BER Highlights

Cultivation and genetic analysis have been the primary means used to study gene function and the behavior of microbes. However, these classical approaches require isolating and culturing microorganisms in the lab. This severely limits scientists’ knowledge of microbes that cannot yet be cultivated in the lab. Moreover, the interactions that occur between microbes when they grow together in a community cannot be studied in isolated organisms. To address these challenges, this research developed two technologies to test functions and interactions directly within laboratory microbiomes. Environmental transformation sequencing (ET-seq) delivers a mobile genetic element (transposon) into a microbial community. The transposon inserts randomly into the genes of some bacterial species. By sequencing the genomes of all the microbes in the community, scientists can detect community members that are transformed by the transposon and how frequently. In that way, they identify genetically tractable species. Those species can be specifically targeted for manipulation of selected genes using DNA-editing all-in-one RNA-guided CRISPR–Cas transposase (DART). The researchers also combined both techniques to demonstrate the enrichment of targeted bacterial species, confer novel metabolic traits, and measure gene fitness of bacteria within a community context in a lab setting. These new capabilities will provide important new insights into the activities of uncultivated microbes and the functions of key genes, metabolites, and proteins, for example, in soil carbon cycling and mediating beneficial microbial interactions with plants for sustainable bioenergy.

Scientists have several approaches to predict functional properties of a given protein that use the protein’s amino acid sequence to build a computational model. Scientists create such models employing both classical statistical methods and modern-day machine learning computational approaches. One of those statistical methods, called regression analysis, associates a given amino acid sequence with an experimentally measured functional property of a protein. To increase the amount of data available to make functional predictions for a protein, researchers include sequences of evolutionarily-related proteins as additional input. In general, those evolutionarily-related proteins are likely to share the property of the protein of interest, albeit often without direct experimental evidence. Researchers use a machine learning modeling approach based on the statistical properties of those sequences. In the study highlighted here, researchers combined regression analysis and evolutionary data to propose a simple, effective machine learning approach. The researchers found that this simple combination approach is competitive with, and often outperforms, more sophisticated methods.

Viruses are a vastly understudied component of microbiomes. In this study, researchers from Oak Ridge National Laboratory (ORNL), the Massachusetts Institute of Technology, Harvard University, and the University of Tennessee created a novel method to create a classification tree for viruses at an unprecedented scale. The method can be used with any taxonomy-based classification tool to better identify viruses and their impacts in the microbiome. The 715,672 metagenome viruses that the Joint Genome Institute (JGI), a Department of Energy (DOE) user facility, has identified potentially make up only a small fraction of viruses that exist, though incorporating them increases the pool of viral taxa for classification by approximately 90-fold. While the uniqueness and diversity of the JGI viruses makes them more difficult to classify in samples with Kraken2, the new method is still 82 percent accurate in identifying the correct JGI viral sequences and more than 90 percent accurate in identifying the sequence as a JGI-identified virus. Using a parallel version of Kraken2 called ParaKraken, the researchers showed that it is possible to identify viral sequences in metagenomic Populus genotype and compartment samples. Furthermore, viral taxa comprise between 6-20% (mean 15%) of the sequence reads in metagenomic samples. The results provide a means to better understand the role that viruses may play in plant biology.

Researchers at Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) used microfluidics technology to develop a transparent soil habitat called the rhizosphere-on-a-chip. Scientists can use the rhizosphere-on-a-chip to grow Brachypodium distachyon, a model grass species, for as long as one month. This allows researchers to image the development of the plant’s roots through the synthetic soil pore space. The research team used computational simulations to predict how small molecules would diffuse through the rhizosphere-on-a-chip if they were exuded from the boundaries of the roots. These simulations predicted the occurrence of hotspots of concentrated carbon in the form of consumable nutrients like amino acids. To experimentally validate the presence of these hotspots, the research team attached their rhizosphere-on-a-chip to a permeable membrane and used a technique called liquid micro-junction surface sampling probe mass spectrometry to spatially sample the root exudates through the membrane. This study confirmed the presence of carbon hotspots within the synthetic pore space and revealed that amino acids are not exuded uniformly along the roots.

The plant hormone ABA is a critical component of the stress acclimation mechanism in many plants. Plants produce ABA in response to a wide array of water associated stresses such as drought, salinity, cold and heat. As ABA levels rise, a plant’s growth can slow due to the inhibition of processes such as cell division and elongation. In this study, researchers from Stanford University, Louisiana State University, and the Salk Institute compared the response of stress sensitive and stress tolerant species, so called “extremophytes,” which can tolerate excessively high levels of environmental stress. While one of these halophytes exhibits similar responses to ABA as stress sensitive species, the species Schrenkiella parvula shows growth acceleration due to an enhancement of cell elongation in the roots.

For this study, the researchers used RNAseq-based transcriptomic characterization of these species as well as DNA Affinity Purification and Sequencing (DAP-seq) to establish the first cross-species gene regulatory network for stress response based on direct measurements of protein-DNA binding landscapes. The comparison of the regulatory networks between species identified targets of ABA signaling that are conserved across species while contrasting these networks allows us to identify a subnetwork that was highly divergent in Schrenkiella parvula and controlled the biosynthesis of the growth hormone auxin. Loss of the regulatory connections between ABA and auxin provide a mechanism to explain why ABA has lost its growth inhibitory effects in this extremophyte.

This research used a combination of genomic, quantitative, genetic, landscape, and niche modeling approaches to contrast the diversity of tetraploid (4X) and octoploid (8X) switchgrass across hundreds of naturally occurring genotypes (in this case, a plant’s complete genetic makeup) and 10 common gardens. The team included researchers from the University of Texas at Austin, the HudsonAlpha Institute for Biotechnology, Michigan State University, South Dakota State University, the U.S. Department of Agriculture, the University of Missouri, Argonne National Laboratory, Texas A&M University, Overton, Oklahoma State University, and the Joint Genome Institute at Lawrence Berkeley National Laboratory. The study discovered that 8X populations have arisen multiple times from different genetic backgrounds, and that these 8X populations contain novel combinations of genetic diversity. The study also found that much of the variation in physical characteristics that is seen in 4X switchgrass is also observed across the 8X cytotype. However, the 4X and 8X cytotypes diverge in their response to climate variations between the common gardens, indicating a generalist (8X)-specialist (4X) tradeoff. Furthermore, niche modeling suggests that niche evolution between 4X and 8X is linked to climate adaptation. Overall, these results indicate that the 8X cytotypes represent a unique combination of genetic variation that has allowed the expansion of switchgrass’ ecological niche. The knowledge gained from 8X switchgrass is a valuable resource towards the effort to generate climate resilient switchgrass for bioenergy production.

Reporter genes are attached to other genes of interest to provide an inexpensive, rapid, and sensitive assay for studying gene delivery and gene expression. These reporters have long been an essential tool for live-cell imaging. Today, imaging and analysis are becoming more accessible through the development of UV-visible fluorescent reporters. This research from scientists at Oak Ridge National Laboratory aimed to advance the use and efficiency of these reporters in two herbaceous plant species (Arabidopsis and tobacco) and two woody plant species (poplar and citrus).

After designing and building a GFP UV reporter protein (eYGFPuv) that provides enhanced signals for all tested plant species, the researchers demonstrated that strong fluorescence could be captured using either a fluorescence microscope or UV light. Moreover, this UV‐excitable reporter can be observed across a wide range of scales from sub‐meter level seedlings to whole plants without need for special emission filters. For instance, by using a simple UV flashlight, the scientists demonstrated how this new reporter can facilitate rapid quantification of transformation efficiency in plant systems. These improved features will make this newly developed GFP-UV reporter a valuable tool for a wide range of applications in plant science research.

Measurements show that the active layer of permafrost in Ny Ålesund, Svalbard (79 degrees north latitude) around the Bayelva River in the Leirhaugen glacier moraine is a small net carbon sink at the brink of becoming a carbon source. In many permafrost-dominating ecosystems, researchers have shown that microbes in the active layers drive organic matter degradation and greenhouse gas production, creating positive feedback on climate change. However, the microbial metabolisms linking the environmental geochemical processes and the populations that perform them have not been fully characterized.

In this study, researchers from the University of Tennessee, Princeton University, the Alfred Wegener Institute in Germany, and Humboldt-University of Berlin used geochemical, enzymatic, and isotopic data paired with experiments on cultures and metagenomic libraries of two active layer soil cores (BPF1 and BPF2). Relative to BPF1, BPF2 had much more labile organic matter. The d13C values for inorganic carbon did not correlate with those of organic carbon in BPF2, suggesting lower heterotrophic respiration. An increase in the d13C of inorganic carbon with depth either reflects an autotrophic signal or mixing between a heterotrophic source at the surface and a lithotrophic source at depth. Potential enzyme activity of xylosidase and N-acetyl-b-D-glucosaminidase increases twofold at 15 degrees C, relative to 25 degrees C, indicating cold adaptation in the cultures and bulk soil. Potential enzyme activity of leucine aminopeptidase across soils and cultures was two orders of magnitude higher than other tested enzymes, implying that organisms use leucine as a nitrogen and carbon source in this nutrient-limited environment. Besides demonstrating large variability in carbon compositions of permafrost active layer soils only 84 meters apart, results suggest that the Svalbard active layer microbes are often limited by organic carbon or nitrogen availability and have adaptations to the current environment, and metabolic flexibility to adapt to the warming climate.

To establish synthetic gene circuits capable of predictably regulating gene expression in plants, scientists adapted a large collection of bacterial gene regulators for use as synthetic activators or repressors of gene expression in plants, also known as transcription factors. Using a transient expression system, the researchers demonstrated that the synthetic transcription factors and their target DNA sequences (promoters) are able to direct specific and tunable control of gene expression. They designed synthetic promoters that responded to one synthetic transcription factor to work as simple logic gates that responded to one input, while more complex gates required synthetic promoters that responded to multiple inputs. The research found these logic gates to control expression in predictable ways according to the specific Boolean rules encoded in the engineered genes.

To implement synthetic gene circuits in a multicellular context, the researchers used Arabidopsis roots as a model system where endogenous promoters drove tissue-specific expression of the synthetic transcription factors. The gene circuits generated novel expression patterns that were the result of successfully performing logical operations. The researchers further used one of the logic gates to quantitatively control the expression of a hormone signaling regulator to tune the amount of root branching in the root system of Arabidopsis. These results demonstrate that it is now possible to program gene expression across plant cell types using genetic circuits, providing a roadmap to engineer more resilient bioenergy crops.

After sequencing the DNA of algal microbiome cultured in the microplate, the team revealed that certain bacteria responded to the algal production of organic carbon in a spatially dependent manner. Specifically, they found that bacteria associated with the algae reached higher abundances when placed closer to the algal culture well. This result fits with expectations for real phycosphere environments. The researchers also unexpectedly found that cultivation of the diatom Phaeodactylum in the microplate led to yields 20 times greater than batch cultures due to continuous supplementation of nutrients.

The new porous microplate incubation method is highly effective for algal cultivation, allowing the diatom Phaeodactylum to accumulate to its theoretical physical limit, densely packed with cell-to-cell distances equal to their cell radius. This result may be important to efforts to produce increased and more efficient algal biomass production at large scales. Moreover, the porous microplate system facilitates investigation of community-level microbial interactions in complex small-scale ecosystems mediated by metabolite exchange. The system shows that the algal phycosphere is a complex ecosystem which allows multiple microbial groups to thrive in different locations within this microscale environment.

Soil viruses are abundant, but scientists have a poor understanding of how they respond to the environment and climate. This study addressed this gap by comparing the diversity, abundance, lifestyle, and metabolic potential of DNA viruses in three grassland soils with historical differences in average annual precipitation: low in eastern Washington, intermediate in Kansas, and high in Iowa. Bioinformatics analyses identified a total of 2,631 viral contigs, including 14 complete viral genomes from three deep metagenomes. The viruses were primarily bacteriophages targeting dominant bacterial taxa. The most significant differences among the three sampled locations were found in arid eastern Washington. Viral abundance in the low-precipitation Washington sample was significantly higher than in the other two locations. The diversity of viral and host bacteria was also higher in the Washington sample. The data also suggested that more infection cycles occur in the historically drier soil. Overall, the observed and predicted relationships between soil viruses and various biotic and abiotic variables can help predict viral responses to environmental change.

Researchers made several improvements to the existing ClusterCAD tool. ClusterCAD is a free online platform that simplifies the process of designing and testing engineered enzyme variants for synthetic biology applications. The tool allows users to browse polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) gene clusters, which are groups of genes that encode pathways that can produce chemicals.

With this update, users can now design NRPS clusters within the platform. The researchers also expanded the platform’s database of clusters to be more comprehensive and informative. Other updates provide users with more high-quality clusters available for study, expanded starting points for PKS and NRPS engineering, and improved database versatility and search tools. These updates allow users to explore more chemicals that can be produced through PKS and NRPS engineering.

Researchers from the University of Tennessee, Knoxville and Oak Ridge National Laboratory used three pretreatment solvents to partially deconstruct and break away plant lignin from other cell wall components so the lignin modules can be extracted. The composition of the extracted lignin (and therefore its potential value) from wild type and genetic variants of switchgrass by each of the solvent conditions is reflected by its measurable molecular mass and remaining chemical bonds. Based on several types of analytical data, the researchers observed differences among the lignin extracts, indicating that various specific types of bioproducts can be generated from the lignin extract from each set of solvent and conditions. The molecular interactions among the solvents, cellulose, and lignin are key to the process. Characterizing those interactions for the set of pretreatment solvents was therefore another research goal. The researchers used computer simulations based on the analytical data to provide insight into the number of molecular interactions between the lignin and solvent molecules. The findings indicate that the ability to form those interactions is important for enabling lignin depolymerization.

The study showed that each of the three solvents and the switchgrass genetic variations are effective in generating lignin extracts of differing forms, suited for distinct uses. For example, the tetrahydrofuran pretreated lignin should be suitable for further depolymerization into monoaromatic compounds. The information from this study can aid in the selection of pretreatment based on the type of precursor modules needed for a particular use.

Researchers estimate that winter carbon losses in northern ecosystems are greater than the amount of carbon taken up during the average growing season and are primarily driven by microbial decomposers. Viruses modulate microbial carbon cycling via induced mortality and metabolic controls, but researchers do not know whether viruses are active under winter conditions (anoxic and sub-freezing temperatures). This study used stable isotope probing (SIP) targeted metagenomics to reveal the genomic potential of active soil microbial populations under simulated winter conditions, with an emphasis on viruses and virus-host dynamics. Arctic peat soils were incubated at sub-freezing anoxic conditions with oxygen 18-enriched water or natural abundance water for 184 and 370 days. The researchers sequenced 23 SIP-metagenomes and identified 46 bacterial populations (spanning 9 phyla) and 243 viral populations that actively took up oxygen-18 in soil and respired carbon dioxide throughout the incubation. Active bacterial populations represented only a small portion of the detected microbial community and were capable of fermentation and organic matter degradation. In contrast, active viral populations represented a large portion of the detected viral community and one third were linked to active bacterial populations. Prior to this work, viral activities had never been confirmed under sub-freezing conditions in soil. There was a stark difference in the identity and function of the active bacterial and viral community compared to the unlabeled community that would have been overlooked with a non-targeted standard metagenomic analysis. It is critical to understand the identity, functional capacity, and activities of bacteria and viruses that cause carbon turnover in soils during winter to better predict their biogeochemical implications.

The plasticity of lignin synthesis allows scientists to engineer lignin polymers that have added value and are easier to break down. As much as 10 percent of poplar lignin is naturally comprised of the enzyme p-hydroxybenzoate (pHB). This group is attached to the lignin polymer by weak ester linkages relative to ether linkages that abound in lignin. This makes it easy to “clip-off” in biomass deconstruction. Once pHB is separated from biomass, it is both a valuable platform chemical and can be upgraded to other biochemicals and bioproducts. Scientists do not fully understand the pathway by which pHB is synthesized in plants. However, by expressing bacterial chorismite pyruvate lyase (CPL) in plastids, plants have been shown to produce more pHB.

In this study, scientists at the Great Lakes Bioenergy Research Center produced transgenic hybrid poplar lines to express bacterial CPL. This resulted in a 50 percent increase of stable pHB in mature trees and 10 times more in developing trees compared to control trees. This work demonstrates how engineering bioenergy crops can improve the efficiency and value of industrial biomass deconstruction by increasing the amount of easily cleavable and valuable chemical groups.

Obtaining genomes of uncultivated microbes directly from the environment using DNA sequencing is a recent advance that allows scientists to discover and characterize novel organisms. Sequencing the DNA of all the microbes in a given environment produces a “metagenome.” Performing genetic analysis of metagenomes has emerged as a way to explore microbial traits and behaviors and community interactions in an environmental context. Methods for obtaining metagenome-assembled genomes (MAGs) have varying degrees of success, depending on the techniques used. An increasing number of researchers generate microbiome sequences, but many do not have ready access to the expertise, tools, and computational resources necessary to extract, evaluate, and analyze their genomes.

The KBase team added and updated several metagenome analysis tools, data types, and execution capabilities to provide researchers the tools that accelerate the discovery of microbial genomes and uncover the genetic potential of microbial communities. A recent paper in Nature Protocols presents a series of analysis steps, using KBase apps and data products for extracting high quality MAGs from metagenomes. These capabilities, including computing, data storage, and sharing of data and analyses, are provided free to the public via the KBase web platform. This protocol allows scientists to both generate putative genomes from organisms only found in the environment and analyze them with tools to understand who they are, what they are doing, who they are interacting with, and their role in the ecosystem.

Scientists at North Carolina State University developed a single-step phage engineering method that takes advantage of the CRISPR-Cas9 system and a DNA recombination and repair mechanism. Using this method, the researchers engineered the genome of the phage lambda (λ) of Escherichia coli. They replaced a non-essential region of the λ genome with a selectable marker and an engineered base editor. This editor enabled precise modifications of the bacterial genomic DNA by converting one of the four DNA bases (or letters A, C, T, and G) into another. The researchers used this tool to convert a C into a T in specific genes, either within the bacterial chromosome or carried in a DNA plasmid. They demonstrated that the approach was successful by inactivating multiple genes, including the endogenous lacZ gene and plasmid-encoded fluorescent reporter and antibiotic resistance genes.

To demonstrate the effectiveness of this precise genome editing tool within a community context, the team used a fabricated ecosystem (EcoFAB) device. Recapitulating a soil microbial ecosystem, they filled the EcoFAB with sterile quartz sand and inoculated it with a soil bacterial community composed of E. coli and two other known bacterial species. They then added the engineered “editor” λ phage that can only infect E. coli to the EcoFAB. After incubating the phage with the bacterial community, they managed to base-edit 28 percent of the E. coli cells in the EcoFAB. These results highlight the potential of phages as DNA delivery vehicles for targeted members of a mixed soil community for precise genome editing.

Phylogenetic information shapes expectations regarding microbial capabilities. In fact, this is the basis of currently used methods that link gene surveys to metabolic predictions of community function. Sulfate reduction, an important anaerobic metabolism, impacts carbon, nitrogen, and hydrogen transformations in numerous environments across the planet and is known to be restricted to organisms from selected bacterial and archaeal phyla. The authors used genome-resolved metagenomic analyses to determine the metabolic potential of microorganisms from six complex marine and terrestrial environments. By analyzing >4,000 genomes, they identified 123 near-complete genomes that encode dissimilatory sulfite reductases involved in sulfate reduction. They discovered roles in sulfur cycling for organisms from 16 microbial phyla not previously known to be associated with this process. Additional findings include some of the earliest-evolved sulfite reductases in bacteria, identification of a novel protein unique to sulfate-reducing bacteria, and a key sulfite reductase gene in putatively symbiotic candidate phyla radiation (CPR) bacteria. This study fundamentally reshapes expectations regarding the roles of a remarkable diversity of organisms in the biogeochemical cycle of sulfur.

Transport between the soil surface and groundwater is commonly mediated through deeper portions of variably saturated sediments and the capillary fringe, where variations in temperature and water saturation strongly influence biogeochemical processes. Temperature control is particularly important because room temperature is not representative of most soil and sediment environments. The authors described and tested a novel sediment column design that allows laboratory simulation of thermal and hydrologic conditions found in many field settings. The 2.0 m–tall column was capable of replicating temperatures varying from 3 to 22°C, encompassing the full range of seasonal temperature variation observed in the deep, variably saturated sediments and capillary fringe of a semi-arid floodplain in western Colorado, United States. The water table was varied within the lower 0.8-m section of the column, while profiles of water content and matric (capillary) pressure were measured. CO₂ collected from depth-distributed gas samplers under representative seasonal conditions reflected the influences of temperature and water-table depth on microbial respiration. Thus, realistic subsurface biogeochemical dynamics can be simulated in the laboratory through establishing column profiles that more accurately represent seasonal thermal and hydrologic conditions.

Model projections of CO₂ and CH₄ emissions in permafrost systems vary widely between land models. In this study, the researchers used ecosys to examine how climate change will affect these emissions in a polygonal tundra site at Utqiagvik (formerly Barrow) Alaska. The model has been thoroughly tested against NGEE–Arctic thermal, hydrological, and biogeochemical observations. During the Representative Concentration Pathway (RCP) 8.5 climate change scenario from 2015 to 2085, rising air temperatures, atmospheric CO₂, and precipitation (P) increased net primary productivity consistently with biometric estimates. Concurrent increases in heterotrophic respiration (Rh) were offset by increases in CH₄ emissions. Both these increases were smaller if boundary conditions were altered to increase landscape drainage, highlighting the importance of these large-scale hydrological dynamics for carbon cycle predictions.

In early 2015, Department of Energy scientists at Pacific Northwest National Laboratory planned a laboratory incubation experiment to characterize the chemical and biological properties of sub-Arctic, active-layer soils subjected to changes in temperature and moisture. This experiment required (1) a multidisciplinary team that was not located in one time zone; (2) integration of various data; (3) rapid performance of quality control and diagnostics, so that if instrument problems arose the team would lose only the minimum amount of time and data; and (4) tight integration of data, statistical analyses, and manuscript results. The team designed a data processing and analytical system written in an open-source and widely used language for statistical computing and graphics, and placed it in a publicly available “repository” that stored all code and data, making them available in real time. Using an automated analytical pipeline in an open repository provided significant advantages for the project, but the costs of such an approach and investments required should also be considered.

Cell-surface receptor proteins play an important role in signal perception and processing, which, in turn, influence growth and development. The membrane-bound LecRLKs comprise a large family of such proteins. LecRLKs are specific to plants and are believed to be involved in responses to external stimuli such as pathogens and environmental stresses. Scientists with Oak Ridge National Laboratory’s Plant-Microbe Interface project report the first genome-wide analysis and classification of LecRLKs in the perennial woody model plant Populus, a bioenergy feedstock tree important for carbon sequestration, ecological systems studies, and biomass production. The researchers found that the LecRLK family was greatly expanded in Populus, with notably high levels of expression in the roots as compared with other plant tissues. They hypothesize that since the root system provides the interface for soil microbes, LecRLKs expressed in the roots may function to perceive microbial signals, which, in turn, influence plant health and tolerance of biotic and abiotic stresses. This first comprehensive study of LecRLKs in a woody plant lays the basis for functional characterization of an important protein family.

The chemical identity and 3D position of individual atoms in inorganic materials can be revealed using the powerful APT technique, which combines mass spectrometry with advanced microscopy. However, use of APT to study soft biological materials has been limited because of difficulties in specimen preparation. To address this problem, researchers from the Department of Energy’s (DOE) Environmental Molecular Sciences Laboratory (EMSL), and Pacific Northwest National Laboratory developed an advanced specimen preparation approach to study soft biological materials using APT. The new specimen preparation approach involves embedding ferritin in an organic polymer resin that lacks nitrogen to provide chemical contrast for visualizing atomic distributions. The team used the Helios Nanolab dual-beam focused ion beam/scanning electron microscope (FIB/SEM) at EMSL, a DOE scientific user facility, to carve and lift out an appropriate sample for APT analysis. Then, using EMSL’s APT, they directly mapped the distribution of phosphorus at the surface of the ferrihydrite mineral, thereby providing insight into the role of phosphorus in stabilizing the ferrihydrite structure. The robust sample preparation method can be directly extended to further enhance the study of biological, organic, and inorganic nanomaterials relevant to energy and the environment.

Sea spray aerosols are a highly complex mixture of sea salt and organic components that are generated through wave action and bubble bursting where the air and sea meet. Obtaining detailed information about the structure and composition of these aerosols is crucial for understanding their role in cloud formation and their influence on climate. However, studying sea spray aerosols using conventional electron microscopy requires high-vacuum conditions that alter aerosol structure and prevent scientists from characterizing the natural configuration of these particles in the atmosphere. To address this problem, a team of researchers from the University of California, San Diego; Department of Energy’s (DOE) Environmental Molecular Sciences Laboratory (EMSL); and University of Iowa developed a new approach that used cryogenic transmission electron microscopy. This approach involved flash freezing sea spray aerosol particles to preserve their natural configuration and then studying their structure with electron microscopy. The researchers used the environmental transmission electron microscope and scanning/transmission electron microscope at EMSL, a DOE user facility. Using this unique approach, the team of researchers was able to detect mixed salts and soft materials characterized by distinct biological, chemical, and physical processes. The researchers also demonstrated this approach could be used to study chemical and morphological changes that occur when particles are exposed to various environmental conditions, such as changing humidity. The ability to trap aerosols under environmentally relevant conditions will open new avenues for addressing many important questions about the chemical complexity and structure of aerosol particles and how they impact climate and the environment.

Researchers at the Massachusetts Institute of Technology applied a multipronged strategy to engineer Y. lipolytica to produce several lipid molecules with applications as biofuels and other oleochemicals such as fatty acid ethyl esters, fatty alkanes, fatty acids, fatty alcohols, and triacylglycerides. This strategy included engineering Y. lipolytica lipid metabolism by expressing enzymes from other microorganisms within specific subcellular compartments within the yeast cells where specific lipids or their precursors are metabolized. Another approach was to engineer a chimeric enzyme to regulate the chain length of specific fatty acids. Finally, to increase the availability of acetyl-CoA building blocks for fatty acid synthesis, alternative acetyl-CoA pathways were designed to avoid the normal repression of acetyl-CoA synthesis by low nitrogen concentration in the medium. Production of different lipid molecules in these engineered strains was increased between 2 and 20 fold, paving the way toward developing industrial strains for commercial production of biodiesel and bioproducts from renewable sources.

Many ecophysiological and biogeochemical processes respond rapidly to changes in biotic and abiotic conditions, while ecosystem-level responses develop much more slowly (e.g., over months, seasons, years, or decades). To better understand the role of the slow responses in regulating interannual variability in net ecosystem exchange (NEE), the study partitioned NEE into two major ecological terms: gross primary productivity (GPP) and ecosystem respiration (Reco). The researchers tested a set of hypotheses on seasonal scales using flux and environment data collected from 2000 to 2015 in an oak-grass savanna area in California, where ecosystems annually experience a wet winter and spring and five-month-long summer drought. Results showed that the spring season (April through June) contributed more than 50% of annual GPP and Reco. An analysis of outliers found that each season could introduce significant anomalies in annual carbon budgets. The magnitude of the contribution depends on biotic and abiotic seasonal circumstances across the year and the particular sequences. The study found that (1) extremely wet springs reduced GPP in the years of 2006, 2011, and 2012; (2) soil moisture left from those extremely wet springs enhanced summer GPP; (3) groundwater recharged during the spring of 2011 was associated with the snowpack depth accumulated during the winter between 2010 and 2011; (4) dry autumns (October–December) and winters (January–March) decreased Reco significantly; and (5) grass litter produced in previous seasons might increase Reco, and the effect of litter legacy on Reco was more observable in the second year of two consecutive wet springs. These findings confirm that biotic and abiotic extremes and legacies can introduce variations to annual ecosystem carbon balance, other than those that might be explained by the fast responses.

The radiative forcing of CO₂ is the leading contribution to climate change from anthropogenic activities. Calculating CO₂ radiative forcing requires detailed knowledge of spectral line parameters for thousands of infrared absorption lines. A reliable spectroscopic characterization of CO₂ forcing is critical to scientific and policy assessments of present climate and climate change. The results of this study show that CO₂ radiative forcing in a variety of atmospheres is remarkably insensitive to known uncertainties in the three main CO₂ spectroscopic parameters: line shapes, line strengths, and half widths. Uncertainties in radiative forcing due to line mixing were specifically examined as this process is critical in determining line shapes in the far wings of CO₂ absorption lines. Radiative forcing computed with a Voigt line shape also was examined. Overall, the spectroscopic uncertainty in present-day CO₂ radiative forcing is less than 1 percent, indicating a robust foundation in current understanding of how rising CO₂ warms the climate system.

When scientists supported by the Department of Energy’s (DOE) Atmospheric System Research (ASR) program embarked on this study to prepare internally consistent ice physical and optical properties, they expected to corroborate past derivations of bullet rosette mass as a function of maximum dimension (a physical property of the ice). The team worked with in situ observations from two cirrus clouds sampled during the 2010 Small Particles in Cirrus (SPartICus) field campaign supported by DOE’s Atmospheric Radiation Measurement (ARM) Climate Research Facility. The team found that while their crystals had projected areas, which are directly measured, that were similar to those in previous studies, their calculations yielded substantially greater crystal masses than previously found. The researchers identified several likely sources of error in previous studies. First, virtually no direct measurements of individual crystal mass exist for cirrus particles, so masses must be calculated based on crystal shape and maximum dimension. Second, the maximum dimension commonly used for projected area is randomly oriented, whereas that used for idealized calculations of mass is true maximum dimension; they find that randomly oriented maximum dimension is substantially smaller. Finally, large uncertainties are expected in particle mass derived from measured particle size distributions and total ice mass, and such measurement uncertainties (in both bin-wise number concentration and total ice mass) have remained essentially uncharacterized in the literature to date.

The Green Ocean Amazon field campaign sought to quantify and understand how aerosol and cloud lifecycles in a particularly clean background in the tropics were influenced by pollutant outflow from a large tropical city. The experiment was conducted by a large, multi-organization team, including scientists from both Brazilian and U.S. institutions, and was carried out in the environs of Manaus, Brazil, an isolated urban region of over 2 million people. The city is surrounded by a natural forest for over 1000 km in every direction. The city, encompassing a large industrial zone, uses high-sulfur oil as its primary fuel for electricity generation and emits large quantities of soot. Particle concentrations increase 10 to 100 times in the pollution plume compared to when pristine conditions prevail. The intersecting research sites downwind of Manaus oscillated between one of the least perturbed natural continental sites on Earth and one in which the pollution emissions of a tropical metropolis interact with the natural emissions of the rainforest. These findings will help researchers understand how aerosol and cloud lifecycles, including cloud-aerosol-precipitation interactions, are influenced by pollutant outflow from a tropical megacity. The goal is to provide data for a more accurate Earth system model to describe tropical regions and, in particular, the Amazon basin, where the hydrologic cycle is one of the primary heat engines of global circulation.

Permafrost is highly heterogeneous, and the impacts of thaw differ dramatically depending on geography, biochemistry, and microbial residents. A recent review summarizes the current state of knowledge about microbial ecology both within permafrost and in the soil layers activated as permafrost thaws, with an emphasis on the use of modern, high-throughput sequencing technologies to understand permafrost-associated microbial communities and their response to climate change. Understanding of the microbial mechanisms controlling greenhouse gas emissions is in its infancy. Metagenomics must be coupled with enhanced measurements of geochemistry and microbial processes to develop a comprehensive understanding of microbial function and activity in permafrost. Predictive understanding will require information generated by both laboratory-based experiments and long-term in situ studies. In the near future, it is imperative for knowledge generated by metagenomics and other omics approaches to be incorporated into climate models to fully integrate microbiology, geochemistry, geophysics, and hydrology for a better representation of Arctic ecosystems.

Multiple stable isotopes of hydrogen and oxygen occur naturally in water. Due to the mass differences between the isotopes, physical processes such as transitions between the vapor and condensed phases of water can change the relative proportion of various isotopes. Thus, examining isotope ratios in samples of precipitation and water vapor can provide insight into hydrological cycle processes that affected the samples. Understanding controls on the stable isotopic composition of precipitation and vapor in the tropics can provide important constraints on the representation of convective processes in models and correct interpretation of isotope-based paleoclimate proxies. The stable isotopic composition of water vapor, precipitation, and seawater was measured at the ARM facility on Manus Island, Papua New Guinea. The results demonstrate variability in the stable isotopic composition of precipitation and vapor in individual precipitation events and over a 10-day period. Isotope ratios progressively increased throughout the period of measurement, coincident with a transition from high to low regional convective activity. Vapor isotope ratios approached equilibrium with seawater during the quiescent period and likely reflected downwind advection of distilled vapor and re-evaporation of rainfall during the period of regional convection. In individual storms, isotope ratios in precipitation were strongly correlated with isotope ratios in surface vapor; however, they were not strongly correlated with surface meteorological data, including precipitation rate, in all storms. Yet across all events, precipitation deuterium excess was negatively correlated with surface temperature, sea level pressure, and cloud base height and positively correlated with precipitation rate and relative humidity. Results from the short campaign support the interpretation that isotope ratios in precipitation and vapor in the western tropical Pacific are indicators of regional convective intensity at the timescale of days to weeks. However, a nonstationary relationship between rain rate and stable isotope ratios in precipitation during individual convective events suggests that condensation, rain evaporation, moisture recycling, and regional moisture convergence do not always yield an amount effect relationship on intra-event timescales. Apart from aiding in understanding modern convective processes, such metrics hold important implications for interpreting archives of past isotopic variability. For example, these results suggest that interpretation of deuterium excess from low-latitude ice cores as reflective of evaporative conditions at the moisture source may be oversimplified; more investigation is required to understand how the signal evolves across the differing timescales represented in various isotope archives. Together, these findings offer observationally based interpretive guidance for proxies that reflect isotope ratios of precipitation in terms of precipitation characteristics in the tropics.

A plant productivity-optimized nutrient acquisition model was integrated into one of the most widely used global terrestrial biosphere models, the Community Land Model (CLM). Global plant nitrogen uptake is dynamically simulated in the coupled model based on the carbon costs of nitrogen acquisition from mycorrhizal roots, non-mycorrhizal roots, symbiotic nitrogen-fixing microbes, and remobilization of nutrients from senescing leaves. Mycorrhizal uptake represented the dominant pathway by which nitrogen is acquired, accounting for about 66 percent of the nitrogen uptake by plants. Overall, the coupled model improves the representations of plant growth limitations globally. Such model improvements are critical for predicting how plant responses to altered nitrogen availability (from nitrogen deposition, rising atmospheric carbon dioxide, and warming temperatures) may impact the land carbon sink.

Orchids, like the majority of terrestrial plants, form symbiotic relationships between their plant roots and soil fungi, known as mycorrhizal associations. However, unlike other terrestrial plants, orchids rely on their mycorrhizal fungal partners for nutrient supply during the feed germination and development stages. Following these stages, most orchid species develop leaves and are capable of self-nourishment, whereas some species continue to rely on their fungal partners for an organic carbon supply. In this study, a team led by University of Turin researchers investigated the orchid mycorrhizal fungus Tulasnella calospora as both a free-living mycelium and in symbiosis with the photosynthetic orchid long-lipped serapias, or Serapias vomeracea. For the first time, researchers looked at the fungal genes that may have been involved in both the uptake and transfer of nitrogen to the host plant. RNA sequencing for the project was performed at the U.S. Department of Energy’s (DOE) Joint Genome Institute (JGI), a DOE Office of Science user facility.

The team also used JGI’s fungal genome database MycoCosm to identify fungal genes coding for proteins that were involved in nitrogen uptake and transfer. They found that the T. calospora genome has two genes coding for ammonium transporters and several genes coding for amino acid transporters, proteins that play roles in the nitrogen nutrient pathway. Overall, the orchid mycorrhizal fungi’s use of nitrogen may broaden the habitat ranges of orchids, allowing them to grow in a variety of soil types. Of more general interest to the DOE, this study provides important insights for this process and furthers understanding of plant-microbial symbioses that are vital for plant health and may inform understanding of microbial symbioses relevant to bioenergy feedstock plants.

Hidden belowground is a vast network of fungi that operates in a complex economy within forests, scavenging for nutrients and trading them to trees for carbon sugars. Researchers in a Department of Energy-supported study figured out how to detect this underground network from space. Understanding how different forests get their nutrients is critical to predicting how forests may grow—or be growth-stunted due to lack of nutrients—into the future. The type of mycorrhizal fungi is a key piece of that puzzle in determining how forests will respond to future changes in climate, carbon dioxide, water, and temperature. Scientists have known for many years which tree species associate with which fungi, but mapping every single tree species across large scales such as landscapes or continents has not been possible. The researchers used Landsat satellite measurements of forest canopies to detect mycorrhizal associations. They gathered data from 130,000 trees throughout the United States to test their approach, finding that they could predict 77% of the differences in mycorrhizal associations known on the ground from satellite observations alone.

Coincident profiling observations from Doppler lidars and radars operated by the Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (1) Doppler radar velocity (DRV), (2) Doppler lidar velocity (DLV), and (3) Doppler radar spectrum width (DRW) measurements. The agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement during both drizzling and non-drizzling conditions. This finding suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced, regardless of the size of the particles present in the radar volume. This finding also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. An important implication of the conditional agreement of the two techniques at the cloud base height is that the influence of the droplet size distribution on the width of the Doppler radar spectrum can be derived. This derived term can be used to constrain retrievals of drizzle properties and provides a means to validate microphysics parameterizations in numerical models.

A number of different retrieval products for cloud condensate from ARM observations are assessed for five different geographical regions for multiple years and seasons. The retrieval reliability varies with cloud type, but for cloud categories largely unaffected by precipitation, a comparison across sites and longer time periods is possible. These observations confirm previously documented variability behavior as a function of cloud fraction, but also reveal a systematic regime dependence that is not captured by existing parameterizations. Condensate variability measured as a fractional standard deviation (FSD) in warm boundary-layer clouds is greater in the tropics than in the mid- and high-latitudes for scenes with comparable cloud type and fraction, with the observed FSD varying from 1.2 in the tropics to 0.4 in the Arctic. A parameterization of the cloud liquid condensate FSD based on the grid box mean total water amount and cloud fraction is formulated and shown to better capture the observed range of FSD values across the different geographical sites and seasons.

The Deardorff 1980 (D80) subgrid turbulence model is perhaps the most ubiquitous scheme used in LES studies of atmospheric boundary-layer flows. This model is often included as the default closure scheme in a variety of codes and numerical weather prediction models. In this study, researchers investigated the three commonly employed corrective adjustments of the D80 closure model. These include a stability-dependent length scale, formulation for the subgrid turbulent Prandtl number, and enhancement of near-surface dissipation. They implemented a modified formulation of the D80 closure, then compared simulated flow statistics in the lower portion of a representative nocturnal stable boundary layer (SBL) case from LES with realistic forcing using the original D80 scheme and the modified version of the scheme. LES data were compared with observations from the ARM program’s Southern Great Plains (SGP) site in Lamont, Oklahoma. The modified scheme shows overall improvement in reproducing vertical profiles of wind speed and potential temperature in the SBL near-surface region. Conclusions regarding turbulence kinetic energy and friction velocity are not as definitive, although there are signs of improved agreement with measurement data. Examination of the stability parameter and near-surface sensible heat flux suggests the modified scheme better captures effects of stability in the considered flow case. The proposed modification offers a more straightforward and interpretable framework for the parametrization of subgrid turbulence in LES of atmospheric boundary layers.

Scientists have observed carbon dioxide’s (CO₂) greenhouse effect at Earth’s surface for the first time. Although the influence of atmospheric CO₂ on the planet’s energy balance is well established, this effect had not been experimentally confirmed outside the laboratory until now. The researchers, led by the U.S. Department of Energy’s (DOE) Lawrence Berkeley National Laboratory, measured atmospheric CO₂’s increasing capacity to absorb thermal radiation emitted from Earth’s surface over an 11-year period at two locations in North America. They used precise spectroscopic instruments operated by DOE’s Atmospheric Radiation Measurement (ARM) program at the ARM research sites in Oklahoma and Alaska. The instruments measure thermal infrared energy that travels down through the atmosphere to the surface and can detect the unique spectral signature of infrared energy from CO₂. Other instruments detect the unique signatures of phenomena that also can emit infrared energy such as clouds or vapor. The combination of measurements enabled the scientists to isolate the signals solely attributed to CO₂. They found that CO₂ was responsible for a significant uptick in radiative forcing at both locations, about 0.2 Watts/m². They linked this trend to the 22 ppm increase in atmospheric CO₂ between 2000 and 2010. The measurements also enabled the scientists to detect, for the first time, the influence of photosynthesis on the balance of energy at the surface. They found that CO₂-attributed radiative forcing dipped in the spring as flourishing photosynthetic activity pulled more of the greenhouse gas from the air. Their results agree with theoretical predictions of the greenhouse effect due to human activity. The research also provides further confirmation that the calculations used in today’s climate models are on track when it comes to representing the impact of CO₂.

Using integrated hydrologic simulations that couple vegetation and land-energy processes with surface and subsurface hydrology, the researchers studied the relative importance of transpiration as a fraction of all the water moving from the land surface to the atmosphere (commonly referred to as transpiration partitioning) at the continental scale. They found that both the total flux of water and transpiration partitioning are connected to water table depth. Because of this connection, including groundwater flow in the model increases transpiration partitioning from 47% (±13%) to 62% (±12%). This finding suggests that groundwater flow, which is generally simplified or excluded from other continental-scale simulations, may provide a missing link to reconciling observations and global models of terrestrial water fluxes.

Studying sawgrass peatlands of south Florida, researchers from Florida Atlantic University quantified differences in plant photosynthetic efficiency and canopy structure between the historic dominant sedge and encroaching native willow to determine the degree to which vegetation carbon and water cycling is altered by shifts in community dominance. Leaf gas exchange of both carbon dioxide (plant photosynthetic uptake) and water (plant transpiration release) was greater for willow, which also used water less efficiently during photosynthesis (greater water loss per carbon gain). Additionally, the willow’s spreading, multitiered branch growth pattern produced more than double the leaf area index (leaf area per ground area). When scaled to the landscape, the elevated water loss rate and leaf density result in substantial increases in wetland water loss through transpiration with even small spatial extent of shrubs. Autogenic drying of wetlands may also accelerate litter and soil decomposition by increasing aerobic conditions, further compromising the health of these peatlands.

For more than half of the world’s population, rice is the primary staple crop. As a grass, it is a close relative of the candidate bioenergy feedstock switchgrass. A team led by University of California, Davis, and including researchers at the U.S. Department of Energy Joint Genome Institute (DOE JGI), a DOE Office of Science User Facility and the Joint Bioenergy Institute (JBEI), a DOE Bioenergy Research Center, have assembled the first major large-scale collection of mutations for grass models. They used the model rice cultivar Kitaake (Oryza sativa L. ssp. japonica), and compared the genes against the reference rice genome of another japonica subspecies called Nipponbare available on the DOE JGI Plant Portal Phytozome.

Through fast-neutron irradiation, the time-consuming procedures involving plant transformation or tissue culture were bypassed, allowing for faster development of rice mutant collections. The DOE JGI resequenced 1,504 rice mutants and identified structural variants and mutations. The work follows a pilot, genome-wide study begun two years ago, in which 41 rice mutants were sequenced and analyzed to identify mutations and structural variants. This new, large-scale collection of more than 90,000 mutations affecting nearly 60 percent of all rice genes is now available on a publicly accessible database called KitBase, is a comprehensive resource that will allow researchers to quickly identify rice lines with mutations in specific genes and to characterize gene function. Among other uses, the collection will allow bioenergy researchers to quickly identify mutations involved in cell wall biosynthesis, critical for increasing plant yields.

There are more than 50,000 microbial genome sequences in the DOE JGI’s Integrated Microbial Genomes publicly accessible database, and many of them have been uncovered through the use of metagenomic sequencing and single-cell genomics. Despite their utility, these sequencing and genomics techniques have limits: single-cell genome amplifications are time-consuming, often incomplete, and metagenomic sequencing generally works best if the environmental sample is not too complex. In eLife, a team of researchers from Stanford University reports the development of a microfluidics-based, mini-metagenomics approach to mitigate these challenges. The technique starts with reducing the environmental sample’s complexity by separating it, using microfluidics, into 96 subsamples each with 5 to 10 cells. Then, the genomes in the cells in each subsample are amplified and libraries are created for sequencing these mini-metagenomes. The smaller subsamples can be held to single-cell resolution for statistical analyses. Co-occurrence patterns from many subsamples can also be used to perform sequence-independent genome binning. The technology was developed through resources provided by the DOE JGI’s Emerging Technologies Opportunity Program, which was launched in 2013. The aim of this program is to use these new technologies to tackle energy and environment applications, adding value to the high-throughput sequencing and analysis being done for DOE JGI users. The team validated the technique using a synthetic microbial community, and then applied it to samples from the Bijah and Mound hot springs at Yellowstone National Park. Among their findings was that the microbes at Mound Spring had higher potential to produce methane than the microbes from Bijah Spring. They also identified a microbial genome from Bijah Spring that could reduce nitrite to nitrogen. Applying this new technology to additional sample sites will add to the range of hitherto uncharacterized microbial capabilities with potential DOE mission applicability.

On a large scale, snow regulates the temperature of Earth’s surface and alters the general circulation of the climate. At a smaller scale, it affects regional climate and water resources. Light-absorbing particles, primarily black carbon (BC), brown carbon, and dust, impact how well the snow reflects light, thereby influencing Earth’s albedo. Researchers, led by scientists at the Department of Energy’s Pacific Northwest National Laboratory, used a regional modeling framework to simulate BC and dust and their direct radiative forcing in snowpack. They found that the simulations are consistent in spatial variability with observations for black carbon and dust mass concentrations (BCS and DSTS, respectively) in the top snow layer, while they underestimate BCS in clean regions and overestimate BCS in some polluted regions. BCS and DSTS result in a similar magnitude of radiative warming in the snowpack, which is comparable to the amount of surface radiative cooling due to BC and dust in the atmosphere. To produce the simulations, the research used the Weather Research and Forecasting (WRF) model, a state-of-the-art regional model with a chemistry component. They coupled it with the snow, ice, and aerosol radiative (SNICAR) model that includes the most sophisticated representation of snow metamorphism processes available for climate study. The coupled model simulated black carbon and dust concentrations and their radiative forcing in seasonal snow over North China in January through February 2010, with extensive field measurements used to evaluate the model performance. The findings highlight a need for more observations, particularly concurrent measurements of atmospheric and snow aerosols and the deposition aerosol fluxes, in future campaigns.

The ongoing drought in California is causing statewide water stress and severe economic loss and has raised an important scientific question: Will California continue to experience more drought in the coming decades? Using multi-ensemble simulations of the Community Earth System Model (CESM) and multimodel simulations archived in the Coupled Model Intercomparison Project phase 5 (CMIP5), Department of Energy scientists at Pacific Northwest National Laboratory and researchers at Utah State University found that water cycle extremes, including both extreme drought and flood, are projected to increase at least 50 percent to the end of the 21st century. They found that this projected increase in water cycle extremes is associated with a strengthened relationship to El Niño and the Southern Oscillation (ENSO). In particular, the association is with extreme El Niño and La Niña events that modulate California’s climate not only through its warm and cold phases, but also its precursor patterns. Two sets of the sensitivity experiments with CESM substantiated the role of the changing ENSO cycle on the water cycle extremes in California under global warming.

Under continuous drought conditions since 2012, California’s drought considerably worsened in the winter of 2013–2014. This change fueled an extreme fire season in 2014. Using the satellite-retrieved burned area and the index indicating extreme fire risk, scientists at Pacific Northwest National Laboratory and Utah State University found that the 2014 fire season is the second largest in terms of burned area in northern California since 1997 (second only to 2012), and stands the highest since 1979 in rankings of extreme fire risk over the entire state. The research used the Keetch-Byram Drought Index (KBDI) based on the historical observation and multi-ensemble simulations of the Community Earth System Model (CESM). Measures of extreme fire risk are also expected to increase in the future despite an overall lack of change in the mean fire probability and annual precipitation, as simulated by CESM for the next 50 years. Manmade global warming is likely one of the causes that will exacerbate the areal extent and frequency of extreme fire risk, though the influence of internal climate variability on the 2014 and future fire season is difficult to ascertain.

The aerosol indirect effect, by altering cloud radiative forcing, is one of the largest uncertainties in understanding climate change. Researchers, including Department of Energy scientists at Pacific Northwest National Laboratory, examined multi-year climate variability associated with sea salt aerosols and their contribution to the variability of pre-industrial shortwave cloud forcing (SWCF) using a 150-year simulation of the Community Earth System Model version 1.0 (CESM1). The results suggest that changes in sea salt and related cloud and radiative properties on interannual timescales are dominated by the El Niño-Southern Oscillation (ENSO) cycle. Sea salt variability on longer timescales is associated with low-frequency variability in the Pacific Ocean similar to the Interdecadal Pacific Oscillation, but does not show a statistically significant spectral peak. The researchers found that sea salt aerosol variability may contribute to short-wave cloud forcing (SWCF) variability in the tropical Pacific, explaining up to 20 percent to 30 percent of the variance in that region. Elsewhere, there is only a small sea salt aerosol influence on SWCF through modifying cloud droplet number and liquid water path that contributes to the change of cloud effective radius and cloud optical depth (and hence cloud albedo), producing a multi-year aerosol-cloud-wind interaction.

Black carbon (BC) and organic carbon (OC) particles—from forest fires, diesel engines, and other fuel combustion—ride on atmospheric currents and reach high and remote places such as the Tibetan Plateau, affecting snow melt and glaciers, which, in turn, record the history of these particles. Researchers at the Department of Energy’s Pacific Northwest National Laboratory and Institute of Tibetan Plateau Research (Chinese Academy of Sciences) designed a new way to identify sources of these particles and the cause of their historical trend in a Tibetan glacier using a tracer tagging technique in a climate model [Community Atmosphere Model version 5 (CAM5)]. They analyzed high temporal resolution measurements of BC and OC covering the time period of 1956 to 2006 in an ice core over the southeastern Tibetan Plateau that show a distinct seasonal dependence of BC and OC with higher respective concentrations but a lower OC/BC ratio in the non-monsoon season than during the summer monsoon. Using a global aerosol-climate model, in which BC emitted from different source regions can be explicitly tracked, they quantified BC source–receptor relationships between four Asian source regions and the southeastern Tibetan Plateau as a receptor.

The model results showed that BC recorded in the southeastern Tibetan glacier primarily originated in South Asia primarily during the non-monsoon season (October to May), followed by East Asia during the summer monsoon (June to September). The ice core record also indicates stable and relatively low BC and OC deposition fluxes from the late 1950s to 1980, followed by an overall increase to recent years, a trend consistent with the BC and OC emission inventories and fuel consumption of South Asia. Moreover, the increasing trend of the OC/BC ratio since the early 1990s indicates a growing contribution of coal combustion and biomass burning to the emissions. The estimated radiative forcing induced by BC and OC impurities in snow has increased since 1980, suggesting an increasing influence of carbonaceous aerosols on the Tibetan glacier melting and the availability of water resources in the surrounding regions. The findings contribute to insights into the impact of carbonaceous particles on glacier melting and potential mitigation actions.

Due to constraints on computing resources, weather and climate calculations can only be done at finite—and often coarse—temporal resolutions, inevitably causing error. A novel technique, developed by scientists at Pacific Northwest National Laboratory, Sandia National Laboratories, and University of Michigan, efficiently quantified and attributed time-resolution errors in the Community Atmosphere Model version 5 (CAM5). Their work is the first publication to evaluate the time-step convergence, namely the reduction of numerical error as a result of a decrease in time-step length, in its strict mathematical sense, in a full-fledged atmospheric general circulation model. This is also the first attempt in the climate modeling community to quantitatively compare time-stepping errors, associated with different physical processes, in a model’s operational configuration. The team found that the temperature error in CAM5 converges at a rate of 0.4 instead of 1.0, indicating the error does not decrease as quickly as expected when the temporal resolution is increased. They performed sensitivity simulations to evaluate various subgrid-scale physical parameterizations in isolation. These simulations led to the conclusion that the representation of stratiform clouds is the primary source of time-stepping error in CAM5. The research showed that in this model, processes associated with the slowest convergence rates also produced the largest errors and strongest artificial sensitivities. Slow convergence is thus a ‘‘?ag’’ for model components that do not accurately represent the intended physical balance of processes and require more attention for improvement.

California receives half of its total annual precipitation in five to 15 days of the year, making its precipitation patterns some of the most intermittent in the United States. Importantly, most of this precipitation falls during the winter months and largely in the northern and mountainous parts of the state as snow, which acts like a natural surface water reservoir and is released during dry portions of the year. Thus, the integrity of California’s economy, and agricultural identity, is largely dependent on ample snowpack accumulation in the Sierra Nevada. Unfortunately, over the past 50 years, numerous observational studies have shown that snowpack has been in steady decline throughout much of the western United States, including the northern Sierra Nevada.

A recent study analyzed the efficacy of a new cutting-edge modeling technique, variable-resolution modeling using the Community Earth System Model (VR-CESM), at horizontal resolutions of 14 km and 28 km (and three topographic characterizations) in representing Sierra Nevada snowpack [i.e., snow water equivalent (SWE) and snow cover (SNOWC)]. VR-CESM was compared with a suite of observational, reanalysis, and dynamically downscaled model results. Overall, considering California’s complex terrain, intermittent precipitation, and that the VR-CESM simulations were only constrained by prescribed sea surface temperatures and sea ice extent data, a 0.68 centered Pearson product-moment correlation, negative mean winter SWE bias of <7 mm, interquartile range well within the values exhibited in the reanalysis datasets, and mean winter SNOWC within 7% of the expected satellite derived value, the efficacy of the VR-CESM framework was shown.

VR-CESM is a novel tool for modeling the climate system and represents a hybrid of global and regional climate models. It is envisioned that this new modeling framework will bring added value to the snowpack modeling community with the benefit of a global solution, accounting for major teleconnections and regional high-resolution, with better representation of winter storms and orographic forcings. Additionally, VR-CESM can be run for a fraction of the cost of a high-resolution global climate model run, on a local server (<1000 processors), with 20 to 40 day turnarounds on 25-year simulation periods, and provide model resolutions (28 km to 14 km), which decision makers (especially in the western United States water sector), may find more useful in regional planning endeavors. The enhanced representation of snowpack and relative computational efficiency of VR-CESM lends itself well to future investigations of other snowpack-dependent regions of the western United States, as well as ensemble-based climate change scenario analysis. This research is underway.

Many scientists expect that carbon emitted from the burning of greenhouse gases and its accompanying heat will be predominantly sequestered within the deep ocean instead of the atmosphere. Understanding the mechanisms and quantifying the rate and variability of this sequestration has profound implications for predicting the rate of atmospheric warming over the next century. A recent publication by Department of Energy-supported scientists at Los Alamos National Laboratory describes a new approach to track motion and mixing in an ocean model. Horizontal and vertical structure of mixing is quantified, along with its dependence upon eddy velocities, using the high-performance Lagrangian particle tracking (LIGHT) software within the Model for Prediction Across Scales Ocean (MPAS-O). The model computes ocean mixing directly from particle statistics to better understand the processes driving mixing and suggests improved methods to simulate them, which is vital for improved ocean and climate modeling.

To simulate the variety of cloud types observed in the atmosphere, climate modelers have historically used different representations for different clouds. This approach causes discontinuities as simulated conditions change such that one cloud scheme turns off and another one turns on. A unified cloud parameterization overcomes this limitation by being general enough that it can be used to represent all cloud types, ensuring smooth transitions of the simulated clouds as environmental conditions change. A team of climate modelers from the National Center for Atmospheric Research, University of Wisconsin–Milwaukee, University of Washington, and Pacific Northwest National Laboratory found improvements in simulated clouds when a statistical interface between cloud properties and cloud processes was introduced in a turbulence scheme to accommodate a diversity of overlapping cloud microphysical conditions within model grid cells. This was allowed to extend throughout the lower atmosphere to simulate all clouds. The researchers expect that alternate methods of accounting for unresolved variability, such as quadrature, could reduce the computation cost of sampling the variability.

The Michaelis–Menten (MM) kinetics and reverse Michaelis–Menten (RMM) kinetics are two popular mathematical formulations used in many land biogeochemical models to describe how microbes and plants would respond to changes in substrate abundance. However, the criteria of when to use either of the two are often ambiguous. A recent Department of Energy-supported study shows that these two kinetics are special approximations to the equilibrium chemistry approximation (ECA) kinetics, which is the first-order approximation to the quadratic kinetics that solves the equation of an enzyme–substrate complex exactly for a single-enzyme and single-substrate biogeochemical reaction. The popular MM kinetics and RMM kinetics are thus inconsistent approximations to their foundation–law of mass action, in that the MM kinetics fails to consider the mass balance constraint from substrate abundance, and the RMM kinetics fails to consider the mass balance constraint from organism abundance. In contrast, when benchmarked with the quadratic kinetics, which is the exact solution to the substrate-uptake problem formulated with the total quasi-steady-state approximation for a single-substrate-single-enzyme system, the ECA appropriately incorporates the mass balance constraints from both substrates and organisms, and predicts consistent parametric sensitivity across a wide range of substrate and organism abundances. This finding resolves the ambiguity in choosing which substrate kinetics for a consistent biogeochemical modeling. The ECA kinetics is expected to motivate a new generation of more robust biogeochemical models for earth system models.

Predictive understanding of northern peatland hydrology is a necessary precursor to understanding the fate of massive carbon stores in these systems under the influence of present and future climate change. Current models have begun to address microtopographic controls on peatland hydrology, but none have included a prognostic calculation of peatland water table depth for a vegetated wetland, independent of prescribed regional water tables. A recent study introduces a new configuration of the Community Land Model (CLM), which includes a fully prognostic water table calculation for a vegetated peatland. The structural and process changes to CLM focus on modifications needed to represent the hydrologic cycle of the bog environment with perched water tables, as well as distinct hydrologic dynamics and vegetation communities of the raised hummock and sunken hollow microtopography characteristic of peatland bogs. The modified model was parameterized and independently evaluated against observations from an ombrotrophic raised-dome bog in northern Minnesota (S1-Bog), the site for the Spruce and Peatland Responses Under Climatic and Environmental Change experiment (SPRUCE). Simulated water table levels compared well with site-level observations. The new model predicts hydrologic changes in response to planned warming at the SPRUCE site. At present, standing water is commonly observed in bog hollows after large rainfall events during the growing season, but simulations suggest a sharp decrease in water table levels due to increased evapotranspiration under the most extreme warming level, nearly eliminating the occurrence of standing water in the growing season. Simulated soil energy balance was strongly influenced by reduced winter snowpack under warming simulations, with the warming influence on soil temperature partly offset by the loss of insulating snowpack in early and late winter. The new model provides improved predictive capacity for seasonal hydrological dynamics in northern peatlands and a useful foundation for investigating northern peatland carbon exchange.

Chemistry-climate models provide a valuable means for projecting future air quality in a changing climate, but recent assessments have lacked commensurate observational comparisons to establish their credibility in reproducing current cycles in surface ozone over polluted regions. The models in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) were used in the recent assessment of the Intergovernmental Panel on Climate Change (IPCC) and represent the most advanced attempt to simulate global surface ozone in a future climate. However, to have confidence in the models’ projections, their ability to simulate the observed, present-day surface ozone climatology must be evaluated.

A recent study tested the current generation of global chemistry-climate models in their ability to simulate observed, present-day surface ozone. Models are evaluated against hourly surface ozone from 4,217 stations in North America and Europe that are averaged over 1° x 1° grid cells, allowing commensurate model-measurement comparison. Models are generally biased high during all hours of the day and in all regions. Most models simulate the shape of regional summertime diurnal and annual cycles well, correctly matching the timing of hourly (~15:00) and monthly (mid-June) peak surface ozone abundance. The amplitude of these cycles is less successfully matched. The observed summertime diurnal range (~25 parts per billion (ppb)) is underestimated in all regions by about 7 ppb, and the observed seasonal range (~21 ppb) is underestimated by about 5 ppb except in the most polluted regions where it is overestimated by about 5 ppb. The models generally match the pattern of the observed summertime ozone enhancement, but they overestimate its magnitude in most regions. Most models capture the observed distribution of extreme episode sizes, correctly showing that about 80 percent of individual extreme events occur in large-scale, multi-day episodes of more than 100 grid cells. The models also match the observed linear relationship between episode size and a measure of episode intensity, which shows increases in ozone abundance by up to 6 ppb for larger-sized episodes. This study concludes that the skill of the models evaluated provides confidence in their projections of future surface ozone.

Cirrus clouds play an important role in regulating Earth’s radiative budget and water vapor distribution in the upper troposphere. Ice crystals in cirrus clouds may form by both homogeneous freezing of solution (aerosol) droplets and heterogeneous ice nucleation on insoluble aerosol particles, called ice nuclei. There are two processes that are currently missing in the ice nucleation parameterization used by the Community Atmosphere Model version 5 (CAM5). First, pre-existing ice particles may deplete available water vapor in the air and prohibit the ice nucleation process. Second, due to in-cloud variability of saturation ratio, the homogeneous nucleation can take place only in a small portion of the cloudy area. Motivated by these problems, a team of scientists, including a U.S. Department of Energy researcher at Pacific Northwest National Laboratory, implemented a new ice nucleation treatment in CAM5. The team found that the impact of considering pre-existing ice crystals and in-cloud variability of supersaturation is significant, and it increases the contribution of heterogeneous ice nucleation to ice crystal number production in cirrus clouds. Compared to observations, the work improved the new model in both the ice number concentrations and the probability distributions of ice number concentration simulated.

Superparameterized models are a new type of atmospheric model used in climate models that capture detailed cloud behavior by embedding a high-resolution cloud-resolving model (CRM) within a climate model gridbox. Superparameterized general circulation models (GCM) are in their infancy, have never been carefully tuned, and are incompletely understood especially in terms of the mechanisms that allow attractive forms of emergent behavior linked to organized deep convection. A recent Department of Energy-supported study explores the effect of reducing the large-scale model time step, which has the byproduct of increasing the frequency with which the planetary versus cloud resolving scales are allowed to interact. The experiments reveal interesting reductions in cloud biases, and a mysterious shift to a climate that has more bottom-heavy tropical convection, stronger rainfall extremes, and more faithfully satisfies the weak-temperature gradient. These results are relevant to understanding convective organization physics and informing climate model development in the next generation of convection-permitting GCMs.

In the ocean, vertical diffusion is several orders of magnitude smaller than horizontal diffusion. Ocean models have difficulty in reproducing low values of vertical diffusion due to spurious mixing intrinsic to the numerical algorithms. Recent work supported by the Department of Energy shows that spurious vertical mixing may be reduced by several advanced techniques.

The Model for Prediction Across Scales-Ocean (MPAS-Ocean) was validated against five long-standing ocean models using five domains, ranging from simple idealized test cases to real-world simulations. MPAS-Ocean produces results commensurate with the other models, validating the functionality of the new model. In addition, MPAS-Ocean produced less spurious mixing than other models, by up to a factor of ten, as measured by the resting potential energy. This result is due to a combination of the vertical coordinate, hexagon-type horizontal grid, and a tracer advection scheme designed for these grids. Ocean models are often categorized by their vertical coordinate. The Arbitrary Lagrangian-Eulerian method (ALE) of the MPAS-Ocean model offers great flexibility, so users can choose from numerous vertical coordinates: z-level (fixed), z-star (expands with sea surface), z-tilde (grid moves with fast waves), sigma (terrain-following), and idealized isopycnal (density surfaces). All of these modes were validated in idealized test cases and compared to other ocean models, including the Parallel Ocean Program (POP), Modular Ocean Model (MOM), MIT General Circulation Model (MITgcm), Regional Ocean Modeling System (ROMS), and Hallberg Isopycnal Model (HIM). The z-type coordinates were validated using real-world cases. MPAS-Ocean performed similarly or better than long-standing ocean models, and certain configurations of the vertical coordinate dramatically reduced the spurious mixing. Thanks to improved algorithms, MPAS-Ocean will better represent physical mixing processes in climate simulations, leading to more accurate climate studies.

Understanding patterns of protein abundance and diversity is critical for assessing soil ecosystem function. Unclear, however, is how minerals interact with proteins to affect nitrogen availability in soil environments. To address this question, a team of scientists from Oregon State University, Environmental Molecular Sciences Laboratory, and Leibniz Zentrum für Agrarlandschaftsforschung characterized reactions of a model protein called Gb1 with a mineral that contains manganese oxide (birnessite) and one that does not (kaolinite). They used nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopies at EMSL, a Department of Energy (DOE) scientific user facility. Their findings suggest that a mineral’s impact on a protein depends on the type of mineral present in the environment. In contrast to kaolinite, the manganese oxide-containing birnessite fragmented Gb1 to produce soluble peptides available to soil biota. The results confirm the existence of an abiotic pathway for the formation of organic nitrogen compounds for direct uptake by plants and microorganisms, highlighting the potential influence abiotic protein degradation could have on soil nitrogen turnover and bioavailability. Moreover, the study highlights NMR and EPR spectroscopies as valuable tools to observe reactions between proteins and minerals to shed light on soil ecosystem function.

High-yield production of bioproducts and fuels in microbial systems requires metabolic flux to be directed toward an engineered pathway. However, this redirection of metabolic flux is difficult to achieve because cells tend to divert metabolic flux toward native cellular processes. Engineered metabolic pathways have been confined to organelles such as the mitochondrion or the vacuole to isolate them from the host’s metabolism, but the cell needs those organelles for its normal functions and, therefore, they cannot be completely repurposed. On the other hand, yeast can live without peroxisomes, making this an ideal organelle to isolate newly designed metabolic pathways and their products. A research team at UC Berkeley has discovered a protein signal that allows the efficient targeting of engineered proteins into the peroxisome. The researchers also devised a high-throughput method to measure the efficiency of the process and demonstrated the feasibility of the approach by introducing a simple metabolic pathway that produces a colored compound into the yeast peroxisome. The strategy can now be used to sequester useful metabolic pathways into the peroxisome to produce high yields of valuable chemicals and fuels.

This research has resulted in the development of a genetic sensor that provides a fluorescent readout proportional to the intracellular concentration of 3-hydroxypropionate, a valuable plastic precursor also called 3HP. This sensor required the introduction of several enzymes into the model bacterium Escherichia coli to convert 3HP into acrylate (another plastic precursor). Next, the gene for a fluorescent reporter whose expression is activated by acrylate also was introduced into the same E. coli strain so that when acrylate is produced, fluorescence can be detected and used as proxy for the amount of 3HP synthesized. With this system, the researchers could easily identify a strain and culture conditions that produced over 20 times more 3HP than previously achieved. At the same time, this research demonstrated the first heterologous pathway for microbial production of acrylate. The investigators proved the flexibility of the approach by designing a similar sensor to monitor muconate (used to make nylon) and glucarate (needed for manufacturing detergents and other chemicals). The fluorescent biosensors developed by this research combined with fluorescence-based cell sorting will accelerate the development of sustainable production of relevant chemicals such as biofuels and biopolymers in engineered microbial systems.

The use of ¹³C-MFA can provide key insights into the metabolic networks of microbial cells that are used for producting biofuels or valuable chemicals. This technique can be combined with either NMR spectrometry or mass spectrometry to infer metabolic fluxes within cells based on the characteristic rearrangement of ¹³C tracers through metabolic pathways. However, position-specific ¹³C-labeling of metabolites has been particularly difficult to obtain using conventional NMR or mass spectrometry techniques, hindering accurate estimations of metabolic fluxes. To overcome this problem, researchers from the Department of Energy’s Environmental Molecular Sciences Laboratory (EMSL; a national scientific user facility), Washington State University, Duke University Medical Center, and Miami University developed a new technique that combines ¹³C-MFA with non-uniform sampling (NUS), which dramatically reduces the time required to collect high-resolution NMR data. NUS techniques acquire only a subset of NMR data points and use sophisticated reconstruction methods that ultimately allow extraction of complete sets of chemical shift information. Using EMSL’s 600 MHz and 800 MHz NMR spectrometers, the research team demonstrated that their approach provides detailed information about position-specific labeling patterns that can be incorporated into metabolic flux models. By enabling more accurate estimations of metabolic fluxes in complex biological systems, the new technique could shed light on environmental nutrient cycling and enhance synthetic biology-based engineering efforts to modify living systems for production of metabolites or other products of interest, such as biofuels or fine chemicals.

Organic acids, particularly dicarboxylic acids, play a key role in the formation of atmospheric aerosol particles. These particles, in turn, can promote formation of cloud droplets, thereby having a major impact on climate. However, the precise mechanisms by which organic acids promote aerosol particle formation, especially during early stages, have remained unclear. To address this question, researchers from the Department of Energy’s Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility, and Pacific Northwest National Laboratory studied the properties of dicarboxylic acid homodimer complexes. These complexes are thought to play an important role in atmospheric aerosol particle formation. The researchers integrated experimental photoelectron spectroscopy measurements and high-performance computational capabilities of EMSL’s NWChem computational chemistry code and Cascade high-performance computer, as well as the Computer Network Information Center, Chinese Academy of Sciences. Experimental data showed that the dimer complexes, which are two identical molecules linked together, are extremely stable. Theoretical calculations revealed that strong hydrogen bonds are extremely important in enhancing the stability of these complexes and that thermodynamically, they are likely to form under low evaporation rates. Taken together, the findings suggest that dicarboxylic acid homodimer complexes play an important role in promoting the formation and growth of atmospheric aerosol particles.

Various regions of the aquifer responded differently to the snowmelt-driven hydrologic perturbation based on redox state, with dissolved oxygen penetrating deeply into oxidized regions, and being rapidly consumed via abiotic reactions in naturally reduced regions, liberating Fe²⁺ and U⁶⁺ species. Microbial community composition varied across spatial and temporal scales. During periods of elevated river stage associated with increasing dissolved oxygen concentrations in the aquifer, microbial community composition favored putative chemolithoautotrophs and heterotrophs, while putative fermenters within the candidate phyla radiation (CPR) were greatly enriched (e.g., members of the Microgenomates and Parcubacteria) during water table fall. Reactive transport modeling was able to capture the dynamic behavior of both the geochemistry and microbiology at the site during the fluctuating hydrology, suggesting that a predictive framework can be developed to better understand biogeochemical responses to future hydrologic dynamics.

The yeast Yarrowia lipolytica is capable of accumulating a large amount of lipids when nitrogen is limited. This ability, along with its amenability to genetic methods, has made Y. lipolytica an attractive model for generating high-value lipids for biofuel production. However, relatively little is known about the factors that regulate enzymatic pathways responsible for lipid accumulation in this species. To address this knowledge gap, a team of researchers from Pacific Northwest National Laboratory (PNNL) integrated metabolome, proteome, and phosphoproteome data to characterize lipid accumulation in response to limited nitrogen in Y. lipolytica. The researchers used a microscopy system that integrates nonlinear two-photon excitation, laser-scanning confocal microscopy, and fluorescence lifetime imaging at the Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) scientific user facility. In this first global study of protein phosphorylation in Y. lipolytica, the researchers focused their analysis on changes in the expression and phosphorylation state of regulatory proteins, including kinases, phosphatases, and transcription factors. They found that lipid accumulation in response to nitrogen limitation results from two distinct processes: (1) higher production of malonyl-CoA from excess citrate increases the pool of building blocks for lipid production, and (2) decreased capacity for β-oxidation reduces lipid consumption. These findings provide new genetic targets that could be manipulated to improve lipid yields in future metabolic engineering efforts.

Cumulus clouds play an important role in energy and water transfers in the climate system. However, representation of such clouds in the regional and global climate models is one of the major error sources of weather and climate predictions. Using the cloud-resolving modeling (CRM) simulations of convective clouds at the midlatitudes and tropics, a team of scientists, led by a U.S. Department of Energy researcher at Pacific Northwest National Laboratory, found the cumulus cloud fraction and convective transport of moisture by the unsolved cumulus clouds are strongly grid-spacing dependent. The team found that there are strong grid-spacing dependencies of updraft and downdraft fractions regardless of altitudes, cloud life stage, and geographical location. The single updraft approach for representing unsolved cumulus clouds significantly underestimates updraft eddy transport of water vapor because it fails to account for the large internal variability of updrafts, while a single downdraft represents the downdraft eddy transport of water vapor well. The team developed a new representation, accounting for the updraft variability and well representing the convective transport calculated from CRM simulations at different model grid-spacings.

Many detection and attribution and pattern scaling studies assume that the global climate response to multiple forcings is additive: the response over the historical period is statistically indistinguishable from the sum of responses to individual forcings. In a recent study, researchers used the National Aeronautics and Space Administration’s Goddard Institute for Space Studies (GISS) and National Center for Atmospheric Research Community Climate System Model (CCSM) simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive to test this assumption for multiyear trends in global-average, annual-average temperature and precipitation at multiple timescales. Findings show that responses in models forced by pre-computed aerosol and ozone concentrations are generally additive across forcings; however, the study demonstrates that there are significant nonlinearities in precipitation responses to different forcings in a configuration of the GISS model that interactively computes these concentrations from precursor emissions. These nonlinearities are attributed to differences in ozone forcing arising from interactions between forcing agents. Study results suggest that attribution to specific forcings may be complicated in a model with fully interactive chemistry and may provide motivation for other modeling groups to conduct further single-forcing experiments.

High-resolution predictions of land surface hydrological dynamics are desirable for improved investigations of regional- and watershed-scale processes. Direct deterministic simulations of fine-resolution land surface variables present many challenges, including high computational cost. In a recent Department of Energy (DOE)-supported study, statistically based reduced-order modeling techniques were used to facilitate emulation of fine-resolution simulations. An emulator, a Gaussian process regression, was used to approximate fine-resolution four-dimensional soil moisture fields predicted using a three-dimensional surface-subsurface hydrological simulator (PFLOTRAN). A dimension-reduction technique known as “proper orthogonal decomposition” is further used to improve the efficiency of the resulting reduced-order model (ROM). The ROM reduces simulation computational demand to negligible levels compared to the underlying fine-resolution model. In addition, the ROM constructed was equipped with an uncertainty estimate, allowing modelers to construct a ROM consistent with uncertainty in the measured data. The ROM is also capable of constructing statistically equivalent analogues that can be used in uncertainty and sensitivity analyses. The technique was applied to four polygonal tundra sites near Barrow, Alaska, that are part of DOE’s Next-Generation Ecosystem Experiments (NGEE)-Arctic project. The ROM is trained for each site using simulated soil moisture from 1998 to 2000 and validated using the simulated data for 2002 and 2006. The average relative root-mean-square errors of the ROMs are under 1 percent. The study shows that this statistical method successfully captures detailed physics in a computationally affordable way, and may be a suitable approach for modeling complex physical systems such as evolving tundra.

Well known is that wide parcels of air accelerate more slowly than narrow parcels, and the effect is stronger when the parcel is near the surface. These effects are simulated crudely in climate models, which have coarse resolutions and thus cannot simulate narrow parcels and their vigorous vertical motion; such models are operating in the hydrostatic limit. Although this effect is well-known, it has not been well-quantified nor understood.

A recent study supported by the Department of Energy derives theoretical equations to explain the physics behind these phenomena, by finding mathematical formulae for the acceleration of buoyant parcels as functions of both parcel aspect ratio (width/height) and surface proximity. These formulae may be especially useful both in gaining physical insight into atmospheric convective systems, and in mapping out the gray zone of numerical modeling, as limitations of hydrostatic effect are reached and non-hydrostatic effects become important.

Many global climate models (GCMs) can run in a mode where a single column (representing a particular latitude and longitude) from the full three-dimensional model is run independently, forced by initial and boundary conditions from observations or from GCM output. The single-column model (SCM) is important for climate model parameterization development because it is simple (allowing users to dig deeply into the processes governing model behavior) and computationally efficient (permitting users to quickly try many code permutations).

Version 5 of the Community Atmosphere Model (CAM5), which serves as the basis for the Department of Energy’s Accelerated Climate Model for Energy (ACME) initiative, includes prognostic aerosol equations but does not specify initial or boundary conditions for aerosols in SCM mode. A recent study notes this lack, documents its effect, and tests several solutions. The findings show that lack of aerosol information causes major problems for SCM studies involving non-convective clouds, but has little impact on convective cloud regimes because convection schemes currently do not use aerosol information. Fixing this problem is important because SCM functionality is often used for non-convective cloud regimes and because future model versions will include aerosol effects on convection. All three fixes for aerosols in the SCM were effective; the best solution depends on the case study being run and the modeler’s goals.

Feedstock recalcitrance is the greatest barrier to cost-effective production of cellulosic biofuels. To overcome this recalcitrance, existing commercial cellulosic ethanol facilities employ thermochemical pretreatment with subsequent addition of fungal cellulase. However, processing cellulosic biomass without thermochemical pretreatment may be possible using thermophilic, cellulolytic bacteria. Researchers at the Department of Energy’s (DOE) BioEnergy Science Center (BESC) examined the ability of various thermophilic bacteria to solubilize autoclaved, but otherwise unpretreated cellulosic biomass. Carbohydrate solubilization of mid-season harvested switchgrass after 5 days ranged from 24 percent to 65 percent, with Clostridium thermocellum showing the best results among the four thermophiles tested. This finding was as much as fivefold better than with the standard method using a fungal cellulase cocktail and yeast fermentation. Other findings showed that there was equal fractional solubilization of glucan and xylan, and, importantly, that there was no biological solubilization of the noncarbohydrate fraction of biomass. A fivefold improvement over standard treatment was observed when using the most effective biocatalyst. Using thermophilic bacteria in biomass-solubilizing systems may enable a reduction in the amount of nonbiological processing required and, in particular, substitution of cotreatment for pretreatment.

TCAP was designed to provide a detailed set of observations to tackle an area of unknowns about aerosol particle optical properties in an area where human-caused effects are present. A team of researchers led by Department of Energy (DOE) scientists at Pacific Northwest National Laboratory (PNNL) organized a year-long deployment of the Atmospheric Radiation Measurement (ARM) Mobile Facility to Cape Cod, Massachusetts, for the 12-month duration of the TCAP project. The surface measurements were augmented by two separate one-month long deployments of the ARM Aerial Facility (AAF), one in the summer and one in winter. Few datasets currently combine the range of detailed measurements like those made during TCAP over a range of seasons; in particular, measurements to examine the chemical composition of aerosol particles, their optical properties, and their ability to act as seeds for cloud drops. Using the AFF data, the team found that elevated layers of aerosols occurred on four of six cloud-free days sampled during the summer deployment period. These layers, with increased amounts of biomass burning material and nitrate compared to aerosol at other altitudes, have a large impact on the amount of sunlight reaching Earth’s surface. This TCAP data will be used to better constrain regional and global models.

The most effective commercial enzyme cocktails of carbohydrate-active enzymes (CAZymes) used in vitro to pretreat biomass are derived from fungal cellulases. These cellobiohydrolases, endoglucanases, and β-d-glucosidases act synergistically to release sugars for microbial conversion. The genome of the thermophilic bacterium C. bescii encodes a potent set of CAZymes, found primarily as multidomain enzymes. This set of CAZymes exhibits high cellulolytic and hemicellulolytic activity on and allows utilization of a broad range of substrates, including plant biomass, without conventional pretreatment. CelA, the most abundant cellulase in the C. bescii secretome, uniquely combines a GH9 endoglucanase and a GH48 exoglucanase in a single protein. E1 is an endo-1,4-β-glucanase from A. cellulolyticus linked to a family 2 carbohydrate-binding module shown to bind primarily to cellulosic substrates and has been shown in vitro to work synergistically with CelA. To test if the addition of E1 to the C. bescii secretome would improve its cellulolytic activity, U.S. Department of Energy (DOE) BioEnergy Science Center (BESC) scientists cloned and expressed the E1 gene in C. bescii under the transcriptional control of the C. bescii S-layer promoter, and secretion was directed by the addition of the C. bescii CelA signal peptide sequence. Increased activity of the secretome of the strain containing E1 was observed on both carboxymethylcellulose (CMC) and Avicel. Activity against CMC increased on average 10.8 percent at 65 °C, and 12.6 percent at 75 °C. Activity against Avicel increased on average 17.5 percent at 65 °C and 16.4 percent at 75 °C. Thus, expression and secretion of E1 in C. bescii enhanced the cellulolytic ability of its secretome in agreement with in vitro evidence that E1 acts synergistically with CelA to digest cellulose. This result offers the possibility of engineering additional enzymes for improved biomass deconstruction into C. bescii effectively.

Researchers at the Department of Energy’s (DOE) Great Lakes Bioenergy Research Center (GLBRC) have used chemically synthesized nanostructure-initiator mass spectrometry (NIMS) probes derivatized with tetrasaccharides to study the reactivity of several enzymes representative of GH function. Patterns of reactivity identified with these NIMS probes provide a diagnostic approach to assess reaction selectivity as well as comparative apparent rate information. Their results show diagnostic patterns for reactions of a β-glucosidase, relaxed but varied specificity of several endoglucanases, and high specificity of a cellobiohydrolase with the model substrate. The researchers also modeled time-dependent reactions of these enzymes by numerical integration, providing a quantitative basis to make functional distinctions among reactive properties, thus providing a new approach to enhance the annotation of GH phylogenetic trees with functional measurements. This research was carried out in collaboration with researchers at DOE’s Joint BioEnergy Institute (JBEI).

The microbial production of biofuels and chemicals often does not reach the theoretical maximum yield, even for engineered strains, thereby limiting the reliability of large-scale bioprocessing. To understand the limitations, scientists have started to investigate the reasons for phenotypic diversity of cells within a culture. A team of scientists from the University of Idaho, Environmental Molecular Sciences Laboratory (EMSL), and Massachusetts Institute of Technology used advanced microfluidics combined with Epifluorescent and Raman microscopy at EMSL to study differences in the ability of individual cells of low-yield and high-yield strains of the fungus Yarrowia lipolytica to produce lipids. The researchers found lipid production fluctuated sporadically with time in both strains. The researchers labeled this newly discovered phenomenon “bioprocessing noise.” Furthermore, the high-yield fungal strain showed reduced bioprocessing noise in lipid production than the low-yield fungal strain. This finding indicates differences in the activity of key metabolic genes that contribute to bioprocessing noise and thus cellular diversity in lipid production. Moreover, this variability was amplified by environmental factors such as chemical gradients of nutrients or waste products surrounding cells. Taken together, these findings show extracellular and intracellular fluctuations interact to place an upper limit on the reliability of lipid production and total yield of lipids. This research could pave the way for new strategies to improve the reliability and efficiency of using engineered microbial strains for the production of lipids that could then be converted to valuable biofuels or chemicals.

Uranium poses a serious risk to groundwater contamination at the Department of Energy’s (DOE) Hanford Site and other locations worldwide. Its chemistry is complex because uranium can exist in several different oxidation states, each having different properties. Remediation strategies have focused on developing approaches for converting the highly soluble U(VI) form of uranium into the less soluble U(IV) form, which poses less risk of contamination due to its lower mobility in groundwater and soil. While much research has focused on these two forms of uranium, remediation efforts have been limited by the lack of knowledge about the intermediate U(V) form. To address this question, a team of researchers recently examined in unprecedented detail the structural and thermodynamic properties of U(V)-containing compounds called metal monouranates. U(V)-containing monouranates allow in-depth structural and stability investigations. The research team used a variety of advanced structural and spectroscopic techniques combined with calorimetric measurements and computational modeling. Mossbauer and X-ray photoelectron spectroscopy (XPS) analyses were performed at the RadEMSL radiochemistry facility at the Environmental Molecular Sciences Laboratory (EMSL), a DOE national scientific user facility. This project received major support from the DOE Energy Frontier Research Center, “Materials Science of Actinides.” It was led by the University of California, Davis, and included participation by a team of scientists from Pacific Northwest National Laboratory; Los Alamos National Laboratory; Argonne National Laboratory; and Lawrence Berkeley National Laboratory; as well as the Nuclear Energy Center of the Negev, Israel; University of California, Berkeley; University of Michigan; and University of Chicago. The research team confirmed the presence of U(V) in the thermodynamically stable metal monouranates CrUO4 and FeUO4. The structural and thermodynamic behavior of U5+ elucidated in this work is relevant to applications in the nuclear industry and radioactive waste disposal. For example, the thermodynamic studies suggest these compounds are highly stable, making them potentially useful in precipitating uranium from oxidizing aqueous environments.

Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change, and extreme weather events. Trees store circa 90% of the total aboveground biomass (AGB) in tropical forests, and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity, and inherent variations in tree allometry and architecture. The researchers parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. They used 727 trees from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this dataset, the researchers assembled six scenarios designed to span existing gradients in floristic composition and size distribution to select models that best predict AGB at the landscape level across successional gradients. They found that good individual tree model fits do not necessarily translate into reliable AGB predictions at the landscape level. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests requires the inclusion of predictors that express inherent variations in species architecture. Reliable biomass assessments for the Amazon basin still depend on the collection of allometric data at the local and regional scales and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.

Earthquakes can produce significant tree mortality and consequently affect regional carbon dynamics. Unfortunately, detailed studies quantifying the influence of earthquakes on forest mortality are rare. This study assesses the committed forest biomass carbon loss associated with the 2008 Wenchuan earthquake in China with a synthetic approach that integrates field investigation, remote-sensing analysis, empirical models, and Monte Carlo simulations. The newly developed approach significantly improved the forest disturbance evaluation by quantitatively defining the earthquake impact boundary and detailed field survey to validate the mortality models. Based on this approach, a total biomass carbon of 10.9 Tg C was lost in the Wenchuan earthquake, which offset 0.23% of the living biomass carbon stock in Chinese forests. Tree mortality was highly clustered at the epicenter, declining rapidly with distance away from the fault zone. These findings suggest that earthquakes represent a significant driver to forest carbon dynamics, and the earthquake-induced biomass carbon loss should be included in estimating forest carbon budgets.

A research team, including Department of Energy (DOE) scientists at Pacific Northwest National Laboratory (PNNL), PNNL’s Joint Global Change Research Institute, and a U.S. Department of Agriculture researcher at Washington State University, transplanted soils between two elevations of semi-arid Rattlesnake Mountain, located in eastern Washington state. They chose sites separated by 500 m of elevation with similar plant species and soil types, but very different temperature and rainfall patterns. This experiment was initiated in 1994; 17 years later the team resampled the transplanted soils and controls, measuring carbon dioxide (CO₂) production, temperature response, enzyme activity, and bacterial community structure. After incubating the soils for 100 days, they found that transplanted soils (i.e., soils that had been moved between the two sites in 1994) respired roughly equal cumulative amounts of carbon as the nontransplanted soils. Soils transplanted from the hot, dry lower site to the cooler, wetter upper site exhibited almost no respiratory response to temperature—as the temperature rose, they barely responded—but soils originally from the upper cooler site respired at higher rates. However, the bacterial community structure of transplants did not change. These findings show that the climate changes experienced by the transplanted soils prompted significant differences in microbial activity, but no observed change to bacterial structure. These results support the idea that environmental shifts can influence soil carbon through metabolic changes in the soil microbial population, and that those microbes, responsible for the soil-to-atmosphere CO₂ flux, may be constrained in surprising ways.

This paper presents a data synthesis model to generate ecosystem data in climate simulations. This model is capable of (1) extracting key features of different physical properties in time and frequency domain, and (2) discovering and synthesizing the physical relationships between ecosystem variables in different feature spaces.

In evergreen tropical forests, the extent, magnitude, and controls on photosynthetic seasonality are poorly resolved and inadequately represented in Earth system models. Combining camera observations with ecosystem carbon dioxide fluxes at forests across rainfall gradients in the Amazon, this work shows that aggregate canopy phenology, not seasonality of climate drivers, is the primary cause of photosynthetic seasonality in these forests. Specifically, synchronization of new leaf growth with dry season litterfall shifts canopy composition toward younger, more light-use efficient leaves, explaining large seasonal increases (~27%) in ecosystem photosynthesis. Coordinated leaf development and demography thus reconcile seemingly disparate observations at different scales and indicate that accounting for leaf-level phenology is critical for accurately simulating ecosystem-scale responses to climate change.

Plant respiration results in an annual CO₂ flux to the atmosphere that is six times as large as that due to the emissions from fossil fuel burning, so changes in either will impact future climate. As plant respiration responds positively to temperature, a warming world may result in additional respiratory CO₂ releases and, hence, further atmospheric warming. Plant respiration can acclimate to altered temperatures (e.g., by downward reduction of their entire temperature-response curve in warmer conditions), weakening the positive feedback of plant respiration to rising global air temperature. However, lack of evidence on long-term (weeks to years) acclimation to climate warming in field settings currently hinders realistic predictions of respiratory release of CO₂ under future climatic conditions. To address this knowledge gap, a study was conducted from 2009 to 2013 to assess the acclimation capacity of more than 1,200 individuals of 10 dominant North American boreal and temperate tree species grown in ambient and warmed (+3.4 °C) plots in a unique open-air warming experiment in both open and understory forest habitats at two sites (~150 km apart) at the boreal-temperate forest ecotone in Minnesota, USA. For 1,620 leaves of these individuals, respiration was measured from 12 °C to 37 °C. Results found strong acclimation of leaf respiration to both experimental warming and seasonal temperature variation for juveniles of all 10 species. Plants grown and measured at temperatures 3.4 °C above ambient increased leaf respiration by 5% on average compared to plants grown and measured at ambient temperatures; without acclimation, these increases would have been 23%. Thus, acclimation eliminated 80% of the increase in leaf respiration expected of nonacclimated plants. Acclimation of leaf respiration per degree temperature change was similar for experimental warming and seasonal temperature variation. Moreover, the observed increase in leaf respiration per degree increase in temperature was less than half as large as the average reported for prior studies, which were conducted largely over shorter time scales in laboratory settings. If such dampening effects of leaf thermal acclimation occur generally, the increase of terrestrial plant respiration rates in response to climate warming may be less than predicted and, thus, may not raise atmospheric CO₂ concentrations as much as anticipated.

Researchers monitored nitrogen in tundra plants and soils during 5 years of experimental warming to quantify how plant access to soil nitrogen changed during permafrost thaw. Nitrogen is a scarce nutrient in high-latitude ecosystems, and plant access to soil nitrogen currently limits plant growth. Within 5 years of warming, plant-available nitrogen in soils increased. Warmed plants were able to grow larger and take up more carbon from the atmosphere than their unwarmed (control) neighbors. Though the study showed that plant biomass increased with warming, it is unlikely that the observed increase in plant carbon storage will be greater than losses of permafrost carbon at this site. In sum, plant carbon uptake offsets, in part, carbon releases from soils, but the system remains a net source of carbon to the atmosphere as a result of permafrost thaw and thus contributes toward accelerating climate change.

It is well known that the methylation of mercury (Hg) is mediated by bacteria and produces neurotoxic methylmercury (MeHg), which is also highly bioaccumulative in living organisms. However, the specific environments or locations in which MeHg is created are not well understood or identified. The recent finding of the specific genes (hgcAB) involved in Hg methylation provides a potential tool for scientists to identify the specific environments or locations where MeHg is created. Because the hgcAB genes are highly conserved, a team of scientists from Oak Ridge National Laboratory, Smithsonian Environmental Research Center, and Texas A&M University realized that they had a foundation for broadly evaluating spatial and niche-specific patterns of microbial Hg-methylation potential in natural environments. The team primarily used assembled and annotated data publicly available from the Department of Energy’s Joint Genome Institute to query hgcAB diversity and distribution in >3,500 publically available microbial metagenomes, encompassing a broad range of global environments. The hgcAB genes were found in nearly all anaerobic, but not aerobic, environments including oxygenated layers of the open ocean. Critically, hgcAB was effectively absent in ~1500 human and mammalian microbiomes, suggesting a low risk of endogenous MeHg production. New potential methylation habitats were identified, including invertebrate digestive tracts, thawing permafrost, coastal “dead zones,” soils, sediments, and extreme environments, suggesting multiple routes for MeHg entry into food webs. Several new taxonomic groups capable of Hg methylation emerged, including lineages having no cultured representatives. Phylogenetic analysis points to an evolutionary relationship between hgcA and genes encoding the corrinoid iron-sulfur proteins functioning in the ancient Wood-Ljungdahl carbon fixation pathway, suggesting that methanogenic archaea may have been the first to perform these biotransformations.

Currently, the most effective commercial enzyme cocktails of carbohydrate-active enzymes (CAZymes) used in vitro to pretreat biomass are derived from fungal cellulases. These cellobiohydrolases, endoglucanases, and β-d-glucosidases act synergistically to release sugars for microbial conversion. The genome of the thermophilic bacterium, Caldicellulosiruptor bescii, encodes a potent set of CAZymes, found primarily as multidomain enzymes. This set of CAZymes exhibit high cellulolytic and hemicellulolytic activity on and allow utilization of a broad range of substrates, including plant biomass without conventional pretreatment. CelA, the most abundant cellulase in the C. bescii secretome, uniquely combines a GH9 endoglucanase and a GH48 exoglucanase in a single protein. E1 is an endo-1,4-β-glucanase from Acidothermus cellulolyticus linked to a family 2 carbohydrate-binding module shown to bind primarily to cellulosic substrates and has been shown in vitro to work synergistically with CelA. To test if the addition of E1 to the C. bescii secretome would improve its cellulolytic activity, the E1 gene was cloned and expressed in C. bescii under the transcriptional control of the C. bescii S-layer promoter, and secretion was directed by the addition of the C. bescii CelA signal peptide sequence. Increased activity of the secretome of the strain containing E1 was observed on both carboxymethylcellulose (CMC) and Avicel. Activity against CMC increased on average 10.8 % at 65 °C and 12.6 % at 75 °C. Activity against Avicel increased on average 17.5 % at 65 °C and 16.4 % at 75 °C. Thus, expression and secretion of E1 in C. bescii enhanced the cellulolytic ability of its secretome in agreement with in vitro evidence that E1 acts synergistically with CelA to digest cellulose. This result offers the possibility of effectively engineering additional enzymes for improved biomass deconstruction into C. bescii.

Enzymes play a critical role in all aspects of life by speeding up specific chemical reactions in living cells. The glycoside hydrolases (GHs) are a group of enzymes that catalyze the breakdown of large quantities of organic matter in nature, specifically cellulose and hemicellulose, and that are being applied industrially to the conversion of biomass to useful products. GHs speed up the cleavage of an otherwise very stable chemical bond through a complex process that is not well understood. New research led by scientists at Oak Ridge National Laboratory (ORNL) on the key steps in the action of xylanase, a GH that cuts xylan chains in hemicellulose (a major component of biomass) into smaller units, has shown how this enzyme coordinates the movement of hydrogen ions to speed up the breakdown process. The scientists combined information from several neutron and X-ray crystallography experiments to visualize the exact atomic structure of the xylanase during the initial steps of the reaction. They found that a side chain of the enzyme amino acid residue that is key to its activity moves between two orientations to first accept a hydrogen ion and then deliver it to the place where the xylan is to be cut. In the former orientation, the side chain is more basic and thus is able to grab a hydrogen ion from water, whereas in the latter it becomes more acidic and ready to initiate the catalytic process. This publication is the first from the new Macromolecular Neutron Diffractometer (MaNDi) at ORNL’s Spallation Neutron Source. Scientists at Los Alamos National Laboratory, Argonne National Laboratory, the University of Toledo, and universities and user facilities in the People’s Republic of China, Sweden, and Germany collaborated in the research.

Aerosol particles have an important role in the climate system by absorbing and/or scattering radiation as well as by changing cloud reflectivity (albedo), cloud lifetime, and precipitation. The aerosol effects depend in part on their concentration and vertical distribution, which is influenced by wet removal (by rain/snow) and vertical transport (how they move within the atmosphere). A team supported by the Atmospheric System Research program studied wet scavenging of aerosols (examining how aerosol particles are removed from clouds) by continental deep convective clouds for a supercell storm complex observed over Oklahoma during the Deep Convective Clouds and Chemistry campaign. The team developed a new passive-tracer-based transport analysis framework to characterize convective transport using vertical profiles of several passive trace gases. The new analysis framework is used to estimate the efficiency of aerosol wet scavenging and to evaluate cloud-resolving simulations made with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem).

Compared to the new observation-based analysis, WRF-Chem greatly underestimates aerosol scavenging efficiencies by 32% and 41% for aerosol mass and number, respectively. Adding a new treatment of secondary activation, which allows aerosols to form cloud droplets not only at the cloud base but also above cloud base, significantly improved the simulations, producing results that are only 7% and 8% lower than observation-based estimates. This finding emphasizes the importance of secondary activation (above the cloud base) for aerosol wet removal in deep convective storms. This study provides a framework that can be extended to different types of storms and could be used to evaluate the diverse parameterizations of convective transport and wet scavenging used in global models.

Lignocellulosic biomass is the largest source of organic matter on Earth, making it a promising renewable feedstock for producing biofuels and chemicals. Currently, however, the main bottleneck in biofuel production is the low efficiency of cellulose conversion, which leads to high production costs. To date, C. thermocellum is the most efficient microorganism known for solubilizing lignocellulosic biomass. Its high cellulose digestion capability has been attributed to the organism’s efficient cellulases consisting of both a free enzyme system and a tethered cellulosomal system, wherein multiple carbohydrate active enzymes are organized by primary and secondary scaffoldin proteins to generate large protein complexes attached to the bacterial cell wall. U.S. Department of Energy (DOE) BioEnergy Science Center (BESC) researchers recently discovered that C. thermocellum also expresses a type of cellulosomal system that is not bound to the cell wall, a “cell-free” cellulosomal system. Researchers believe the cell-free cellulosome complex functions as a “long-range” cellulosome because it can diffuse away from the cell and degrade polysaccharide substrates distant from the bacterial cells. This discovery reveals that C. thermocellum utilizes not only all the previously known cellulase degradation mechanisms (cellulosomes and free enzymes), but also a new category of scaffolded enzymes not attached to the cell. This unexpected finding explains C. thermocellum’s superior performance on biomass, demonstrating that nature’s strategies for biomass conversion are not yet fully understood and could provide further opportunities for microbial enzyme discovery and engineering efforts.

The mechanism of methane formation puzzled scientists for years, mainly because intermediates in the catalytic cycle had not been identified. The enzyme that catalyzes the chemical step of methane synthesis or oxidation is methyl-coenzyme M reductase (MCR). Two proposed mechanisms for how methane is generated differ in whether the first step in the MCR catalytic reaction involves an organometallic methyl-nickel(III) or a methyl radical intermediate. A third mechanism involving methyl anion and Ni(III)-SCoM species also is possible. All three mechanisms propose formation of distinct intermediates. To uncover the true MCR mechanism, researchers from the University of Michigan, Ann Arbor, and Pacific Northwest National Laboratory combined rapid kinetic studies and spectroscopic approaches with high-performance computing resources at the Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy scientific user facility, and the National Energy Research Scientific Computing Center located at Lawrence Berkeley National Laboratory. Their rapid kinetic studies revealed no evidence for a methyl-Ni(III) species proposed by the first mechanism. Rather, spectroscopic results provided direct evidence that Ni(II)-thiolate and methyl radical intermediates proposed in the second potential mechanism are key intermediates in methane formation. Moreover, computational analyses revealed the formation of the methyl radical intermediate is thermodynamically favored. Temperature-dependent transient kinetics also closely matched density functional theory predictions of the methyl radical mechanism. Additional calculations ruled out formation of a methyl anion intermediate proposed by the third mechanism. Taken together, the findings provide clear support for a methyl radical–based mechanism of methane formation. Furthermore, the findings have broad applicability for developing technologies to make and activate methane for alternative fuel as well as reducing greenhouse gas warming.

Biological production of chemicals and fuels using microbial transformation of sustainable carbon sources, such as pretreated and saccharified plant biomass, is a multistep process. Each of the steps—deconstruction of the cellulose, hemicellulose, and lignin that are bound together in the plant cell wall; addition of enzymes to release sugars; and conversion into the desired biofuel—is done in separate pots. Significant effort has gone into developing efficient solutions to these discrete steps, but few studies report the consolidation of the multistep workflow into a single pot reactor system. Researchers at the Department of Energy’s (DOE) Joint BioEnergy Institute (JBEI) demonstrate a one-pot biofuel production process that uses an IL (1-ethyl-3-methylimidazolium acetate) for pretreating switchgrass biomass. This IL is highly effective in deconstructing lignocellulose, but leaves behind a residue that is toxic to standard cellulase and the microbial production host. JBEI scientists established that an amino acid mutation in the gene rcdA leads to an E. coli strain that is highly tolerant to ILs. To develop a strain for a one-pot process, they engineered this IL-tolerant strain to express a d-limonene production pathway. The JBEI researchers also screened previously reported IL-tolerant cellulases to select one that would function with the range of E. coli cultivation conditions and expressed it in the IL-tolerant E. coli strain to secrete this IL- tolerant cellulase. The final strain was found to digest pretreated biomass and use the liberated sugars to produce the jet fuel candidate precursor d-limonene in a one-pot process.

Global climate models have difficulty reproducing the correct location and timing of precipitation over the central United States. One possible reason for this difficulty involves the Southern Great Plains “low-level jet (LLJ)”, a phenomenon of enhanced wind speeds at heights below 3 km that plays an important role in transporting moisture from the Gulf of Mexico to the Great Plains. A team of Department of Energy (DOE) researchers used data from DOE’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site to identify LLJ characteristics and evaluate how well six commonly used reanalysis products, which combine numerical weather models with data assimilation models, were able to reproduce the characteristics. The study focused on data from the Mid-latitude Continental Convective Clouds Experiment (MC3E) collected over the ARM SGP site in April and May 2011, as well as a 10-year period from 2001 to 2010 that provides a comparison with the MC3E study. The team compared all six reanalysis products to MC3E data and only three of them to the 10-year data. They found that reanalyses are able to represent most aspects of the composite LLJ profile, but there are large discrepancies in the number of observed jets and those derived from reanalyses. Underestimating the frequency of strong LLJs leads to an underestimation of the moisture transport. When the 10-year period is considered, all three reanalyses underestimate the moisture transport associated with strong LLJs by factors ranging between 1.4 and 2.7, impacting the models’ ability to produce accurate timing and location of precipitation in the Great Plains. There are indications that increased horizontal and vertical resolution improves the ability of the reanalyses to produce strong LLJs, but other factors not addressed in this study might also be important.

Rising atmospheric carbon dioxide (CO₂) concentrations are projected to increase plant inputs to soil, which may stimulate soil carbon decomposition. Many studies attempting to quantify this priming effect use a simple analytical framework that is inappropriate for inferring complex dynamics. Using a multipool soil carbon model, a recent study shows that changes in carbon flows that would be attributed to priming in a one-pool model (using overall respiration and carbon stocks) can be explained without a change in decomposition rate constants of individual pools. Furthermore, a sensitivity analysis demonstrates the potential range of “false priming” responses inferred from simple, first-order models. The researchers argue that, in addition to standard measurements of carbon stocks and CO₂ fluxes, quantifying the fate of new plant inputs requires isotopic tracers and microbial measurements. They discuss the pitfalls of using simple model structures to infer complex dynamics and suggest appropriate model structures and necessary observational constraints for projections of carbon feedbacks.

The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models. Simulations using existing models exhibit peak evapotranspiration during the wrong season and rain occurring too early in the day. A team of researchers supported by the Terrestrial Ecosystem Science and Atmospheric System Research programs and using data from the GOAmazon campaign show that those biases are not present in an approach opposite to that taken by general circulation models, in which they resolve convection and parameterize large-scale circulation as a function of the resolved convection.

The ability to simulate the seasonality of the hydrologic cycle in the Amazon using this approach is attributed to (1) the representation of the morning fog layer, and (2) more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present during the wet season, but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget. These results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents. The study indicates understanding of tropical climates over land can be considerably advanced by using coupled land–atmosphere models with explicit convection and parameterized large-scale dynamics.

In the atmosphere as clouds form, grow, and dissipate, they mix with the air around them. This mixing of the water-saturated air inside the clouds with the drier air outside the clouds impacts the size and number of cloud droplets. The details of this mixing process, however, have been a source of controversy in the cloud microphysics community for decades. Two theories have been developed to describe how clouds mix with the environment: (1) homogeneous mixing, in which all droplets evaporate a little bit until the air becomes saturated; and (2) inhomogeneous mixing, in which some water droplets evaporate completely while others are unchanged.

In a recent study, researchers present in-cloud observations from a new instrument that finally settle the matter. The new instrument, Holographic Detector for Clouds (HOLODEC), was developed in part with support from the Atmospheric Radiation Measurement (ARM) Climate Research Facility. The HOLODEC takes detailed three-dimensional images of cloud droplets during aircraft flights, enabling the measurement of both the sizes and spatial distribution of cloud droplets within clouds at unprecedented scales. The holographic measurements show that in cumulus clouds, the data are in strong agreement with the inhomogeneous mixing hypothesis.  The droplet size distributions show large changes in number density as drier air mixes with the cloudy air, but a nearly unchanging mean droplet diameter. Essentially, clouds have distinct edges down to the centimeter scale. This result is important for correctly representing cloud microphysical processes within numerical weather and climate models, because the same amount of water divided into many small drops or a few large drops has very different optical properties.  The differences affect how much sunlight is reflected by clouds, as well as other aspects of the cloud development and lifecycle, such as precipitation development.

Black spruce trees at the southern edge of the vast boreal forest are being exposed to progressive increases in temperature due to climate change. Temperature increases could change the balance between photosynthetic uptake of carbon dioxide (CO₂) and respiratory release of CO₂, which could further affect climate change. Since black spruce trees retain their needles for several years, the different age classes may have different responses to temperature increases. Thus, to understand and model how the boreal forest will function in the future, seasonal- and age-specific photosynthetic and respiratory temperature response functions must be measured. From 2011 to 2014, research was undertaken in a nutrient-limited black spruce and Sphagnum bog forest in northern Minnesota in the United States. Measurements were collected seasonally on different needle age classes from mature trees and included photosynthetic capacity, foliar respiration (Rd), and leaf biochemistry. Scientists from Oak Ridge National Laboratory used the results to model the predicted total annual carbon uptake by the trees under normal and elevated temperature scenarios. Temperature responses of key photosynthetic parameters were dependent on season and less responsive in the developing new needles (Y0) as compared with 1-year-old (Y1) or 2-year-old (Y2) needles. Each process initially increased with temperature, peaking between 19 °C and 38 °C, then declined at higher temperatures. Different age classes differed in their leaf structure and photosynthetic capacity, which resulted in 64% of modeled total annual carbon uptake from the older Y1 and Y2 needles (56% of the tree leaf area), and just 36% from Y0 cohorts (44% of tree leaf area). Under warmer climate change scenarios, the contribution of young needles was even less, just 31% of annual carbon uptake for a modeled 9 °C rise in summer temperature. Results suggest that net annual carbon uptake by black spruce could increase under elevated temperature and become more dependent on the older needle age classes. This study illustrates the physiological and ecological significance of different leaf ages, and indicates the need for seasonal- and leaf age-specific model parameterization when estimating carbon uptake capacity of boreal forests under current or future temperatures.

Over the last 5 decades, monitoring systems have been developed to detect changes in carbon (C) accumulations in the atmosphere and oceans, but the ability to detect changes in the behavior of the global carbon cycle is still hindered by measurement and estimate errors. In a recent study, researchers developed a rigorous and flexible framework for assessing the temporal and spatial components of estimate errors and their impact on uncertainty in net carbon uptake by the biosphere. They present a novel approach for incorporating temporally correlated random error into the error structure of emission estimates. Based on this approach, they conclude that the 2σ uncertainties of the atmospheric growth rate have decreased from 1.2 Pg C yr^-1 in the 1960s to 0.3 Pg C yr^-1 in the 2000s due to an expansion of the atmospheric observation network. The 2σ uncertainties in fossil fuel emissions have increased from 0.3 Pg C yr^-1 in the 1960s to almost 1.0 Pg C yr^-1 during the 2000s due to differences in national reporting errors and differences in energy inventories. Lastly, while land use emissions have remained fairly constant, their errors still remain high and thus their global carbon uptake uncertainty is not trivial. Currently, the absolute errors in fossil fuel emissions rival the total emissions from land use, highlighting the extent to which fossil fuels dominate the global carbon budget. Because errors in the atmospheric growth rate have decreased faster than errors in total emissions have increased, a 20% reduction in the overall uncertainty of net carbon global uptake has occurred. Given all the major sources of error in the global carbon budget that could be identified, the results are 93% confident that terrestrial carbon uptake has increased and 97% confident that ocean carbon uptake has increased over the last 5 decades. Thus, arguably one of the most vital ecosystem services that the biosphere currently provides is the continued removal of approximately half of atmospheric carbon dioxide emissions from the atmosphere, although there are certain environmental costs associated with this service, such as the acidification of ocean waters.

Plant biodiversity is often correlated with ecosystem functioning in terrestrial ecosystems. However, little is known about the relative and combined effects of above- and belowground biodiversity on multiple ecosystem functions [e.g., ecosystem multifunctionality (EMF)] or how climate might mediate those relationships. A recent study teases apart the effects of biotic and abiotic factors, both above- and belowground, on EMF on the Tibetan Plateau in China. The researchers found that a suite of biotic and abiotic variables account for up to 86% of the EMF variation, with the combined effects of above- and belowground biodiversity accounting for 45% of the EMF variation. These results have two important implications: (1) including belowground biodiversity in models can improve the ability to explain and predict EMF, and (2) regional-scale variation in climate, and perhaps climate change, can determine, or at least modify, the effects of biodiversity on EMF in natural ecosystems.

In science, researchers collaborate so that they can complement existing disciplinary expertise, gain access to specialized equipment, or expand the depth and breadth of datasets that can be used to derive new knowledge. Motivated by this latter objective, a research team has compiled a global database (GlobResp) that details rates of leaf dark respiration and associated traits from sites that span Arctic tundra to tropical forests. This database builds on earlier research and was supplemented by recent field campaigns and unpublished data. In keeping with other trait databases, GlobResp provides insights on how physiological traits, especially rates of dark respiration, vary as a function of environment and how that variation can be used to inform terrestrial biosphere models and land surface components of Earth system models. Although an important component of plant and ecosystem carbon budgets, respiration has only limited representation in models. This database gives users a unique perspective of the climatic controls on respiration, thermal acclimation and evolutionary adaptation of dark respiration, and insights into the covariation of respiration with other leaf traits.

Peatlands store a third of global soil carbon. Drought and drainage coupled with climate warming present the main threat to these stores. Hence, understanding drought effects and inherent feedbacks related to peat decomposition has been a primary global challenge. However, widely divergent results in recent studies concerning drought effects challenge the accepted paradigm that waterlogging and associated anoxia are the overarching controls locking up carbon stored in peat. By linking field and microcosm experiments, a recent study shows how previously unrecognized mechanisms regulate the buildup of phenolics, which protects stored carbon directly by reducing phenol oxidase activity during short-term drought and, indirectly, through a shift from low-phenolic Sphagnum and herbs to high-phenolic shrubs after long-term moderate drought. The study demonstrates that shrub expansion induced by drought and warming in boreal peatlands might be a long-term, self-adaptive mechanism not only increasing carbon sequestration but also potentially protecting historic soil carbon. The researchers propose that the projected “positive feedback loop” between carbon emissions and drought in peatlands may not occur in the long term.

Large uncertainty exists in model projections of the land carbon sink response to increasing atmospheric carbon dioxide (CO₂). Free-Air CO₂ Enrichment (FACE) experiments lasting a decade or more have investigated ecosystem responses to a step change in atmospheric CO₂ concentration. To interpret FACE results in the context of gradual increases in atmospheric CO₂ over decades to centuries, a recent study used a suite of seven models to simulate the Duke Forest and Oak Ridge FACE experiments extended for 300 years of CO₂ enrichment. It also determined key modeling assumptions that drive divergent projections of terrestrial carbon uptake and evaluated whether these assumptions can be constrained by experimental evidence. All models simulated increased terrestrial carbon pools resulting from CO₂ enrichment, though there was substantial variability in quasi-equilibrium carbon sequestration and rates of change. In two of two models that assume that plant nitrogen uptake is solely a function of soil nitrogen supply, the net primary production response to elevated CO₂ became progressively nitrogen limited. In four of five models assuming that nitrogen uptake is a function of both soil nitrogen supply and plant nitrogen demand, elevated CO₂ led to reduced ecosystem nitrogen losses and thus progressively relaxed nitrogen limitation. Many allocation assumptions resulted in increased wood allocation relative to leaves and roots, which reduced the vegetation turnover rate and increased carbon sequestration. In addition, self-thinning assumptions had a substantial impact on carbon sequestration in two models. Accurate representation of nitrogen process dynamics (in particular nitrogen uptake), allocation, and forest self-thinning is key to minimizing uncertainty in projections of future carbon sequestration in response to elevated atmospheric CO₂.

The global carbon balance is vulnerable to disturbances that alter terrestrial carbon storage. Disturbances to forests occur along a continuum of severity, from low-intensity disturbance causing the mortality or defoliation of only a subset of trees to severe stand-replacing disturbance that kills all trees; yet, considerable uncertainty remains in how forest production changes across gradients of disturbance intensity. In a recent study, researchers used a gradient of tree mortality in an upper Great Lakes forest ecosystem to: (1) quantify how aboveground wood net primary production (ANPPw) responds to a range of disturbance severities and 2) identify mechanisms supporting ANPPw resistance or resilience following moderate disturbance. They found that ANPPw declined nonlinearly with rising disturbance severity, remaining stable until > 60 % of the total tree basal area senesced. As upper canopy openness increased from disturbance, greater light availability to the subcanopy enhanced the leaf-level photosynthesis and growth of this formerly light-limited canopy stratum, compensating for upper canopy production losses and a reduction in total leaf area index (LAI). As a result, whole-ecosystem production efficiency (ANPPw/LAI) increased with rising disturbance severity, except in plots beyond the disturbance threshold. These findings provide a mechanistic explanation for a nonlinear relationship between ANPPw and disturbance severity, in which the physiological and growth enhancement of undisturbed vegetation is proportional to the level of disturbance until a threshold is exceeded. These results have important ecological and management implications, demonstrating that in some ecosystems moderate disturbance levels minimally alter forest production.

Large quantities of organic carbon are stored in frozen soils (permafrost) within Arctic and sub-Arctic regions. A warming climate can induce environmental changes that accelerate the microbial breakdown of organic carbon and the release of the greenhouse gases carbon dioxide and methane. This feedback can accelerate climate change, but the magnitude and timing of greenhouse gas emissions from these regions and their impact on climate change remain uncertain. In a recent study, researchers find that current evidence suggests a gradual and prolonged release of greenhouse gas emissions in a warming climate and present a research strategy with which to target poorly understood aspects of permafrost carbon dynamics.

Knowledge of soils in the permafrost region has advanced immensely in recent decades, despite the remoteness and inaccessibility of most of the region and the sampling limitations posed by the severe environment. These efforts have significantly increased estimates of the amount of organic carbon stored in permafrost-region soils and improved understanding of how pedogenic processes unique to permafrost environments built enormous organic carbon stocks during the Quaternary. This knowledge also has called attention to the importance of permafrost-affected soils to the global carbon cycle and the potential vulnerability of the region’s soil organic carbon (SOC) stocks to changing climatic conditions. In a recent review, researchers briefly introduce the permafrost characteristics, ice structures, and cryopedogenic processes that shape the development of permafrost-affected soils and discuss their effects on soil structures and organic matter distributions within the soil profile. They examine the quantity of organic carbon stored in permafrost-region soils, as well as the characteristics, intrinsic decomposability, and potential vulnerability of this organic carbon to permafrost thaw under a warming climate. Overall, frozen conditions and cryopedogenic processes, such as cryoturbation, have slowed decomposition and enhanced sequestration of organic carbon in permafrost-affected soils over millennial timescales. Due to the low temperatures, the organic matter in permafrost soils is often less humified than in more temperate soils, making some portion of this stored organic carbon relatively vulnerable to mineralization upon thawing of permafrost.

Simulating precipitation is very diffcult for global atmospheric models because precipitation requires accurate handling of many different processes to achieve success. Problems occur with errors in rain location, rates, and timing. A team of U.S. Department of Energy scientists from Pacific Northwest National Laboratory used a regional weather model to explore how global models will behave when used with higher resolutions more typical of regional atmospheric models. An important parameter in simulating clouds is the “convective timescale,” which represents how quickly convective clouds act on the surrounding atmosphere and whose resolution dependency is uncertain. Combining physics packages from the global Community Atmosphere Model version 5 (CAM5) with the regional Weather Research and Forecasting model for realistic weather conditions, they found that a shorter timescale results in a more accurate precipitation amount over the central United States during the simulated period. However, this short timescale worsens the precipitation diurnal cycle, with the convection too tightly linked to the daytime surface heating, and thus occurring too close to noon. Longer timescales greatly improve the diurnal cycle but result in less precipitation and thus produce a low bias. To investigate the simulated precipitation occurrence, strength, and diurnal cycle, the team compared the model results with observations using a grid with approximately ¼° grid spacing for a period in late April and May 2011 during the Midlatitude Continental Convective Clouds Experiment (MC3E). The analysis of rain rates shows that with longer timescales, the frequency distribution of rain can be improved, particularly for the extreme rain rates. Ultimately, without changing other aspects of the physics, a decision between accurate diurnal timing and rain amount must be made when choosing an appropriate convective timescale due to structural deficiencies in the cloud portion of the model. This information is important for designing climate models to operate at ¼° resolution during the next few years.

Orographic forcing is an efficient and dominant mechanism for harnessing water vapor into consumable fresh water in the form of precipitation, snowpack, and runoff. Mountain water resources not only support human activities, but are also vital to diverse terrestrial and aquatic ecosystems. To study the long-term effect of solar radiation effect over three-dimensional (3-D) mountains and snow on surface energy and hydrology, the 3-D radiative transfer parameterization developed for the computation of surface solar fluxes has been incorporated into the Community Climate System Model version 4 [(CCSM4); Community Atmosphere Model version 4 (CAM4)/Community Land Model version 4 (CLM4)] global model and applied at a resolution of 0.23°x0.31° over the Rocky Mountains and Sierra Nevada areas in the western United States. In the 3-D radiative transfer parameterization, the surface topography data have been updated from a resolution of 1 km to 90 meters to improve parameterization accuracy. In addition, the upward-flux deviation [3D–plane-parallel (PP)] adjustment has also been modified to ensure that energy balance at the surface is conserved in global climate simulations based on 3-D radiation parameterization. Findings show that deviations of the net surface fluxes are not only affected by 3-D mountains, but also influenced by feedbacks of clouds and snow in conjunction with long-term simulations. Deviations in the sensible heat and surface temperature generally follow the patterns of net surface solar flux. Including 3D-mountain effects significantly increases (decreases) solar radiation at higher (lower) elevations, leading to increased (reduced) snowmelt. Combined with precipitation changes influenced by changes in the surface fluxes, runoff is significantly reduced in mountainous regions after the snow accumulation peaks in April. The 3-D mountain effects could have an important impact on vegetation by changing the energy and water available to plants. With the larger differences in solar radiation, soil moisture, and soil temperature developing in late spring and early summer, changes in photosynthetic rate and plant phenology may affect leaf area index and gross primary production. These findings will be further investigated in the future using longer simulations to quantify the 3-D mountain effects on radiation and the impacts on water and carbon cycles and vegetation globally.

Sometimes, an experiment provides an answer that departs significantly from what is expected. These unexpected results point in the direction of new physics (i.e., new processes that are not yet accounted for in the theories). A recent study reported on one such unexpected result. Scientists think they understand how a patch of convecting atmosphere communicates with the rest of the atmosphere: it is all about gravity waves. So, if a patch of convecting atmosphere is disconnected from its surroundings, but its interactions are modeled with the surroundings using a model of those gravity waves, then the patch of convecting atmosphere should behave the same. This is referred to as a “self-consistency test.” In a particular limit (small domain size L, or small timescale tau), the self-consistency tests should be passed with ease, but, this is not what happens. New physics awaits.

The loss of Arctic sea ice has emerged as a leading signal of global warming. Sea ice loss, together with acknowledged impacts on other components of the Earth system, has led to the term ‘New Arctic.’ Global coupled climate models predict that ice loss will continue through the 21st century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions among the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. A recent study summarizes present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific water, river discharges, and summer sensible heat and shortwave radiative fluxes at the ocean and ice surface—and speculates how such processes may change in the New Arctic. The complexity of the coupled Arctic system and the logistical and technological challenges of working in the Arctic Ocean require a coordinated interdisciplinary and international program that not only improves understanding of this critical component of global climate, but also provides opportunities for developing human resources with the skills required to tackle related problems in complex climate systems. This study proposes a research strategy that includes: 1) improved mapping of the upper and mid-depth Arctic Ocean, 2) enhanced quantification of important processes, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat flux mechanisms and their interactions.

Bubbles bursting at the ocean surface produce sea spray aerosol droplets. This process changes sea spray chemistry by transferring organic matter from ocean water into the marine boundary layer. These bubbles can contain several classes of organic compounds that are emitted and transported through the air. In the atmosphere, these particles can affect cloud properties, impacting the amount of sunlight clouds reflect away from Earth. A team of scientists, including U.S. Department of Energy researchers at Pacific Northwest National Laboratory and Los Alamos National Laboratory, found that emitted aerosol particles containing long-chain carbon molecules can contribute significantly to the atmospheric particle population and affect concentrations of cloud condensation nuclei (CCN). CCN, in turn, influence how clouds form and develop and impact the climate by modifying Earth’s albedo (reflectivity). The team developed an observational approach that accounts more completely for macromolecular chemical resolution within the sea and then utilizes the distributions to predict the organic mass composition in fine-mode sea spray aerosols. This new approach permits estimation of oceanic concentrations and bubble film surface coverages for several classes of organic compounds. Additionally, this research may provide useful mapped estimates of macromolecular distributions as a research guide for aerosol studies, such as the design of ship and aircraft-based experiments.

The Madden-Julian oscillation (MJO)—a continent-sized cyclic pattern of rainy and dry weather moving slowly eastward across the tropical Indian and Pacific Oceans—is strongly affected by seasonal and year-to-year sea-surface temperature (SST) variations, yet MJO drivers and variability remain a subject of uncertainty and ongoing research. A recent Department of Energy-supported study explored how MJO is impacted by atmospheric interactions across a wide range of space-time scales. The superparameterized Community Atmosphere Model (SPCAM), a modified climate model using a sophisticated approach to explicitly simulate tropical convective clouds fundamental to MJO, is used to explore MJO response to anomalies in seasonal SST distributions associated with the Indian Ocean dipole (IOD). The simulations demonstrate critical new findings: (1) SPCAM reproduces the observed disruption on the MJO signal as it crosses Indonesia, (2) MJO disruption is linked to circulation and moisture anomalies on seasonal time scales as well as variations driven by atmospheric eddies that are active on weekly time scales, and (3) SST perturbations in the equatorial Pacific Ocean, not the Indian Ocean, are the dominant contributor to MJO disruption over Indonesia. Interestingly, IOD-driven MJO weakening does not occur due to local dynamics over the Indian Ocean as might be expected. Rather, the MJO disruption dynamics are traced back to Central Pacific SST perturbations that coexist with the IOD event and seem to be indirectly associated with an El Niño-IOD relationship. This finding has profound implications for understanding MJO’s future based on the future pattern of SSTs.

A recent study measured the changes in dissolved organic carbon compounds during anoxic incubations of low-centered polygon soils from the Barrow Environmental Observatory in Alaska (Herndon et al. 2015a). Analyses used Fourier transform infrared and ultraviolet-visible spectroscopies to identify an initial increase in soluble carbohydrate and organic acid pools, followed by a decline in organic acids. These results describe the upstream microbial processes of soil organic matter decomposition that feed anaerobic microbial fermentation, methanogenesis, and iron reduction, which are highly temperature-sensitive processes and thus likely to control rate and magnitude of methane emissions from thawing permafrost. In a companion study, samples from mineral and organic soils were analyzed at the Stanford Synchrotron Radiation Lightsource to further characterize the geochemistry of active layer soils and permafrost (Herndon et al. 2015b). From those results, the researchers infer that geochemical differences induced by water saturation dictate microbial products of soil organic matter decomposition, and that iron geochemistry is an important factor regulating methanogenesis in anoxic tundra soils. Together, these coordinated datasets provided a conceptual framework from which to parameterize and enhance fine-scale biogeochemical models from the Next-Generation Ecosystem Experiments-Arctic project that specifically represent these anaerobic processes. The datasets are being used to assess the effects of newly represented iron-reduction processes on simulations of carbon dioxide, methane, and pH production in one-dimensional models.

The spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R, is found to vary according to both the average of R over a given footprint and the footprint size or averaging scale. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of the spatial variability of rain rate as a function of footprint size and averaging scale that can be readily implemented into large-scale numerical models. The variability in both the rainwater amount and rain rate as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both the rainwater amount and rain rate is provided for the analyzed tropical scenario. This research shows how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

In this study, researchers explored the triggering mechanisms of diurnal rainfall events due to multiscale processes and how to best represent them in climate models. Rainfall events for seven summers (2002-2008) were simulated with a single-column version of the Community Atmosphere Model using both the default cumulus parameterization (ZM) for this climate model and a cumulus parameterization more widely used in weather models and able to produce the correct rainfall timing. By comparing the model output obtained using the two schemes to observations from the Department of Energy’s (DOE) ARM Climate Research Facility, they identified the critical triggering mechanisms for producing the appropriate timing of convective rainfall in the climate model. One key issue was that observations showed that strong temperature inversions often developed in the nighttime and were sustained until morning, so the most convective air parcels were often about the planetary boundary layer; however, the ZM convective scheme required all convective air parcels to originate in the boundary layer. Relaxing this constraint and allowing convective parcels that originate above the boundary layer was the key to simulating nighttime rainfall. A second issue was that the ZM scheme produced unrealistic frequent weak rainfall events; adding a convective inhibition constraint to prevent the occurrence of convection when the energy barrier of dry layers is too large for the air parcels to be lifted to the level of free convection eliminated these events. When both changes were included, the ZM scheme produced a significantly improved daily rainfall cycle.

Regenerating forests influence the global carbon cycle, and understanding how climate change will affect patterns of regeneration and carbon storage is necessary to predict the rate of atmospheric carbon dioxide (CO₂) increase in future decades. While experimental CO₂ elevation has revealed that young forests respond with increased productivity, there remains considerable uncertainty as to how the long-term dynamics of forest regrowth are shaped by elevated CO₂ (eCO₂). In a recent study, researchers used the mechanistic size- and age-structured Ecosystem Demography model to investigate the effects of CO₂ enrichment on forest regeneration, using data from the Duke Forest Free-Air Carbon Dioxide Enrichment (FACE) experiment, a forest, and an eddy-covariance tower for model parameterization and evaluation. They found that the dynamics of forest regeneration are accelerated, and stands consistently hit a variety of developmental benchmarks earlier under eCO₂. Because responses to eCO₂ varied by plant functional type, successional pathways and mature forest composition differed under eCO₂, with mid- and late-successional hardwood functional types experiencing greater increases in biomass compared to early-successional functional types and the pine canopy. Over the simulation period, eCO₂ led to an increase in total ecosystem carbon storage of 9.7 Mg carbon/ha. Model predictions of mature forest biomass and ecosystem-atmosphere exchange of CO₂ and water were sensitive to assumptions about nitrogen limitation; both the magnitude and persistence of the ecosystem response to eCO₂ were reduced under nitrogen limitation. These simulations demonstrate that eCO₂ can result in a general acceleration of forest regeneration, while altering the course of successional change and having a lasting impact on forest ecosystems.

Many arid and semiarid ecosystems have soils covered with well-developed biological soil crust communities (biocrusts) made up of mosses, lichens, cyanobacteria, and heterotrophs living at the soil surface. These communities are a fundamental component of dryland ecosystems and are critical to dryland carbon cycling. To examine the effects of warming temperatures on soil carbon balance in a dryland ecosystem, a recent study used infrared heaters to warm biocrust-dominated soils to 2°C above control conditions at a field site on the Colorado Plateau. The researchers monitored net soil exchange (NSE) of carbon dioxide (CO₂) every hour for 21 months using automated flux chambers (5 control and 5 warmed chambers), which included the CO₂ fluxes of the biocrusts and the soil beneath them. They observed measurable photosynthesis in biocrust soils on 12 percent of measurement days, which correlated well with precipitation events and soil wet-up. These days included several snow events, providing what is believed to be the first evidence of substantial photosynthesis underneath snow by biocrust organisms in drylands. Overall, biocrust soils in both the control and warmed plots were net CO₂ sources to the atmosphere, with control plots losing 62 ± 8 g carbon m^-2 (mean ± SE) over the first year of measurement and warmed plots losing 74 ± 9 g carbon m^-2. Between the control and warmed plots, the difference in soil carbon loss was uncertain over the course of the entire year due to large and variable rates in spring, but on days during which soils were wet and crusts were actively photosynthesizing, biocrusts that were warmed by 2 oC had a substantially more negative carbon balance (i.e., biocrust soils took up less carbon and/or lost more carbon in warmed plots). Taken together, these data suggest a substantial risk of increased carbon loss from biocrust soils with higher future temperatures, and highlight a robust capacity to predict CO₂ exchange in biocrust soils using easily measured environmental parameters.

Research conducted during the Department of Energy’s (DOE) GOAmazon field campaign provides a new twist to a recently proposed theory about atmospheric particulates and paints a clearer picture of how these particles behave. The research found that atmospheric particles tied to plant life can be either solid or liquid, depending on the environment in which they form. These findings expand on a previous study that posited such particles favor a solid state. The previous research, which found that atmospheric particles over forests are in a solid or semi-solid state, was conducted in a boreal (pine) forest in Finland. There, pine trees release alpha-pinene, an organic building block that reacts with other substances such as ozone to produce atmospheric organic particulate matter. The research team decided to test that theory in the Amazon rainforest, which has about 80 percent humidity, compared to the pine forest’s 30 percent. In the Amazon, the reaction products of the compound isoprene provide the basic building block for atmospheric organic particulate matter. The team found that 80 percent of the time, the atmospheric organic particles that formed in the Amazon were in a liquid state. Liquid particles absorb molecules from the gas phase and grow. Semi-solid particles, on the other hand, grow layer by layer and remain smaller, which affects the types of clouds that form and their propensity to rain. The results of the present study highlight a biome-dependent distribution of liquid and non-liquid particulate matter over forested regions. These differences arise both because of intrinsic differences related to emissions of volatile organic compounds and oxidation pathways, as well as extrinsic differences in climatology of relative humidity and temperature, among other possible factors. Climate models must be able to treat aerosol particles as either liquid or solid, depending on the region, to accurately model their climate impacts.

Marine stratocumulus clouds are considered significant contributors to cloud-climate feedbacks and are a large source of uncertainty in climate model simulations. Many past studies of marine stratocumulus have focused on ‘‘ideal’’ stratocumulus regions of the southeast Pacific Ocean or California coast, while ignoring regions where stratiform low clouds form behind midlatitude baroclinic weather systems. From its location on the subtropics-midlatitude boundary, the Azores is influenced by both the Azores High, a semi-permanent region of high pressure, and midlatitude baroclinic storm systems. Therefore, the Azores experiences a wide range of cloud structures, from fair-weather scenes to stratocumulus sheets and deep convective systems. In this study, researchers combined three types of datasets to study cloud variability in the Azores: a satellite analysis of cloud regimes, a reanalysis characterization of storminess, and data from a 19-month Department of Energy (DOE) ARM field campaign that occurred on Graciosa Island. Combined analysis of the three datasets provides a detailed picture of cloud variability and the respective dynamic influences, with emphasis on low clouds that constitute a major uncertainty source in climate model simulations. The cloud regime analysis shows that the Azores cloud distribution is similar to the mean global distribution and can therefore be used to evaluate cloud simulation in global models. Regime analysis of low clouds shows that stratocumulus decks occur under the influence of the Azores High, while shallow cumulus clouds are sustained by cold-air outbreaks, as revealed by their preference for postfrontal environments and northwesterly flows. An evaluation of climate model output over the Azores shows that all models severely underpredict shallow cumulus clouds, while most models also underpredict the occurrence of stratocumulus cloud decks in this region. This study also demonstrates that regime-based methods applied to in situ and satellite observations can be used to study cloud processes and evaluate models ranging from process-resolving to global climate models. The presence of a permanent ARM site in the Azores will provide a wealth of data to study a wide range of cloud fields and their environment. The present study demonstrates that all the tools are now in place to perform process-resolving model simulations of individual cases observed during the ARM field campaign and to generalize the case study results and attempt to explain whether major general circulation model cloud deficiencies relate to the poor representation of atmospheric dynamics mechanisms or to issues related to the parameterization of cloud microphysical processes.

Biological soil crusts (biocrusts)—communities of mosses, lichens, cyanobacteria, and heterotrophs living at the soil surface—are fundamental components of drylands worldwide, and their destruction dramatically alters biogeochemical processes, hydrology, surface energy balance, and vegetation cover. Impacts of physical disturbances on biocrusts (e.g., trampling by livestock and damage from vehicles) have been a long-standing concern, and concern is also increasing over the potential for climate change to alter biocrust community structure. Using long-term data from the Colorado Plateau, a recent study examined the effects of 10 years of experimental warming and altered precipitation on biocrust communities and compared the effects of altered climate with those of long-term physical disturbance (more than 10 years of replicated human trampling). Surprisingly, altered climate and physical disturbance treatments had similar effects on biocrust community structure. Warming, altered precipitation, and physical disturbance from trampling all promoted early successional community states. Although the pace of biocrust community change varied significantly among treatments, these results suggest that multiple aspects of climate change will affect biocrusts to the same degree as physical disturbance. This finding is particularly disconcerting in the context of warming, as temperatures for drylands are projected to increase beyond those imposed as treatments in this study.

Ecosystem responses to rising carbon dioxide (CO₂) concentrations are a major source of uncertainty in climate change projections. Data from ecosystem-scale Free-Air CO₂ Enrichment (FACE) experiments provide a unique opportunity to reduce this uncertainty. The recent FACE Model–Data Synthesis project aimed to use information gathered in two forest FACE experiments to assess and improve land ecosystem models. A new ‘assumption-centred’ model intercomparison approach was used, in which participating models were evaluated against experimental data based on the ways in which they represent key ecological processes. By identifying and evaluating the main assumptions causing differences among models, the assumption-centred approach produced a clear roadmap for reducing model uncertainty. In a recent paper, researchers explained this approach and summarized the resulting research agenda. They encourage the application of this approach in other model intercomparison projects to fundamentally improve predictive understanding of the Earth system.

Atmospheric mass balance analyses suggest that terrestrial carbon storage is increasing, partially abating the atmospheric carbon dioxide (CO₂) growth rate, although the continued strength of this ecosystem service remains uncertain. This research presents a new, satellite-derived global terrestrial Net Primary Production (NNP) dataset, which shows a significant increase in NPP from 1982 to 2011. However, comparison against Earth system model (ESM) estimates reveals a significant divergence, with satellite-derived increases (2.8 ± 1.5%) less than half of ESM-derived increases (7.60 ± 1.67%) over the 30-year period. By isolating the CO₂ fertilization effect and comparing against a synthesis of available free-air CO₂ enrichment data, the researchers provide evidence that much of the discrepancy may be due to an over-sensitivity of ESMs to atmospheric CO₂, potentially reflecting an under-representation of climatic feedbacks and a lack of representation of nutrient constraints. Understanding of CO₂ fertilization effects on NPP needs rapid improvement to enable more accurate projections of future carbon cycle-climate feedbacks. The study suggests that better integration of modeling, satellite, and experimental approaches offers a promising way forward.

Cyanobacteria are photosynthetic bacteria that, like plants, consume carbon dioxide and produce oxygen through photosynthesis. All cyanobacterial membranes contain diesel-range C15-C19 hydrocarbons in high concentration and the production pathways for these metabolites are exclusive to cyanobacteria. In this study, the model cyanobacterium, Synechocystis sp. PCC 6803, was modified to produce no alkanes, and the resulting strain grew poorly at low temperatures. To understand the growth defect, the researchers assessed the redox kinetics of how cyanobacteria convert solar energy into chemical energy in the form of adenosine triphosphate (ATP) and the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH). ATP and NADPH are produced using a linear and a cyclic pathway with the pigment-protein complex, photosystem I (PSI), as the hub to both. The modified strain made greater use of the cyclic pathway, which raises the ATP:NADPH ratio, especially at low temperature. This use helps to balance reductant requirements and maintain the redox poise of the electron transport chain. While previous theories held that the cyclic pathway was used in a fixed ratio to the linear pathway, the researchers demonstrated that the cyclic pathway responds dynamically to the environment and that alkanes play a role in this response. Flux balance computational analysis showed that an intermediate use of the cyclic pathway (circa one-fourth that of the linear pathway) maximized growth as well. From this analysis, the team concluded that the lack of membrane alkanes required greater use of the cyclic pathway, presumably to maintain redox poise. In turn, such an increase compromises growth by activating energy-inefficient pathways. This study highlights the unique and universal role of medium-chain hydrocarbons in cyanobacteria: they regulate redox balance and reductant partitioning in these photosynthetic cells under stress.

The diverse microbial communities that inhabit the zones within and surrounding the roots of plants, the “root microbiome,” have a significant influence on the host plant’s health and vitality. The root microbiome of Populus, a genus of trees that are a potential bioenergy feedstock, contains a high abundance of microbes known as β- and γ-Proteobacteria. Both of these classes include multiple bacterial species known to promote plant growth. To understand the contribution of individual microbiome members in a community, researchers at Oak Ridge National Laboratory (ORNL), funded by the Department of Energy’s (DOE) Plant-Microbe Interfaces Science Focus Area and U.S. Department of Agriculture-DOE Plant Feedstocks Genomics for Bioenergy program, studied a simplified community consisting of Pseudomonas (γ-Proteobacteria) and Burkholderia (β-Proteobacteria) bacterial strains inoculated on sterile Populus cuttings under controlled laboratory conditions. Alone and in combination, the two species increased root growth and photosynthetic potential and activated unique pathways relative to uninoculated controls.   Complementary data such as photosynthetic efficiency, gene expression, and metabolite expression data, in individual and in mixed inoculated treatments, indicate that the molecular effects of these bacterial strains are unique and additive. This work is the first constructed community study to show the additive host effects of bacteria, and the results suggest that microbiome function may be predicted from the synergistic effects of individual members of the microbial community.

Lignin’srecalcitrance to chemical or biological digestion presents a major obstacle to the production of second-generation biofuels and valuable coproducts from lignin’s monoaromatic units. A catabolic pathway for the enzymatic breakdown of aromatic oligomers linked via β-aryl ether bonds typically found in lignin was reported in the bacterium Sphingobium sp. SYK-6. In a collaborative effort, researchers from the Department of Energy’s (DOE) Great Lakes Bioenergy Research Center (GLBRC) and Joint BioEnergy Institute (JBEI) determined the X-ray crystal structures and biochemical characterizations of several glutathione-dependent β-etherases that participate in the cleavage of lignin. Results from these studies reveal important new aspects of the enzyme mechanisms and the determinants of substrate specificity. As β-aryl ether bonds account for 50 percent to 70 percent of all inter-unit linkages in lignin, understanding the mechanism of enzymatic β-aryl ether cleavage has significant potential for informing ongoing studies on lignin valorization.

Ethylene is one of the most industrially important chemicals derived from petroleum. Therefore, scientists have been trying to develop biological systems to produce ethylene in a sustainable way. Expression of a heterologous bacterial ethylene-forming enzyme (EFE) in E. coli has resulted in the production of ethylene, but the yields were too low for industrial purposes. Researchers at the National Renewable Energy Laboratory and University of Colorado Boulder conducted a study of the effects of different nutrients and substrates present in the growth medium for the EFE-expressing E. coli strain to be able to predict which genes significantly affect ethylene yields. Guided by those findings, they re-engineered E. coli to minimize competing pathways within central metabolism and to overproduce key enzymes predicted to increase ethylene productivity. The re-engineered strain produced more than twice as much ethylene relative to the original EFE-expressing E. coli strain. Those yields can be further improved by identifying and engineering additional enzymes and regulatory factors that prevent higher metabolic flow toward ethylene biosynthesis. This work advances the development of a sustainable ethylene production industry that is not dependent on fossil fuels.

Scientists from the Department of Energy’s (DOE) Great Lakes Bioenergy Research Center (GLBRC) studied how Bayberry fruits accumulate massive quantities of a unique surface wax with a structure similar to triacylglycerol seed oils. Research on plants that produce such large amounts of surface lipids is providing insights into the molecular features and biochemical pathways for plant lipid secretion and thus may help in developing strategies to engineer lipid production in non-seed tissues. The GLBRC scientists examined changes in fruit anatomy and details of the chemical structures secreted by Bayberry fruits, and quantified the accumulation of wax through fruit development. Biochemical pathway analysis by [¹⁴C]-labeling and transcript analysis by RNA-seq revealed features of Bayberry wax accumulation that are distinctly different from conventional triacylglycerol production. Together, these results indicate that the extracellular glycerolipids in Bayberry wax are synthesized by a novel pathway that differs from previously defined triacylglycerol biosynthesis pathways. An increased understanding of this process may prove useful in engineering plants for secretion of high-energy and high-value lipids, particularly those that have toxic or negative consequences when accumulated inside cells.

Variations in precipitation regimes can shift ecosystem structure and function by altering frequency, severity, and timing of plant water stress. Being able to predictively understand impacts of precipitation regimes on plant water stress is crucial in a changing climate. The research team, led by Oak Ridge National Laboratory (ORNL), formulated complementary, physiologically-linked indices of precipitation variability (PV) and related them to continuous measurements of predawn leaf water potential—a fundamental indicator of plant water status—in six tree species with different water use strategies in a central U.S. forest. These indices explained nearly all interannual variations in water stress levels for all species. These species differed in sensitivities to variations in precipitation regimes with the differences more pronounced in response to PV than to amount. Further, they exhibited stress tradeoffs between low and high PV, suggesting that how different plant species respond to PV is part of species-specific water use strategies in a plant community facing the uncertainty of fluctuating precipitation regimes. The new indices provide simple ways to quantify physiological drought and the ecological impacts of precipitation regimes in a changing climate.

Scientists have long known that anaerobic fungi living in the guts of herbivores play a significant role in helping those animals digest plants. However, culturing these fungi in the lab is difficult because they cannot survive in the presence of oxygen and must be grown in sealed containers. A research team led by Michelle O’Malley at the University of California, Santa Barbara, isolated three species of these fungi in feces from goats, horses, and sheep. The enzymes expressed by these fungi work together to break down crude, untreated plant biomass. The research showed that the fungi adapt their enzymes to the different kinds of plant materials eaten by these animals, so that wood, grass, or agricultural waste all can be efficiently digested. Each of the fungi studied was found to contribute in a characteristic way, tailoring their combined action to the particular type of biomass being digested. These findings could help in identifying distinctive enzymes from other anaerobic gut fungi, with potential applications for biomass processing and sustainable biofuel production.

Genomes contain the information underlying an organism’s molecular functions. One way to compare the entire genomes of different organisms is to compare their gene-family content profiles, which is effectively a comparison of their functional potential. Standard networks, when used to model phylogenomic similarities, are not capable of capturing some of the underlying complexity of the relationships between genomes. To address this limitation, scientists at Oak Ridge National Laboratory, funded through the Department of Energy’s Plant-Microbe Interfaces Science Focus Area, developed a new three-way similarity metric and constructed three-way networks modeling the relationships among 211 bacterial genomes. They found that such three-way networks find cross-species genomic similarities that would otherwise have been missed by simpler models such as standard networks. Interactions within and between the multiple species that make up the complex microbial communities associated with plant roots are believed to influence the plant’s overall health and vigor and may contribute to the plant’s ability to survive adverse environmental conditions. This research is the first time the concept of three-way networks has been applied in the field of comparative genomics. These networks will be a useful tool to model and reveal complex interspecific bacterial relationships that are not found using the conventional two-way network models, and could pave the way toward deciphering intricate plant-microbe and microbe-microbe interactions.

Atmospheric chemistry is almost entirely driven by sunlight. Scientists understand photochemical reactions that happen with gaseous molecules in air, but reactions happening inside and on surfaces of atmospheric particles, such as those produced in large cities on smoggy days, remain unexplored. To address this unknown, a team of researchers from the University of California at Irvine, University of British Columbia, Environmental Molecular Sciences Laboratory [EMSL; a Department of Energy (DOE) national scientific user facility], and Pacific Northwest National Laboratory explored the effect of environmental conditions on photodegradation rates of atmospherically relevant pollutants embedded in a film of secondary organic material (SOM). The researchers used liquid chromatography (LC) coupled to a photodiode array detector and electrospray ionization high-resolution mass spectrometer (LC-PDA-MS) measurements at EMSL to study three types of SOM. Photodegradation rates of the pollutant 2,4 dinitrophenol were slower at lower temperatures and lower relative humidity—conditions that make SOM more viscous. Additional analyses suggested increased viscosity hinders motion of the molecules in SOM, thereby slowing down the rate of their photodegradation. The findings show that pollutants trapped inside viscous particles, which are more abundant in cold, dry parts of the atmosphere, may take longer to decompose than expected. Future efforts to expand the scope of the study could reveal how environmental conditions influence the photodegradation of compounds known to affect human health.

The mobility of uranium in subsurface environments depends strongly on its oxidation state, with chemically reduced U^IV phases being significantly less soluble than U^IV minerals. A team of scientists from Argonne National Laboratory, Illinois Institute of Technology, and Bulgarian Academy of Sciences compared the oxidation kinetics and mechanisms of two potential products of U^IV reduction in natural systems: a nanoparticulate UO₂ phase and an amorphous U^IV-Ca-PO₄ phase. The valence and molecular structure of uranium was tracked by synchrotron x-ray absorption spectroscopy. Similar oxidation rates for the two phases were observed in solutions equilibrated with atmospheric O₂ and CO₂. Addition of up to 400 µM Ca and PO₄ decreased the oxidation rate by an order of magnitude for both UO₂ and U^IV-phosphate. In the absence of Ca or PO₄, the product of UO₂ oxidation was Na-uranyl oxyhydroxide, whereas the product of U^IV-Ca-PO₄ oxidation was a U^IV-phosphate phase (autunite). In the presence of Ca or PO₄, the oxidation proceeded to U^IV-phosphate for both pre-oxidation forms of U^IV. Addition of Ca or PO₄ changed the mechanism of oxidation by causing the formation of a passivation layer on the particle surfaces.

Sediments and floodplain soils in the East Fork Poplar Creek (EFPC) watershed in Oak Ridge, Tennessee, are contaminated with high levels of Hg from an industrial source at the headwaters. While baseflow conditions have been monitored, concentrations of Hg and MeHg during high-flow storm events (when the stream is more hydrologically connected to the floodplain) had not yet been assessed. This study evaluates EFPC baseflow and event-driven Hg and MeHg dynamics 5 km upstream of the confluence with Poplar Creek to determine the importance of hydrology to instream concentrations and downstream loads, and to ascertain if dynamics are comparable to systems without an industrial Hg source. Particulate Hg (Hg_P) and MeHg were positively correlated with discharge (r2=0.64 and 0.58, respectively) and total suspended sediment (r2=0.97 and 0.89, respectively). Dissolved Hg (Hg_D) also increased with increasing flow (r2=0.18) and was associated with increases in dissolved organic carbon (DOC; r2=0.65) similar to dynamics observed in uncontaminated systems. Dissolved MeHg (MeHg_D) decreased with increases in discharge (r2=0.23) and was not related to DOC concentrations (p=0.56), dynamics comparable to relatively uncontaminated watersheds with a small percentage of wetlands (<10%). While stormflows exert a dominant control on Hg_P, MeHg_P, and Hg_D concentrations and loads, baseflows were associated with the highest MeHg_D concentration (0.38 ng/L) and represented the majority of the annual MeHg_D load.

CRAGE saves time as constructs can be inserted in a single step in one day instead of having serial inserts over several days. Novel secondary metabolites not normally produced by the hosts were uncovered. Secondary metabolites are the basis for hundreds of invaluable agricultural, industrial, and medical products. CRAGE can be adapted to other organisms such as fungi and archaea.

An ensemble variationally constrained objective analysis of atmospheric large-scale forcing data has been developed for the March 2000 Intensive Observing Period at the ARM SGP site. The ensemble approach uses the uncertainty information of the background data, error covariance matrices, and constraint variables in the ARM constrained variational analysis. The ensemble forcing data are applied to drive the Community Atmosphere Model 5 (CAM5) single-column model and the simulated clouds are compared with MICROBASE cloud retrievals to diagnose the source of model biases. The results show that most of the model biases are larger than the uncertainty from large-scale forcing data plus uncertainty from observations, pointing the simulated cloud biases to model parameterization deficiencies. Sensitivity studies show that background data, error covariance matrix, and constraint variables all contribute to the uncertainty range of the analyzed state variables and large-scale forcing data, especially to the vertical velocity and advective tendencies. Background data have the largest impact. CAM5 simulations of clouds forced by the ARM ensemble forcing data systematically overestimate high clouds, while underestimating low clouds when compared with ARM MICROBASE cloud retrievals. These model biases cannot be explained by the uncertainty of large-scale forcing data and the uncertainty of observations, which points to the deficiencies of physical parameterizations.

Reductive immobilization of hexavalent chromium (Cr(VI)), often forming iron-chromium (Fe-Cr) precipitates, is a frequent remediation alternative, yet the relationship between the conditions of precipitate formation, the structural and chemical properties of the precipitates, and the rate and extent of precipitate oxidation by Mn oxides is needed. This study provided a systematic investigation of the rates of Cr(VI) reduction by both abiotic minerals and a chromium-reducing bacterium, the properties of the resulting Fe-Cr precipitates, and the susceptibility for reoxidation and remobilization of Cr(VI) upon precipitate exposure to the manganese oxide birnessite.

The properties of the resulting Fe-Cr solids and their behavior upon exposure to birnessite differed significantly. In microcosms where Cr(VI) was reduced by Desulfovibrio vulgaris strain RCH1, and where hematite or Al-goethite were present as iron sources, there was significant initial loss of Cr(VI) in a pattern consistent with adsorption, and significant Cr(VI) was found in the resulting solids. The solid formed when Cr(VI) was reduced by FeS contained a high proportion of Cr(III) and was poorly crystalline. Reaction between birnessite and the abiotically formed Cr(III) solids led to production of significant dissolved Cr(VI) compared to the no-birnessite controls. This pattern was not observed in the solids generated by microbial Cr(VI) reduction, and could be due to re-reduction of any Cr(VI) generated upon oxidation by birnessite via active bacteria or microbial enzymes.

The results of this study suggest that Fe-Cr precipitates formed in groundwater remediation may remain stable only in the presence of active anaerobic microbial reduction. If exposed to environmentally common Mn oxides such as birnessite in the absence of microbial activity, there is the potential for rapid (re)formation of dissolved Cr(VI) above regulatory levels.

Chesapeake Bay is the largest and most productive estuary in the United States, containing more than 1,500 square miles of wetlands that provide critical habitat for fish, shellfish, and wildlife. Well documented summertime algae blooms deplete the oxygen content of the bay’s water and challenge the survival of benthic invertebrates and macro-organisms (e.g., shellfish), as well as pelagic organisms in the overlying waters. The prevailing theory is that excessive levels of nutrients such as phosphorus (P) are entering the bay from point and nonpoint sources, and that they are the primary culprit in this ecosystem management challenge. In an effort to better understand and constrain the mechanisms and processes involved in P cycling between the bay sediments and overlying waters, a team of researchers from the University of Delaware, Old Dominion University, and the Department of Energy’s Environmental Molecular Sciences Laboratory (EMSL), analyzed sediment cores from the mid-bay portion of the Chesapeake Bay. To understand the mineralogy of the sediments, particularly the composition and stability of iron-containing minerals, they used Mössbauer spectroscopy and X-ray diffraction capabilities at EMSL, and to understand P transport, they used P isotopic techniques. The team found that the degradation of organic matter in the anoxic sediments results in the regeneration of inorganic P, and that, in contrast, the P from terrestrial and atmospheric inputs becomes bound to iron oxide in the sediments and very little is remobilized into the overlying waters. These results indicate that the cycle at the sediment-water interface works as follows: organic debris from dead algae settles in the sediments, and then degradation of the organic debris results in the liberation of inorganic P, which diffuses upward into the overlying water to resupply P to algae. The algae continue to grow and sustain a dead benthic zone that cannot support shellfish. Because high P levels and low-oxygen conditions are now common in many coastal environments, these findings will have important implications for managing these ecosystems.

Hanging over many major cities for days on end, and especially during summer, is a brownish haze that some scientists think contributes to air quality issues and climate forcing because of its potential for absorbing sunlight and trapping surface heat. A team of scientists from Pacific Northwest National Laboratory and the Department of Energy’s Environmental Molecular Sciences Laboratory (EMSL) has started to examine the chemistry of brown carbon, a type of particle found in that haze. The team used analytical capabilities at EMSL, including high-resolution mass spectrometry, to study the particles that form around the chemical toluene, a common pollutant emitted to the atmosphere and found in the haze. They discovered that the addition of nitrogen oxide, which is found in the exhaust from combustion engines, produced heat-trapping particles, and that at high levels, the particles not only held significantly more heat but also turned yellowish brown. The research, highlighted on the cover of a recent issue of Physical Chemistry Chemical Physics, provides new insights that could improve atmospheric and climate models.

Accurate global and national inventories of fossil fuel carbon dioxide (CO₂) emissions are fundamental to carbon cycle research and important for research studying potential impacts and vulnerabilities of greenhouse gas induced climate change. China is the world’s largest emitter of carbon from fossil fuel use and cement production. The Department of Energy’s Carbon Dioxide Information Analysis Center (CDIAC) has long compiled annual time series of fossil fuel CO₂ emissions for the globe and individual countries using data published by the United Nations. A recent study provides new fossil fuel CO₂ emissions estimates for China based on new, previously unpublished Chinese data. These new estimates are markedly lower than earlier estimates, including CDIAC’s, for recent years (e.g., 0.35 GtC for 2013) thanks to extensive, new measurements of Chinese coal and cement properties. Even with these downward revisions of fossil fuel carbon releases from China, China remains the world’s largest fossil fuel emitter, but the emissions reductions have implications for balancing the global carbon cycle budget and projections for future emissions scenarios.

As land-use models used for understanding climate change mitigation and adaptation responses increase in sophistication, the spatial scales have become more resolved. In addition to allowing finer-scale analysis of land-use trends, increasing spatial resolution also may lead to different model outcomes at regional and global scales. A study by Department of Energy researchers at Pacific Northwest National Laboratory (PNNL) isolated the impacts of increased resolution on regional-scale model outcomes in the agriculture and land-use component of PNNL’s Global Change Assessment Model (GCAM). The work presents a new method for visualizing and analyzing data from land-use models, which typically contain too many output variables to be assessed simultaneously. To address this problem, the team applied statistical methods developed by ecologists for analyzing ecosystem differences across environmental gradients to model output, using a set of scenarios differentiated by land-use region size and greenhouse gas emissions mitigation levels. Specifically, nonmetric, multidimensional scaling is applied to a pair-wise distance matrix, collapsing variability along eight different land-cover classes and six scenarios into a two-dimensional coordinate plane. The study demonstrated that land-use regions in GCAM should be climatically and physiographically homogeneous to prevent infeasible transitions in land-use types. The researchers found that for studies focused on broad-scale trends, there is little apparent benefit to push enhancements in spatial resolution. In future studies, the team will focus on the importance of country-to-region assignments in land-use and energy modeling, and the consequences of such groupings for future emissions mitigation assessments.

Ecosystem-scale manipulation experiments represent large science investments that require well-designed data acquisition and management systems to provide reliable, accurate information to project participants and third party users. The Spruce and Peatland Responses Under Climatic and Environmental Change (SPRUCE) project is such an experiment funded by the Department of Energy’s Terrestrial Ecosystem Science program. The SPRUCE experimental mission is to assess ecosystem-level biological responses of vulnerable, high-carbon terrestrial ecosystems to a range of climate warming manipulations and an elevated carbon dioxide (CO₂) atmosphere. SPRUCE provides a platform for testing mechanisms controlling the vulnerability of organisms, biogeochemical processes, and ecosystems to climatic change (e.g., thresholds for organism decline or mortality, limitations to regeneration, biogeochemical limitations to productivity, and cycling and release of CO₂ and methane to the atmosphere). As a result, the SPRUCE experiment will generate a wide range of continuous and discrete measurements. In a recent publication, project researchers lay out their approach to meeting the challenges of designing and constructing an efficient data system for managing high volume sources of in situ observations in a remote and harsh environmental location. The approach covers data flow starting from the sensors and ending at the archival and distribution points, discusses types of hardware and software used, examines design considerations that were used to choose them, and describes the data management practices chosen to control and enhance the data’s value.

Soils play an important role in the cycling of carbon and other nutrients with the atmosphere, and they are also known to contain a vast amount of carbon and are responsible for most emissions of greenhouse gases to the atmosphere. A key reservoir in soils for carbon and other nutrients is soil organic matter (SOM), which consists of a mixture of above and belowground plant litter and animal and microbial residues that are being decomposed. To understand the local, regional, and global cycling of carbon and other elements, it is important to attempt to characterize SOM. Researchers from the U.S. Department of Energy’s Idaho National Laboratory and Environmental Molecular Sciences Laboratory (EMSL) have, for the first time, done just that, comparing the molecular composition of SOM from different ecosystems using EMSL’s ultra-high resolution mass spectrometry. As expected, the SOM from these different ecosystems was heterogeneous; however, they also determined that by using different solvents (e.g., hexane and methanol), they could consistently extract specific, but different types of compounds from SOM. While the use of multiple solvents will result in the richest representation of the diverse molecular constituents from any SOM sample, other scientists will now know which selective solvent to use to extract specific molecular constituents from a particular type of SOM to answer specific science questions. This work clarifies the range of molecular constituents in SOM and sets the stage for enabling a greater understanding of carbon and nutrient cycling in soils.

CO₂ and CH₄ degassing was measured in two rainforest streams at La Selva, Costa Rica: one stream fed only by young (<10 years old) local groundwater recharged within the watershed, and another fed by about two-thirds young groundwater and one-third older groundwater (about 3,000 years old) from a large regional aquifer system. Regional groundwater inputs had no measurable effect on stream gas exchange velocity, stream water CH₄ concentration, or stream CH₄ emissions, but it significantly increased stream water CO₂ concentration and degassing. CO₂ emissions from the stream receiving regional groundwater averaged 5.5 moles of carbon per m² of stream surface per day, about 7.5 times higher than the average from the stream with no regional groundwater input. Carbon emissions from both streams were dominated by CO₂, with CH₄ accounting for only 0.06 percent to 1.70 percent of the total (average CH₄ degassing rate from both streams was 0.005 moles of carbon per m² of stream surface per day). Annual stream degassing fluxes normalized by watershed area were 299 and 48 moles of carbon per m² of watershed surface in the watersheds with and without inputs of old regional groundwater, respectively. Stream degassing of CO₂ is a major carbon flux in the watershed receiving inputs of old regional groundwater, and is similar in magnitude to the average net ecosystem exchange estimated by eddy covariance. Examining the effects of watershed connections to underlying hydrogeological systems can help avoid overestimation of ecosystem respiration and advance understanding of the carbon source and sink status and overall carbon budgets of terrestrial ecosystems.

When exposed to moderately oxidizing conditions, ⁹⁹Tc is readily converted to pertechnetate (TcO₄^–), a highly soluble anion that can migrate into groundwater and the environment. Existing methods for onsite monitoring of TcO₄^– in groundwater require a complicated series of analytical steps due to the low selectivity and sensitivity of Tc. A team of scientists from Pacific Northwest National Laboratory (PNNL), Environmental Molecular Sciences Laboratory [EMSL; a U.S. Department of Energy (DOE) user facility], University of Cincinnati, and Florida State University searched for a suitable material for sensing TcO₄^– in water. The team evaluated simple salts of transition metal complexes that change in color and luminescence properties upon exposure to the Tc anion using the SPEX Fluorolog 2 fluorimeter at EMSL. They found one specific platinum salt that undergoes a dramatic color and brightness change upon exposure to TcO₄^–; the salt was highly sensitive and enables detection of TcO₄^– at levels well below the drinking water standard established by the U.S. Environmental Protection Agency. Modeling and simulation work using EMSL’s Cascade supercomputer enabled the team to determine that the high selectivity was due to the unique electronic structure of the platinum salt. Unlike currently available methods for TcO₄^– sensing, the new approach does not require separation, concentration, or other pretreatment steps. Thus, the rapid, sensitive, and accurate TcO₄^– sensing system is ideal for real-time deployment at contaminated sites. Future implementation of this type of ion recognition system has great potential for remediation efforts and could be essential in addressing a broad range of environmental and health concerns.

Steam reforming of biomass-derived compounds is a promising strategy for hydrogen production. To realize the full potential of this approach, scientists must identify which catalyst is optimal for producing the highest yield of hydrogen. To address this question, a team of researchers from Pacific Northwest National Laboratory (PNNL) combined experimental and theoretical methods to study steam reforming of ethylene glycol over MgAl₂O₄-supported Rh, Ni, and Co catalysts. Computational work and advanced catalyst characterization were performed at the Environmental Molecular Sciences Laboratory (EMSL), a Department of Energy (DOE) national scientific user facility. Compared to the highly active Rh and Ni catalysts, which achieve 100 percent conversion of ethylene glycol, the steam reforming activity of the Co catalyst was comparatively lower, with only 42 percent conversion under the same reaction conditions. However, use of the Co catalyst rather than the Rh and Ni catalysts resulted in a three-fold drop in methane (CH₄) selectivity—a measure of the percentage of ethylene glycol converted to CH₄. Calculations revealed the lower CH₄ selectivity for the Co catalyst, as compared to the Rh and Ni catalysts, is primarily due to the higher barrier for CH₄ formation. The findings demonstrate that the Co catalyst leads to a higher yield of hydrogen, at the expense of CH₄, compared with the Rh and Ni catalysts. Additionally, the Co catalyst was also found to offer enhanced catalyst stability compared with the more conventional Ni and Rh catalysts. This information could be used to develop efficient methods for converting biomass-derived compounds into hydrogen for petroleum refining, the production of industrial commodities such as fertilizers, and electricity production via fuel cells.

Glacier and ice sheet models, like other components of the climate system, require simpler and computationally efficient formulations (parameterizations) when implemented into a full global climate model. For ice sheets, a two-dimensional (2D) solution of simplified (low-order) equations is often used. Furthermore, mountain glaciers, generally located in remote and difficult to access regions, are often hard to simulate due to a lack of necessary model input data, most specifically accurate information on glacier geometry. For this reason, it is often convenient to measure glacier geometry only along a central flowline and to model evolution of those glaciers using a 2D flowline model with parameterizations for capturing across-flow geometric effects.

To test the simpler methods, a computationally slow 3D full set of (Stokes) equations is required. The Department of Energy-sponsored Scientific Discovery through Advanced Computing (SciDAC) project Predicting Ice Sheets and Climate Evolution of Extremes (PISCEES) recently published a full-solution result. Researchers systematically studied the applicability of a 2D, first-order Stokes approximation flowline model, modified by geometric shape factors, for the simulation of land-terminating glaciers by comparing it with a 3D, “full”-Stokes ice-flow model. The researchers then explored the sensitivities of the flowline and Stokes models to ice geometry, temperature, and forward model integration time using steady-state and transient, thermomechanically uncoupled and coupled numerical experiments. Their findings show that the 2D, first-order flowline model may produce inaccurate results for (1) steep glaciers with complex basal topography, (2) polythermal glaciers that contain temperate basal ice and experience basal sliding, and (3) coupled thermomechanical glacier evolution over long time periods (~103 years). They conclude that the 2D first-order flowline model should be applied and interpreted with caution when modeling glacier changes under a warming climate or over long periods of time.

Large-scale applied general equilibrium models play a key role in enabling decision makers to evaluate the implications of proposed energy or climate strategies. A growing concern, however, has been whether these models produce reliable projections, which necessitates improving model accuracy and gauging performance on an ongoing basis. In a recent study, Department of Energy-funded researchers at the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change enhanced and tested the MIT Economic Projection and Policy Analysis (EPPA) model. They introduced a new strategy for modeling the final consumption of various goods, compared results of historical simulations against actual data, and conducted sensitivity analyses of future projections to key parameters under various scenarios. The researchers found that historical simulations of energy use performed better for developed regions than developing regions; the new consumer modeling strategy improved representation of shifts in consumption patterns; and emissions results were more sensitive to gross domestic product growth than energy and nonenergy substitution elasticity or autonomous (non-price-driven) efficiency improvement. The improved model thus can provide more accurate projections for decision makers to refer to when evaluating the cost-effectiveness of different climate change mitigation strategies.

A new approach for modeling photolysis rates (J values) in atmospheres with fractional cloud cover has been developed and implemented as Cloud-J, a multiscattering eight-stream radiative transfer model for solar radiation based on Fast-J. Using observations of the vertical correlation of cloud layers, Cloud-J 7.3c provides a practical and accurate method for modeling atmospheric chemistry. The combination of the new maximum-correlated cloud groups with integration over all cloud combinations by four quadrature atmospheres produces mean J values in an atmospheric column with root mean square (rms) errors of 4% or less compared with 10% to 20% errors using simpler approximations. Cloud-J is practical for chemistry–climate models, requiring only an average of 2.8 Fast-J calls per atmosphere versus hundreds of calls with the correlated cloud groups, or 1 call with the simplest cloud approximations. Another improvement in modeling J values, the treatment of volatile organic compounds with pressure-dependent cross sections, also is incorporated into Cloud-J.

Most of the research on efficient, scalable algorithms in computational science and engineering has focused on the forward problem: given parameter inputs, solve the governing equations to determine output quantities of interest. In a recent study, researchers considered the broader question: given a model containing uncertain parameters, noisy observational data, and a prediction quantity of interest (QOI), how can efficient and scalable algorithms be constructed to (1) infer model parameters from the data (the deterministic inverse problem), (2) quantify uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model for predictions with quantified uncertainties on the QOI (the forward uncertainty propagation problem)? The researchers developed efficient, scalable algorithms for this end-to-end, data-to prediction process in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. Ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid, the observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field inferred is a basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the ice sheet’s base. The QOI is the present-day ice mass flux from the Antarctic continent to the ocean. The work required for executing this data-to prediction process is independent of the state dimension, parameter dimension, data dimension, and number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, observational data typically provide sparse information on model parameters. This property is exploited to construct a low rank approximation of the parameter-to-observable map via randomized singular value decomposition (SVD) methods and adjoint-based actions of Hessians of the data misfit functional.

The amount of ice present in clouds can affect cloud lifetime, precipitation, and radiative properties. Ice formation in clouds is facilitated by the presence of airborne ice nucleating particles. Sea spray is one of the major global sources of atmospheric particles, but it is unclear to what extent these particles are capable of nucleating ice. Sea spray aerosol contains large amounts of organic material that is ejected into the atmosphere during bubble bursting at the organically enriched sea-air interface or sea surface microlayer. Researchers, including a Department of Energy scientist at Pacific Northwest National Laboratory, show that organic material in the sea surface microlayer nucleates ice under conditions relevant for mixed-phase cloud and high-altitude ice cloud formation. The ice nucleating material is likely biogenic and less than ~0.2 μm in size. The researchers found that exudates (organic material secreted by an organism) separated from cells of the marine diatom T. Pseudonana nucleate ice. The researchers propose that organic material associated with phytoplankton cell exudates is a likely candidate for the observed ice nucleating ability of the microlayer samples. Global model simulations of marine organic aerosol in combination with the study’s measurements suggest that marine organic material may be an important source of ice nucleating particles in remote marine environments such as the Southern Ocean, North Pacific, and North Atlantic. Including organic ice nuclei in models is expected to have a significant impact on their properties and their behavior under changing climate conditions.

Black carbon particles, either airborne or deposited on snow surfaces, can cause earlier snowmelt and potentially glacier retreat in the Himalayas and on the Tibetan Plateau. Since particles are emitted from both natural and manmade sources in a number of regions, understanding where they originate and how they are generated and transported is important for developing guidance to mitigate their impact on the environment. These concerns prompted a team of U.S. Department of Energy scientists from Pacific Northwest National Laboratory and collaborators from the Key Laboratory for Semi-Arid Climate Change (Ministry of Education, China) to characterize the various means by which black carbon particles arrive on the plateau and in the mountains. The team compared simulations from the Community Atmosphere Model (CAM5 with source-tagging capability) to ground and satellite observations from the Himalayas, Tibetan Plateau, and surrounding areas. The model simulation agreed well with seasonal variations in near-surface, airborne black carbon concentrations and provided confidence in the modeling framework. The team’s analysis showed that the amount of black carbon from different regions varies according to season and location. Their estimates indicate that the largest contribution to the black carbon burden and deposition is from biofuel and biomass burning emissions in South Asia, followed by fossil fuel emissions, first from South Asia and, second, from East Asia. Local emissions in the Himalayas and on the plateau contribute only 10 percent of the black carbon in the region, but small local changes in emissions would have a big effect. These findings contribute insights into the impact of black carbon on snow and glacier melting and potential for mitigation actions.

Predicting future climate change remains a high priority as well as a complex challenge for science. Insufficient physical understanding and relatively coarse grid resolution limit the ability of global circulation models (GCMs) in this endeavor. Despite increased computational power enabling higher resolution, GCMs still must rely on parameterizations (computational methods to simplify complex physical processes) to represent the subgrid variability of clouds, aerosols, and their interactions. In research led by Department of Energy scientists at Pacific Northwest National Laboratory, scientists investigated the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to dozens of uncertain parameters mainly related to cloud and aerosol processes in the Community Atmosphere Model (CAM version 5). They found that extreme precipitation characteristics are sensitive to a fewer number of parameters, precipitation does not always respond monotonically to parameter change, and the influence of individual parameters does not depend on sampling approaches or related parameters selected. The study was a fast-process investigation responding to parameter perturbation in the current climate, over a 5-year period with prescribed sea surface temperatures. The study better explains the CAM5 model behavior associated with parameter uncertainties and will guide the next step to reducing model uncertainty in precipitation via calibration of the most uncertain model parameters and developing new parameterizations.

Globally, about one-third of the sunlight that reaches Earth is reflected back to outer space before ever reaching the surface. Most of this sunlight is reflected by cloud droplets, which act like tiny mirrors, deflecting the sun’s rays and cooling the planet. The amount of sunlight reflected by clouds depends both on the extent of clouds and their properties, including the number and size of water droplets within the clouds. In a recent study, researchers at Pacific Northwest National Laboratory (PNNL), University of Washington, Los Alamos National Laboratory, and University of Leeds found that small particles originating from ocean phytoplankton are responsible for most of the seasonal and geographic differences in the number of droplets in clouds over vast stretches of the ocean in the Southern Hemisphere. This, in turn, affects the fraction of sunlight reflected by the clouds, also known as cloud albedo. The team assembled a collection of datasets related to cloud properties, marine aerosols, and meteorological variables (such as wind speed), using a combination of information from satellite observations and atmospheric and ocean models. By comparing these datasets and others, the researchers showed that about half of the seasonal and geographic variation in cloud drop number over the oceans between 35 degrees and 55 degrees south latitude can be predicted using models describing aerosol particles that are primarily of marine biogenic origin. This finding suggests that marine critters are responsible for much of the variation in cloud albedo in this region. The effects of ocean biology on clouds are largest in the oceans of the Southern Hemisphere, a geographic region where current climate models perform poorly relative to other parts of the world. This finding will improve the representation of cloud albedo in global atmospheric models, which may help to improve simulations of past and present climate as well as future climate projections.

Aerosols affect clouds in several ways, including influencing the number and size of cloud droplets, and therefore cloud radiative properties and lifetime (aerosol “indirect effects”). Although these effects occur at subgrid scale, increasing model resolution to 0.25° grid spacing, improvements in the cloud simulation may enable improved simulation of aerosol-cloud effects. A team of scientists led by U.S. Department of Energy researchers at Pacific Northwest National Laboratory quantified the resolution sensitivity of cloud and precipitation susceptibilities to aerosols, as well as aerosol indirect forcing in the Community Atmosphere Model Version 5 (CAM5). The team ran the model in a realistic climate at four different horizontal grid spacings with the model meteorology strongly nudged toward the very high-resolution Year Of Tropical Convection analysis. They found that aerosol effects on clouds vary with model resolution. A better characterization of aerosol-cloud interactions can be achieved by increasing model resolution as the CAM5 aerosol and cloud parameterizations are able to produce more realistic simulations with the higher-resolution model, despite the fact that most aerosol and cloud processes are still at subgrid scale even for the highest resolution explored in this study. The higher-resolution simulation has a stronger cloud brightness effect, but smaller cloud lifetime effect, with an overall 15% decrease in aerosol-cloud (cooling) effect.

Understanding subsurface electron flow is vital in understanding elemental cycling and remediating subsurface pollutants, including those from recent energy technologies and historic waste sites. Research into the flow of electrons can show how certain minerals and bacteria work together via reduction-oxidation reactions to shape the geochemical landscape at Earth’s near surface and possibly halt toxins from spreading. The scientific challenge is how to unravel complex communities of organisms and mineral assemblages in nature into key cooperative subsystems that can be studied in the laboratory to determine how they work. In a recent study, scientists at the University of Tuebingen, University of Manchester, and Pacific Northwest National Laboratory discovered that during the day, one species of bacteria withdraws electrons from the iron-based mineral magnetite. At night, another species adds electrons back to the mineral, where the electrons reside until the daytime bacteria are active. The phototrophic Fe(II)-oxidizing Rhodopseudomonas palustris TIE-1 and the anaerobic Fe(III)-reducing Geobacter sulfurreducens work together to use magnetite’s iron ions as both electron sources and sinks under different day and night conditions. The researchers used a host of instruments to make this discovery, including transmission electron microscopy resources at the Department of Energy’s Environmental Molecular Sciences Laboratory. The research shows that the common iron oxide mineral magnetite can serve as a naturally occurring battery for two very different types of bacteria that depend on iron to survive, revealing that a single mineral can serve as a platform for microbial diversity in nature.

Lignocellulosic biomass has the potential to be a major source of renewable sugar for biofuel production. However, the lignin component, a complex and interlinked phenolic polymer, associates with secondary cell wall polysaccharides, rendering them less accessible to enzymatic hydrolysis to convert them to sugars. Therefore, before enzymatic hydrolysis, biomass must first be pretreated to make it more susceptible to saccharification and release high yields of fermentable sugars. To reduce the impact of lignin on limiting saccharification, researchers at the Department of Energy’s Joint BioEnergy Institute (JBEI) engineered Arabidopsis lines where lignin biosynthesis was repressed in fiber tissues but retained in the plant’s vessels, and polysaccharide deposition was enhanced in fiber cells. Growth of these engineered plants showed little to no apparent negative impact on growth phenotype. Analyses of these engineered Arabidopsis plants were conducted to determine if the engineered plants would yield more sugars than wild type. Both wild type and engineered plant biomasses were treated with an ionic liquid at either 70°C for 5 hours or 140°C for 3 hours. After pretreatment at 140°C and subsequent saccharification, the relative peak sugar recovery from biomass of engineered plants and wild type was not statistically different. However, reducing the pretreatment temperature to 70°C resulted in a higher peak sugar recovery for the engineered lines, but a significant reduction in the peak sugar recovery obtained from the wild type. These results demonstrate that employing cell wall engineering to decrease the recalcitrance of lignocellulosic biomass has the potential to drastically reduce the energy required for effective pretreatment.

The hydrological cycle is influenced by climate, but also regulated extensively by human activities such as irrigation and groundwater pumping that also respond to climate. A team of scientists, led by U.S. Department of Energy researchers at Pacific Northwest National Laboratory, presented a first-of-its-kind study that looks at impacts of irrigation on both surface water and groundwater resources at the global scale under the Coupled Model Intercomparison Project Phase 5 climate scenarios. The team conducted three different sets of numerical experiments driven by bias-corrected climate projections from five general circulation models (GCMs) to analyze the effect of irrigation on global surface water (SW) and groundwater (GW) resources. They found that irrigation could lead to SW/GW depletion in many intensely irrigated regions. Irrigation depending primarily on SW tends to have larger impacts on low-flow than high-flow conditions, suggesting increased vulnerability for drought. By the end of this century, combined effects of increased irrigation water demand and amplified temporal-spatial variability of water supply may lead to severe local irrigation water scarcity. The team highlighted the need to account for the effects of irrigation and its water sources in assessing regional climate change impacts.

Unresolved questions about the dynamics of convective clouds can be lumped into two broad categories, namely, how do these clouds get created, and how do they evolve once created? For the first process, there are two possibilities: (1) Pools of warm, humid air at the surface launch off the surface under the force of their own buoyancy. (2) Pools of warm, humid air are forced off the surface by other, colder pools of air that collide with them.
Which process dominates? To find out, researchers derived a decomposition of forces that cleanly separates between these two effects: effective buoyancy (driven by buoyancy alone) and the inertial acceleration (driven by the motion of the fluid alone). Solving for these two terms requires solving a Poisson equation, which was done in the context of high-resolution, large-eddy simulations of deep convection. The results are unambiguous: air parcels are launched off the surface by the forcing from colder pools that collide with them, not by the force of their own buoyancy. This finding is a critical piece of input for convective parameterizations in global climate models.

Clouds play an important role in the climate system’s global energy and water cycles. Representation of clouds, especially cumulus clouds, remains a great modeling challenge due to their variability in time and space and the coarse grid spacing in regional and global climate models. Even at the cloud-resolving scale, models with bulk (computationally inexpensive) microphysical formulas have difficulties simulating the convective (updraft) properties of cumulus clouds. Using the Weather Research and Forecasting (WRF) model, researchers at the Department of Energy’s Pacific Northwest National Laboratory found that compared to observations, the spectral-bin (more physical, more computationally expensive) microphysics method provides better simulations of precipitation and vertical velocity of the cumulus convective cores than two methods that use double-moment (mass and number) bulk microphysics. The spectral-bin microphysics method reproduces the observed updraft intensity well, alleviating much of the overestimation of updraft speeds produced by the bulk method. This finding suggests that a cloud microphysical method can improve model simulation of convective cloud properties. The researchers then used the spectral-bin microphysics model output as benchmark simulations for their study of scale-dependence of convection transport (Part II of the study). They also discovered that mass flux (rate of mass flow), a quantity on which cumulus representations are based, is very sensitive to different microphysical methods for tropical convection, indicating strong microphysics modification to convection. But, the modeled mass fluxes of cloud systems in the mid latitudes are not sensitive to the choice of microphysics methods. Cloud microphysical measurements of rain, snow, and graupel in convective cores will be critically important to further understand and elucidate performances of cloud microphysics methods.

Dynamic ice sheet models are a new component for Earth system models such as the Department of Energy’s (DOE) Accelerated Climate Modeling for Energy (ACME). As these ice sheet models are developed, it is challenging for developers to easily test the code, both computationally and in comparison with measurements. To address this issue, DOE’s Predicting Ice Sheets and Climate Evolution of Extremes (PISCEES) SciDAC project has released the first automated capability to verify continental ice sheet models with an advanced and robust software capability, the Land Ice Verification and Validation kit, or LIVV kit, which has been released for the broader ice-sheet community. This python, web-based software package is a comprehensive and extensible model testing framework, and it enables computational and climate scientists to quickly and easily verify model changes on multiple desktop and Leadership Class Computing platforms, document the changes quantitatively and graphically, and use the results to build confidence in the new, DOE-sponsored Community Ice Sheet Model (CISM). LIVV’s performance aspect allows users to identify performance bugs as well as computational performance improvements and degradations quantitatively as the model is developed. It includes scalability (i.e., showing how well the model can be parallelized) analysis for large problems. This first release allows model developers outside DOE to utilize a continental-scale ice sheet evaluation capability, and it is the basis for future development of a comprehensive validation capability of stand-alone ice sheet and coupled Earth system models. This software package will enable faster development and, going forward, provide increased confidence in an ice sheet model predictive capability.

Understanding the future pattern and scale of climate change requires an understanding of how the human systems that drive climate change will evolve. However, human systems are vulnerable to and will adapt and respond to a changing climate. Changes in land productivity, water availability, or demand for heating and cooling services could significantly alter the nature of human resource management and therefore feed back to the drivers of climate change itself. Human and Earth systems co-evolve, yet the modeling tools used to project the behavior of these systems into the future typically treat them as independent processes. As a result, the magnitude and nature of such interactions are not well understood.

In a recent study funded by the Department of Energy’s Office of Science, a team of scientists from three national laboratories—Lawrence Berkeley, Pacific Northwest, and Oak Ridge national laboratories—have combined efforts to create a new integrated Earth system model (iESM). The iESM merges the human system components of an integrated assessment model and the physical, hydrological, ecological, and biogeochemical components of an Earth system model. This unified software framework is designed with flexibility and extensibility in mind. It permits the component models to be operated and developed separately, or to be run together in a coupled mode designed to probe interactions among them. The study documents the structure and rationale behind the iESM coupling framework and demonstrates its ability to reproduce one-way coupling from the integrated assessment model to the Earth system model that was previously conducted in an offline mode as part of the 5th Coupled Model Intercomparison (CMIP5) effort. The iESM also reproduces offline model output from individual model components to within machine precision.

The iESM, which will soon be released to the global climate research community, represents a major new model capability that permits the exploration of process-level interactions among human and Earth systems that were previously not represented in the existing suite of computational tools and procedures. While the initial version of the iESM focuses on carbon cycle interactions, the extensible nature of the software framework ensures that more complex interactions among human and Earth systems are able to be represented as well. Planned extensions include human emissions of short-lived climate forcers, climate impacts on human energy systems, and two-way interactions between climate and managed water systems.

Drought and heat-induced tree mortality is accelerating in many forest biomes as a consequence of a warming climate, resulting in a threat to global forests unlike any in recorded history. Forests store the majority of terrestrial carbon, thus their loss may have significant and sustained impacts on the global carbon cycle. In a recent paper, researchers used a hydraulic corollary to Darcy’s law, a core principle of vascular plant physiology, to predict characteristics of plants that will survive and die during drought under warmer future climates. Plants that are tall with isohydric stomatal regulation, low hydraulic conductance, and high leaf area are most likely to die from future drought stress. Thus, tall trees of old-growth forests are at the greatest risk of loss, which has ominous implications for terrestrial carbon storage. This application of Darcy’s law indicates today’s forests generally should be replaced by shorter and more xeric plants, owing to future warmer droughts and associated wildfires and pest attacks. The Darcy’s corollary also provides a simple, robust framework for informing forest management interventions needed to promote the survival of current forests. Given the robustness of Darcy’s law for predictions of vascular plant function, the researchers conclude with high certainty that today’s forests will be subject to continued increases in mortality rates that result in substantial reorganization of their structure and carbon storage.

Bioreduction is being actively investigated as an effective strategy for subsurface remediation and long-term management of Department of Energy (DOE) sites contaminated by metals and radionuclides [i.e., uranium (VI)]. These strategies require manipulation of the subsurface, usually through injection of chemicals (e.g., electron donor), which mix at varying scales with the contaminant to stimulate metal-reducing bacteria. Evidence from DOE field experiments suggests that mixing limitations of substrates at all scales may affect biological growth and activity for U(VI) reduction.

To study the effects of mixing on U(VI) reduction, researchers used selenite, Se(IV), instead of U(VI) in the lab because Se(IV) is easier to handle and microbial reduction of Se(IV) and U(VI) is similar in that two immobilization pathways are involved. In one pathway, the soluble contaminant [Se(IV) or U(VI)] is biologically reduced to a solid [Se0 or U(IV)]. In the other pathway, sulfate, which is commonly present in groundwater, is first biologically reduced to sulfide; this product then abiotically reacts with the soluble contaminant [Se(IV) or U(VI)] to form a solid [selenium sulfide or U(IV)]. While the first pathway is well understood, the second pathway has not been widely studied. Another unique aspect of this study is that researchers investigated mixing and reaction in a microfluidic flow cell with realistic pore geometry and flow conditions that mimic the transverse-mixing dominated reaction zone along the margins of a selenite plume undergoing bioremediation due to injected electron donors in the presence of background sulfate. Microbial and chemical reaction products were characterized using advanced microscopic and spectroscopic methods. A continuum-scale reactive transport model also was developed to simulate this experiment.

Results demonstrate that engineering remediation of metal-contaminated sites via electron-donor addition can lead to secondary and abiotic reactions that can immobilize metals, in addition to previously studied biotic reactions. The improved understanding of selenite immobilization as well as the improved model can help in the design of in situ bioremediation processes for groundwater contaminated by selenite or other contaminants [e.g., U(IV)] that can be immobilized via similar pathways.

Processes occurring in soils and aquifers play a crucial role in contaminant remediation and carbon cycling. The flow of water through porous media like soils and aquifers is essential for contaminant remediation and carbon cycling and depends on the permeability, which determines how much water flows for a given hydraulic driving force. Widely recognized is that colloids (fine particles including soils, chemical precipitates, and bacteria) often control permeability and that colloid deposit morphology (the structure of deposited colloids) is a fundamental aspect of permeability. Until recently, however, no experimental techniques were available to measure colloid deposit morphology within porous media. A recent study, led by the University of Colorado Denver in collaboration with Lawrence Berkeley National Laboratory, used a custom-designed experimental apparatus to perform a series of experiments using static light scattering (SLS) to characterize colloid deposit morphology within refractive index matched (RIM) porous media during flow through a column. Real-time measurements of permeability, specific deposit, and deposit morphology were conducted with initially clean porous media at various ionic strengths and water velocities. Decreased permeability (i.e., increased clogging) correlated with colloid deposit morphology, specifically with lower fractal dimension and smaller radius of gyration.

These observations suggest a deposition scenario in which large and uniform aggregates become deposits, reducing porosity, and lead to higher fluid shear forces, which then decompose the deposits, filling the pore space with small and dendritic fragments of aggregate. Accordingly, for the first time, observations are available to quantify the relationship between the macroscopic variables of ionic strength and water velocity and the pore-scale variables of colloid deposit morphology, which can be conceptualized as an emergent property of the system. This research paves the way for future studies to quantify the complex feedback process between flow, chemistry, and biology in soils and aquifers.

Soil isotopes are commonly used in environmental, agricultural, and biogeochemical studies to track sources and fate of labeled compounds, and also because they facilitate quantification of the intensity of a process relative to others. In a recent study, researchers worked to (1) elucidate the linear and nonlinear contributions of temperature to the reaction rate of isotopically labeled reactants, (2) highlight whether effects arise in other parameters, and (3) provide a comprehensive sensitivity analysis of kinetic isotopic effects over the concentration-temperature space using mathematical modeling of the effects in (1) and (2). To accomplish this, nine independent experiments of nitrate (NO₃^–) denitrification were analyzed using the Arrhenius law and the Eyring’s transition-state theory to highlight how temperature affects reaction rate constants, affinities, and kinetic isotopic effects. For temperatures between 20 and 35 °C, the Arrhenius law and the transition-state theory described equally well observed temperature increases in ¹⁴NO₃^– and ¹⁵NO₃^– denitrification rates. These increases were partly caused by an increase in frequency factor and a slight decrease in activation energy (enthalpy and entropy). Parametric analysis also showed that the affinity of ¹⁴NO₃^– and ¹⁵NO₃^– toward a microbial enzyme increased exponentially with temperature and a strong correlation with the rate constants was found. Experimental time and temperature-averaged fractionation factor αP/S showed only a slight increase with increasing temperature (i.e., lower isotopic effects); however, a comprehensive sensitivity analysis in the concentration temperature domain using average thermodynamic quantities estimated here showed a more complex response; α_P/S was relatively constant for initial bulk concentrations [NO₃^–]₀ ≤ 0.01 mol kg^-1, while substantial nonlinearities developed for [NO₃^–]₀ ≥ 0.01 mol kg^-1 and appeared to be strongly correlated with microbial biomass, whose concentration and activity varied primarily as a function of temperature and available substrate. Values of α_P/S ranging between 0.9 and 0.98 for the tested temperatures suggested that interpretations of environmental isotopic signatures should include a sensitivity analysis to the temperature as this affects directly the rate constants and affinities in biochemical reactions and may hide process- and source-related isotopic effects.

Stomatal conductance (gs) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of gs in predictions of global water and carbon cycle changes, a global scale database and an associated globally applicable gs model that enable predictions of stomatal behavior are lacking. In a recent study, researchers present a database of globally distributed gs obtained in the field for a wide range of plant functional types (PFTs) and biomes. They found that stomatal behavior differs among PFTs according to their marginal carbon cost of water use, as predicted by the theory underpinning the optimal stomatal model and the leaf and wood economics spectrum. They also demonstrate a global relationship with climate. These findings provide a robust theoretical framework for understanding and predicting gs behavior across biomes and across PFTs that can be applied to regional, continental, and global-scale modeling of ecosystem productivity, energy balance, and ecohydrological processes in a future changing climate.

Permafrost soils contain enormous amounts of organic carbon whose stability is contingent on remaining frozen. With future warming, these soils may release carbon to the atmosphere and act as a positive feedback to climate change. Significant uncertainty remains on the post-thaw carbon dynamics of permafrost-affected ecosystems, in particular since most of the carbon resides at depth where decomposition dynamics may differ from surface soils, and since nitrogen mineralized by decomposition may enhance plant growth. Using a carbon–nitrogen model that includes permafrost processes forced in an unmitigated warming scenario, researchers show that the permafrost region’s future carbon balance is highly sensitive to the decomposability of deeper carbon, with the net balance ranging from 21 Pg of carbon to 164 Pg carbon losses by 2300. Increased soil nitrogen mineralization reduces nutrient limitations, but the impact of deep nitrogen on the carbon budget is small due to enhanced nitrogen availability from warming surface soils and seasonal asynchrony between deeper nitrogen availability and plant nitrogen demands. Although nitrogen dynamics are highly uncertain, the future carbon balance of this region is projected to hinge more on the rate and extent of permafrost thaw and soil decomposition than on enhanced nitrogen availability for vegetation growth resulting from permafrost thaw.

Fine roots constitute a significant source of plant productivity and litter turnover across terrestrial ecosystems, but less is known about the quantitative and qualitative profile of phenolic compounds within the fine-root architecture, which could regulate the potential contribution of plant roots to the soil organic matter pool. To understand the linkage between traditional macro-elemental and morphological traits of roots and their molecular-level carbon chemistry, researchers analyzed seasonal variations in monomeric yields of the free, bound, and lignin phenols in fine roots (distal five orders) and leaves of Ardisia quinquegona. Fine roots contained two-fold higher concentrations of bound phenols and three-fold higher concentrations of lignin phenols than leaves. Within fine roots, the concentrations of free and bound phenols decreased with increasing root order, and seasonal variation in the phenolic profile was more evident in lower-order than in higher-order roots. The morphological and macro-elemental root traits were decoupled from the quantity, composition, and tissue association of phenolic compounds, revealing the potential inability of these traditional parameters to capture the molecular identity of phenolic carbon within the fine-root architecture and between fine roots and leaves. These results highlight the molecular-level heterogeneity in phenolic carbon composition within the fine-root architecture, and imply that traits that capture the molecular identity of the root construct might better predict the decomposition dynamics within fine-root orders.

Surface-to-atmosphere emissions of dimethyl sulfide (DMS) may impact global climate through the formation of gaseous sulfuric acid, which can yield secondary sulfate aerosols and contribute to new particle formation. While oceans are generally considered the dominant sources of DMS, a shortage of ecosystem observations prevents an accurate analysis of terrestrial DMS sources. Using mass spectrometry, researchers recently quantified ambient DMS mixing ratios within and above a primary rainforest ecosystem in the central Amazon Basin in real time (2010–2011) and at high vertical resolution (2013–2014). Elevated, but highly variable DMS mixing ratios were observed within the canopy, showing clear evidence of a net ecosystem source to the atmosphere during both day and night in both the dry and wet seasons. Periods of high DMS mixing ratios lasting up to 8 hours [up to 160 parts per trillion (ppt)] often occurred within the canopy and near the surface during many evenings and nights. Daytime gradients showed mixing ratios (up to 80 ppt) peaking near the top of the canopy as well as near the ground following a rain event. The spatial and temporal DMS distribution suggests that ambient levels and their potential climatic impacts are dominated by local soil and plant emissions. A soil source was confirmed by measurements of DMS emission fluxes from Amazon soils as a function of temperature and soil moisture. Furthermore, light- and temperature-dependent DMS emissions were measured from seven tropical tree species. This study has important implications for understanding terrestrial DMS sources and their role in coupled land-atmosphere climate feedbacks.

Carbon uptake rates in many forests are sustained, or decline only briefly, following disturbances that partially defoliate the canopy. The mechanisms supporting such functional resistance to moderate forest disturbance are largely unknown. Researchers used a large-scale experiment to identify mechanisms sustaining carbon uptake through partial canopy defoliation. The Forest Accelerated Succession Experiment in northern Michigan employs a suite of carbon-cycling measurements within paired treatment and control meteorological flux tower footprints. They found that enhancement of canopy light-use efficiency and maintenance of light absorption maintained net ecosystem production and aboveground wood net primary production (NPP) when leaf-area index (LAI) of the treatment forest temporarily declined by nearly half its maximum value. In the year following peak defoliation, redistribution of nitrogen in the treatment forest from senescent early successional aspen and birch to nongirdled later successional species facilitated the recovery of total LAI to predisturbance levels. Sustained canopy physiological competency following disturbance coincided with a downward shift in maximum canopy height, indicating that compensatory photosynthetic carbon uptake by undisturbed, later successional subdominant and subcanopy vegetation supported carbon-uptake resistance to disturbance. These findings have implications for ecosystem management and modeling, demonstrating that forests may tolerate considerable leaf-area losses without diminishing rates of carbon uptake. They conclude that the resistance of carbon uptake to moderate disturbance depends not only on replacement of lost leaf area, but also on rapid compensatory photosynthetic carbon uptake during defoliation by emerging later successional species.

Although tropical forests account for only a fraction of the planet’s terrestrial surface, they exchange more carbon dioxide with the atmosphere than any other biome on Earth and thus play a disproportionate role in the global climate. Over the next 20 years, the tropics will experience unprecedented warming, yet exceedingly high uncertainty persists about their potential responses to this imminent climatic change. In a recent study, researchers investigated overall model uncertainty of tropical latitudes and explored the scientific benefits and inevitable trade-offs inherent in large-scale manipulative field experiments. With a Coupled Model Intercomparison Project Phase 5 analysis, they found that model variability in projected net ecosystem production was nearly three times greater in the tropics than for any other latitude. Through a review of the most current literature, they concluded that manipulative warming experiments are vital to accurately predict future tropical forest carbon balance, and they further recommend establishing a network of comparable studies spanning gradients of precipitation, edaphic qualities, plant types, and land use change. In addition, they provide arguments for long-term, single-factor warming experiments that incorporate warming of the most biogeochemically active ecosystem components (i.e., leaves, roots, and soil microbes). Hypothesis testing of underlying mechanisms should be a priority, along with improving model parameterization and constraints. No single tropical forest is representative of all tropical forests; therefore, logistical feasibility should be the most important consideration for locating largescale manipulative experiments.

Disturbance-induced tree mortality is a key factor regulating the carbon balance of a forest, but tree mortality and its subsequent effects are poorly represented processes in terrestrial ecosystem models. Thus unclear is whether models can robustly simulate moderate (noncatastrophic) disturbances, which tend to increase biological and structural complexity and are increasingly common in aging U.S. forests. Researchers recently tested whether three forest ecosystem models—Biome-BGC (BioGeochemical Cycles), a classic big-leaf model, and the ZELIG and ED (Ecosystem Demography) gap-oriented models—could reproduce the resilience to moderate disturbance observed in an experimentally manipulated forest (Forest Accelerated Succession Experiment in northern Michigan, where 38% of canopy dominants were stem girdled and compared to control plots). Each model was parameterized, spun up, and disturbed following similar protocols and run for 5 years post-disturbance. The models replicated observed declines in aboveground biomass well. Biome-BGC captured the timing and rebound of observed leaf area index (LAI), while ZELIG and ED correctly estimated the magnitude of LAI decline. None of the models fully captured the observed post-disturbance carbon fluxes, in particular gross primary production or net primary production (NPP). Biome-BGC NPP was correctly resilient but for the wrong reasons, and could not match the absolute observational values. ZELIG and ED, in contrast, exhibited large, unobserved drops in NPP and net ecosystem production. The biological mechanisms proposed to explain the observed rapid resilience of the carbon cycle typically are not incorporated by these or other models. Thus, an open question is whether most ecosystem models will simulate correctly the gradual and less extensive tree mortality characteristic of moderate disturbances.

Eddy covariance measurements of carbon dioxide (CO₂) exchange have to be partitioned into offsetting gross fluxes, canopy photosynthesis, and ecosystem respiration to understand biophysical controls on the net fluxes. Additionally, independent estimates of canopy photosynthesis (G) and ecosystem respiration (R) are needed to validate and parametrize carbon cycle models that are coupled with climate and ecosystem dynamics models. Carbon flux partitioning methods, however, may suffer from spurious correlation, because derived values of canopy photosynthesis and ecosystem respiration both contain common information on net carbon fluxes at annual time scales.

Researchers hypothesized that spurious correlation between canopy photosynthesis and ecosystem respiration can be minimized using day–night conditional sampling of CO₂ exchange, with daytime fluxes dominated by photosynthesis and nighttime fluxes dominated by respiration. To test this hypothesis, the research team derived explicit equations that quantify the degree of spurious correlation between photosynthesis and respiration. Theoretically, day and night samples of net carbon exchange share a different common variable, daytime ecosystem respiration, and the degree of spurious correlation depends upon the variance of this shared variable. This theory was applied to ideal measurements of carbon exchange over a vigorous, irrigated, and frequently harvested alfalfa field in the sunny and windy region of the Sacramento-San Joaquin Delta of California, where soil CO₂ efflux is strong. Results showed a correlation coefficient between canopy photosynthesis and ecosystem respiration of -0.79. This relatively high correlation between canopy photosynthesis and respiration was mostly real as the degree of spurious correlation was only -0.32.

This analysis was expanded to the FLUXNET database, which spans a spectrum of climate and plant functional types. On average, the correlation between gross photosynthesis and ecosystem respiration, using day–night sampling, was close to minus one (-0.828 ± 0.130). For perspective, a large fraction of this correlation was real, as the degree of spurious correlation (Eq. (22)) was -0.526. Consequently, the potential for spurious correlation between canopy photosynthesis and ecosystem respiration across the FLUXNET database was moderate. Looking across the database, the researchers found that the least negative spurious correlation coefficients (>-0.3) were associated with seasonal deciduous forests. The most negative spurious correlations coefficients (<-0.7) were associated with evergreen forests found in most boreal climates.

Lignocellulosic materials offer an attractive replacement for food-based crops used to produce ethanol, but understanding the interactions within the cell wall is vital to overcome the highly recalcitrant nature of lignocellulosic biomass. One factor imparting plant cell wall recalcitrance is lignin, which can be manipulated by making changes in the lignin biosynthetic pathway. Changes to lignin gene expression in switchgrass and Populus have shown increased sugar release and reduced recalcitrance. Researchers at the Department of Energy’s BioEnergy Science Center have sought to transfer these results to eucalyptus, a fast-growing, warm climate, woody biofeedstock also suitable for cellulosic biofuel production. The researchers genetically engineered reduced gene expression of two key lignin biosynthesis enzymes, cinnamate 4-hydroxylase (C4H) and p-coumaroyl quinate/shikimate 3′-hydroxylase (C3’H), in eucalyptus. The engineered plants were evaluated for cell wall composition and reduced recalcitrance. Eucalyptus trees with down-regulated C4H or C3’H expression displayed lowered overall lignin content than the control samples. The C3’H and C4H down-regulated lines also had different lignin compositions when compared to the control eucalyptus trees. Both the C4H and C3’H down-regulated lines had reduced recalcitrance as indicated by increased sugar release, which was determined using enzymatic conversion assays utilizing both no pretreatment and a hot water pretreatment. Lowering lignin content rather than altering lignin content was found to have the largest impact on reducing recalcitrance of the transgenic eucalyptus variants. The development of lower recalcitrance trees opens up the possibility of using alternative pretreatment strategies in biomass conversion processes that can reduce processing costs.

Every year, large amounts of methane (CH₄) are produced in coastal wetlands and deep ocean sediments through the decay of organic material or seepage from geological reservoirs. Fortunately, microbes consume the majority of this potent greenhouse gas before it reaches Earth’s atmosphere. Although these subsurface environments are typically depleted of oxygen, methane can still be oxidized by symbiotic partnerships between methane-consuming archaea and sulfate-reducing bacteria that collaboratively transfer electrons from methane to sulfate (rather than O₂) to generate useful energy. Observed near sites of environmental CH₄ production, consortia of cells performing anaerobic oxidation of methane (AOM) form mixed balls composed of tens to hundreds of cells, but the exact mechanism by which they consume CH₄ and share energy is not fully understood. In a new study, scientists at the California Institute of Technology used high-resolution microscopy paired with mass spectrometry (NanoSIMS) to examine the relationship between spatial distribution of microbes and metabolic processes in AOM consortia. To their surprise, the researchers found that metabolically active partner microbes did not need to be closely associated with each other, even though each organism performs only half of the critical methane-consuming reaction. Using data from these studies, the team constructed a computational model of consortial metabolism that predicted an extracellular conduit allowing direct transfer of electrons between the organisms. By re-examining the genomes of both microbes, the team identified a previously overlooked set of genes in the archaeal partner encoding an electron transfer system similar to those observed in known electroconductive bacteria. Histological staining was then used to detect this system in active AOM consortia, revealing components arrayed across the extracellular space between the microbes. These results indicate the presence of a biological wiring system within AOM consortia that allows the two partners to more efficiently consume methane, share resulting energy, and form larger consortial structures than would otherwise be possible. These findings reveal another new aspect of the diverse metabolic capacities present in the microbial world and considerably advance our understanding of a key microscale mechanism driving a carbon cycle process of global significance.

Harsh pretreatments are often used to make lignocellulose sugars more accessible for conversion to biofuels. These pretreatments can cause problems for subsequent stages of biofuel production. For example, dilute-acid pretreatment of lignocellulosic biomass creates potent inhibitors of microbial growth and fermentation such as furfural and 5-hydroxymethyl-furfural (5-HMF). The enzymatic reduction of these furan aldehydes to their corresponding less toxic alcohols is an engineering approach that has been successfully implemented in both Saccharomyces cerevisiae and ethanologenic Escherichia coli. However, this approach has not yet been investigated in thermophiles relevant to biofuel production through consolidated bioprocessing (CBP), such as Caldicellulosiruptor bescii. To test if C. bescii could be engineered to be more tolerant of these inhibitors, researchers from the Department of Energy’s BioEnergy Science Center (BESC) constructed a strain of C. bescii using a butanol dehydrogenase encoding gene from Thermoanaerobacter pseudethanolicus 39E (BdhA), which had previously been shown to have furfural and 5-HMF reducing capabilities. Heterologous expression of the NADPH-dependent BdhA enzyme conferred increased resistance of the engineered strain to both furfural and 5-HMF relative to the wild-type and parental strains. Further, when challenged with 15 mM concentrations of either furan aldehyde, the ability to eliminate furfural or 5-HMF from the culture medium was significantly improved in the engineered strain. This study represents the first example of engineering furan aldehyde resistance into a CBP-relevant thermophile and further validates C. bescii as being a genetically tractable microbe of importance for lignocellulosic biofuel production.

The implications of global change for the sustainability of global and regional agroecosystems and food security continue to be a priority research concern. Corresponding insights have considerable implications for land use and land cover change, the carbon cycle, feedbacks to the climate system, energy-water-land coupled system dynamics, and broader socioeconomics as reflected in integrated assessment models. Agroecosystems are inherently complex. In particular, few aspects of agroecosystems are unaffected by human agency — the capacity of actors to act, directly or indirectly, to affect change. Hence, attempts to conceptualize or model agroecosystems as purely biophysical systems may result in biased insights or mask important consequences. At the same time, agency is contingent on scale. Therefore, diagnosing and predicting socioeconomic and ecological influences on agroecosystems are facilitated by conceptualizing, observing, and modeling the system at scales that are relevant to the questions that are being asked.

Researchers at Oak Ridge National Laboratory have explored a broad range of modelling tools and frameworks that can be applied to agroecosystem predictions. The researchers discovered that the processes in these models, including human agency, are generally designed to address a relatively bounded problem, which leads to a number of modeling limitations. First, models are often limited with respect to the scales they explicitly represent, and therefore may neglect consideration for institutional behavior, jurisdictional issues, or different levels of management responses. Second, models encounter significant challenges not simply with scales, but also with scaling. For example, farm level models do not consider issues of procurement, supply chains, and markets, which are influenced by agency at higher spatial and institutional levels. Third, the capacity to represent complex systems and their behavior is contingent on the availability of input data for model variables and processes as well as data for model calibration and validation. Fourth, the normative decisions made by model developers and users regarding what management options should be included ultimately influence model behavior and the results that are generated.

The research team asserts that a range of research pathways can help alleviate these challenges. Explicitly identifying the scales and levels relevant to the development or application of agroecosystem models can assist in identifying their strengths and weaknesses. This information can be used to prioritize model development or data needs and to identify model limitations and knowledge gaps affecting interpretation and use of results. Greater emphasis on model integration or coupling can be an effective pathway for linking top-down and bottom-up models to incorporate agency across multiple scales and levels. Meanwhile, the use of socioeconomic scenarios to define alternative futures can help to create context around models for those aspects of human systems that are not explicitly modeled.

Hydropower, the current dominant renewable energy source, can facilitate the deployment of other variable renewable energy resources used in part to reduce greenhouse gas emissions and provide a stable and sustainable source of electricity. Improved information on hydropower potential and its spatial distribution can help decisionmakers guide the deployment of hydropower plants. Hydropower potential information is also an important input to integrated assessment and energy–economic models, which are used to help explore future energy systems, climate impacts, and transition pathways to lower-carbon futures over decadal to century time scales. In this study, researchers at the Department of Energy’s (DOE) Pacific Northwest National Laboratory assessed global hydropower potential using water runoff and stream flow data, along with turbine technology performance, cost assumptions, and consideration of protected areas. The results provide the first comprehensive quantification of global hydropower potential including: gross, technical, economic, and exploitable. The hydropower is estimated in petawatt hours per year, a measurement defined to quantify electrical use per hour in terms of a quadrillion watts. The research shows that hydropower has the potential to supply a significant portion of world energy needs, although this potential varies substantially by region. Globally, exploitable hydropower potential is comparable to total electricity demand in 2005. Regionally, hydropower plays different roles in each country, mainly because of regional variation in potential relative to electricity demand. In addition, hydropower estimates are sensitive to a number of regionally defined parameters: design capacity, cost assumptions, turbine efficiency, stream flow, fixed charge rate, and protected land. The research emphasizes hydropower’s reliable role for future energy systems, especially when compared to other renewable energy resources with larger uncertainty in their future potentials.

Ongoing integrated modeling efforts focus on devising sustainable climate change mitigation policies and jointly considering potential synergies and constraints within the climate-energy-water nexus. While there is evidence that climate warming will contribute to increasing intensity and duration of drought, understanding the overall impact of climate change mitigation on water resources requires accounting for the impact of mitigation-induced changes in water demands from human activities. In a study led by Department of Energy (DOE) scientists at Pacific Northwest National Laboratory (PNNL), researchers used a regional integrated assessment model and a regional Earth system model at high spatial and temporal resolutions over the United States to compare the implications of two representative concentration pathways under consistent socioeconomic conditions. By using integrated, high-resolution models of human and natural system processes, the scientists show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emission mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation.

Current atmospheric models are not perfect predictors of wind conditions or “inflow” at heights spanned by industrial-scale wind turbines (~40 to 200 m above ground level). Wind forecasting improvement of as little as 10% to 20% could result in hundreds of millions of dollars in annual operating cost savings for the U.S. wind industry. One candidate for improving wind forecasts is the choice of a land surface model (LSM) employed in numerical weather prediction models. The LSM controls the exchange of energy between the surface and the atmosphere and may have a large effect on inflow in the lower boundary layer.

Scientists used the Weather Research and Forecasting (WRF) model and data from the Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site to investigate the LSM’s impact on the near-surface wind profile, including heights reached by multimegawatt wind turbines.

Simulations of wind profiles and surface energy fluxes were made using five LSMs of varying degrees of sophistication in dealing with soil-plant-atmosphere feedbacks. Surface flux and wind profile measurements from the ARM SGP site in Oklahoma were used to validate the model simulations. WRF was run for three different two-week periods covering varying canopy and meteorological conditions. The LSMs predicted a wide range of energy flux and wind shear magnitudes even during the cool autumn period when less variability was expected.

Simulations of energy fluxes varied in accuracy by model sophistication; LSMs with very simple or no soil-plant-atmosphere feedbacks were the least accurate. However, the most complex models did not consistently produce more accurate results. Errors in wind shear were also sensitive to LSM choice and were partially related to energy flux accuracy. The variability of LSM performance was relatively high, suggesting that LSM representation of energy fluxes in WRF remains a large source of model uncertainty for simulating wind turbine inflow conditions. Future simulations could be done during periods of concurrent wind power data to assess the relationship between surface energy exchange, wind shear, and power production at the wind farm located a few miles west of the SGP site.

To make confident predictions about future global and regional climate, global climate models (GCMs) must be capable of reproducing the present-day distribution of global heat and moisture. However, many GCMs exhibit a persistent bias in temperature over the mid-latitude continents, which is present in both short-range forecasts as well as long-term climate simulations. A common approach to evaluating model biases is to focus on the model-mean state, but this approach makes an unambiguous interpretation of the bias origins difficult, given that biases are often the result of the superposition of impacts of different processes over multiple time steps in the model.

A team of scientists funded in part by the Department of Energy’s (DOE) Atmospheric System Research and Regional and Global Climate Modeling programs developed a new methodology to objectively disentangle and quantify contributions from clouds and other processes in the creation of a surface warm bias in climate models. A unique feature of this approach is its focus on the growth of the temperature error at the time-step level. Compositing the error growth by the coinciding bias in total downwelling radiation provides unambiguous evidence for the role that clouds play in the creation of the surface warm bias during certain portions of the day. Furthermore, application of an objective cloud-regime classification allows for the detection of the specific cloud regimes that matter most for the bias creation. The new model evaluation methodology relies heavily on the availability of high-temporal resolution observations of temperature, cloud properties, and surface radiation from DOE’s Atmospheric Radiation Measurement (ARM) Climate Research Facility.

The scientists applied their new method to two state-of-the-art GCMs that exhibit a distinct warm bias over the ARM Southern Great Plains (SGP) site. The analysis finds that in one GCM, biases in deep-convective and low-level clouds contribute most to the temperature-error growth in the afternoon and evening, respectively. In the second GCM, deep clouds persist too long in the evening, leading to a growth of the temperature bias. The reduction of the temperature bias in both models in the morning and the growth of the bias in the second GCM in the afternoon could not be assigned to a cloud issue, but are more likely caused by a land-surface deficiency. This new analysis approach provides specific guidance to model developers about the processes on which they should focus development efforts to resolve existing model biases.

Despite orders of magnitude difference in atmospheric reactivity and great diversity in biological functioning, little is known about monoterpene speciation in tropical forests. In a recent study, researchers report vertically resolved ambient air mixing ratios for 12 monoterpenes in a central Amazon rainforest, including observations of the highly reactive cis-β-ocimene [160 parts per trillion (ppt)], trans-β-ocimene (79 ppt), and terpinolene (32 ppt), which accounted for an estimated 21% of total monoterpene composition, yet 55% of the upper canopy monoterpene ozonolysis rate. All 12 monoterpenes showed a mixing ratio peak in the upper canopy, with three demonstrating subcanopy peaks in seven of 11 profiles. Leaf-level emissions of highly reactive monoterpenes accounted for up to 1.9% of photosynthesis, confirming light-dependent emissions across several Amazon tree genera. These results suggest that highly reactive monoterpenes play important antioxidant roles during photosynthesis in plants and serve as near-canopy sources of secondary organic aerosol precursors through atmospheric photooxidation via ozonolysis.

Carbon cycle models often lack explicit belowground organism activity, yet belowground organisms regulate carbon storage and release in soil. Ectomycorrhizal fungi are important players in the carbon cycle because they are a conduit into soil for carbon assimilated by the plant. It is hypothesized that ectomycorrhizal fungi can also be active decomposers when plant carbon allocation to fungi is low. In this study, researchers developed a simulation model of the plant-mycorrhizae interaction where a reduction in plant productivity stimulates ectomycorrhizal fungi to decompose soil organic matter. The model output suggests that ectomycorrhizal activity accounts for a portion of carbon decomposed in soil, but this portion varied with plant productivity and the mycorrhizal carbon uptake strategy simulated. Lower organic matter inputs to soil were largely responsible for reduced soil carbon storage. Using mathematical theory, the researchers demonstrated that biotic interactions affect predictions of ecosystem functions. Specifically, they developed a simple function to model the mycorrhizal switch in function from plant symbiont to decomposer. The study shows that including mycorrhizal fungi with the flexibility of mutualistic and saprotrophic lifestyles alters predictions of ecosystem function.

The atmospheric boundary layer, which is the lowest layer of the atmosphere, directly feels the Earth’s surface and is strongly affected by processes such as large-scale dynamics, solar heating and nocturnal radiative cooling, evapotranspiration, and frictional drag. Accurate prediction of the boundary layer’s height and characteristics is important for a wide range of atmospheric processes including surface temperature; cloud formation; aerosol mixing, transport, and transformation; and chemical mixing, transport, and transformation. The structure of the boundary layer varies, becoming more stable and less convective at night as the surface starts to cool. The nocturnal stable boundary layer (SBL) generally can be classified into the weakly stable boundary layer (wSBL) and very stable boundary layer (vSBL) regimes. Within the wSBL, turbulence is relatively continuous, whereas in the vSBL, turbulence is intermittent and not well characterized. Better understanding of the differing characteristics of each SBL type is needed so they can be accurately simulated in numerical weather and climate models.

Scientists analyzed thermodynamic and kinematic data collected by a suite of instruments at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north central Oklahoma to better understand both SBL regimes and their differentiating characteristics. In particular, the team examined the relationship between wind speed and SBL characteristics. Composite normalized profiles of potential temperature, wind speed, vertical velocity variance, and the third-order moment of vertical velocity were produced for weak, moderate, and strong turbulence regimes. The team found that a threshold wind speed must be exceeded at lower heights (down to the surface) in order for strong turbulence to develop. Within the wSBL, turbulence is generated at the surface and transported upward. In the vSBL, values of vertical velocity variance are small throughout the entire boundary layer, likely due to the strong surface inversion that typically forms after sunset. The temperature profile tends to be approximately isothermal in the lowest portions of the wSBL and does not substantially change over the night. Within both SBL types, stability in the residual layer tends to increase as the night progresses. This stability increase is likely due to differential warm air advection, which frequently occurs in the southern Great Plains when southerly low-level jets and a typical north–south temperature gradient are present. Differential radiative flux divergence also contributes to this increase in stability. This increased understanding of different SBL characteristics can be used to evaluate and improve weather and climate models.

Every natural soil ecosystem hosts a great diversity of microbes that consume complex organic matter and transform it to simpler small carbon compounds (metabolites) or gaseous endproducts such as carbon dioxide. This decompositional microbial activity transforms organic compounds in the soil, playing a critical role in the global carbon cycle. To determine the functional characteristics of a microbial community’s different members, it is necessary to understand the complex mixture of metabolites present in their environment and to determine which compounds are preferentially consumed by each microorganism. Researchers at Lawrence Berkeley National Laboratory and collaborating institutions have used new exometabolomics techniques to quantitatively analyze the compounds consumed by seven bacterial species isolated from soil crusts in the desert environment of the Colorado Plateau. In these arid environments, most of the organic matter is produced by photosynthetic bacteria and released in the form of metabolites that other microbes can consume and further transform. The investigators discovered that each of the seven species consumes only 13% to 26% of the nearly 500 metabolites produced by these bacteria, and only 0.4% of the metabolites are used by all of them. These different feeding habits may represent a form of ecological niche specialization and may play important roles in maintaining non-overlapping diversity within microbial consortia. This study constitutes a significant advance in our understanding of how microbes in terrestrial ecosystems transform soil organic matter and may affect atmospheric carbon dioxide levels.

Clostridium thermocellum has the natural ability to convert cellulose to ethanol, making it a promising candidate for consolidated bioprocessing (CBP) of cellulosic biomass to biofuels. In addition to ethanol, however, C. thermocellum produces a number of unwanted fermentation products such as organic acids and gaseous hydrogen, which divert energy and carbon from the desired fermentation product, ethanol. Researchers at the Department of Energy’s BioEnergy Science Center sought to eliminate these non-target fermentation products in order to increase ethanol yields. In doing so, they created C. thermocellum strain AG553 by deleting genes involved in the production of acetate, formate, lactate, and hydrogen gas. Strain AG553 showed a two- to three-fold increase in ethanol yield relative to the wild type on all substrates tested. When grown in a defined medium with 5 g/L of soluble disaccharide cellobiose as the carbon source, the mutant strain produced greater than two-fold more ethanol than the wild type strain. It exceeded 70% of theoretical ethanol yield with no appreciable amounts of other fermentation products detected and H2 production reduced five-fold. Wild type C. thermocellum will naturally acidify a non-buffered medium during fer­mentation by production of organic acids and limit ethanol production by limiting growth. The elimination of organic acid production suggested that strain AG553 might be capable of growth under higher substrate loadings in the absence of pH control. The maximum titer of wild type C. thermocellum was only 14.1 mM ethanol on 10 g/L Avicel. For strain AG553, final ethanol titer peaked at 73.4 mM in on 20 g/L Avicel, at which point the pH decreased to a level that does not allow growth of C. thermocellum, likely due to carbon dioxide accumulation. With the elimination of the non-target fermentation metabolic pathways, AG553 is the best ethanol-yielding CBP strain to date. It will serve as a platform strain for further metabolic engineering for the bioconversion of lignocellulosic biomass into advanced biofuels other than ethanol.

Both observations and cloud-resolving models have frequently revealed that convective clouds over ocean grow from relatively moist boundary layers near the edges of evaporatively driven cold pools. Despite the relevance of these rings for the initiation and organization of cumulus clouds, there has been considerable debate about their origin. The prevailing hypothesis is that evaporation of rain drops within the sub-cloud layer provides the extra moisture that subsequently gets spread out radially by the gust front of the cold pool. However, the sub-cloud layer could be relatively moist simply because deep convection is favored in such environments in the first place. Or, surface latent-heat fluxes could provide the excess moisture found near cold pool edges. Scientists supported by the Department of Energy’s Atmospheric System Research and Scientific Discovery through Advanced Computing (SciDAC) programs carried out large-eddy simulations of a single cloud and cold pool to determine the individual contributions to the water-vapor field.

Their simulations reveal that the dominating contribution to these water-vapor rings comes from surface latent-heat fluxes. In contrast, the source from evaporated rain drops is rather small inside these rings. During the initial phase of cold pool formation the displaced sub-cloud layer air is relatively moist only because the sub-cloud layer has already been relatively moist before rain started falling into it.

Using a simple vertical velocity equation, the scientists demonstrate that evaporation can only explain roughly one third of the observed perturbation. During the cold pool’s early development, the time a descending air parcel is exposed to the rain shaft below cloud base is set by buoyant acceleration. Using this analytical framework, they show that this exposure time is short compared to the time required to evaporate sufficient moisture into the sub-cloud layer. The reasons for this finding are (a) the small saturation deficit in the sub-cloud layer (thus small evaporation rates) and (b) the sufficiently strong negative buoyancy provided by the weight of rain drops.

A key step in converting cellulosic biomass into sustainable fuels and chemicals is thermochemical pretreatment to reduce plant cell wall recalcitrance. An improved understanding of the chemistry of lignin as it undergoes this processing is critical to the development of renewable biofuel production. Researchers at the Department of Energy’s BioEnergy Science Center (BESC) have studied the behavior of lignin during dilute acid pretreatment (DAP). They isolated lignin from poplar and switchgrass using a cellulolytic enzyme system and then treated it under DAP conditions. Results highlighted that lignin is subjected to depolymerization reactions within the first 2 minutes of DAP, and these changes are accompanied by increased generation of aliphatic and phenolic hydroxyl groups of lignin. These developments are followed by a competing set of depolymerization and repolymerization reactions that lead to a decrease in the content of guaiacyl lignin units and an increase in condensed lignin units as the reaction residence time is extended beyond 5 minutes. A detailed comparison of changes in functional groups and molecular weights of cellulolytic enzyme lignins demonstrated that several structural parameters related to lignin’s recalcitrant properties are altered during DAP conditions. This deeper understanding of the chemical structure of lignin as it undergoes processing is an important step toward the goal of efficient conversion of lignocellulose into renewable biofuel products.

Lignocellulose-derived hydrolysates contain several different inhibitors (collectively called lignotoxins or LTs) that arise during pretreatment of biomass. Determining the mechanisms by which yeast or bacteria are adversely affected by LTs is a key step toward improving the efficiency of fermentation and bioconversion. Prior work has established that LTs present in ammonia pretreated corn stover hydrolysates inhibit growth and sugar utilization in Escherichia coli. Researchers at the Department of Energy’s Great Lakes Bioenergy Research Center (GLBRC) have now keyed in on two phenolic amine LTs, feruloyl amide (FA) and coumaroyl amide (CA). These inhibitors are important because these two alone are sufficient to recapitulate the inhibitory effects of all LTs present. Analysis of the metabolome in untreated versus treated cells indicated that these phenolic amides cause rapid accumulation of 5-phosphoribosyl-1-pyrophosphate (PRPP), a key precursor in nucleotide biosynthesis. Moreover, isotopic tracer studies confirmed that carbon and nitrogen flux into nucleotides is inhibited by the amides, suggesting that these phenolic amines are potent and fast-acting inhibitors of purine and pyrimidine biosynthetic pathways. Biochemical studies showed that the amides directly inhibit glutamine amidotransferases, with FA acting as a competitive inhibitor of the E. coli enzyme responsible for the first committed step in de novo purine biosynthesis. Supplementation of cultures with nucleosides was sufficient to reverse the effect of the amides, suggesting the ability to bypass the block in de novo nucleotide biosynthesis via salvage pathways. Collectively, these results provide a direct mechanism for the inhibitory effects of phenolic amides, knowledge that will inform future design of biocatalysts for improved bioconversion.

A new international study, with important contributions by researchers from Lawrence Berkeley National Laboratory supported by the Department of Energy’s Scientific Discovery through Advanced Computing (SciDAC) program, is the first to use a high-resolution, large-scale computer model to estimate how much ice the West Antarctic Ice Sheet (WAIS) could lose over the next couple centuries, and how much that ice loss could add to sea-level rise. The results provide a more precise estimate of West Antarctica’s future than was previously possible. The Intergovernmental Panel on Climate Change’s 4th and 5th Assessment Reports both note that the acceleration of West Antarctic ice streams in response to ocean warming could result in a major contribution to sea-level rise, but the models were unable to satisfactorily quantify that response. The novel aspect of this study is the use of a high-resolution ice-sheet model over a larger area and longer time scale than previously attempted, which helps to capture details of the physics involved that may be crucial to the broad picture. West Antarctica is one of the fastest warming regions on Earth, and its ice sheet has dramatically thinned in recent years. The WAIS is out of balance because it is losing significant amounts of ice to the ocean, and these losses are not being offset by snowfall. The lost ice, drained by the ice sheet’s several ice streams, amounts to a significant contribution to sea-level rise, which is expected to increase in the future. The research results reflect uncertainty in future greenhouse gas emissions, snowfall, and ocean circulation, but the choice of a high-resolution model enabled the researchers to reduce the numerical error that often plagues ice-flow models. The simulations indicate that future WAIS change would be dominated by thinning in the Amundsen Sea Embayment, just as it is today, until at least the 22nd century. But other regions of West Antarctica could thin to a similar extent if the ocean warms sufficiently. In their most extreme simulation, where the ice shelves progressively disintegrate over the next century, most of the major ice streams retreat by hundreds of kilometers. The WAIS as a whole would contribute some 80,000 km³ of lost ice to sea-level rise by 2100 and 200,000 km³ by 2200. This ice loss corresponds to a 20-cm increase in global sea level by the end of this century—enough to fill the Caspian Sea—and close to 50 cm by 2200. While these amounts would be enough to threaten low-lying cities and countries, the researchers point out this is an extreme scenario. This comprehensive high-resolution study is a significant improvement from previous calculations, which were lower in resolution or scale, enabling researchers to make more accurate predictions about West Antarctica’s future.

With increasing emphasis on sustainable energy sources, lipid-derived biofuels are a promising substitute for fossil fuels. In particular, the yeast species Yarrowia lipolytica has strong potential as a biofuel-producing organism because it accumulates large amounts of lipids, but little is known about the key biological processes involved. A recent study led by scientists from the Department of Energy’s Environmental Molecular Sciences Laboratory (EMSL) and Pacific Northwest National Laboratory identified and characterized major pathways involved in lipid accumulation from glucose in this yeast species. The researchers obtained metabolomic and lipidomic profiles of the yeast cells using EMSL’s mass spectrometry capabilities, and they used confocal, electron, and helium ion microscopes in EMSL’s Quiet Wing to visualize changes in cellular structures over time. The team found that when fed glucose, the cells accumulated lipids rapidly and that lipid production peaked at 48 hours, but they also found that the highest proportion of a biofuel-friendly lipid occurred at 24 hours. By 72 hours, the cells began to produce thicker cell walls. These omics profiling results provide insights into possible targets for metabolic engineering to improve lipid production in Y. lipolytica. The visual results demonstrating that the cells produce thicker cell walls as they age suggest that the genes involved in cell wall synthesis are a potential target for improving the efficiency of lipid production.

Consolidated bioprocessing (CBP) has the potential to reduce biofuel and biochemical production costs by processing cellulose hydrolysis and fermentation simultaneously, without the addition of premanufactured cellulases and other hydrolytic enzymes. In particular, Clostridium thermocellum is a promising thermophilic CBP host because of its high cellulose decomposition rate. Toward this end, researchers at the Department of Energy’s BioEnergy Science Center (BESC) researchers engineered C. thermocellum to produce isobutanol, an advanced biofuel. Metabolic engineering for isobutanol production in C. thermocellum is hampered by enzyme toxicity during cloning, time-consuming pathway engineering procedures, and slow turnaround in production tests. Engineering of the isobutanol pathway into C. thermocellum was facilitated by first cloning plasmids into Escherichia coli before transforming these constructs into C. thermocellum for testing and optimization. Among these engineered strains, the best isobutanol producer was selected. Interestingly, both the native ketoisovalerate oxidoreductase (KOR) and the heterologous ketoisovalerate decarboxylase (KIVD) were expressed and found to be responsible for isobutanol production. A single crossover integration of the plasmid into the chromosome resulted in a stable strain not requiring antibiotic selection. This strain produced 5.4 g/L of isobutanol from cellulose in minimal medium at 50°C within 75 hours, corresponding to 41% of theoretical yield. While there is significant room for further optimization, this initial engineering of a cellulolytic thermophile to produce an advanced biofuel demonstrates the potential of this strategy to help create a sustainable and commercially viable biofuel.

A key obstacle to large-scale production of biofuels is the resistance of biomass to deconstruction into simple biomolecules that can be converted to the desired fuels. This so-called recalcitrance is being studied intensively at the cellular level. Non-destructive, simultaneous chemical and physical characterization of materials at the nanoscale is a highly sought-after capability for understanding the underlying mechanisms of this cell wall recalcitrance to deconstruction. However, a combination of physical limitations of existing nanoscale technologies has made achieving this goal challenging. To overcome these obstacles, researchers at the Department of Energy’s BioEnergy Science Center (BESC) have developed a hybrid approach for nanoscale material characterization based on nanomechanical force microscopy in conjunction with infrared photoacoustic spectroscopy. The researchers targeted the outstanding problem of spatially and spectrally resolving plant cell walls. Nanoscale characterization of plant cell walls and the effect of complex phenotype treatments on biomass are challenging but necessary in the search for sustainable and renewable bioenergy. The BESC scientists were able to reveal both the morphological and compositional substructures of the cell walls. They found that the measured biomolecular traits are in agreement with the lower-resolution chemical maps obtained with infrared and confocal Raman microspectroscopies of the same samples. These results should prove relevant in fields such as energy production and storage, as well as medical research, where morphological, chemical, and subsurface studies of nanocomposites, nanoparticle uptake by cells, and nanoscale quality control are in demand.

The representation of convective clouds (i.e., clouds formed from rising air motions) is a key uncertainty in climate models due to the small scales of convective elements relative to model grid size and the complex interactions between large-scale circulation and local surface conditions on time scales of less than a day. To properly understand and simulate these complex interactions in numerical models, relationships between the various scales from the local land surface to the large-scale background state of the atmosphere must be consistently quantified. In particular, to improve weather and climate models of convection, scientists need to understand under what conditions convective clouds are triggered by local changes in heat and moisture and when they are more influenced by the larger-scale atmospheric conditions.

Researchers previously introduced the idea of the Heated Condensation Framework (HCF) as a tool for addressing the issue of separating local from non-local impacts on convection. In a recent study, scientists used data from the Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site to illustrate the full suite of HCF variables and to demonstrate their capability for 1) quantifying the necessary moisture and heat inputs to trigger convection; 2) identifying the transition height separating two boundary layer regimes; 3) identifying which convective events were triggered locally; and 4) identifying sources of model bias in the convective state. The newly developed relationships provide a comprehensive way of assessing the atmosphere’s convective state, and, in particular, isolate the influence of the large-scale background state on convective initiation. Because the approach only requires atmospheric profiles of temperature and humidity to produce the entire suite of variables, models can be compared directly against observations enabling targeted model development. The capabilities presented here enable better process understanding of how the land surface may influence convective initiation, which can help improve future weather and climate models.

Researchers are calling for an expansion of international networks of deep soil manipulation experiments in the field, with coordination, common variables, integration, and collaboration. Siting along environmental and land-use gradients will accelerate understanding of soil organic carbon (SOC) cycling. Data are lacking to unravel the importance of various mechanisms controlling deep SOC cycling in different soils under different environmental conditions. Field manipulation experiments will overcome limitations of laboratory studies, enabling testing for cause and effect and isolating direct response function in real ecosystems. Reduced uncertainty of the role of soils as positive or negative feedbacks to global climate change will improve climate projections. Also, mitigation strategies and solutions for ecological and agricultural challenges can be developed and tested at the networks’ facilities.

The Arctic experiences intrusions of high aerosol levels termed “Arctic haze,” especially between winter and late spring. Strong east-west pressure gradients can cause a poleward transport of pollution from mid latitudes, primarily from Eurasia. This haze may play an important role in affecting cloud properties, and hence their radiative impacts at the surface, in winter.

Scientists used 4 years of observations from the Atmospheric Radiation Measurement (ARM) Climate Research Facility measurement site in Barrow, Alaska, to determine the indirect effects of Arctic haze pollution on surface cloud radiative forcing by low-level clouds in the Arctic. The study shows that the cloud radiative impact on the surface is a net warming effect between October and May and a net cooling in summer. During episodes of high surface haze aerosol concentrations and cloudy skies, both the net warming and net cooling are amplified, ranging from +12.2 Wm-2 in February to -11.8 Wm-2 in August. In liquid clouds, approximately 50% to 70% of this change is caused by changes in cloud particle size, with the remainder caused by unknown atmospheric feedbacks that increase cloud water path. While the yearly averaged warming and cooling effects nearly cancel, the timing of the forcing may be a relevant control of the amplitude and timing of sea ice melt.

Low-level clouds play an important role in the Arctic surface energy budget due to their high frequency and extensive lifetimes. These “mixed-phase” clouds simultaneously contain ice crystals and liquid drops within the same cloud layer and are able to persist for long periods due to balances among complex dynamical, microphysical, and thermodynamic processes in the Arctic boundary layer. One of these complex, and not well-understood processes, is the nucleation (or formation) of new ice particles. Past studies have been unable to explain how new ice particles can continue to form for hours in Arctic mixed-phase clouds without unrealistically high aerosol concentrations.

Scientists used data from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), a field campaign conducted by the Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility near Barrow, Alaska, to evaluate a new model of ice nucleation. The model accounts for time-dependent changes in ice nucleation by considering that aerosol particles that are most efficient at forming ice will be the first to nucleate, and thus the properties of aerosol and nucleation rates will change over time. Evaluation of the new model with the ARM observations illustrates that the model can produce a reasonable representation of the ice water path and ice crystal size distributions in the observed mixed-phase clouds. The study also finds that the production of new ice crystals in the upper part of the cloud is controlled mostly by the competition among radiative cooling (resulting in more aerosol particles becoming efficient ice nuclei as the temperature decreases), cloud-top entrainment (bringing fresh particles into the cloud), and nucleation scavenging (ice-forming aerosol particles removed as the ice crystals fall out of the cloud). The relative contribution of each process is mostly determined by the cloud-top temperature and entrainment rates. These results suggest that modeling the time evolution of the aerosol population’s ability to form ice is required to accurately model Arctic mixed-phase cloud processes.

A team of scientists from the University of Michigan and Sandia National Laboratories investigated the impact of a regionally refined nested area in the Community Atmosphere Model (CAM version 5) with its Spectral Element (CAM-SE) from the Department of Energy/National Center for Atmospheric Research.. They found that the addition of a refined patch over the Atlantic basin does not noticeably affect the global circulation. In the area where the refinement is located, large-scale precipitation increases with the higher resolution. This increase is partly offset by a decrease in precipitation resulting from convective parameterizations, although total precipitation is also slightly higher at finer resolutions. Large-scale equatorial waves are not significantly impacted when traversing multiple grid spacings. Despite the grid transition region bisecting northern Africa, local zonal jets and African easterly wave activity are highly similar in both simulations. The frequency of extreme precipitation events increases with resolution, although this increase is restricted to the refined patch. Topography is better resolved in the nest as a result of finer grid spacing. The spatial patterns of variables with strong orographic forcing (such as precipitation, cloud, and precipitable water) are improved with local refinement. Additionally, dynamical features, such as wind patterns, associated with steep terrain are improved in the variable-resolution simulation when compared to the uniform coarser run. This study indicates that the variable-resolution modeling with CAM-SE is free of numerical artifacts and has become a mature technique for regional climate studies.

High-resolution climate modeling can reveal new insights into the climate system’s many multiscale interactions. At grid resolution around 30 km and finer, mesoscale phenomena like tropical cyclones (TCs), topographically forced local wind patterns, or mesoscale convective systems start to become resolved and their climatology can be investigated in global climate models (GCMs). This provides an in-depth look at the model skill at fine resolutions and potential deficiencies in the physical parameterization packages. However, high-resolution climate modeling is costly from a computational viewpoint, and so far only very few global modeling studies with grid spacing of 30 km and below have been conducted. Therefore, new efforts are underway to use variable-resolution (VR) grids that lower the computational demand while providing high-resolution regional climate information over areas of interests.

In a recent study, scientists from the University of Michigan demonstrated that the regionally refined Community Atmosphere Model (CAM version 5) with its spectral element (SE) dynamical core reproduces many of the Atlantic hurricane statistics. In particular, they zoomed into the North Atlantic Ocean basin with a 0.25-degree (28-km) mesh, which was embedded within a 1-degree (111-km) global grid. Two 23-year simulations with prescribed sea surface temperatures and sea ice were conducted (with and without the refined nest) to investigate hurricane climatologies and impact of the enhanced resolution on TCs. The VR simulation contains significantly more TCs than the unrefined simulation. Its increased resolution in the Atlantic region enables it to resolve much more intense storms, with multiple storms strengthening to Saffir-Simpson category 3 intensity or higher. Both count and spatial distribution of TC genesis and tracks in the VR simulation are well matched to observations and represent significant improvements over the unrefined simulation. Some degree of interannual skill also is noted, with the VR grid able to reproduce the observed connection between Atlantic TCs and the El Nino Southern Oscillation (ENSO). Potential ‘upscale’ effects are noted in the VR simulation, suggesting stronger TCs in refined nests may play a role in meridional transport of momentum, heat, and moisture. These sorts of resolution-change influences were further explored in a subsequent publication. Both studies indicate that VR modeling with CAM-SE is free of numerical artifacts and has become a mature technique for regional climate studies.

Carbon dioxide (CO₂) exchange between terrestrial systems and the atmosphere are an important element of the carbon cycle and greenhouse gas climate forcing. High-frequency eddy-covariance measurements of net ecosystem CO₂ exchange (NEE) with the atmosphere are valuable resources for model parameterization, calibration, and validation. However, uncertainties in measured data (i.e., data gaps and inherent random errors) create problems for researchers attempting to quantify uncertainties in model projections of terrestrial ecosystem carbon cycling. In a recent study, researchers demonstrated that a model data fusion method (residual bootstrap) produces defensible annual NEE sums by mimicking the behavior of random errors, filling missing values, and simulating gap-filling biases. Annual NEE sums are estimated for 53 site years based on nine AmeriFlux eddy-covariance tower sites in the United States. In most cases, the annual estimates were comparable in magnitude with those obtained from gap-filled data. Additionally, compared to the AmeriFlux standardized gap filling, this approach provides better NEE estimates for moderate to longer, and more frequent, data gaps. Annual accumulated uncertainties in NEE at the 95% confidence level were ±30 gC m-2 yr-1 for evergreen needleleaf forests, ±60 gC m-2 yr-1 for deciduous broadleaf forests, and ±80 gC m-2 yr-1 for croplands. The residual bootstrap approach performed worst when gap length was greater than one month or data exclusion was greater than 90% during the growing season, common to other gap-filling techniques. However, this study produced robust results for most site years when monthly data coverage during the growing season is not extremely low. These results therefore suggest that the inclusion of NEE uncertainty estimates and better estimation for moderate to longer, and more frequent, data gaps as provided by the residual bootstrap approach can be beneficial for ecosystem model evaluation.

Secondary organic aerosols (SOAs) are often the dominant components of fine aerosols at many locations globally, but they are also the least understood. Their chemistry and properties are complex and poorly known, but they may play an important role in affecting cloud-aerosol interactions. SOA particles are created by complex multiscale interactions among human activities (fossil-fuel burning), biomass burning, and terrestrial biosphere and marine biogenic emissions that are linked by physical and chemical atmospheric processes. Although SOAs are large contributors to fine particle amounts and radiative forcing, they often are represented crudely in global models. For the first time, research led by U.S. Department of Energy researchers at Pacific Northwest National Laboratory replaced the previous crude SOA treatments with much more advanced treatments in a global climate model. The new treatments account for chemical reactions in the atmosphere that are both sources and sinks of SOA precursor gases (multigenerational aging), low “effective volatility” of SOA particles due to aging processes in the particle-phase, and “missing” semi-volatile/intermediate volatility precursors from global biomass burning and fossil-fuel sources. The new treatments caused large increases in simulated aerosol amounts, lifetimes, and direct radiative forcing compared to previous global modeling treatments and dramatically improved agreement with a suite of surface-based, aircraft, and satellite organic aerosol measurements, especially in regions affected by biomass burning emissions. The ratio of their revised non-volatile SOA to previous semi-volatile SOA burden varied by a factor of 2 to 5. Their new model treatments also largely increased loadings and lifetimes of SOA particles corresponding to continental outflow over marine environments, where cloud reflectivity (albedo) is highly sensitive to cloud seed (cloud condensation nuclei or CCN) concentrations. Their work shows that new and advanced aerosol model treatments are expected to change the radiative forcing of aerosols simulated by current generation global climate models. These findings will have large potential impacts on our understanding of aerosol-cloud-radiative forcing interactions.

A significant portion of the carbon emitted from fossil fuel combustion is taken up by ocean and terrestrial systems. However, drought and heat-induced tree mortality is accelerating in many forest biomes, resulting in a threat to global forests unlike any in recorded history. Forests store the majority of terrestrial carbon, thus their loss may have significant and sustained impacts on the global carbon cycle. Researchers from Los Alamos National Laboratory have used a hydraulic corollary to Darcy’s law, a core principle of vascular plant physiology, to predict characteristics of plants that will survive and die during drought under warmer future climates. They find that plants that are tall are most likely to die from future drought stress. Thus, tall trees of old-growth forests are at the greatest risk of loss, which has ominous implications for terrestrial carbon storage. This application of Darcy’s law indicates today’s forests generally should be replaced by shorter and more xeric plants, owing to future warmer droughts and associated wildfires and pest attacks. The Darcy’s corollary also provides a simple, robust framework for informing forest management interventions needed to promote the survival of current forests. Given the robustness of Darcy’s law for predictions of vascular plant function, they conclude with high certainty that today’s forests are going to be subject to continued increases in mortality rates that will result in substantial reorganization of their structure and carbon storage.

The tracking of cloud vertical motions and how these interact with atmospheric moisture and temperature are key for climate simulation and weather prediction. One of the most fundamental and ubiquitous calculations is the calculation of the properties of a cloud that rises vertically through the atmosphere. In fact, this calculation is performed thousands of times per day at weather centers around the world to quantify atmospheric instability and storm potential. It also is calculated many millions of times per day on supercomputers that are forecasting next week’s weather and next century’s climate. Despite the importance of this process, there is no agreement on how it should be calculated.

A recent study by a researcher at Lawrence Berkeley National Laboratory shows that previous methods for calculating these fluxes are flawed, and a new approach was developed. Three of the most common approaches are to use conservation of moist static energy (MSE), conservation of equivalent potential temperature, or conservation of entropy (the last two are actually the same). The new study shows that none of these is the correct choice: their use can lead to temperature errors on the order of 1 K. While 1 K may not sound like a lot, that is the typical buoyancy of a convecting cloud. The correct conservation principle is MSE minus CAPE, where CAPE is the parcel’s convective available potential energy. This quantity is the sum of the parcel buoyancy from the parcel height to its level of neutral buoyancy. The new results will lead to improvements in model methods for simulating atmospheric convection and dynamics.

Streamflow is a key component of the terrestrial system. By redistributing water and the associated heat content and nutrients through the hillslope, tributary, and stream network, streamflow plays an important role in the regional and global water, energy, and biogeochemistry cycles of the Earth system. To improve streamflow modeling in Earth system models (ESMs), Department of Energy (DOE) scientists at Pacific Northwest National Laboratory (PNNL), with collaborators at the National Aeronautics and Space Administration’s Goddard Space Flight Center and University of Maryland, evaluated the global implementation of the Model for Scale Adaptive River Transport (MOSART) recently developed at PNNL and coupled with the Community Land Model (CLM4.0). To support global modeling using MOSART, a comprehensive global hydrography dataset was derived at multiple resolutions from different sources. The scientists first evaluated the simulated runoff fields against the composite runoff from the Global Runoff Data Center (GRDC). With routing of the runoff from CLM by MOSART, the simulated streamflow reproduced reasonably well the observed daily and monthly streamflow at over 1,600 major world river stations in terms of annual, seasonal, and daily flow statistics. The scientists also evaluated the impacts of model structure complexity. Results showed the spatial and temporal variability of river velocity simulated by MOSART is necessary for capturing streamflow seasonality and annual maximum flood. Other sources of simulation biases include uncertainties in the atmospheric forcing, as revealed by simulations driven by four different climate datasets, and human influences, based on a classification framework that quantifies the impact levels of large dams on the streamflow worldwide. In addition to simulating streamflow, MOSART provides a physically based global framework for modeling stream temperature and river biogeochemistry, both currently under or not represented in ESMs.

Understanding the interactions of key Earth system processes is important for projecting how human activities will affect global climate. A recent study introduces Hector v1.0, a simple climate model developed by a team of researchers from the Department of Energy’s Pacific Northwest National Laboratory and collaborators from the University of Maryland. Hector was designed to be fully integrated into integrated assessment (IA) modeling tools and studies that provide rapid emulation of key climate parameters. Within this context of integrated analysis, Hector was designed with three goals in mind. First, Hector is an open-source model, which is important because the scientific community, funding agencies, and journals are increasingly emphasizing transparency and open source, particularly in the climate change sciences. Second, Hector offers a framework that allows for ease in editing files, adding new components, and sharing with the scientific community. Third, in addition to being an integral component of IA models, Hector also can operate in stand-alone mode. Hector can answer fundamental scientific questions such as what future concentrations of greenhouse gases will be and how they will affect the balance of heat that enters and leaves Earth’s atmosphere. Hector represents the most critical global-scale Earth system processes while featuring fast computational execution times, clear understanding, and straightforward output analysis. Hector compares well to other similar climate models, as well as the more complex Earth system models. Because of these qualities, Hector has the potential to be a key analytical tool in IA research, scientific research more generally, and decision-making.

Surface weather conditions are closely governed by the large-scale circulation of the atmosphere. Recent increases in the occurrence of some extreme weather phenomena have led to multiple mechanistic hypotheses linking changes in atmospheric circulation to increasing extreme event probability. However, observed evidence of long-term change in atmospheric circulation has been difficult to interpret, and therefore proven inclusive, but new efforts have revealed important insights. A research team, supported in part by the Department of Energy’s Integrated Assessment Research program, identified statistically significant trends in the occurrence of mid-atmospheric circulation patterns, which partially explain observed trends in surface temperature extremes over seven mid-latitude regions of the Northern Hemisphere. Utilizing self-organizing map (SOM) cluster analysis, the researchers detected robust pattern trends in a subset of these regions during both the satellite observation era (1979–2013) and the recent period of rapid Arctic sea ice decline (1990–2013). Particularly substantial influences include the contribution of increasing trends in anticyclonic circulations to summer/autumn hot extremes over portions of Eurasia and North America, and the contribution of increasing trends in northerly flow to winter cold extremes over central Asia. Their results indicate that although a substantial portion of the observed change in extreme temperature occurrence has resulted from regional- and global-scale thermodynamic changes, the risk of extreme temperatures over some regions also has been altered by recent changes in the frequency, persistence, and/or maximum duration of regional circulation patterns. These results have important implications for the field of integrated assessment research insofar as they demonstrate that the observed changes in temperature extremes have not been caused exclusively by a linear response to increasing greenhouse gas concentrations. Therefore, explicit treatment of atmospheric dynamics is required, if even in more computationally efficient ways, within integrated assessment modeling frameworks.

Microbes are being engineered for a wide range of applications such as producing biofuels, biobased chemicals, and pharmaceuticals. Although currently available tools are useful for this process, further improvements are needed to lower the barriers scientists face if they plan to enter this growing field. Researchers at the Department of Energy’s Joint BioEnergy Institute have developed an innovative microfluidic platform for assembling DNA fragments, a critical step in the entire process. The new system uses volumes 10 times lower than current microfluidic platforms and has integrated region-specific temperature control and on-chip transformation. Integration of these steps in a single device minimizes the loss of reagents and products compared to conventional methods, which require, for example, multiple pipetting steps. For assembling DNA fragments, researchers implemented three commonly used DNA assembly protocols on the new microfluidic device: Golden Gate assembly, Gibson assembly, and yeast assembly (i.e., TAR cloning, DNA Assembler). Assembly of two combinatorial libraries of 16 plasmids each demonstrated the utility of these microfluidic methods. Each DNA plasmid was transformed into Escherichia coli or Saccharomyces cerevisiae using on-chip electroporation and further sequenced to verify the assembly. This platform likely will enable new research that can integrate this automated microfluidic platform to generate large combinatorial libraries of plasmids, helping to expedite the overall synthetic biology process for biofuels development.

A major barrier to efficient conversion of lignocellulosic materials to biofuels is the sensitivity of microbes to inhibitory compounds formed during biomass pretreatment. Aromatics derived from lignocellulose are a major class of inhibitors that typically are not metabolized by microbes commonly used as biocatalysts. However, the purple nonsulfur bacterium Rhodopseudomonas palustris is known to utilize aromatic compounds such as benzoate or p-hydroxybenzoate under anaerobic conditions. Researchers at the Department of Energy’s Great Lakes Bioenergy Research Center (GLBRC) have now shown that R. palustris is able to remove a majority of the aromatic compounds present in corn stover hydrolysates while leaving the sugars intact. The conditioned hydrolysate supported improved growth of a second microbe that was not able to grow in untreated hydrolysate. GLBRC researchers also found that most of the aromatic compounds were metabolized via the known R. palustris benzoyl-coenzyme A (CoA) pathway. Furthermore, the use of benzoyl-CoA pathway mutants prevents complete degradation of the aromatics and allows for production of selected products that may be recovered as coproducts from fermentations. This work presents the first demonstration of a microbe’s ability to metabolize and remove mixed aromatics in biomass hydrolysate, compounds that are detrimental to most microbes and generally unsuitable as carbon sources. This knowledge may inform the design of new microbes for bioconversion that can generate valuable coproducts from fermentation of sugars in lignocellulosic biomass.

Precipitation plays a crucial role in the availability of water for people, agriculture, and ecosystems. Quantitative predictions of precipitation amount, frequency, and location still remain one of the ground challenges in the hydrological and atmospheric sciences due to the complex interactions between large-scale atmospheric circulations, local-scale cloud circulations, and cloud microphysical processes that affect the properties of precipitation. Detailed observations of the temporal and spatial variability of rain drop size distributions, in concert with other atmospheric and environmental measurements, can provide important information about what causes changes in precipitation properties between different storms—an important step toward a physically consistent description of precipitation physics that can be included in numerical models.

Scientists using two state-of-the-art radar systems at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site have developed a new method for simultaneously obtaining the details of the rain drop size distribution (DSD) and parameters of the local air state related to turbulence and wind shear. The method uses radar observations at two different wavelengths and takes advantage of specific features observed in radar Doppler spectra that are caused by the wavelength dependence of scattering and absorption properties. A fundamental advantage of the new method is that DSD properties are retrieved via the differential attenuation technique, which looks at the differences in signal attenuation between the two wavelengths and is not affected by radar calibration or by standing water on the radome or antenna.

Using the current two radar wavelengths, the approach is applicable to rain rates between roughly 1 and 30 mm/hr. The methodology can be extended to other radar wavelength combinations, which could lead to a seamless retrieval of precipitation properties from light drizzle to heavy rainfall. The proposed methodology shows great potential in linking microphysics to dynamics in rainfall studies, and can be used to study rain microphysical processes such as coalescence and rain drop breakup, ultimately leading to improved parameterizations of rain processes in cloud models.

Correctly predicting snowfall properties in numerical models is important not just for weather forecasts, but for long-term climate simulations. Errors in predicting snowflake fall speeds can cause simulated clouds to disappear too quickly or live too long, resulting in further errors in their impact on Earth’s radiation budget. Research and weather radars can observe scattering from snowflakes, but their complex shapes and particles make it difficult to relate observations of radar scattering by snowfall to the physical properties of the snow particles. A recent deployment of the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility to the University of Helsinki Hyytiälä Forestry Field Station in Finland, combined with the availability of excellent in situ ground-based snow particle measurements, provided an unprecedented snowfall dataset. This dataset provides the first opportunity to relate collocated ground-based triple frequency radar observations with in situ measurements of snowfall at the ground to produce relationships that can be used to characterize snowfall properties in future radar observations.

A team of scientists, including researchers funded by DOE’s Atmospheric System Research program, analyzed three snowfall cases from the campaign. These cases cover light to moderate snowfall rates with transitions from heavily rimed snow to open-structured, low-density snowflakes. The triple-frequency radar measurements show rich temporal and spatial structure throughout the cloud during each of the three cases; these structures often seem to be related to riming and aggregation zones within the cloud. A comparison of the radar signatures from the lowest altitudes with the ground-based in situ measurements reveals that in the presence of large (>5?mm) snow aggregates, the triple-frequency radar observations do not follow the curve of classical spheroid scattering models.  Additionally, rimed particles appear along an almost horizontal line in the triple-frequency space, which had not been observed before. Overall, the three case studies indicate a close connection of the triple-frequency radar signatures to snow particle structure, bulk snowfall density, and characteristic size of the snowfall particle size distribution.

Quickly rising clouds are associated with many important phenomena, including hail, turbulence, and lightning. Despite the important impacts of fast updrafts, also called thermals, a surprisingly large uncertainty remains about the forces that generate these updraft speeds. What sets the speeds of these rising clouds? Do these cloud updrafts experience drag? If so, what are the magnitudes of the drag and buoyancy forces? Scientists funded by the Department of Energy’s Atmospheric System Research program provide answers to these questions in two recent studies.

Schematically, the acceleration of a cloud thermal can be written as acceleration equals buoyancy minus drag. The slippery-thermal hypothesis, advocated for in a previous study, states that drag is negligible and that the dominant balance in this equation is acceleration equals buoyancy. An alternative hypothesis, which is dubbed the sticky-thermal hypothesis in these studies, is that drag balances buoyancy.

To test these hypotheses, the scientists tracked cloud thermals. In one study, they tracked thousands of cloud thermals in a large-eddy simulation of deep convection, averaged their properties around a vertical axis through their top, and identified the thermal’s volume using its stream function. Averaging both the buoyancy and the drag over the cloud thermal, they found buoyancy and drag to be in very close balance. In another study, they tracked cloud thermals using stereo photogrammetry in which two synchronized cameras measured three-dimensional positions. Because they were able to measure speeds within the flow (Lagrangian speeds) as opposed to speed relative to a certain point (Eulerian speeds, such as those measured by Doppler radar), they could analyze the data using a simple momentum equation: acceleration equals buoyancy minus drag. They found that a substantial amount of drag (a drag coefficient on the order of one) was needed to match both the stereo-photogrammetric data and the known buoyancy of clouds from previous in situ measurements and the large-eddy simulations. Theoretical calculations reveal that wave drag could easily be the source of this drag. In other words, cloud thermals are sticky.

Understanding the physical processes that lead to the formation, growth, and precipitation of clouds is vital to improving climate models. Previous studies have shown that accurate knowledge of relationships among the dimensions of length (L), width (W), and maximum dimension (D) of ice crystals is important because they are used to construct shape models for calculating the single-scattering  and for determining the microphysical (e.g., cross-sectional area and fall velocity) properties of ice crystals. Additionally, new modeling approaches that explicitly predict particle properties, rather than using predefined ice categories as in traditional schemes, require statistical databases of L, W, and D of ice crystals. Existing databases of such properties are expanded to include cirrus clouds with different origins such as those originating from synoptic fronts, orographic (surface) influence, or in-cloud anvil growth from thunderstorms. The dimensions and aspect ratios (AR, which describes the dimension of the major axis divided by the dimension of the minor axis of crystals) were determined as functions of temperature and geophysical location.

The Cloud Particle Imager (CPI) records images of cloud particles with high resolution (2.3 µm) on a 1 million pixel charge coupled device. High-resolution images of ice crystals were recorded at temperatures between -87°C and 0°C during the following U.S. Department of Energy field campaigns: the 2006 Tropical Warm Pool International Cloud Experiment (TWP-ICE), 2008 Indirect and Semi-Direct Aerosol Campaign (ISDAC) in the Arctic, and 2010 Small PARTicles In CirrUS (SPARTICUS) campaign at the Southern Great Plains in Oklahoma. In situ ice crystal data from hexagonal plates, columns, and the components of bullet rosettes, which are the fundamental building blocks of ice crystal forms, were cataloged. These large databases are essential in representing the enormous spread of microphysical and radiative properties of ice crystals for retrieval algorithms and numerical modeling studies, and they will ultimately further enhance the predictive capabilities of climate models.

Limonene, a major component of citrus peel oil, has a number of applications related to microbiology. Limonene has antimicrobial properties, but also has potential as a biofuel component, making it the target of renewable production efforts through microbial metabolic engineering. For both applications, an understanding of microbial sensitivity or tolerance to limonene is crucial, but the mechanism of limonene toxicity was unknown. Researchers at the Department of Energy’s Joint BioEnergy Institute have characterized a limonene-tolerant strain of Escherichia coli and found a mutation in a gene encoding alkyl hydroperoxidase, which alleviates limonene toxicity. They found that the acute toxicity previously attributed to limonene was largely due to the common oxidation product limonene hydroperoxide, which forms spontaneously in aerobic environments. The mutant AhpC protein was able to alleviate this toxicity by reducing the hydroperoxide to a more benign compound. The researchers found that the degree of limonene toxicity is a function of its oxidation level and that nonoxidized limonene has relatively little toxicity to wild-type E. coli cells. These results have implications for both the renewable production of limonene and limonene’s applications as an antimicrobial.

Potential water requirements are a significant concern for large-scale production of biofuel crops. Studying water use for plant communities across years of varying water availability can indicate how terrestrial water balances will respond to climate change and variability as well as to land cover change. Perennial biofuel crops, likely grown mainly on marginal lands of limited water availability, provide an example of a potentially extensive future land-cover conversion. Researchers at the Department of Energy’s Great Lakes Bioenergy Research Center measured growing-season evapotranspiration based on daily changes in soil profile water contents in five perennial systems—switchgrass, Miscanthus, native grasses, restored prairie, and hybrid poplar—and in annual maize (corn) in a temperate humid climate (Michigan, USA). Three study years (2010, 2011, and 2013) had normal growing-season rainfall, whereas 2012 was a drought year with about half to a third normal rainfall. Overall growing-season mean evapotranspiration for the four years did not vary significantly among corn and the perennial systems. Differences in biomass production largely determined variation in water-use efficiency. Miscanthus had the highest water-use efficiency in both normal and drought years, followed by maize; the native grasses and prairie were lower and poplar was intermediate. Measured water use by perennial systems was similar to maize across normal and drought years and contrasts with earlier modeling studies suggesting that rain-fed perennial biomass crops in this climate have little impact on landscape water balances, whether replacing rain-fed maize on arable lands or successional vegetation on marginal lands. Results also suggest that crop evapotranspiration rates, and thus groundwater recharge, streamflow, and lake levels, may be less sensitive to climate change than has been assumed.

A large-scale weather feature known as the Madden-Julian Oscillation (MJO) is the largest contributor to variability in tropical clouds and rainfall on weekly to monthly timescales. Global climate models (GCMs) have trouble accurately simulating the initiation, strength, and evolution of the MJO, indicating that there are still gaps in conceptual theories of the MJO or their implementation in numerical models. Scientists, funded in part by the Atmospheric System Research program, used data from the Atmospheric Radiation Measurement (ARM) MJO Investigation Experiment, along with satellite data, to evaluate the sensitivity of a GCM’s MJO simulation to physical factors including entrainment, rain evaporation, downdrafts, and cold pools. This study found that differences among model versions occur primarily at intermediate values of column water vapor, where the transition from shallow to deeper convection occurs.  Simulations that have too rapid a transition from shallow to deep convection, due to weak entrainment or lack of convective organization, have poor MJO simulations.  Shallow convection is important for MJO initiation because it allows sources such as surface evaporation and large-scale transport to slowly import moist static energy into the middle levels of the atmosphere, eventually triggering the MJO propagation. Premature deep convection exports the moist static energy too quickly. These results suggest that both cloud/moisture-radiative interactions and convection-moisture sensitivity are required to produce a successful MJO simulation and strongly support the “moisture mode” conceptual theory of the MJO.

Archaea, a domain of single-celled microorganisms, represent a significant fraction of Earth’s biodiversity, yet much less is known about Archaea than bacteria. One reason for this lack of knowledge is relatively poor genome sampling, which has limited accuracy for the Archaeal phylogenetic tree. To obtain a better understanding of the diversity and physiological functions of members of the Archaea domain, a team of scientists from the University of California, Berkeley, The Ohio State University, Columbia University, Lawrence Berkeley National Laboratory, the Department of Energy’s (DOE) Joint Genome Institute, Pacific Northwest National Laboratory, and DOE Environmental Molecular Sciences Laboratory used genome-resolved metagenomics analyses to investigate the diversity, genome sizes, metabolic capabilities, and potential environmental niches of Archaea from the Rifle, Colorado, uranium mill tailings site. The team used DOE JGI to sequence DNA from Rifle sediment and groundwater samples, and they not only identified new sequences for more than 150 Archaea but were able to reconstruct the complete genomes of two Archaea that were demonstrated to be representative of two different phyla. Transcriptomic studies conducted using EMSL capabilities on one of these microbes demonstrate that they have small genomes and limited metabolic capabilities; however, these metabolic capabilities are associated with carbon and hydrogen metabolism. These results suggest that these Archaea are either symbionts or parasites that depend on other organisms for some of their metabolic requirements. This research approximately doubled the known genomic diversity of Archaea, reconstructed the first complete genomes for Archaea using cultivation-independent methods, and enabled an extensive revision of the Archaeal tree of life. In addition, these findings can be incorporated into genome-resolved ecosystem models to more accurately reflect the role played by Archaea in the global carbon cycle.

The radioactive metallic element neptunium (Np) is created when uranium (U)-based nuclear fuel is burned in electricity-producing commercial reactors and in plutonium-producing reactors operated for military purposes. Np(V) has been accidentally released to the environment at former Department of Energy (DOE) weapons production sites as well as other locations through a variety of circumstances. Because Np(V) has a high aqueous solubility, it is readily transported in groundwater. Predictions for the transport of Np(V) in groundwater are based on studies of U(VI), in part because U(VI) is easier and cheaper to study. However, there are major differences in the crystal chemistry of Np(V) and U(VI), suggesting they might be incorporated into mineral structures differently, and thereby immobilized in groundwater differently. In a recent study, researchers from the University of Notre Dame and Pacific Northwest National Laboratory examined factors that impact the structural incorporation of Np(V) and U(VI) ions into carbonate and sulfate minerals. Using spectroscopic and imaging instruments in RadEMSL, a radiochemistry facility at DOE’s Environmental Molecular Sciences Laboraty, the team found that carbonate minerals incorporated both ions at far higher levels than sulfate minerals. In addition, they found that Np(V) and U(VI) are incorporated into carbonate minerals at dramatically different levels, and that Np(V) can be readily incorporated into carbonate minerals, thereby reducing its mobility in groundwater.

Organic matter in soils is a key reservoir for carbon and plays a significant role in nutrient biogeochemical cycling. Because of limited understanding of the molecular composition of soil organic matter (SOM), scientists are challenged to decipher the range of chemical processes in soils and to predict how terrestrial carbon fluxes will respond to changing climatic conditions and land use. To address this need, a team of scientists from the University of Idaho and Department of Energy’s Environmental Molecular Sciences Laboratory (EMSL) extracted SOM from multiple ecosystems using a variety of organic solvents, and then analyzed the SOM using EMSL’s ultra-high resolution mass spectrometry capabilities. The team found different solvents extracted different types of compounds from soils, significantly expanding the ability to sensitively detect and identify the vast suite of diverse organic molecules that compose SOM. These findings enable targeted extraction approaches to elucidate differences in organic matter among soils from different ecosystems. These findings also demonstrate that by using multiple solvents on the same soil material, scientists will be able to obtain a more complete characterization of the organic matter in a specific soil sample. Increased understanding of SOM composition in soils from multiple ecosystems is expected to improve predictions of how terrestrial carbon fluxes will respond to future climate change.

Extreme heat events are likely to become more frequent in the coming decades due to climate change. Exposure to extreme heat depends not only on changing climate, but also on changes in the size and spatial distribution of the human population. A recent analysis provides a new projection of population exposure to extreme heat for the continental United States that takes both of these factors into account. Using projections from a suite of regional climate models driven by global climate models and forced with the A2 scenario from the Special Report on Emissions Scenarios by the Intergovernmental Panel on Climate Change and a spatially explicit population projection consistent with the socioeconomic assumptions of that scenario, changes in exposure are projected into the latter half of the 21^st century. The results show that U.S. population exposure to extreme heat increases four- to six-fold over observed levels in the late 20^th century, and that changes in population are as important as changes in climate in driving this outcome. Aggregate population growth, as well as redistribution of the population across larger U.S. regions, strongly affects outcomes while smaller-scale spatial patterns of population change have smaller effects. The relative importance of population and climate as drivers of exposure varies across regions of the country. This research was funded in part by the Office of Biological and Environmental Research’s Integrated Assessment Research and Regional and Global Climate Modeling programs.

Different techniques to generate large collections of cells intentionally mutated in a number of targeted genes are currently available, and specific mutants in those collections can be readily identified. However, to manipulate complex traits involving multiple genes, it is necessary to identify individual cells that contain several mutated genes. Tracking individual cells that harbor specific combinations of two or more mutations separated by long distances within their genome is a time-consuming and effort-intensive process. In a recent study, researchers at the University of Colorado in Boulder reported the development of a new method called “TRACE” that allows the identification of single bacterial or eukaryote cells with mutations in about six targeted genes. The technique uses mathematical modeling to design short DNA fragments (or primers) that specifically bind to the targeted mutation sites. These primers are synthesized in a way that allows amplification of the targeted regions and subsequent joining of the amplification products into a single DNA molecule. By performing the amplification and joining of the DNA products in an emulsion where each cell in the population is confined to a single droplet, the six targeted sites can be analyzed by high-throughput sequencing to identify which cells contain mutations in one or more of the sites. In proof-of-concept experiments, the team used TRACE to identify a combination of mutant genes that confer the bacterium Escherichia coli tolerance to the toxicity of cellulose hydrolysate and the biofuel isobutanol. Because of the much higher throughput of TRACE relative to other genotyping methods, this technology will substantially accelerate the engineering of microbes for the production of biofuels and other chemicals.

Producing biofuels and bioproducts from biomass requires the construction of efficient biosynthetic pathways. The introduction of heterologous enzymes into the well-established model microbe, Escherichia coli, can have the benefits of expanding the metabolite produced while avoiding feedback inhibition. Researchers at the Department of Energy’s Joint BioEnergy Institute expressed several heterologous type I fatty acid synthases (FAS) in E. coli that functioned in parallel with the native FAS. The most active heterologous FAS expressed in E. coli was Corynebacterium glutamicum FAS1A and resulted in the production of oleochemicals including fatty alcohols and methyl ketones. Chain length distribution of fatty alcohols produced shifted with coexpression of FAS1A with the acyl carrier protein/coenzyme A (CoA)-reductase from Marinobacter aquaeolei (Maqu2220). Coexpression of FAS1A with the Micrococcus luteus acyl-CoA-oxidase (FadM, FadB) resulted in the production of methyl ketones, although at a lower level than cells using the native FAS. This work is believed to be the first example of in vivo function of a heterologous FAS in E. coli. Functional expression of these large enzyme complexes in E. coli will enable their study without the need to culture the native organisms as well as enable the study of FAS from uncultured organisms. In addition, using FAS1 enzymes for oleochemical production has several potential advantages, and further optimization of this system could lead to strains with more efficient conversion of biomass to desired biofuels and bioproducts.

As bioenergy cropping systems are developed, their greenhouse gas (GHG) emissions will be a key component of sustainability evaluations. Nitrous oxide (N₂O) is a potent GHG and a substantial proportion of the total GHG footprint associated with feedstock production. N₂O emitted from soils is primarily the result of microbial activities, which are influenced by various environmental factors including temperature and oxygen and water availability. The impact of each of these factors differs among various cropping systems. To understand how traditional and biomass feedstock cropping systems might vary with regard to N₂O emissions, researchers at the Department of Energy’s Great Lakes Bioenergy Research Center compared the establishment phase N₂O emissions of annual monocultures of continuous corn and corn-soybean-canola rotations; perennial monocultures of switchgrass, Miscanthus, and hybrid poplar; and perennial polycultures of early successional species, native grasses, and native prairie species. Measurements were done over a 2- to 4-year period following planting over which several perennial crops attained “full capacity” biomass production. They found that during the establishment phase, perennial bioenergy crops emit less N₂O than annual crops, especially when not fertilized. Emissions for perennials were about three times less than for annuals on a per hectare basis. N₂O peak fluxes were associated with periods of rain following fertilizer application. And finally, the results show that simulation models trained on single systems performed well in most monocultures but worse in polycultures, which means models including N₂O emissions should be parameterized specifically for particular plant systems. The results suggest that perennial biomass feedstock cropping systems have the potential for a lower GHG burden even during their establishment phase.

The Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) site in Lamont, Oklahoma, is home to one of the longest records of actively sensed cloud information anywhere in the world. Despite the best efforts of facility staff, however, instruments occasionally fail or are taken down for maintenance, resulting in holes within the observational record.  These gaps lead to uncertainty in monthly statistics of observed variables such as cloud fraction that are often used to evaluate model simulations or diagnose trends in the observations.

Researchers funded by the Atmospheric System Research (ASR) program used a statistical technique called self-organizing maps (SOM) to reduce uncertainties in the instrument record. The analysis took advantage of the fact that cloud occurrence is partly controlled by the large-scale environment and that the long time series of ARM measurements allows robust classification into meteorological regimes. Testing a number of SOM configurations, the analysis showed that uncertainty in the monthly total cloud fraction record can be reduced significantly and that the largest gain is provided by SOMs that have a large number of classes and separate data by month.  Using the new technique, uncertainty in monthly total cloud fraction was reduced in half from previous values.

This proof-of-concept work opens the door to a number of other opportunities. The methodology is adaptable to other ARM sites. Further, the results suggest that a combination of ARM observations and reanalyses can provide a better historical record of cloud occurrence prior to the existence of actively sensed observations. Finally, this work can move beyond cloud fraction and the techniques can be applied to other variables such as records of specific cloud types.

Aerosols are a large source of uncertainty in climate model predictions of radiative forcing.  To evaluate aerosol processes in global models, colocated measurements of meteorology, radiation, and aerosols are needed. A team of scientists funded by the Department of Energy’s Atmospheric System Research (ASR) program and the Atmospheric Radiation Measurement (ARM) Climate Research Facility studied long-term trends of submicrometer aerosol composition and mass concentration measured by an aerosol chemical speciation monitor (ACSM) at the ARM Southern Great Plains (SGP) site. They measured organic mass spectral matrix using a rolling window technique to derive distinct source factors, evolution processes, and physiochemical properties. The rolling window approach enabled the capture of dynamic variations of the chemical properties in the organic aerosol factors over time.

The team found that organics dominated the observed aerosol mass concentration for most of the study with the exception of winter, when ammonium nitrate increases due to cooler temperatures and the transport of gaseous precursors from surrounding urban and agricultural areas. Sulfate mass concentrations have little seasonal variation and have mixed regional and local sources. In the spring, biomass burning organic aerosol emissions increase and are mainly associated with local fires. Isoprene and carbon monoxide emission rates represent the spatial distribution of biogenic and anthropogenic sources, respectively. The combined spatial distribution of isoprene emissions and air mass trajectories suggest that biogenic emissions from the southeast contribute to secondary organic aerosol formation at the SGP site during the summer.

The observations illustrate that aerosol particles at the SGP site derive from a complex mixture of local sources, with varying seasonal behavior, and atmospheric transport. In combination with colocated measurements of meteorology and radiation, the long-term aerosol chemistry measurements at the SGP site can be used to evaluate the treatment of these complex processes in regional and global climate models.

Much of the uncertainty in climate model projections stems from limited understanding of cloud and precipitation processes and the parameterization of these processes in global climate models (GCMs). Results from high-resolution models, such as large-eddy simulation (LES) models, can serve as benchmarks for developing GCM parameterizations. However, before LES can be considered as a benchmark, LES solutions should be evaluated against observational constraints to ensure that they accurately represent observed physical processes.

Scientists supported by the Department of Energy’s Atmospheric System Research (ASR) program conducted a study to determine whether the new scanning radars at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site provide useful model constraints for documenting the time-evolving structure of clouds, precipitation, and associated processes. The site recently complemented its single-column measurements with new scanning radars that better suit the sampling of LES-scale domains. Since large-scale atmospheric circulations are much larger than an LES model domain, LES models must be “forced” by providing atmospheric conditions at their boundaries.  An additional emphasis in the study was to evaluate the sensitivity of cloud and precipitation properties to differences in the spatial scale and temporal details of the large-scale forcing datasets.

This study focused on a case of shallow cumulus transitioning to precipitating cumulus congestus. Unlike typical idealized LES cases, this case exhibited substantial synoptic-scale variability and strong, height-dependent forcing. The model captured, at least in a general sense, the transition from shallow cumulus to a multilayer cloud system that included deeper cumulus congestus. These results were encouraging, given the substantial synoptic variability and highly idealized modeling framework. Results indicated that measurements obtained from scanning radar such as cloud-top height distributions better highlighted the differences across the ensemble of simulations, compared to metrics obtained from vertically profiling instruments. Multidimensional measures of cloud and precipitation geometry from scanning radar systems (e.g., precipitation onset, precipitation area, and cloud-top probability distribution functions) demonstrated several key advantages for the simulation driven with the time-varying forcing. Linking persistent biases in simulation results to differences in the scale of the forcing or bulk measures of forcing terms was difficult, suggesting that bulk representations of forcing quantities are insufficient in understanding cloud system evolution.

Subtropical marine boundary layer clouds play a significant role in cloud-climate feedbacks due to their warm temperatures and high reflectivity; small changes in cloud cover or droplet size resulting from changes in meteorology or interactions with aerosol particles can lead to large changes in the energy budgets of these clouds.  During its first marine deployment, the second Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF-2) was installed on a container ship that made 36 transects between the port of Los Angeles, CA, and Honolulu, HI, to provide detailed observations of the meteorological variables and aerosol, cloud, and surface radiation properties in this region. The campaign provided one of the most comprehensive datasets available in this important marine boundary layer cloud region.

Scientists funded by the Department of Energy’s Atmospheric System Research (ASR) program combined the ARM in situ observations of aerosol concentrations with satellite retrievals and numerical model meteorological outputs to understand how the atmospheric circulation regulates the synoptic and monthly variations in aerosol concentration over a vast area of the northeast Pacific. Analysis of monthly observations of cloud condensation nuclei (CCN) reveal an annual cycle with peaks during spring and summer. The seasonal variation mostly occurs within 30° of the California coast, whereas monthly variations near Hawaii are modest. The analysis suggests that the annual cycle in CCN is consistent with a wind-transport mechanism associated with large-scale circulations and the California low-level jet, rather than with seasonal changes in precipitation. The study also examined the sensitivity of cloud properties to aerosol number concentration, and results support emerging new evidence from aircraft observations indicating that the aerosol-cloud interaction in low subtropical clouds is substantially stronger than that inferred from previous ground-based and satellite observations and from climate models.

Human-driven climate changes, which are expected to impact human and natural systems, are often expressed in terms of global-mean temperature. Much attention also is given to the interannual variability and rates of change of global average temperatures. Given that faster rates of change result in less time for human and natural systems to adapt, scientists at Pacific Northwest National Laboratory’s Joint Global Change Research Institute performed an analysis using global model simulations that indicate the world is entering a period where multidecadal rates of change are becoming larger than those seen over the last millennium. They found that current trends in greenhouse gas and aerosol emissions are now moving the Earth system into a new regime, in terms of multidecadal rates of change that are unprecedented for at least the last 1,000 years. In the Climate Model Intercomparison Project phase 5 (CMIP5) archive over 40-year periods, the rate of global-mean temperature increases to 0.25±0.05 (1s) °C per decade by 2020, that, in turn, represents an unprecedented rate of change that has not been experienced at least for the past 1 to 2 millennia. Increases in greenhouse gas forcing, coupled with the decreasing influence of atmospheric aerosol particles, are now driving the climate system into this new state. Regional rates of change in Europe, North America, and the Arctic are higher than the global average. These findings show the need for research on the impacts of such near-term rates of change. Support for this analysis was provided through the Integrated Assessment Research Program, a program of the Department of Energy’s Office of Biological and Environmental Research, and the Global Technology Strategy Project, a public-private collaboration.

Future costs of low-carbon technology options are a key factor in determining the challenges of reducing greenhouse gases, as well as describing in detail the technology basis of lower emissions scenarios such as the RCP 2.6 and RCP 4.5 used in the Climate Model Intercomparison Project (CMIP) process. To explore the implications of uncertainty about future technology costs, a team of scientists led by Haewon McJeon from the Department of Energy’s Pacific Northwest National Laboratory used information provided by multiple expert elicitation surveys on the future cost of key low-carbon technologies and used it as input for three integrated assessment models. The team’s large set of simulations using the Global Change Assessment Model (GCAM), Market Allocation Model of the U.S. energy system (MARKAL_US), and World Induced Technical Change Hybrid model (WITCH) were used to assess the implications of technology performance probability distributions over key model outputs. They were able to detect which sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered, or the stringency of the carbon-reduction strategies considered. The team found that the future emission trajectory is most responsive to the capital cost of nuclear power plants. Under the climate-constrained scenarios, they found that the cost of biofuel processing also has a large impact, especially when coupled with carbon capture and storage to produce negative emissions. Overall, this effort improves understanding of uncertainty in emissions scenarios and improves the utility of models to inform technology research and development.

To accurately assess the impacts of biofuel crop production on regional ecosystem services such as crop yields, carbon and nutrient cycling, soil erosion, water quality, and pest and disease control, it is necessary to have an accurate picture of which crop rotation systems are utilized by growers. Despite the availability of databases such as the Cropland Data Layer (CDL), which provide remotely sensed data on U.S. crop types on a yearly basis, crop rotation patterns remain poorly mapped due to the lack of tools that allow for efficient and consistent analysis of multiyear CDLs. Researchers at the Department of Energy’s Great Lakes Bioenergy Research Center created an algorithm that can select representative crop rotation systems by combining and analyzing multiyear CDLs. Among the findings using this algorithm is that only 82 representative crop rotations accounted for over 90% of the spatiotemporal variability of the more than 13,000 rotations in the Western Corn Belt; it also can detect pronounced shifts from grassland to monoculture corn and soybean cultivation. Furthermore, the area estimates of the rotation systems are comparable to those obtained from agricultural census data. Given this algorithm’s novel capability to flexibly and efficiently derive representative crop rotation patterns in a spatially and temporally explicit manner, it is expected to be a useful tool for providing input data to drive agro-ecosystem models and for detecting shifts in cropping patterns in response to environmental and socio-economic changes.

Cyanobacteria, a broadly distributed class of photosynthetic bacteria, are attractive candidates for development as “chassis organisms” for production of biofuels and other products. In comparison to photosynthetic algae, cyanobacteria grow more quickly, are capable of growth in a broad range of conditions, and possess much simpler (and thus more easily engineered) genomes. However, developing systems-level understanding of integrated metabolic networks in cyanobacteria will be necessary before more sophisticated bioengineering approaches can be applied to further optimize performance or more easily introduce new biosynthetic modules. A new study by researchers at Washington University examines systems biology properties of the recently discovered cyanobacterial strain Synechococcus elongatus UTEX 2973, which grows at double the rates of other members of this species under high light intensities. Using a comparative genomics approach, the team was able to identify a surprisingly small set of genetic differences between UTEX 2973 and slower growing S. elongatus strains, amounting to 55 amino acid substitutions and a small missing region encoding six genes seen in the slower growing strains. Leveraging capabilities at the Department of Energy’s Environmental Molecular Sciences Laboratory, these findings were validated using global proteomics analysis, confirming predicted amino acid substitutions and showing that UTEX 2973 is missing five of the six predicted proteins. Although these proteins are currently of unknown function, UTEX 2973 fails to form cytoplasmic glycogen granules observed during growth of the other strains. This observation suggests that UTEX 2973 may not store photosynthetically fixed carbon, but instead immediately uses it as substrate fueling accelerated growth. UTEX 2973 can be genetically manipulated using tools developed for related cyanobacterial strains, and the team currently is developing a mutant library to explore the specific mechanistic basis of the UTEX 2973’s rapid growth phenotype. These findings expand our knowledge of cyanobacterial systems biology and present Synechococcus elongatus UTEX 2973 as a promising potential biotechnological chassis organism for the direct conversion of sunlight and CO₂ into biofuels and other compounds.

The biofuels industry has devoted significant efforts to converting lignocellulosic substrates into sugars that can be fermented into biofuels or other bioproducts. However, one of the major bottlenecks for cost-effective conversion in biorefineries has been the fermentation inhibition of yeast or bacteria by pretreatment degradation products. To engineer microbial strains for improved conversion, it is important to understand the inhibition mechanisms that affect the fermentative organisms in the presence of a lignocellulosic hydrolysate. One way in which these processes can be understood is by developing a synthetic hydrolysate media with a composition similar to the one that will be found after pretreating lignocellulosic biomass. Researchers at the Department of Energy’s Great Lakes Bioenergy Research Center characterized the plant-derived decomposition products present in ammonia fiber expansion (AFEX) pretreated corn stover hydrolsate (ACH), and a synthetic hydrolysate (SH) was formulated based on that ACH composition. The SH was used to evaluate the inhibitory effects of various families of decomposition products during fermentation using Saccharomyces cerevisiae strain 424A (LNH-ST). The SH did not entirely match the ACH performance; however, the major groups of inhibitory compounds were identified and used for further evaluation and comparison. Their characterization showed that the compounds present in ACH that were most inhibitory to fermentation were nitrogenous compounds, especially amides, though this result is associated with a concentration effect, given that nitrogenous compounds were the most abundant. Comparing inhibition due to amides in AFEX pretreatment versus inhibition due to carboxylic acids and other compounds formed in alternative pretreatment methods, they discovered that amides are significantly less inhibitory to both glucose and xylose fermentation. This means that ACH would be easily fermentable by yeast without any further detoxification. These studies help to map where to focus research efforts to overcome pretreatment byproduct inhibition of fermentation.

Photosynthetic microbial mats dominated by cyanobacteria achieve high rates of productivity using little more than sunlight, atmospheric gases (CO₂ and N₂), and trace nutrients. These complex, stratified ecosystems thus can provide experimentally tractable models to investigate functional properties of microbial communities and serve as valuable analogues for bioenergy production systems. The high rates of photosynthetic productivity observed in microbial mats are made possible by microbial nitrogen fixation, the process of converting N₂ gas into biologically useful forms of nitrogen. Identifying which community members perform this process would provide a key to understanding overall community function. A team of investigators led by Lawrence Livermore National Laboratory scientists have reported new findings on nitrogen fixation in photosynthetic microbial mats using a combination of community gene expression analysis (metatranscriptomics), high-resolution microscopy, and nanoscale mass spectrometry (nanoSIMS). Metatranscriptomic analysis provided an overview of metabolically active community members capable of N₂ fixation, thus providing an initial roster of target species worthy of further examination. Microscopically enabled nanoSIMS then provided the capability to narrow the search, tracking isotopically labeled nitrogen through the community at the scale of single cells. By coupling these two technologies, the team was able to identify specific members of the cyanobacterial portion of the community as the dominant N₂ fixers and examine their spatial relationships within the overall community structure. These findings highlight the importance of pairing omics-driven techniques with complementary approaches that provide validation of functional predictions. By coupling cutting-edge experimental capabilities, researchers are developing a more sophisticated understanding of the biological rules that govern community structure and function, potentially enabling construction of analogous systems devoted to high-efficiency bioenergy production.

When a pathogen attacks a plant, the plant mounts an immune response that alerts the rest of the plant, a response called systemic acquired resistance (SAR). The chemical compound(s) responsible for inducing the immunity is a topic of intense interest for agriculture, including for bioenergy crops. For example, the application of a 9-carbon-atom-chain (C-9) dicarboxylic acid, azaleic acid, induces immunity, but the similar C-8 and C-10 diacids do not. One hypothesis is that the azaleic acid, but not the related acids, moves to distant parts of the plant. New radiochemistry imaging research at Brookhaven National Laboratory has developed a rapid method to label these three acids with Carbon-11 (¹¹C, half-life of 20.4 min) for short-term (minutes to hours) tracking of their movement within the plant, and with Carbon-14 (¹⁴C, half-life of 5730 years) for long-term (hours to days) studies. When applied to a leaf, [¹¹C]-azaleic acid shows substantial movement within an hour. When [¹⁴C]-azaleic acid is applied to the roots, it distributes throughout the whole plant within a day. These studies demonstrate that azaleic acid has the potential to be a mobile signaling molecule. The radioactive-carbon labeled diacids will have utility as scientific tools to unravel SAR mechanisms and other phenomena that impact production of robust bioenergy crops.

Cloud vertical structure, a key parameter affecting the impact of clouds on Earth’s energy balance, is among the most difficult atmospheric quantities to observe. Operational long-term measurements of cloud vertical structure are only available from remote-sensing measurements made at a few ground sites around the world, such as the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) sites. However, radiosondes are routinely launched for meteorological observations around the world, and their information on temperature and humidity profiles can be used to derive information on cloud vertical layers. DOE scientists have now developed a new method to detect cloud layers from radiosonde data. The method was rigorously evaluated using multiple years of radar measurements from the ARM sites. Overall, the location of cloud layers derived from radiosonde and cloud radar measurements agree reasonably well. Some near-surface cloud layers were classified as cloud layers by the radar but as clear from radiosonde measurements; a few cloud layers at high altitudes were detected by the radiosonde but missed by the cloud radar. Absolute differences in cloud-base heights and cloud-top heights from radiosonde and cloud radar matched retrievals at the ARM Southern Great Plains (SGP) site were less than 500 m for 86% and 79% of the cases analyzed, respectively. The large differences between cloud boundaries from the two retrieval methods are mostly due to balloon drift, resulting in the radiosonde sampling different parts of the cloud field than the vertically pointing radars. Application of the new method to radiosonde datasets from around the world will increase the available information on cloud vertical structure, which will be useful for understanding cloud impacts on the radiation budget and for evaluation of cloud structure in climate models.

Dust from soils is an important “natural” source of aerosols, contributing a major portion of aerosol scattering (cooling) and absorption (warming) of solar radiation. However, capturing the correct source amount and sizes of aerosols as the wind blows dust from different soil and desert sources into the atmosphere is a challenge in climate models. A multi-institutional team, including a U.S. Department of Energy scientist from Pacific Northwest National Laboratory, applied a new particle size distribution (PSD) of emitted dust, improving previous estimates of remote dust contributions. The new PSD is based on a simple, but physical fragmentation relation and is constrained using measured sizes. Fine surface particulate matter in the western United States is influenced not only by local sources, but also by trans-Pacific transport of Asian and African dust, with Asian dust contributing between 0.2 and 1.0 mg/m3 in the spring. The new PSD was applied to the GEOS-Chem chemical transport model and applied globally to all dust source regions. The team found that the new PSD for emitted dust in the GEOS-Chem model reduced large discrepancies between the simulated surface-level fine dust amount measured in the western United States. They also improved the ratio of fine to coarse dust, something that simply adjusting the total dust emissions did not accomplish. The model with the new PSD better simulates fine dust surface concentrations in the western United States, which is important when considering sources contributing to non-attainment of air quality standards, as well as for simulating climate and hydrological changes.

Global models are especially challenged to simulate pollution aerosols, including the dark aerosol black carbon (BC), in the remote Arctic, far from the BC source regions. Models typically greatly underestimate BC and fail to simulate the peak values observed in springtime, when aerosols deposited on snow enhance snow melting rates. To improve simulation of Arctic aerosols in the Community Atmosphere Model (CAM5), U.S. Department of Energy scientists at Pacific Northwest National Laboratory improved processes associated with aerosol chemical aging that affects their uptake by cloud water, wet removal, and transport by convective clouds, all key to determining the amount of aerosols reaching remote regions. The team created a new scheme that better synthesized aerosol transport and removal by convective clouds for CAM5. An explicit treatment of BC aging with slower aging assumptions produced a 30-fold increase in the Arctic winter BC burden. The new model was evaluated using surface and aircraft measurements. With the improvements, the Arctic BC burden has a 10-fold increase in the winter months and a 5-fold increase in the summer, resulting in a better simulation of the BC seasonal cycle. The modifications also produce much better aerosol optical depth when compared to multiyear surface-based retrievals of aerosol optical depths, both globally and regionally. The improved aerosol distributions also improved aspects of the CAM5 climate simulation, including global cloud water amount and cloud radiative forcing. Overall, the model aerosol process improvements make CAM5 a better tool to study the role of aerosols in Earth’s climate system.

A warming climate is expected to melt large portions of the Greenland and Antarctic ice sheets, contributing to sea level rise. However, since the behavior of ice sheets as they melt is poorly known, it is difficult to estimate how soon major changes will occur. Important melting behaviors in the Greenland ice sheet include surface melting, iceberg breakoff from around the edges, and enhanced sliding as meltwater slips through cracks to the bedrock lubricating ice sheet slippage into the sea. The last of these factors, lubrication, was carefully estimated in a recent study by scientists from multiple institutions, including U.S. Department of Energy researchers from Los Alamos National Laboratory. A wide range of observations suggest that water generated by melt at the ice sheet’s surface reaches the bed by both fracture and drainage through moulins (roughly circular, vertical to nearly vertical well-like shafts within a glacier through which water enters from the surface). However, the observations are insufficient to determine whether the water enhances ice flow. The research team performed a modeling analysis, varying the flow formulations to find two contrasting possibilities: continuously increasing or bounded changes in lubrication and glacier speed with increased meltwater input. These contrasting scenarios were applied to four sophisticated ice sheet models in a series of experiments for a warmer future scenario, forced by changes in likely ice sheet surface mass changes, lubrication changes, and a combination of these factors. The team determined that the additional sea level rise brought about by lubrication is small (= 8 mm) in comparison with that from experiments forced only by changes in surface mass balance (~170 mm). Although changes in lubrication generate widespread effects on the flow and form of the ice sheet, they do not substantially affect net mass loss. These experiments predict that by year 2200, increases in the ice sheet’s contribution to sea level rise from basal lubrication will be no more than 5% of the contribution from surface mass budget forcing alone.

The Asian longhorned beetle (Anoplophora glabripennis ) is an invasive species first discovered in the United States in 1996. It attacks both healthy and stressed hardwood trees, including the bioenergy candidate feedstocks poplar and willow, and has no natural enemies in this environment. The microbial community in the beetle’s midgut is capable of breaking down the lignin, cellulose, and hemicellulose in the trees to acquire needed nutrients, but little is known about the processes involved. To learn more about how microbial communities in the guts of such wood-boring insects break down these woody tissues, a team including researchers from the Department of Energy’s (DOE) Joint Genome Institute (JGI) sequenced, assembled, and analyzed the Asian longhorned beetle’s midgut metagenome.

In the study published in Plos ONE , the team compared the metagenome assembly from the wood beetles to annotated assemblies in DOE JGI’s IMG/M database. These datasets came from microbial communities associated with herbivores that feed to plant tissues, insects that feed on specific plant tissues, and insects (e.g., termites) that feed on woody tissues. The findings revealed that the beetle’s midgut contained a community dominated by aerobes, which research­ers expected, noting that large-scale lignin-degrading reactions require oxygen and have only been demonstrated in aerobic environments. They identified several genera of fungi and bacteria in the assembly; many of the microbes have been associated with break down of lignocellulose, hemicellulose, and other similar compounds. The metagenome assembly also led to the identifi­ca­tion of candidate genes for a variety of functions, including lignin-degrading enzymes, cellu­lases, xylose utilization, and fermentation as well as for nitrogen and nutrient acquisition.

This study is the first large-scale functional metagenomic analysis of the midgut micro­bial community of a beetle with known lignin-degrading capabilities. Lignin is one of the most recalcitrant components of plant biomass. The candidate genes identified from by the functional profile could lead to novel enzymes that might either be useful for industrial biofuels applications or else be used to control this invasive insect.

Iron plays a vital role in environmental biogeochemistry, exchanging electrons with microorganisms to transform more soluble Fe(II) to less soluble Fe(III). The iron cycle is also coupled to the climatically relevant carbon and nitrogen cycles, as well as other elemental cycles. By pulling apart the kinetics and detailed interactions between iron particles and microorganisms, researchers hope to gain insights into which aspects of these processes are important at larger scales. A team of scientists from Pacific Northwest and Lawrence Berkeley National Laboratories used stopped-flow spectrometry and micro X-ray diffraction at the Environmental Molecular Sciences Laboratory (EMSL) and X-ray absorption and magnetic circular dichroism spectroscopies at the Advanced Light Source (ALS) to investigate the oxidation kinetics of iron nanoparticles exposed to a bacterial protein, decaheme c-type cytochrome (Mto). When MtoA from Sideroxydans lithotrophicus was exposed to iron nanoparticles, the MtoA extracted electrons from the structural Fe(II) in the nanoparticles starting at the surface and then continuing to the interior, leaving behind the Fe(III) and not damaging the crystal structure. The team intends to further investigate this process using proteins known to transfer electrons in other environmentally relevant microorganisms, and using other types of iron-containing minerals. This research provides the first quantitative insights into the transfer of electrons from minerals to microbes, and provides a clear picture of how microorganisms accelerate or control iron biogeochemistry and cycling in natural systems. This knowledge sheds light on elemental cycling processes coupled to the iron cycle, including carbon, nitrogen, sulfur, and other metals.

One of the challenges in assembling plant genome “contigs,” fragments of the entire genome that are identified by the assembly algorithms, is that they are not easily linked together or even placed in their proper order. In an effort to mitigate this problem, researchers with the U.S. Department of Energy’s (DOE) Joint Genome Institute (JGI) teamed with other researchers to develop another approach for assembling contigs.

In a study published in The Plant Journal, the team reports on the results of testing the approach they call POPSEQ with the barley genome. The plant was selected for DOE JGI’s 2011 Community Sequencing Program portfolio in part for its potential as a bioenergy feedstock crop. Grown on four million acres in the United States, the crop could be used to produce cellulosic ethanol from the straw. More than 80 percent of the 5.1 billion-base genome is composed of repeats, adding to its complexity.

Using POPSEQ, researchers assembled the barley genome while testing a number of variables. For example, they used datasets obtained from different mapping populations, or, in another case, assembled the genome based solely on short reads. The team reported that the results from these tests were comparable with the assembly previously produced by the International Barley Sequencing Consortium. “By comparison,” they wrote, “POPSEQ is inexpensive, rapid, and conceptually simple, the most time-consuming step being the construction of a mapping population…The method is independent of the need for any prior sequence resources,” and this proof of principle demonstrates that POPSEQ can be effectively applied to many species.

Clouds with bases between five and seven kilometers of Earth’s surface, also known as mid-level clouds, that occur year round over West Africa may have major impacts on Earth’s energy budget. Using observations collected by the Atmospheric Radiation Measurement (ARM) Mobile Facility deployed in Niamey, Niger, in 2006, scientists from Europe published a comprehensive, first-ever survey of different cloud types over West Africa and estimated their impact on the region’s climate. The team identified four types of clouds in the region: cirrus or high-level clouds with bases above 8 kilometers, mid-level clouds with bases between 5-7 kilometers, low-level clouds (bases within 5 kilometers), and deep convective clouds. The latter two produce rain in the region. Of these four cloud types, mid-level clouds appear to have the strongest impact on Earth’s energy budget. They scatter incoming sunlight but trap outgoing energy. As the only clouds to do so year round, mid-level clouds exert a major impact on West African climate. The only other cloud type that exerts comparable influence on radiation is the thunderstorm-causing ‘anvil’ cloud. These clouds have flat bottoms that spread laterally, sometimes for hundreds of kilometers, but occur in the region only during the monsoon season. Their impact on radiation is thus limited. Climatologists agree that clouds produce by far the largest source of uncertainty in climate models. It is difficult to measure the impact of clouds on Earth’s energy budget, and more so in places like West Africa, where setting up instrumentation is a logistical challenge. The authors hope their research will provide much-needed information to calibrate weather prediction and climate models with the observed characteristics of clouds over African arid regions.

Lipids derived from oil-rich microorganisms such as bacteria, yeast, and microalgae offer a promising source of renewable fuels and chemicals. However, genetic and biochemical mechanisms regulating lipid accumulation in microorganisms are poorly understood. A recent study revealed a novel molecular pathway involved in microbial lipid accumulation. Researchers from the Department of Energy’s (DOE) Great Lakes Bioenergy Research Center (GLBRC) and the University of Wisconsin-Madison used the cryotransmission electron microscope at the DOE Environmental Molecular Sciences Laboratory to study lipid accumulation in the microbe Rhodobacter sphaeroides. Using fatty acid levels to assess membrane lipid content, the team found that the total fatty acid content per cell increased three-fold under low oxygen and anaerobic conditions compared to high oxygen conditions. They also found that the microbes’ lipid and pigment accumulation processes were separable, and they identified a transcription factor called PrrBA that is required for fatty acid accumulation in response to low oxygen levels. This new approach to maximize lipid production through an alteration in the activity of a single transcriptional regulator could lead to the development of strategies for engineering this microbe to increase yields for large-scale production of lipids for biofuels and chemicals.

Microbial sensitivity to heat, or thermosensitivity, depends on the stability of cellular proteins and their ability to remain in an active, folded state. Research to improve microbial survival and function at higher temperatures has mainly focused on strategies for increasing the structural stability of individual proteins. A new approach called structural systems biology directly assesses the genome-scale metabolic potential of a model organism, E. coli, for thermostability. Using this approach, metabolic reactions of E. coli were integrated with three-dimensional structures of each catalytic enzyme. To simulate E. coli growth at various temperatures, protein (structural) activity functions were defined to impose temperature constraints on the metabolic models. This combined metabolic-structural method allows researchers to integrate temperature-dependent information about enzyme function with simulations of microbial metabolic growth. This approach enabled simulation of E. coli growth under various temperature conditions that was in good agreement with experimental growth data. It also provided mechanistic interpretations of mutations that conferred greater thermostability in E. coli. This new approach has important implications for developing industrial microbes as biocatalysts.

In addition to sequencing the genomes of microbes, plants, fungi, and metagenomes, the U.S. Department of Energy’s (DOE) Joint Genome Institute (JGI) develops tools to improve the assembly and analysis of the DNA sequences that it generates. One tool, HGAP (Hierarchical Genome Assembly Process), provides a fully automated workflow for users of the Pacific Biosciences’ single molecule, real-time DNA sequencing machine. The “PacBio” sequencer generates initial DNA sequences up to 10 or more times longer than those provided by other technologies, which is a great assistance in the assembly of sequences into more complete genomes, but at a higher cost and lower accuracy. Competing sequencing technologies involve creating multiple DNA libraries, conducting multiple runs, and combining the data. I n contrast, HGAP requires just a single, long-insert, shotgun DNA library, enabling the resolution of long regions of repeated DNA sequence that often complicate other assembly methods. This new assembly method was tested using three microbes previously sequenced by DOE JGI. The HGAP produced final assemblies with >99.999% accuracy when compared to the reference sequences for these microbes. Next steps in the project will focus on extending HGAP’s utility beyond microbes to the larger genomes of more complex organisms. By improving sequence assemblies in this way, sequencing information can more readily be developed into understanding the role of biological processes and genes in DOE bioenergy and environmental missions.

Fueled by exponential increases in computational and storage capabilities of high-performance computing platforms, climate model simulations are evolving toward higher numerical fidelity, complexity, volume, and dimensionality. Data holdings are projected to reach hundreds of exabytes worldwide by 2020. Such explosive growth presents both challenges and opportunities for scientific breakthroughs. A U.S. Department of Energy (DOE) funded project, Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), is addressing these challenges, with a team from four DOE laboratories (Lawrence Berkeley, Lawrence Livermore, Los Alamos, and Oak Ridge); two universities (Polytechnic Institute of New York University and University of Utah); National Aeronautics and Space Administration at Goddard Space Flight Center; and two private companies (Kitware and Tech-X).

UV-CDAT software tools address:

1) problems with “big data” analytics;

2) the need for reproducibility;

3) requirements to push ensemble analysis, uncertainty quantification, and metrics computation to new boundaries;

4) heterogeneous data sources (simulations, observations, and re-analysis); and

5) provision of an overall architecture for incorporating existing and future software components.

The team designed a Python-based framework that integrates several disparate technologies under one infrastructure. United by standard common protocols and application programming interfaces, UV-CDAT integrates more than 40 different software components. The primary goal of this nationally coordinated effort is to build an ultrascale data analysis and visualization system empowering scientists to engage in new and exciting data exchanges, thus enabling breakthrough climate science. The framework is established to evolve and incorporate the new software tools that the science and scientific community require.

Global wetlands are believed to be climate sensitive and are the largest natural emitters of methane (CH₄), a potent greenhouse gas. However, the emission size and change with climate is poorly constrained. Earth system models are developing the capability to simulate these wetlands and their methane production. The Wetland and Wetland CH₄ Intercomparison of Models Project is evaluating the ability of models to simulate large-scale wetland characteristics and their methane emissions, which are essential for evaluating key uncertainties in methane emission mechanisms and parameters. Ten modeling groups, including U.S. Department of Energy researchers at Lawrence Berkeley National Laboratory, ran eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO₂) forcing datasets. The models demonstrate extensive disagreement in their simulations of wetland areal extent and CH₄ emissions in both space and time. All models show a strong positive response to increased atmospheric CO₂ concentrations in both CH₄ emissions and wetland area. In response to increasing global temperatures, on average, the models decreased wetland area and CH₄ fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation, with a consistent small positive response in CH₄ fluxes and wetland area. We presently do not have sufficient wetland methane observation datasets to evaluate model fluxes, severely restricting our ability to model global wetland CH₄ emissions with confidence. The large range in predicted CH₄ emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH₄ emission models, even after accounting for uncertainties in wetland areas. Clearly, significant research is needed both in modeling and measuring wetland sources of methane and their responses to climate.

Nearly 75 years ago, Alfred C. Redfield observed a similarity between the elemental composition of marine plankton in the surface ocean and dissolved nutrients in the ocean interior. This stoichiometry among carbon (C), nitrogen (N), and phosphorus (P) continues to be a central tenet in ocean biogeochemistry and is used to infer a variety of ecosystem processes, such as phytoplankton productivity and rates of nitrogen fixation and loss. Over the years, however, model, field, and laboratory studies have shown that different mechanisms can explain both constant and variable ratios of C to N and P among ocean plankton communities. The range of C/N/P ratios in the ocean and their predictability are the subject of much active research. In a recent study, partially funded by the U.S. Department of Energy’s Office of Biological and Environmental Research, global patterns in the elemental composition of phytoplankton and particulate organic matter in the upper ocean were assessed using published and unpublished observations of particulate P, N, and C from a broad latitudinal range, supplemented with elemental data for surface plankton populations. The authors showed that the elemental ratios of marine organic matter exhibit large spatial variations, with a global average that differs substantially from the canonical Redfield value. Moreover, elemental ratios exhibit a clear latitudinal trend. Specifically, a ratio of 195:28:1 is observed in the warm, nutrient-depleted low-latitude gyres; a ratio of 137:18:1 in warm, nutrient-rich upwelling zones; and a ratio of 78:13:1 in cold, nutrient-rich high-latitude regions. Thus, it appears that the coupling between oceanic C, N, and P cycles may vary systematically by ecosystem, which, in turn, is reflected in these observed latitudinal tendencies.

Atmospheric aerosols are emitted by fossil fuel combustion and other human activities and affect climate by scattering (cooling) or absorbing (warming) incoming solar radiation. The effect depends on the particles’ chemical composition. However, the estimate of how much aerosols warm or cool climate is uncertain, as various models have calculated a wide range of forcings. These forcing uncertainties come from differing aerosol simulation algorithms that have differing aerosol-climate interactions (such as aerosol transport and rainout) and different background climate properties such as surface brightness. A recent study by a group of climate scientists, including U.S. Department of Energy-funded researchers at Pacific Northwest National Laboratory, found a way to isolate the uncertainty due to the aerosol calculations from the influence of the models’ different background climates. The team used 12 different climate models and prescribed the same aerosol fields for each model. They found that a surprisingly large diversity in aerosol forcing comes from the host climate models’ differences in model-simulated clouds and surface properties, which could explain about half the overall sulfate aerosol forcing diversity in the forcing estimate. The study demonstrates the importance of considering the aerosol climate context when working to reduce uncertainty in forcing estimates.

Black carbon (BC), from incomplete combustion of fossil fuels and biofuels, is a strongly absorbing component of atmospheric aerosols that warms the atmosphere due to its absorbing properties. However, uncertainties in BC sources and properties have made it challenging to simulate, and uncertainties about its effects on climate persist. Researchers, including U.S. Department of Energy scientists at Pacific Northwest National Laboratory, conducted BC simulations using 12 different global models. The team found that at least 20% of the diversity in BC’s direct radiative forcing estimated by global aerosol models is due to differences in the simulated vertical profile of BC mass. A significant fraction of the variability comes from high altitudes, as more than 40% of the total BC radiative forcing is exerted above 5 km. The efficiency with which BC can induce radiative forcing depends on external factors, such as surface albedo, water vapor, background aerosol distributions, and, most notably, the vertical distribution of clouds. Therefore, BC above bright areas such as clouds has an enhanced positive radiative forcing (absorption effect). By combining the models’ own concentration profiles with a common 4D (spatial and temporal) efficiency profile of radiative forcing per gram of BC, the team recalculated and compared the exerted radiative forcing of BC’s direct effect at various altitudes and spatial regions. This study on the importance of BC’s vertical profile suggests that observational studies are needed to better characterize its global distribution, including in the upper troposphere.

Atmospheric aerosols are emitted by fossil fuel combustion and other human activities and affect climate by scattering (cooling) or absorbing (warming) incoming solar radiation. This effect depends on the particles’ chemical composition. While the net effect has been estimated to be cooling, the exact size of the effect is poorly constrained. A recent multimodel study, including contributions from U.S. Department of Energy researchers at Pacific Northwest National Laboratory, used 15 detailed global aerosol models to simulate and document changes in aerosol distribution and impact on the Earth’s energy balance over the industrial era. The direct aerosol effect (DAE) due to scattering and absorption of solar radiation by anthropogenic sulfate, black carbon (BC), organic aerosols, and other species from fossil fuel, biofuel, and biomass burning emissions was estimated by contrasting simulations using emissions for the years 1750 and 2000. Comparing these new model results to previous model versions from the team, they found very similar spreads in both total DAE and individual aerosol component radiative forcing. However, the radiative forcing of the total DAE is stronger negative, and radiative BC forcing from fossil fuel and biofuel emissions is stronger positive in the present study than in the previous one. Furthermore, models having large forcing for absorbing components also have large forcing for scattering components. The authors argue that the net aerosol forcing uncertainty is less than for individual aerosol components.

Climate model predictions of how much the planet is warming because of rising greenhouse gases vary widely due to different simulated responses of clouds to warming. Model cloud predictions are variable because clouds are among the least well simulated components in spite of much effort over many years to improve their simulations by climate models. In this study, U.S. Department of Energy scientists from Lawrence Livermore National Laboratory measured whether or not cloud simulations have improved in the newest generation of climate models being assessed for reports by the Intergovernmental Panel on Climate Change. The team examined the ability of 19 climate models to simulate climatological cloud amount, reflectivity, and altitude in comparison with satellite observations and found that cloud simulations are improving. In the newest models, a bias associated with too many highly reflective clouds has been widely reduced, and the best models have eliminated this bias. With increased amounts of clouds with lesser reflectivity, there is a significant reduction in the “too few – too bright” problem where the time-mean radiation balance is well simulated by having the compensating errors of too few clouds that are too reflective. Improved cloud simulations in climate models is a necessary, but insufficient step towards increased confidence in their predictions.

Four to five million years ago, in the early Pliocene epoch, Earth had a warm, temperate climate and carbon dioxide (CO₂) concentrations similar to today’s, but with very different climate patterns. The gradual cooling that followed led to the establishment of modern temperature patterns, possibly in response to an atmospheric CO₂ concentration reduction on the order of 100 parts per million, towards preindustrial values. In a new study, partly funded by the U.S. Department of Energy, a team of scientists synthesized the available geochemical proxy records of sea surface temperature. They found that, compared with today, the early Pliocene climate had much less change in temperature with latitude and longitude, but similar maximum ocean temperatures. Using an Earth system model, the authors show that none of the mechanisms currently proposed to explain Pliocene warmth can simultaneously reproduce all three of these crucial features. The authors suggest that a combination of several dynamical feedbacks currently underestimated in the models, such as those related to ocean mixing and cloud albedo, may have been responsible for these climate conditions. The study reinforces the need to improve constraints on cloud and ocean feedback systems.

Researchers took advantage of cloud system observations in two very different environments to study factors that influence tropical convective cloud system development. The Atmospheric Radiation Measurement (ARM) program conducted field studies in two different tropical locations—Darwin, Australia, and Niamey, Niger. Darwin is a tropical coastal site, while Niamey is an arid site fairly close to the Sahara desert. The researchers used radiosonde observations from ARM and other international agencies to initialize high-resolution model simulations and compared the resulting cloud fields to radar and satellite observations to determine whether the model was correctly capturing the cloud properties. The model was able to reproduce characteristics of the observed mesoscale convective systems (MCSs) in both locations. The African cloud systems had a scale of nearly 400 km, while the Australian systems were much smaller (approximately 100 km). Once satisfied with the model simulation quality, the researchers performed sensitivity studies to understand what environmental aspects led to cloud system variations at the two locations. The model experiments found that the Australian cloud systems had stronger convective updrafts, while the African clouds had stronger mesoscale ascent outside of the convective areas. Differences in vertical wind shear and larger amounts of dust aerosol at Niamey also contributed to the variations found in the two regions. The high-resolution model simulations enabled quantitative descriptions of water transport between the convective, stratiform, and anvil regions of the cloud systems and quantification of water sources and sinks from microphysical processes, providing information that can be used to help determine parameters in cloud parameterizations used in general circulation models (GCMs).

Mixed-phase clouds, in which super-cooled water droplets and ice crystals coexist in the same volume of air, persist for long time periods over the Arctic due to a delicate balance between cloud-top radiative cooling, microphysical heating, ice sedimentation, and large-scale forcing. Because mixed-phase clouds are radiatively significant and thermodynamic phase affects cloud radiative properties, knowledge of phase distribution is critical for understanding the role of mixed-phase clouds in the climate system. The phases of small particles are especially important because they can contribute to more than half of the total extinction in the clouds and are also important for understanding nucleation processes occurring in the clouds. Typically, small particles in mixed-phase clouds are assumed to be liquid, while larger particles are assumed to be ice. Atmospheric System Research program researchers have used in situ aircraft measurements from two recent Atmospheric Radiation Measurement (ARM) field campaigns in the Arctic to challenge those assumptions. They performed detailed image analysis of particles with maximum diameters of less than 60 microns taken during the two campaigns. The researchers were able to identify particle sizes and probe focusing conditions under which reliable information about such small particles could be obtained. For each image, they calculated the area ratio and projected area of a particle divided by a circle with diameter equal to the maximum particle diameter, showing that the average area ratio of the small cloud particles was correlated with the ratio of liquid water content to total water content. A stronger correlation was found when large cloud droplets were present. This analysis indicated that a large average area ratio could be used to discriminate liquid cloud droplets from small ice crystals. The study’s most important finding was that the assumption that all small particles in mixed-phase clouds are super-cooled water droplets does not hold true. This finding may have important ramifications for developing parameterizations of single scattering and sedimentation properties in mixed-phase clouds and retrieving cloud properties from ground- and satellite-based remote sensors.

Uranium groundwater contamination resulted from mining for use as an energy source, as well as from past enrichment and weapons production activities at U.S. Department of Energy (DOE) sites. Understanding the impact of uranium contamination on water sources and developing appropriate remediation strategies are needed both to protect public safety and to continue the use of uranium in a balanced energy portfolio. Ground­water travels underground through a complex mixture of soils and sediments. A magnetic iron oxide mineral, magnetite, is commonly found in these sediments. Magnetite can significantly slow uranium migration, acting like a “rechargeable battery” for continued uranium removal from groundwater. It performs this task by sequestering the uranium as nanoparticles of uranium dioxide within underground sediments. Researchers at Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory now have found that titanium, a common impurity in these natural magnetic iron minerals, obstructs the formation of the uraninite nanoparticles, resulting in the formation of novel molecular-sized uranium-titanium structures. This previously unknown association of uranium with titanium affects uranium’s mobility in subsurface groundwater. Incorporating this knowledge into ongoing modeling efforts will improve scientists’ ability to predict future migration of subsurface contaminant plumes and provide detailed information needed for long-term stewardship of DOE legacy sites. The researchers used ANL’s Advanced Photon Source to study how uranium interacts with magnetite within the complex subsurface chemical environment.

Gene sequencing has become very fast and inexpensive, yet the bottleneck of producing reliable functional annotations of gene sequences remains a challenge. Functional annotations commonly use a protocol based on pairwise sequence comparison algorithms such as the Basic Local Alignment Search Tool (BLAST). However, these methods can miss important phylogenetic relationships such as orthology. Phylogenetic methods that explicitly reconstruct evolutionary relationships in multigene families have a higher precision for whole genome functional annotation. A new phylogenetic web server and analysis platform, PhyloFacts, integrates experimental and annotation data from different resources including SwissProt, Gene Ontology, Pfam, BioCyc, Enzyme Commission, and third-party orthology databases. These data are then used to provide functional annotations for user-inputted protein sequences. PhyloFacts also allows users to drill down and view provenance and supporting data for functional annotations. PhyloFacts makes use of Hidden Markov Model (HMM) algorithms to place user-submitted sequences into precalculated phylogenetic relationships, or trees. As a result, its functional subclassifications have greater precision when compared with other orthology web services. Funding for PhyloFacts was provided as part of the Department of Energy’s Systems Biology Knowledgebase (KBase) enabling tools program and will be a component of future KBase services.

The traditional view of adaptive gene regulation is that bacteria adapt to sense their environment and then selectively tune the expression of their genes for optimal growth efficiency and survival (i.e., fitness) under those conditions. Numerous observations of seemingly nonoptimal gene expression in various microbes suggest, however, that reality is more complex. Researchers at Lawrence Berkeley National Laboratory’s ENIGMA Science Focus Area are gaining a more sophisticated understanding of bacterial gene regulation by examining over a thousand different combinations of gene expression patterns and growth conditions to determine their relation to overall fitness. Four genetically tractable bacterial species representing a broad diversity of microbial lifestyles have been studied: the aquatic metal-reducing environmental microbe Shewanella oneidensis, common intestinal bacterium Escherichia coli, ethanol-producing bacterium Zymomonas mobilis, and anaerobic sulfate-reducing bacterium Desulfovibrio alaskensis. In all four organisms, evidence of adaptive gene regulation was observed for only a small minority of genes; most gene expression was determined to be neutral or even detrimental to growth efficiency and fitness under experimental conditions. While these observations need testing in more realistic environmental settings and in microbial communities, the team concludes that under laboratory conditions, most gene expression is nonadaptive and reflects some form of indirect control unrelated to functional properties of specific genes. These study results add a new layer of complexity to our knowledge of the forces governing gene expression in microorganisms. They have important implications in understanding fundamental systems biology of microbes and attempts to engineer organisms with modified functional capabilities. This publication was selected as a research highlight in the June 2013 issue of Nature Reviews Microbiology.

Recent years have seen rapid changes in the Arctic, including a rapid decline of summer sea ice. It is crucial for models to be able to capture these changes, including seasonal growth and melting of sea ice. These processes include complex interactions between sea ice and salty ocean water and ocean biogeochemistry. When sea ice first freezes, it incorporates salty ocean water into microscopic brine inclusions. Over time, this brine drains out in a process known as gravity drainage, resulting in a desalination of the sea ice. This drainage, in turn, sets up a circulating flow of brine with the ocean that provides an important nutrient source for organisms living in the brine inclusions. U.S. Department of Energy-funded researchers at Los Alamos National Laboratory (LANL) have developed a new thermodynamic module for the LANL sea-ice model, CICE, which simultaneously determines both the time varying temperature and sea-ice salinity. This new module improves on the previous version of CICE, which had a fixed salinity profile. Observational data from both tank experiments and fieldwork are used to guide and test the development of a simple gravity drainage scheme suitable for inclusion in a global climate model. The researchers have found that gravity drainage consists of two modes: rapid desalination near the base of the ice, and slower desalination throughout the ice. The model results compare well with both the experimental and fieldwork data.

Mercury exists in several different forms in the environment, and some of these forms are quite toxic. Research is being conducted to gain a fuller understanding of how different forms of mercury interact with minerals and how these interactions influence mercury’s conversion into hazardous forms, or, conversely, its reduction to volatile metallic mercury. New studies of the behavior of mercury (II; the generally soluble, oxidized form of mercury) have shown that the common iron-containing mineral magnetite with a large proportion of ferrous (reduced) iron is effective in converting mercury (II) into mercury metal. If chloride ion was present in significant concentrations (as it often is in natural environments), then the mercury was reduced more slowly, and some of it was in the metastable mercury (I) chloride form. The studies, carried out by scientists at the University of Iowa, Argonne National Laboratory, and Illinois Institute of Technology, used X-ray spectroscopy stations at Argonne’s Advanced Photon Source to study the changing forms of mercury.

Minerals have a profound effect on the fate and transport of contaminants in subsurface environments. Surface complexation modeling (SCM) enables predictions of adsorption over a broader range of conditions than can be accommodated by adsorption isotherm equations or ion exchange models. A newly published review article discusses the current status of SCM and its applications to a range of systems. The main focus is on multidentate surface complexes, formed when an ion or molecule in solution binds to two or more adjacent active sites on the surface. Spectroscopic measurements often provide evidence for the presence of multidentate surface complexes, but there has been ambiguity and confusion in the literature regarding the best ways to incorporate such complexes into SCM. The article describes and evaluates several approaches to modeling these interactions and discusses examples of model applications, as well as the need for improvements in textbooks, computer programs, and the clarity of future publications to bridge the gap between theory and practice in SCM. This section is illustrated by a modeling discussion of surface complexation of uranium (VI) on the mineral goethite, a system that is a research focus of the Department of Energy’s Office of Biological and Environmental Research (BER). Many of the experimental results referenced in this review were obtained in BER research projects. The article concludes with advice for SCM users.

A key question in Earth system science is: Will warming lead to increased soil organic matter decay and an accelerated release of soil carbon as CO₂? If yes, a self-reinforcing feedback would result with warming begetting warming. In 1991, a replicated, in situ soil-warming experiment was established at the Harvard Forest in central Massachusetts to address this question. Rates of CO₂ production have been measured monthly for microbial and root respiration from April through November. Initially, warmed plots had higher respiration than controls, but after about a decade, the warming-accelerated CO₂ production decreased and returned to background levels. However, during the last seven years of the study (years 16–22), soil respiration again increased in the heated plots relative to the control plots – a long-term response to soil warming never before documented. Based on measurements made over the first 15 years that showed the depletion of the soil’s labile carbon pool, the investigators hypothesized that much of the carbon respired over the last seven years has come from the recalcitrant soil carbon pool. Using¹³C compound-specific soil incubation studies, they found that long-term soil warming increases the microbial carbon-use efficiency (CUE) associated with the degradation of complex (recalcitrant) carbon compounds such as phenol, but that the CUE of simple carbon compounds such as glucose was not temperature sensitive. Additional preliminary data shows a shift in microbial community structure in the heated plots that indicates an increase in taxa or pathways adapted to recalcitrant carbon decomposition. This long-term study suggests that the soil
microbial community will adapt to long-term warming in a way that will lead to a depletion of the recalcitrant soil carbon stocks and a self-reinforcing feedback to the climate system.

Predicted responses of transpiration to elevated atmospheric CO₂ concentrations are highly variable among process-based models. To better understand and constrain this variability, forest carbon and water flux data from the free-air CO₂ enrichment (FACE) experiments at Duke University and Oak Ridge National Laboratory were compared to simulations from 11 ecosystem models. A primary objective was to identify key underlying assumptions in model structure that cause differences in model predictions of transpiration and canopy water-use efficiency. Model-to-model and model-to-observations differences resulted from four key sets of assumptions: (1) the nature of the stomatal response to elevated CO₂; (2) the roles of the leaf and atmospheric boundary layer; (3) the treatment of canopy interception; and (4) the impact of soil moisture stress. The degree of coupling between carbon and water fluxes, and how that coupling is calculated, is one of the key assumptions that determines how well the models compare with observations. This study yields a framework for analyzing and interpreting model predictions of transpiration responses to elevated CO₂. This approach highlights key areas for immediate model improvement, hypotheses for experimental testing, and opportunities for data synthesis to significantly reduce discrepancies among models.

As convective clouds grow, they mix with drier air through the entrainment process. Entrainment reduces a
cloud’s liquid water content, lowering its buoyancy, increasing its decay rate, and altering its microphysical characteristics. Regional and global climate models require assumptions about entrainment to simulate cloud properties and lifetimes, so observations of entrainment rate are needed. Entrainment observations have typically been made using aircraft, which are expensive and therefore limited. U.S. Department of
Energy researchers have developed a new method to calculate entrainment rate in shallow cumulus clouds using long-term measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility. The method combines measurements from four different remote sensing instruments, the
aerosol observing system (AOS) and a simple cloud parcel model. An iterative process adjusts the entrainment rate in the model until the modeled cloud characteristics converge to the observations. The
method also produces uncertainty estimates on the retrieved entrainment rate values. In this initial study, the researchers have illustrated the method by applying it to three months of data at the SGP site. Next, they will apply the method to the many years of historical data at the SGP site and to the ARM site in Darwin,
Australia, providing an unprecedented database of entrainment rates in shallow cumulus for analysis and model evaluation.

Determining how radioactive material sticks to soil and affects its movement into nearby water sources is a major challenge for cleaning up nuclear waste sites. This waste, which may include uranium, can be diffuse as well as difficult to isolate and remove. To reduce the cost and complexity of complete removal, innovative and inexpensive methods are needed to expedite cleanup efforts around the world, especially in sites with vast areas of contamination. Scientists at Pacific Northwest National Laboratory discovered that the surface of a
common soil mineral, aluminum oxide, adheres to uranium, making it less mobile. The researchers assembled a detailed picture of how uranium adheres to the mineral surface using a computational model. By modeling the behavior of uranium in a complex subsurface environment, they were able to show that uranium sticks to the
surface of aluminum oxide without changing it in any way and that a more acidic environment improves how well the two stick together. This cluster model approach allows for a straightforward comparison
between different sorption mechanisms, and predictions can be directly related to X-ray adsorption experiment measurements. This approach can be used to model surface reactivity and be further utilized in other complex model systems. It also may lead to efficient, more affordable solutions for cleaning contaminated ground.

Climate models indicate that the reduction of surface albedo caused by black carbon (BC) contamination of snow contributes to global warming and near-worldwide melting of ice. However, model predictions of
BC-caused snow albedo reduction over a range of BC levels and snow grain sizes have not been verified by measurements. The main reason is that the BC effect is typically masked in natural environments by other variables that influence albedo, such as snow grain size, snow density, snow depth, and the interaction of sunlight with the underlying surface, tree cover, and solar zenith angle. Researchers from Lawrence Berkeley National Laboratory developed an approach to isolate the effect of black carbon (BC) on snow albedo through
laboratory experimentation with newly developed processes for making both pristine and BC-laden snow and techniques for measuring the morphology, albedo, and BC content of this snow. These methods enabled quantification of the snow albedo reduction associated with increasing amounts of BC and as a function of snow grain size. The study verified that black carbon contamination at levels that have been found in natural settings appreciably reduces snow albedo. Increasing the size of snow grains decreased snow albedo and amplified the radiative perturbation of black carbon, which justifies the aging-related positive feedbacks that are included in
climate models. Moreover, these data provide an extensive verification of a snow, ice, and aerosol radiation model, which will be included in the next assessment of the Intergovernmental Panel on Climate Change.

The conversion of plant biomass to liquid transportation fuel using consolidated bioprocessing (CBP) technology is a promising, cost-efficient strategy to develop energy from renewable sources. CBP takes advantage of the ability of certain microbes to convert sugars contained within the plant cell wall to high-energy chemicals such as ethanol or butanol, but the efficiency can be hampered by the recalcitrance of certain plant materials to deconstruction. While plant cell wall composition and corresponding resistance to breakdown varies considerably within plant species, this genetic diversity can potentially be exploited if plant material is efficiently screened for such properties. Researchers at the U.S. Department of Energy’s (DOE) BioEnergy Science
Center (BESC), together with scientists funded by the U.S. Department of Agriculture-DOE Plant Feedstocks Genomics for Bioenergy program, report the development of a robust assay for biomass digestibility and conversion using the anaerobic bacterium Clostridium phytofermentans. This bacterium is capable of directly converting a wide array of fermentable biomass components to ethanol without the addition of costly, exogenous, deconstruction enzymes. The assay, which measures ethanol production under the influence of different variables, was tested on both herbaceous grasses and woody plants. Significant differences in ethanol production within individual plant species were found, indicating detection of subtle genetic differences. This method provides a means of assessing feedstock quality for digestibility and ethanol production that will facilitate genetic analysis of energy crops for amenability to biological conversion.

Cloud formation occurs when aerosol particles take up moisture from the atmosphere. The water uptake rate
is important to constraining the effect of aerosols on cloud brightness, or the “aerosol indirect effect,” resulting
from pollution emissions, but the rate at which this occurs has been poorly constrained and has been formulated in terms of particle size, composition, and humidity. A new study, partially funded by the U.S. Department of Energy, used a large dataset to constrain the kinetics of water uptake as expressed by the condensation coefficient, αc. Estimates of αc for droplet growth from activation of ambient particles vary considerably (over five orders of magnitude, from 10^-5 to 1!) and represent a critical source of uncertainty in estimates of global cloud droplet distributions and the aerosol indirect forcing of climate. The authors analyzed 10 globally relevant datasets of cloud condensation nuclei to constrain the value of αc. They found that rapid uptake kinetics (αc > 0.1) is uniformly prevalent. This finding resolves a long-standing issue in cloud physics, as the uncertainty in water vapor uptake on droplets is considerably less than previously thought.

Aerosol particles brighten clouds and contribute to climate cooling, but to
calculate these effects in climate models requires lengthy calculations to average out the natural “noisiness” of clouds. Now, U.S. Department of Energy researchers from the Scripps Institute of Oceanography, University of Washington, and Pacific Northwest National Laboratory have shown that by nudging the winds simulated in the Community Atmosphere Model (CAM5) toward the winds measured in the atmosphere, the aerosol effects on cloud brightness can be identified much more quickly. This nudging greatly reduced variations in the column liquid water in clouds without changing the sensitivity of the column liquid water to the aerosol, thus
permitting global estimates of aerosol effects on clouds in much shorter simulations. Simulations with preindustrial and present-day emissions of aerosol and aerosol precursor gases were both nudged toward the same winds so that the weather systems were similar in both simulations. They also performed simulations with preindustrial and with present-day emissions without nudging such that the weather systems could evolve freely in order to check their results. This study gives climate researchers a valuable tool for important climate change projection experiments.

In climate models, radiation from the sun’s rays are assumed to
only interact with the Earth’s surface straight up and down. However, when there is steep topography, as in mountainous regions, a 3D scheme may be needed to capture the impacts on mountain climates. Researchers have now implemented a parameterization of the interactions between 3D radiative transfer and mountain topography in a regional climate model that includes a detailed land surface model. The parameterization accounts for deviations of the downward solar fluxes from flat surfaces. U.S. Department of Energy scientists at Pacific Northwest National Laboratory and at the University of California—Los Angeles investigated the effects of 3D radiative transfer over a western U.S. region focusing on the Sierra Nevada
Mountains. Two simulations, with and without the 3D radiative transfer parameterizations, were performed. Comparison of the simulations shows that mountain topography can induce up to -50 W/m² to +50 W/m² deviations in solar fluxes reaching the surface in the Sierra Nevada Mountains. In response to these changes, surface temperature can increase by up to 1^oC on the sunny side of the mountains, leading to
enhanced snowmelt and increased soil moisture. The team found that
mountain areas receive more solar radiation during early morning and
late afternoon with a corresponding increase in surface temperature.
However, the 3D-radiation impact is smaller in the middle of the day
leading to a relative cooling effect. These changes are reflected in
a reduced diurnal temperature range and changes in sensible and
latent heat fluxes. The relatively large changes in diurnal variability and surface fluxes motivate the need to assess the climatic effects of 3D radiative transfer in mountains and the implications to the hydrological cycle in mountainous regions worldwide.

The U.S. Department of Energy has a long-term responsibility to contain uranium leaked into the environment at mining and processing sites. Uranium has a complex chemistry that determines whether it is immobilized or moves out of a contaminated area, potentially into water supplies. New research on the transformation of uranium (VI) to uranium (IV)—the most common oxidation states of the element—discovered that bacterial biomass in the ground impacts this transition. Studies were carried out at the Rifle (Colorado) Integrated Field Research Challenge site, by scientists from the SLAC National Accelerator Laboratory and Berkeley Lab, to determine how uranium (VI) exposed to natural conditions at the site behaved and to determine the underlying controlling biological and chemical mechanisms. The experiments showed that uranium (IV) unexpectedly was present both as a monomeric, biomass-associated uranium (IV) species and, to a much
lesser extent, as nanoparticles of uraninite (UO₂). The researchers attribute the presence of the former to the binding of uranium (IV) to phosphate groups in biomass following the chemical transformation of uranium (VI) to uranium (IV) by reaction with iron sulfides or bacterial enzymes. Since a substantial portion of the uranium is found in this form, models of uranium transport in contaminated subsurface environments need to recognize the existence of multiple pathways for reduction of uranium (VI), including the
biological factors identified in this research.

Fluorine-18 is a radioactive isotope that emits positrons. Using positron emission tomography (PET), scientists can image the movement and localization, in living organisms, of molecules that contain fluorine-18. Fluorine-18-labeled-fluorosugars, that is, natural sugars into which fluorine-18 atoms have been incorporated, enable study of the mechanisms by which living organisms use and process these biomolecules and offer opportunities to observe sugar distribution and metabolism in real time. Fluorine-18 fluoro-deoxyglucose (FDG) has already been established as an important PET imaging agent in human medicine. It is well known that vascular plants transport the bulk of their carbohydrate load in the form of sucrose. Now, U.S. Department of Energy scientists at the University of Missouri—Columbia have synthesized fluorine-18-fluoro-deoxy-sucrose (FDS) and used it to obtain the first images of corn plant leaves that demonstrate realtime transport of the sugar. Their results will enable investigators to image sucrose metabolism in living plants and, from these images, gain insight into metabolic pathways in plants with potential value for biofuel production.

As clouds evolve, outside dry air is mixed in (or entrained) by turbulent motions in the clouds. This dry air causes cloud droplets to evaporate. As also observed in the field, cloud modelers assume either “homogeneous” mixing (all cloud droplets evaporate the same amount) or “heterogeneous” mixing (some droplets evaporate more than others). These different mixing processes give rise to distinct cloud properties. Having an accurate representation of these processes is critical for improving large scale models. Unfortunately, there has been no single parameterization that spans the full spectrum of observed entrainment-mixing processes. Now, U.S. Department of Energy scientists at Brookhaven National Laboratory (BNL) have filled this gap and developed a new model formulation, based on in situ aircraft measurements collected at the Atmospheric Radiation Measurement program’s Southern Great Plains site and numerical simulations with the Explicit Mixing Parcel Model (EMPM). They introduced a new microphysical measure, the homogeneous mixing degree, and explored the potential of using this measure to quantify a continuum of entrainment-mixing mechanisms and relate it to the entrainment-mixing dynamics. The parameterization may now be used in models that have both droplet mass and number information (“two-moment microphysics schemes”). BNL scientists are implementing the scheme into a cloud-resolving model and investigating its influence on these cloud model results. The long-term goal is to develop a mixing scheme for use in global climate models.

Clouds reflect incoming energy from the sun but trap outgoing energy from the Earth. How much energy clouds retain versus reflect determines their
emissivity— their ability to act as a source of energy themselves. Satellite-based observations provide information about the top but not the bottom of clouds. Thus, ground-based observations are still important to understand the effect of clouds on the atmosphere and surface radiation balance. Scientists used the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) dataset collected from Shouxian, China, in 2008 to simulate the downwelling radiances on the surface. Results show that emissivity of clouds decreases as the height of their bases increases. That is, the higher the bases of the clouds, the less those clouds can act as sources of energy. These results significantly improve our ability to quantify the impact of clouds forming at different altitudes on Earth’s energy budget.

Land cover and land use, topography, and soil properties contribute to land surface heterogeneity around the world. As the resolution of climate models increases, it is critical to capture fine-scale land features in the land-surface datasets that drive the models. U.S. Department of Energy scientists at Pacific Northwest National Laboratory have developed a high-resolution, gridded dataset at 0.05 degree resolution for the Community Land Model (CLM). This
dataset includes plant functional types (PFTs), leaf area index (LAI), stem area index (SAI), and non-vegetated land cover composition. When they compared the new surface parameters with those currently used in CLM4 at 0.5 degree resolution, the researchers found that the new parameters resolve more diverse subgrid PFTs within each 0.5 degree grid cell. The new dataset also shows more contributions from shrubs, grass, and crops as opposed to bare soil
and a global decrease in LAI in boreal forests, but a large increase in LAI in tropical forests. This study demonstrated the use of the new high-resolution data in a coupled land-atmosphere model coupled to the CLM at 12 km resolution over the western United States. This analysis showed the important spatial details in surface fluxes being resolved by high-resolution modeling, which, in turn, would influence the climate.

Researchers, including U.S. Department of Energy scientists from Pacific Northwest National Laboratory, used two aircraft-based field campaigns to
understand the distribution and mixing state of carbonaceous aerosols in California. One campaign sampled aerosols over southern California to understand the role of particle composition on air quality and climate change. The other campaign followed the evolution of organics and soot as urban emissions were transported from Sacramento into the Sierra Nevada foothills. These studies, conducted in May and June 2010, assessed the particle mixing state throughout most of California. Even though atmospheric particle composition in both regions was influenced by urban sources, the mixing state was found to vary greatly. Nitrate and soot were the dominant species in southern California, while sulfate and organics were more prevalent in northern California. The mixing state varied temporally in northern California, where soot mixed with organics became the prevalent particle type toward the end of the study as regional pollution levels increased. Nearly 97% of submicron particles contained carbonaceous material, and nearly 88% of all particles sampled showed signs of atmospheric aging. These studies demonstrate that the majority of ambient carbonaceous particles in California are internally mixed and heavily influenced by the secondary species that are most prevalent in this particular region. Considerations of regionally dominant sources and secondary species, as well as temporal variations of aerosol physical and optical properties, will be required to obtain more accurate predictions of aerosol climate impacts in California and elsewhere.

High clouds associated with tropical mesoscale convective systems (MCSs) can extend thousands of kilometers and last tens of hours, strongly impacting the global radiation budget. Accurately representing these clouds in climate models requires understanding of microphysical processes that control cloud properties and lifetime. U.S. Department of Energy scientists at Brookhaven National Laboratory and their collaborators performed cloud resolving model simulations using three microphysics
parameterizations of varying complexity, evaluating them against
satellite-retrieved cloud properties. A new algorithm to identify and
track MCSs was also developed and applied to observations and model
simulations over the Tropical Western Pacific (TWP) to track the full
lifetime of individual cloud systems. The results demonstrated that
MCS simulations are sensitive to microphysics parameterizations. The
most crucial element was the fall velocity of frozen particles (i.e.,
ice, snow, and graupel). While model simulations all had similar
updraft characteristics, microphysics parameterizations that produced
particles with lower fall velocities produced a larger buildup of ice
in the upper troposphere, leading to longer lasting and/or larger
MCSs than observed in satellite observations. In terms of cloud
properties, the performance of more complex two-moment schemes was
not superior to that of the simpler one-moment schemes for these
tropical cloud systems. This result indicates that improvements to
microphysical parameterizations need to focus on better
representation of processes such as ice nucleation and aggregation of
ice crystals into snowflakes that affect number concentration in
tropical high clouds.

Scientists working to improve atmospheric climate simulations have few systematic methods to determine what aspect of the atmosphere is responsible for poor simulations, such as rainfall from particular cloud types. A U.S. Department of Energy team from Pacific Northwest National Laboratory and Scripps Institution of
Oceanography used an uncertainty quantification (UQ) technique to improve convective precipitation in the Community Atmosphere Model version 5 (CAM5). In this model, the simulated precipitation looks
reasonable but the partitioning of rain between convective and stratiform clouds is very different from observation-based estimates. The team examined the sensitivity of precipitation and circulation to
key parameters in the deep convection scheme in CAM5, using a statistical algorithm that can progressively converge to optimal parameter values. They then evaluated the impact of improved deep
convection on the global circulation and climate, including extreme rain events. Their results showed that the simulated convective precipitation is most sensitive to certain model parameters related to convective timescales, air mass entrainment rate, and the maximum permitted cloud downdraft mass flux fraction. Using the optimal parameters constrained by observations from the Tropical Rainfall Measuring Satellite Mission, the model remarkably improved the simulation of the convective to stratiform precipitation ratio and rain rates. As the optimal parameters are used, they also found improvement in aspects of the atmospheric circulation and simulated climate extremes. These new UQ statistical methods will help scientists converge more quickly toward improved model parameters.

Half the world’s population is chronically infected with Helicobacter pylori, which causes gastritis, gastric ulcers, and an increased incidence of gastric adenocarcinoma. Treatment is becoming less effective because of increasing antibiotic resistance, suggesting that a specifically targeted approach to eradicate this organism would be beneficial. H. pylori’s survival and its ability to colonize in the acidic stomach depend on the presence of HpUreI, a proton-gated inner-membrane urea channel protein, which enables chemical reactions that balance acidic effects. HpUreI has thus been identified as a clinical target. An HpUreI structure, revealed at the Stanford Synchrotron Radiation Lightsource at the SLAC National Accelerator Laboratory, shows an arrangement of six protomers that form a compact hexameric ring about 95 Å in diameter and 45 Å in height. The hexamer’s center is filled with an ordered lipid plug. Each protomer encloses a channel of transmembrane helices, with specific side chains lining the entire channel, and defines two constriction sites in the middle of each channel. This first three-dimensional channel structure from the AmiS/UreI superfamily provides unique information that may guide the discovery of small-molecule inhibitors, offering the possibility of clinical treatment without the use of conventional antibiotics.

Gene positioning and regulation of nuclear architecture are thought to influence gene expression. Soft X-ray tomography (SXT) imaging shows that silent olfactory receptor (OR) genes from different chromosomes in mouse olfactory neurons converge in a small number of heterochromatic foci. These foci are OR exclusive and form in a differentiation-dependent manner specific to cell type. OR gene aggregation is developmentally synchronous with the downregulation of the lamin B receptor (LBR) and can be reversed by ectopic LBR expression in mature olfactory neurons. LBR-induced reorganization of nuclear architecture and disruption of OR aggregates perturbs the singularity of OR transcription and disrupts the olfactory neurons’ targeting specificity. These observations indicate spatial sequestering of heterochromatinized OR family members as a basis of monogenic and monoallelic gene expression. This research was conducted using resources at the Advanced Light Source at Lawrence Berkeley National Laboratory.

Subsurface microbial communities are highly diverse and comprise an enormous fraction of Earth’s biomass, but lack of knowledge related to their ecological function makes understanding their ongoing biogeochemical processes difficult. Using synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectromicroscopy to probe biofilm samples from a cold subsurface sulfur spring, researchers recently determined how bacteria and archaea work together to influence global sulfur and carbon cycles. By revealing the bright spectral signals of akylic and methyl groups, together with sulfur functional groups, SR-FTIR unambiguously identified the bacteria’s sulfur-oxidizing metabolic activity. Archaeal cells, which were the dominant population in this biofilm, showed no such activity, suggesting a thriving mutual metabolism of archaea and bacteria. The research was conducted using resources at the Advanced Light Source at Lawrence Berkeley National Laboratory.

Cyanobacteria are important photoautotrophic organisms that can capture carbon dioxide and convert it into a suite of organic compounds such as high-density liquid fuels. Using synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectromicroscopy as a high-throughput imaging method, researchers tracked metabolic phenotypes of Synechocystis 6803, which was engineered for enhanced production of alkanes and free fatty acids. Multivariate SR-FTIR data analysis revealed biochemical shifts in the engineered cells. These results demonstrate the applicability of
SR-FTIR spectromicroscopy for rapid metabolic screening and phenotyping of live individual cells. The research was conducted using resources at the Advanced Light Source at Lawrence Berkeley National Laboratory.

As a major component of the biological nitrogen cycle, the bacterial enzyme nitrogenase (N₂ase) converts nitrogenfrom air into ammonia, thereby making it accessible to plant life. The enzyme achieves this feat at a metal-sulfur cluster called the FeMo cofactor by a mechanism that still is not wellunderstood. Research to better understand how metals and metal clusters interact with nitrogen and reduced nitrogen species is exploiting the soft X-ray region via
transition metal L-edgeand nitrogen K-edge spectroscopy. Complementary studies haveused the stopped-flow infrared system in the mezzanine spectroscopysuite at the Advanced Light Source at Lawrence Berkeley National Laboratory to probe time-dependent binding of the carbon monoxide molecule CO to N₂ase.

Chlorosomes make up a highly specialized supramolecular light-harvesting antenna complex found in green photosynthetic bacteria. They are of interest in the development of synthetic devices for solar harvesting and conversion because the organization of bacteriochlorophylls in the chlorosome provides a mechanism for highly efficient light collection and energy funneling to the photosynthetic reaction centers. Researchers investigated sol-gel chemistry as an approach to entrap and stabilize chlorosomes isolated from Chloroflexus aurantiacus. The Bio-SANS beamline at the High Flux Isotope Reactor at Oak Ridge National Laboratory enabled the characterization of the sol-gel matrix properties, as well as the size, shape, and aggregation state of the entrapped chlorosomes. This approach offers new possibilities for developing artificial solar-harvesting and
energy-conversion devices based on naturally occurring photosynthetic systems.

Nanosized vesicles have potential as drug carriers or diagnostic agents because of their ability to entrap and release molecules into their core region in a controlled way. An easy and robust route was developed to fabricate uniform porphysomes consisting of porphyrin-polylactide (PPLA) conjugates that can overcome the obstacles faced with previous systems. The Bio-SANS beamline at the High Flux Isotope Reactor at Oak Ridge National Laboratory was used to characterize these new particles and identify the hollow shell structure characteristic of vesicles. These PPLA porphysomes may have potential as a new and stable platform for drug delivery and ultrasonic imaging, especially in cancer theranostics. This research was featured on the Sept. 28, 2012, cover of Chemical Communications.

The hydrosulphide ion (HS–), a critical element in the origin of life on Earth, is important in physiology and cellular signaling. The HS–species is also the terminal product when an anaerobic bacterium derives its oxidative power from sulphate instead of oxygen. A recent study conducted on beamlines at the National Synchrotron Light Source revealed the structure of the hydrosulphide ion channel (HSC), a membrane-pore molecule, elucidating how HS– is able to escape from pathogenic Clostridium difficile cells. In the same protein family, the formate channel (FocA), which has a fold similar to HSC, has been shown to play two other roles related to bioenergy and environmental science. In the first case, hydrogen gas production in Escherichia coli depends on the selective decomposition of formate, whose concentration depends on FocA. In the second, when Euglena experiences long-term chronic exposure to cadmium ions, it overexpresses a FocA protein. This protein has been proposed as a marker for long-lasting cadmium pollution in water.

A key step in the production of biofuels from biomass is hydrolytic breakdown of cellulose, a major component of all plants, into simple, fermentable sugars. Many natural systems carry out this breakdown, and much research is devoted to find systems that are highly efficient and thus candidates for inclusion in a biofuel production system. A new study of a subfamily of glucosidase enzymes (6-P-β-glucosidases), critical to
efficient hydrolysis of cellulose, uses x-ray crystallography to determine their structures and how they bind to cellulose molecules. The researchers isolated these enzymes from two bacteria commonly found in the digestive tracts of many mammals, including humans: Lactobacillus plantarum and Streptococcus mutans. They obtained structures of the enzymes alone and bound to key cellulose breakdown molecules, using the Structural Biology Center’s stations at Argonne National Laboratory’s Advanced Photon Source. Different bacteria show different
P-β-glucosidase and P-β-galactosidase activities. The structures and functional studies enabled the scientists to define structural features shared by glucosidases and galactosidases and those that are unique to the 6-P-β-glucosidases subfamily. Both enzymes show hydrolytic activity against 6’-P-β-glucosides but exhibit surprisingly different kinetic properties and affinities for substrates. Considering the conservation of the overall structures and active sites of various 6-P-β-glucosidases, the differences at their ligand binding subsites and the entrance to the active site are likely the determinants of their substrate specificities. These new findings will help scientists studying the design of efficient enzyme systems for biofuel production and will also have implications for human health.

Precipitation is one of the most poorly simulated physical processes in general circulation models (GCMs). One difficulty with modeling precipitation is that precipitation is affected by a variety of complex processes that need to ben parameterized in large-scale models. The single-column model (SCM), which isolates a single-grid column from a global model, is a useful and effective tool to study the parameterization schemes in GCMs. However, most SCM intercomparison studies with Atmospheric Radiation Measurement (ARM) data focused on special cases or week-to-month-long periods. To make a statistically meaningful comparison and evaluation on modeled precipitation, three-year-long SCM simulations of seven GCMs participating in the Brookhaven National Laboratory (BNL) led “FASTER” project at the ARM Southern Great Plains (SGP) site have been completed. The results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant differences and deficiencies such as problems in frequency-intensity trade-off during cold seasons, too much rain during the day rather than at night, and differences in how various models partition rain between convective and stratiform clouds. Further analysis reveals distinct meteorological backgrounds for model precipitation underestimation and overestimation, offering clues to why the models are deficient.
The different SCM performances and associations with large-scale forcing and thermodynamic factors shed useful insights on cloud and convection parameterizations and will guide future model development.

The international Climate Model Intercomparison Project (CMIP5) has produced an enormous number of
climate and Earth system model simulations to help scientists understand climate change and variability. Performing those simulations and analyzing the data requires a great deal of sophisticated and high performance software, some of which is freely available to the community as open source. A recent issue of the journal Geoscientific Model Development was devoted to “Community Software to Support the Delivery of CMIP5.” Coupling software in an Earth system model, sometimes called ‘the coupler,’ is the software used to get the ocean, atmosphere, land surface, and sea ice models to talk to each other and simulate one system. Researchers at Argonne National Laboratory developed the Model Coupling Toolkit (MCT) used as the foundation coupling software in the U.S. Department of Energy/National Science Foundation-developed Community Earth System Model (CESM), one of the biggest contributers to CMIP5. This coupling software performs well on high-performance, massively parallel computers and is straightforward to integrate into a climate model, important “coupler” criteria. In addition to the CESM, five out of seven European-developed climate models contributing to CMIP5 used a coupler called OASIS whose developers recently announced a new version of their software that includes MCT.

Atmospheric aerosols from human activities such as fossil fuel combustion influence surface temperatures, mainly contributing to climate cooling. Although aerosol concentrations increased during the past century, they have been declining in many regions due to the recent imposition of pollution controls. A team of scientists, including U.S. Department of Energy researchers at Pacific Northwest National Laboratory, evaluated 10 Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) model simulations of aerosols and estimated the climate impacts for past and future simulations. The team found that the models represent present-day total aerosol optical depth (AOD), a measure of atmospheric blockage of radiation, relatively well, though many models underestimate AOD. Contributions from individual aerosol chemical components are quite different among models. The models captured most AOD trends during the years 1980 to 2000, but under-predicted increases over the Yellow/Eastern Sea. They strongly underestimate absorbing AOD trends from black carbon or soot in many regions. This study found climate feedbacks, including cloud responses, contribute substantially (35% to 58%) to modeled historical aerosol radiative forcing. The largest 1850 to 2000
negative aerosol forcings (leading to cooling) are over and near Europe, South and East Asia, and North America, which are major emission regions. There remains considerable uncertainty in how climate feedbacks to aerosols, including cloud responses, are influencing climate.

When black carbon (BC) or soot, emitted during combustion of fossil fuels such as diesel or coal or from wood burning, is deposited on ice or snow, it reduces surface brightness or albedo, enhancing melting. Once melted, a much darker surface is exposed, accelerating a tendency toward climate warming. However, the size of this effect is not well known. Research in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), including by U.S. Department of Energy scientists at Pacific Northwest National
Laboratory, evaluated the historical BC aerosols simulated by eight ACCMIP models against ice core records, long-term surface mass concentration observations, and recent Arctic BC snowpack measurements. The global BC burden from pre-industrial to the present day increased by 2.5–3 times with little variation among models, roughly matching the 2.5-fold increase in total BC emissions estimated during the same period. The models had a large divergence at both Northern Hemisphere and Southern Hemisphere high latitude regions for BC burden and at Southern Hemispheric high latitude regions for deposition fluxes reflecting differences in poleward transport among models. Clearly, substantial work remains to refine model simulations of BC and its removal and deposition on snow before a clear understanding of its snow-albedo climate effects will be possible.

Climate model simulations include the influences of atmospheric chemistry and aerosols, yet there are uncertainties in how models formulate and parameterize chemistry, aerosols, and their influence on Earth’s radiation, clouds, and other climate features. The latest phase of the international Climate Model Intercomparison Project (CMIP) included a parallel effort in which model groups compared the treatment and effect of chemistry and aerosols in climate models (Atmospheric Chemistry and Climate Model Intercomparison Project; ACCMIP). An international group of scientists, including U.S. Department of Energy researchers at
Pacific Northwest and Lawrence Livermore National Laboratories, participated. The project consisted of a series of single time-slice experiments targeting long-term changes in atmospheric composition between 1850 and 2100. The focus was to document composition changes and the associated radiative forcing during
this period. The team studied 16 ACCMIP models in a wide range of horizontal and vertical resolutions, vertical extent, chemistry schemes, and interaction with radiation and clouds. While the groups specified anthropogenic and biomass burning emissions for all time slices in the ACCMIP protocol, they found that natural emissions are responsible for a significant range across models, especially in the case of ozone precursors. Model-to-model
comparisons of changes in temperature, specific humidity, and zonal wind between 1850 and 2000 and between 2000 and 2100 were mostly consistent; however, simulated meteorology for some outlier models
was different enough to significantly affect their atmospheric chemistry simulations. Isolation and comparison of the chemistry and aerosol effects on climate, as performed in this exercise, will be an important element of understanding overall climate change within the CMIP experiments.

The extreme complexity of earth system models (ESMs) is necessary to represent the many processes underlying terrestrial carbon cycle processes. However, simple models may be useful to qualitatively
understand projected dynamic responses to warming and to identify processes missing in the models. A U.S. Department of Energy scientist at Lawrence Berkeley National Laboratory developed a simple model for vegetation carbon response by tracking the movement of the most statistically similar climate at every location in an ESM over past time and recalculating the carbon flux within the Fifth Climate Model Intercomparison Project (CMIP5) ESMs. The most important area of disagreement between this simple method and the full ESM calculations are in the southern boreal forest, where ESMs project carbon gains, while the simplified
approach projects carbon losses. This finding suggests that potential carbon losses such as forest disturbance and mortality, known to be missing in the ESMs, need to be better represented to robustly predict the carbon response in this region.

Atmospheric aerosols from both natural and anthropogenic sources have a net cooling effect on climate by
blocking incoming solar radiation and brightening clouds. Pollution aerosols also have deleterious health and environmental effects. The most important anthropogenic aerosol type is sulfate, which
results from oxidation of sulfur dioxide. Sulfur dioxide, emitted during the combustion of fossil fuels such as coal and gasoline, has increased in the atmosphere since industrialization. In recent decades, however, various emission control strategies and technologies have been implemented. Ongoing monitoring of sulfur
dioxide emissions is needed to track, and simulate in climate models, the extent to which aerosols may offset greenhouse gas warming in various regions. Researchers, including a U.S. Department of Energy scientist at Pacific Northwest National Laboratory (PNNL), estimated sulfur dioxide emissions from 2000
through 2011. The work verified a previous PNNL study that found an increase in emissions from 2000 to 2005. The new work found that emissions have declined in recent years largely due to increased
emission controls in North America and China. The study found that sulfur dioxide emissions in the recently released Representative Concentration Pathway scenarios, used by the Fifth Climate Model Intercomparison Project (CMIP5), are consistent with these inventory estimates.

In the Arabian Peninsula region, frequent winter storms carry desert dust throughout the Mediterranean area, affecting visibility, health, climate, and ecosystems in water bodies. Using a Weather Research and
Forecasting model with chemistry (WRF-Chem), a team that included a researcher at the U.S. Department of Energy’s Pacific Northwest National Laboratory simulated various aspects of dust phenomena over the Arabian Peninsula and Red Sea during a typical winter dust event. They found that the presence of dust particles
in the atmosphere causes a significant reduction in the amount of sunlight reaching the surface during the dust event. They also found that dust aerosols have a significant impact on the energy and nutrient balances of the Red Sea. The (simulated) cooling under the dust plume could have profound effects on both the sea surface
temperature and circulation. The model projected two maximum daily rates that corresponded to two periods with the highest aerosol optical depth captured by ground and satellite observations. The model also projected that the dust plume was thick, extensive, and mixed in a deep boundary layer at an altitude of 3–4 km.
Further analysis of dust generation and its spatial and temporal variability is extremely important for future projections and better understanding of the climate and ecological history of the Red Sea.

Atmospheric particles, or aerosols, influence climate by blocking incoming solar radiation and by influencing
clouds. One of the least understood effects of aerosols is their influence on very cold clouds, which exist toward polar regions and high in the atmosphere. High-atmosphere clouds trap long-wave radiation and warm climate. Dust from natural sources, such as deserts, and from human activities, including disruption of soils and some industrial activities, appears to have an important effect on cold clouds. Researchers, led by a U.S. Department of Energy scientist at Pacific Northwest National Laboratory, used the Community Atmospheric Model version 5 (CAM5) to study the effect of dust on upper tropospheric cirrus clouds through their tendency to enhance ice particle formation as vapor or droplets that freeze on dust (heterogeneous ice nucleation). These ice particles typically fall out or precipitate. Although scarce, heterogeneous ice nuclei could impact ice crystal number concentration, compared to standard droplet (homogeneous) freezing, by initiating ice nucleation earlier, depleting available water vapor, and hindering the occurrence of homogeneous freezing. Using two model formulations that consider homogeneous and heterogeneous nucleation and the competition between them, the team found heterogeneous nucleation on dust aerosols reduces the occurrence frequency of homogeneous
nucleation and thus the ice crystal number concentration in the northern hemisphere. These results highlight the importance of quantifying the number concentrations and properties of heterogeneous ice nuclei (mainly dust) in the upper troposphere.

Proposed climate mitigation measures do not account for the non-greenhouse gas climate impacts of land-use change such as the regional effects of changing albedo or evapotranspiration. The same is true of the stabilization targets modeled for the Fifth Climate Model Intercomparison Project (CMIP5) Representative Concentration Pathways (RCPs). A recent U.S. Department of Energy (DOE) study examined the climate implications of two different scenarios that stabilize radiative forcing by greenhouse gases and aerosols at the same level, but with dramatically different patterns of land-use change over the 21^st century. The study relied on a new modeling framework, the Integrated Earth System Model (iESM) being developed by three DOE labs, which couples the human decisionmaking components of an integrated assessment model, the Global Change Assessment Model (GCAM), with the Global Land-Use Model (GLM), and a state-of-the-art global climate model, the Community Earth System Model (CESM). The iESM is able to replicate the model coupling procedure in CMIP5 and can provide insight into the importance of non-greenhouse gas climate forcing from land-use change. The study also used offline land and radiative transfer models to identify forcing and feedback mechanisms that contribute to the climate effects of land-use change in different regions. The study found that Boreal deforestation strongly influences climate due to increased albedo coupled with a regional-scale water vapor feedback. Globally, the mitigation scenario with high biofuel use and correspondingly high levels of deforestation yielded a 21^st century warming trend that is 0.5 °C cooler than baseline, driven by a decrease in radiative forcing that is distributed unevenly around the globe. These results demonstrate that neither climate change nor actual radiative forcing is uniquely related to atmospheric forcing targets, but depend on the socioeconomic pathways followed to meet each target.

Changes in land use from increased cultivation of biofuel crops can alter climate by increasing greenhouse gas emissions and by changing the reflective properties of Earth’s surface. A new modeling study by U.S. Department of Energy (DOE) researchers at the Massachusetts Institute of Technology Joint Program on the
Science and Policy of Global Change investigates how (1) land-use policies and economic factors influence where and how biofuel crops are planted, (2) potential implications for land-use change and
greenhouse gas emissions, and (3) the overall effect on global and regional climates. The study uses the DOE-supported Integrated Global Systems Model (IGSM) to simulate the climate effects of two
possible global biofuels futures—one that allows conversion of natural areas to meet the increased demand for land, and a second that encourages more intense use of existing managed land and restricts deforestation. Findings show that increased biofuel crop cultivation has a negligible effect on global temperature as
warming from increased greenhouse gas emissions from cultivation is balanced by cooling caused by increased surface reflectivity of cropland.  Although global temperature will only be minimally
affected, more substantial regional warming may occur, and not necessarily in the regions where biofuel crops are grown. The model predicts the Amazon Basin and Central Africa will warm by as much
as 1.5°C. This effect is stronger in the first case that includes the conversion of forests into crop land. The effect is less pronounced when deforestation is limited. This indicates that future activities that promote land-use intensification may result in more tolerable future environmental conditions for local populations in tropical regions.

Understanding how bacteria, algae, and other microbes influence or communicate with each other by exchanging molecules could provide insights useful for advancing sustainable bioenergy. Scientists at the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory used a novel technique that noninvasively analyzes microbes to profile chemicals produced by a cyanobacterium to influence nearby microorganisms.
Synechococcus sp. PCC 7002 was found to steadily secrete two molecules, sucrose and glucosylglycerol, that nearby bacteria could use as resources. The technique that was used to chemically profile the microbial communities in both space and time is nanospray desorption ionization electrospray mass spectrometry, or nano-DESI, developed at the Environmental Molecular Sciences Laboratory (EMSL), a DOE scientific user facility. This research appeared on the March 2013 cover of Analyst.

A fundamental question in microbial ecology is how do community diversity and composition change in response to perturbations. Most ecological studies have a limited ability to deeply sample community structure or a limited taxonomic resolution to track changing microbial diversity. To address this issue,
researchers at the University of California, Berkeley, developed a method to assemble full length 16S rRNA sequences from short-read sequencing to assay the abundance and identity of organisms that represent as little as 0.01% of sediment bacterial communities. This approach, termed EMIRGE and optimized for large sequencing data size, allows researchers to differentiate the community composition among samples acquired before and after an environmental perturbation. Briefly, EMIRGE relies on a database of candidate 16S sequences for a template-guided assembly. An iterative method, sequencing reads are first aligned and probabilistically attributed to candidate 16S genes. Subsequently, candidate gene abundances and consensus sequences are adjusted based on the calculated probabilistic read attribution. The results were highly reproducible across very high alpha microbial diversity and abundant organisms from phyla that do not have cultivated representatives. This method allows for sensitive, accurate profiling of the “long tail” of low-abundance organisms that exist in many microbial communities and can resolve population dynamics in response to environmental change.

Methylmercury is a potent neurotoxin produced from inorganic mercury by anaerobic bacteria in natural environments. Until now, however, the genes and proteins involved have remained unidentified. A team of scientists from Oak Ridge National Laboratory and collaborators from the Universities of Missouri and Tennessee identified a two-gene cluster required for mercury methylation by Desulfovibrio desulfuricans ND132 and Geobacter sulfurreducens PCA. In both bacteria, deletion of either or both genes resulted in the elimination of their ability to methylate mercury. Among bacteria and archaea with sequenced genomes, related genes (orthologs) are present in confirmed methylators but absent in non-methylators, suggesting a common mercury methylation pathway in all methylating bacteria and archaea sequenced to date.

Understanding how genomes of organisms change over time underlies much of biology and its practical applications. Plasmids are DNA molecules that can replicate independently of chromosomal DNA in a cell. This enables organisms to “collect” and move genes to other organisms through lateral gene transfer (like “genomic email”) and contributes to prokaryotic genome evolution. To understand the depth and breadth of the prokaryote plasmid gene pool, scientists have isolated, sequenced, and compared plasmids from two wastewater sludge communities. The authors studied the “mobilome,” a name for the mobile elements in a community genome, by specifically targeting, separating, and purifying closed circular supercoiled DNAs (CCSD) originating from the plasmids. They found that the plasmids isolated from the sludge wastewater microbial communities turned out to contain primarily uncharacterized coding sequences. Besides lending credence to the idea that plasmids are crucial to genome innovation, evolution, and community structure and functioning,
this study generated a large library of new genes involved in wastewater sludge degradation and processing that could enable new approaches to microbial wastewater cleanup. The study was enabled by the DOE Joint Genome Institute.

Plants respond to developmental cues and environmental stresses by controlling both the level and activity of various hormones. A highly adaptable scaffold
enables the evolution of promiscuous activity within the auxin-responsive GH3 enzyme family, leading to diversification of substrate specificity and evolution of metabolic control systems. Newly reported crystal structures provide a glimpse into substrate recognition and control of hormones involved in plant growth, development, and defense, enabling deeper understanding of plant metabolism intricacies. The research was conducted using resources at the Advanced Photon Source at Argonne National Laboratory.

The New Delhi metallo-beta-lactamase (NDM-1) gene makes multiple pathogenic microorganisms resistant to all known beta-lactam antibiotics including carbapenems, which are considered as “last resort” antibiotics. Researchers used the Advanced Photon Source at Argonne National Laboratory to determine the structural basis for NDM-1’s promiscuous activity via a combination of crystallographic and biochemical studies and theoretical calculations that elucidated a pH-dependent set of pathways. Based on these findings, future active drugs can be predicted.

Department of Energy researchers at Pacific Northwest National Laboratory led a team to investigate the scientific utility of atmospheric data collected by the Atmospheric Radiation Measurement (ARM) scientific user facility over a decade of observations in the equatorial tropical western Pacific (TWP), an
important climatic region. Strong solar heating, warm sea surface temperatures, and the annual progression of the Intertropical Convergence Zone (ITCZ) across this region generate abundant convective systems that have a profound impact on global climate and precipitation. To accurately evaluate tropical cloud systems in models, measurements are needed of tropical clouds, the environment in which they reside, and their impact on the radiation and water
budgets. Because of the remote location, ground-based datasets of cloud, atmosphere, and radiation properties from the TWP region traditionally came primarily from short-term field experiments. However, these short-term datasets provided only limited statistical and climatological information. To provide long-term measurements of the surface radiation budget in the tropics and the atmospheric and cloud properties that affect it, ARM established the TWP measurement sites in 1996. This analysis gives examples of the wide range of scientific use of these unique long-term datasets, including characterization of cloud properties, analysis of cloud radiative forcing, model studies of tropical clouds and processes, and validation of satellite algorithms. The impact of recently installed instrumentation on new opportunities for tropical atmospheric science is also discussed. The study highlights contributions of ARM TWP data to increased knowledge of tropical cloud systems and the tropical surface radiation budget.

Switchgrass (Panicum virgatum L.) is a prime bioenergy feedstock candidate due to its high biomass yields, minimal input requirements, broad adaptability, and perenniality. However, its large genome size, complicated genetics, and lack of a reference genome make efforts to improve switchgrass extremely challenging. Some of these difficulties can be overcome with genotyping-by-sequencing (GBS), a relatively low-cost method that targets a fraction of the genome for sequencing. GBS has already been used in many plant species to find molecular markers called single nucleotide polymorphisms (SNPs). To be both accurate and economical, however, this strategy requires a fully sequenced and assembled reference genome. To respond to this challenge, researchers funded in part by the joint U.S. Department of Agriculture-U.S. Department of Energy Plant Feedstocks Genomics for Bioenergy Program used GBS to develop a SNP discovery platform that does not require a reference genome and that can be applied to any complex plant species. This pipeline, called the Universal Network-Enabled Analysis Kit (UNEAK), was validated with maize and then successfully tested on switchgrass. Over one million SNPs were discovered in the switchgrass collection and used to construct high-density linkage maps, providing insight into the genetic diversity, population structure, phylogeny, and evolution of this species. UNEAK is providing an invaluable resource for switchgrass improvement programs.

Global production of agricultural fertilizers has vastly increased the amount of nitrogen compounds entering natural terrestrial ecosystems. Although it is clear that increased nitrogen availability boosts primary productivity (i.e., plant growth) in ecosystems, the impacts of this nitrogen influx on the decomposition of dead plant material by soil microbes remain poorly understood. A collaborative team of U.S. Department of Energy researchers at the Universities of Michigan and Oklahoma examined carbon decomposition by soil fungi and bacteria at an experimental forest site in Michigan. GeoChip 4.0, a DNA microarray containing probes for thousands of functional genes, was used to measure expression of genes involved in degradation of complex carbon compounds in soil samples from sites that have been exposed to elevated nitrogen input for the past 18 years. Compared to nearby control plots, sites with elevated nitrogen showed significant decreases in the diversity and overall expression levels of fungal and actinobacterial genes involved in deconstruction of cellulose, lignin, and other plant compounds. This finding correlates with a long-term observation of decreased carbon decomposition rates in soils at the nitrogen-elevated sites and points to the specific mechanism underlying this shift. These findings shed new light on poorly understood processes occurring in forest soils and improve our ability to better predict how ecosystems will respond to changing environmental variables.

A team of scientists from Pacific Northwest National Laboratory, University of North Dakota, and National Aeronautics and Space Administration found that the lifetime of midlatitude convective systems lasting less than six hours is mainly attributable to the intensity of the initial convection. Systems lasting longer than six hours were associated with up to 50 percent higher mid-tropospheric relative humidity and up to 40 percent stronger middle to upper tropospheric wind shear. This resulted in continuous growth of the stratiform rain area, prolonging the system’s lifetime. The team used statistical
analysis of satellite, ground radar, and reanalysis datasets to study these deep convective systems consisting of intense convective cores, large stratiform rain regions, and extensive non-precipitating anvil clouds. This study focused on the factors that affect system lifetime and anvil cloud production, with important implications for the impact of these cloud systems on Earth’s radiation budget. An automated satellite tracking method was used in conjunction with a recently developed multisensor classification to analyze the evolution of convective system structure in a Lagrangian framework over the central United States. Regression analysis showed that anvil cloud areal coverage is strongly correlated with the size of the convective core, updraft strength, and stratiform rain area. Upper tropospheric wind speed and wind shear also play an important role for convective anvil cloud production. This research provides insight into the variety of factors that affect the life cycle of convective systems.

As a cloud grows, air outside the cloud is entrained, or drawn into the cloud due to turbulent motions at cloud boundaries. The amount and characteristics of the entrained environmental air, which is generally much drier than the cloudy air, impact the cloud’s growth and microphysical properties. Therefore, the entrainment rate is an important parameter that needs to be better understood to improve climate model simulations. U.S. Department of Energy researchers implemented a new method, in which individual particles in a high-resolution model simulation are tracked, to study the cloud entrainment process. The new method produces higher entrainment rates in convective clouds than previous methods because it is able to track the fast recycling of air into and out of the cloud that other methods could not. Over half of the air entrained by the simulated cloud was found to have been previously resident in the cloud, indicating that assumptions about the thermodynamic properties of entrained air may need to be revisited.

U.S. Department of Energy (DOE) researchers continue to test the performance of model cloud microphysics, or the way droplets and ice crystals form, evolve, and precipitate in models. Microphysics parameters influence cloud evolution and climate conditions and the complex schemes require extensive and ongoing testing against observations. Simulations of frontal stratiform precipitation events are sensitive to the representation of snow in the cloud microphysics parameterization, while convective precipitation events are mainly sensitive to the representation of the largest rimed (ice-coated) ice species, either graupel (cold ice-water condensed on a snow crystal) or hail (ball of dense layered ice). DOE scientists at Brookhaven National Laboratory and their collaborators performed model microphysics sensitivity experiments of the representation of snow and rimed ice species for two composites of 15 stratiform and 15 convective observed precipitation events. Cloud properties and surface precipitation characteristics of all events were rigorously evaluated against satellite- and radar-derived observations. Simulations that include graupel and a temperature-dependent snow parameter during both convective and stratiform events yielded results that consistently agreed better with satellite observations. The enhanced ice depositional growth rates in these experiments led to significantly improved cloud-top heights. Compared to previous model experiments, surface precipitation was less sensitive to whether graupel or hail was chosen as the rimed ice species. However, capturing peak precipitation rates required including graupel in the microphysics scheme. This study used precipitation and cloud observations to constrain and improve the requirements for cloud microphysical schemes for both convective and stratiform cloud modeling.

Melting of ice sheets from Greenland and Antarctica will lead to sea level change. It is critical that processes influencing the melt and glacier flow rates be understood and captured in models. Several Greenland and Antarctic ice shelves have deep inverted channels in the direction of ice flow and running along the underside of the ice floating over the ocean. U.S. Department of Energy researchers have developed a coupled ice-ocean model to understand the formation and evolution of submarine melt channels beneath the floating ice shelf of Greenland’s Petermann glacier. The model uses the Community Ice Sheet Model (CISM) to model the flow of grounded and floating (shelf) ice and an ocean layer (or “plume”) model to represent interaction with the underlying ocean. Melting, bedrock topography, and flow processes at the point where the glacier departs into the ocean stencils channels into the ice base as it passes by. These channels help to control and preserve the ice shelf against excessive submarine melting. The calculations revealed that warming of subsurface waters would increase submarine melting. Surprisingly, slight cooling of subsurface waters could also generate a reorganization of the submarine melt pattern and catastrophic thinning of the ice shelf. Increased discharge of (fresh) subglacial melt water at the grounding line also increases overall submarine melting through increased entrainment of relatively warm ocean waters. The study has revealed complex interactions in the ice-ocean system as well as conditions and variables that will require scrutiny and more detailed modeling in future studies.

Because ice clouds are nucleated by aerosol particles, changes to the aerosol composition may alter the ice crystal properties, ice-cloud reflectivity of incoming sunlight, and absorption of outgoing long-wave radiation. This aerosol-ice-cloud effect is poorly understood, largely unconstrained, but potentially quite significant. U.S. Department of Energy researchers, including at Pacific Northwest National Laboratory, quantified these aerosol “indirect” effects (AIE) on high-altitude cirrus clouds, using several different ice nucleation formulations in two different advanced General Circulation Models (GCMs): the Community Atmosphere Model version 5 (CAM5), and the European Center Hamburg model version 5 with the Hamburg Aerosol Model. They investigated (a) the climate states simulated by different ice nucleation schemes, (b) anthropogenic effects on ice clouds, and (c) the role of black carbon (soot) as ice nuclei in ice clouds. Different ice nucleation formulations in the two climate models result in different balances between “homogeneous” nucleation (freezing of cold sulfate droplets) and “heterogeneous” nucleation (cloud particle freezing enhanced by contact against a solid sulfate-coated dust particle). However, the magnitude of AIE on ice clouds is remarkably similar with the total ice AIE estimated at 0.27 ±0.10 W m^-2, a warming effect. This warming effect represents a 20 percent offset of the simulated total shortwave scattering of incoming radiation (cooling) AIE of -1.6 W m^-2. Black carbon (soot) aerosols have a small AIE (-0.06 W m^-2) for the ice nucleation efficiencies within the range of laboratory measurements. This study is one of the first to estimate the warming effect of aerosols on high-altitude ice clouds.

Researchers report that the impact of the 2008-2009 global financial crisis (GFC) on emissions was short-lived owing to strong emissions growth in emerging economies, a return to emissions growth in developed economies, and an increase in the fossil-fuel intensity of the world economy. Carbon dioxide emissions from fossil-fuel consumption rose 3 percent in 2011 to 9.5 billion metric tons and are expected to increase a further 2.6 percent by the end of 2012. From 2000 to 2011, emissions have grown at an average of 3.1 percent per year. The significance of these findings is that global carbon dioxide emissions continue to track the high end of various emission scenarios, expanding the gap between current emission trends and the emission pathway required to keep the global-average temperature increase below 2 degrees Celsius. If this emission growth trend continues, the global mean temperature is likely to increase by more than 5 degrees Celsius by 2100.

Arbuscular mycorrhizal (AM) fungi form intimate affiliations with the roots of many plant types. This classic example of symbiosis is commonly understood to involve AM fungi helping the plants take up soil nutrients. In exchange, the fungi receive some of the sugars generated by the plants from photosynthesis. Although AM fungi play a large role in carbon and nitrogen cycling in terrestrial environments, details of how they actually function remain poorly understood. In particular, the impact of AM fungi on soil microbe communities has not been examined in detail due to the difficulty of tracking nanoscale processes in complex soil habitats. U.S. Department of Energy researchers at the University of California Berkeley and Lawrence Livermore National Laboratory used a combination of “omics” tools and nanoscale tracking of isotopically labeled compounds to dissect interactions of AM fungi and soil microbial communities in carefully constructed soil microcosms. Plant-affiliated AM fungi were allowed to colonize small chambers containing soil samples and radiolabelled dead plant material (litter). The team found that the AM fungi have a significant impact on surrounding microbial community composition, increasing the abundance of microbes involved in plant litter degradation. During degradation of litter in soil, microbes play an important role in liberating nitrogen compounds bound in dead plant matter. The team observed significant uptake of microbially released nitrogen (but not carbon) by the AM fungi. These findings reveal another layer of complexity in this symbiotic system and yield another important puzzle piece towards understanding the complex routes by which carbon and nitrogen flow through ecosystems.

U.S. Department of Energy (DOE) scientists at Pacific Northwest National Laboratory capitalized on the multiple sensors available at DOE’s Atmospheric Radiation Measurement (ARM) Climate Research Facility site in Darwin, Australia, to understand how and when midlevel clouds form in the tropics. Midlevel clouds impact the energy budget and vertical profile of heating in the atmosphere, yet the radiative and latent heating impacts are difficult to calculate because they depend on understanding the frequency and phase of the clouds. The scientists observed cloud formation using a four-year climatology of vertically pointing lidar and radar data to get a complete picture of how clouds at this altitude occur. The team combined this technique with data from radiosondes launched on weather balloons to gather atmospheric measurements and a scanning precipitation radar that observes precipitation. Their results show that thin, midlevel clouds more frequently follow stratiform precipitation during the active monsoon rather than the break monsoon period. Cloud layers are more likely to coincide with warmer, more stable layers during the break period. In the active monsoon phase, when storms come from the ocean, these midlayer clouds are more often found after ice precipitation melts and cools the layer, causing more water vapor to condense into a cloud. In the break monsoon phase, the clouds come primarily from over land. A greater percentage of those midlevel clouds come from direct injection of cloud particles into the layer. This study provides a unique climatology based on four years of observations at the Darwin ARM site.

As climate model resolution increases toward resolving convection, the representation of cloud microphysics-the way models represent parameters such as cloud drops, ice crystals, rain, and snow formation-requires increased precision. With greater complexity also comes an increased computational burden, so it is important to understand what level of complexity is appropriate for the computationally expensive high-resolution climate simulations. U.S. Department of Energy scientists at Brookhaven National Laboratory and their collaborators, through sensitivity studies of an idealized squall line with the Weather Research and Forecasting model (WRF), showed that there is a benefit of using two variables to describe the size distribution evolution of all hydrometeors, liquid as well as ice. It was also shown that two equally complex schemes (Milbrandt and Yau, 2005 [MY] and Morrison et al. 2009 [MTT]) still behave very differently in terms of surface precipitation and moist processes aloft. These differences could be entirely related to their different treatments of how raindrops break up and grow as they fall. Over the past years, the focus in microphysics modeling often has been on the role of droplet size distribution assumptions in state-of-the-art schemes, but this study has identified that an equally large variability is associated with processes such as ice initiation, growth processes, and raindrop breakup.

Cloud droplet sizes and behaviors are influenced both by local dynamical effects, such as vertical velocity, and aerosol particles. It is challenging to discern these influences and formulate cloud behavior properly in response to both dynamical and aerosol effects. U.S. Department of Energy scientists at Brookhaven National Laboratory and their collaborators have studied these effects on cloud droplets using data collected in cumulus clouds during the Routine AAF [Atmospheric Radiation Measurement (ARM) Aerial Facility] Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign. Their focus was on the effect of vertical velocity on cloud droplet number concentration and droplet size distribution. This observational study showed that with increasing vertical velocity, the droplet number concentration increases while the size distribution range decreases. These effects happen to be opposite that of enhanced aerosol numbers. Thus, this study is an important step toward understanding and discerning the relative influences of cloud dynamical vigor and aerosol particles on cloud properties.

Summer monsoons deliver about three-quarters of South Asia’s annual rainfall, influencing fresh water supplies, agriculture, and energy production. Small changes in monsoons can have large impacts on local living conditions, affecting crop yields, prolonging droughts, or fostering floods. Recent studies have suggested various mechanisms and effects for how pollution aerosols in South Asia impact the monsoon. Aerosols cool the underlying surface and reduce the north-south temperature gradient leading to slow-response climate effects. High-altitude absorbing aerosols may cause short-term, localized enhancement of convective uplift. Using a global climate model with a fully predictive aerosol life cycle, U. S. Department of Energy researchers from Pacific Northwest National Laboratory investigated the fast and slow responses of the South Asian monsoon system to anthropogenic aerosol forcing. They show that the feedbacks associated with the slower sea surface temperature (SST) change caused by aerosols play a more important role than the aerosol’s direct impact on radiation, clouds, and land surface (rapid adjustments) in shaping the total equilibrium climate response of the monsoon system to aerosol forcing. Inhomogeneous SST cooling caused by anthropogenic aerosols eventually reduces the north-south tropospheric temperature gradient and the easterly shear of zonal winds over the region, slowing the local Hadley cell circulation, decreasing the northward moisture transport, and causing a reduction in precipitation over South Asia. Although total responses in precipitation are closer to the slow responses in general, the fast component dominates over land areas north of 25°N. The results also show an east-west asymmetry in the fast responses to anthropogenic aerosols causing increases in precipitation west of 80ºE but decreases east of it. This study provides insights into the various impacts of aerosols on the South Asian monsoon.

Growing plants on marginal lands, or lands unsuitable for conventional agricultural crops, is a promising route towards attaining sufficient cellulosic biomass for the production of biofuels without compromising food crops. However, both the availability of such lands as well as the potential environmental impacts (e.g., greenhouse gas emissions) resulting from widespread biofuel crop production remain uncertain. Researchers at the U.S. Department of Energy’s Great Lakes Bioenergy Research Center (GLBRC) report results from the first assessment of the total biomass potential of these lands, including an estimate of greenhouse gas benefits and the productivity potential of unmanaged lands. Using 20 years of data from 10 Midwest states, the researchers compared both productivity and greenhouse gas impacts of several potential biofuel feedstocks, including corn, poplar, alfalfa, and old field vegetation, and then used supercomputers to model the biomass production required to support local biorefineries. The assessment shows that if properly managed, marginal lands could provide sufficient biomass to support a viable cellulosic biofuel production industry while benefiting conservation efforts and the environment.

Populus, a fast-growing perennial tree, holds potential as a bioenergy crop due to its ability to produce large amounts of biomass on non-agricultural land. For woody perennial plants such as poplar, there is a tight coupling between growth and photosynthesis during the plant’s lifetime. To understand this process, researchers at the U.S. Department of Energy’s BioEnergy Science Center (BESC) have measured more than 11,000 proteins in different tissues of poplar, including mature leaves, young leaves, roots, and stems. They have developed a poplar proteome atlas that shows which proteins are present in the various tissue types at a given point in time. By mapping the proteins back to tissue-specific metabolic pathways, the BESC scientists demonstrated that the same organ can participate in two different growth stages. Their findings confirm prior hypotheses that mature leaves appear to function primarily in the generation of energy via photosynthesis while young leaves partition resources between growth and photosynthesis. This study illustrates that a comprehensive systems approach to proteomics can yield valuable information on the lifecycle of bioenergy-related plants. The paper is the cover article for the latest issue of Molecular and Cellular Proteomics.

Scientists provide a detailed description of the datasets and methodology used to compute the global carbon dioxide (CO₂) budget and associated uncertainties for the period 1959–2011. The objective is to quantify the major sources and sinks in the global carbon cycle budget, understand changes and trends in carbon sources and sinks, characterize the uncertainty associated within individual carbon budget source and sink terms, and provide benchmark data for mitigation efforts and policy discussions. The scientists discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. Among these, they estimate that global emissions of CO₂ from fossil fuel combustion and cement production were 9.5±0.5 PgC yr in 2011, 3% above 2010 levels. The analysis also shows that China is now clearly the world’s largest fossil-fuel emitter with 2.5 Pg C, or 28%, of the world’s fossil-fuel CO₂ emissions. The United States is second at 1.5 Pg C, or 16%. All carbon data presented can be downloaded from the Carbon Dioxide Information Analysis Center (DOI: 10.3334/CDIAC/GCP V2012).

A major challenge in research to enable large-scale production of biofuels is developing enzymes that are highly efficient in converting biomass components into usable fuels. Enzymes are proteins that are configured to catalyze such conversions. Many protein structures are known, including those of many valuable enzymes. Much less is known about how small changes in a protein’s composition can change its three-dimensional structure and control its catalytic efficiency, or even convert a protein with no catalytic function into one that is an efficient catalyst. New research shows the structural basis for conversion by directed evolution of a non-catalytic small protein into an enzyme that is an effective catalyst for linking RNA molecules. The scientists used an Extended X-ray Absorption Fine Structure (EXAFS) station at the Stanford Synchrotron Radiation Lightsource (SSRL) to determine the active-site structure of the newly synthesized enzyme. The EXAFS experiments were able to show the exact chemical environment of each zinc atom in the new enzyme, leading to an explanation of why it had developed the catalytic activity. The research was carried out by a team of scientists from the University of Minnesota and SSRL and is published in Nature Chemical Biology.

Some studies on the impacts of climate change use a “damage” function that assigns a dollar value to the physical effects of climate change. U.S. Department of Energy researchers at the Massachusetts Institute of Technology’s (MIT) Joint Program on the Science and Policy of Global Change believe that this approach is limited because of the large uncertainty surrounding climate change. The MIT Integrated Global Systems Model (IGSM), described in a special edition of Climate Change, integrates the Earth system with an economic model that allows researchers to describe human activities that contribute to environmental change or are affected by it. The MIT approach also provides an opportunity to understand the complex dynamics of the system’s interactions. For example, the possible effects of tropospheric ozone on crop productivity and yields can be explored individually as part of a process representation rather than a blended, generalized economic assumption. One challenge is the ability to describe specific physical relationships in the Earth system that are not known because the climate system has not yet experienced those changes (e.g., connecting climate change to outbreaks of pests). In the IGSM, the researchers confront this and other challenges by focusing on physical impacts that can be described and quantified and by conducting uncertainty analyses to better understand the full range of potential future effects. This mixed approach of valuing impacts, evaluating physical and biological effects, and working to better describe uncertainties in the Earth system can contribute to understanding the options and implications for various mitigation and adaptation strategies.

Cloud droplet size is an important variable for understanding the impact of clouds on Earth’s radiation budget because different size droplets reflect different amounts of sunlight. Cloud droplet size can be impacted by meteorology, cloud type, aerosol concentration, and other factors, so accurate observations of cloud droplet size are needed to evaluate the ability of models to reproduce the correct droplet size under different conditions. U.S. Department of Energy (DOE) scientists have developed a new method of determining cloud droplet size and liquid water path that uses zenith radiance measurements from single ground-based instruments at DOE Atmospheric Radiation Measurement (ARM) and the National Aeronautics and Space Administration’s Aerosol Robotic Network (AERONET) sites. The new retrieval has an accuracy of 11% to 22%, depending on the cloud conditions, and compares favorably to methods combining multiple instruments or using more expensive instruments such as cloud radars.  By using only a single instrument, the new retrieval can be implemented at more measurement sites worldwide, providing more information for climate models.

Marine boundary layer (MBL) clouds cover large areas of the world’s oceans and coastal areas and play a major role in the global climate system. Feedbacks associated with changes in MBL clouds are one of the largest sources of uncertainty in cloud feedbacks in global climate model simulations. The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Mobile Facility was recently deployed to the Azores and obtained the most extensive (19 months) and comprehensive dataset of MBL clouds to date. The observations show that cumulus and stratocumulus cloud types often occur simultaneously, with each cloud type tied to a distinct thermodynamic layer. Theoretical models of stratocumulus cloud formation often assume that the boundary layer is well mixed, meaning that there are few horizontal or vertical gradients in potential temperature or water vapor mixing ratio. The frequent presence (> 90%) of transition layers in the sounding observations indicates that this well-mixed assumption rarely holds true for the Azores. The percentage of these decoupled layers is much higher than observed in previous studies in the eastern Equatorial Pacific, indicating important differences between MBL clouds in different regions. These findings, as well as statistics on cloud frequency, precipitation occurrence, and cloud updrafts, will be used to improve models of MBL clouds.

The use of large amounts of nitrogen fertilizer in modern agriculture has resulted in massive releases of nitrous oxide (N₂O) into the atmosphere. Although shorter lived than CO₂, N₂O is over 300 times more potent as a greenhouse gas, so understanding its role and behavior in global climate change is important. Soil microbes naturally consume ammonia in fertilizers, converting it into N₂O or dinitrogen gas (N₂), a harmless component of the atmosphere. Previous attempts to estimate the abundance of microbes that perform these processes have significantly overestimated N₂O production, suggesting that a large, but undetected group of microbes is converting ammonia to N₂. In a new study, researchers have used a comparative genomics approach to identify new gene sequences involved in conversion of ammonia to N₂ and demonstrated that this genetic pathway is present in several abundant groups of soil microbes not previously thought to be involved in nitrogen conversion. Preliminary experiments suggest that these organisms are capable of this form of metabolism in the laboratory and that the relevant genes are present in soil samples. These results have revealed an important missing piece in our understanding of the terrestrial nitrogen cycle. Further research on the physiology of these organisms and determination of their environmental abundance should improve model predictions for release of greenhouse gasses from soils of bioenergy landscapes or other agricultural systems.

Microbes continue to offer surprises by their range of capabilities and versatility. When studying a microbe in its natural environment for a particular application, scientists often find that it also does something quite different and useful. A new study of the basic biological processes of methane-producing bacteria (methanotrophs) found that Methylocystis strain SB2 can also grow on acetate or ethanol and degrade a wide range of halogenated hydrocarbons. A specific pollutant-degrading protein, particulate methane monooxygenase (pMMO), attacked pollutants of interest while the bacteria used ethanol to grow. Ethanol added to contaminated groundwater enhances the ability of the groundwater to “flush” pollutants such as trichloroethylene and tetrachloroethylene. The authors suggest that the resulting aqueous ethanol-pollutant solution can be passed through a methanotrophic bioreactor where both ethanol and the pollutants are removed by a bacterium like Methylocystis strain SB2. The study, which began as a project to understand how methanotrophs that produce a metal-binding compound (methanobactin) affect the behavior of copper and mercury in the environment, led to new discoveries that could provide novel bioremediation strategies.

Understanding how cells repair DNA damage from ionizing radiation is a major focus of low-dose radiation biology research. New research now explains how a multifunctional protein protects against low dose radiation-induced DNA damage. The translationally controlled tumor protein (TCTP) is a highly conserved protein found in mammals, plants, and yeast. TCTP participates in numerous cellular processes, including protein synthesis, cell growth, and allergic reactions. A critical role of TCTP is found in a cell’s ability to repair DNA damage and maintain genomic integrity in response to stressful agents. The investigators had previously observed adaptive responses when normal human cells were exposed to low doses of gamma rays that mimic human exposure during diagnostic radiography or occupational activities. Specifically, these irradiated cells exhibited significantly less chromosomal damage than observed in nonirradiated cells. Their new findings show that this protective effect only occurs in the presence of TCTP. This study demonstrates that after exposure to low doses of ionizing radiation, signals are activated that have the potential to stimulate protective mechanisms that could reduce the risk from radiation exposure. The new study was carried out by scientists at the University of Medicine and Dentistry of New Jersey and the Fourth Military Medical University in the People’s Republic of China.

The tiny cyanobacterium Prochlorococcus is among the most abundant and important in the oceans, and distinct variants (“ecotypes”) exist at different water depths. An estimated 100 million cells of this unicellular organism can be found in a single liter of seawater. These cyanobacteria help remove some 10 billion tons of carbon from the atmosphere each year. New research addresses a long-held assumption that the size of a microbial population in the marine community corresponds to its level of activity in terms of carbon uptake and growth rate, thus determining its impact on global biogeochemical cycles. Researchers, including scientists at the U.S. Department of Energy’s Joint Genome Institute, studied the activity levels of several Prochlorococcus ecotypes at several locations in the Pacific and Atlantic oceans. The results suggest that the theory does not fully explain the link between abundance levels and activity. In their article, the authors state: “Our results suggest that low abundance microbes may be disproportionately active in certain environments and that some of the most abundant may have low metabolic activity.” “We observed uncoupling of abundance and specific activity of Prochlorococcus in the Sargasso Sea depth profile, which highlights deficiencies in our understanding of marine microbial ecology and population structure.” They conclude that marine ecosystem functioning is likely to be more complex and dynamic than previously thought. This finding has significant implications for understanding the role of the oceans in the global carbon cycle.

X-ray crystallography is commonly used to determine how the atoms comprising a biological macromolecule are arranged. However, it has one serious limitation: it cannot observe directly where all of the hydrogen atoms are located, because they scatter x-rays very weakly (or in the case of H⁺, not at all). This underscores a major weakness of x-ray crystallography since an enzyme’s behavior usually depends on the arrangement of hydrogen atoms around its active site. In contrast, neutron crystallography can reveal the position of hydrogen atoms since they scatter neutrons as strongly as the other atoms found in proteins (i.e., C, N, O, and S). A recent study used neutron crystallography to show how carbonic anhydrase (HCA), an enzyme found in all life forms, binds acetazolamide (AZM), a carbonic anhydrase inhibitor drug clinically used to treat disorders ranging from glaucoma to epilepsy to altitude sickness. Experiments at the Protein Crystallography Station at Los Alamos National Laboratory (LANL) identified the hydrogen atoms at the binding site. The results clearly show the ionization state of AZM and how drug binding displaces key active site water molecules in HCA. It also revealed the hydrogen bonding interactions between the drug and the enzyme and showed the role of certain water molecules in drug binding. The experiments demonstrate that neutron beams provide crucial and specific information that will assist in applications such as structure-based drug design. The research was carried out by scientists at LANL and the University of Florida.

Many aspects of land hydrology in climate models are uncertain and important for correctly simulating climate and cloud changes. A new, more precise system of estimating land model uncertainties has been designed and implemented in the Community Land Model (CLM4) by U.S. Department of Energy scientists at Pacific Northwest National Laboratory and Oak Ridge National Laboratory. They analyzed the sensitivity of simulated surface heat and energy fluxes to selected hydrologic parameters in CLM4 by applying a new method of uncertainty quantification (UQ) to 13 Ameriflux tower sites that span a wide range of climate conditions and provide measurements of surface water, energy, and carbon fluxes. UQ is used to select the most influential CLM parameters for increased focus and research. The results suggest that the CLM4 simulated latent/sensible heat fluxes show the largest sensitivity to parameters associated with subsurface runoff. This work is the first UQ study on the CLM4 and has demonstrated that uncertainties in hydrologic parameters could have significant impacts on the simulated water and energy fluxes and land surface states, which will in turn affect atmospheric processes and the carbon cycle.

In their effort to derive energy from iron, bacteria may set off a cascade of reactions that reduce rocks to soil and free biologically important minerals. These findings from a team at the Environmental Molecular Sciences Laboratory (EMSL) at Pacific Northwest National Laboratory are based on a model microbial community called the Straub culture, a lithotrophic culture or literally an “eater of rock,” that can turn non-carbon sources such as iron into energy. This energy is produced via a biochemical pathway driven by a series of electron exchanges, which, in the case of the Straub culture, is initiated by taking an electron from, or oxidizing, iron. To gain insight into how lithotrophs behave in the environment, the Straub culture was incubated with media containing fine particles of an iron-rich mica called biotite. After two weeks, Mössbauer spectroscopy was used to compare a biotite control to biotite incubated with the Straub culture to quantify how much iron exists in what oxidation states in the sample. In the biotite, Mössbauer confirmed that the microbes did oxidize iron from Fe(II) to Fe(III). Transmission electron microscopy revealed that this oxidation affected the biotite structure, leading to changes that resemble those observed in nature. This work offers new insight into the roles of microbes in soil production and in the biogeochemical cycling of minerals (e.g., iron oxidation) and suggests that microbes have a direct effect on rock weathering.

Arctic clouds are a major controller of the surface net radiative budget, and it is important that climate models produce these clouds correctly to accurately represent Arctic climate. The Arctic Ocean’s surface alters between ocean and sea ice. This variation, along with atmospheric dynamics and thermodynamics, affects Arctic cloud properties. U.S. Department of Energy (DOE) scientists at Lawrence Livermore National Laboratory (LLNL) developed a method to evaluate Arctic clouds in the Community Earth System Model’s (CESM) two most recent global atmospheric models that are used in the coupled transient climate projections, the Community Atmospheric Model Version 4 and 5 (CAM4 and CAM5). Clouds were first examined during distinctly separate dynamical and thermo-dynamical conditions, which were called synoptic regimes. Next, cloud fractions for each regime were examined when the regime occurred over open-ocean, sea ice, and land. The scientists ran CAM4 and CAM5 using the DOE Cloud-Associated Parameterizations Testbed (CAPT) framework to ensure the dynamics and thermodynamics in the models were similar to the observations. From CAM4 to CAM5, there was a large community effort to improve the representation of boundary layer clouds, which are prevalent in the Arctic. This analysis demonstrated that the new boundary layer turbulence and cloud microphysical schemes in CAM5 produced a global atmospheric model with improved Arctic cloud sensitivity to lower tropospheric stability and Arctic surface type.

In the design and bioengineering of metabolic pathways for clean bioenergy and other applications, it is important to understand and eventually manipulate the movement of atoms in these biochemical reactions. For example, assessing how a reactant compound is transformed into a targeted product allows researchers to optimize for efficiency in the pathways. A new computational system (Minimum Weighted Edit-Distance or MWED) allows mapping of all the non-hydrogen atoms in biochemical reactions from the initial reactants to the final products. MWED relies on predicting the propensity of forming or breaking chemical bonds during a biochemical reaction. It then calculates and optimizes all possible solutions to the reaction of interest. Because it also uses a mixed-integer linear programming technique, it is three-fold faster than other, similar techniques. The MWED all atom pathway mapping was benchmarked on 2,446 manually curated biochemical reactions from the KEGG database. The researchers found that only 22 MWED-predicted reactions were in error (error rate of 0.9%) due mainly to difficulties in representing stereochemistry in the reactions. MWED offers research scientists an extremely fast and highly accurate method to model all atoms in biochemical reactions, both for novel bioengineering as well as for tracking isotopically labeled atoms in metabolic experiments.

U.S. Department of Energy (DOE) plant biology research seeks to optimize plant productivity, both for biofuel development and for carbon sequestration in biomass. Taking a lesson from medical technology, plant biologists are now using sophisticated imaging technology to learn more about nutrient utilization in plants by watching the movement of those nutrients in real time. Positron emission tomography (PET) imaging has been used to study carbon transport in live plants using ¹¹CO₂, but because plants typically have very thin leaves, littlemedium is availablefor the emitted positronsto undergo an annihilation event within the plant leaf resulting in limited sensitivity for PET imaging.To address this problem DOE’s Thomas Jefferson Laboratory has developed a compact beta-positive, beta-minus particle imager (PhytoBeta imager) for ¹¹CO₂ leaf imaging. The detector is equipped with a flexible arm to allow its placement on or under a leaf while maintaining its original orientation. The detector has been used to generate dynamic images of carbon translocation in a leaf of the spicebush (Lindera benzoin) under two transient light conditions. The PhytoBeta detector system and methodology opens new possibilities for short-lived radioisotope use in plant biology research,especially for problems relatedto carbon utilization, transport, and sequestration.

Membrane transport proteins play a key role in controlling the movement of a wide variety of carbon sources into microbial cells, including complex sugars and plant structural polymers derived from lignin. The transporter profile also influences the composition and structure of microbial communities in soils. However, the functioning of these proteins has not been adequately characterized. Researchers at Argonne National Laboratory have studied a specific type of transporter called the ATP-binding cassette (ABC) proteins. Using a combination of functional characterization (ligand-binding thermal screens), analytical tools for structural analysis (x-ray crystallography), and a computational framework, the functions of ABC transporters have been identified and better defined. The binding strength of various ABC transporters to aromatic products of lignin degradation was determined, and a set of ABC microbial transporters not previously identified with aromatic product transport was found. High-resolution crystal structures were produced for seven of the strongly bound molecular complexes, providing insights into the molecular basis for the observed strong binding. They revealed essential details about the modes of molecular interactions (e.g., hydrogen bonds) and the physical configuration of the active binding site. Knowledge derived from these experiments creates a foundation for developing a sequence-based computational method to predict what molecules will bind similar, but uncharacterized transporters in other microbes.

A new modeling study reveals that a large pulse of meltwater flowing north from North America into the Arctic Ocean is likely responsible for the major climate shift that occurred 12,900 years ago, and suggests the need for enhanced scrutiny of current melting of Arctic land and sea ice. The last major cold episode on Earth, the Younger Dryas, was 12,900 years ago and is considered to have been triggered by a large meltwater flood from lakes along the edge of the Laurentide Ice Sheet that covered much of North America. This influx of freshwater into the Arctic Ocean is thought to have weakened the ocean “conveyer belt” of currents known as the Atlantic Meridional Overturning Circulation. The weakened conveyor belt in turn may have diminished the flow of warm water to high latitudes, and led to the cold Younger Dryas period. Climate scientists have debated whether this flood of ice-sheet meltwater first flowed northwest into the Arctic or directly into the western North Atlantic via the St. Lawrence River. To see which flood route best explained the abrupt drop in temperature at the onset of the Younger Dryas, a sophisticated, high-resolution, ocean sea-ice model was developed and used to study the impact of meltwater from the two outlets on the Atlantic Meridional Overturning Circulation. Simulation results showed that meltwater from the St. Lawrence Valley would have weakened this circulation by approximately 15% whereas freshwater from the Mackenzie Valley would have weakened this conveyer belt by >30%, suggesting that the Mackenzie Valley was the likely route for meltwater that triggered the Younger Dryas. This work highlights the Arctic as a primary driver for abrupt climate change, and is especially relevant considering the rapid changes in sea-ice and Greenland ice sheet melting in this region over the last 10 years.

To use a microbe as a factory to make a desired product, a bacterial strain that already produces the compound is treated to generate many mutants, some of which may produce more of the product. From all these new variants, the challenge is to identify those microbes that make the largest amounts of the desired compound. This is particularly difficult when the target compound (e.g., a biofuel) does not confer any selective advantage to the microbe. To solve this problem, researchers at the U.S. Department of Energy’s (DOE) Lawrence Berkeley National Laboratory and DOE Joint BioEnergy Institute designed a “biosensor”—a genetic regulator that “senses” the presence of the desired product (e.g., butanol). The expression of a gene that confers an advantage to the microbe, such as resistance to the antibiotic tetracycline, is then induced by the presence of the biosensor. Butanol biosensor-containing Escherichia coli cells, for example, grow in the presence of the antibiotic only if the medium also contains butanol. Finally, plasmids capable of synthesizing various amounts of butanol were introduced into E. coli containing the butanol biosensor and growing in tetracycline-containing medium. High butanol-producing cells could readily be identified by their faster growth rates. This approach will facilitate the selection of microbial strains that produce large quantities of any small molecule, an important step toward the development of renewable biofuels.

Sustainable and cost-effective production of biofuels from plant biomass is hindered by the cost of pretreatment and low sugar yields after enzymatic hydrolysis of plant cell wall polysaccharides. Many studies have looked at enzymatic action on individual biomass components, but in nature, the plant cell wall is a complex, networked structure that interacts concertedly with pretreatment enzymes. To fully understand the mechanisms of enzymatic plant cell wall deconstruction for optimal production of bioenergy from biomass, it is imperative to understand the whole system. Scientists at the U. S. Department of Energy’s (DOE) BioEnergy Science Center (BESC) and DOE National Renewable Energy Laboratory (NREL) have addressed this problem by using a combination of advanced microscopic imaging methods in a correlative, real-time manner to examine both fungal and bacterial enzyme systems. With this new technology, they are able to localize the enzymatic sites of action without compromising the cell wall’s structural integrity. The results suggest that an optimal strategy for enhancing fermentable sugar yield from enzymatic deconstruction is to modify lignins to be more amenable to removal through pretreatment while maintaining polysaccharide integrity, improving accessibility to enzyme action.

In many harsh desert environments, microbial “biocrust” communities dominated by photosynthetic bacterial species (cyanobacteria) cover up to 70% of the land surface and play important roles in nutrient cycling, water retention, and stabilizing soil against erosion. These communities are highly adapted to the specific environmental conditions of arid ecosystems, and it is unclear what impacts climate change processes may have on them. Operating in collaboration with DOE’s long-term Free-Air CO₂ Enrichment (FACE) program, researchers at Los Alamos National Laboratory have published new findings on the effects of 10 years of controlled elevated CO₂ exposure on cyanobacterial biocrusts using environmental metagenomics. Natural biocrusts exposed to elevated CO₂ (550 ppmv) were shown to have significantly reduced abundance of cyanobacteria relative to plots exposed to ambient CO₂ concentrations (360 ppmv). These findings were correlated with an observed loss of biocrust coverage in the elevated CO₂ plots, although curiously, total soil biomass measurements did not change significantly. Loss of cyanobacterial abundance appears to be at partially related to increased damage from oxidative stress, with genes involved in resistance to this kind of stress appearing more frequently in the elevated CO₂ samples. Although more study is needed, these results present preliminary evidence suggesting that increasing atmospheric CO₂ concentrations have a deleterious impact on desert biocrusts and may result in decreased performance by these communities.

A study led by a U.S. Department of Energy (DOE)-funded scientist examines the impact of the El Niño-Southern Oscillation (ENSO) on temperature extremes for both observations and coupled climate model simulations. The recently developed observed gridded dataset of climate extremes indices (HadEX2) shows marked contrasts in seasonal composites of the monthly maximum value of daily maximum temperatures during the cold and warm phases of ENSO. Extreme maximum temperatures are significantly cooler over Australia, southern Asia, Canada, and South Africa during strong La Niña events compared to El Niño events and significantly warmer over the contiguous United States and southern South America. Two versions of the DOE-National Science Foundation Community Climate System Model (CCSM3 and CCSM4) are contrasted for their ability to capture these relationships given their very different simulations of ENSO. The CCSM3 ENSO simulation has a strong biennial frequency that is more narrowly confined along the equator than observations, while the CCSM4 ENSO simulation is more realistic in both frequency and pattern. While both models capture some aspects of the observed regional changes across the globe, the fidelity of the ENSO simulation appears to be crucial for simulating the magnitude and sign of the extreme maximum temperature relationships. Over the United States in particular, the composite pattern of maximum temperature extremes with ENSO is weak and opposite in sign for CCSM4 compared to that observed for CCSM3. CCSM4 is much improved, capturing the observed increase in U.S. maximum temperature extremes during La Niña with realistic amplitude, pattern, and statistical significance. In a future emissions scenario of CCSM4, the contrast between maximum temperature extremes during El Niño and La Niña events strengthens over Australia whereas it weakens slightly over the United States. Further understanding of the mechanisms leading to these projected changes will enable better predictions of regional changes due to ENSO.

Often, computational tools used to reduce oil use and greenhouse gas emissions are not coordinated and can impact the other tool’s efficiency. New U.S. Department of Energy (DOE) research from the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change has demonstrated the importance of jointly considering the effects of instruments used to reduce oil consumption and greenhouse gas emissions. Karplus and colleagues studied a sector-specific tool—fuel economy standards—and compared the results to those under an economy-wide tool, a cap-and-trade system. They then combined the two instruments to assess those effects as well. Using the DOE-supported Emissions Prediction and Policy Analysis Model (EPPA), a component of a broader integrated assessment model, the study finds that fuel economy standards are projected to be six to 14 times more expensive than a fuel tax and the results take longer to be seen. The model predicts that increasing fuel efficiency reduces the per-mile cost of driving, incentivizing a modest increase in travel that offsets the total reduction. Fuel efficiency is also only applied to the newest vintage of vehicles, taking many years to affect efficiency of the fleet. The tax provides a direct incentive for households to reduce gasoline use, both by investing in vehicle fuel efficiency and reducing total mileage.

Microbes play a key role in determining the chemical form of metal and radioactive contaminants in the environment. They shuttle electrons back and forth with metal ions, often over long distances. Researchers at the University of Minnesota have found new evidence for how this happens by examining how the thickness of a biofilm produced by Geobacter sulfurreducens affects electron transfer. They used spectroscopic methods involving ultraviolet and visible light with a potentiometric system that exposes the biofilm to a controlled voltage. The investigators discovered that a gradient of electrons developed if the biofilm grew beyond a few cell thicknesses. This gradient was identified when an increased potential, i.e., an increased pull on the electrons produced by a more positive electrode, could not increase the rate electrons traveled out of the thicker biofilm. Unlike thin biofilms where only a small percentage of cytochromes retained electrons, the thicker biofilm showed a substantial number of cytochromes still retained electrons, even when subjected to increased voltage. These results will be helpful in developing new interaction models of metallic contaminants with microbial communities in the environment, particularly in light of the fact that previous studies have led to significantly different descriptions of how the electron transfer process works.

U.S. Department of Energy (DOE) scientists used a seven-year dataset from the Atmospheric Radiation Measurement (ARM) scientific user facility’s Southern Great Plains site to evaluate the six surface flux parameterizations used in the Weather Research and Forecasting (WRF) model and three U. S. global climate models (GCMs). Surface momentum, sensible heat flux, and latent heat flux are critical processes that need to be accurately represented in large-scale weather and climate models. However, direct observational evaluation of the parameterization schemes for these fluxes is rare. The long-term observations of surface fluxes collected at the ARM Southern Great Plains site were used to evaluate the model parameterizations under a variety of stability conditions, diurnal cycles, and seasons. Statistical analyses show that the momentum flux parameterization agrees best with the observations, followed by latent heat flux, sensible heat flux, and the evaporation ratio/Bowen ratio. The overall performance of these parameterizations depends on atmospheric stability and is best under neutral stratification conditions, deteriorating under both more stable and more unstable conditions. The results also demonstrate the need for improving land-surface models and for the measurement of surface properties that allow their full evaluation.

An international group of researchers, including U.S. Department of Energy (DOE) scientists at Pacific Northwest National Laboratory, constructed a systematic examination of regional-scale climate models and their projections for North America. Using a multimodel ensemble approach, they compared the results of physical climate process models on a regional scale to precipitation and temperature observational data. The controlled baseline data showed that the ensemble results mostly outperformed any single model. The researchers organized the North American Regional Climate Change Assessment Program (NARCCAP) to evaluate temperature and precipitation results from six regional climate models over 1980–2004. For the first time in model assessments over North America, the international team adopted metrics to evaluate specific features of different models. Establishing common protocols in a controlled set of experiments, they came up with a baseline to compare each model’s results. Their comparison showed that while no single model stood out, working in ensemble the models often returned the best results compared to observational data. The NARCCAP effort provided a unique opportunity to systematically compare and evaluate North American regional model data. The work was featured in the cover story of the October issue of the Bulletin of the American Meteorological Society.

Indigenous microbial communities can be used to immobilize radioactive or toxic contaminants in subsurface sediments, thus reducing their spread and associated risk. This strategy relies on encouraging the growth of these communities by providing them with nutrients. The microbes reduce the normally present iron-Fe(III) to iron-Fe(II), which in turn converts many metallic contaminants, including uranium, chromium, and technetium, from soluble to insoluble forms. Being able to visualize the flow of water through the sediment as it delivers both the nutrients and the contaminants to the microbes, as well as the three-dimensional density of Fe(II), is critical for understanding the progressive biological processes that produce Fe(II) and the evolution of flow patterns through the sediment. Scientists at Lawrence Berkeley National Laboratory have validated the utility of two radiotracers, ^99mTc-pertechnetate (which measures Fe(II) density) and ^99mTc-DTPA (which is a flow tracer) in bioreduced sediment. This work, recently published in Environmental Science and Technology, shows that these technetium radiotracers can be used to examine and guide the development of new bioremediation processes in environmental systems.

Poplar is a promising bioenergy feedstock due to its rapid growth and large biomass, and because sugars extracted from the lignocellulosic biomass (wood) of these native trees can be fermented to form renewable biofuels. These sugars are embedded within lignin, a complex, rigid structure that is critical to the overall health of the plant but that also impedes extraction of the sugars. New U.S. Department of Energy research is providing insight into how the lignocellulosic material forms in poplar. The process involves the expression of a cascade of genes whose regulation is poorly understood. The researchers at North Carolina State University report their discovery of a single protein (“controller” protein) that regulates this cascade on multiple levels to ensure normal growth, doing so in a way never before seen in plants. The controller protein was found outside the cell nucleus. In the presence of one of four other related proteins, it is carried into the nucleus where the two proteins bind. The newly formed molecule then suppresses expression of the regulatory gene cascade. This discovery helps define how wood formation occurs at the molecular level, furthering our understanding of a process critical to plant growth. The results will help guide research to optimize bioenergy production from biomass.

In comparison to multicellular plants and animals, bacteria are relatively simple, typically existing as single cells. However, some bacteria cooperate to form surprisingly sophisticated structures. The photosynthetic microbe Nostoc punctiforme forms long filaments of connected cells. At regular spacing along these filaments, individual cells differentiate to form heterocysts, non-photosynthetic cells that convert nitrogen gas into biologically useful nitrogen compounds. This patterning allows these microbes to separately perform both photosynthesis (which produces O₂ as byproduct) and “fix” nitrogen using enzymes that are poisoned by oxygen, cooperatively exchanging the resulting nutrients between the cell types. In a new study, U.S. Department of Energy (DOE) researchers at the University of California, Davis, describe genetic mechanisms responsible for the establishment and maintenance of this distinctive pattern in growing filaments. When the expression of a series of regulatory genes (the “pat system”) was experimentally manipulated, filaments formed with abnormal distributions of heterocysts. By analyzing these patterns and tracking the distribution of related proteins in dividing cells, the investigators were able to develop a new model describing the regulatory interactions resulting in the pattern that allows optimal photosynthesis and nitrogen fixation in the filaments. The results of this study provide valuable new insights into the mechanisms used by microbes to tune their functional attributes through the use of structural patterns and could lead to the development of new tools for optimizing processes in biological systems engineered for bioenergy applications.

The long-term measurement records from the Atmospheric Radiation Measurement (ARM) site on the Southern Great Plains (SGP) show evidence of a surface irradiance bias in the global Numerical Weather Prediction model from the European Centre for Medium-Range Weather Forecasts (ECMWF). This has been a long-standing problem in the model, and previous studies have suggested that low clouds may contribute. To guide improvements to the model’s cloud and radiation parameterizations, the origin of the bias was explored for different cloud regimes to highlight the particular cloud processes that are contributing to the error. Comparisons between observed and modeled cloud fraction profiles over six years at the SGP site identified overcast low cloud conditions during the spring and fall seasons as a major contributor to the model bias. These findings will provide guidance for a targeted improvement of cloud parameterizations.

Hydrogen atoms are notoriously difficult to locate in proteins, yet they are key atoms in many of the chemical reactions of life and comprise one-half of a protein’s atoms. X-ray crystallography has been used to determine the atomic structure of many proteins and macromolecular complexes, but only a small fraction of the hydrogen atoms in these molecules can be located using this technique. In contrast, neutrons are scattered by hydrogen atoms, enabling determination of the position of these atoms in a protein molecule, though usually only to a medium resolution of about 2Å. Now, scientists at the Los Alamos Neutron Science Center have used the Protein Crystallography Station to determine the structure of a protein with the positions of its hydrogen atoms defined to an ultrahigh resolution of 1.1Å, the highest resolution ever for a neutron structure of a protein. They were able not only to locate nearly 95% of the hydrogen atoms in the protein at this resolution, but could determine the location of the hydrogen bonds that help determine the three-dimensional structure of the folded protein, and in some cases see how individual hydrogen atoms vibrate about their position in the protein. This new capability will improve understanding of the activity of many proteins, as well as guide computational modeling of systems such as protein-substrate and protein-drug complexes. The research was a collaboration of scientists at the University of Toledo, Los Alamos National Laboratory, and Oak Ridge National Laboratory.

Five papers have been published with four more in review for a special issue on the U.S. Department of Energy (DOE) Carbonaceous Aerosol and Radiative Effects Study (CARES) experiment. The CARES experiment, conducted in June 2010 in Central Valley, California, was a comprehensive effort designed to improve the understanding of the possible interactions between urban and natural (biogenic) emissions in the production and transformation of atmospheric aerosols and the resulting impact on climate change. The field study’s primary objective was to investigate the evolution of secondary organic and black carbon aerosols and their climate-related properties in the Sacramento urban plume as it was routinely transported into the forested Sierra Nevada foothills area. Urban aerosols and trace gases experienced significant physical and chemical transformations as they mixed with the reactive biogenic hydrocarbons emitted from the forest. Two heavily instrumented ground sites-one within the Sacramento urban area and another about 40 km to the northeast in the foothills area-were set up to characterize the evolution of meteorological variables, trace gases, aerosol precursors, aerosol size, composition, and climate-related properties in freshly polluted and “aged” urban air. On selected days, the DOE G-1 aircraft was deployed to make similar measurements upwind and across the evolving Sacramento plume in the morning and again in the afternoon. DOE also supported the NASA B-200 aircraft, which carried remote-sensing instruments, to characterize the vertical and horizontal distribution of aerosols and aerosol optical properties within and around the plume. Preliminary findings from the campaign include expanded insight into the interactions of biogenic and anthropogenic secondary organic aerosols, as well as unexpected behavior of optical and volatility properties of organic aerosols in this region.

Cellular biochemical networks govern biological function and are strongly influenced by the exchange of molecules between the cell and its environment. Modeling this exchange process and its impact on cellular networks for whole microbial cells will be a key step in developing biology-based applications in bioenergy and other Department of Energy (DOE) mission areas. However, it has been a problem to represent nutrient exchange with the environment for genome-scale kinetic models, in a manner consistent with the existence of a steady state. New research has developed a mathematical model that establishes sufficient conditions for a non-equilibrium steady-state for cellular biochemical networks. The research proves the theorem that reactions conserving mass and kinetic rate laws are sufficient conditions for the existence of a non-equilibrium steady state. The new study demonstrates how to mathematically model the exchange of molecules between any cell and its environment. The results of this DOE Scientific Discovery through Advanced Computing (SciDAC) research by Fleming and Thiele of the University of Iceland are foundational for future efforts to computationally model non-equilibrium steady states as part of whole cell microbial models.

Black carbon (BC) in the atmosphere has a strong effect on global and regional climate. Some estimates suggest that the positive (warming) radiative forcing by BC is second only to CO₂, making it an important near-term climate mitigation target. In a recent study, direct measurements of BC absorption enhancements and average mixing state for BC in the atmosphere around California are reported from two field campaigns: the 2010 CalNex study and the U.S. Department of Energy’s Carbonaceous Aerosols and Radiative Effects Study (CARES). The CalNex measurements were made onboard the R/V Atlantis, whereas the CARES measurements were made at a ground site in the Sacramento urban area. Observations indicate that the BC absorption enhancements for ambient particles around large urban centers do not vary much with photochemical aging, are significantly less than predicted from traditional theory, and are in contrast to laboratory experiments. These findings suggest that the warming by BC may be overestimated in climate models. Further, they indicate a role for absorption at short visible wavelengths by non-BC aerosol components [brown carbon (BrC) in urban environments], which are not well quantified in current measurements or models.

As the threat of climate change grows, the importance of understanding possible regional impacts-especially to temperature and precipitation-also grows. Researchers from the Massachusetts Institutes of Technology (MIT), Pennsylvania State University, and Tufts University have widened the scope and flexibility of analysis by quantifying the likelihood of particular regional outcomes, adding in socio-economic data, different emission scenarios, and various levels of risk and uncertainty. In a recent study, the researchers developed hybrid frequency distributions by combining climate-model projections and analysis from the Intergovernmental Panel on Climate Change (IPCC) with the MIT Integrated Global System Modeling (IGSM) framework. The study finds that while some regions are affected by emission reduction measures more than others, when comparing business-as-usual with a greenhouse gas stabilization scenario, lowering emissions does reduce the odds of regional warming. In fact, the most extreme warming outcome from the business-as-usual case is eliminated entirely. South and West Africa, the Himalayan region, and the greater Hudson Bay basin are expected to see some of the largest relative warming. At the same time, the odds of regional precipitation changes are seen as both increases and decreases by the middle of this century. In the business-as-usual scenario, there is a greater chance that western Europe and southern Africa will overall be dryer, while the Amazon and northernmost Siberian regions will become wetter.

In biology, some enzymes are highly specialized and catalyze specific reactions with a few or only one substrate, while other enzymes are promiscuous and can catalyze reactions using a variety of substrates. This phenomenon also has been observed experimentally for microbes involved in bioenergy-related processes. What is not understood, however, is why, within an organism, some enzymes are highly specialized while others remain generalists. Recently, researchers addressed this question using whole genome metabolic reconstructions and analysis, including dynamical simulations of environmental changes to understand microbial responses. Their findings indicate that enzymes with very specialized function maintain a higher flux, or processing rate, and require more regulation of their activities. This higher flux and higher regulation allows these enzymes to be more responsive and adaptive to environmental surroundings and changes then their less specialized counterparts. This work also illustrates that understanding environmental cellular physiology is greatly enhanced when using a systems biology approach rather than approaches that are focused on single enzyme simulations. These new results offer a means of translating genomic information into functional capabilities, with particular relevance for microbes involved in biofuel production.

Improving our ability to forecast tropical cyclones and to mitigate their destructive potential requires knowledge of various environmental factors that influence a cyclone’s path and intensity. U.S. Department of Energy scientists at Pacific Northwest National Laboratory found that tropical cyclones intensify considerably when passing over ocean regions with a barrier layer. (A barrier layer in ocean environments, or mixed layer depth, is defined as the depth where the density increases from the surface value due to a prescribed temperature decrease of some value (e.g., 0.2°C) from the surface value while maintaining constant surface salinity value.) Using a combination of observations and model simulations, the team demonstrated that barrier layers, formed through high fresh water input reducing the salinity in the upper tropical oceans, significantly increase the intensity of tropical cyclones. When tropical cyclones pass over these regions, the increased stratification and stability within the layer reduce storm-induced vertical mixing and sea surface temperature cooling. Their findings underscore the importance of observing salinity structure in deep tropical barrier layer regions. As the hydrological cycle responds to global warming, any associated changes in the barrier layer distribution must be considered in projecting future tropical cyclone activity.

Understanding how lignin degradation compounds are transported into microbial cells for further processing into biofuels and for other biotechnology purposes is essential. Using the bacterium Rhodopseudomonas palustris as a model to study the transport of these compounds, researchers from Argonne and Brookhaven national laboratories have applied high-throughput genomic and biophysical approaches to determine the characteristics of the proteins that bind the lignin-degradation products. These binding proteins are part of a large complex, the ABC transporter, that moves chemical compounds through the cell membrane into the cell. The researchers found that the proteins bind aromatic compounds with high affinity and tested the physical configuration of these binding proteins with and without the aromatic degradation products present. The results suggested that the shape of the proteins does not change, but that local changes do occur in the tertiary structure where degradation compounds bind. This molecular reconfiguration could position the aromatic compounds to be more easily transported through the cell membrane. The combination of theoretical models validated by these studies and experimental approaches should be applicable to other organisms relevant to biofuels research.

Achieving efficient and cost-effective breakdown of cellulosic plant biomass remains a significant barrier to the development of economically competitive biofuels that do not compete with food supplies. The hot spring bacterium Caldicellulosiruptor has been shown to efficiently degrade biomass (e.g., switch grass and corn stover) at temperatures over 160° Fahrenheit, but further characterization and engineering of this organism for biofuel production has proven challenging due to a lack of tools for genetic manipulation. Researchers at the DOE BioEnergy Science Center (BESC) have now developed the first system allowing the stable introduction of foreign DNA elements into this microbe. This breakthrough is based on the identification of a Caldicellulosiruptor “immune system” that normally protects the bacterium from viral infection, destroying outside DNA before it can be integrated into the host genome. The BESC team was able develop a set of targeted nucleic acid modifications that protects DNA from the host immune system and allows the introduction of new genes and regulatory elements into the organism. Now that Caldicellulosiruptor is a step closer to the model status of an easily manipulated microbe like E. coli, the team can more effectively study the organism’s unique cellulose-degrading properties and engineer new metabolic pathways that would allow direct conversion of plant biomass into next-generation biofuels.

Algae are of major interest to researchers who are developing alternative energy sources. For example, lipids making up algal membranes can be transformed into biodiesel. One photosynthetic alga, Coccomyxa subellipsoidea C-169, was recently isolated in Antarctica and now is the first alga from a polar region to have its genome sequenced. Surprisingly, the alga thrives at temperatures close to 20°C, though it is tolerant of the cold temperatures in the Antarctic. C. subellipsoidea was sequenced by the DOE Joint Genome Institute, and its predicted protein families were compared with those from several other sequenced green algae. The researchers found that the polar alga had more enzymes involved in lipid metabolism, such as those that desaturate fatty acids. This greater versatility of lipid metabolism is thought to have contributed to its adaptation to cold. The research will provide insights on novel enzymes that may prove useful to researchers working to harness algae for biodiesel production.

Algae can produce a wide variety of biofuels, chemical building blocks, nutrients, and proteins using sunlight as an energy source and carbon dioxide or other simple carbon compounds. DOE scientists at Lawrence Berkeley Lab have developed a new method to deliver radioactive or fluorescently labeled small molecules or protein probes into algal cells to monitor cellular messengers such as mRNA, gene expression or to develop biosensors. A molecular probe’s ability to pass through the cell membrane is often restricted by its water and lipid solubility. The new method overcomes these restrictions, enabling transport of molecules across the cell wall and membrane barriers. The transporter technology is broadly applicable and can be used for the delivery of labeled probes into algal cells for the development of sensitive biological assays for dynamic imaging of gene expression. The technique is being further developed to transport genetic materials and for probing changes in the carbon metabolism of these cells. These advances will enable scientists to improve algae as a tool for a wide variety of applications.

Microbes in nature often live in communities containing many different species. Identifying the mix of species is necessary for many studies of microbes for energy and environmental missions. Genome sequencing of an entire community (determining the metagenome) is often the best means of discovering which species are in a given community. However, it is a major challenge to pull out and assemble the whole genomes of the individual microbes comprising the community. Although many of the species are difficult to culture in a laboratory, they often contain a wealth of novel capabilities that must be characterized to enable understanding of the processes that take place in a community. Researchers at the DOE Joint Genome Institute have shown, through simulations and analysis of reference microbial communities, that extracting and assembling single genotypes requires 20X sequencing coverage. Their results also suggest that a higher coverage of sequencing will not enhance the assembly of individual organism. This result will help researchers plan metagenomic sequencing experiments for a wide range of DOE-relevant microbial communities.

Uranium in the environment exists in soluble and insoluble forms. Understanding transformations among these forms is a key aspect to developing models of uranium transport in subsurface environments. Uranyl ion [oxidized, uranium (VII)] is the main soluble form, while the mineral uraninite [reduced, uranium(IV)] is the most prominent insoluble form. Recent research has suggested that uraninite may not be the only reduced form of uranium, with non-crystalline species also present in environmental samples. However, until now it has been difficult to measure the amounts of these species. New DOE research has led to a reliable method for measuring the amounts of crystalline and non-crystalline uranium(IV) in environmental samples. The non-crystalline forms were selectively extracted from environmental samples under alkaline conditions that did not extract the crystalline uraninite, followed by separate measurement of the extracted and unextracted uranium by inductively coupled plasma optical emission spectrometry. The new method will be particularly useful in studying chemical forms of uranium at field sites where interaction of the uranium with minerals and bacteria complicates prediction of uranium movement in the environment.

New estimates of ocean surface salinity change over the past 50 years mark a clear symptom of climate change. Salinity measurements are valuable because they respond to changes in the water cycle (as manifest through precipitation and surface evaporation) over the poorly sampled global oceans that cover 71 percent of the Earth’s surface. These and other observations were compared with results from the Coupled Model Intercomparison Project (CMIP3; climate model output from simulations of past, present, and future climate), and the relationship between salinity changes and global water cycle changes was examined. The observationally based estimate of water cycle intensification (4 percent intensification from 1950 to 2000) is twice that predicted by most CMIP3 models, although the responses of individual models varied. Changes in the pattern of surface salinity provide independent evidence that wet regions are becoming wetter and dry regions drier, an expected result for a warming Earth. The CMIP3 projections of future climate change suggest that this pattern of change will intensify.

A major hurdle to the development of economically competitive biofuels remains the difficulty of separating long sugar chains from plant biomass (cellulose and hemicellulose) from the tough network of lignin that gives strength and resilience. Pretreatment of plant material by ionic liquids (ILs), a class of salts that are molten at room temperature, is highly effective in disrupting biomass structure and liberating cellulose chains for subsequent conversion to biofuel compounds by fermentative microbes. However, residual IL molecules are highly toxic to biofuel-producing microbes and must be fully removed from the cellulose fraction prior to conversion, an expensive and time-consuming process. To understand this IL toxicity and enable development of resistant strains of microbes, researchers at the Joint Bioenergy Institute (JBEI) examined shifts in gene expression of a novel biomass-degrading bacterium when exposed to an IL. Enterobacter lignolyticus was surprisingly resistant to IL exposure, altering its cell membrane composition, activating a series of pumps to remove IL from the cell interior, and balancing osmotic pressure across the cell membrane. Many of the response mechanisms were specific to IL exposure and were not triggered by exposure to standard salts. These findings provide new insights into the mechanisms used by microbes to tolerate exposure to ionic liquids and may lead to the improvement of IL tolerance in biofuel-producing microbes through targeted genetic engineering.

Switchgrass is considered to be a promising biofuel feedstock because of its ability to produce high biomass yields on marginal lands with minimal inputs. Several efforts to improve switchgrass as a dedicated bioenergy crop have been initiated, but breeding efforts are hampered by the outbred, tetraploid nature of this species and by limited knowledge of its chromosome architecture. Researchers at the USDA-Agricultural Research Service have used sophisticated molecular, cytological, and imaging techniques to tease apart and unambiguously identify the nine relatively small and otherwise undistinguishable base chromosomes of a dihaploid switchgrass line, producing the first karyotype (systematized arrangement of the total chromosome complement) of this bioenergy crop. The scientists were able to distinguish the two switchgrass ecotypes as well as the two basic subgenomes using this resource. This new capability will greatly facilitate identification of specific gene pools (e.g., regionally adapted cultivars) for switchgrass improvement toward the goal of making it a productive biomass crop. The research was supported in part by the joint USDA-DOE Plant Feedstocks Genomics for Bioenergy Program.

The sensitivity of the Atlantic Meridional Overturning Circulation (AMOC) to high-latitude freshwater input is a key uncertainty in the climate system. Considering the importance of the AMOC for global heat transport and the vulnerability of the Greenland Ice Sheet to global warming, assessing this sensitivity is critical for climate change projections. A unique set of computational experiments were conducted at Los Alamos National Laboratory to investigate the adjustment of the AMOC to enhanced melt water from the Greenland Ice Sheet under present-day conditions. This is the first time that the response of a global, high-resolution strongly-eddying ocean model was systematically compared to that of a typical coarser-grid ocean Intergovernmental Panel on Climate Change-class climate model. The overall decline of the AMOC on decadal time scales is quantitatively similar (<10%) in the two configurations. However, the time-varying transient response is significantly different; the AMOC decline and reduction during wintertime convection is markedly more gradual and persistent in the strongly-eddying configuration. The strongly-eddying ocean model also responds more strongly to a traditional, single dump of freshwater, in contrast to the low-resolution model, in which the spatial distribution of the freshwater flux anomaly does not matter for the AMOC response. This study reveals the conditions under which climate projections based on coarse models need to be revisited with higher-resolution investigations.

Climate change may alter lake area and cause other changes in high-latitude, terrestrial-surface properties, which, in turn, affect climate. BER scientists at Lawrence Berkeley National Laboratory (LBNL) and Lawrence Livermore National Laboratory used a lake model, recently developed at LBNL, in the Community Land Model (CLM4-LISSS) and coupled into the Community Earth System Model. This new version corrected a previous underestimation of lake area under present conditions and predicted spring cooling and fall warming of 1°C throughout large areas of Canada and the United States. The predicted diurnal temperature range decreased by up to 4°C in the summer, bringing predictions closer to observations. A projected loss of lakes in some permafrost regions under doubled CO₂ slightly enhanced net daytime warming in those regions. Correcting the under-estimation of mainly boreal lake area caused changes in distant Southern Ocean winds, which play an important role in the carbon cycle driving CO₂ upwelling from the deep ocean into the atmosphere. These changes were also analyzed in an idealized ocean-only “aqua-planet” model with prescribed sea-surface temperatures for which relatively small (2°C) decreases in high-latitude surface temperatures caused shifts in the Inter-Tropical Convergence Zone and Southern Ocean winds. The improved CLM lake model represents an important step forward in simulating potential climate feedbacks in high-latitude systems. In addition to atmospheric interactions, changes in inundation and thermokarst lakes can lead to potentially important changes in surface greenhouse gas emissions.

Greenhouse gas emissions from fossil fuel combustion contribute to climate warming, but to better estimate the amount of warming, uncertainties about the concentrations and the climate response need to be addressed. Some greenhouse gases are chemically reactive in the atmosphere, have additional uncertainties about their chemical production and destruction, and may be potential targets for short-term mitigation. New DOE research at the University of California at Irvine has taken the first systematic look at the uncertainties in attributing and projecting human-driven increases in greenhouse gases. The Representative Concentration Pathways future emissions scenarios used in the current Intergovernmental Panel on Climate Change assessment have large uncertainties for methane (CH₄, 25%) and nitrous oxide (N₂O, 50%), and thus do not accurately project future abundances. The new study combines observational and model data with uncertainties that constrain the pre-industrial (natural) and current (natural + anthropogenic) greenhouse gas budgets. These data include pre-industrial abundances, current abundances and trends, lifetimes past and projected, and atmospheric distributions. Statistical methods were applied to assess, for the first time, the current budgets, anthropogenic fractions, and projected abundances with uncertainties. The methane lifetime was found to be 9.1 ± 0.9 years. Anthropogenic emissions contribute 64% of total emissions. N₂O and CH₄ emissions and abundances, including uncertainties, were also projected and values sometimes deviated from projections given by integrated assessment models. The research helps identify aspects of these chemical systems requiring further research and improves both the projections and mitigation potentials for greenhouse gases.

Accurately simulating climate change requires inclusion of full interactions between tiny aerosol particles, clouds, and climate. This, in turn, requires that aerosol size and mixing conditions be resolved and that multiple species be carried in the climate model. A new aerosol scheme that includes these features is now available for the Community Earth System Model (CESM1). DOE researchers at Pacific Northwest National Laboratory led the development of a Modal Aerosol Module (MAM) for the Community Atmospheric Model version 5 (CAM5), the atmospheric component of CESM1. MAM can simulate the aerosol size distribution and mixing states between different aerosol components, and can treat numerous aerosol physical and chemical processes. Two versions of MAM were developed: a complete version with seven aerosol modes serving as the benchmark and used for detailed aerosol studies, and a simplified version with three aerosol modes used for decade-to-century climate simulations. MAM does a good job of simulating the temporal and spatial distributions of aerosol mass, number, and size distribution, and aerosol optical depth compared to observations, although some biases, such as underestimation of black carbon in the Arctic and underestimation of aerosol loading near source regions, will require further development. MAM is being used in CESM1 for the Intergovernmental Panel on Climate Change Fifth Assessment Report. MAM has also been adopted by other major global and regional models (e.g., NASA GEOS-5 and the Weather Research Forecast Model). The complexities of aerosol properties and processes and limitations of computer resources have made it a challenge for global climate models (GCMs) to realistically represent aerosols. MAM’s ability to minimally represent aerosols in GCMs while capturing the essentials of aerosol forcing is a substantial achievement.

The numerical modeling of glacier and ice sheet evolution is a subject of growing interest, because of the potential for models to inform estimates of global sea level change. DOE-funded researchers have recently developed and published a new ice sheet numerical model for calculating the three-dimensional velocity and pressure fields within a glacier or ice sheet, based on a high-fidelity mathematical model for the full equations of motion in the ice sheet, highly accurate numerical methods, and fast computational methods. The model is verified and validated with standard manufactured and benchmark solutions for ice flow. These same test cases are used to demonstrate the new model’s accuracy and efficiency. Ongoing work will focus on incorporating the new model as a dynamical core with the DOE-developed Model Prediction Across Scales (MPAS) Ice Sheet model that will improve our ability to estimate global sea level change based on changes in glaciers and ice sheets.

Atmospheric chemical and aerosol species and their precursors are released by energy combustion and from natural processes. The species affect the atmospheric energy budget and the climate system, so it is important that they are included in climate model simulations. However, because the species typically have short lifetimes (i.e., days to months), it is challenging to capture their spatial and temporal distribution. In this study, partially funded by DOE, the newest version of atmospheric chemistry in the global Community Atmosphere Model version 4 (CAM4), the atmospheric component of the Community Earth System Model (CESM), is described and evaluated. CAM4 offers a variety of configurations for the representation of tropospheric and stratospheric chemistry, wet removal, and online and offline meteorology. Major model biases include a negative bias in the high-latitude carbon monoxide distribution, a positive bias in upper-tropospheric/lower-stratospheric ozone, and a positive bias in summertime surface ozone over the United States and Europe. Aerosol optical depth tends to be underestimated over most regions, with large surface concentration biases for most species, but with good sulfate simulation over the United States. Overall, the model-data comparison indicates that the offline simulation driven by GEOS5 (Goddard Earth Observing System Model, Version 5) meteorological analyses provides the best simulation, possibly due in part to the increased vertical resolution. Ongoing efforts will focus on improving the simulation of chemistry in CAM4 to better understand and project the climate and pollution consequences of various energy pathways.

Regional models used for weather prediction have an ongoing need for testing and improvement, particularly for capturing cloud and radiation properties. In a recent study, DOE researchers from Brookhaven National Laboratory used data from the decade-long (1997 to 2008) DOE Atmospheric Radiation Measurement (ARM) surface-based continuous measurements over the Southern Great Plains (SGP) site to evaluate the ability of three major Numerical Weather Prediction models to simulate cloud radiative behaviors, cloud fraction, and cloud albedo. Like the observations, all the reanalyses show a strong annual cycle and relatively weak diurnal or interannual variations of the cloud properties. Further examination shows that the cloud properties are strongly related to near-surface relative humidity, and the model behaviors and biases relative to change in relative humidity, temperature, and other meteorological features were evaluated. A combined statistical analysis is presented and used to quantify the overall model performance in simulating the mean, standard deviation, and correlation with observations and a ranking of model performances in simulating different quantities. The study presents an evaluation tool applying ARM measurements to models that will be useful for ongoing and future model developments.

Getting an accurate projection of water cycle changes for the southwestern United States (SW) is becoming increasingly more urgent in light of regional drought trends and changes in the Colorado River’s flow. A research team, including a DOE scientist from Pacific Northwest National Laboratory, analyzed the future climate from four pairs of regional and global climate models (RCMs and GCMs). The region’s water cycle is dominated by winter storms that maintain a positive annual net precipitation (precipitation minus evapotranspiration). The research team found that the regional models simulate greater transport of moisture eastward over the mountains because the air flow over the topographically complex mountains is better resolved. Under global warming, this enables the RCMs to capture a response that allows more moisture to converge on the windward side of the mountains. The analysis shows that compared to GCMs, RCMs simulate enhanced transient moisture convergence in the SW, although both robustly simulate large-scale drying due to enhanced moisture divergence by the divergent mean flow in a warmer climate. Because the RCMs with their sharper topographic relief more accurately simulate enhanced moisture convergence, they indicate that the SW is less susceptible to experience drought compared to GCMs.

Natural microbial communities usually are made up of a large variety of species. Knowing the community’s composition is important for addressing DOE energy and environmental missions. Sequencing of the community’s combined genome (the ‘metagenome’) is now the best way to characterize these communities, but to make sense of the data, it is important to accurately account for all of the experimental and instrumental errors in the process. Up to now, the instrumental errors have been routinely estimated, but not the sample collection and preparation errors. As part of the DOE Systems Biology Knowledgebase project, researchers at Argonne National Laboratory have developed an open-source program called DRISEE (duplicate read inferred sequencing error estimation) to account for both types of errors. DRISEE identifies errors that could be due to sample collection, intermediary DNA processing techniques, or to the instruments themselves. Using DRISEE, the authors reproduce known error rates from a given set of standard data. They then apply this method to show that many factors can contribute to errors in sequencing including read length and sample preparation. Although this method so far only applies to 454 and Illumina sequencing, it will provide valuable assistance to scientists trying to assemble genomes from metagenomic data by helping them determine if the sequence data has a true error and should be disregarded or if it is a natural sequence variation and should be included.

Much of the world’s coal was generated 300-360 million years ago during the Carboniferous period. Wood (a major pool of organic carbon that is highly resistant to decay largely due to its lignin content) was deposited, transformed to peat, and eventually transformed to coal. But coal formation may also have declined from an unlikely source: fungi. These fungi had enzymes (ligninases) capable of degrading lignin, a category of enzyme important for the Department of Energy’s bioenergy mission, since lignin in plant biomass hinders biomass conversion to biofuels. An international team of scientists from Clark University and DOE’s Joint Genome Institute has proposed that a species of fungus, first appearing at about the end of the Carboniferous period, could more efficiently break down dead plant matter, possibly leading to the decline in coal formation. By comparing the genomic sequences of 31 fungi, including 12 sequenced for this study, the researchers showed that genes able to degrade lignin first appeared at the end of this period. Instead of becoming coal, the plant biomass decayed and the carbon was released into the atmosphere as carbon dioxide. This research provides insights into the origin of ligninases that can be used to develop processes for converting plant and tree biomass into bioenergy products.

Aerosols primarily scatter and absorb radiation back to space, shielding Earth’s surface from the sun’s energy and counteracting some of the warming induced by greenhouse gases. Global models have had some success in simulating the amount of aerosols in the lower atmosphere, but predictions have differed greatly among models and have undergone little constraint due to lack of measurements. Understanding the vertical distribution is important for predicting aerosol-cloud interactions and for accurate accounting of aerosol absorption enhancement over clouds. A team of international aerosol modelers, including Department of Energy scientists at Pacific Northwest National Laboratory, simulated global vertical distributions of aerosols and evaluated the results using measurements of aerosol extinction (scattering + absorption of sunlight) by a lidar instrument on a NASA satellite. All models simulated the observed decrease of the extinction with altitude; however, most models overestimated the extinction at altitudes between 6 and 10 km, particularly over the oceans and industrial regions. By predicting too much of the aerosol above clouds, the simulated aerosol is less easily scavenged and is more effective at making Earth appear darker from space and hence warming the Earth. This has implications for estimates of aerosol effects on climate and reveals a need for focused model development and testing.

Cellulose fibers provide the structural framework for plant cell walls and are critical for plant growth, stability, and normal function. These same properties of cellulose fibers are also the main obstacle for efficient conversion of biomass to biofuels. Molecular dynamic simulations can aid in understanding cellulose fiber crystallinity and its resilience to deconstruction; however, since the fibers are very large, realistic molecular simulations require extensive run times on leadership-class supercomputers. Recently Scientific Discovery through Advanced Computing (SciDAC) supported researchers at Oak Ridge National Laboratory, in collaboration with RIKEN National Lab in Japan, developed a coarse-grained simulation method termed REACH (Realistic Extension Algorithm via Covariance Hessian) that will enable more efficient simulation of large cellulose fibers. The REACH method reduces the complexity of the simulation (coarse graining) and directly relates molecular force parameters from the more complex all-atom simulation to the faster REACH simulation. Using this method, the researchers simulated the behavior of a cellulose fiber of 36 chains and 40 to 160 degrees of polymerization with a speed of up to 24 nanoseconds per day of computation. The REACH simulations are in agreement with previous findings that the hydrophobic face of the cellulose fiber is more easily deconstructed than the hydrophilic face. An extension of REACH is now being developed that will account for larger amplitude strand separation motions of the fibers thought to precede subsequent deconstruction.

Subsurface minerals help control the chemical form of contaminants such as uranium (U). The redox (reduction and oxidation) state of soils and sediments exists on a continuum from oxidized to reduced and can affect the mobility of uranium plumes. Under oxidized conditions, U is rather soluble as a uranyl ion in the U⁶⁺ valence state, whereas under reducing conditions U can become immobilized in the less-soluble U⁴⁺ valence state. Researchers at the University of Iowa and Argonne National Laboratory have found that a complex mixture of ferrous iron (Fe²⁺)-bearing minerals in a naturally reduced soil is capable of reducing and immobilizing uranium. Using Mössbauer spectroscopy at the University of Iowa and synchrotron x-ray absorption spectroscopy at the Advanced Photon Source at Argonne, the researchers found that uranium was reduced by Fe²⁺ in clay minerals and by a less-common, transient, and highly reactive Fe²⁺-mineral called green rust. The researchers also observed that the reduced U⁴⁺ atoms formed a product different from the uraninite mineral (UO₂) commonly observed in laboratory studies, providing evidence for the diversity in chemical speciation of reduced U in natural systems. This study provides detailed information necessary for understanding toxic and radioactive contaminant mobility which will contribute to the long-term stewardship of U.S. Department of Energy legacy sites.

Methanogenic archaea and sulfate-reducing bacteria (SRBs) both play important roles in the carbon cycle of soils, wetlands, and other environments with limited oxygen availability. SRBs are versatile consumers of a variety of organic compounds, while methanogens primarily convert hydrogen and CO2 into methane. Neither of these organisms is capable of independent growth on lactate, a small organic compound that is an important intermediate in food webs, but can consume it when working together in a partnership called syntrophy. Researchers at the University of Washington and Lawrence Berkeley National Laboratory have published a new study that helps explain how this partnership works. They carried out a high-resolution transcriptomic study of changes in gene expression of the methanogen Methaococcus maripaludis during syntrophic growth on lactate with the SRB Desulfovibrio vulgaris as a partner. The methanogen shows a substantial shift in genes associated with conversion of hydrogen to methane, switching over to a parallel set of enzymes that may be better adapted to low rates of hydrogen production and other conditions associated with syntrophy. These results advance our understanding of microbial production of a potent greenhouse gas and highlight the important role of subtle interactions between organisms that influence environmental processes.

One challenge to the commercialization of microbial production of hydrogen using sunlight is that the oxygen produced by photosynthesis decreases hydrogen production. Various biological mechanisms have evolved to separate the two reactions and scientists have been looking for engineering solutions, but the challenge is not yet solved. Scientists at the Pacific National Northwest Laboratory now have shown for the first time that a single-celled cyanobacterium, Cyanothece, is able to produce hydrogen and oxygen simultaneously without interruption for at least 100 hours. The bacteria produce hydrogen at relatively high rates without high cell density or inducing circadian rhythms, as required in studies by other researchers. Furthermore, there is little photo-damage and decay of the photosynthesis apparatus, perhaps enabled by the removal of excess electrons by the hydrogen production. These results and the improved understanding of the underlying cyanobacterial physiology will help advance the biotechnology of microbial hydrogen production.

The fast-growing black cottonwood (Populus trichocarpa), a fast-growing tree that inhabits stream and river banks across a long north-south range of western North America, has been identified as a promising bioenergy crop. Many genetic and genomic resources for Populus have been developed and are being used to study the molecular basis of desirable traits such as biomass yield, cell wall characteristics, and environmental adaptation. To develop superior Populus cultivars for bioenergy feedstocks, it is necessary to understand the genetic and genomic structure of the Populus population to reliably detect phenotype-genotype associations, which informs suitable breeding approaches. Researchers at the DOE BioEnergy Research Center (BESC), together with the DOE Joint Genome Institute (DOE JGI), sequenced the genomes of 16 different black cottonwood varieties, broadly spanning north to south of the species’ native range, and determined the population structure and genetic variation on a geographic scale. They found that significant genetic differentiation existed and was strongly correlated with latitudinal location of the sampled trees, suggesting that this species may have survived the past glaciation in multiple locations along the northwest of North America. The study demonstrates that advanced population genetics approaches should be more feasible in Populus than previously thought, increasing the potential for genetic improvement of Populus as a biofuel feedstock.

The Revelle Medal is awarded to an individual “for outstanding contributions in atmospheric sciences, atmosphere-ocean coupling, atmosphere-land coupling, biogeochemical cycles, climate, or related aspects of the Earth system.” Wofsy is being recognized for a distinguished career in the factors that regulate atmospheric composition, including experimental field studies of the carbon cycle using long-term eddy-covariance measurements of atmosphere-biosphere exchange in tropical, boreal, and midlatitude forests. Wofsy is currently supported by BER’s Terrestrial Ecosystem Science program and is working on land-biosphere interactions (biogenic volatile organic compound and trace gas emissions) in Brazil. Wofsy was also an organizer for the Next-Generation Ecosystem Experiment: Tropics workshop held in Bethesda, Md., in June 2012.

The biotechnology and biofuels industries are particularly interested in cellulases, enzymes that break down cellulose, the most abundant organic compound on Earth and the component that makes up 33 percent of all plant matter. Cellulases from a group of aerobic bacteria called Actinobacteria are of special interest as sources of enzymes useful for biofuel production from lignocellulosic biomass. They have distinct features and cellular organization when contrasted to those in anaerobic bacteria (such as the Clostridia). The DOE Joint Genome Institute (JGI) has sequenced the genomes of 11 diverse strains of these bacteria. Comparative analysis using the JGI’s Integrated Microbial Genomes system followed by experimental verification identified eight cellulolytic Actinobacterial species that were not previously known to degrade cellulose. Of seven organisms tested, six showed activity in assays for cellulases. One organism, Catenulispora acidiphilia, previously unknown to break down cellulose, has 15 predicted cellulases and may be used in future biofuel production. This work, conducted under the umbrella of the JGI’s Genomic Encyclopedia of Bacteria and Archaea (GEBA) project, broadens the repertoire of useful enzymes beyond those previously recognized.

Methanogenic microbes known as Archaea carry out many chemical transformations essential for anaerobic carbon recycling in virtually all environments. However, little is known about how raw materials for, and products of, these transformations are transported between an Archaeal cell and its environment. Research now has determined the structure of a key surface-layer protein of a methane-generating microbe, Methanosarcina acetivorans, enabling new insights into how this microbe communicates with its surroundings. The new information enables construction of a diagram of the cell envelope’s surface layer, showing the pores through which chemical species move back and forth. Three types of pores with distinctly different sizes and shapes were identified. All of them are small and highly negatively charged, which means that they are highly selective about which substances can pass through the layer into the cell. DNA sequencing of several related species of Methanosarcinales suggests that the structures of their surface layer proteins are similar to the one in M. acetivorans. These results provide valuable information for understanding the role of these microbes in producing methane in natural environments, a potentially major factor in global carbon cycling. The research was led by Robert Gunsalus of the UCLA-DOE Institute of Genomics and Proteomics.

Microbes carry out a wide range of chemical transformations. Understanding the mechanisms of these processes can lead to new biological insights and practical applications. For example, removal of polycyclic aromatic hydrocarbons (PAHs) from contaminated soils is facilitated by microbial degradation. The PAH phenanthrene can be broken down by Arthrobacter phenanthrenivorans, a bacterium isolated from a creosote-polluted site in Greece and that uses phenanthrene as a carbon and energy source. A team of researchers, including a collaborator from the DOE Joint Genome Institute, has purified and analyzed two phenanthrene-breakdown enzymes from this microbe. Based on the similarity of the two genes’ sequences and their common expression in the presence of the PAH, the authors suggest that one of the genes is a duplication of the other even though they are located in very different parts of the genome. Similar results are found in other related bacteria. These types of comparative studies may aid in the design of strategies using microbes for DOE missions or other applications, such as wastewater treatment, biodegradation, and biocatalysis.

Lignin is a key building block in plant cell walls and one of the two most abundant biopolymers on Earth. It is also highly resistant to breakdown, complicating efforts to use plant biomass for producing biofuels. No animals and few fungi or bacteria are able to degrade lignin. However, the white rot fungus Ceriporiopisis subvermispora not only degrades lignin but leaves the cellulose in biomass intact. An international team of scientists has sequenced and annotated (assigned possible functions to genes) the genome of this fungus to learn more about its mechanisms of lignin degradation. Using experiments and a comparison with the sequence of its more studied relative Phanaerochaete chrysosporium, the scientists identified differences in the degradation genes between the two fungi and developed new hypotheses about the mechanisms that enable these fungi to target lignin but not cellulose. These results may assist in the development of improved pathways for the conversion of biomass to biofuels as well as provide improvements in deconstruction of wood for the pulp and paper industry. The study included researchers at the DOE’s Joint Genome Institute (DOE-JGI).

Direct comparison of climate-model simulated clouds with satellite observations has been difficult because there are not direct equivalents between the model representation of clouds and what satellites are able to see. To largely solve this issue, a diagnostic tool—the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP)—was developed by a group of scientists worldwide, including scientists at Lawrence Livermore National Laboratory (LLNL). By mimicking the satellite view of an atmospheric column with model-specified physical properties, COSP enables a meaningful comparison between modelled clouds and satellite observations overcoming the significant ambiguities in the direct comparison of model simulations with satellite retrievals. LLNL scientists, working with scientists at the National Center for Atmospheric Research (NCAR), have used COSP to assess the latest version of the NCAR/DOE Community Atmosphere Model (CAM5). Multiple independent satellite datasets and their corresponding instrument simulators in COSP were combined to systematically evaluate the model performance. Compared with the earlier atmospheric model version (CAM4), the new CAM5 model, with its more advanced physics, significantly reduces the long-standing errors in simulated clouds by increasing the total cloud fraction, decreasing optically thick clouds, and increasing mid-level clouds. The COSP diagnostics revolutionize the comparison technique, enabling consistent inter-model and observation-model comparisons. Ultimately, by better identifying model cloud biases, COSP will help to reduce uncertainty in climate predictions. This paper was included in the CCSM Earth System Model CESM1 special collection.

DOE scientists at Pacific Northwest National Laboratory found that dust kicked up from the desert floor acts like a heat pump in the atmosphere, fueling the annual climate system called the North American Monsoon (NAM). NAM occurs during June, July, and August over the U.S. Southwest and northern Mexico and is characterized by surface heat and episodes of heavy rainfall. The region receives over 70 percent of its annual precipitation during these three months. The researchers used sophisticated simulation techniques to investigate the effect on the atmosphere of dust emitted from U.S. Southwest deserts to fuel the intensity of the monsoon system. The study simulated 15 years with dust emissions and 15 years without dust emissions, using the regional model WRF-Chem for the time period from 1995-2009, and compared the results with surface mass and satellite and surface aerosol optical depth observations. The enhanced dust increases precipitation by up to 40 percent during the summer rainy season in Arizona, New Mexico, and Texas. The study, the first on the U.S. Southwest summer monsoon, found that the heat pump effect is consistent with how dust acts on West African and Asian monsoon regions. Understanding how dust contributes to atmospheric heating is important for predicting drought and rainfall patterns throughout the world.

Aerosols affect the energy balance by scattering and absorbing sunlight and through their influence on cloud droplet and ice particle number concentrations. Climate simulations must account for all important radiative forcing mechanisms, including those from human-caused aerosols. DOE scientists at Pacific Northwest National Laboratory (PNNL) developed a detailed aerosol microphysical scheme, including seven distinct aerosol modes, each with its own size distribution and chemical mixing properties. However, it is computationally too expensive to represent this complexity in multi-century climate simulations. To address this challenge, the team developed a simpler three-mode aerosol scheme and compared simulations using the minimal representation of the aerosol to a more complex benchmark, showing that the minimal representation is both accurate enough for climate change simulations and sufficiently inexpensive to enable multi-century simulations. For either scheme, direct aerosol scattering and absorption effects nearly cancel one another. However, the aerosol indirect effect on clouds has a substantial cooling effect from enhanced low-level clouds in spite of a 25% offset from enhanced high-altitude clouds. The simpler, more efficient representation is being used in the Community Earth System Model to simulate future climate change for the Intergovernmental Panel on Climate Change.

Structure and function are intimately linked but do not necessarily predict the other. For example, X-ray crystallography provides 3-D atomic structural information about biological macromolecules but does not define important details about metal ions. However, the oxidation state of metal ions at an enzyme’s active site has a critical effect on enzyme behavior. Thus, an enzyme’s catalytic function derives from the electronic structure of those atoms influencing or directly participating in the reaction, information not revealed by the scattering methods used in X-ray crystallography. A new technology has been developed that simultaneously carries out crystallography and UV-visible and Raman spectroscopy to determine the atomic structure of the entire protein, and electronic and vibrational structures of the metal ions or cofactors within. The combined instrumentation has been used to study the process of demethylation of an organic substrate molecule by an enzyme whose active site includes an iron-sulfur cluster. The authors used spectroscopy to follow the change in the oxidation state of the cluster during the crystallography data collection and to formulate a mechanism for the process. The results provide insight into an important class of phenomena that control cellular behavior. The technology was developed by scientists at the Protein Crystallography Research Resource at the National Synchrotron Light Source at Brookhaven National Laboratory. The new study was led by Allen M. Orville of Brookhaven and Pinghua Liu and Karen N. Allen of Boston University and is published in the Journal of the American Chemical Society.

Using measurements to evaluate the impacts of aerosols on cloud properties can help narrow climate model uncertainties by identifying where model problems occur and where model representations are robust for aerosol-cloud interactions. DOE scientists at Lawrence Livermore and Pacific Northwest National Laboratories have quantified the aerosol impacts on cloud droplet effective radius (aerosol first indirect effect, FIE) for non-precipitating, low-level, single-layer liquid phase clouds simulated in the Community Atmospheric Model version 5 (CAM5) at three Atmospheric Radiation Measurement (ARM) sites. The aerosol FIE is quantified in terms of a relative change in cloud droplet effective radius for a relative change in aerosol amount under conditions of fixed liquid water amount. The study shows that CAM5 simulates aerosol-cloud interactions reasonably well for this specific cloud type and the simulated FIE is consistent with the long-term ARM observations at the examined locations. The high sensitivity of aerosol FIE to cloud liquid water amount and aerosol variable and low sensitivity to location and time are also consistent with observational studies. If this study has general applicability for other cloud types and locations, it suggests that the possible overestimation of aerosol climate impacts found by other studies may be a problem from other aerosol indirect effects, such as cloud lifetime effects, rather than the FIE.

Cloud fraction is the dominant modulator of radiative fluxes. For this study, DOE scientists at Pacific Northwest National Laboratory and Lawrence Livermore National Laboratory evaluated cloud fraction simulated in the IPCC AR4 GCMs against long-term, ground-based measurements. They focused on the vertical structure, total amount of cloud, and its effect on cloud shortwave transmissivity. Comparisons were performed for three climate regimes represented by the Atmospheric Radiation Measurement (ARM) sites: Southern Great Plains (SGP); Manus, Papua New Guinea; and North Slope of Alaska (NSA). Both inter-model deviation and model bias against observation were investigated. The results show that the model observation and inter-model deviations have similar magnitudes for the total cloud fraction and the normalized cloud effect, and these deviations are larger than those in surface downward solar radiation and cloud transmissivity. Similar deviation patterns between inter-model and model measurement comparisons suggest that the climate models tend to generate larger biases against observations for variables with larger inter-model deviation. The ARM measurements enabled the team to evaluate the seasonal variation of cloud vertical structures in the GCMs.

The polar ice edge, or marginal ice zone (MIZ), is a key area since it is erodes first; however, it is not known how much of the erosion results from atmospheric heating or from oceanic advection of warm waters. This DOE-funded model study uses the DOE-supported POP ocean and CICE sea-ice models to investigate and compare these processes. The model passes an important test of sea-ice distribution changes: when the model is driven by the observed-reanalysis winds of the 1990s, it successfully simulates the observed dipole pattern of ice concentration changes characteristic of the changes associated with North Atlantic Oscillation (NAO) pressure and circulation changes. The model successfully simulates the first mode of sea ice concentration variability, which is characterized by a dipole pattern of ice concentration anomalies, coherent with the atmospheric NAO pressure pattern. The model ocean-ice system was forced with NCEP/NCAR atmospheric reanalysis and then run for the two NAO periods during the 1990s. The upper ocean mixed layer heat budgets were analyzed in the Barents, Nordic, and Irminger seas to determine the winter-to-winter changes in the ocean heat advection and mixed layer net fluxes, and these were then related to the ice changes. The researchers found that bottom ice melt dominates the top ice melt, signifying the role of the ice-ocean heat exchange for the ice thermodynamics. The ocean advection anomalies were also closely related to anomalous bottom ice melt rates. However, although the oceanic temperature advection is of the same order of magnitude as the net atmospheric heat fluxes, the latter are always larger. Entrainment of heat from the deeper ocean may also play a key role in the upper ocean heat balance, which may be strongly influenced by ocean heat advection. Future research will consider the role of the deeper ocean upwelling and continue to investigate the relative importance of atmospheric and oceanic processes in eroding polar sea-ice.

Sunlight is essential for plant development and growth, yet many details of the mechanisms by which plants respond to sunlight are poorly understood. A recent study published in Science provides new information about the molecular changes initiated by exposure to the UV-B portion of sunlight. The research used small-angle x-ray scattering (SAXS) experiments to characterize how the plant photoreceptor UVR8 changes shape when exposed to UV-B radiation. Two UVR8 molecules are complexed together as a dimer in plant cells and break apart on exposure to UV-B. The separate molecules then interact with a series of proteins in the cell to signal the presence of solar radiation. A specific mutation in UVR8 was found to “retune” the molecule’s response from UV-B to UV-C radiation. The results will be useful in understanding how to optimize biomass crop growth. The SAXS studies were carried out at the SIBYLS experimental station at the Advanced Light Source at the Berkeley Lab. The study was led by Elizabeth Getzoff of the Scripps Research Institute.

The Atlantic Meridional Overturning Circulation (AMOC) acts as a heat conveyer belt, bringing warm tropical water northward in the Atlantic Ocean and carrying cold dense water back southward. Previous model studies suggest that AMOC can trigger abrupt climate change when runoff from melting ice sheet water is added into the North Atlantic. DOE-funded scientists have investigated the role of the Bering Strait (the 50-mile-wide gateway between the Atlantic and Pacific oceans) in abrupt climate change using computationally intensive simulations. They find that as long as the Bering Strait remains open, abrupt climate changes driven by ocean circulation are unlikely. Such climate instabilities occurred frequently during the last glacial period, ranging from 80,000 to 11,000 years ago. Increased freshwater in the North Atlantic would weaken the AMOC, altering the transport of heat and salinity between the Atlantic and Pacific. The study suggests that as long as the Bering Strait remains open, AMOC will likely not exhibit this chain of events. The study reveals how a relatively small geographic feature could have potentially far-reaching impacts and may have influenced global climate patterns over the past 100,000 years, according to the authors.

As the global population grows to possibly 10 billion by 2100, there will be greater demands for food, energy, and land. At the same time, world leaders have set a goal of restraining temperature to within 2°C of the pre-industrial level. This will become harder as energy use and emissions increase with population growth. Emissions strategies might also consider land changes, because deforestation accounts for almost 20 percent of annual greenhouse gas emissions, more than the entire global transportation sector. A new report by Massachusetts Institute of Technology researchers at the Joint Program on the Science and Policy of Global Change analyzes the effects of land-use emission mitigation and biofuels production. This study uses the DOE-sponsored Integrated Global System Model (IGSM), a linked system that represents the agriculture, energy, and forestry sectors in an economy-wide model. The report finds that if an aggressive global tax is applied to energy emissions alone, it would not be possible to achieve the 2°C target. However, if the tax also encompasses land-use emissions, and biofuels are used, the target becomes more realistic. Nevertheless, there is a significant tradeoff because prices for food, crops, livestock, and forest products rise substantially due to mitigation costs borne by the sector and higher land prices. While wealthier regions will continue to spend less of their income on food over time, the poorest regions will spend more of their income on food. The results suggest that environmental, food, and energy challenges are likely to put significant pressure on land resources over the century, especially if efforts to reduce greenhouse gases include land changes.

Systems biology approaches to bioenergy and environmental research are enabled by reliable models of processes in living cells. Advances in genome sequencing and computational modeling have led to the development of over 100 genome-scale network reconstructions (constraint-based models). Rapid increases in this number are expected, so methods that use algorithms to compare functional characteristics between organisms will be increasingly important. Scientists at the University of Wisconsin have reported a novel approach that embeds two constraint-based models into an optimization model. This combination identifies those genes and reaction pathways that contribute most to differences in metabolic functionality. The authors identified several differences in metabolism in two cyanobacteria that have potential for biofuel production, Synechococcus and Cyanothece. For example, they demonstrated the necessity for a particular protein (plastocyanin) for photosynthesis in Cyanothece, but not in Synechococcus. The new approach also aids the curation of constraint-base models by identifying pathways that are coded by the organism, but that are missing from the model.

In biology, the location of molecules in a cell often dictates the function of the biological system. A new type of high-resolution mass spectrometer developed by users from the FOM Institute for Atomic and Molecular Physics (AMOLF) in The Netherlands, and scientists from the Environmental Molecular Sciences Laboratory (EMSL), a DOE scientific user facility located in Richland, Washington, now allows the biological research community to identify and map the location of biomolecules in a sample with higher mass accuracy and mass resolving power than ever before. Because biological molecules with very different functions can have almost identical masses, this holistic analysis will open new doors in biological research and offer scientists unique insights into biological systems and how they work. Called C₆₀ SIMS FTICR MS, the new tool couples C₆₀ (also called buckminsterfullerene, or buckyball) secondary ion mass spectrometry (SIMS), which has high spatial resolution chemical imaging capabilities and minimizes damage to biological samples during analysis, with high-magnetic field (9.4 or 12 Tesla) Fourier Transform Ion Cyclotron Resonance (FTICR) mass spectrometry, which has impressive mass spectral performance. Featured on a recent cover of Analytical Chemistry, the team demonstrated the potential of C₆₀ SIMS FTICR MS using mouse brain tissue. They achieved mass accuracy and mass resolving power 10 times higher than previously reported for SIMS. This achievement is an exciting development for the biological research community, and system optimizations are already underway, including efforts to achieve sub-micrometer resolution and build advanced data handling and analysis tools.

Strains of bacteria from the genus Yersinia are pathogenic and have a wide virulence range. For example, Y. pseudotuberculosis causes intestinal distress, while Y. pestis causes plague. To better understand and potentially design ways to mitigate the effects of Yersinia on human health, a research team from the University of Texas Medical Branch, J. Craig Venter Institute, Pacific Northwest National Laboratory, and Environmental Molecular Sciences Laboratory (EMSL), a DOE scientific user facility in Richland, Washington, took on the task of refining the genome maps of three Yersinia strains. The team used one of EMSL’s mass spectrometers to obtain proteomic data and combined these data with microarray data to annotate both the proteome and transcriptome of the three Yersinia strains. The data confirmed the validity of nearly 40% of the computationally predicted genes and resulted in the discovery of 28 novel proteins expressed under conditions relevant to infections. In addition, 68 previously identified protein coding sequences were shown to be invalid. This new multi-faceted approach layers several types of evidence and substantially improves the genome annotation process. Importantly, the team’s work established refined genome annotations that provide essential information needed for a better understanding of how the plague functions, may provide new targets for therapeutics, and should speed the characterization of other pathogenic bacteria.

Airborne particles impact human health, cause haze, and influence climate. New findings from researchers at the University of California, Irvine; Pacific Northwest National Laboratory (PNNL); Imre Consulting; and Portland State University may explain why the abundance of secondary organic aerosols (SOA), which make up more than half of airborne particle mass, has been significantly underestimated by currently accepted air quality and climate models. SOAs are derived from the oxidation of volatile organics, such as pinene, a substance excreted from pine trees. Using the SPLAT II mass spectrometer at PNNL’s Environmental Molecular Sciences Laboratory (EMSL), a unique instrument that allows users to study fundamental processes governing the chemistry and physics of particles at the nano- and microscale, the team showed that a-pinene reacts with ozone and nitrate to form organic nitrates and ozonolysis products, and that the latter nucleates and forms seed particles on which other products condense to form SOAs. The findings are contrary to expectations, including the view that SOAs evolve in the atmosphere as equilibrated liquid droplets and evaporate with time. Instead, the data show that SOA particles are quasi-solids that stick around for a long time. If found to be a general phenomena in the atmosphere, aerosol models may need to be reformulated to better predict SOA evolution in both indoor and outdoor environments, including climate prediction models.

Microbes are very effective at carrying out a wide range of chemical reactions, even ones that involve substances toxic to higher life forms. Many groundwater sites contaminated with compounds such as trichloroethene (TCE), a pervasive groundwater pollutant often used by industry as cleansers or degreasers, are decontaminated by microbes. Dehalococcoides are the only family of bacteria known to break down TCE to ethene, a harmless chemical compound often used to help ripen fruits. A team of researchers has conducted a metagenomic analysis of a stable dechlorinating community derived from sediment collected at the Alameda Naval Air Station (ANAS) in California. The team identified the other members of this microbial community, since microbes such as Dehalococcoides are known to dechlorinate chemicals more effectively in the presence of other microorganisms. This study showed that all of the genes that code for enzymes involved in dechlorination were associated with Dehalococcoides, emphasizing its importance as the dominant dechlorinating microbe in the ANAS microbial community. Understanding the composition and functioning of communities such as this one will contribute to similar remediation efforts on a variety of cleanup challenges that DOE faces, as well as other processes (e.g., plant nutrition, carbon processing) that microbial communities carry out. The research was based on sequencing carried out by the DOE Joint Genome Institute (JGI).

Plutonium (Pu) adsorption to and desorption from mineral surfaces plays a major role in controlling its mobility in the environment. However, laboratory measurements of Pu sorption are typically conducted at much higher concentrations (10^-6 to 10^-10 M) than found in subsurface water (< 10^-12 M). As a result, there is a concern that Pu behavior determined in lab measurements might not be representative of sorption occurring under actual subsurface conditions. A new study carried out at Lawrence Livermore National Laboratory (LLNL) overcomes this obstacle. It provides measurements of the sorption of dissolved Pu (V) onto surfaces of a common clay mineral (Na-montmorillonite) over an unprecedentedly large range of initial plutonium solution concentrations (10^-6 to 10^-16 M). Concentration measurements at the low end of this range were made possible by the unique capabilities of the Center for Accelerator Mass Spectrometry at LLNL. The team’s results indicate that the plutonium adsorption behavior on montmorillonite was linear over the range of concentrations studied, indicating that plutonium sorption behavior from laboratory studies at higher concentrations can be extrapolated to sorption behavior at low, environmentally relevant concentrations.

Contributions to the carbon cycle in the ocean’s water column below the penetration of sunlight have not yet been explained either mechanistically or quantitatively, although a significant part of ocean carbon fixation is known to be due to microbial activities. Current oceanographic models suggest that archaea, the prevalent microbial domain in the oceans, do not adequately account for the carbon that is being fixed in the dark ocean. New research using sequencing technology has identified microbes involved in capturing carbon in the twilight zone, the region of the ocean that lies between 200 meters and 1,000 meters beneath the surface. This study discovered specific types of bacteria (the other domain of prokaryotic microbes besides the archaea) that may be responsible for this major, previously unrecognized component of the dark ocean carbon cycle. The report’s authors isolated and identified bacteria from water samples collected in the South Atlantic and North Pacific oceans. They found that “…previously unrecognized metabolic types of dark ocean bacteria may play an important role in global biogeochemical cycles, and their activities may in part reconcile current discrepancies in the dark ocean’s carbon budget.” A better model of carbon cycling in the oceans will help experts predict future CO₂ concentrations in the atmosphere and oceans and impacts of altered CO2 fluxes on ocean biogeochemistry. This work involved researchers from the DOE Joint Genome Institute.

Mercury has become a global pollutant due to its release into the atmosphere during coal burning and into freshwater systems as part of agricultural runoff and direct industrial discharge. Once in freshwater systems, specific types of microorganisms are known to transform mercury into methylmercury (MeHg), a highly toxic form of mercury. Scientists from Oak Ridge National Laboratory (ORNL) recently examined the microbial communities from the sediments of six different surface streams in Oak Ridge, Tennessee, to identify bacteria that could be contributing to MeHg production. Using 16S rRNA pyrosequencing, the researchers correlated the presence of a group of known MeHg producers, the Deltaproteobacteria, with MeHg in all of the Hg contaminated streams. Within the Deltaproteobacteria group, Desulfobulbus species are considered to be prime candidates for being involved in Hg methylation in these streams.

Between May and October 2011, the U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program, conducted a field campaign at the ARM Southern Great Plains (SGP) site to evaluate the High Dynamic Range All-Sky Imaging System (HDR-ASIS), a new instrument for quantitative image-based monitoring of sky conditions and solar radiation. The geometry of incident solar radiation has been widely shown to be a major determinant of photosynthesis rates, atmospheric CO₂ exchange, and photosynthetic light use efficiency in terrestrial ecosystems. USGS developed the HDR-ASIS to provide time series, ground-based observations to address this constraint (i.e., the geometry of incident solar radiation). Field tests are ongoing, and the final instrument is envisioned to be critical for improving ecosystem process modeling. The instrument evaluation included intercomparisons with several ARM instruments. HDR-ASIS time-series data products are available from the ARM archive, following initial data processing, quality control, and analysis.

Creating longer-life lithium-ion (Li-ion) batteries could help reduce our dependence on fossil fuels, affecting everything from vehicles to manufacturing. Current Li-ion batteries perform well, but over time the anode typically fails. A team of scientists from the Environmental Molecular Sciences Laboratory (EMSL), a DOE scientific user facility located in Richland, Washington, and users from Pacific Northwest National Laboratory (PNNL), Oak Ridge National Laboratory (ORNL), Applied Sciences Inc., and the General Motors Global Research & Development Center recently pinpointed the atomic-level changes that lead to anode failure in Li-ion batteries. The team created test anodes composed of hollow carbon nanofibers (CNFs) coated with a thin layer of amorphous silicon (Si). They then used EMSL’s in situ transmission electron microscope (TEM) to examine the performance of these anodes. Upon charging the anodes, the team observed a transformation of the amorphous Si into a crystalline state, and upon discharging, a return to a non-crystalline state. These results were further supported by performing theoretical calculations. Understanding the structural and phase transformation characteristics of CNFs with Si coatings provides Li-ion battery designers with the information they need to optimize silicon’s high storage capacity while maximizing the reliability of Li-ion batteries by manipulating coating layer thickness, CNF diameter, and the bonds between the coating layer and CNFs. For images of the transformation and for more information about this Si-carbon electrode research, see the news item “Silicon-carbon electrodes snap, swell, don’t pop.”

As the spatial resolution used in climate simulations becomes finer, the models become capable of representing more intense convective rain events (showery rain). However, the mass of precipitation in a cloud is not accounted for in many global atmospheric models (AGCMs), including the Community Atmosphere Model (CAM), but it may have an important impact on cloud evolution. DOE-funded researchers examined results from a cloud resolving model (CRM) that uses extremely high horizontal resolution and explicitly resolves atmospheric convection. They found that the weight of precipitation can increase atmospheric pressure by significant amounts over areas as large as 25km x 25km—an area similar to the grid box sizes in the coming generation of AGCMs. A simple representation of the pressure perturbation caused by precipitation mass was constructed and introduced into the latest version of CAM v5. Effects on both the intensity spectrum of precipitation and its mean distribution were found. This additional pressure tended to reduce the strength of the most intense small-scale upward motion and the frequency of intense precipitation in the model.

Recent price spikes have raised concerns that climate change could increase food insecurity by reducing grain yields in coming decades. However, commodity price volatility is also influenced by other factors, which may either exacerbate or buffer the effects of climate change. DOE-funded research reveals that U.S. corn price volatility exhibits higher sensitivity to near-term climate change than to energy policy influences or agriculture-energy market integration, and that the presence of a biofuels mandate enhances the sensitivity to climate change by more than 50%. The climate change impact is driven primarily by intensification of severe hot conditions in the primary corn-growing region of the United States, which causes U.S. corn price volatility to increase sharply in response to global warming projected over the next three decades. Closer integration of agriculture and energy markets moderates the effects of climate change, unless the biofuels mandate becomes binding, in which case corn price volatility is exacerbated. Despite the substantial impact on U.S. corn price volatility, the researchers observed a relatively small impact on food prices. Overall, results suggest that energy markets and associated policy decisions could substantially exacerbate the impacts of climate change, even for the relatively modest levels of global warming that are likely to occur over the near-term decades.

Marine biogeochemistry influences high-latitude climate through fluxes of greenhouse gases and aerosol precursors, and it is now becoming clear that such processes extend from open waters well into the sea-ice pack. DOE-funded investigators have constructed the first simulations of sea ice sources for dimethyl sulfide (DMS), the primary natural carrier of sulfur atoms from the ocean to the atmosphere. The sulfur is oxidized to form sulfate, which reduces incoming solar radiation. Complete nutrient cycling and ecodynamics were introduced into the Los Alamos sea ice model (CICE), a component of the Community Earth System Model, with interactive silicon, nitrogen, and sulfur processing attached. Under brine stress, the model ice algal metabolism produced sufficient organosulfur to support high concentrations of DMS in leads and marginal waters. Dissolved distributions along the migrating pack edge were compared with available measurements for the trace gas, which proved to be rare. Significant sulfur fluxes to the atmosphere are attributable to sea ice biology in peripheral seas such as the Okhotsk or Bering and also throughout the Canadian Archipelago. Emissions follow the seasonally retreating ice margin. However, the observational database is so sparse that alternate scenarios could not be excluded. A renewal of measurement activity was recommended to remedy this situation. Upcoming Arctic sea ice changes are likely to significantly impact high-latitude aerosols through sulfur channels represented in the model.

Thanks to fundamental research by a team of DOE scientists from Pacific Northwest National Laboratory (PNNL), Central China Normal University, and Wuhan University, sodium-ion batteries could be part of the future of storage and on-demand use of energy from wind farms. Large-scale energy storage and the ability to release electricity on demand requires high-capacity, low-cost batteries. The team developed a new alloy for use in sodium-ion batteries that stores nearly twice as much energy as carbon electrodes used in popular lithium-ion batteries. The team designed the anode by combining, at the nanoscale, a tin and antimony alloy with carbon. Using one of the transmission electron microscopes at the Environmental Molecular Sciences Laboratory (EMSL), a DOE scientific user facility located in Richland, Washington, the team found that the alloy reaction could be used to store sodium ions in the anode. The new anode also quickly charges and discharges without significant losses in capacity. The team is optimizing the alloy composition and structure and examining the structural change of the different alloy phases during the reactions to further improve its use for even higher capacities, increased durability, and faster charge/discharge cycles.

Perennial switchgrass (Panicum virgatum L.) is capable of producing high biomass yields with low inputs on marginal lands, making it one of the most promising candidate bioenergy feedstocks. Breeding programs are underway to enhance and improve switchgrass as a viable agricultural crop, but these efforts are hampered by the limited genetic and genomic information currently available. The switchgrass genome is now being sequenced, but its highly complex structure makes assembly difficult. Researchers at the DOE Joint Genome Institute (JGI) and the DOE Joint BioEnergy Institute (JBEI) report on the construction, sequencing, and analysis of two “Bacterial Artificial Chromosome” (BAC) libraries from switchgrass. These libraries contain relatively large DNA segments and represent essentially a random sampling of the genome, allowing the researchers to analyze structure and function at a genome-wide scale. Comparisons with sequences from other bioenergy-relevant grasses reveal that switchgrass is closely related to sorghum, indicating that the fully sequenced sorghum genome would serve as a good reference for assembling switchgrass gene space. The resources generated here will have utility for a number of applications, including identification of switchgrass gene functions relevant to bioenergy production.

Cellobiose, the two glucose basic repeat unit of cellulose, is formed during enzymatic or acidic hydrolysis of plant biomass, an early step in the production of biofuels. DOE researchers at the University of California, Irvine, have investigated the stability of cellobiose in water using high-level quantum molecular dynamics at DOE’s NERSC high-performance computing facility. The results from these simulations suggest that water dynamics play a leading role in stabilizing cellobiose in particular low energy states. The findings also indicate that long-range interactions between the water molecules and the sugar give rise to collective motions that could impact downstream enzymatic functions in the production of biofuels. These results provide new insight into a key step in the conversion of biomass to fuel molecules.

Understanding and characterizing the uncertainty in future projections of terrestrial system changes (e.g., land use, land cover, and land use change) is an active area of research. Food consumption is among the most fundamental drivers of these terrestrial system changes. Food consumption, in turn, is shaped by global change through interactions with socioeconomic changes such as population growth and economic prosperity. The paper develops a new model of food demands for use in human-Earth system dynamics models and employs hindcasting and advanced statistical techniques to characterize the food-demand model’s performance and derive numerical values for model parameters.

The new model addresses a long-standing issue in human-Earth system dynamics modeling, namely the evolution of food demand that accompanies large changes in income and agricultural prices occurring in widely varying countries over decades. As people’s wealth increases, their diets change, with important ramifications for agricultural and terrestrial systems more generally. Similarly, changes in prices that might emerge, for example, from drought, will affect the foods people eat. This paper takes a new approach to the representation of these changes that is rooted in decades of historical data and the latest understanding of how people have changed their diets and their food consumption over time across the world. Using historical information from countries around the world, the model projects the demand for two different types of food, staples commodities (for example, grains like corn and wheat) and non-staples (foods like fruits and vegetables).

An important element of the paper is the application of advanced statistical techniques – Bayesian Monte Carlo parameter estimation – to establish numerical values for the parameters of the food demand model. The robustness of the model was tested by developing the model parameters using a “training set” and then applying them to a “testing” data set. Divided data into testing and training sets is a form of “hindcasting”, because the projection is not being made into the future, but rather into data from the past and is therefore testing model performance over history. These “hindcast” experiments demonstrated that the model did a similarly good job of predicting values in both the testing and training data sets. An additional benefit of the statistical techniques used in this paper is that that the statistical characterization of the model parameters can be used to create uncertainty distributions for projections of future food demands in coupled human-Earth system models.

The use of hindcasting and advanced statistical techniques is less common in the development of the human system components of coupled, human-Earth system models than in the physical science components. Targeted approaches like those in this paper provide a template for increasing their future use in coupled, human-Earth system models.

Methane-producing microbes (i.e., methanogens) play a key role in the global carbon cycle and could significantly contribute to climate change due to the potent greenhouse gas properties of methane. These organisms occupy a central place in the biogeochemistry of soils, wetlands, and permafrost. However, it remains difficult to predict how they may respond to changing environmental conditions due to limited understanding of their biology. In a new study by DOE investigators at the University of Illinois, the first fully curated genome-scale metabolic model has been assembled for the methanogen Methanosarcina acetivorans. M. acetivorans is unique among methanogens in its ability to convert organic compounds such as acetate to methane, but it cannot perform the more traditional conversion of hydrogen and CO₂. The new model’s predictions have been validated using flux balance analysis and gene knockouts. The model provides new information on the integration of central and peripheral metabolic pathways, an important step in developing a systems biology approach to understanding this methanogen’s behavior. These findings significantly increase our predictive understanding of this important class of microbes providing a powerful new tool to test hypotheses on their potential roles in climate change.

Researchers have long been challenged with how to provide credible simulations of streamflow given different climate change scenarios. This challenge extends to the development of meaningful estimates of uncertainty and to historical observations for model conditioning. Ultimately, methods must account for the differences in how watersheds ‘behave’ if located in a different climate for long periods of time. DOE researchers developed a new Bayesian framework that uses a trading-space-for-time methodology, an idea adapted from seismic hazard modeling. The approach builds from similarities between spatial gradients of hydrologic response at the basin scale and temporal gradients (if a basin is placed in a different climatic regime). The new method is tested in five U.S. watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8°C and changing mean precipitation by -30% to +40% from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded -10% or +20%. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high- and low-flow conditions. The approach is independent of the hydrological model used and can be used directly (without the need for a hydrologic model) in integrated assessment models.

Chemical pretreatment of plant biomass prior to enzymatic breakdown significantly improves the release of sugar molecules, which are subsequently converted to biofuel compounds by fermentative microbes. However, pretreatment also introduces a variety of stress factors that can interfere with these fermentative organisms, including residual chemicals, toxins released from the biomass, high concentrations of sugars, and production of biofuels themselves. Researchers at the DOE Great Lakes Bioenergy Research Center (GLBRC) describe the integration of gene expression and physiological stress responses in an ethanol-producing strain of Escherichia coli during growth on corn stover that had been pretreated using ammonia fiber expansion (AFEX) and enzymatic digestion. Their results indicate that osmotic pressure resulting from high sugar concentrations and toxicity due to ethanol production were the two most important stressors to E. coli under these conditions, and that the cells activated a cascade of carefully timed stress tolerance pathways in response to these factors. Identification of these pathways provides new targets for metabolic engineering to improve stress tolerances of biofuel-producing microbes, leading to the development of more sophisticated approaches to leverage microbes’ natural abilities to sense and respond to environmental stress.

DOE scientists have created a new ensemble cloud data product named the ARM Cloud Retrieval Ensemble Dataset (ACRED) for the climate modeling community. ACRED consists of cloud microphysical properties retrieved from nine different cloud retrieval algorithms using ARM ground-based radar/lidar measurements over many years on a common grid that is comparable to climate model output. Differences between these cloud retrieval products provide a crude estimate of uncertainties in current cloud retrievals. Understanding the uncertainty in ARM cloud retrievals is important for improving model-observation comparison and better constraining climate models. ACRED is expected to greatly facilitate the use of ARM cloud data by climate modelers and advance climate science in general.

The ability to effectively model and predict integrated functional properties across complex groups of microbes is critical to understanding major environmental processes. Advances in this area would also facilitate development of novel bioengineering approaches utilizing the unique functional compartmentalization that enables microbial communities to efficiently perform complex cooperative processes. In a new perspective essay, DOE researchers Karsten Zengler and Bernhard Palsson of the University of California San Diego describe a conceptual approach to extend systems biology tools developed to understand metabolic functions of single organisms to more complex multispecies communities. This is a considerable challenge since detailed physiological information is only available for the small fraction of microbes that can be cultivated. Cultivation independent approaches such as metagenomics provide a snapshot of overall functional potential but little information on dynamic processes or interactions between members. Building on preliminary successes with modeling interactions in simple two member partnerships, the authors suggest that a combination of these “bottom up” and “top down” approaches that incorporates efficient targeting of organisms performing processes of interest, high-resolution imaging of spatial process relationships, and more refined environmental ‘omics techniques could yield predictive computational models of microbial community function.

Cyanobacteria are prime candidates for the biological production of biofuels, especially hydrogen. They photosynthesize in sunlight, have relatively fast growth rates, are tolerant to extreme environments, and can accumulate high amounts of intracellular compounds and produce large quantities of H₂. New research has combined a new genome-scale, constraint-based model of the cyanobacterium Cyanothece with experiments in a novel photobioreactor. The model and experiments provide new insights into the effect of light quality on metabolism and the bacteria’s mechanisms for balancing reductant and electron flows. The model differs from similar models of other cyanobacteria in its detailed treatment of the photosynthesis and respiratory systems. The photobioreactor features dual sources of monochromatic light that can vary photon flux with wavelengths that are tuned to the two bacterial photosynthesis systems. The results will guide development of genome-scale metabolic models for other cyanobacteria and may help with the genetic manipulation of photosynthetic microorganisms to improve biofuel production. These findings were presented by a team of DOE scientists led by Pacific Northwest National Laboratory and the University of Wisconsin.

The retrieval of cloud liquid water is an important climatic measurement because the earth radiative balance is strongly affected by cloud cover. The microwave radiometer, which is deemed to be the primary instrument for making these measurements, uses radiative transfer models to determine this property. Because of the propensity of clouds with small liquid water path (LWP) amounts and the importance of these clouds, the research community has recently started using higher frequencies, in the 90-200 GHz region. These frequencies have higher sensitivity to small amounts of cloud liquid water and, thereby, can improve the retrieval of liquid water by reducing the random uncertainties in the current LWP retrievals. Thus the accuracy of the radiative transfer code in the spectral region above 90 GHz was a major research focus. Scientists used measurements from microwave radiometers at the Southern Great Plains and North Slope of Alaska sites to assess four radiative transfer models. An ancillary dataset of measurements from cloud radars, ceilometers, radiosondes, and atmospheric emitted radiance interferometers was used to derive additional information (such as cloud boundaries, cloud temperature, and cloud LWP) that could be used as input for the analysis of the code. The study compared measurements of microwave absorption with model computations in supercooled liquid clouds that have temperatures between 0°C and -30°C. Findings from this study will be implemented by the Atmospheric Radiation Measurement Climate Research Facility to improve the accuracy of these measurements.

To simulate the exchange of water and energy between the ground and the atmosphere, the flow of water over and through the land surface must be accurately simulated. DOE-funded scientists at Pacific Northwest National Laboratory and Oak Ridge National Laboratory and a collaborator from the Chinese Academy of Sciences tested the simulation of water flow in the Community Land Model (CLM4) by comparing model simulations of runoff, surface water, and energy flux at various locations using streamflow gauge measurements from the U.S. Geological Survey and measurements from various flux towers across North America. The original model predicted excessive runoff variations that are not realistic when compared to observations. The team demonstrated that hydrologic simulations from CLM4 might be improved by calibrating the model parameters to better approximate actual site conditions. In addition, they showed it is important to represent spatial heterogeneity in land cover, vegetation, soil, and topography for better simulation of streamflow by increasing the spatial resolution when applying the model to a mountainous watershed. The research demonstrates the important constraint of soil hydrology on the surface energy budget and highlights the need to improve runoff parameterizations in land and surface models. The team identified several methods to improve the simulations, mainly by improving how the subsurface runoff is parameterized.

Perennial grasses are a potential source of feedstocks for “second-generation” cellulosic bioethanol because they efficiently accumulate large amounts of biomass and can be grown on marginal lands not suitable for conventional agricultural food crops. Among these grasses, Miscanthus is one of the most promising bioenergy crops in the Midwest because of its extremely high biomass yields, in particular the species Miscanthus x giganteus. However, efforts to breed improved varieties of Miscanthus are hampered by its complicated genome structure and lack of genetic tools. With support from the Joint USDA-DOE Plant Feedstocks Genomics for Bioenergy program, researchers report the first genetic linkage maps of Miscanthus using molecular markers derived from the closely related sugarcane grass. Genetic similarity between Miscanthus, sorghum, and sugarcane allowed comparative studies between the three species, revealing information into the genomic relationships among them and also allowing the first genetic map length estimate of Miscanthus. These resources provide a framework that will significantly enhance Miscanthus improvement efforts by facilitating identification of biomass-relevant genes and marker-assisted selection in this important bioenergy crop.

A cell’s internal and external pH plays a critical role in influencing many cellular chemical reactions and functions. Yet measuring pH without the appearance of artifacts in these challenging cellular and extracellular nanoscale environments is very difficult. New silicon nanowire (SiNW) pH sensors that possess long-term stability in these difficult environments have been developed by scientists at Lawrence Berkeley National Laboratory and their collaborators. The sensors were produced using a top-down fabrication process combining electron beam lithography (EBL) with conventional photolithography. A passivation layer (silicon nitride applied using plasma enhanced chemical vapor deposition) is coated on the SiNW’s surface to enhance electrical insulation and ion-blocking properties. This study shows that the application of these techniques results in improved stability of the sensor and enhances its performance. The paper explains how to achieve reliable performance in biological systems and discusses the trade-off between stability and pH sensitivity of the sensor response.

Climate models have been used to predict future climate changes, including Arctic sea ice loss under future warming climate and Arctic processes that are highly sensitive to feedbacks between clouds and the surface. The representation of Arctic clouds in the newly released Community Atmospheric Model version 5 (CAM5) was examined and tested by a team of researchers, including Department of Energy (DOE) scientists from Pacific Northwest National Laboratory, Lawrence Livermore National Laboratory, and Brookhaven National Laboratory. The model was run in forecast mode using the DOE-supported Cloud-Associated Parameterizations Testbed (CAPT) framework to facilitate comparison with observations from the DOE Atmospheric Radiation Measurement (ARM) Indirect and Semi-Direct Aerosol Campaign (ISDAC) and Mixed-Phase Arctic Cloud Experiment (M-PACE). ISDAC and M-PACE were conducted at the North Slope of Alaska site in April 2008 and October 2004, respectively. The team found that CAM5 generally simulates cloud cover in the Arctic successfully; however, it underestimates the observed cloud liquid water content in low-level stratocumulus. The underestimate of low-level clouds causes CAM5 to significantly underestimate the surface downward longwave radiative fluxes by 20-40 W m-2, which would in turn compromise the model’s ability to accurately simulate Arctic climate. Model improvements on cloud microphysics such as the processes controlling conversion of liquid to frozen precipitation and on aerosol parameterizations are needed and highlighted in this research.

Being able to see chemistry happen inside living cells in real time provides important understanding about how the cells function. However, imaging the behavior of cellular receptors that respond to small molecules is especially challenging when they are inside a cell rather than on the surface. A new approach to find compounds that can locate these intracellular receptors in living cells has been developed by scientists at Lawrence Berkeley National Laboratory in collaboration with scientists at the University of California, Berkeley, Ames Laboratory, and National University of Singapore. Candidate compounds are initially screened for the ease with which they enter cells and then for their rapid clearance out of the cells if they are not bound inside the cell (i.e., their ‘non-stickiness’). The screening is carried out in mammalian cells and the non-sticky compounds are then used to study complex multicellular systems such as plant roots using live-cell microscopy. A structure and flux-function co-relationship study was carried out to construct an atlas of structure-flux responses, the first of its kind. Radiolabeling studies using this information have enabled scientists to image gene expression in the living cell with positron emission tomography (PET).

Plant feedstocks for next-generation biofuels (e.g., lignocellulosic biomass) will come from many different sources depending on the geographic region and will likely include high biomass-producing species such as switchgrass, pine, poplar, and sorghum. Genome-enabled tools promise to facilitate breeding efforts to maximize biomass quality and yield in these plants; however, most of these species lack a complete genome sequence and many have only limited genetic tools available. To enable genome-based improvement of lignocellulosic biofuel feedstock species, researchers at Michigan State University, with support from the joint USDA-DOE Plant Feedstocks Genomics for Bioenergy program, have developed the Biofuel Feedstock Genomics Resource (BFGR). This web-based portal and database contains data from 54 bioenergy-relevant plant species, together with annotation and tools that allow identification and analysis of genes important for improvement of bioenergy traits, molecular marker analysis, and mapping to specific biochemical and metabolic pathways. Importantly, the database provides comparative analysis tools to allow scientists investigating species that lack a genome sequence to identify critical genes and develop experimentation to determine gene function. The BFGR will provide a valuable resource for plant breeders to use in improving bioenergy feedstocks for biofuel production.

The gold standard in ice sheet modeling is the “full-Stokes model,” which solves the nonlinear (non-Newtonian) Stokes equations for the three components of velocity and the pressure. However, it is a computationally difficult and expensive problem to solve and has inspired the creation of numerous approximate models that are cheaper and often inaccurate. A DOE scientist at Los Alamos National Laboratory used an approximate method that maximizes or minimizes functions for the full-Stokes model and showed how to reformulate the resulting system of equations into an equivalent, but much smaller problem for just the horizontal velocity components that has much more favorable properties. These features of the new formulation are illustrated and validated using a simple, but nontrivial Stokes flow problem involving a sliding ice sheet. This procedure will lead to new and efficient directions in ice sheet modeling.

Quantifying feedbacks between terrestrial carbon cycling and changing climate conditions remains one of the major sources of uncertainty in predicting climate change impacts. A lack of mechanistic understanding of biogeochemical processes mediated by soil microbes and how they are affected by climate change variables is a significant element of this problem. New ‘omics techniques for high-throughput characterization of microbial community structure and function are now providing powerful tools to examine these processes in intact ecosystems. Researchers at the University of Oklahoma have studied the impacts of long-term warming experiments (10+ years) on soil microbes at a grassland field site. The study describes compositional and functional shifts in the microbial communities related to elevated temperature and resulting changes in overlying vegetation and soil moisture. These effects were correlated with an increase in CO₂ efflux from soils, which was tied to stimulation of microbial community members and enzyme activities associated with degradation of labile (but not recalcitrant) soil carbon sources. The team also observed an accelerated microbial cycling of nitrogen, phosphorous, and other soil nutrients that appeared to help stimulate plant growth and at least partially ameliorate the net loss of carbon from the system. These findings point to the complex role of microbial communities in climate impacted ecosystem processes. Further study will be needed to tease apart their net effects on carbon feedbacks.

Atmospheric convection in the tropical regions is one of the main mechanisms of transporting solar energy from the equator to the polar regions (convection) of our planet. To project how climate change will affect global precipitation, it is important that models accurately simulate the upwelling and divergence of moisture in tropical clouds. DOE scientists at Pacific Northwest National Laboratory showed that global climate models are not accurately depicting the true depth and strength of tropical clouds that have a strong hold on the general circulation of atmospheric heat and the global water balance. The team surveyed tropical divergence in three global climate models, three global reanalyses (models corrected with observational data), and four sets of atmospheric measurements from field campaigns. Their survey uncovered significant uncertainties in current climate simulations and, in future projections, of the intensity and vertical structure of the low-level convergence of moisture to and upper-level divergence of heat away from the tropics. In the tropics and subtropics, deep divergent circulation is the largest contributor to net precipitation. Further, all global circulation models studied portray this process as deeper and stronger than what is observed in field measurements. Their analysis points to the need for model improvements to project water cycle changes in the 21st century.

The three-dimensional structures of proteins and other macromolecules often provide a starting point for designing new approaches to solving problems in a wide range of applications from bioenergy to medicine. The high-resolution structure of a specific protein can be used to identify small molecules that would bind to the protein and increase or decrease its activity to achieve a desired change in a biological system. A new study has determined the structures of an enzyme found in the malaria parasite (Plasmodium falciparum). The enzyme is not found in humans but is required by the parasite for the formation of its outer membrane. Several high-resolution structures were obtained for the enzyme in several stages of its functioning as well as with a small molecule that inhibits it. The structural information helped identify the enzyme’s active site and will be used as a starting point to seek drugs to treat infections by the malaria parasite. The results, published in the Journal of Biological Chemistry, were obtained by scientists from Washington University at the highly productive beamline 19ID of the DOE Structural Biology Center at Argonne National Laboratory’s Advanced Photon Source.

A systems biology approach to biological research requires ready access to information from many investigators conducting a wide variety of experiments. DOE’s BioEnergy Science Center (BESC) is undertaking large experimental campaigns to understand the biosynthesis and biodegradation of biomass and to develop biofuel solutions. BESC is generating large volumes of diverse data, including genome sequences, omics data, and diverse assay results. To assist the community of bioenergy researchers, BESC has developed a public Knowledgebase repository (besckb.ornl.gov) that they describe in the journal Bioinformatics. The BESC Knowledgebase serves as a central repository for experimentally generated data and provides an integrated, interactive, and user-friendly analysis framework. The Knowledgebase portal makes tools available for visualization, integration, and analysis of data produced by BESC or obtained from external resources. The aim of this database is to provide a resource for a systems-level understanding of cellular processes involved in plant formation, degradation, and biofuel production. The BESC Knowledgebase fits within the scope of a larger Knowledgebase activity across the DOE Genomic Science Program.

Accurate representation of cloud processes is critical for understanding and simulating climate and cloud-climate feedbacks. One process that appears to play a critical role in cloud evolution, but which is not well understood or simulated in models, involves the entrainment of dry surrounding air into a cloud. It is not well known to what extent entrainment has a uniform effect on the cloud droplets (homogeneous mixing), so that all droplets evaporate at a similar rate, or whether some drops shrink much more than others (inhomogeneous mixing). The different behaviors would have a significant influence on subsequent cloud microphysics (such as a cloud’s ability to rain) and on radiative effects. Recent work examining these cloud behaviors is reported by researchers at Brookhaven National Laboratory who used the Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site during the March 2000 Cloud Intensive Observation Period. Data were analyzed from 16 non-drizzling flight legs in five warm continental stratocumulus clouds. The data indicated that inhomogeneous entrainment-mixing processes occurred more often than the homogeneous entrainment-mixing mechanism. The researchers derived a more robust characterization of entrainment-mixing processes, including a probabilistic description using a dimensionless number that indicates the degree of homogeneous versus inhomogeneous mixing. The authors argue that the common wisdom of classifying entrainment-mixing processes into several distinct types appears oversimplified. Rather, the derivation of a mechanism continuum over these types is desirable but challenging. This new study provides an important first step in that direction.

A team of researchers from universities and national laboratories has published a groundbreaking climate simulation using the Community Climate System Model Version 4 (CCSM4). A global, weather-scale atmospheric model was coupled to a global ocean model that fully resolves tropical and mid-latitude eddies. The models’ horizontal resolutions are 0.25° and 0.1°, five and ten times, respectively, those of standard atmosphere and ocean models used in coupled climate simulations. This is the first effort to simulate the full climate system at such high horizontal resolution for a multi-decade period, and it enables the explicit simulation of climatically important processes. For example, the model realistically developed intense category 4 tropical cyclones causing colder water from below the surface mixed layer to move upward, producing characteristic cold sea surface temperature wakes under and to the right of storm tracks. It also correctly depicted the deepening of and warming below the ocean mixed layer. Additionally, the model realistically reproduced the structure and pathways of explicitly resolved South Atlantic Agulhas ocean eddies, the main constituent of the upper limb of the Atlantic meridional overturning circulation. These are absent in the simulated oceans of standard climate models and are incorrectly represented in high-resolution ocean-only experiments forced with atmospheric fields derived from observations. This new prototype simulation demonstrates that sub-grid scale parameterizations are scale dependent and require improvements and adjustments to remove persistent mean climate biases before high-resolution simulations become routine.

Increasing concentrations of atmospheric carbon dioxide (CO₂) can affect biotic and abiotic conditions, such as microbial activity and water content, in soil. In turn, these changes might be expected to alter the production and consumption of the important greenhouse gases nitrous oxide (N₂O) and methane (CH₄). However, studies on fluxes of N₂O and CH₄ from soil under increased atmospheric CO₂ have not been quantitatively synthesized. Here, a DOE-funded study from Northern Arizona University used a meta-analysis of increasing CO₂ (ranging from 463 to 780 ppm by volume), demonstrating that increasing CO₂ stimulates both N₂O emissions from upland soils and CH₄ emissions from rice paddies and natural wetlands. Because enhanced greenhouse-gas emissions add to the radiative forcing of terrestrial ecosystems, these emissions are expected to negate at least 16.6% of the climate change mitigation potential previously predicted from an increase in the terrestrial carbon sink under increased atmospheric CO₂ concentrations. The study’s results, therefore, suggest that the capacity of land ecosystems to slow climate warming has been overestimated.

Soil organic matter (SOM) is a heterogeneous mixture of partially decomposed, plant- and microbial-derived materials that plays an important role in the global carbon cycle. The carbon associated with some components of SOM can persist belowground for centuries to millennia, making these pools crucial carbon reservoirs. However, these long-lived carbon pools and the factors controlling their persistence in soil are not well characterized, because they are extremely challenging to isolate. A team led by Argonne National Laboratory used a novel approach combining sequential physical and chemical fractionations with two naturally occurring carbon isotopic tracers to divide the SOM into pools with average turnover times that ranged from 1 to over 3,000 years. They discovered that the SOM pools associated with soil minerals, which typically have been characterized as having extremely long lifetimes, are actually composed of a mixture of rapidly cycling pools and pools with much longer residence times. Further, the study found that the rapidly cycling pools accounted for a much greater proportion of the total soil carbon than is generally represented in SOM models. These results provide new insight into soil carbon dynamics, since SOM pools with long turnover times were previously thought to be relatively homogenous and practically inert. The findings can inform models used to predict the contributions of soils to the carbon cycle and the responses of SOM to climatic change.

Understanding and predicting the impacts of climate change and the stability of carbon stored in terrestrial ecosystems is an important part of planning future energy strategies. This Oak Ridge National Laboratory-led study compared the turnover time of labile soil carbon, in relation to temperature and soil texture, in several forest ecosystems that are representative of large areas of North America. Carbon (C) and nitrogen (N) stocks and C:N ratios were measured in the forest floor, mineral soil, and two mineral soil fractions (particulate and mineral-associated organic matter) at five AmeriFlux sites (a network that provides continuous observations of ecosystem-level exchanges of CO2, water, and energy across the Americas) along a latitudinal gradient in the eastern United States. With one exception, forest floor and mineral soil carbon stocks increased from warm, southern sites (with fine-textured soils) to cool, northern sites (with more coarse-textured soils). The exception was a northern site, with less than 10% silt-clay content, that had a soil organic carbon stock similar to the southern sites. Moving from south to north, the turnover time of labile soil organic C increased from approximately 5 to 14 years. Consistent with its role in stabilization of soil organic carbon, silt-clay content was positively correlated with stable C at each site. Latitudinal differences in the storage and turnover of soil C were related to mean annual temperature, but soil texture superseded temperature when there was too little silt and clay to stabilize labile soil C and protect it from decomposition. Overall, this study suggests that large labile pools of forest soil C are at risk of decomposition in a warming climate, especially in coarse textured forest soils.

A multi-component plant-soil biogeochemical model for the herbaceous energy crop switchgrass was developed and evaluated using data from a long-term bioenergy plantation in the southeastern United States. DOE scientists at Oak Ridge National Laboratory used the model to simulate biomass production, nitrogen dynamics, and carbon sequestration in soils beneath switchgrass over a 30-year period, revealing a strong coupling of carbon and nitrogen dynamics, both above- and below-ground. The lead scientist concluded that the extent to which biogeochemical cycles are coupled is a critical determinant of sustainability in systems where biomass growth and removal occurs annually. More efficient use of nitrogen in the production of biomass deserves further investigation. Based on model simulations, researchers believe that reductions in nitrogen fertilization are possible given rates of organic matter decomposition and soil nitrogen mineralization. Overall, the model simulations reveal a suite of feedbacks and tradeoffs in the production of feedstock for transportation fuels, but the author suggests that long-term production and removal of biomass from switchgrass fields for transportation fuels is possible.

Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth’s surface by altering biophysical processes. When continental-scale land clearing is included in climate models, cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is a key component of the model predictions; without it other processes overwhelm the albedo effect to generate warming. Ongoing activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback. It is not known if the intrinsic biophysical mechanism on its own can consistently change surface temperatures. The effect of deforestation on climate has also not been demonstrated over large areas from direct observations. Now, DOE researchers show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85°±0.44K (mean ± one standard deviation) north of 45°N (essentially north of the U.S.-Canadian border) and 0.21°±0.53K southwards. Below 35°N (south of Tennessee, all of Texas and New Mexico, and southern California), there is weak evidence that deforestation leads to warming. Results are based on temperature comparisons at forested eddy covariance towers in the United States and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are also an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectations of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models.

Engineer Novosphingobium aromaticivorans to funnel heterogeneous mixtures of lignin-derived aromatic compounds to 2-pyrone-4,6-dicarboxylic acid (PDC), a bioplastic precursor.

Mitigation strategies for reducing CO₂ emissions include (1) substituting fossil fuels with bioenergy from forests on the assumption that emitted carbon is recaptured through new biomass growth to achieve zero net emissions, and (2) forest thinning to reduce emissions from wildfires. DOE-supported scientists from Oregon State University used forest inventory data to show that fire prevention measures and large-scale bioenergy harvest in U.S. West Coast forests will lead to 2%-14% (46-405 TgC) higher emissions over the next 20 years compared to current management practices. These results contradict some previous studies suggesting that biofuels from forests would be carbon neutral or even reduce greenhouse gas emissions. The investigators studied 80 forest types in 19 ecoregions and found that the current carbon sink in 16 of these ecoregions is sufficiently strong that it cannot be matched or exceeded through substitution of fossil fuels by forest bioenergy. The only exception was forests in high fire-risk zones that become weakened due to insect outbreaks or droughts, which impairs their growth and carbon sequestration and sets the stage for major fires. In the remaining three ecoregions, immediate implementation of fire prevention and biofuel policies may yield net emission savings. The study also concluded that forest policy should consider current forest carbon balance, local forest conditions, and ecosystem sustainability in establishing how to decrease emissions.

The nation’s dependence on imported fossil fuels could be alleviated, at least in part, by the domestication of dedicated bioenergy crops such as native perennial switchgrass for lignocellulosic ethanol production. Switchgrass is a promising feedstock candidate because it produces high yields of biomass on marginal lands unsuitable for production of food crops. In addition, perenniality (the ability of a plant to survive over winter and resume growth in the spring) is important for sustainability, since the unharvested below-ground tissues help maintain the integrity and nutrient status of the soil. Perennial biomass cultivars will need to tolerate fluctuations in temperature and rainfall, traits influenced by the overall health of below-ground tissues. Research¬ers at the USDA-ARS in Lincoln, Nebraska, with funding from the joint USDA-DOE Plant Feedstocks Genomics for Bioenergy Program, analyzed changes in gene expression patterns in below-ground tissues (crowns and rhizomes) of the switchgrass cultivar ‘Summer’ to gain insight into the genetic mechanisms regulating these processes. The results revealed that these tissues are metabolically active, including pathways involved in basal cell metabolism and stress response. In addition, several novel gene sequences of unknown function were identified, which may represent genes specific to these tissues and with unique functions. These analyses should yield further insights into perenniality that will improve switchgrass as a sustainable bioenergy feedstock.

Found within the plant cell wall, lignin is a complex polymeric compound that provides the plant with both mechanical support and protection from pests and pathogens. However, the structural rigidity of this compound also inhibits efficient conversion of the sugars within plant cell walls into biofuels, making lignin a major obstacle to the efficient production of biofuels from cellulosic feedstocks. Three types of lignin are usually found in nature: H-, G-, and S-lignins. They are synthesized by polymerization of their respective monolignol units. However, lignin biosynthesis can be relatively flexible, sometimes allowing different and more unusual monolignols to be incorporated. Researchers at the DOE BioEnergy Science Center (BESC) and DOE Great Lakes Bioenergy Research Center (GLBRC) report the identification and characterization of a new type of polymer, C-lignin, composed almost exclusively of caffeyl units. Detected in the Vanilla orchid, a few related orchids, and some cactus species, this unique new lignin was found only in the seed coats, with more conventional lignins observed in other plant tissues. These results may lead to a greater understanding of the lignin biosynthetic pathway, as well as new approaches for engineering biomass that can be more easily and efficiently digested for conversion into biofuels.

There is evidence that boreal trees and shrubs are invading tundra regions due to global warming at high latitudes. New computer simulations by DOE-funded scientists indicate that an invasion of shrubs, in turn, can further warm the northern high latitudes at a rate that depends on the plants’ height. The scientific team, composed of Lawrence Livermore National Laboratory, Lawrence Berkeley National Laboratory, and National Center for Atmospheric Research (NCAR) scientists, conducted a series of idealized experiments with the NCAR-DOE Community Climate System Model (CCSM) to investigate the potential impact of a large-scale, tundra-to-shrub conversion on permafrost and the boreal climate. They found that an increase in the total shrub fraction from 32% to 51% of the land north of 60°N (Alaska and north) triggered a substantial regional atmospheric warming in the spring and summer by 1) reducing the land surface albedo (reflectance of light), and 2) increasing the water vapor content of the atmosphere through increased transpiration (loss of water vapor from plants). The team also found that the strength and timing of these two mechanisms depends highly on the height of the shrubs (i.e., the time at which branches and leaves protrude above the snow). Taller and aerodynamically rougher shrubs lower the albedo earlier in the spring and transpire more efficiently than shorter shrubs, increasing soil warming and destabilizing the permafrost. The addition of an interactive ocean model produces additional warming through reduction in the amount of sea ice (which lowers further the surface albedo) and an increase in ocean evaporation (which adds more water vapor to the atmosphere). The study highlights the significant warming influence of high-latitude vegetation changes that should be included in climate simulations.

The plant cell wall polysaccharide pectin is often associated with the tissue softening that occurs during fruit ripening. However, this complex compound is also involved in secondary cell-wall synthesis in grasses and woody plants, helping to give the plant rigidity, but also impeding the deconstruction of plant biomass and hence its conversion into biofuels. Researchers at the DOE BioEnergy Research Center (BESC) have discovered that the pectin-synthesizing enzyme GAUT1 forms an unusual, two-protein complex with a similar protein (GAUT7) that constitutes a critical part of a pectin-synthesizing protein complex. They also showed that this complex plays a role in secondary cell-wall synthesis. Manipulating the formation of this complex may provide a way to modify secondary cell walls, which could either increase available biomass or improve its digestibility for biofuel production.

Significant amounts of mercury have contaminated some DOE cleanup sites, such as the Oak Ridge Reservation. Mercury mobility is strongly dependent on its chemical form, with the elemental metal being volatile and hence mobile in the environment, while oxidized forms are much less mobile (though more toxic). New research at Argonne National Laboratory has provided improved understanding of the role of bacteria in controlling the chemical form of mercury in subsurface environments. The research group used x-ray absorption spectroscopy experiments at the Advanced Photon Source to study the sorption of oxidized HgII to Bacillus subtilis, a bacterium commonly found in soils. They found that HgII sorbs to bacterial cells via both high-affinity sulfhydryl binding groups and low-affinity carboxyl groups on the cell surfaces. The HgII that is sorbed to cells via the sulfhydryl groups remains unavailable for reduction by magnetite, a reactive iron-containing mineral often found in sediments, even after two months of reaction time. These results identify a mechanism by which mercury might be immobilized in the environment and help provide a clearer picture of the complex system of interactions of mercury in the subsurface.

A recent report in Nature Climate Change shows that global fossil-fuel CO₂ emissions in 2010 surpassed 9 petagrams of carbon (Pg C) for the first time, more than offsetting the 1.4% decrease in 2009 attributed to the 2008 worldwide financial crisis. The research, supported by DOE’s Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory, shows that the impact of the 2008-2009 global financial crisis on emissions was short lived due to strong emissions growth in emerging economies, a return to emissions growth in developed economies, and an increase in the fossil-fuel intensity of the world economy following the crisis. The 2010 growth was due primarily to high growth rates in a few key emerging economies namely China (10.4%, 0.212 Pg C) and India (9.4%, 0.049 Pg C). This is the latest CDIAC annual report of time series estimating releases of carbon from fossil-fuel use and cement production on global and national scales. These data quantify the major anthropogenic sources of carbon in the global carbon cycle budget and support research to understand national trends in fossil-fuel CO₂ emissions. CDIAC produces gridded products needed for modeling activities (e.g., Intergovernmental Panel on Climate Change Fifth Assessment Report) and provides benchmark data for mitigation efforts and policy discussions.

The major barrier to the efficient conversion of biomass from plant feedstocks to biofuels is breaking down the plant cell wall so that the sugars locked within can be released. This barrier is due to the presence of lignin, a complex compound that cross links the walls and provides rigidity to the plant. Plants that are genetically modified to have less lignin can be broken down more easily, but often these plants show severely stunted growth. Plants have a stress hormone (salicylic acid (SA)) that is known to impact plant growth and development and whose levels are inversely proportional to lignin levels. Researchers at the DOE BioEnergy Science Center (BESC) have found that genetically removing SA from Arabidopsis plants that were also modified to produce low levels of lignin restores normal growth to these plants while maintaining low lignin content. These results support the hypothesis that low lignin, high biomass yielding plants can be engineered to produce sustainable biofeedstocks for biofuel production.

Biodiesel production typically starts with oil-rich energy crops such as soybean, palm, or rapeseed, which are harvested and converted into fatty acids from which biodiesels or other fuels are derived. The cost of expanding crop production is a limiting factor in allowing biodiesel to compete with fossil fuel sources. One alternative is to avoid the plant entirely and directly synthesize the precursor fatty acids in bacteria, bypassing several upstream steps, reducing production costs, and raising final yields. A team of researchers, including members of the DOE Joint Genome Institute, now has developed a process to engineer bacteria to produce biodiesel with the help of a novel fatty acid synthesis enzyme. The enzyme, identified and characterized from several bacterial sequences, was inserted into the commonly used model microbe E. coli to prove that it was involved in fatty acids synthesis. The fatty acid pathway was further engineered to improve the generation of biodiesel precursors. This new work provides an alternative route for the synthesis of biofuel molecules. The pathway they describe is a first step in the generation of biodiesel and, with further optimization, may lead to the production of a cost-efficient, next-generation biofuel. The results have just been published in Applied and Environmental Microbiology.

The low efficiency and high cost of enzymes used to break down plant material into sugars remains a major barrier to economically competitive production of cellulosic biofuels. Consolidated biomass processing, in which a single microorganism both produces cellulose-degrading enzymes and converts the resulting sugars to a desired biofuel, presents a promising alternative to improve efficiency and reduce costs, but few organisms naturally possess both capabilities. Researchers at the Joint Bioenergy Institute (JBEI) have now engineeered a modified strain of the workhorse industrial microbe E. coli that expresses a tailored set of cellulases, allowing it to degrade both the cellulose and hemicellulose chains released from switchgrass pretreated with ionic liquid. This was accomplished by cloning cellulase genes from Cellvibrio japonicus, a soil microbe with similar protein secretion systems to E. coli, and modifying the genes to allow proper timing and level of cellulase expression in the host. The team then added metabolic pathways that allowed E. coli to convert resulting sugars to either of two drop-in automotive biofuels (biodiesel and butanol) or a jet fuel precursor terpene compound. This presents a promising new advance in consolidated biomass processing, and, given the relative ease of genetic modification in E. coli, offers tremendous potential for subsequent engineering to increase conversion efficiency or synthesize a broader range of fuels.

The latest issue of Genome Technology leads off its 6th Annual Young Investigator survey with a profile of Tanja Woyke of the DOE-Joint Genome Institute. This annual year-end article focuses on young investigators carrying out cutting-edge “omics” research, the post-genomic exploration of the biological meaning of sequenced genomes from microbes, plants, and environments. Woyke is profiled for her work developing and using single-cell genomics, the technology permitting elaboration of the entire genome sequence of a single microbial cell, without prior cultivation (to which the vast majority of environmental microbes are resistant). Woyke is exploring microbial taxa from the unexplored regions of the microbial tree of life about which almost nothing is known, either about physiological capacities or evolutionary relationships. The promise of this work is that single-cell genomics will enable the exploration of microbes important for bioenergy processes, waste cleanup, and carbon cycling.

Using a 10-year set of extensive measurements made at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility in the U.S. Southern Great Plains, researchers found unprecedented strong evidence that aerosols drastically alter clouds and precipitation. Aerosols— tiny particles in the air, like dust or soot—affect clouds and precipitation through different mechanisms. Aerosols can serve as cloud condensation nuclei (CCN) that impact cloud microphysics and precipitation processes, or they can directly modulate radiative and latent energy, changing the atmospheric stability dynamics and thermodynamics that dictate cloud development. The interplay of these effects can either suppress or foster cloud and precipitation processes, depending on the specific circumstances. This study showed that increased aerosol concentrations increased the cloud top height and thickness—most significantly in the summer by up to a factor of 2—for clouds with a warm base (above 15°C) and mixed-phase tops (below -4°C). Precipitation frequency increased with aerosols for deep clouds with high water content and decreased for clouds with little water. The observational findings are successfully reproduced with a state-of-the-art cloud-resolving model demonstrating that these aerosol processes are well represented in the model.

Uranium is one of the major contaminants at DOE cleanup sites. It was usually released into the environment as the highly soluble uranyl ion (uranium (VI)). This ion interacts with bacteria and minerals in the ground to form reduced uranium (IV), notably in the mineral uraninite, a form that is much less soluble than uranium (VI). Less soluble uranium (IV) species are less likely to be moved out of the initially contaminated zone and into nearby rivers or aquifers by groundwater. New research has shown that biologically produced uraninite in a natural underground environment dissolves much more slowly than uraninite prepared in the laboratory. Researchers have developed a model showing that the slower dissolution is due to the presence of biomass that limits the reoxidation rate of the uranium (IV) in uraninite and diffusion of oxidized uranium into the groundwater. This understanding will be used in developing improved models of uranium transport in contaminated environments. Field studies were carried out at the Old Rifle, Colorado, Integrated Field Research Challenge site, while experiments to determine the forms of uranium present were conducted at the Stanford Synchrotron Radiation Lightsource.

Technetium-99 (Tc-99) is a major fission product of uranium-235 and comprises a large component of radioactive waste. Under normal environmental conditions, it exists in its highly oxidized (VII valent) form, which is stable, very soluble, and migrates easily through the environment. If reduced, it readily oxidizes back to this stable form. This reduction-oxidation activity of Tc-99 hampers its separation from spent fuel rods and cleanup of radioactive tank waste and presents a problem in the identification of a suitable waste form. A new Tc-photoreduction study employs polyoxometalates, nanometer-sized metal oxide aggregates that precipitate the highly soluble Tc-99 allowing for its recovery and isolation. This DOE-funded project involves graduate students and postdoctoral fellows as part of an effort to develop a new cadre of radiochemists.

The Arctic is a particularly challenging region to simulate accurately in a climate model, yet it is an important region because it is undergoing rapid change. In preparation for the upcoming Climate Model Inter-comparison Project (CMIP5), part of the Intergovernmental Panel on Climate Change, DOE researchers at Lawrence Berkeley National Laboratory assessed the Arctic climate in the fourth version of the Community Climate System Model (CCSM4), the version most often used in the CMIP5. An ensemble of 20th Century CCSM4 simulations was compared to a variety of reanalysis and measurement products to assess CCSM’s ability to accurately simulate the present-day Arctic atmosphere. Analyses included evaluations of surface air temperature, sea-level pressure, the atmospheric energy budget, precipitation and evaporation, cloud properties, and lower tropospheric stability. The model demonstrated the best performance in simulating surface air temperature. Errors in sea-level pressure fields were significant at certain times of year, impacting the atmospheric circulation. The model has too few clouds, and the clouds it does produce are generally too thick. Precipitation was overestimated in the Arctic and the lower atmosphere was demonstrated to be excessively stable. The results from this evaluation will help guide future model developments and in the interpretation of the CMIP5 simulations.

Researchers from the MIT Joint Program on the Science and Policy of Global Change have found that anaerobic digesters could supply as much as 5.5% of national electricity generation by 2050. Anaerobic bacteria that break down organic wastes produce methane that can be used to generate renewable electricity. Diverting methane emissions towards electricity generation also reduces total U.S. greenhouse gas emissions and may qualify for low-carbon energy subsidies and methane-reduction credits. Anaerobic digesters also reduce odor and pathogens in manure storage, and digested manure can be applied to crops as a fertilizer. Researchers used the MIT Emissions Prediction and Policy Analysis (EPPA) model to test the effect of emissions scenarios on the adoption of anaerobic digesters. The researchers estimate that cattle, swine, and poultry manure deposited in lagoons or pits currently has the potential to produce 11,000 megawatts of electricity. The study found that, under a representative emissions mitigation scenario, anaerobic digesters are introduced in 2025 when the price of CO₂e is $76/ton. By 2050, use of anaerobic digesters would mitigate 151 million metric tons of CO₂e, mostly from methane abatement.

Sulfate-reducing bacteria (SRBs) play important roles in the decomposition of organic matter, cycling of nutrients, and transformation of heavy metals in subsurface environments. Sensing and responding to minute shifts in nutrient levels, potentially damaging or toxic conditions, and the presence of other microbes is critical to their lifestyle. Systems involving two components, paired sets of sensor and regulator proteins that control gene expression, are an important sense/response mechanism in bacteria, but it remains extremely difficult to establish relationships between the systems and larger networks of regulated genes. Researchers at Lawrence Berkeley National Laboratory have now completed the first-ever map of two-component regulatory systems for the model microbe SRB Desulfovibrio vulgaris using a cell-free approach based on direct binding of purified regulator proteins to genome fragments. Genes involved in nutrient acquisition, growth, stress response, and community assembly were mapped onto specific response regulators, providing a greatly enhanced understanding of how SRBs react to changing environmental conditions and mediate key processes in the subsurface.

Earth’s microbes live in staggeringly diverse environments, colonizing habitats with extremes of temperature, pH, salt concentration, or presence of toxic compounds. Archaea, a domain of single-celled microbes sharing traits with bacteria and simple eukaryotes, are well known for their ability to thrive in harsh environments. How this impressive adaptive capability is achieved has remained a mystery. Now, a team of investigators at the Institute for Systems Biology has completed a groundbreaking study on the role of gene regulation in environmental niche adaptation by Halobacterium salinarum, an archaeal microbe that grows in high salt environments. Using a combination of comparative genomics and hypothesis-driven molecular biology experiments, the team found that a specific class of regulatory genes had been duplicated during the archaea’s evolution and controls a nested set of “niche adaptation programs.” These programs control cascades of gene expression essential for adaptation to particular environments. Diversification of these control elements has resulted in a “division of labor” such that overlapping regulatory networks flexibly balance large-scale functional shifts under changing conditions, where rapid adaptation increases fitness. Describing mechanisms that control niche adaptation in microbes allows us to better understand how microbial communities function in natural environments, and provides an intriguing glimpse into fundamental design rules governing biological systems.

In Earth’s Arctic regions, frozen soils (permafrost) sequester an estimated 1.6 trillion metric tons of carbon, more than 250 times the amount of greenhouse gas emissions attributed to the United States in 2009. Concerns are growing about the potential impact on the global carbon cycle when rising temperatures thaw the permafrost and release the trapped carbon. Microbes may significantly influence the eventual outcome through their involvement in carbon cycling. New research on permafrost microbes has discovered a previously unknown, yet abundant microbe that produces methane, a far more potent greenhouse gas than carbon dioxide. A draft of this microbe’s genome was determined by assembling DNA fragments isolated from permafrost. The DOE Joint Genome Institute (JGI) had previously identified several microbes that produced methane (“methanogens”) as a metabolic byproduct, and used this knowledge to identify enough fragments of the new microbe’s DNA to assemble a draft of its genome. The abundance of this novel methanogen implies that it could be an important factor in methane production under permafrost thawing conditions. The research, published in Nature, was carried out by scientists at JGI, Lawrence Berkeley National Laboratory, and U.S. Geological Survey.

Climate-driven vegetation mortality is occurring globally and is predicted to increase in the near future. The expected climate feedbacks of regional-scale mortality events have intensified the need to improve the mortality algorithms used for future predictions, but uncertainty regarding mortality processes precludes mechanistic modeling. By integrating new evidence from a wide range of fields, DOE-supported scientists at Los Alamos National Laboratory conclude that hydraulic function and carbohydrate and defense metabolism have numerous potential failure points. These failure points are interdependent both with each other and by providing an avenue for increased mortality from destructive pathogens and insect populations (e.g., bark beetle). Crucially, most of these mechanisms and their interdependencies are likely to become amplified under a warmer, drier climate. Improved understanding of climate-driven mortality will improve our ability to predict future impacts of climate change on vegetation.

Climate change is predicted to cause forest tree distributions to higher latitudes and elevations, which will require seedling recruitment beyond current forest boundaries. However, predicting the likelihood of successful plant establishment beyond current species’ ranges under changing climate is complicated by the interaction of genetic and environmental controls on seedling establishment. DOE-supported scientists at the University of California, Merced, transplanted germinated seedlings of limber pine (Pinus flexilis) from high- and low-elevation sites in common gardens along a gradient from subalpine forest into the alpine zone and examined differences in physiology and morphology between and among seed source sites. The results of the study suggest that tree seedlings germinating from lower-elevation seed consistently outperformed seedlings from higher-elevation seed, even above the current tree line. This suggests that inherent (e.g., genetic) differences between seed source populations could be an important factor affecting species range expansions or shifts due to climate change.

Vast quantities of methane, a potent greenhouse gas, are trapped in oceanic hydrate deposits. There is concern that a rise in ocean temperatures will induce dissociation of these hydrate deposits, potentially releasing large amounts of carbon into the atmosphere. The recent discovery of active methane gas venting along the shallow continental slope west of Svalbard in northern Norway suggests that this process may already have begun, but the source of the methane has not yet been determined. DOE researchers have performed two-dimensional simulations of hydrate dissociation in conditions representative of the Arctic Ocean margin to assess whether such hydrates could contribute to the observed methane gas release. The results show that shallow hydrate deposits subjected to recently observed or future predicted temperature changes at the seafloor result in the release of methane at magnitudes and locations similar to what has been observed. Localized gas release is observed for most cases of gradual and rapid warming. These model results resemble recently published observations and strongly suggest that hydrate dissociation and methane release due to climate change may be real, that it could occur on decadal timescales, and that it may already be occurring.

Understanding water flow and transpiration in plants is an important component of understanding land-atmosphere interactions, but methods to make these measurements are poorly developed. Thermal dissipation probes are widely used to estimate the movement of water through woody plant stems, branches, and roots. The mathematical treatment of the heat-transfer characteristics that underlie this technique are complex. Models that allow ecologists to evaluate the performance of those techniques are lacking, thus limiting advancements in process-level understanding and technology development. Scientists at Oak Ridge National Laboratory (ORNL) have now developed a model of conductive and convective heat transfer in sapwood that takes into account the thermal properties of wood and the physical dimensions and thermal characteristics of the probes that can be used to identify shortcomings in the thermal dissipation approach to measuring water use in trees. After validating the model’s performance using data from field studies, the team observed that the fundamental calibration equation upon which the technique is based was highly sensitive to variation in water content, sapwood density, radial gradients, wound diameter, and other operational characteristics of this technique. Uncertainty analysis suggested that significant over- and under-estimation of sap flow was possible using the traditional calibration equation. Improved estimates of water use and latent energy exchange should further understanding of land-atmosphere interactions when applied to a variety of ecosystems such as the AmeriFlux study sites.

Wetlands store a substantial amount of carbon in deposits of deep soil organic matter and play an important role in global fluxes of carbon dioxide and methane. Fine roots active in water and nutrient uptake are recognized as important components of biogeochemical cycles in nutrient-limited wetland ecosystems. However, quantification of fine-root dynamics in wetlands has generally been limited to destructive approaches. Minirhizotrons, cameras that enable non-destructive viewing of plant roots through clear tubes permanently inserted into the soil, have now been adapted for use in wetland ecosystems. An Oak Ridge National Laboratory-led methodology workshop examined a number of potential solutions for the challenges associated with the deployment of minirhizotron technology in wetlands, including minirhizotron installation and anchorage, capture and analysis of minirhizotron images, and upscaling of minirhizotron data for analysis of biogeochemical pools and parameterization of land-surface models. The authors conclude that despite their limitations, minirhizotrons provide critical information on relatively understudied fine-root dynamics in wetlands needed to advance our knowledge of ecosystem carbon and nutrient cycling in these globally important ecosystems.

Estimates of global carbon sinks have large uncertainties that complicate estimates of Earth’s capacity to buffer rising atmospheric carbon dioxide (CO₂). Photosynthesis is a major contributor to these carbon sinks. A DOE-funded team led by Ralph Keeling at the Scripps Institution of Oceanography followed the path of oxygen atoms on CO₂ molecules during photosynthesis to create a new way to measure the efficiency of the world’s plants. The ratio of two natural isotopes of oxygen in CO₂ told researchers how long the CO₂ had been in the atmosphere and how fast it had passed through plants. From this, they estimated that the global rate of photosynthesis is about 25 percent faster than thought. This new approach linked the changes in oxygen isotopes to El Niño, the global climate phenomenon associated with a variety of unusual weather patterns including low rainfall in tropical regions of Asia and South America. The naturally occurring isotopes of oxygen, 18O and 16O, are present in different proportions in the water inside leaves during dry, El Niño periods in the tropics. This oxygen ratio in leaf waters is passed along to CO₂ when CO₂ mixes with water inside leaves. This exchange of oxygen between CO₂ and plant water also occurs in regions outside of the tropics that are not as affected by El Niño and where the 18O/16O ratio is more “normal.” The team measured the time it took for the global 18O/16O ratio to return to normal following an El Niño event to infer the speed at which photosynthesis is taking place. They discovered that the ratio returned to normal faster than expected indicating that global photosynthesis occurs at a greater rate than previously assumed. The rate, expressed in terms of how much carbon is processed by plants in a year, has now been revised upward from the previous estimate of 120 Pg of carbon a year to a new annual rate between 150-175 Pg. These results suggest that the uncertainty in estimating global carbon sinks is even greater than previously thought.

Plants take up nitrate using transmembrane proteins of the Nitrate Transporter (NRT)/Peptide Transporter family (NPF). Understanding nitrogen uptake, translocation, and utilization is key to improve nitrogen-use efficiency (NUE).

Numerous DOE sponsored, long-term Free-Air CO₂ Enrichment (FACE) experiments have provided novel insights into the ecological mechanisms controlling the cycling and storage of carbon in terrestrial ecosystems. These studies have significantly contributed to our ability to project how ecosystems respond to increasing CO₂ concentrations in the Earth’s atmosphere. In this synthesis and review led by Oak Ridge National Laboratory, important lessons emerged by evaluating a set of hypotheses that initially guided the design and longevity of forested FACE experiments. Net primary productivity is increased by elevated CO₂, but the response can diminish over time. Carbon accumulation in ecosystems is driven by the distribution of carbon among plant and soil components with differing turnover rates and by interactions between the carbon and nitrogen cycles. Plant community structure may change, but elevated CO₂ has only minor effects on microbial community structure. FACE results have provided a strong foundation for next-generation experiments in unexplored ecosystems. FACE results also inform coupled climate-biogeochemical models of the ecological mechanisms controlling ecosystem response to the rising atmospheric CO₂ concentration.

The accumulation of anthropogenic CO₂ in the Earth’s atmosphere and its impact on the rate of climate warming also impacts plant growth. Here, DOE-funded scientists synthesize data from the Rhinelander FACE (Free Air CO₂ Enrichment) experiment in which three developing northern forests have been exposed to combinations of elevated CO₂ and O₃. Enhanced growth (~26% increase) under elevated CO₂ was sustained by greater root exploration of soil for growth-limiting nitrogen, as well as rapid rates of litter decomposition and microbial nitrogen release during decay. Despite initial declines in forest productivity under elevated O₃, compensatory growth of O₃-tolerant trees resulted in equivalent growth under ambient and elevated O₃. After a decade, productivity has remained enhanced under elevated CO₂ and has recovered under elevated O3. The mechanisms responsible for these CO₂ and O₃ effects need to be represented in coupled climate-biogeochemical models simulating interactions between the global carbon cycle and climate warming.

Disturbances such as fire, insect infestations, or extreme storms are often evaluated for their impacts on forest ecosystem carbon cycling. However, in addition to the direct effects of killing trees on the carbon cycle, these changes in land cover can also lead to changes in albedo, a measure of the absorbance versus reflectance of solar radiation from the Earth’s surface. In a recent paper, DOE scientists O’Halloran et al. compare these three disparate forest disturbance events and show that they cause similar magnitudes of change in albedo and carbon flux. Due to the long time scale for forest growth, such changes are likely to persist for decades, and both need to be represented in Earth system models.

How do clouds change with climate change? This is one of the great unsolved problems of climate change whose solution is critical because cloud changes may counter or enhance temperature changes. Climate models struggle to accurately represent clouds, because the equations used cannot completely describe clouds. Satellite observations of clouds have provided important tests for models, but models and satellites “view” clouds differently since satellites only get time-limited snapshots of clouds. Scientists at Lawrence Livermore National Laboratory, in collaboration with scientists worldwide, have created a diagnostic tool known as the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) that enables scientists to compare satellite and climate model views of clouds. COSP converts model clouds into pseudo-satellite observations with an approach that mimics the satellite view of an atmospheric column with model-specified physical properties. COSP is now used worldwide by most of the major models for climate and weather prediction, and it will play an important role in the model evaluation being reviewed in the next report of the Intergovernmental Panel on Climate Change. This study already reveals information on cloud representation in climate models: for example, an under-representation in all models of mid-level and cumulus clouds, and better performance by a detailed regional model compared to global models. In sum, COSP facilitates a more rapid improvement of climate models, and it will ultimately reduce uncertainty in climate predictions.

Carbonic anhydrase (CA) converts bicarbonate ion to carbon dioxide and back. It is a key part of the metabolism of humans, animals, plants, and microbes that involves carbon dioxide. Engineered and stabilized forms of CA are being studied for use to capture CO₂ from flue gas at coal-fired power plants and as part of algal biofuel production. Three recent publications improve our understanding of how CA works using the unique capabilities of DOE’s National Synchrotron Light Source (NSLS) and Los Alamos Neutron Science Center (LANSCE). X-ray crystallography at the NSLS was used to show how human CA recognizes molecules to which it might bind. These data support the authors’ hypothesis from thermodynamic considerations that “the shape of the water in the (HA) binding cavity may be as important as the shape of the cavity.” The second study, used neutron diffraction of human CA at LANSCE to show that the catalytic site CA changes when the pH of the water around it decreases from 10.0 to 7.8. This observation, the first of its kind, enabled the authors to define more clearly the proton transfer that occurs when CA catalyzes the carbon dioxide—bicarbonate conversion. These studies will help scientists re-engineering CA designs for CO₂ capture, biofuel production, and other applications.

The NSLS studies were carried out by scientists at Brookhaven’s Macromolecular Crystallography Research Resource jointly with scientists from Harvard University, while the LANSCE experiments were carried out by scientists at Los Alamos’ Protein Crystallography Station in collaboration with scientists from the University of Florida.

The sugars in plant cell walls have the potential to be converted on a large scale to biofuels; however, these sugars are locked in a rigid lignin matrix, inhibiting their extraction and conversion into biofuels. Researchers have now discovered a potential way around this obstacle through studies of the maize Corngrass1 (Cg1) gene, which promotes maintenance of juvenility in maize plants. Since juvenile plant material contains less lignin, they hypothesized that this mutant might produce plants whose sugars would be more easily extracted and converted into biofuels. When the Cg1 gene was transferred into other plants, including the potential bioenergy crop switchgrass, the amount of starch and subsequent glucose release was significantly higher than from the wild type plants even without expensive pretreatment. These results offer a promising new approach for the improvement of dedicated bioenergy crops. The research was carried out at the USDA-ARS, University of California, Berkeley, DOE’s Joint BioEnergy Institute, and the Energy Biosciences Institute, and supported in part by the joint USDA-DOE Plant Feedstocks Genomics for Bioenergy program. It is published in the Proceedings of the National Academy of Sciences.

Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. However, whereas some SOM persists for millennia, other SOM decomposes readily, according to phenomena that we currently do not understand. This limits our ability to predict how soils will respond to climate change. DOE scientists from Lawrence Berkeley National Laboratory have recently demonstrated that SOM molecular structure alone does not control SOM stability; in fact, environmental and biological controls predominate, such as interdependence of compound chemistry, reactive mineral surfaces, climate, water availability, soil acidity, soil redox state, and the presence of potential degraders in the immediate environment. In other words, the persistence of soil organic carbon is primarily not a molecular property, but an ecosystem property. The authors also propose ways to include this understanding in a new generation of experiments and soil carbon models that will improve predictions of the SOM response to global warming.

DNA sequencing technologies generate a tremendous amount of genomic data compared to just a few years ago. Today, however, most genomic data is for small DNA fragments that need to be assembled back into a whole genome to elucidate the biological function of the parent organism. This represents a computational challenge for the sequencing community, in particular when the amount of genomic data reaches more than a hundred million fragments. DOE Joint Genome Institute researchers have now developed an efficient algorithm, Meraculous, to assemble the short genomic fragments into whole genome sequences. Meraculous can quickly and accurately assemble microbial genomes with a fraction of the computer memory required for more traditional methods, thanks to the use of novel techniques in graph theory and in memory-efficient hashing schemes. JGI staff have tested this method on Pichia stipiti, a microbe that efficiently produces ethanol from the five-carbon sugar xylose and found that they were able to quickly reconstruct 95% of the genome, error free. Research at JGI continues to advance this algorithm with applications to more complex plant genomes planned.

Development of next-generation biofuels will require economical production of high-energy compounds that are compatible with existing vehicle engines and fuel distribution infrastructures. To this end, researchers at the DOE Joint Bioenergy Institute (JBEI) have been exploring potential fuel properties of molecules in the terpene family. Many terpene molecules possess properties similar to petroleum-derived fuel compounds, and industrial microbes such as yeast and E. coli have been previously engineered for terpene compound synthesis for pharmaceutical production. In a new study published in Nature Communications, JBEI scientists describe production of the terpene bisabolane, a molecule with fuel properties similar to D2 diesel. After identifying bisabolane as a promising biofuel, the team embarked on a series of targeted genetic modifications to terpene synthesizing E. coli and yeast strains, resulting in microbial production of the compound using simple sugars as the starting material. Unlike other biofuels such as ethanol and isobutanol, bisabolane was found to be relatively nontoxic to the microbes and thus could potentially be produced at higher yields. Efforts are currently underway to screen the fuel properties of biologically produced bisabolane and develop improved fermentation strategies that would enable scaling of production to commercial levels.

Redirecting a microbe’s metabolic pathways to make desired products frequently results in slower growth, lower yield, and other negative impacts that reduce production efficiency. This is often related to the accumulation of toxic intermediates at metabolic “bottlenecks” in microbes lacking natural pathways to use, redirect, or dispose of these compounds. Researchers at the DOE Joint Bioenergy Institute (JBEI) have observed this phenomenon in E. coli strains expressing an engineered pathway for the synthesis of terpene, a precursor of several different hydrocarbon biofuels. To alleviate this toxicity, the team screened genome databases to identify variants of the enzyme in other organisms that are able to process the problematic compound. The enzymes were expressed in vitro and assayed for activity, and genes encoding the most promising candidates were engineered into E. coli. This produced a set of strains with varying synthesis properties under different growth conditions. Subsequent manipulation of gene expression levels, cofactor pools, and redox conditions resulted in a 120% improvement in terpene production over the initial strain. These results further improve an already promising industrial microbe and demonstrate the potential of coupled systems biology and targeted metabolic engineering for enhancing biofuel production.

The Conservation Reserve Program (CRP) contains over 13 million hectares of former croplands now in grasslands, providing a reservoir of biodiversity, water quality, and carbon sequestration benefits. However, these benefits could be lost if the land is converted back to agricultural use for biofuel production. Scientists from the DOE Great Lakes Bioenergy Research Center analyzed the effects that converting CRP lands to annual crops for biofuel production (continuous corn and corn-soybean rotation, each either tilled or permanent no-till) would have on greenhouse gas (GHG) emissions as compared with directly harvesting perennial grasses on these lands for cellulosic ethanol. They report that although a no-till management regime of an annual bioenergy crop would reduce the carbon debt significantly compared with tilling, harvesting perennial grasses would result in virtually no GHGs lost, because the disruption required when converting to annual crops would be avoided. This is the first time field trials have been used instead of model predictions. The trials show that carbon debt can be avoided and climate change mitigated by directly using unconverted CRP grasslands for cellulosic feedstock production. The results will be helpful in developing strategies for producing bioenergy crop systems.

Transporter proteins control the flow of large and small molecules in and out of the cell and are a primary means for organisms to interface with the environment. Transporters affect cellular metabolic capabilities and influence signaling pathways and regulatory networks that are key to the cell’s behavior. DOE researchers have confirmed the efficacy of a high-throughput methodology to rapidly and specifically identify the molecules transported by these proteins. The new technique measures the change in the melting temperature of proteins. Using Rhodopseudomonas palustris as a test case, they found a variety of compounds bound to the transporters studied that were not predicted using standard computational methods. These findings illustrate the potential of this method to expand our ability to predict the response of microbes and cells to environmental changes, such as the utilization of environmental nutrients and the ejection of toxic compounds.

Pretreatment of bioenergy feedstocks produces complex chemical changes that need to be understood to evaluate the effectiveness of different pretreatment regimens. Feedstock imaging can provide useful information, but high molecular specificity is required to identify components such as cellulose and hemicellulose and to produce useful spatial images. Simple mass spectrometry (MS) is limited by the complexity of the plant tissue. University of Florida researchers have successfully overcome this difficulty by applying matrix-assisted laser desorption/ionization mass spectrometry (MALDI) linear ion trap tandem MS technology. In tandem MS, the material goes through two consecutive rounds of MS instead of one. While single MALDI MS images of young Populus wood stems show an even distribution of both cellulose and hemicellulose, tandem MS produces very different images of the distribution of the two plant components. The new strategy offers the high molecular specificity needed for analyzing complex lignocellulosic biomass and will be applicable to many plant species that are potential bioenergy resources.

Poplar, a model organism for woody perennials, is a promising bioenergy feedstock for producing cellulosic biofuels. Poplar roots establish intimate associations with various microorganisms, both bacterial and fungal, that are beneficial to both plant and microbe. However, these associations are still poorly understood. Researchers at Oak Ridge National Laboratory have published the first results of a comprehensive study of the poplar rhizosphere (soil in direct contact with plant roots) and endophytic (living within plant tissues without causing harm) microbial communities from mature, natural poplar stands. They investigated microbial diversity among root endophyte and associated rhizosphere communities from two poplar populations differing in soil and stand characteristics near the Caney Fork River in central Tennessee. Although soil was not a major determinant of microbial distribution and diversity, the rhizosphere and endophyte communities of both bacteria and fungi were distinct. The results suggest that tissues within naturally occurring poplar roots provide a unique niche for these microorganisms. The research has implications for the growth and management of poplar plantations established for biofuel production.

Plants and other organisms synchronize their internal processes with the environment through circadian clocks to cope with natural cycles of light and temperature. These temporal rhythms coordinate physiological and metabolic processes with daily and seasonal changes by helping coordinate gene expression that enable organisms to adapt. Researchers at Oregon State University and collaborators used a combination of genomics and bioinformatics technologies to investigate daily rhythms in gene expression in the monocot plant rice and the dicot plant poplar. They compared their findings to work previously performed in the model plant Arabidopsis. They found a high degree of conservation across the three species among the cycling patterns of many circadian clock genes. This new research indicates that a core regulatory network is conserved across higher plants, although some cases of species-specific diurnal/circadian-associated regulatory circuits were observed. The findings have implications for engineering plants with enhanced vigor, fitness, and adaptation to changing environments. The research was supported in part by the joint USDA-DOE Plant Feedstocks Genomics for Bioenergy program.

The long-term ARM Cloud Retrieval Ensemble Dataset (CRED) was introduced recently for all ARM permanent research sites (i.e., Southern Great Plains in Oklahoma, Tropical Western Pacific, and the North Slope of Alaska) to the climate change research community. CRED is a multi-year cloud microphysical property ensemble dataset created by assembling nine existing ground-based cloud retrievals that have been developed by different research groups. The intent of the dataset is to provide a rough estimate of the uncertainties in current retrieved cloud microphysical properties for climate model evaluation and development. The current CRED dataset contains: (1) cloud liquid effective radius, liquid water content, and liquid water path, (2) cloud ice effective radius, ice water content, and ice water path, (3) cloud liquid optical depth and ice optical depth at solar wavelength, and (4) cloud fraction and cloud total column fraction. These quantities are averaged over one-hour time intervals with a vertical resolution of 45 meters, consistent with the ARM Climate Modeling Best Estimate (CMBE) dataset, to facilitate the use of the cloud property data by climate modelers.

The creation of CRED represents an integrated effort from a focus group that consists of Atmospheric System Research scientists with expertise in cloud retrievals and ARM experts. For more details, please see the README file (login, registration required) and the technical report (report ) associated with this dataset.

Urban regions account for an increasing fraction of global air pollutants, but urban-scale aerosol processing is not included in global atmospheric models due to the computational demands of modeling at such detailed temporal and spatial scales. Now, a DOE team from the MIT Joint Program on the Science and Policy of Global Change has developed a detailed air quality meta-model that includes this processing. This urban processing model was used in a global 3-D chemical transport model to simulate the effects of cities around the world on aerosol chemistry, physics, and radiative effects at the global scale. The study compares the new method with the traditional approach of diluting total aerosol emissions across global model grid cells, which does not capture the heterogeneity of urban and non-urban areas within each grid cell. The researchers found that the urban processing model predicted a lower concentration of atmospheric aerosols than the dilution method, particularly in the Northern Hemisphere and during the summer season. In addition, the urban processing model showed increased concentrations of primary aerosols, like black carbon and organic carbon, and decreased concentrations of secondary aerosols, like sulfates. The results show that the traditional dilution method leads to significantly more negative aerosol radiative forcing compared to results that include detailed urban-scale processing.

If a single organism could breakdown cellulosic biomass and synthesize biofuels, a process known as consolidated bioprocessing, it could significantly increase the efficiency and reduce the costs of biofuel production. Some biomass-degrading microbes such as Clostridium thermocellum can also synthesize ethanol, but they are poisoned by relatively low ethanol concentrations compared to sugar fermenters such as yeast or E. coli. Researchers at the DOE Bioenergy Science Center (BESC) have now identified a key gene in C. thermocellum that is related to enhanced ethanol tolerance. The team analyzed genomes of C. thermocellum mutants that could tolerate higher than normal ethanol concentrations, and found a consistently modified gene involved in alcohol metabolism. By analyzing the structure of the encoded protein, it was determined that the mutation causes significant alterations to central ethanol metabolism. The identification of this gene will enable more targeted metabolic engineering approaches to improve production of ethanol and other biofuels in C. thermocellum and other biomass-degrading microbes useful for consolidated bioprocessing.

Recent reports indicate that common anaerobic subsurface microbes respire metal-containing minerals and radionuclide contaminants via appendages, known as “nanowires,” on their cell surface. These nanowires facilitate electron transport from central metabolism inside the cell to electron acceptors on the outside of the cell. New results from a DOE team led by the University of Massachusetts show that microbial pili composed of natural proteins exhibit metal-like conductivity in the absence of cytochromes and function as “nanowires,” a finding that could have far-reaching biotechnological and bioelectronic implications. Researchers have shown that they could manipulate biofilms grown in microbial fuel cells, “tuning” electrical conductance depending on the expression of specific genes associated with pili (“nanowire”) production. Furthermore, X-ray diffraction and electrical studies of purified “nanowire” filaments attribute the electron-conducting behavior to the molecular structure of the pili that results in close alignment of aromatic groups within the amino acid components facilitating p-orbital overlap and charge delocalization. The data help to explain how these microorganisms respire solid minerals and radionuclide contaminants in anaerobic subsurface environments and has far-reaching implications for nanomaterial biodesign and biotechnology.

Some microbes contain enzymes that can break down lignocellulosic biomass, such as that found in switchgrass or Miscanthus. But there are few suitable methods for finding these enzymes in complex microbial communities. Researchers at the DOE Joint BioEnergy Institute (JBEI) have developed a new method that uses nanostructure initiator mass spectroscopy (NIMS). It enables rapid and accurate characterization of enzymes in complex microbial and environmental samples (e.g., microbial compost). Using this new technology, JBEI researchers have characterized a broad range of environmental and purified microbial samples, further optimizing selected samples for enzymatic activity and stability in the presence of ionic liquids, which are being tested by JBEI for use in biofuel production. This new NIMS-based approach may aid in finding more efficient ways to convert biomass into lignocellulosic biofuels.

Due to its abundance and high cellulose content, wood has great potential as raw material for the production of biofuels. However, wood also contains lignin, a hard-to-degrade polymer that poses a major obstacle to converting its cellulose into liquid fuels. White rot fungi have evolved mechanisms to digest lignin and cellulose, and scientists are trying to take advantage of these capabilities. Now, new research using genome sequencing and comparative analysis of the brown rot fungus Serpula lacrymans has discovered a different strategy used by this boreal forest fungus to extract the energy-rich cellulose from conifer wood. A comparison of the gene content in white and brown rot fungi indicates that the enzymatic machinery to degrade lignin has been eliminated in brown rot fungi, enabling it to specifically target cellulose, separating it from the recalcitrant lignin. The researchers also discovered that in the presence of wood, S. lacrymans produces variegatic acid, a phenolate compound that helps in reducing iron ions to Fe⁺², which are required for the initial non-enzymatic steps in cellulose degradation upon wood colonization by the fungus. These insights provide researchers with new strategies to potentially bypass the problem of eliminating lignin from renewable woody feedstocks for transportation fuel production. The research has just been published in Science and was carried out by an international consortium including researchers at DOE’s Joint Genome Institute in Walnut Creek, CA, and its partners HudsonAlpha Institute for Biotechnology (Huntsville, AL) and Pacific Northwest National Lab (Richland, WA).

Photosynthetic algae are a potential bioenergy source; however, significant unknowns about their basic metabolic properties have hindered development of algae for biofuel production. DOE researchers now present a new metabolic network reconstruction and a genome-scale model of light-driven metabolism for the alga Chlamydomonas reinhardtii. This approach represents a significant advance over previous metabolic models for this organism since it incorporates greatly improved functional gene annotations, experimental validation of gene expression, and quantitative reaction measurements under different light conditions. This model allows enhanced understanding and prediction of photosynthetic growth properties (including lipid synthesis) under varying conditions and provides a broad knowledgebase of potential targets for directed metabolic engineering. This publication was featured in the Editor’s Choice section of the August 12^th issue of Science.

The forest soil environment is teeming with microbial communities, including a group of mutualistic fungi known as the ectomycorrhizae. These organisms develop a close association with tree roots, establishing an exchange of nutrients and sugars essential for the health of both plant and microbe. While this phenomenon has been known for a long time, the signaling and regulatory mechanisms of this exchange are poorly understood. Researchers at the DOE Oak Ridge National Laboratory, as part of an international collaboration, have identified and characterized a protein called Mycorrhizal Induced Small Secreted Protein 7 (MiSSP7) that is secreted from the ectomycorrhizal fungus Laccaria bicolor in response to signals diffused from the roots of poplar trees, a promising bioenergy feedstock. They found that this very small protein is imported into the nucleus of the host plant cell where it alters the expression of certain plant genes, similar to the manner in which fungal pathogens work. The result is a “reprogram-ming” of plant cells, through which a beneficial, symbiotic relationship between fungus and plant is established. This relationship enhances growth and productivity of the tree. Understanding the underlying mechanism will help address diverse DOE missions, including bioenergy production, environmental remediation, and carbon cycling and sequestration.

Climate model simulations are based on solving equations of motions in the atmosphere and ocean. To make the solutions feasible, the equations must be simplified, retaining only the most important terms. However, the solutions to these simpler equations can become unstable, resulting in unrealistic simulations. To address this issue, damping or dissipation is added to smooth out the unrealistic behaviors. Models either implement this dissipation directly, or the numerical methods used to solve the equations include smoothing effects. All climate models need smoothing, but there has not been a systematic evaluation of how various models achieve this. DOE-funded researchers have investigated and compared the dissipation processes used in the fluid dynamics component of climate models, providing a comprehensive survey of the diffusion, filters, and fixers in dynamical schemes of over 20 general circulation models. They focused on dissipation used in the Community Atmosphere Model (CAM), part of the DOE-supported Community Earth System Model (CESM) at the National Center for Atmospheric Research. Using idealized test cases, the investigators isolated causes and effects of individual dissipation mechanisms, demonstrating that the choice of the dissipation processes directly impacts the accuracy and stability of the simulations. Dissipation even has the potential to alter the large-scale circulation pattern and thereby the outcome of climate simulations. The survey reveals the important role that the stabilizing methods have on atmospheric dynamics and offers practical guidance in choosing adequate subgrid-scale mixing schemes.

Perennial grasses such as switchgrass are considered prime candidates for bioenergy feedstocks because of their potential for substantial biomass yields on marginal lands. An approach that promises further improvement in this species is genetic transformation, the introduction and expression of desirable genes from other sources to increase yields and reduce recalcitrance. Current transformation technology, however, uses promoters (segments of DNA that control the expression of desired genes) from other plants making them inefficient for use in switchgrass. Researchers from the DOE BioEnergy Science Center (BESC) now report the identification of novel promoter regions from a specific switchgrass gene that is found in all eukaryotes and that can be used for efficient genetic transformation in switchgrass. A variety of transgenic plants constructed with these promoters exhibited significantly higher gene expression levels than observed using the non-switchgrass promoters, showing great potential for driving transgenic expression in switchgrass and other plants. This is the first characterization of native switchgrass promoter sequences for transgene expression. The results will facilitate improvement of switchgrass and other bioenergy feedstocks through engineering of key bioenergy-relevant traits.

Understanding the formation and evolution of small ice particles in clouds has been a long-standing issue in cloud physics. Improving measurements of ice particles is critical for improving the predictive capabilities in the models since small ice particles (less than 100 microns) may play a significant role in radiation transfer and precipitation formation. In an effort to improve understanding of ice particles in clouds, the Atmospheric Radiation Measurement Climate Research Facility, a DOE scientific user facility, recently completed the Small Particles in Cirrus (SPartICus) experiment to examine cirrus clouds. A central focus was to address the challenging problem of large ice particles shattering on the inlets and tips of cloud particle probes, a process that produces copious ice particles that can be mistakenly measured as real ice particles. Currently, two approaches are being used to mitigate the problem: (1) redesigned probe tips and (2) improved post processing techniques. Results from SPartICus show that modified probe tips significantly reduce the number of shattered particles, but that a new particle arrival time algorithm is even more effective than the redesigned probe tips in giving accurate ice particle measurements. The analysis techniques in this paper can also be used to estimate an upper bound for the effects of shattering. This new technique provides an enhanced tool for characterizing the properties of clouds so that their representation can be improved in global climate models.

A three-dimensional climate model has been updated to better examine the environmental impacts of large-scale, offshore wind turbine deployment by DOE researchers at the MIT Joint Program on the Science and Policy of Global Change. Building on previous research suggesting that land-based wind turbines large enough to meet ~10% of predicted world energy needs in 2100 could cause localized surface warming, the updated model’s spatial resolution was increased and six additional simulations were modeled to address the potential environmental and intermittency issues for differing offshore installation areas and spatial densities. In contrast to the land-based results, the MIT researchers found that offshore wind turbine installations cause a local surface cooling effect, exceeding 1° K in the highest density case. This cooling is primarily due to the enhanced latent heat flux from the sea surface to the lower atmosphere, driven by an increase in turbulent mixing caused by the wind turbines. The study also found that the perturbation to global circulation caused by the large-scale deployment of offshore wind turbines is relatively small compared to the case of land-based installations. The study also demonstrated significant seasonal wind variations, highlighting an intermittency issue for potential power generating and distributing systems over several major offshore sites.

Cellobiose, a two glucose basic repeat unit of cellulose, is formed in enzymatic or acidic hydrolysis of plant biomass and is the precursor compound that microbes digest to produce cellulosic biofuels. Because this process happens outside of the microbial cell, understanding the structure and stability of cellobiose in solution provides a framework for improving microbial biofuel production. Interestingly, the low-temperature, gas-phase stable, preferred structure of cellobiose is cis, while the high temperature structure is trans. However, in cellulose itself, cellobiose is always in the trans state. Researchers believe that the stability of trans-cellobiose could be due to the water environment that surrounds it. Now, an international collaborative study has found that water molecules hydrate cellobiose collectively instead of binding to cellobiose separately and sequentially as was previously assumed. The team used DOE’s National Energy Research Scientific Computing Center, a high-performance computing facility, to simulate cellobiose dynamics together with vibrational spectroscopy experiments. Their results suggest that water dynamics could play a critical role in determining the most stable structure of cellobiose. The next step in this research will be to produce a simulation of cellobiose that includes the quantum and dynamically polar nature of water. It is anticipated that this new research will provide insight into how to optimize the hydrolysis of plant-derived cellulose, a key step in the production of biofuels. The computational aspects of the research were funded by DOE’s SciDAC program.

River flow has decreased significantly in recent decades, but the causes are not well understood. DOE scientists investigated how climate (temperature and precipitation changes), rising atmospheric CO₂ concentrations (independent of effects on climate), increasing anthropogenic nitrogen deposition, and land-use change influenced continental river flow from 1948 to 2004. Nitrogen and CO₂ affect vegetation, which alters ground hydrology. The study used the Community Land Model version 4 (CLM4) with a coupled global river routing scheme. Model results were compared to river flow from the world’s 50 largest rivers. Both mean river flow and river flow trends from model predictions were significantly correlated with observed values. Model results show a significant decreasing trend in global river flow and indicate that climate is the dominant factor responsible for the downward trend. Nitrogen deposition and land-use change account for about 5% and 2.5% of the decrease in simulated global scale river flow, respectively, while rising atmospheric CO₂ concentration causes an upward trend. However, the relative role of each driving factor is variable across regions in the simulations. For example, the decreasing trend in river flow for the Amazon River basin is primarily explained by CO₂, while land-use change accounts for 27% of the downward trend in river flow for the Yangtze River basin. The study suggests that to better understand river flow trends, it is not only necessary to take climate into account, but also to consider atmospheric composition, carbon-nitrogen interaction, and land-use change. This multi-factor approach to the analysis of Earth system response to climate and anthropogenic forcing is particularly important for understanding regional-scale dynamics.

It is critical to understand the role of agricultural practices on soil greenhouse gas (GHG) emissions as expanded collections of agricultural residues are considered for bioenergy production and shifts are made to farming dedicated bioenergy crops. Production and consumption of carbon dioxide, methane, and other GHGs are predominantly mediated by soil microbes, yet the relationship between functional processes and microbial diversity in these systems is poorly understood. Researchers at the DOE Great Lakes Bioenergy Research Center (GLBRC) have examined agricultural GHG production, linking these processes to microbial community activities. The study included agricultural soils under various management practices, both successional grasslands on abandoned agricultural land and mature forests or grasslands that had never been farmed. GHG production and consumption rates were correlated to soil microbial community composition. Rates of methane consumption were found to be highest in non-agricultural forests and grasslands, which also showed the greatest diversity of methane-consuming microbes (i.e., methanotrophs). Successional sites were intermediate in terms of both methane consumption and methanotroph diversity, suggesting a gradual recovery process following disruption by traditional tillage agriculture. These results have important implications in considering sustainable establishment and long-term management of bioenergy landscapes and predictive modeling of GHG emissions.

This study addressed the impact of a class of short-lived climate forcers, radiatively active substances whose rate of turnover in the atmosphere makes them candidates for possible climate mitigation strategies. The study describes estimates of different ozone precursor emission contributions from different geographical regions towards global net radiative forcing. Ozone (O₃) precursor emissions influence regional and global climate and air quality through changes in tropospheric O₃ and other oxidants, which also influence methane (CH₄) and sulfate aerosols (SO₄⁼). The Task Force on Hemispheric Transport of Air Pollution Source-Receptor global chemical transport model (CTM) was used to simulate changes in the tropospheric composition of O₃, CH₄, SO₄⁼, and global net radiative forcing (RF) for 20% reductions in global CH₄ burden and in anthropogenic O₃ precursor emissions (nitrogen oxides (NO_x), non-methane volatile organic compounds (NMVOC), and carbon monoxide (CO)) from four regions (East Asia, Europe and Northern Africa, North America, and South Asia). NOx reductions produced positive RF, while CH₄, NMVOC, and CO reductions yielded a negative RF. A positive RF (more incoming energy) tends to warm the system, while a negative RF (more outgoing energy) tends to cool it. RF is also more sensitive to NO_x and NMVOC emission reductions from regions closer to the equator. Variability in the global warming potential among different regions for NO_x and NMVOCs suggests that the availability and use of regionally specific estimates would be important. This information will be useful for policymakers as they try to control future air quality and climate change.

While biophysical climate research depends strongly on historical records, a challenge for socio-economic research concerns the need to look forward and act today based on expectations of the future. DOE researchers from the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change have published a new study comparing the results of their Emissions Prediction and Policy Analysis (EPPA) model solved as a recursive dynamic or as a forward-looking model. A recursive dynamic solution is obtained by solving for equilibrium in all markets one period at a time, reflecting the historical evolution of capital stock, GDP growth, prices, and other time-dependent variables. In a forward-looking model, all time periods are solved simultaneously using a simplified model, and so decisions made in early periods take into account the values of economic variables in all future periods. The goal was to see how the forward-looking solution approach affected results, since more complex versions of the EPPA model cannot be solved as forward-looking models. The study analyzes three different U.S. climate scenarios with varying levels of emissions abatement occurring between 2012 and 2050. The researchers found that the forward-looking and the recursive models produced very similar emissions abatement and carbon price paths, reassuring consistency, as it implies that the recursive model, which can be extended to longer time horizons and more geographic regions and include greater technological detail, approximates well the results of the forward-looking approach in terms of prices and emissions.

“Brown rot” and “white rot” fungi from forest floors are among the few organisms on Earth that can fully degrade both the long, repeated sugar chains (cellulose and hemicellulose) and the complex, interlinked network of aromatic compounds (lignin) that make up woody plant material. The two classes of fungi use distinct (but poorly understood) enzyme systems to break down biomass and show strong preferences for particular types of wood. A collaborative team of researchers at the DOE Great Lakes Bioenergy Research Center and the DOE Joint Genome Institute have examined representative species of brown and white fungi to determine which specific genes involved in biomass deconstruction are deployed to attack aspen or pine wood. These studies revealed that the two types of fungi used distinct deconstruction systems, and the expression of these systems was heavily influenced by the type of wood being degraded. Many genes identified in the study correspond to known biomass degradation enzymes, but a significant fraction have no currently known catalytic function and will be the subject of further investigation. The results of this study increase our understanding of molecular mechanisms that allow degradation of biomass and could lead to the identification of new systems for plant deconstruction and biofuels production.

A team of researchers from the University of East Anglia and Pacific Northwest National Laboratory have determined, for the first time, the molecular structure of the proteins that enable the bacterium Shewanella oneidensis to transfer an electrical charge. The bacteria survive in oxygen-free environments by constructing small wires that extend through the cell wall and contact minerals—a process called iron respiration or “breathing rocks.” Proteins within these wires pass electrons outward to create an electrical charge. Using resources at the Environmental Molecular Sciences Laboratory (EMSL), including X-ray crystallography, the scientists gained new insights about how these proteins work together to move electrons from the inside to the outside of a cell. Identifying the molecular structure of these proteins is a key step toward potentially using microbes as a source of electricity; for example, by connecting them to electrodes to create microbial fuel cells. Because the bacteria also trap and transform the minerals they contact, the new information could advance the development of microbe-based agents for use in environmental remediation such as cleaning up legacy radioactive waste. EMSL is a Department of Energy national scientific user facility.

Over the past two decades, scientists have linked the decrease in the pH levels of the global oceans and the corresponding slowdown in coral growth to the increasing levels of carbon dioxide trapped in the atmosphere and which, in turn, are being absorbed in the ocean. As coral reefs are the primary habitat for several marine organisms, their decline has significant impacts on the health of the marine ecosystems and ocean productivity. To better understand how corals contribute to the global carbon cycle, the DOE Joint Genome Institute (JGI) generated a dataset of expressed sequence tags or ESTs, small portions of a genome that can be used to help identify unknown genes and chart their locations along the sequence, from the reef-building coral Acropora palmate. In a study published online May 25, 2011, in PLoS ONE, a team of researchers including DOE JGI’s Erika Lindquist compared the A. palmate EST dataset to an EST dataset of another reef-building coral to identify the proteins involved in helping corals adapt to global climate change. The comparative analysis identified several proteins evolving at an accelerated rate, such as those involved in immunity, reproduction and sensory perception. “The category that was the most enriched with rapidly evolving proteins —cell adhesion—may also be related to symbiosis,” noted the study authors in their paper. These proteins are expected to evolve under positive selection due to the need for readjustments, e.g., due to the “arms race” between the coral and the bacterial symbionts. This research provides insights into the impacts of climate change at the biological level.

Subsurface uranium is a significant contaminant at U.S. Department of Energy sites. Remediation solutions include immobilizing contaminants to prevent them from reaching groundwater. Using a model organism isolated from a uranium seep of the Columbia River, scientists recently quantified how extracellular polymeric substances (EPS) in subsurface environments can be used to immobilize heavy metal and radionuclide contaminants such as uranium [U(VI)]. In geologic systems, EPS can help bind microbes to mineral surfaces, influence cellular metabolism, and influence the fate and transport of contaminants. Using a novel biofuel reactor designed by scientists from the Environmental Molecular Sciences Laboratory (EMSL), the team prepared biofilms of a Shewanella species that produces EPS, and quantitatively analyzed the contribution of EPS to U(VI) immobilization. Using EMSL’s nuclear magnetic resonance capabilities to analyze chemical residues in EPS samples and cryogenic fluorescence spectroscopy to obtain sensitive U(VI) measurements, they tested the reactivity of loosely associated EPS and bound EPS with U(VI). The scientists found that, when reduced, the isolated cell-free EPS fractions could reduce U(VI) and the bound EPS contributed significantly to its immobilization, primarily through redox-active proteins. For loosely associated EPS, sorption of U(VI) was attributed predominantly to reaction with polysaccharides. These results could lead to the development of improved remediation techniques for subsurface contaminants.

DOE’s Atmospheric Radiation Measurement (ARM) Climate Research Facility is a multi-platform national scientific user facility that supports research for addressing the major uncertainties of climate models—clouds and aerosols—with heavily instrumented fixed research sites in Oklahoma, Alaska, and the tropical Western Pacific. It also provides mobile and aerial measurement platforms to support research at key sites around the world. This week, through an intergovernmental agreement with India, an ARM mobile facility began operating at the Aryabhatta Research Institute of Observational Sciences (ARIES) in India for the Ganges Valley Aerosol Experiment (or GVAX). Measurements obtained during the nine-month field study will enable scientists to study how aerosols—small particles like dust and soot—in the air affect the formation of clouds and whether they increase or decrease the amount of precipitation that falls from them. Their findings will be used to improve computer models that simulate Earth’s climate system. Some studies suggest that haze over the Ganges Valley region will increase temperature and pressure, which could draw moisture from the ocean and intensify seasonal monsoons. Other studies indicate the increased heat will cause clouds to dry up. To refine these possibilities, data are needed that span both the summer and winter monsoon seasons. This is the first large-scale field study that the United States or any other country has conducted in India that is related to environmental and climate issues. This collaborative experiment, in addition to DOE and the Indian government, involves NOAA, NSF, and the Navy.

Members of the Caldicellulosiruptor genus of bacteria, originally discovered in terrestrial hot springs, are unique in their ability to efficiently degrade cellulosic plant biomass at temperatures over 70°C. Researchers at the DOE Bioenergy Science Center (BESC) at Oak Ridge National Laboratory previously sequenced the genomes of several Caldicellulosiruptor species and characterized their abilities to degrade corn stover, switchgrass, and other biomass feedstocks. In a new study, BESC scientists used mass spectrometry-based proteomics to compare the complex mixture of enzymes secreted by two Caldicellulosiruptor species during cellulose degradation. Both of the organisms deployed carefully regulated configurations of multifunctional cellulase modules, tethered cellulose binding elements, and proteins that bind released sugars and return them to the cell. All of these elements were traced back to encoding genes on sequenced genomes. The secreted cellulase fractions from the Caldicellulosiruptors were found to work optimally at 85°C and pH 5, indicating significantly higher thermal stability and acid tolerance than current commercially available cellulase cocktails. These results present a promising source of novel cellulase enzymes for industrial development and provide new insights into the diversity of tools that microbes have at their disposal for biomass breakdown.

The simulation of rain processes in climate models is of crucial importance in determining cloud properties and thus the energy balance of climate models. Many current climate models treat these processes, such as the conditions for conversion of cloudwater to rainwater, as free tuning parameters to adjust cloud properties so that simulated top-of-atmosphere radiative fluxes broadly agree with observations. This empirical tuning has various disadvantages. In particular, there is a lack of explicit physical constraints in the tuning process and the need for retuning at different horizontal resolutions. In this publication, partially funded by DOE, the authors describe a novel method to constrain warm rain processes climate models based on observations. Rainfall formation is linked to cloud and precipitation conditions in a new way that is also independent of model resolution. The method might ultimately help to effectively eliminate these free tuning parameters in climate models. The new method was implemented into the University of Hawaii’s regional climate model iRAM. A series of test integrations were performed at horizontal resolutions ranging from 0.25°x0.25° to 2°x2°. The constrained approach was compared with a conventional approach commonly found in current climate models. Comparisons with an observational climatology of cloud liquid water amount reveal significant improvements, in particular a better consistency between different model resolutions. The study enables improved constraint for determining rain formation in climate models.

New research in Climatic Change finds that many areas of the globe are likely to permanently move into an unprecedented extreme heat regime over the next four decades should greenhouse gas concentrations continue to increase. The study, partially funded by DOE and conducted by scientists at Stanford University, includes analyses of a large suite of global climate model experiments and observational data revealing that global warming is already resulting in a novel heat regime. In addition, the authors find that global climate models are able to capture the observed intensification of seasonal hot conditions, demonstrating emergence of an extreme heat regime in which the coolest summer of the 21^st century is hotter than the hottest summer of the late 20^th century. In contrast to the common perception that high-latitude areas face the most accelerated response to global warming, the results demonstrate that tropical areas may exhibit the most immediate and robust emergence of unprecedented heat, occurring within the next two decades. The research implies that many areas outside of the tropics may exhibit a 50% likelihood of permanent emergence by the mid 21^st century, including areas of the United States, Europe, and China, even with relatively moderate average global warming.

Rapid economic growth over the last 30 years in China has led to a significant increase in aerosol loading, which is mainly due to the increased emissions of its precursors such as SO₂ and NO_x. In this study, partially supported by DOE, the authors show that these changes significantly affect wintertime clouds and precipitation over the East China Sea downwind of major emission sources. Satellite observations show a 50% increase of cloud droplet number concentration from the 1980s to 2005. In the same time period, precipitation frequency reported by voluntary ship observers was reduced from more than 30% to less than 20% of the time. A back trajectory analysis showed the pollution in the investigation area to originate from the Shanghai-Nanjing and Jinan industrial areas. A model sensitivity study was performed, isolating the effects of changes in emissions of the aerosol precursors SO₂ and NO_x on clouds and precipitation using a state-of-the-art regional model including chemistry and aerosol indirect effects. The model was able to simulate similar changes in cloud droplet number concentration over the East China Sea when the current industrial emissions in China were reduced to the 1980 levels. Modeled changes in precipitation were somewhat smaller than the observed changes but still significant. The study reveals a significant impact of local pollution on precipitation.

Among the options for reducing the emission of greenhouse gases such as carbon dioxide to the atmosphere is the injection of supercritical CO₂ into the deep subsurface for long-term storage. However, some scientists wonder whether ongoing geochemical processes in the subsurface will ensure that the supercritical CO₂ would remain sequestered. Efforts to study these processes require instrumentation that can handle samples at supercritical CO₂ pressure and temperatures. In response to this need, a team of scientists from the Environmental Molecular Sciences Laboratory (EMSL), a DOE scientific user facility in Richland, WA, Wright State University, and Lawrence Berkeley National Laboratory has developed a high-pressure atomic force microscope (AFM) that enables the first-ever measurements of the atomic-scale topography of solid surfaces that are in contact with supercritical carbon dioxide (scCO₂) fluids. Obtaining in situ, atomic-scale information about mineral-fluid interfaces at high pressure is particularly useful for understanding geochemical processes relevant to carbon sequestration. The ability to take in situ images as a function of time allows researchers to measure atomic-scale reaction rates by visualizing the dynamic processes that occur on the mineral surface and eliminates the need to alter experimental conditions between images. The new apparatus significantly extends the ability to make AFM measurements in environmental conditions not previously possible (in either commercial AFM instruments or in the few specially designed hydrothermal AFMs), and is designed to handle pressures up to 100 atmospheres at temperatures up to approximately 350 degrees Kelvin. The research team demonstrated the new microscope by imaging the disappearance of a hydrated calcium carbonate film on the calcite mineral surface in scCO2. The team met the technical challenge of maintaining precise control of pressure and temperature in the fluid cell, which is necessary to mitigate noise associated with density changes in a compressible fluid. The new apparatus can be used to study other gaseous or aqueous high-pressure solid-fluid chemical processes in addition to geochemical processes.

Methane is an important greenhouse gas. About half of annual methane emissions to the atmosphere result from terrestrial ecosystem sources that are poorly understood and represented in climate models. To characterize uncertainties, study feedbacks between methane fluxes and climate, and guide future model development and experimentation, DOE-funded researchers developed and tested a new methane biogeochemistry model (CLM4Me) integrated in the land component (Community Land Model; CLM4) of the Community Earth System Model. They compared the seasonality and magnitude of predicted CH4 emissions to observations from 18 sites and three global atmospheric inversions. They also used the model to characterize the sensitivity of regional and global methane emission estimates to uncertainties in model parameterizations. Several parameters critical to realistic prediction of future methane emissions dominated the uncertainty (with up to a factor of four in potential range of methane emissions). Most sensitive parameters include the temperature sensitivity of methane production and the treatment of methane chemistry. In a 21st century scenario, they found that predicted declines in high-latitude inundation may limit increases in high-latitude methane emissions. Finally, to address the high level of remaining uncertainty, the study describes observations and experiments that could improve regional and global methane biogeochemical models and therefore future predictions.

This month’s Quarterly Journal of the Royal Meteorological Society Special Issue gives an overview of the scientific results of the Convective and Orographically-induced Precipitation Study (COPS). The ARM Mobile Facility participated in the large, international COPS experiment that was conducted from June to August 2007 in a low-mountain area in southwestern Germany and eastern France covering the Vosges Mountains, the Rhine Valley and the Black Forest Mountains. An unprecedented combination of in situ instruments and remote-sensing systems constituted the largest combination of multi-wavelength passive and active remote-sensing systems deployed during a field campaign to date. The objective of COPS was to improve the skill of quantitative precipitation forecasting (QPF) in the models, one of the major challenges of atmospheric science. The focus of COPS was on analyses and model representations of the physical and chemical processes responsible for the deficiencies in QPF over low-mountain regions. Some of the most severe deficiencies in QPF have been identified in regions of orographic terrain, which are prone to flash-flood events, a major driver of COPS. One of the ARM-specific pagers examined the daytime cloud radiative effect (CRE) (i.e., the difference of cloudy and clear-sky net fluxes) for the period of the mobile facility deployment. The analysis shows that CRD uncertainty for overcast, single-layer water clouds is determined by uncertainties in liquid water content and effective radius.

The sustainability of higher ecosystem production under elevated atmospheric carbon dioxide (CO,₂) is unknown. Nitrogen (N) is often limited in ecosystems as a result of N incorporation into long-lived biomass and soil organic matter. As a result, N limitation could eventually limit or nullify increasing forest productivity under elevated CO₂ (i.e., “Progressive N Limitation”). In the first six years of the Oak Ridge National Laboratory (ORNL) FACE experiment, there was no apparent evidence that N limitation was exacerbated by elevated CO₂ or that N limitation reduced sweetgum tree growth. However, the CO₂ stimulation of sweetgum tree growth has more recently declined and was tentatively attributed to N limitation. Using stable N isotopes, temporal trends in sweetgum leaf litterfall ¹⁵N abundance provided strong evidence that N availability in the ORNL FACE plots has in fact declined over time, and declined faster in plots exposed to elevated CO₂, providing evidence for progressive N limitation. Although these results cannot be generalized for other FACE sites, examination of leaf litterfall d¹⁵N may provide an accurate indicator of soil N availability and progressive N limitation.

The potential migration of plutonium in the environment is a concern at DOE sites such as the Hanford Nuclear Reservation and the Nevada Test Site, as well as an issue in nuclear waste disposal for nuclear energy development. Using a number of transmission electron microscopy techniques Lawrence Livermore National Laboratory researchers and collaborating Clemson University scientists have provided important new understanding of the formation and the biogeochemical mechanisms controlling plutonium migration. Once thought immobile in the subsurface, it has been recently recognized that plutonium is capable of being transported with the colloidal faction of groundwater. The researchers examined the interaction of plutonium nanocolloids with environmentally relevant minerals such as iron-containing goethite and silicon-containing quartz. The studies revealed the molecular basis of potential binding through epitaxial growth between the plutonium nanocolloids and colloid goethite that may be a possible mechanism for enhanced plutonium transport. The results improve our understanding of how molecular-scale behavior at the mineral-water interface may facilitate transport of plutonium at the field scale, providing important molecular-level input to improve contaminant transport models and the prediction of plutonium behavior.

The promise of fuel cells as a clean-burning alternative to fossil fuels is highly attractive, but to reach their full potential, the internal components of fuel cells need to be more durable and less expensive. Catalysts inside fuel cells require a support structure that provides a base for electrical conductivity and permits an even distribution of the catalyst on the support. Current technology employs either a metal oxide or a type of carbon (e.g., graphene) as a support structure for catalysts such as platinum; however, platinum atoms tend to clump on carbon supports and to be unevenly distributed on metal oxide supports. Using both experimental and supercomputing capabilities at the Environmental Molecular Sciences Laboratory (EMSL), a scientific user facility in Richland, WA, a team of scientists from Pacific Northwest National Laboratory, Princeton University, and Washington State University not only created a new combination of platinum and support structure materials, but they demonstrated that with the new materials the catalyst was about 40% more efficient and the support structure was three times more durable and resistant to corrosion than currently used materials. By integrating EMSL’s experimental and computational capabilities, this research team was able to create and test a new material that has significant potential for improving the longevity of fuel cells and for reducing their cost. This research was supported by DOE’s Office of Energy Efficiency and Renewable Energy.

DOE scientists at MIT have released a publicly available version of a major component of the Integrated Global Systems Model (IGSM). IGSM is a comprehensive integrated assessment model, one of six such models in the world designed to explore the interactions between humans and the climate system, including the causes of global climate change and the potential societal and environmental consequences of such change. A major component of IGSM, Emissions Prediction and Policy Analysis (EPPA), is a multi-sector, multi-region computable general equilibrium (CGE) model of the world economy. EPPA projects economic variables (e.g., GDP, energy use, sectoral output, and consumption) and emissions of greenhouse gases (CO₂, CH₄, N₂O, HFCs, PFCs, and SF₆) and other air pollutants (CO, VOC, NOx, SO₂, NH₃, black carbon, and organic carbon) from combustion of carbon-based fuels, industrial processes, waste handling, and agricultural and other land-use activities. The licensed version of EPPA is available for non-commercial use with the intent of increasing transparency and advancing the development of new methods in integrated assessment research.

Advances in genome sequencing and the capability to develop genome-scale metabolic models have enabled the ability to predict microbial interactions. An analysis of two microbes known to compete in situ during tests of uranium bioremediation predicts how life strategies and growth rates for each are altered by substrate and nutrient availability and the implications of these interactions on uranium bioremediation strategies. DOE researchers from the University of Massachusetts and University of Toronto working with metabolic models for two metal-reducing microorganisms (Rhodoferax and Geobacter) present in the subsurface at a uranium bioremediation test site in Rifle, CO, explain how the introduction of acetate and the availability of ammonium impacts growth rates and the life strategies of these two organisms. Acetate addition in the absence of ammonium favors Geobacter metabolism consistent with field observations. However, the models predict that Rhodoferax metabolism should be favored in the presence of ammonium due to a higher overall growth rate. The results help explain field observations of decreased uranium bioreduction activity in areas with elevated ammonium concentrations. Unlike Geobacter species, Rhodoferax species are not known to reduce uranium indicating ammonium concentration as an important design criterion for uranium bioremediation.