11/23/2016

Multiple Observational and Modeling Perspectives Reveal Key Factors Controlling Arctic Cloud Phases

Teamwork provides insight into complicated cloud processes.

The Science

Observations over the last half century show that the Arctic environment has changed at a faster rate than the rest of the planet. Clouds have important impacts on the surface energy budget and, thus, on the melting or growth of land- and ocean-based ice, so they may play a key role in these changes. Many Arctic clouds are “mixed-phase,” consisting of both ice and liquid particles simultaneously. Correctly predicting the partitioning of mass and transitions between these two phases is critically important for understanding cloud impacts on Arctic climate because ice particles and liquid droplets impact atmospheric radiative transfer in substantially different ways.

The Impact

A team of scientists, primarily funded by the Department of Energy’s (DOE) Atmospheric System Research activity, developed a working group on cloud phase to bring together expertise from multiple observational and modeling perspectives and determine the key processes that control cloud phase partitioning. The team chose to a focus on a persistent stratiform mixed-phase cloud observed at DOE’s Atmospheric Radiation Measurement (ARM) site in Barrow, Alaska, on March 11-12, 2013. This case is of particular interest because substantial temporal variability in the liquid-cloud layer and associated ice precipitation was observed during the cloud’s 37-hour duration. The team found that major influences on the cloud were the large-scale advection of different air masses with different aerosol concentrations and humidity content, cloud-scale processes such as a change in the thermodynamical coupling state, and local-scale dynamics influencing ice particle residence time. Other factors (e.g., radiative shielding by a cirrus cloud and the influence of the solar cycle) played only a minor role in this specific case study.

Summary

The team used an extensive suite of ground-based remote-sensing instruments, including lidar and multifrequency vertically pointing and scanning radars operated at the ARM North Slope of Alaska atmospheric observatory in Barrow, combined with information on aerosol light scattering and absorption from National Oceanic and Atmospheric Administration instruments. To provide large-scale context for the case study and to examine important processes in more detail, multiple model approaches were employed. Limited area model simulations are used to identify processes that cause the descent of the cloud layer as well as the role of surface and large-scale forcing in the observed precipitation and phase partitioning transitions. Short-term forecasts from the Monitoring Atmospheric Composition and Climate (MACC) model are used to gain a wider perspective on aerosol transport at and around Barrow during the case study period, and help understand to what degree locally observed shifts in aerosol amount and type might be attributed to advection versus local processing.

During the 37-hour duration of the mixed-phase cloud over Barrow, substantial temporal variability in the liquid-cloud layer and associated ice precipitation was observed. Observational and modeling resources were brought together to understand the processes that control the cloud-phase partitioning and its transition in time. Evidence suggests that three main factors contributed to the abrupt change in phase partitioning for this case study: (1) Large-scale advection of different air masses with different moisture content and indications of different aerosol concentrations played a role. During the time of highest ice and liquid water contents, the airmass over Barrow had a relatively high aerosol concentration and was supported by moist advection at cloud level. (2) Cloud-scale processes, specifically the cloud-surface thermodynamic coupling state, changed at the time of this airmass transition. (3) Model simulations suggest that the ice particle residence time, which is linked to local-scale dynamics, also was important in the change of phase partitioning. The simulated ice water path was found to be higher during times of strong updrafts that dominated during the early part of the case study. After the transition, updrafts weakened and ice crystals fell more quickly from the cloud system. The radiative shielding of a cirrus cloud on March 12 and the influence of the solar cycle were found to be of minor importance for turbulence modulation in the mixed-phase cloud, and thus likely did not play key roles in the transition. A lack of observations of aerosol properties, including ice nuclei concentrations and vertical profiles of aerosol particle concentrations and size, poses a large challenge for understanding phase transitions. Additionally, this case study suggests that the interplay of aerosol-induced cloud microphysical properties with cloud dynamic and thermodynamic processes also may be critically important.

Principal Investigator(s)

Heike Kalesse
Leibniz Institute for Tropospheric Research
[email protected]

Gijs de Boer
University of Colorado
[email protected]

Funding

H. Kalesse conducted this study within the framework of the DFG project COMPoSE, GZ: KA 4162/1-1. G. de Boer contributed to this research under funding from the U.S. Department of Energy’s (DOE) Atmospheric System Research (ASR) program (project numbers: DE-SC0008794 and DE-SC0013306), as well as the U.S. National Science Foundation (ARC 1203902). M. Shupe was supported by DOE ASR grant DE-SC0011918. M. Ahlgrimm’s contribution to this work was supported by DOE ASR grant DE-SC0005259. This research also was supported in part under DOE ASR grants DE-SC00112704 (E. Luke) , DE-SC0013953 (M. Oue), and DE-SC0006974 and DE-SC0014239 (D. Zhang).

References

H. Kalesse, G. DeBoer, A. Solomon, M. Oue, M. Ahlgrimm, D. Zhang, M. Shupe, E. Luke, and A. Protat, “Understanding rapid changes in phase partitioning between cloud liquid and ice in stratiform mixed-phase clouds: An Arctic case study.” Monthly Weather Review 144, 4805-826 (2016). DOI: 10.1175/MWR-D-16-0155.1.