New Method for Efficiently Representing Complex Aerosol Distributions

Study shows that cloud condensation nuclei activity can be accurately captured by sparse set of representative particles.

The Science

A key challenge in simulation of aerosol interactions with clouds is capturing processes and properties across multiple scales. Aerosol impacts on clouds depend on particle-level variation in size and composition, but this small-scale complexity is not easily captured in large-scale atmospheric models. Existing aerosol schemes in large-scale models simplify the representation of the aerosol mixing state for large-scale simulation, leading to an error in the representation of aerosol effects on clouds and radiation. In their 2017 paper, Fierce and McGraw introduce a new framework for representing multivariate aerosol size-composition distributions, which captures the multivariate complexity of aerosol size-composition distributions using only a small set of weighted particles.

The Impact

The study is a first step toward a new paradigm in aerosol simulation that will enable large-scale models to accurately and efficiently represent key features of multivariate aerosol distributions. The new framework replaces complex multivariate aerosol distribution with a sparse set of representative particles. Whereas existing aerosol schemes are either too simple to accurately represent climate-relevant aerosol properties or too complex for large-scale simulation, the new sparse-particle representation will enable accurate simulation of particle-level properties in large-scale atmospheric models.


Fierce and McGraw describe a new technique for constructing sparse representations of realistically complex aerosol populations from distribution moments. The study shows that cloud condensation nuclei activity of particle-resolved simulations, which track tens to hundreds of thousands of computational particles, are accurately represented using only a few sparse particles. This sparse representation of the aerosol mixing state, designed for use in quadrature-based moment models, is constructed from a linear program constrained by low-order moments and combined with an entropy-inspired cost function. The critical supersaturation at which each sparse particle becomes CCN active is computed as a function of its size and composition. Continuous CCN activation spectra are then computed from the sparse critical supersaturation values using constrained maximum entropy distributions. Unlike reduced representations common to large-scale atmospheric models, such as modal and sectional schemes, the approach described here is not confined to pre-determined size bins or assumed distribution shapes. This study is a first step toward a quadrature-based aerosol scheme that will track multivariate aerosol distributions with both reliable accuracy and sufficient computational efficiency for large-scale simulations.

Principal Investigator(s)

Laura Fierce
Brookhaven National Laboratory


LMF is supported by UCAR through a NOAA Climate & Global Change Postdoctoral Fellowship and the US Department of Energy’s Atmospheric System Research program. RLM is supported by the US Department of Energy’s Atmospheric System Research program.


Fierce, L. and McGraw, R. L. “Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations.” J. Geophys. Res. Atmos., 122(18), 9867-9878 (2017). [DOI:10.1002/2016JD026335]