09/21/2017

# 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.

## Summary

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

lfierce@bnl.gov

## Funding

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.

## References

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]