From 100,000 to 8: Representing Complex Aerosol Patterns with Far Fewer Particles
Study shows how aerosols interacting with clouds can be accurately captured by sparse set of representative particles.
The Science
Capturing processes and properties across multiple scales is a big challenge in simulating aerosols interacting with clouds. How aerosols impact clouds depends on particle-level variation in size and composition. However, small-scale complexity is not easily captured in large-scale atmospheric models. Today’s models of large cities, ecosystems, and the global atmosphere simplify the aerosols. The result? Errors in the presentation of aerosol effects on clouds and energy transfer. Researchers created a framework that represents the different sizes and compositions of aerosols using only 8 weighted particles. This simple substitution adequately captures the complexity of the system, but can more realistically be used for calculations.
The Impact
Current simulations are either too simple to really study climate-relevant aerosols or too complex to give answers about large areas. The study is a first step in creating a simulation that is neither too simple nor too complex. The new method enables accurately and efficiently representing key features in large-scale atmospheric models.
Summary
Scientists 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 an active cloud condensation nucleus is computed as a function of its size and composition. Continuous cloud condensation nuclei 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 researchers’ approach is not confined to pre-determined size bins or assumed distribution shapes. This study is a first step towards a quadrature-based aerosol scheme that will track multivariate aerosol distributions with both reliable accuracy and sufficient computational efficiency for large-scale simulations.
Contact
BER Program Managers
Ashley Williamson
Atmospheric System Research Program Manager
Department of Energy
[email protected]
Shaima Nasiri
Atmospheric System Research Program Manager
Department of Energy
[email protected]
Principal Investigator
Laura Fierce
Brookhaven National Laboratory
[email protected]
Funding
L.M.F. is supported by the University Corporation for Atmospheric Research (UCAR) through a National Oceanic and Atmospheric Administration Climate & Global Change Postdoctoral Fellowship and the Department of Energy, Office of Science, Office of Biological and Environmental Research, Atmospheric System Research program. R.L.M. is supported by the Department of Energy, Office of Science, Office of Biological and Environmental Research, Atmospheric System Research program.
Publications
L. Fierce and R.L. McGraw, “Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations.” Journal of Geophysical Research: Atmospheres 122, 9867 (2017). [DOI: 10.1002/2016JD026335]
Related Links
Department of Energy Atmospheric System Research: New Method for Efficiently Representing Complex Aerosol Distributions
Highlight Categories
Performer: DOE Laboratory
Additional: Collaborations , Non-DOE Interagency Collaboration
