Abstract View
Error in Aerosol Mixing State Induced by Aerosol Representation Assumptions
ZHONGHUA ZHENG, Matthew West, Nicole Riemer, University of Illinois at Urbana-Champaign
Abstract Number: 263
Working Group: Aerosols, Clouds and Climate
Abstract
Modal aerosol models are a popular choice to represent aerosols in regional chemical transport models and earth system models because they are computationally efficient while capturing aerosol size distribution and composition. Modal models are, to some extent, “mixing state-aware,” since they represent the aerosol by several overlapping modes (subpopulations), and hence resolve composition differences within given size ranges. However, choices need to be made regarding the number of modes and which chemical species they contain, as well as regarding the rules to transfer aerosol number and mass between modes. These choices may introduce considerable yet poorly-characterized structural uncertainty in aerosol simulations. This raises the question: how well do modal models represent mixing state? This study aims to verify the global distribution of aerosol mixing state represented by modal models by using benchmark simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. We used the aerosol mixing state index χ as a metric to quantify aerosol mixing state. To achieve a spatiotemporal comparison, we calculated the mixing state index using output from the Community Earth System Model with the modal MAM4 aerosol module, and compared the results with the mixing state index from a machine learning-enabled surrogate model based on high-detail particle-resolved simulations. The two methods yielded very different spatial patterns of mixing state index. In some regions, the yearly-averaged χ value computed by the modal model was up to 70 percentage points different than the benchmark values. These errors tended to be zonally structured, with the modal method predicting a more internally mixed aerosol at low latitudes, and a more externally mixed aerosol at high latitudes, compared to the benchmark.