From Static to Spatiotemporal: Dynamic Volatility Distribution Parameterizations for Global Organic Aerosol Simulations

Abstract

Organic aerosols (OA) are a major component of fine particulate matter that affects air quality and climate, yet atmospheric models often underestimate their concentrations, partly reflecting uncertainties in the assumed volatility distribution of primary organic aerosol emissions. Using the NASA GISS ModelE Earth system model, we test four hypotheses: OA biases decrease when volatility distributions are differentiated by (H1) emission source, (H2) anthropogenic sector, (H3) geographic region, and (H4) VBS volatility bin. Simulations are evaluated against IMPROVE over the Continental US, ACTRIS over Europe, and the Chen et al. (2024, https://doi.org/10.1038/s41467‐024‐48902‐0 ) field‐campaign data set over China; for Africa and India, where direct OA observations are sparse, modeled OA is compared with MERRA‐2 reanalysis and complemented by MODIS/CALIPSO aerosol optical depth (AOD). Optimized volatility distributions reduce OA biases by up to 35% relative to the baseline, with optimal profiles varying across regions, seasons, and sectors. To extend these gains to data‐limited regions, we train Random Forest, XGBoost, and a back‐propagation neural network to construct spatiotemporally variable distributions. Although this improves performance, purely statistical predictions produce unrealistic summertime overestimations because they neglect the underlying chemistry. We therefore impose a physicochemical constraint based on RO ₂  + NO chemistry, activated where exceeds a piecewise‐regression threshold, and anthropogenic plus biomass‐burning OA emissions exceed the monthly 75th percentile, which reduces biases by up to 77% in the most overestimated regions. Aerosol‐population diagnostics show that volatility changes redistribute organic mass among primary‐organic, black‐carbon–organic, and organic–sulfate populations, providing a physically consistent pathway for improving OA representation.