From Column to Surface: Connecting the Performance in Simulating Aerosol Optical Properties and PM₂.₅ Concentrations in the NASA GEOS-CCM Model

Abstract

Aerosols are a key climate forcer and harmful to human health at the surface. Accurately modeling aerosol optical properties, mass loading and their relationship is important for constraining aerosol-climate forcing and characterizing particulate matter pollution exposure. We investigate the drivers of uncertainties in the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) in simulating aerosols by focusing on the link between aerosol optical properties and mass. We compare a GEOSCCM hindcast with long-term coincident observations including satellite AOD measurements, speciated PM₂.₅ datasets from observations-model data fusion, and ground-based measurements of aerosol mass and optical properties. We analyze regional trends and seasonal variations of AOD and PM₂.₅, and surface aerosol properties, including relative humidity’s role in hygroscopic enhancement. This work also presents the first extensive assessment of GEOSCCM’s aerosol component with observational data. Our findings show that biases in PM₂.₅ components and relative humidity significantly impact simulated aerosol scattering at the surface, while scattering efficiency assumptions align with observations. This indicates that errors in simulated scattering relate more to simulated aerosol speciated mass and relative humidity than optical properties and size distribution assumptions in GEOSCCM. Our work highlights the importance of relative humidity biases on aerosol scattering enhancement for climate models where meteorology is not prescribed. Findings suggest improvements in GEOSCCM aerosols mass and optical properties could be achieved through updating emission inventories, especially over biomass burning regions, reducing nitrate biases, and improving relative humidity simulation.