Speaker: Fangqun Yu (SUNY Albany)
Topic: Use of machine learning and up-to-date algorithms to improve GISS ModelE aerosol number simulations: Implications for indirect aerosol forcing

All cloud droplets formed in the atmosphere start with tiny particles that act as cloud condensation nuclei (CCN). These particles can alter cloud properties and precipitation, and their effects on cloud radiative forcing remain the largest source of uncertainty in climate prediction. The GISS global climate models have been making and will continue to make important contributions to the Coupled Model Intercomparison Project (CMIP) and IPCC climate change assessments. The recent historical simulations of the GISS ModelE2.1 for CMIP6 are based on either a mass-based One-Moment Aerosol (OMA) or a quadrature-method-of-moments-based MATRIX aerosol module. The aerosol radiative forcing values and trends over the past decades differ for MATRIX and OMA, even though the host model and emissions were the same. One critical step toward better modeling aerosols' effect on clouds is to have a robust representation of major aerosol processes that are important for determining particle number concentrations (PNC).