Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four-mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non-BC species and large mass ratio of coating to BC (R_BC, the mass ratio of non-BC species to BC in BC-containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle-resolved simulations to the CAM6 with MAM4 (MAM4-ML). In MAM4-ML, we use R_BC to partition accumulation mode particles into two new modes, BC-free particles and BC-containing particles. We adjust R_BC to make the modeled BC mixing state index (χ_mode) match the one predicted by the ML model (χ_ML). On a global average, the mass fraction of BC-containing particles in accumulation mode decreases from 100% (MAM4-default) to 48% (MAM4-ML). The globally averaged χ_mode decreases from 78% (MAM4-default) to 63% (MAM4-ML, 19% reduction) and agrees well with χ_ML (66%). The R_BC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC-containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.