Minisymposium Presentation

Aerosol mixing state refers to the way that different chemical components are distributed amongst the particles in an aerosol population. It is an important emergent property of the atmospheric aerosol that affects aerosol radiative forcing and aerosol–cloud interactions. However, current aerosol models used in global Earth system models are limited in their capability to represent aerosol mixing state, thereby suffering from largely unquantified structural uncertainty. In contrast, particle-resolved aerosol simulations are able to capture aerosol mixing state faithfully, however, they are too computationally expensive to be directly implemented into Earth system models.

This study integrates machine learning and particle-resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the Earth system model, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. This work is a prototypical example of using machine learning emulators to add information to Earth system model simulations and to quantify structural uncertainty in Earth system models.