Session

Description Aerosols and clouds have large impacts on climate, with macroscale climate impacts depending on the size and composition of individual aerosol particles and cloud droplets. Large uncertainties exist in simulating aerosols and clouds and in assessing their climate impacts on the global scale. The objective of this minisymposium is to bring together researchers focusing on data-driven methods for the development of cloud and aerosol model parameterizations. These methods have the potential to reduce structural and parametric uncertainty and to improve the consistency of the representation of aerosol and cloud processes across spatial and temporal scales. Our speakers will present on machine learning for Earth system prediction and predictability, reduced mechanisms of atmospheric chemistry, Bayesian methods to estimate aerosol process rates, and the learning of highly-efficient reduced order models of cloud microphysics. While our minisymposium will focus on the area of climate, the methods have the potential to be applicable in other areas that have similar multi-scale structures.