Simulating cloud microphysics across scales for predicting climate extremes

Cloud microphysics is critical for weather and climate prediction. Cloud microphysics is one of the main uncertainties in climate forcing (through aerosol-cloud interactions) and climate response (cloud feedbacks). Furthermore, cloud microphysics is critical for simulating extreme weather events under climate change. Representing cloud physics usually involves a series of different modeling approaches, which are only loosely connected across scales. It also requires coupling cloud microphysics with other uncertain parameterizations for cloud organization, cloud-scale turbulence and aerosols. In this presentation a framework and some approaches to working across scales from detailed cloud models to regional and global models at a range of scales will be discussed. Illustrations of using machine learning and detailed models to better represent cloud microphysics at the global scale will be shown, with prospects for future improvement, with the ultimate goal of a traceable hierarchy of cloud physics and cloud scale turbulence able to reproduce observations from the cloud to the global scale.