Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations

Building off ClimSim, this paper is the first to yield a stable and accurate machine-learning parameterization of subgrid processes (including explicit cloud condensate coupling) in a comprehensive atmospheric model learned from embedded convection-permitting simulations.