Interpretable Machine Learning‐Based Radiation Emulation for ICON

Abstract The radiation parameterization is one of the computationally most expensive components of Earth system models (ESMs). To reduce computational cost, radiation is often calculated on coarser spatial or temporal scales, or both, than other physical processes in ESMs, leading to uncertainties in cloud‐radiation interactions and thereby in radiative temperature tendencies. One way to address this issue is to emulate the radiation parameterization using machine learning (ML), which is typically faster and has good accuracy in high‐dimensional parameter spaces. This study investigates the development and interpretation of an ML‐based radiation emulator using the ICOsahedral Non‐hydrostatic model with the RTE+RRTMGP radiation code, which calculates radiative fluxes based on the atmospheric state and its optical properties. With a Bidirectional Long Short‐Term Memory architecture, which can account for vertical bidirectional auto‐correlation, we can accurately emulate shortwave and longwave heating rates with a mean absolute error of and , respectively. Further, we analyze the trained neural networks using Shapley Additive exPlanations and confirm that the networ