Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model

Abstract

<p>In the preparation of the global kilometre-resolution coupled ICON climate model, it is necessary to calibrate cloud microphysical parameters. Here we explore the avenue towards optimally calibrating such parameters using machine learning. The emulator developed by Watson-Parris et al. (2021) is employed in combination with a perturbed-parameter ensemble of limited-area atmosphere-only ICON simulations for the North Atlantic ocean. In a first step, the autoconversion scaling parameter is calibrated, using satellite-retrieved top-of-atmosphere and bottom-of-atmosphere radiation fluxes. For this purpose, limited area simulations of the north atlantic are performed with ICON. In which different cloud microphysical parameters are changed, in order to evaluate possible influences on the output of radiation fluxes.</p>