EGU23-5003, updated on 22 Feb 2023
https://doi.org/10.5194/egusphere-egu23-5003
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Hannah Marie Eichholz1,2, Jan Kretzschmar1, Duncan Watson-Parris3, Josefine Umlauft2, and Johannes Quaas1,2
Hannah Marie Eichholz et al.
  • 1University Leipzig, theoretical Meteorology, clouds and global climate, Leipzig, Germany (hannah.eichholz@uni-leipzig.de)
  • 2Center For Scalable Data Analytics And Artificial Intelligence, University Leipzig, Leipzig, Germany
  • 3Department of Atmospheric, Oceanic and Planetary Physics University of Oxford, Oxford, UK

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.

How to cite: Eichholz, H. M., Kretzschmar, J., Watson-Parris, D., Umlauft, J., and Quaas, J.: Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5003, https://doi.org/10.5194/egusphere-egu23-5003, 2023.