Ellen Sarauer · Mierk Schwabe · Philipp Weiss · Axel Lauer · Philip Stier · Veronika Eyring

Cambridge University Press · 2025

We developed a cloud microphysics parameterization for the icosahedral nonhydrostatic modeling framework
(ICON) model based on physics-informed machine learning (ML). By training our ML model on high-resolution
simulation data, we enhance the representation of cloud microphysics in Earth system models (ESMs) compared to
traditional parameterization schemes, in particular by considering the influence of high-resolution dynamics that are
not resolved in coarse ESMs. We run a global, kilometer-scale ICON simulation with a one-moment cloud
microphysics scheme, the complex graupel scheme, to generate 12 days of training data. Our ML approach combines
a microphysics trigger classifier and a regression model. The microphysics trigger classifier identifies the grid cells
where changes due to the cloud microphysical parameterization are expected. In those, the workflow continues by
calling the regression model and additionally includes physical constraints for mass positivity and water mass
conservation to ensure physical consistency. The microphysics trigger classifier achieves an F1 score of 0.93 on
classifying unseen grid cells. The regression model reaches an R2 score of 0.72 averaged over all seven microphysical
tendencies on simulated days used for validation only. This results in a combined offline performance of 0.78. Using
explainability techniques, we explored the correlations between input and output features, finding a strong alignment
with the graupel scheme and, hence, physical understanding of cloud microphysical processes. This parameterization
provides the foundation to advance the representation of cloud microphysical processes in climate models with ML,
leading to more accurate climate projections and improved comprehension of the Earth’s climate system.

Cambridge University Press (2025)
https://doi.org/10.1017/eds.2025.10016

Creative Commons BY 4.0