The sensitivity of cloud micro- and macrophysical properties to cloud microphysics parameterisations and simulation setup
Contrasting effects of intensity and organisation on the structure and lifecycle of deep convective clouds
Simulating the Earth system with interactive aerosols at the kilometer scale
Physics-informed machine learning-based cloud microphysics parameterization for earth system models
Abstract:
In this study, we develop a physics-informed machine learning (ML)-based cloud microphysics parameterization for the ICON model. By training the ML parameterization on high-resolution simulation data, we aim to improve Earth System Models (ESMs) in comparison to traditional parameterization schemes. We investigate the usage of a multilayer perceptron (MLP) with feature engineering and physics-constraints, and use explainability techniques to understand the relationship between input features and model output. Our novel approach yields promising results, with the physics-informed ML-based cloud microphysics parameterization achieving an R2 score up to 0.777 for an individual feature. Additionally, we demonstrate a notable improvement in the overall performance in comparison to a baseline MLP, increasing its average R2 score from 0.290 to 0.613 across all variables. This approach to improve the representation of cloud microphysics in ESMs promises to enhance climate projections, contributing to a better understanding of climate change.