Towards a prototype hybrid ICON-ML model with physics-aware machine learning parameterizations

Earth System Models (ESMs) are fundamental to understanding and projecting climate change. While they have demonstrated continuous improvements over the last decades, systematic errors and large uncertainties in their projections remain. A large contribution to these uncertainties stems from the representation of unresolved processes such as clouds and convection that occur at scales smaller than the model grid spacing. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. High-resolution models with horizontal grid spacing of a few kilometers or less alleviate many biases of coarse-resolution models, but at high computational costs. Yet short simulations from high-resolution models can be used to inform machine learning (ML)-based parameterizations that are then incorporated into hybrid (physics+ML) ESMs. This new generation of hybrid models promises to reduce systematic errors and enhance projection capabilities compared to current state-of-the-art ESMs [1, 2]. In an effort to design a comprehensive hybrid ESM, the ICOsahedral Non-hydrostatic (ICON) model is equipped with a variety of physics-aware ML parameterizations, including moist convection, cloud cover and radiation. This talk will present an overview of the modelling activities undertaken within this framework, with a special focus on the developed ML-based cloud cover parameterization. This parameterization takes the form of an interpretable non-linear equation discovered through a combination of ML techniques including symbolic regression and sequential feature selection [3]. We demonstrate that, with this new parameterization, ICON runs stably over several decades and reduces global biases in cloud cover and radiation metrics. In addition, the new equation is controlled by only 10 free parameters that we automatically calibrate to achieve more accurate climate projections. This approach of discovering a low-dimensional data-driven equation for a parameterization with subsequent tuning of the hybrid model can be used in any host ESM provided suitable training data.

 

References:

[1] Eyring, V., Collins, W.D., Gentine, P. et al., Pushing the frontiers in climate modeling and analysis with machine learning, Nat. Climate Change, doi:10.1038/s41558-024-02095-y, 2024.

[2] Eyring, V., Gentine, P., Camps-Valls, G., Lawrence, D.M., and Reichstein, M., AI-empowered Next-generation Multiscale Climate Modeling for Mitigation and Adaptation, Nat. Geosci., doi:10.1038/s41561-024-01527-w, 2024.

[3] Grundner, A., Beucler, T., Gentine, P. and Eyring, V., Data-driven equation discovery of a cloud cover parameterization, J. Adv. Model. Earth Sys., doi:10.1029/2023MS003763, 2024.