AI-empowered Next-generation Multiscale Climate Modeling for Mitigation and Adaptation

Earth System Models (ESMs) are fundamental to understanding and projecting climate change. They have continued to improve, but systematic errors and large uncertainties in their projections remain. A large contribution to this uncertainty stems from the representation of processes such as clouds and convection that occur at scales smaller than the resolved model grid. This impacts the models’ ability to accurately project global and regional climate change, climate variability, and extremes. High-resolution, cloud resolving models with horizontal grid resolution of a few kilometers alleviate many biases of coarse-resolution models, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. Yet short simulations from high-resolution models can serve as information to develop machine learning (ML)-based parametrisations that are then incorporated into hybrid (physics+ML) ESMs that promise to have significantly reduced systematic errors and enhanced projection capability compared to current ESMs. We argue that an AI-empowered multiscale approach is needed, making use of km-scale climate models and hybrid ESMs that include essential Earth system processes and feedbacks yet are still fast enough to deliver large ensembles for better quantification of internal variability and extremes. The talk presents progress in hybrid modelling work with the ICOsahedral Non-hydrostatic (ICON) atmospheric model from the European Research Council (ERC) Synergy Grant on "Understanding and Modelling the Earth System with Machine Learning (USMILE)" as well as key challenges and visions how to enhance climate modeling with ML (Eyring et al., 2024a,b).

References:

Eyring, V., W. D. Collins, P. Gentine, E. A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, C. S. Bretherton, H.M. Christensen, D. J. Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias-Suarez, I. Lopez-Gomez, M. C. McGraw, G. A. Meehl, M. J. Molina, C. Monteleoni, J. Mueller, M. S. Pritchard, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K. Weigel, R. Yu, and L. Zanna, Pushing the frontiers in climate modeling and analysis with machine learning, Nature Climate Change, doi:10.1038/s41558-024-02095-y, 2024.

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