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arXiv · 2311.13691

Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications

Tom Beucler·Erwan Koch·Sven Kotlarski·David Leutwyler·Adrien Michel·Jonathan Koh
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Abstract

We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations. Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the integration of AI and physical modeling for improved reliability, especially for longer prediction horizons, acknowledging the delicate balance between knowledge-based and data-driven components required for optimal performance. Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring techniques that respect physical laws, preserve inter-variable dependencies and spatial structures, and accurately represent extremes at the local scale. Recommendation 3: Promote Inclusive Model Development: Ensure Earth System Model development is open and accessible to diverse stakeholders, enabling forecasters, the public, and AI/statistics experts to use, develop, and engage with the model and its predictions/projections.

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Tom Beucler, Erwan Koch, Sven Kotlarski, David Leutwyler, Adrien Michel, Jonathan Koh. 2023-11-22. Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications. https://arxiv.org/abs/2311.13691

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