Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
This work addresses the need for more accurate and accessible Earth system models for stakeholders in Switzerland and beyond, but it is incremental as it builds on existing reviews and recommendations without presenting new experimental results.
The paper reviews how machine learning is transforming Earth system modeling and provides recommendations for developing reliable hybrid AI-physical models to improve weather and climate predictions, particularly for longer horizons and local scales.
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.