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

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. Figure Caption: Advancements in data collection, data access, hybrid AI-physical Earth system modeling, and downscaling empower stakeholders with increased accessibility to local predictions and projections, encouraging collaborative efforts across disciplines to improve climate change preparedness. Here, we review how machine learning has interactions (Rosenfeld et al., 2014). In the ocean, uncertainties persist due that can be integrated forward in time, serve the to unresolved mesoscale eddies and turbulent double purpose of understanding and prediction processes (Couldrey et al., 2021).