Strategic Governance of AI Models in Earth Science

The article discusses how AI foundation models trained on weather and climate data are increasingly fine‑tuned for a wide range of Earth science tasks beyond traditional weather forecasting. It highlights that current evaluations rely mainly on benchmark skill metrics, which assess forecast accuracy but not the models’ representation of underlying physical processes, a gap that becomes critical under changing climate conditions. The authors propose five priorities for physical evaluation—covering training data, fine‑tuning, behavioral testing, mechanistic interpretability, and output validation—and recommend three actions for the next decade: open AI‑ready evaluation datasets, a shared physics‑based reporting standard, and a dedicated research program on model safety.

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• fine-tuning
• benchmarks
• safety