Strategic Governance of AI Models in Earth Science
AI foundation models pretrained on weather and climate data are increasingly fine-tuned to Earth science tasks well beyond weather forecasting. Their development and adoption are outpacing the scientific community's ability to evaluate them. These models are judged almost entirely by benchmark skill metrics, which measure how closely a forecast reproduces a reference product but not whether a model represents the physical processes governing the system it predicts. Forecast skill and physical reliability are therefore distinct properties. The distinction is most consequential under the nonstationary conditions of a changing climate for which these models were never trained. We identify fi...
| Authors | Makoto Kelp, Amirhossein Arzani, Patricia Castellanos et al. |
|---|---|
| Primary category | physics.soc-ph |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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