Adapting AI Weather Foundation Models to a Changing Climate

AI weather foundation models learn from historical archives, while the climate distribution encountered during deployment continues to evolve. This growing model–climate mismatch motivates continual adaptation without repeated full-model retraining, but the structure of weather forecast errors determines how such adaptation should be performed. We develop an online adaptation framework that exploits two complementary error structures: geographically persistent biases and flow-dependent errors that vary with the atmospheric state. Analysis of Aurora-1.3B reveals strongly concentrated forecast-loss sensitivity in its decoder, motivating flow-dependent corrections within fixed low-dimensional sensitivity subspaces. A residual memory tracks persistent errors remaining after this correction, while a variable-wise online gate controls the contribution of the persistent component. We evaluate Aurora-1.3B using ERA5 from 1959–1968 for offline initialization and a strictly chronological 1969–2022 stream for online evaluation, with updates restricted to previously available verification targets. For 2-metre temperature, distributional departure from the historical reference, measured directly from ERA5, is associated with larger frozen-model errors and greater adaptation gains, while the adapted model maintains more stable skill across evaluation periods. Across 69 output fields and five lead times from 6 to 72 hours, our method reduces MSE and MAE by 4.17% and 2.76%, respectively, relative to $\delta$-Adapter, the strongest of six online adaptation baselines. Evolving climate distributions create the need for continual adaptation, while weather-specific forecast-error structure guides how that adaptation should be performed.