ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
Clark, Anna Kwa, W
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.
citing papers explorer
-
No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
-
AIMIP Phase 1: systematic evaluations of AI weather and climate models
AIMIP Phase 1 sets up a common experiment and five evaluation criteria for AI atmosphere models forced by historical sea surface temperatures, finding they match conventional models on most metrics but underestimate some warming trends and diverge on out-of-sample tests.