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.
Navigating the Noise: Bringing Clarity to ML Parameterization Design With \boldsym- bol\mathcalO\(100) Ensembles
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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.
- Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations