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.
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi-Member and Stochastic Parameterizations
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In the Lorenz '96 testbed, ensemble perturbations regulate decorrelation rates without raising long-term variance, while temporally persistent stochastic parameterizations boost early spread growth and spread-error consistency.
citing papers explorer
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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.
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Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
In the Lorenz '96 testbed, ensemble perturbations regulate decorrelation rates without raising long-term variance, while temporally persistent stochastic parameterizations boost early spread growth and spread-error consistency.