This paper is in the final editing stages of a substantial update with updated code, new results, and revised methodology. We hope to publish it soon.
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Published in NeurIPS 2023 Climate Change AI Workshop, 2023
This paper samples the coupled behavior of neural network convective parameterizations at scale on an unseen, warmer climate to see if design decisions conducive to better online performance in-distribution do the same out-of-distribution.
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Published in NeurIPS 2023 Datasets and Benchmarks Track, 2023
This paper establishes a comprehensive set of datasets and benchmarks for machine learned subgrid parameterizations of convection and radiation in E3SM-MMF. It received the Outstanding Paper award for the Datasets and Benchmarks track at NeurIPS 2023. You can find the official repo for the code here: https://github.com/leap-stc/ClimSim/
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Published in Science Advances, 2023
This paper explores feature transformations that can make machine learned convective parameterizations invariant to distribution-shift in climates.
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