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  • title: ACE: A fast, skillful learned global atmospheric model for climate prediction
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            ACE: A fast, skillful learned global atmospheric model for climate prediction
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            ACE: A fast, skillful learned global atmospheric model for climate prediction

            Dec 15, 2023

            Speakers

            OW

            Oliver Watt-Meyer

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            GD

            Gideon Dresdner

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            JM

            Jeremy McGibbon

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            About

            Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 10 years, nearly conserves column moisture without explicit…

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            NeurIPS 2023

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