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Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle
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2023
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Source: Journal of Advances in Modeling Earth Systems, 15(5)
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Journal Title:Journal of Advances in Modeling Earth Systems
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Description:One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm-resolving model (GSRM). Our past work demonstrating this approach was trained with short (40-day) simulations of GFDL's X-SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our ML using a new year-long GSRM simulation. Our corrective ML models are trained by learning the state-dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a
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Source:Journal of Advances in Modeling Earth Systems, 15(5)
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DOI:
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ISSN:1942-2466;1942-2466;
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Rights Information:CC BY
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Compliance:Library
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