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Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations
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2022
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Source: Journal of Advances in Modeling Earth Systems, 14(9)
[PDF-3.17 MB]
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Journal Title:Journal of Advances in Modeling Earth Systems
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Description:Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794 ) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine-resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi-year ML-corrected simulations. Here four fine-resolution (∼25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by −4, 0, +4, and +8 K. A data set of state-dependent corrective tendencies is then derived through nudging the ∼200 km model to the coarsened
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Source:Journal of Advances in Modeling Earth Systems, 14(9)
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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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