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Conference Paper

Improving climate models using corrective machine learning

Authors

Clark,  Spencer
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Kwa,  Anna
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Watt-Meyer,  Oliver
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Henn,  Brian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

McGibbon,  Jeremy
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Perkins,  Andre
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Brenowitz,  Noah
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Bretherton,  Christopher
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Harris,  Lucas
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Clark, S., Kwa, A., Watt-Meyer, O., Henn, B., McGibbon, J., Perkins, A., Brenowitz, N., Bretherton, C., Harris, L. (2023): Improving climate models using corrective machine learning, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-2145


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018669
Abstract
AI2, partnering with GFDL, has developed a corrective machine learning (ML) methodology to improve weather forecast skill and reduce climate biases in a computationally efficient coarse-grid climate model. The corrective ML is trained by nudging the 3D temperature, humidity and wind fields forecast by the coarse-grid model to a time-dependent global reference and learning the ‘nudging tendencies’ as a function of the column state of the model. The reference can be a reanalysis (for present-climate simulation) or a finer-grid version of the same model that may be more trustworthy across a range of climates. The ML is interpreted as a correction to the combined physics parameterizations of the coarse-grid model. We trained two versions of corrective ML for a 200 km grid version of the US weather forecast model, FV3GFS. First, we train on two-year simulations with a finer 25 km grid version of FV3GFS in four climates specified using sea-surface temperature (SST) offsets of -4K, 0, 4K and 8K from a present-day climatological annual cycle. The ML reduces spatial biases of annual-mean land surface temperature and precipitation in prognostic simulations in all climates and also in an independent simulation with an SST offset ramped from 0K to 4K. Second, we train corrective ML using a year-long reference simulation with GFDL’s X-SHiELD global storm resolving model (dx = 3 km, 79 levels). This ML reduces annual-mean land temperature and precipitation pattern biases by up to 50% in coarse prognostic simulations and further enhances weather forecast skill.