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Correcting weather and climate models by machine learning nudged historical simulations
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  • Oliver Watt-Meyer,
  • Noah Domino Brenowitz,
  • Spencer Koncius Clark,
  • Brian Henn,
  • Anna Kwa,
  • Jeremy J McGibbon,
  • Walter A. Perkins,
  • Christopher S. Bretherton
Oliver Watt-Meyer
Vulcan Inc.

Corresponding Author:oliwm@vulcan.com

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Noah Domino Brenowitz
University of Washington
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Spencer Koncius Clark
Princeton University
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Brian Henn
Scripps Institution of Oceanography, UC San Diego
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Anna Kwa
Vulcan, Inc.
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Jeremy J McGibbon
University of Washington
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Walter A. Perkins
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Christopher S. Bretherton
University of Washington
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Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. These biases are problematic for forecasting on timescales from medium-range weather to centennial-scale climate. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective tendencies from a hindcast simulation nudged towards an observational analysis. We show that a random forest can predict the nudging tendencies from this hindcast simulation using only the model state as input. This random forest is then coupled to FV3GFS, adding corrective tendencies of temperature, specific humidity and horizontal winds at each timestep. The coupled model shows no signs of instability in year-long simulations and has significant reductions in short-term forecast error for 500hPa height, surface pressure and near-surface temperature. Furthermore, the root mean square error of the annual-mean precipitation is reduced by about 20%.