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\begin{document}

\title{Correcting a coarse-grid climate model in multiple climates by machine
learning from global 25-km resolution simulations}



\author[1]{Spencer K. Clark}%
\author[2]{Noah D. Brenowitz}%
\author[2]{Brian Henn}%
\author[2]{Anna Kwa}%
\author[2]{Jeremy McGibbon}%
\author[2]{W. Andre Perkins}%
\author[2]{Oliver Watt-Meyer}%
\author[2]{Christopher S. Bretherton}%
\author[3]{Lucas M. Harris}%
\affil[1]{Allen Institute for Artificial Intelligence / NOAA-GFDL}%
\affil[2]{Allen Institute for Artificial Intelligence}%
\affil[3]{NOAA-GFDL}%


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  \date{June 16, 2024}


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\begin{abstract}
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
\textasciitilde{}200 km version of NOAA's FV3GFS) evolve more like a
fine-resolution model. This study extends that work for application in
multiple climates and multi-year ML-corrected simulations. Here four
fine-resolution (\textasciitilde{}25 km) two-year reference simulations
are run using FV3GFS with climatological sea surface temperatures
perturbed uniformly by -4 K, 0 K, +4 K, and +8 K. A dataset of
state-dependent corrective tendencies is then derived through nudging
the \textasciitilde{}200 km model to the coarsened state of the
fine-resolution simulations in each climate. Along with the surface
radiative fluxes, the nudging tendencies of temperature and specific
humidity are machine-learned as functions of the column state. ML
predictions for the fluxes and corrective tendencies are applied in 5.25
year \textasciitilde{}200 km resolution simulations in each climate, and
improve the spatial pattern errors of land precipitation by 17\% to 30\%
and land surface temperature by 20\% to 23\% across the four climates.
The ML has a neutral impact on the pattern error of oceanic
precipitation.%
\end{abstract}%



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