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\begin{document}

\title{Correcting coarse-grid weather and climate models by machine learning
from global storm-resolving simulations}



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


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  \date{May 26, 2024}


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\begin{abstract}
Global atmospheric `storm-resolving' models with horizontal grid spacing
of less than 5\textasciitilde{}km resolve deep cumulus convection and
flow in complex terrain. They promise to be reference models that could
be used to improve computationally affordable coarse-grid global climate
models across a range of climates, reducing uncertainties in regional
precipitation and temperature trends. Here, machine learning of nudging
tendencies as functions of column state is used to correct the physical
parameterization tendencies of temperature, humidity, and optionally
winds, in a real-geography coarse-grid model (FV3GFS with a
200\textasciitilde{}km grid) to be closer to those of a 40-day reference
simulation using X-SHiELD, a modified version of FV3GFS with a
3\textasciitilde{}km grid. Both simulations specify the same historical
sea-surface temperature fields. This methodology builds on a prior study
using a global observational analysis as the reference. The coarse-grid
model without machine learning corrections has too little cloud, causing
too much daytime heating of land surfaces that creates excessive surface
latent heat flux and rainfall. This bias is avoided by learning
downwelling radiative flux from the fine-grid model. The best
configuration uses learned nudging tendencies for temperature and
humidity but not winds. Neural nets slightly outperform random forests.
Forecasts of 850 hPa temperature gain 18 hours of skill at 3--7 day
leads and time-mean precipitation patterns are improved
30\textbackslash{}\% by applying the ML correction. Adding
machine-learned wind tendencies improves 500 hPa height skill for the
first five days of forecasts but degrades time-mean upper tropospheric
temperature and zonal wind patterns thereafter.%
\end{abstract}%



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