Correcting a coarse-grid climate model in multiple climates by machine
learning from global 25-km resolution simulations
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
~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 (~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 ~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 ~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.