Machine-learned climate model corrections from a global storm-resolving
model: Performance across the annual cycle
Abstract
One approach to improving the accuracy of a coarse-grid global climate
model is to add machine-learned state-dependent corrections to the
prognosed model tendencies, such that the climate model evolves more
like a reference fine-grid global storm-resolving model (GSRM). Our past
work demonstrating this approach was trained with short (40-day)
simulations of GFDL’s X-SHiELD GSRM with 3 km global horizontal grid
spacing. Here, we extend this approach to span the full annual cycle by
training and testing our machine learning (ML) using a new year-long
GSRM simulation. Our corrective ML models are trained by learning the
state-dependent tendencies of temperature and humidity and surface
radiative fluxes needed to nudge a closely related
200~km grid coarse model, FV3GFS, to the GSRM evolution.
Coarse-grid simulations adding these learned ML corrections run stably
for multiple years. Compared to a no-ML baseline, the time-mean spatial
pattern errors with respect to the fine-grid target are reduced by
6-25% for land surface temperature and 9-25% for land surface
precipitation. The ML-corrected simulations develop other biases in
climate and circulation that differ from, but have comparable amplitude
to, the no-ML baseline simulation.