Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle
Anna Kwa, Spencer K. Clark, Brian Henn, Noah Brenowitz, Jeremy McGibbon, Oliver Watt‐Meyer, W. A. Perkins, Lucas Harris, Christopher S. Bretherton
Abstract
Abstract One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) 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 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%–26% 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.