Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle

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.

Keywords

• climate modeling
• machine learning
• global storm‐resolving model
• coarse‐graining