Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning.

ABSTRACT: We showcase a hybrid modeling framework that embeds machine learning (ML) inference into the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (SPEAR) climate model for online sea ice bias correction during a set of global fully coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully coupled simulations: Hybrid_CPL (couple trained; with feedbacks) and Hybrid_IO (ice ocean trained; without feedbacks). Relative to SPEAR, Hybrid_CPL systematically reduces seasonal forecast errors in the Arctic and considerably reduces Antarctic errors for target months May to December, wi

SUBMITTER: Gregory W 

PROVIDER: S-EPMC12757041 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature