Multiscale Machine Learning In Coupled Earth System Modeling

Our international team is based in the US and in France and includes scientists from New York University, Princeton, GFDL, Columbia, LDEO, NCAR, MIT, CNRS-IGE, and CNRS-IPSL.

Our goal is to uncover and capture the unaccounted physical processes at the air-sea-ice interface, which will reduce climate model biases and improve climate projections. Our project is developing interpretable Machine Learning models to deepen our understanding of complex processes in the climate system.