Climate

I am part of M2LInES, an international collaborative project with the goal of improving climate projections, using machine learning to capture unaccounted physical processes at the air-sea-ice interface. In collaboration with Alistair Adcroft and Laure Zanna, I have focused on modeling missing physics in the ocean.

• A Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization P. Perezhogin, C. Zhang, A. Adcroft, C. Fernandez-Granda, L. Zanna. Journal of Advances in Modeling Earth Systems, 16 (10), e2023MS004104. 2024

• Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing S. Dheeshjith, A. Subel, S. Gupta, A. Adcroft, C. Fernandez-Granda, J. Busecke, L. Zanna. ICML 2024 Workshop on Machine Learning for Earth System Modeling.

• Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model P. Perezhogin, L. Zanna, C. Fernandez-Granda. Journal of Advances in Modeling Earth Systems, 15 (10), e2023MS003681. 2023

• Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model C. Zhang, P. Perezhogin, C. Gultekin, A. Adcroft, C. Fernandez-Granda, L. Zanna. Journal of Advances in Modeling Earth Systems, 15 (10), e2023MS003697. 2023

• Benchmarking of machine learning ocean subgrid parameterizations in an idealized model A. S. Ross, Z. Li, P. Perezhogin, C. Fernandez-Granda, L. Zanna. Journal of Advances in Modeling Earth Systems, 15(1), e2022MS003258. 2023