Abstract
While AI has been disrupting conventional weather forecasting, we are only beginning to witness the impact of AI on long-term climate simulations. The fidelity and reliability of climate models has been limited by computing capabilities. These limitations lead to inaccurate representations of key processes such as convection, cloud, or mixing or restrict the ensemble size of climate predictions. Therefore, these issues are a significant hurdle in enhancing climate simulations and their predictions.
Here, I will discuss a new generation of climate models with AI representations of unresolved ocean physics, learned from high-fidelity simulations, and their impact on reducing biases in climate simulations. The simulations are performed with operational ocean model components. I will further demonstrate the potential of AI to accelerate climate predictions and increase their reliability through the generation of fully AI-driven emulators, which can reproduce decades of climate model output in seconds with high accuracy.
Laure Zanna
Professor Zanna is a climate physicist in the Department of Mathematics at the Courant Institute, and the Center for Data Science, NYU. She holds the Joseph B. Keller and Herbert B. Keller Professorship in Applied Mathematics. Her research focuses on understanding, simulating and predicting the role of the ocean in climate on local and global scales. She combines theory, numerical simulations, statistics, and machine learning to tackle a wide range of problems in fluid dynamics and climate, including turbulence, multiscale modeling, ocean heat and carbon uptake, and sea level rise. Since 2020, she is leading M²LInES, an international collaboration sponsored by Schmidt Sciences dedicated to improving climate models using scientific machine learning. In 2020, Prof Zanna received the Nicholas P. Fofonoff Award from the American Meteorological Society "for exceptional creativity in the development and application of new concepts in ocean and climate dynamics", and was the 2022 WHOI Geophysical Fluid Dynamics principal lecturer.
