Machine Learning at the Flatiron Institute Seminar: Laure Zanna

Date & Time

Title: Machine learning for climate modeling

Abstract: Climate simulations remain one of the best tools to understand and predict global and regional climate change. Yet, these simulations demand immense computing power to resolve all relevant scales in the climate system from meters to 1000’s kilometers. Due to the limited computing resources, many aspects of the physics are missing from climate simulations – e.g., ocean mixing or clouds’ effects on temperature and currents. I will describe some of the key challenges in climate modeling and how machine learning tools can help accelerate progress toward accurate climate simulations and reliable climate projections. I will focus on our work capturing ocean turbulence with a range of machine learning techniques, that we have adapted for fluid flows. This will include deep learning with embedded physics constraints, uncertainty quantification for ocean turbulence, and equation discovery of multiscale physics with genetic programming. Some of our work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections.

About the Speaker

Laure Zanna is a Professor in Mathematics & Atmosphere/Ocean Science at the Courant Institute, New York University. Her research focuses on the dynamics of the climate system and the main emphasis of her work is to study the influence of the ocean on local and global scales. Prior to NYU, she was a faculty member at the University of Oxford until 2019, and obtained her PhD in 2009 in Climate Dynamics from Harvard University. She was the recipient of the 2020 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”. She is the lead principal investigator of the NSF-NOAA Climate Process Team on Ocean Transport and Eddy Energy, and M2LInES – an international effort to improve climate models with scientific machine learning. She currently serves as an editor for the Journal of Climate, a member on the International CLIVAR Ocean Model Development Panel, and on the CESM Advisory Board.

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