Distinguished Lecturer, Laure Zanna
Climate Modeling with AI: Hype or Reality?
Tuesday, July 18, 2023 | 11:00 AM | CDS 1750
As part of its Distinguished Speaker Series for 2023, the Faculty of Computing & Data Sciences is proud to welcome Professor Laure Zanna to Boston University on Tuesday, July 18. Professor Zanna will deliver a lecture entitled, "Climate Modeling with AI: Hype or Reality?".
Laure Zanna is a Professor in Mathematics & Atmosphere/Ocean Science at the Courant Institute at 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.
Dr. Zanna 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. Learn more about Dr. Zanna here.
Abstract: Climate simulations remain one of the best tools to understand and predict global and regional climate change. Yet, the accuracy of numerical climate models is constrained by computing power. Uncertainties in climate predictions originate partly from a poor or lacking representation of processes, such as ocean turbulence and clouds, that are not resolved in global climate models but impact the large-scale temperature, rainfall, sea level, etc. Representing these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning (ML) algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty?
This talk will discuss the advantages and challenges of using machine learning for climate projections. The focus is on recent work in which Zanna and fellow researchers leverage ML tools to learn representations of unresolved ocean processes – in particular, learning symbolic expressions. Some of this 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.