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Toward Reliable Projections of Ocean Warming and Climate Change
Laure Zanna (Courant Institute, NYU)
Increasing greenhouse gas emissions cause ocean temperatures and sea levels to rise, as the oceans absorb most of the excess energy in the climate system. Climate simulations are essential for understanding and predicting global and regional ocean warming. However, uncertainty remains regarding the causes and pace of future ocean warming due to inadequate representations of unresolved processes, such as ocean turbulence or clouds, in global climate models. Improving these unresolved processes' representations (parametrizations) is crucial in reducing uncertainty in climate projections.
We propose to rethink the parametrization problem in climate models by leveraging different data sources and machine learning advances. In our work, we discover new equations to describe unresolved ocean turbulence processes, such as energy exchange between spatiotemporal scales, and their impact on large-scale flow. Our ongoing efforts to incorporate data-driven ocean parametrizations have simultaneously exposed new challenges in climate modeling and revealed exciting opportunities for delivering reliable climate projections. The next decade holds great promise for climate modeling in the age of data, computing, and artificial intelligence.
Laure Zanna is a Professor in Mathematics & Atmosphere/Ocean Science at the Courant Institute, New York University. Prior to NYU, she was an Associate Professor in Climate Physics at the University of Oxford until 2019. Her research focuses on the role of the oceans in climate using theory, a hierarchy of models, and data driven methods. Her group's work spans a wide range of topics, including global and regional sea level rise, coupled ocean-atmosphere dynamics, multiscale ocean physics, and uncertainty quantification in climate modeling.
Zanna is the scientific director of M²LInES - Multiscale Machine Learning In Coupled Earth System Modeling - an international collaboration funded by Schmidt Futures to improve climate models with scientific machine learning and the Geoscience Director for the NSF Science and Technology Center LEAP (Learning the Earth with Artificial Intelligence and Physics). She is also the lead PI of the NSF-NOAA Climate Process Team on Ocean Transport and Eddy Energy. She received 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 was the principal lecturer at the WHOI GFD summer school on Data-Driven GFD in 2022. Currently, Zanna serves as an editor for the Journal of Climate, Chair of the AMS Ocean Award Committee, and on the External Advisory Boards of CESM and the NSF AI Institute AI2ES.
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