Understanding, Modeling, Predicting
Our group aims to advance the fundamental understanding of ocean dynamics and its role in the climate system in order to improve climate change projections. Our team works on a wide range of topics including global and regional sea level rise, ocean decadal predictions, the representation of ocean turbulence in climate models, and uncertainty quantification for seasonal predictions. Recently, we have been particularly interested in the role of the ocean dynamics in shaping patterns of ocean heat and carbon storage under climate change, and in the development of physics-aware machine learning models to deepen our understanding of climate processes and their representation in climate models.
[formal bio] 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.
PhD in Climate Dynamics, 2009
MSc in Environmental Sciences, 2003
Weizmann Institute of Science
BSc in Atmospheric Physics, 2001
Tel Aviv University
GFD, Turbulence, Sea Level, Machine Learning
Statistics, Machine learning, Physics Applications
Model parameterization, Turbulence, Mesoscale & submesoscale dynamics
Transient climate change, Ocean heat uptake, Global ocean circulation, Atmosphere-ocean dynamics
Sea Level, Coastal ocean dynamics, Extreme events
Tracer distribution, Ocean carbon uptake, Conceptual models
Blending Physics and Machine Learning for Climate Modeling recording at the Opening Conference of IMSI.
👉 Postdocs: We are looking for 3 postdocs in ML for Climate Modeling
💡Graduate Students: I am accepting PhD students through CAOS, Applied Math, and CDS. Read carefully the application pages. Note: in CAOS, you are admitted to the program, not an adviser or a research project. To see our latest work, browse through this website and my Google Scholar.
Undergraduates, Interns and K-12: We are not accepting any applicants for the foreseeable future.