Speaker: Laure Zanna, Professor of Atmosphere/Ocean Science at New York University, Courant Institute
Numerical simulations used for weather and climate predictions solve approximations of the governing laws of fluid motions on a grid. Ultimately, uncertainties in climate predictions originate from the poor or lacking representation of processes, such as turbulence, clouds that are not resolved on the grid of global climate models. The representation of these unresolved processes has been a bottleneck in improving climate predictions.
The explosion of climate data and the power of machine learning 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?
In this talk, I will discuss the current state of climate modeling and projections and its future, focusing on the advantages and challenges of using machine learning for climate modeling. I will present some of our recent work in which we leverage tools from machine learning and deep learning to learn representations of unresolved processes and improve climate simulations. Our work suggests that machine learning could unlock the door to discovering new physics from data and enhance climate predictions.
4 июл 2024
