Date/Time

Date(s) - 06/16/2021
12:00 pm - 1:00 pm

Join us on Wednesday, June 16th at noon PST for a seminar about Ocean Dynamics with presentations by Miguel Jimenez-Urias, Postdoctoral Fellow of Earth and Planetary Sciences at Johns Hopkins University, and Laure Zanna, Professor of Mathematics and Atmosphere/Ocean Science at New York University.

Zoom link: https://umich.zoom.us/j/92607163508

 

“Oceanic stirring and Mixing of Passive Scalars: A Novel Closure”

Abstract: Tracers that help regulate biogeochemical cycles in the ocean and atmosphere have complex spatial distributions due to the combined effect of stirring by the multi-scale shearing motions that are ubiquitous and persistent in the ocean, and the small-scale diffusive mixing resulting in spatially inhomogeneous, enhanced mixing rates. Computer models need to parameterize the effect of shear dispersion due to restrictions on computer power and numerical stability when running climate-scale ocean simulations. Such parameterizations, however, fail to represent scale dependency, an assumption not strictly applicable to the ocean. In this talk, we present new results describing scale-dependency of shear-dispersion by idealized oceanic flows that can lead to a better understanding and representation of tracer distribution in the oceans.

Miguel Jimenez-Urias is a computational physical oceanographer interested in improving our understanding of ocean circulation, through the use of theory, computer simulations and global ocean circulation models. He is currently a postdoctoral fellow at Johns Hopkins University.

“Blending Machine Learning and Physics to Improve Climate Models”

Abstract: 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 ocean turbulence and 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 simulations and projections.

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 its future, focusing on the advantages and challenges of using machine learning for climate projections. I will present some of our recent work in which we leverage tools from machine learning and deep learning to learn representations of unresolved ocean processes and improve climate simulations. Our work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions.

 

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.

 

The Data Science Coast to Coast (DS C2C) seminar series is co-hosted by the eScience Institute, along with data science institutes at New York University, Rice University, Stanford University, University of California – Berkeley, University of Michigan and the Academic Data Science Alliance (ADSA).

In the first half of 2021, we will host five seminars, each featuring one faculty member and one postdoctoral fellow from two universities.  Each speaker will give a 20-minute talk about ongoing projects and motivating issues, followed by 20 minutes of discussion with the audience. These seminars will be the launching point for follow-on research discussion meetings which will hopefully lead to fruitful collaborative research.