Data Science Coast To Coast — Ocean Dynamics

Data Science Coast To Coast — Ocean Dynamics
Date: Wednesday, June 16, 2021
Time: 12:00–1:00 PM Pacific 

Attend this webinar using this Zoom link.

The Data Science Coast to Coast (DS C2C) seminar series is hosted jointly by seven academic data science institutes — BIDS, NYU’s Center for Data Science, Rice University’s Ken Kennedy Institute, Stanford Data Science, the University of Michigan’s Michigan Institute for Data Science (MIDAS), and the University of Washington’s eScience Institute, and Johns Hopkins University's Institute of Data Intensive Engineering and Science (IDIES) — to provide a unique opportunity to foster a broad-reaching data science community. In the first half of 2021, DS C2C 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 that will hopefully lead to fruitful collaborative research.

Scale-Dependent Shear Dispersion: Stirring and Mixing of Passive Tracers in the Ocean

Miguel Jimenez-Urias, Postdoctoral Fellow, Earth and Planetary Sciences, Johns Hopkins University
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.

Blending Machine Learning and Physics to Improve Climate Models

Laure Zanna, Professor of Mathematics & Atmosphere/Ocean Science, New York University

All events in the series are free to attend, and all who are interested are welcome and encouraged to participate. Questions may be directed to Jing Liu (ljing@umich.edu), Managing Director of MIDAS.