Podcast Episode 1.32

Improving Climate Models with Machine Learning

Most climate models in use today are based upon large-scale, well-understood physical relationships that drive global temperature and precipitation trends.

But the effects of complicated interactions that occur on smaller scales, which may still be significant, are harder to capture in these models.

That is why Dr. Laure Zanna of New York University and her colleagues are employing machine learning techniques, which can “learn” the effects of these interactions without explicitly solving the physics, to improve climate modeling.  Climate Now spoke with Dr. Zanna to learn more.

Featuring:

Laure Zanna
M2LInES Lead Investigator

X

Laure Zanna

M2LInES Lead Investigator

Dr. Laure Zanna is a Professor in Mathematics & Atmosphere/Ocean Science at the Courant Institute, New York University and 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.

Hosted By:

James Lawler
Climate Now Host

X

James Lawler

Climate Now Host
James Lawler is the founder of Climate Now. James started Climate Now as a way to learn about climate change and our energy system. Climate Now’s mission is to distill and communicate the science of our changing climate, the technologies that could help us avoid a climate crisis, and the economic and policy pathways to achieve net zero emissions globally. James is also the founder of Osmosis Films, a creative studio.

Katherine Gorman
Climate Now Host

X

Katherine Gorman

Climate Now Host

Katherine Gorman is a podcast host for Climate Now. She has worked for terrestrial public radio stations across the US, and is also co-host of the podcast “The Talking Machines”. She is excited to democratize the climate conversation and to learn and share knowledge from experts in the field.

Share podcast: