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
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
James Lawler
Climate Now Host
Katherine Gorman
Climate Now Host
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.
