Prospects for Improved Regional Predictions of Arctic Sea Ice
This webinar will provide an overview of a newly developed dynamical seasonal prediction system and its performance in predicting regional Arctic sea-ice conditions.
Registration is required.
This webinar is designed for the sea-ice research community and others interested in information about applications of machine learning in sea-ice forecasting. While this is an open event, attendees are advised that the discussions will largely be of a technical nature.
Webinar Abstract
The rapid decline of Arctic sea ice and the implications for a broad array of stakeholders have spurred a decade of research activity into sea ice predictability and prediction. In this talk, I will introduce a newly developed dynamical seasonal prediction system, GFDL-SPEAR, and assess the performance of this system for regional sea ice predictions. I will discuss the key physical sources of Arctic sea ice predictability, routes to improving sea ice predictions, and fundamental limits on prediction skill. Advancing dynamical Arctic sea ice prediction capabilities will require coordinated efforts between the modeling, observational, and data assimilation communities.
Presenter Bio
Mitch Bushuk is a research scientist at the Geophysical Fluid Dynamics Laboratory, where he works on sea ice predictability and polar climate. He received his PhD in Atmosphere-Ocean Science and Mathematics from New York University in 2015, and his B.Sc. in Mathematics and Physics from the University of Toronto in 2009.