Christopher S. Bretherton (Allen Institute for AI): AI for Climate Speaker Series

Agenda
3:00pm – 3:45pm:  Presentation
3:45pm – 4:00pm:  Q&A
4:00pm – 4:30pm: Reception

AI for Climate Modeling: Present and Future

AI-driven weather forecast models are now more accurate and much faster than the best physics-based models. The open-source Ai2 Climate Emulator (ACE) uses similar technology to accurately emulate both daily weather variability (including extremes) and climate of historical reanalysis or of a reference global atmospheric model. ACE runs 100x faster than a physics-based model of similar grid resolution – 1600 years per day on a 100 km grid with a single GPU.  ACE can be forced by specified sea-surface temperatures or coupled to a slab ocean model (SOM).  When trained on SOM-coupled reference model simulations spanning multiple climates forced by changed CO2 concentrations, ACE can accurately emulate the reference model climate change response.  We have coupled ACE to an AI-based ocean emulator called Samudra; when trained on a pre-industrial control simulation of a reference physically-based GCM simulation, the resulting emulator reproduces its mean climate and ENSO characteristics. Lastly, we discuss key remaining challenges to general-purpose use of ACE for climate modeling applications.

Chris Bretherton directs a climate modeling group at AI2 in Seattle which uses machine learning trained on global storm-resolving model output and observational data to improve climate model simulations. He is an Emeritus Professor of the Atmospheric Science and Applied Mathematics Departments at the University of Washington, where for 35 years he studied cloud formation and turbulence and improved their simulation in atmospheric models. He is an American Meteorological Society Charney Award winner, IPCC author, AMS and AGU Fellow, and a member of the National Academy of Sciences.