AI-Accelerated Climate Science: A New Approach to Understanding Risk

This is the first of a three-part webinar series cohosted by National Academy of Public Administration's International Affairs Standing Panel and Extreme Weather Resilience Hub, Monash University, UC Berkeley's California-China Climate Institute, Chinese University of Hong Kong

Climate risk and weather forecasting are often confused — but they answer very different questions, on very different timescales, with very different levels of uncertainty. 

This opening session introduces the fundamentals governance practitioners need to interpret climate risk information accurately: what risk and uncertainty mean in a climate context, how input assumptions shape model outputs, and how downscaling translates global climate data into local, decision-relevant information. The session will also introduce a new generation of AI-accelerated climate modeling — an approach that promises faster, higher-resolution risk information than traditional methods have allowed. Speakers will discuss what this shift makes possible, and what governance practitioners should understand about its strengths and limits. 

Speakers: Tapio Schneider, Theodore Y. Wu Professor of Environmental Science and Engineering, Caltech, and Lead, Climate Modeling Alliance (CliMA) 

Ailie Gallant, Associate Professor, School of Earth, Atmosphere and Environment, Monash University 

Moderator: Michael Spencer, Research Fellow, Impact Labs, Monash University