Arizona State University: Climate & Water Futures | Try Early, Emulate – On Timescale Considerations for Sampling Extreme Weather Events

A critical challenge for modern climate science is to characterize extreme weather events. From heat waves to hurricanes to cold snaps, extreme events share the common feature of being uncommon, occupying some tail of the climatic probability distribution and thus presenting only scant historical data for analysis. Such sporadically occurring events catch societies and ecosystems offguard when they do occur, and better estimating risks can help greatly to mitigate the impacts. There are several competing approaches to this challenge, each with their own tradeoffs. This talk will present some Monte Carlo strategies to bring out the best in the various approaches, in particular rare event sampling (RES). More generally, it will be argued that extreme event research should employ less random sampling and more deterministic optimization. This incidentally invites synergistic coordination between traditional physics-based codes and differentiable-by-construction machine-learned emulators. Early results on that front will also be presented. Overall, the diversity of definitions of “extreme events” and the computer models that simulate them demand commensurate flexibility in sampling methods. Rising to this challenge will be a long-term collaborative research agenda.

About the Presenter
Justin Finkel is an applied mathematician and climate scientist, working on advancing the science of extreme weather and its relationship to climate. He draws on classic and new tools from applied mathematics to (1) sample extreme events more efficiently, (2) statistically analyze extreme weather ensembles to infer physical mechanisms and sources of predictability, and (3) inform model development as a result. Following an interest in math, physics and climate sparked during high school, Justin obtained a Bachelor’s degree in physics and mathematics at Washington University in St. Louis, followed by a PhD in Computational and Applied Mathematics (the first cohort) at the University of Chicago, but with a long extended visit to the Courant Institute at New York University. His PhD focused on highly idealized models of stratospheric variability, after which he moved “closer to earth” in a postdoctoral appointment at MIT, where he has worked on developing rare event sampling methods for extreme precipitation. In the process, Justin rediscovered the value of idealized modeling, and moving forward plans to leverage the hard-earned insight from both realistic and idealized ends of the model spectrum.

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