[Eoas-seminar] Fw: 2023 Myles Hollander Distinguished lecturer

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From: Stat-all <stat-all-bounces at lists.fsu.edu> on behalf of Elizabeth Slate via Stat-all <stat-all at lists.fsu.edu>
Sent: Monday, August 28, 2023 10:15:50 AM
To: FSU-stat-all <stat-all at lists.fsu.edu>
Subject: [Stat-all] 2023 Myles Hollander Distinguished lecturer

Dear All,

We are excited to announce that Dr. Adrian Raftery, the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington, is the 2023 Myles Hollander Distinguished Lecturer.

Raftery will present “Downscaled Probabilistic Climate Change Projections, with Application to Hot Days,” on October 25, 2023 at 11:00am in the CSL auditorium at Florida State University. The live talk will also be accessible via Zoom. The registration link for the talk will soon be available at  stat.fsu.edu/HollanderLecture.

About Dr. Raftery

Born in Dublin, Ireland, Adrian E. Raftery obtained a B.A. in Mathematics (1976) and an M.Sc. in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then joined the faculty in statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999-2009). His research focuses on Bayesian model selection and Bayesian model averaging, model-based clustering, inference for deterministic simulation models, and the development of new statistical methods for demography, sociology, and the environmental and health sciences.

Raftery has published over 200 articles, edited three volumes of the annual Sociological Methodology compilation, co-edited Statistics in the 21st Century (2002), and co-authored the text Model-based Clustering and Classication for Data Science, with Applications in R (2019). He is an elected member of the U.S. National Academy of Sciences and of the Sociological Research Association, and Honorary Member of the Royal Irish Academy. He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics and elected member of the International Statistical Institute.

Lecture Abstract

The climate change projections of the Intergovernmental Panel on Climate Change are based on scenarios for future emissions, but these are not statistically based and do not have a full probabilistic interpretation. Instead, Raftery et al. (2017) and Liu and Raftery (2021) developed probabilistic forecasts for global average temperature change to 2100. I will describe a method for downscaling these to yield probabilistic long-term spatial forecasts of local average annual temperature change, combining the probabilistic global method with a pattern scaling approach. This yields a probability distribution for average temperature in any year and any place in the future. Finally, we ask, how common dangerously hot days are likely to be at any location by the end of the century, and develop a method for assessing its predictive distribution. We find, for example, that exposure to dangerous heat levels is likely to increase by factors of 3-10 in many parts of the midlatitudes.



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