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Applied Mathematics and Statistics Special Seminar: A Bayesian Hierarchical Model for Climate-Change Detection and Attribution

November 28, 2017 @ 3:00 pm - 4:00 pm

Dr. Dorit Hammerling (NCAR) will present “A Bayesian Hierarchical Model for Climate-Change Detection and Attribution”
Regression-based detection and attribution methods continue to take a central role in the study of climate change and its causes. I will present a novel Bayesian hierarchical approach to this problem, which allows us to address several open methodological questions. Specifically, we take into account the uncertainties in the true temperature change due to imperfect measurements, the uncertainty in the true climate signal under different forcing scenarios due to the availability of only a small number of climate model simulations, and the uncertainty associated with estimating the climate-variability covariance matrix, including the truncation of the number of empirical orthogonal functions (EOFs) in this covariance matrix. We apply Bayesian model averaging to assign optimal probabilistic weights to different possible truncations, and incorporate all uncertainties into the inference on the regression coefficients. We provide an efficient parallel implementation of our method and illustrate its use with a realistic application, which includes a 400-member ensemble of remotely sensed temperature observations.


November 28, 2017
3:00 pm - 4:00 pm
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Chauvenet Hall
1015 14th St.
Golden, CO 80401 United States
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