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Activity Number: 254
Type: Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #313491
Title: Bayesian Hierarchical Modeling for Assessment of Regional Climate Change in the U.S. in Response to Large-Scale Drivers of Climate
Author(s): Zachary Thomas*+ and Mark Berliner
Companies: Ohio State University and Ohio State University
Keywords: Regional Climate Modeling ; Bayesian Hierarchical Modeling ; Ocean Heat Content ; Multivariate Spatial Processes ; Spatio-Temporal Modeling
Abstract:

We present a Bayesian hierarchical modeling approach for assessing the regional impact of climate change in the United States from the beginning of the 20th century through the present. The model is constructed essentially in two stages: (1) a Bayesian statistical physical model for the temporal evolution of ocean heat content in the Atlantic and Pacific oceans in response to global radiative forcing and (2) a dynamic Bayesian spatio-temporal model for characterizing the historical response of temperature (observed at 1218 USHCN stations) to changes in ocean heat content and known modes of interannual variability such as the Southern Oscillation Index (SOI), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). Regional temperature response to these large-scale, global drivers of climate is characterized by clear spatial and temporal patterns which allow for a statistical assessment of local sensitivity to global climate mechanisms.


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