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Activity Number: 167
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #312078 View Presentation
Title: Optimal Fingerprinting in Detecting Changes in Climate Extremes
Author(s): Jun Yan*+ and Xuebin Zhang and Zhuo Wang
Companies: University of Connecticut and Environment Canada and University of Connecticut
Keywords: climate change ; estimating equation ; spatial extreme
Abstract:

To detect changes in climate extremes, the generalized extreme value (GEV) distributions are typically assumed at all sites, and the effect of the signal variable is incorporated in a regression model for the location parameters of the GEV distributions. Nevertheless, no fully satisfactory analog of the optimal fingerprinting method has been available in the context of GEV models with climate extremes. The state-of-the-art method is the independence likelihood approach of Zwiers, Zhang, and Feng (2011, Journal of Climate, 24:881--892), where the spatial dependence was ignored. Our strategies are based on the fact that, at each site, a GEV distribution is assumed, and, hence, the score functions at all sites can be combined in a way, exploiting the spatial dependence, similar to the generalized least squares to improve the efficiency of the inference. The method provides an analog of the optimal fingerprinting method widely used in detecting changes in mean state climate variables. The method showed considerable efficiency gain in a simulation study in comparison to the independence likelihood approach. It was applied to detect changes in extreme temperatures at the regional scale.


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