In his paper in Climate Monitor , TML Wigley uses basic probability arguments to illustrate how a slowly changing climate could potentially affect the frequency of extreme events. In the time since the paper appeared, there has been increased interest in assessing how weather extremes may be altered by climate change. Much of the work has been conducted using extreme value analysis, which is the branch of statistics developed specifically to characterize extreme events. This commentary discusses the advantages of an EVA approach and reviews some EVA techniques that have been used to describe climate change’s potential impact on extreme phenomena. Additionally, this commentary illustrates basic EVA techniques in an analysis of temperatures for central England. In parallel to Wigley’s analysis, a time-varying EVA analysis is compared to a stationary one, and furthermore, the trend from the EVA analysis is compared to the trend in means.
%0 Journal Article
%1 springerlink:10.1007/s10584-009-9627-x
%A Cooley, Daniel
%D 2009
%I Springer Netherlands
%J Climatic Change
%K ClimateChange Extremes
%P 77-83
%R 10.1007/s10584-009-9627-x
%T Extreme value analysis and the study of climate change
%U http://dx.doi.org/10.1007/s10584-009-9627-x
%V 97
%X In his paper in Climate Monitor , TML Wigley uses basic probability arguments to illustrate how a slowly changing climate could potentially affect the frequency of extreme events. In the time since the paper appeared, there has been increased interest in assessing how weather extremes may be altered by climate change. Much of the work has been conducted using extreme value analysis, which is the branch of statistics developed specifically to characterize extreme events. This commentary discusses the advantages of an EVA approach and reviews some EVA techniques that have been used to describe climate change’s potential impact on extreme phenomena. Additionally, this commentary illustrates basic EVA techniques in an analysis of temperatures for central England. In parallel to Wigley’s analysis, a time-varying EVA analysis is compared to a stationary one, and furthermore, the trend from the EVA analysis is compared to the trend in means.
@article{springerlink:10.1007/s10584-009-9627-x,
abstract = {In his paper in Climate Monitor , TML Wigley uses basic probability arguments to illustrate how a slowly changing climate could potentially affect the frequency of extreme events. In the time since the paper appeared, there has been increased interest in assessing how weather extremes may be altered by climate change. Much of the work has been conducted using extreme value analysis, which is the branch of statistics developed specifically to characterize extreme events. This commentary discusses the advantages of an EVA approach and reviews some EVA techniques that have been used to describe climate change’s potential impact on extreme phenomena. Additionally, this commentary illustrates basic EVA techniques in an analysis of temperatures for central England. In parallel to Wigley’s analysis, a time-varying EVA analysis is compared to a stationary one, and furthermore, the trend from the EVA analysis is compared to the trend in means.},
added-at = {2011-12-02T15:45:52.000+0100},
affiliation = {Colorado State University Department of Statistics Fort Collins CO USA},
author = {Cooley, Daniel},
biburl = {https://www.bibsonomy.org/bibtex/208820351c659061b19e7d430d714071e/marsianus},
description = {SpringerLink - Climatic Change, Volume 97, Numbers 1-2},
doi = {10.1007/s10584-009-9627-x},
interhash = {bfd4e79d891b7a183e531ff2b6f67e54},
intrahash = {08820351c659061b19e7d430d714071e},
issn = {0165-0009},
issue = {1},
journal = {Climatic Change},
keyword = {Earth and Environmental Science},
keywords = {ClimateChange Extremes},
pages = {77-83},
publisher = {Springer Netherlands},
timestamp = {2011-12-02T15:45:52.000+0100},
title = {Extreme value analysis and the study of climate change},
url = {http://dx.doi.org/10.1007/s10584-009-9627-x},
volume = 97,
year = 2009
}