The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland
Abstract Statistical extreme value models allow estimation of the frequency, magnitude, and spatio-temporal extent of extreme temperature events in the presence of climate change. Unfortunately, the assumptions of many standard methods are not valid for complex environmental data sets, with a realis...
Published in: | Journal of the Royal Statistical Society Series C: Applied Statistics |
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Oxford University Press (OUP)
2023
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croxfordunivpr:10.1093/jrsssc/qlad020 2023-10-01T03:56:18+02:00 The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland Clarkson, Daniel Eastoe, Emma Leeson, Amber 2023 http://dx.doi.org/10.1093/jrsssc/qlad020 https://academic.oup.com/jrsssc/advance-article-pdf/doi/10.1093/jrsssc/qlad020/49721966/qlad020.pdf https://academic.oup.com/jrsssc/article-pdf/72/4/829/51412773/qlad020.pdf en eng Oxford University Press (OUP) https://creativecommons.org/licenses/by/4.0/ Journal of the Royal Statistical Society Series C: Applied Statistics volume 72, issue 4, page 829-843 ISSN 0035-9254 1467-9876 Statistics, Probability and Uncertainty Statistics and Probability journal-article 2023 croxfordunivpr https://doi.org/10.1093/jrsssc/qlad020 2023-09-08T10:43:18Z Abstract Statistical extreme value models allow estimation of the frequency, magnitude, and spatio-temporal extent of extreme temperature events in the presence of climate change. Unfortunately, the assumptions of many standard methods are not valid for complex environmental data sets, with a realistic statistical model requiring appropriate incorporation of scientific context. We examine two case studies in which the application of routine extreme value methods result in inappropriate models and inaccurate predictions. In the first scenario, incorporating random effects reflects shifts in unobserved climatic drivers that led to record-breaking US temperatures in 2021, permitting greater accuracy in return period prediction. In scenario two, a Gaussian mixture model fit to ice surface temperatures in Greenland improves fit and predictive abilities, especially in the poorly-defined upper tail around 0∘C. Article in Journal/Newspaper Greenland Oxford University Press (via Crossref) Greenland Journal of the Royal Statistical Society Series C: Applied Statistics |
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Open Polar |
collection |
Oxford University Press (via Crossref) |
op_collection_id |
croxfordunivpr |
language |
English |
topic |
Statistics, Probability and Uncertainty Statistics and Probability |
spellingShingle |
Statistics, Probability and Uncertainty Statistics and Probability Clarkson, Daniel Eastoe, Emma Leeson, Amber The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland |
topic_facet |
Statistics, Probability and Uncertainty Statistics and Probability |
description |
Abstract Statistical extreme value models allow estimation of the frequency, magnitude, and spatio-temporal extent of extreme temperature events in the presence of climate change. Unfortunately, the assumptions of many standard methods are not valid for complex environmental data sets, with a realistic statistical model requiring appropriate incorporation of scientific context. We examine two case studies in which the application of routine extreme value methods result in inappropriate models and inaccurate predictions. In the first scenario, incorporating random effects reflects shifts in unobserved climatic drivers that led to record-breaking US temperatures in 2021, permitting greater accuracy in return period prediction. In scenario two, a Gaussian mixture model fit to ice surface temperatures in Greenland improves fit and predictive abilities, especially in the poorly-defined upper tail around 0∘C. |
format |
Article in Journal/Newspaper |
author |
Clarkson, Daniel Eastoe, Emma Leeson, Amber |
author_facet |
Clarkson, Daniel Eastoe, Emma Leeson, Amber |
author_sort |
Clarkson, Daniel |
title |
The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland |
title_short |
The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland |
title_full |
The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland |
title_fullStr |
The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland |
title_full_unstemmed |
The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland |
title_sort |
importance of context in extreme value analysis with application to extreme temperatures in the u.s. and greenland |
publisher |
Oxford University Press (OUP) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1093/jrsssc/qlad020 https://academic.oup.com/jrsssc/advance-article-pdf/doi/10.1093/jrsssc/qlad020/49721966/qlad020.pdf https://academic.oup.com/jrsssc/article-pdf/72/4/829/51412773/qlad020.pdf |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Journal of the Royal Statistical Society Series C: Applied Statistics volume 72, issue 4, page 829-843 ISSN 0035-9254 1467-9876 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1093/jrsssc/qlad020 |
container_title |
Journal of the Royal Statistical Society Series C: Applied Statistics |
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1778525717547450368 |