The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland
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 stati...
Published in: | Journal of the Royal Statistical Society Series C: Applied Statistics |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://eprints.lancs.ac.uk/id/eprint/174204/ https://eprints.lancs.ac.uk/id/eprint/174204/1/The_importance_of_context_in_extreme_value_analysis_with_application_to_extreme_temperatures_in_the_USA_and_Greenland_edit_2.pdf |
id |
ftulancaster:oai:eprints.lancs.ac.uk:174204 |
---|---|
record_format |
openpolar |
spelling |
ftulancaster:oai:eprints.lancs.ac.uk:174204 2024-04-28T08:21:12+00:00 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 2023-09-02 text https://eprints.lancs.ac.uk/id/eprint/174204/ https://eprints.lancs.ac.uk/id/eprint/174204/1/The_importance_of_context_in_extreme_value_analysis_with_application_to_extreme_temperatures_in_the_USA_and_Greenland_edit_2.pdf en eng https://eprints.lancs.ac.uk/id/eprint/174204/1/The_importance_of_context_in_extreme_value_analysis_with_application_to_extreme_temperatures_in_the_USA_and_Greenland_edit_2.pdf Clarkson, Daniel and Eastoe, Emma and Leeson, Amber (2023) The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland. Journal of the Royal Statistical Society: Series C (Applied Statistics), 72 (4). pp. 829-843. ISSN 0035-9254 creative_commons_attribution_4_0_international_license Journal Article PeerReviewed 2023 ftulancaster 2024-04-09T23:34:31Z 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 Lancaster University: Lancaster Eprints Journal of the Royal Statistical Society Series C: Applied Statistics |
institution |
Open Polar |
collection |
Lancaster University: Lancaster Eprints |
op_collection_id |
ftulancaster |
language |
English |
description |
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 |
spellingShingle |
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 |
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 |
publishDate |
2023 |
url |
https://eprints.lancs.ac.uk/id/eprint/174204/ https://eprints.lancs.ac.uk/id/eprint/174204/1/The_importance_of_context_in_extreme_value_analysis_with_application_to_extreme_temperatures_in_the_USA_and_Greenland_edit_2.pdf |
genre |
Greenland |
genre_facet |
Greenland |
op_relation |
https://eprints.lancs.ac.uk/id/eprint/174204/1/The_importance_of_context_in_extreme_value_analysis_with_application_to_extreme_temperatures_in_the_USA_and_Greenland_edit_2.pdf Clarkson, Daniel and Eastoe, Emma and Leeson, Amber (2023) The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland. Journal of the Royal Statistical Society: Series C (Applied Statistics), 72 (4). pp. 829-843. ISSN 0035-9254 |
op_rights |
creative_commons_attribution_4_0_international_license |
container_title |
Journal of the Royal Statistical Society Series C: Applied Statistics |
_version_ |
1797583682870968320 |