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The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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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.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 72, No. 4, 02.09.2023, p. 829-843.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Clarkson, D, Eastoe, E & Leeson, A 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), vol. 72, no. 4, pp. 829-843. https://doi.org/10.1093/jrsssc/qlad020

APA

Clarkson, D., Eastoe, E., & Leeson, A. (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), 829-843. https://doi.org/10.1093/jrsssc/qlad020

Vancouver

Clarkson D, Eastoe E, Leeson A. 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). 2023 Sept 2;72(4):829-843. Epub 2023 Feb 16. doi: 10.1093/jrsssc/qlad020

Author

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. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2023 ; Vol. 72, No. 4. pp. 829-843.

Bibtex

@article{cbee21f842ad40948dadcfc22289a4ba,
title = "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 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.",
author = "Daniel Clarkson and Emma Eastoe and Amber Leeson",
year = "2023",
month = sep,
day = "2",
doi = "10.1093/jrsssc/qlad020",
language = "English",
volume = "72",
pages = "829--843",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland

AU - Clarkson, Daniel

AU - Eastoe, Emma

AU - Leeson, Amber

PY - 2023/9/2

Y1 - 2023/9/2

N2 - 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.

AB - 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.

U2 - 10.1093/jrsssc/qlad020

DO - 10.1093/jrsssc/qlad020

M3 - Journal article

VL - 72

SP - 829

EP - 843

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 4

ER -