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A step towards efficient inference for trends in UK extreme temperatures through distributional linkage between observations and climate model data

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A step towards efficient inference for trends in UK extreme temperatures through distributional linkage between observations and climate model data. / Tawn, Jonathan Angus; Gabda, Darmesah; Brown, Simon.

In: Natural Hazards, Vol. 98, No. 3, 01.09.2019, p. 1135–1154.

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@article{4fd0f70996cb46dd98e252f29ada441b,
title = "A step towards efficient inference for trends in UK extreme temperatures through distributional linkage between observations and climate model data",
abstract = "The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25 km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, that accounts for all the sources of uncertainty, requires a very high dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes.",
keywords = "climatological data, distributional linkage, generalised extreme value distribution, spatial extremes, temperature data",
author = "Tawn, {Jonathan Angus} and Darmesah Gabda and Simon Brown",
year = "2019",
month = sep,
day = "1",
doi = "10.1007/s11069-018-3504-8",
language = "English",
volume = "98",
pages = "1135–1154",
journal = "Natural Hazards",
issn = "0921-030X",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - A step towards efficient inference for trends in UK extreme temperatures through distributional linkage between observations and climate model data

AU - Tawn, Jonathan Angus

AU - Gabda, Darmesah

AU - Brown, Simon

PY - 2019/9/1

Y1 - 2019/9/1

N2 - The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25 km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, that accounts for all the sources of uncertainty, requires a very high dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes.

AB - The aim of this paper is to set out a strategy for improving the inference for statistical models for the distribution of annual maxima observed temperature data, with a particular focus on past and future trend estimation. The observed data are on a 25 km grid over the UK. The method involves developing a distributional linkage with models for annual maxima temperatures from an ensemble of regional and global climate numerical models. This formulation enables additional information to be incorporated through the longer records, stronger climate change signals, replications over the ensemble and spatial pooling of information over sites. We find evidence for a common trend between the observed data and the average trend over the ensemble with very limited spatial variation in the trends over the UK. The proposed model, that accounts for all the sources of uncertainty, requires a very high dimensional parametric fit, so we develop an operational strategy based on simplifying assumptions and discuss what is required to remove these restrictions. With such simplifications we demonstrate more than an order of magnitude reduction in the local response of extreme temperatures to global mean temperature changes.

KW - climatological data

KW - distributional linkage

KW - generalised extreme value distribution

KW - spatial extremes

KW - temperature data

U2 - 10.1007/s11069-018-3504-8

DO - 10.1007/s11069-018-3504-8

M3 - Journal article

VL - 98

SP - 1135

EP - 1154

JO - Natural Hazards

JF - Natural Hazards

SN - 0921-030X

IS - 3

ER -