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

Darmesah Gabda, Jonathan Tawn () and Simon Brown
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Darmesah Gabda: Universiti Malaysia Sabah
Jonathan Tawn: Lancaster University
Simon Brown: Met Office Hadley Centre

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 98, issue 3, No 14, 1135-1154

Abstract: 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, which 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11069-018-3504-8

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