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.
Highlights
Extreme events of environmental processes, such as temperature, sea levels and precipitation, are likely to be affected by global climate change
We have been trying to address the question ‘What are the magnitudes and uncertainties of present and future changes in extreme temperatures?’ Adaptation pathways, so that society can endure future extreme temperatures, could incur significant cost, and it is highly desirable to consider and quantify the uncertainty in projections of future changes in extremes. This question can be answered through a convolution of the local response to global temperature changes and its uncertainty with the uncertainty in global temperature change at a future date of interest
This paper only deals with the first aspect, looking at the local response sensitivity across climate models
Summary
Extreme events of environmental processes, such as temperature, sea levels and precipitation, are likely to be affected by global climate change. Natural Hazards (2019) 98:1135–1154 and so global mean temperature has frequently been used as a metric to represent the time evolution of future climate change (Brown et al 2014). There is a need to estimate changes in extreme temperatures at the local scale and to assess how these relate to global mean temperature change. We treat the annual global mean temperature as a known covariate and build trend models for extreme temperatures relative to that. A full analysis of extreme temperature trends strictly needs to account for the uncertainty in this covariate, but that is outside the scope of this analysis
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