Abstract
Coupled general circulation models are of paramount importance to assess quantitatively the magnitude of future climate change. Usual methods for validating climate models include the evaluation of mean values and covariances, but less attention is directed to the evaluation of extremal behaviour. This is a problem because many severe consequences of climate changes are due to climate extremes. We present a method for model validation in terms of extreme values based on classical extreme value theory. We further discuss a clustering algorithm to detect spacial dependencies and tendencies for concurrent extremes. To illustrate these methods, we analyse precipitation extremes of the AWI-ESM global climate model compared to the reanalysis data set CRU TS4.04. The methods presented here can also be used for the comparison of model ensembles, and there may be further applications in palaeoclimatology.
Highlights
Introduction10 Coupled general circulation models are frequently utilised to assess quantitatively the magnitude of future climate change
Coupled general circulation models are of paramount importance to assess quantitatively the magnitude of future climate change
Usual methods for validating climate models include the evaluation of mean values and covariances, but less attention is directed to the evaluation of extremal behaviour
Summary
10 Coupled general circulation models are frequently utilised to assess quantitatively the magnitude of future climate change. Following an approach by Bernard et al (2013), we use a clustering algorithm to group spatio-temporal climate data into different spatial regions based 45 on their similarity in terms of extremal behaviour and the concurrency of their extremes. This clustering is based on the theory of max-stable copulae, which has been used extensively to investigate spatial dependence of extreme precipitation events, for example in Bargaoui and Bárdossy (2015); Zhang et al (2013); Qian et al (2018). We use bilinear interpolation to 90 scale the data to the 1◦ × 1◦ grid of the reanalysis data set and take into account only those interpolated grid points that correspond to locations with given observed data, excluding the oceans and the regions with incomplete data mentioned above
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