Abstract
Abstract. Studies using climate models and observed trends indicate that extreme weather has changed and may continue to change in the future. The potential impact of extreme events such as heat waves or droughts depends not only on their number of occurrences but also on "how these extremes occur", i.e., the interplay and succession of the events. These quantities are quite unexplored, for past changes as well as for future changes and call for sophisticated methods of analysis. To address this issue, we use Markov chains for the analysis of the dynamics and succession of multivariate or compound extreme events. We apply the method to observational data (1951–2010) and an ensemble of regional climate simulations for central Europe (1971–2000, 2021–2050) for two types of compound extremes, heavy precipitation and cold in winter and hot and dry days in summer. We identify three regions in Europe, which turned out to be likely susceptible to a future change in the succession of heavy precipitation and cold in winter, including a region in southwestern France, northern Germany and in Russia around Moscow. A change in the succession of hot and dry days in summer can be expected for regions in Spain and Bulgaria. The susceptibility to a dynamic change of hot and dry extremes in the Russian region will probably decrease.
Highlights
Multivariate extreme events are likely to impact society greater than their univariate counterparts
The persistence is above 50 % for the whole domain, which means that the probability of the system residing in a compound extreme state is high and these events are grouped in episodes of long duration
We have shown that our climate model ensemble is able to reproduce past dynamics of compound extremes fairly well within acceptable uncertainties
Summary
Multivariate extreme events (in this paper used in the sense of extremes of two or more climate variables occurring simultaneously) are likely to impact society greater than their univariate counterparts. We identify regions in Europe, where the dynamical behavior of the analyzed compound extremes is prone to change These findings highlight that it is the (simple) linear increase of the occurrence of extremes (due to an increase in mean and variability), which is a challenge for adaption and mitigation. The strategy of this study is first to show that the Markov method is able to extract different dynamics of compound extremes for different regions in Europe, based on observational data and model data. We extract temporal change signals of the dynamics of compound extremes based on observations between the periods 1951–1980 and 1981–2010 This information is new and if used as supplementary information to other analyses, could lead to a better understanding of changes of extremes in Europe. A summary and outlook will be given in Sect. 6 and some areas discussed where the application of this method might be of value
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