Spatial classification of typical European heatwaves using clustering

Prolonged heat periods have become a recurring feature of the European climate. Recent events like the 2003 heatwave in France, the 2010 Russian heatwave, and the 2019 European heatwave have caused considerable economic losses due to crop failure, imposed substantial stress on the health system, and caused thousands of heat-related deaths. Due to climate change, an increase in length and frequency of heatwaves has been observed since 1950 in most regions worldwide. However, until now, little knowledge is available on the generalized patterns of heatwaves since most studies focus on the analysis of single historical heatwave events.

This study aims to increase the general understanding of heatwaves by identifying and analyzing stable classes, i.e., recurring patterns, of heatwaves present in Europe. In this study, we use data from a regional climate model large ensemble (Canadian Regional Climate Model version 5, CRCM5-LE) consisting of 50 possible realizations of climate in the years 1981-2010 in the EUR-11 domain. We use the 95th percentile of three days' mean temperature as a threshold of heatwave occurrence. Those events are additionally filtered to at least one percent of the land area to ensure that the events have a considerable spatial extent. We repeatedly apply hierarchical agglomerative clustering to find a dozen stable heatwave patterns in Europe. Those results are in good correspondence with clustering on an observational dataset (E-OBS) and when comparing those to historical events. Therefore it is shown that the catastrophic historical events can be explained as an extreme manifestation of the same recurring pattern.

Moreover, we analyze the obtained typical patterns regarding a precipitation deficit present before or after the event. We find that, e.g., after a summer heatwave in South-East Europe, there is a high chance of having increased precipitation in autumn, while no such trend can be observed in Scandinavia. Moreover, the study serves as a blueprint for the analysis of other spatial extreme events (e.g., droughts).