%0 Journal article
%A Jahn, Sally
%A Hertig, Elke
%T Using Clustering, Statistical Modeling, and Climate Change Projections to Analyze Recent and Future Region‐Specific Compound Ozone and Temperature Burden Over Europe
%R 10.1029/2021GH000561
%J GeoHealth
%V 6
%N 4

%I 
%X High ground‐level ozone concentrations and high air temperatures present two health‐relevant natural hazards. The most severe health outcomes are generally associated with concurrent elevated levels of both variables, representing so‐called compound ozone and temperature (o‐t‐) events. These o‐t‐events, their relationship with identified main meteorological and synoptic drivers, as well as ozone and temperature levels themselves and the linkage between both variables, vary temporally and with the location of sites. Due to the serious health burden and its spatiotemporal variations, the analysis of o‐t‐events across the European domain represents the focus of the current work. The main objective is to model and project present and future o‐t‐events, taking region‐specific differences into account. Thus, a division of the European domain into six o‐t‐regions with homogeneous, similar ground‐level ozone and temperature characteristics and patterns built the basis of the study. In order to assess region‐specific main meteorological and synoptic drivers of o‐t‐events, statistical downscaling models were developed for selected representative stations per o‐t‐region. Statistical climate change projections for all central European o‐t‐regions were generated to assess potential frequency shifts of o‐t‐events until the end of the 21st century. The output of eight Earth System Models from the sixth phase of the Coupled Model Intercomparison Project considering SSP245 and SSP370 scenario assumptions was applied. By comparing midcentury (2041–2060) and late century (2081–2100) time slice differences with respect to a historical base period (1995–2014), substantial increases of the health‐relevant compound o‐t‐events were projected across all central European regions.
%U http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9986
%~  FID GEO-LEO e-docs