ESSL is part of the ClimXtreme research network, funded by the German Ministry of Education and Research and carries out the project CHECC, part of ClimXtreme Module B.
Convective hazards such as large hail, severe wind gusts, tornadoes and heavy rainfall are responsible for high economic damages, fatalities and injuries across the world, in Europe, and in Germany. There are insufficient observations to determine whether trends in such local phenomena exist, but recent studies suggest that conditions associated with such hazards have become more frequent across large parts of Europe in recent decades. These conclusions are in part based on work with Additive Regression Convective Hazard Models (AR-CHaMo) that have been developed using state-of-the-art reanalysis data and observations collected in the European Severe Weather Database (ESWD).
The CHECC project improves AR-CHaMo by using newer reanalysis datasets with higher spatial and temporal resolutions, such as ERA5, COSMO-REA6 and MERRA2. The added resolution is expected to better resolve the conditions that give rise to the convective storms and hence to improved statistical models. More improvement is expected from additional observational data that is retrieved from media archives and thus enhances the severe weather database used for training the models. The robustness of the models will be investigated by applying them to different regions, e.g. Europe and a part of North America.
CHECC uses the models to investigate if significant trends in modelled hazard occurrence can be detected both in the past and in future climate projections. Furthermore, CHECC studies which part of these trends can be attributable to changes in tropospheric flow patterns, by assessing the impacts of any detected changes on the underlying physical drivers of convective events.
Finally, CHECC will explore the use of convection-permitting reanalysis data, such as COSMO-REA2. This is of particular interest as climate projections are gradually becoming available at convection-permitting module resolutions. As part of this section of the study, predictor parameters will need to be modified owing to the higher spatial resolution which requires proxies that describe the convective storms themselves rather than their respective mesoscale environment.