| 19 Sep 2025
Atmospheric blocking and climate extremes in Germany in present and future climate
Abstract. Atmospheric blocking is linked to extreme weather and climate events like heatwaves, heavy rainfall, and calm weather. The statistical relationship between blocking and extreme events in Germany is quantified in atmospheric reanalyses ERA5, ERA-20C, and 20CRv3, and in historical and future CMIP6 climate simulations. This targets the reliability assessment of climate projections regarding extreme events in the 21st century. The analysis of the atmospheric reanalyses in the period 1961–2010 indicates that days with blocking see heatwaves 10–11 times and heavy precipitation events or calms 1.5 to 3 times more often than days without blocking. These empirical relationships are also seen in historical CMIP6 simulations for the large-scale phenomena heatwaves and calms, but not for heavy precipitation events (with odds only 1–1.5 times higher given a day with blocking than without). In the simulated future climate, the relationship of blocking with the three extreme event types changes only moderately. Inconsistent blocking trends in the projections, particularly in summer, obstruct the robust projection of extreme events in Germany despite the stable relationship between blocking and heatwaves and calms in most of the CMIP6 simulations. Furthermore, the results confirm the need for better representation of precipitation extremes in climate models.
The study uses atmospheric reanalyses (ERA5, ERA-20C, 20CRv3) and coupled model simulations (CMIP6) with historical and future forcing (SSP5-8.5) to investigate the link between atmospheric blocking and the occurrence of different types of extreme events (heatwaves, heavy precipitation and calms) in Germany. The key finding emerging from the reanalyses is that weather extremes, and particularly heatwaves, occur more frequently when associated with blocking than in the absence of blocking. This finding is also found in CMIP6 simulations but with considerable discrepancies and spread.
The main strength of the study is the large number of datasets that are analysed (3 reanalyses, 6 CMIP6 models with historical and future simulations), which gives robustness to the methodology. However, the novel contribution of the study with respect to existing literature is not clearly indicated. Some methodological assumptions are insufficiently justified, and their uncertainties should at least be discussed. Moreover, the presentation of results in the manuscript should be reconsidered – the authors use multiple data sources, but their figures practically only show ERA5, when it would be far more interesting to show a comparison between the different reanalyses and CMIP6 models for the metrics being investigated.
While there is potential for interesting results, I believe important work is needed to bring the manuscript to standards worthy of publication, particularly in the presentation of results and writing.
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