Long-term Variances of Heavy Precipitation across Central Europe using a Large Ensemble of Regional Climate Model Simulations

2019 - Earth Syst. Dynam. Discuss.


Ehmele, F.

Additional authors:

Kautz, L.-A., Feldmann, H., Pinto, J.G.




Widespread flooding events are among the major natural hazards in Central Europe. Such events are usually related to intensive, long-lasting precipitation. Despite some prominent floods during the last three decades (e.g. 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses.

This study presents and discusses a validation of different century-long data sets, a large ensemble of decadal hindcasts, and also projections for the upcoming decade. Global reanalysis for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. The simulations show a good agreement with observations for both statistical distributions and time series. Differences mainly appear in areas with sparse observation data. The temporal evolution during the past 60 years is well captured. The results reveal some long-term variability with phases of increased and decreased heavy precipitation. The overall trend varies between the investigation areas but is significant. The projections for the upcoming decade show ongoing tendencies with increased precipitation for upper percentiles. The presented RCM ensemble not only allows for more robust statistics in general, in particular it is suitable for a better estimation of extreme values.