Climate dynamics: temporal development of the occurrence frequency of heavy precipitation in Saxony, Germany

• Published in: Meteorologische Zeitschrift
• Main Authors: Andrea S. Schaller, Johannes Franke, Christian Bernhofer
• Format: Article in Journal/Newspaper
• Language: English
• Published: Borntraeger 2020
• Subjects: heavy precipitation; precipitation extremes; kernel occurrence rate estimation regional climate change; trend variability and stability; shifting seasonality; Meteorology. Climatology; QC851-999; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.1127/metz/2020/0771; https://doaj.org/article/eea09f351b5c42cdb1848e16172dd82c

Several studies showed the impact of global climate change in Germany and Saxony including the risk of increasing precipitation extremes. Here, heavy precipitation was analyzed on the basis of daily precipitation sums using the 95th percentile (index R95p). The long term development was studied for selected stations (1917–2013). Transects with high spatial resolution (1×1 km) (1961–2015) complemented the study to gain information about spatial temporal development of the occurrence of precipitation extremes. The non parametric kernel occurrence rate estimation has been applied to reveal changes in the temporal development of daily totals. The most distinct changes have been found for the seasons and the growing seasons and only slight changes for the calendar year and the meteorological half-years. The findings of this study showed a shifting seasonality with decreasing number of heavy precipitation events in the growing season I (April, May, June) and increasing number of events in growing season II (July, August, September). Furthermore, a distinct periodicity has been revealed in all findings for all seasons, particularly striking in the growing seasons, indicating the influence of large scale drivers as potentially the North Atlantic Oscillation on local precipitation extremes. Our data showed, that kernel occurrence rate estimation is a suitable approach to analyze the temporal development of heavy precipitation with a high temporal and spatial resolution.