Figure 3. a) Slope of the regional trends for the number of seasonal storms; significant trends (α-level=0.05) are marked. b) Median (across stations) seasonal number of convective-like (decorrelation time ≤ 2 hr) and c)other (decorrelation time > 2 hr) storms in the 25% tail; the Sen’s slope (S) and the p-value (pv) of the Regional Mann-Kendall test are reported in case of significant trends (α-level=0.05).
5 Conclusions
We examine changes in extreme sub-daily precipitation intensities for the relevant case of the eastern Italian Alps, where consistent significant changes in annual maximum (AM) intensities were reported (Libertino et al., 2019). Specifically, we aim at detecting and quantifying trends in sub-daily AM and extreme return levels, and linking the observed trends in extremes to specific changes in the local precipitation regime. To do so, we adopt a novel unified framework for extreme value analyses based on ordinary events, and we quantify trends by means of the regional Mann-Kendall test. With respect to traditional change-permitting extreme value models, the here presented method provides a statistical tool for better quantifying changes in extremes in spite of the large stochastic uncertainties, and for better understanding the observed changes by separately considering multi-duration storm intensity distributions and storm occurrence frequency.
Results confirm the presence of significant positive trends in the AM. Trends in the 2 yr return levels estimated yearly using our model are consistent with the observed trends in AM. These trends are more marked for 15 min to 1 hr durations and less marked for 3 hr to 24 hr durations. The model parametrization allows to conclude that these trends are likely due to a combination of (i) increasing number of storm events per year and increasing intensity of the storms, and (ii) changes in the tail properties of the storms. In particular, an increasing, albeit not-significant, trend in tail heaviness at short durations seems to mostly explain the changes in AM and return levels. A significant increase in the proportion of convective-like storms is detected during the summer (JJA). This could explain the observed trends in AM and return levels emerged at the short durations in this study. This agrees with results reported by Fowler et al. (2021a), who highlight that the stronger increases in short-duration extremes are related to feedbacks in convective clouds dynamics at the local scale. The approach can be expanded to directly consider different types of storm events (Marra et al., 2019), following previous works regarding mixed distributions like the Two-component Extreme value distribution (Rossi et al., 1984) or the mixed Gumbel (Kjeldsen et al., 2018).
The trends in this study are derived from a relatively short data series and should be considered as representative of the examined period only (1991-2020). Due to decadal climate variability, they should not be considered as representative of climate change in general, nor extrapolated to predict future conditions (Iliopoulou and Koutsoyiannis, 2020). Nevertheless, our approach could provide insights for better describing local climatologies under change, and for enhancing our understanding of the linkages with changes in the underlying physical processes. This information can be valuable for improving our ability to create and use process-based change-permitting statistical models for hydrometeorological extremes.
Data Availability Statement
Precipitation data was provided by the Provincia Autonoma di Trento and can be retrieved from https://www.meteotrentino.it (Last accessed: September 2021). The codes used for the statistical model are available at https://doi.org/10.5281/zenodo.3971558. The Regional Mann-Kendall trend test was performed based on the functions by J. Burkey, downloaded from https://it.mathworks.com/matlabcentral/fileexchange/22389-seasonal-kendall-test-with-slope-for-serial-dependent-data (retrieved July 2021). The codes developed in the study and the elaborated data for reproducing the results of the paper are available at https://www.dropbox.com/sh/f7cf93racbg5hqv/AADXBHHTKebd5OtG9syKrIJOa?dl=0 for the purpose of peer review and, upon acceptance, will be made publicly available in their final version.
CRediT authors’ contribution
ED : Data curation, Methodology, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing.MB : Conceptualization, Investigation, Writing – review & editing, Supervision. MZ : Visualization, Writing – review & editing. FM : Conceptualization, Methodology, Software, Investigation, Writing – original draft, Writing – review & editing, Supervision.
Acknowledgements
The authors declare no conflict of interests. This study was funded by Provincia Autonoma di Trento through Accordo di Progamma GPR. FM thanks the Institute of Atmospheric Sciences and Climate (ISAC), National Research Council of Italy for the support.
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