Preprints
https://doi.org/10.5194/hess-2021-67
https://doi.org/10.5194/hess-2021-67

  11 Feb 2021

11 Feb 2021

Review status: this preprint is currently under review for the journal HESS.

A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods

Jérôme Kopp, Pauline Rivoire, S. Mubashshir Ali, Yannick Barton, and Olivia Martius Jérôme Kopp et al.
  • Oeschger Centre for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland

Abstract. Temporal (serial) clustering of extreme precipitation events on sub-seasonal time scales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the frequency of sub-seasonal clustering episodes and their relevance for large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (sub-seasonal to decadal). The code is available at the listed GitHub repository.

Jérôme Kopp et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-67', Anonymous Referee #1, 11 Mar 2021 reply

Jérôme Kopp et al.

Data sets

Dataset for "A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods" Jérôme Kopp https://doi.org/10.5281/zenodo.4481893

Model code and software

subseasonal_clustering Jérôme Kopp https://doi.org/10.5281/zenodo.4533688

Jérôme Kopp et al.

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Short summary
Episodes of extreme rainfall events happening in close temporal succession can lead to floods with dramatic impacts. We developed a novel method to individually identify those episodes and deduced the regions where they occur frequently and where their impact is substantial. Those regions are the east and northeast of the Asian continent, central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and north of Argentina/south of Bolivia.