Journal article
Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes
- Abstract:
- Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We fit the GEV distribution to the UNSEEN ensemble with a time covariate to facilitate detection of changes in 100-year precipitation values over a period of 35 years (1981–2015). Applying UNSEEN trends to 3-day precipitation extremes over Western Norway substantially reduces uncertainties compared to estimates based on the observed record and returns no significant linear trend over time. For Svalbard, UNSEEN trends suggests there is a significant rise in precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. We propose a suite of methods to evaluate UNSEEN and highlight paths for further developing UNSEEN trends to investigate non-stationarities in climate extremes.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 4.2MB, Terms of use)
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- Publisher copy:
- 10.1038/s41612-020-00149-4
Authors
- Publisher:
- Nature Research
- Journal:
- npj Climate and Atmospheric Science More from this journal
- Volume:
- 3
- Issue:
- 2020
- Article number:
- 47
- Publication date:
- 2020-11-27
- Acceptance date:
- 2020-09-13
- DOI:
- EISSN:
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2397-3722
- Language:
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English
- Keywords:
- Pubs id:
-
1136632
- Local pid:
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pubs:1136632
- Deposit date:
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2020-10-09
Terms of use
- Copyright holder:
- Kelder et al.
- Copyright date:
- 2020
- Rights statement:
- © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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