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Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

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journal contribution
posted on 03.12.2020, 15:52 by Timo KelderTimo Kelder, M. Müller, L.J. Slater, Tim MarjoribanksTim Marjoribanks, Robert WilbyRobert Wilby, C. Prudhomme, P. Bohlinger, L. Ferranti, T. Nipen
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.

Funding

Norwegian Meteorological Institute

Loughborough University

TWEX project (grant 255037)

History

School

  • Social Sciences and Humanities

Department

  • Geography and Environment

Published in

npj Climate and Atmospheric Science

Volume

3

Publisher

Springer Nature

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

12/10/2020

Publication date

2020-11-27

Copyright date

2020

ISSN

2397-3722

eISSN

2397-3722

Language

en

Depositor

Prof Robert Leonard Wilby. Deposit date: 24 October 2020

Article number

47