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Kelder_2022_Environ._Res._Lett._17_044052.pdf (4.21 MB)

Interpreting extreme climate impacts from large ensemble simulations – are they unseen or unrealistic?

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journal contribution
posted on 2022-03-14, 11:17 authored by Timo Kelder, Niko Wanders, Karin van der Wiel, Tim MarjoribanksTim Marjoribanks, Louise J Slater, Robert WilbyRobert Wilby, Christel Prudhomme
Large-ensemble climate model simulations can provide deeper understanding of the characteristics and causes of extreme events than historical observations, due to their larger sample size. However, adequate evaluation of simulated ‘unseen’ events that are more extreme than those seen in historical records is complicated by observational uncertainties and natural variability. Consequently, conventional evaluation and correction methods cannot determine whether simulations outside observed variability are correct for the right physical reasons. Here, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We illustrate these steps for a 2000-year Amazon monthly flood ensemble simulated by the global climate model EC-Earth and global hydrological model PCR-GLOBWB. EC-Earth and PCR-GLOBWB are adequate for large-scale catchments like the Amazon, and have simulated ‘unseen’ monthly floods far outside observed variability. We find that the realism of these simulations cannot be statistically explained. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. We conclude that there is high sensitivity of simulations outside observed variability to the bias correction method, and that physical credibility checks are crucial to understanding what is driving the simulated extreme events. Understanding the driving mechanisms of unseen events may guide future research by uncovering key climate model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers.

Funding

NERC CENTA Doctoral Training Partnership

NWO 016.Veni.181.049

NWO ALWCL.2016.2

History

School

  • Architecture, Building and Civil Engineering
  • Social Sciences and Humanities

Department

  • Geography and Environment

Published in

Environmental Research Letters

Volume

17

Issue

4

Publisher

IOP Publishing

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by IOP Publishing 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

2022-03-11

Publication date

2022-03-29

Copyright date

2022

eISSN

1748-9326

Language

  • en

Depositor

Dr Tim Marjoribanks. Deposit date: 12 March 2022

Article number

044052

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