Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 541084
Title Extreme precipitation in the Netherlands : An event attribution case study
Author(s) Eden, Jonathan M.; Kew, Sarah F.; Bellprat, Omar; Lenderink, Geert; Manola, Iris; Omrani, Hiba; Oldenborgh, Geert Jan van
Source Weather and Climate Extremes 21 (2018). - ISSN 2212-0947 - p. 90 - 101.
DOI https://doi.org/10.1016/j.wace.2018.07.003
Department(s) Meteorology and Air Quality
WIMEK
Publication type Refereed Article in a scientific journal
Publication year 2018
Abstract

Attributing the change in likelihood of extreme weather events, particularly those occurring at small spatiotemporal scales, to anthropogenic forcing is a key challenge in climate science. While a warmer world is associated with an increase in atmospheric moisture on a global scale, the impact on the magnitude of extreme precipitation episodes has substantial regional variability. Analysis of individual cases is important in understanding the extent of these changes on spatial scales relevant to stakeholders. Here, we present a probabilistic attribution analysis of the extreme precipitation that fell in large parts of the Netherlands on 28 July 2014. Using a step-by-step approach, we aim to identify changes in intensity and likelihood of such an event as a result of anthropogenic global warming while highlighting the challenges in performing robust event attribution on high-impact precipitation events that occur at small scales. A method based on extreme value theory is applied to observational data in addition to global and regional climate model ensembles that pass a robust model evaluation process. Results based on observations suggest a strong and significant increase in the intensity and frequency of a 2014-type event as a result of anthropogenic climate change but trends in the model ensembles used are considerably smaller. Our results are communicated alongside considerable uncertainty, highlighting the difficulty in attributing events of this nature. Application of our approach to convection-resolving models may produce a more robust attribution.

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