Disentangling the anthropogenic climate change influence on recent hydroclimate whiplash events via multiple nudged model simulations

Abstract Hydroclimate whiplash describes swings between extremely dry and wet conditions. The magnitude and physical drivers of changes in observed whiplash events under global warming remain poorly understood. Here we quantify thermodynamically-driven changes in whiplash intensity for nine high-impact events using the Standardised Precipitation Evapotranspiration Index and a unique multi-model ensemble of wind-nudged storyline simulations. Anthropogenic climate change accounts for 9% (range across events: −2 to 72%) of the observed whiplash intensity on a weighted average across events, with positive contributions for nearly all events according to the multi-model mean, and all models agreeing on the positive contribution for about two-thirds of the events. This climate change contribution is predominantly driven by a non-linear increase in potential evapotranspiration, which strengthens drying more during the dry than the wet phase. Model uncertainty on the magnitude of the climate change contribution is substantial, with an inter-model spread of 13% on average across the events. These results highlight a climate change imprint on observed whiplash events and the need for multiple nudged datasets to robustly attribute complex events.

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Publication Details

Journal
npj Climate and Atmospheric Science
Published
2026-10-28
DOI
https://doi.org/10.1038/s41612-026-01578-3
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

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article

Disentangling the anthropogenic climate change influence on recent hydroclimate whiplash events via multiple nudged model simulations

Niklas Merz, Frauke Feser, Emanuele Bevacqua, Sebastian Sippel et al.
2 links
npj Climate and Atmospheric Science
Climate variability and models
article

Disentangling the anthropogenic climate change influence on recent hydroclimate whiplash events via multiple nudged model simulations

Niklas Merz, Frauke Feser, Emanuele Bevacqua, Sebastian Sippel, Helge Goessling, Amal John, Jakob Zscheischler A, Antonio Sánchez-Benítez, István Dunkl
article en

Abstract

Abstract Hydroclimate whiplash describes swings between extremely dry and wet conditions. The magnitude and physical drivers of changes in observed whiplash events under global warming remain poorly understood. Here we quantify thermodynamically-driven changes in whiplash intensity for nine high-impact events using the Standardised Precipitation Evapotranspiration Index and a unique multi-model ensemble of wind-nudged storyline simulations. Anthropogenic climate change accounts for 9% (range across events: −2 to 72%) of the observed whiplash intensity on a weighted average across events, with positive contributions for nearly all events according to the multi-model mean, and all models agreeing on the positive contribution for about two-thirds of the events. This climate change contribution is predominantly driven by a non-linear increase in potential evapotranspiration, which strengthens drying more during the dry than the wet phase. Model uncertainty on the magnitude of the climate change contribution is substantial, with an inter-model spread of 13% on average across the events. These results highlight a climate change imprint on observed whiplash events and the need for multiple nudged datasets to robustly attribute complex events.

npj Climate and Atmospheric ScienceVol. 9(1)
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE), Helmholtz Centre for Environmental Research (DE), Helmholtz-Zentrum Hereon (DE), Technische Universität Dresden (DE), Leipzig University (DE)
Deutsche Bundesstiftung Umwelt, Deutsches Klimarechenzentrum, European Commission, Deutsche Forschungsgemeinschaft, Helmholtz-Zentrum für Umweltforschung, Sächsisches Staatsministerium für Wissenschaft und Kunst, Universität Leipzig, European Organization for the Exploitation of Meteorological Satellites
Climate action
Openalex Percentile: Top 17%
Climate variability and models
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