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https://doi.org/10.5194/nhess-2020-188
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-188
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 26 Jun 2020

Submitted as: research article | 26 Jun 2020

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This preprint is currently under review for the journal NHESS.

A Methodology for Attributing the Role of Climate Change in Extreme Events: A Global Spectrally Nudged Storyline

Linda van Garderen1, Frauke Feser1, and Theodore G. Shepherd2 Linda van Garderen et al.
  • 1Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, Max-Planck-Straße 1, 21502 Geesthacht, Germany
  • 2Department of Meteorology, University of Reading, Reading RG6 6BB, United Kingdom

Abstract. Extreme weather events are associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. On the other hand, the thermodynamic aspects of climate change are already largely apparent from observations, and are far more certain since they are anchored in agreed-upon physical understanding. The storyline method of extreme event attribution, which has been gaining traction in recent years, quantitatively estimates the magnitude of thermodynamic aspects of climate change, given the dynamical conditions. There are different ways of imposing the dynamical conditions. Here we present and evaluate a method where the dynamical conditions are enforced through global spectral nudging towards reanalysis data of the large-scale vorticity and divergence in the free atmosphere, leaving the lower atmosphere free to respond. We simulate the historical extreme weather event twice: first in the world as we know it, with the events occurring on a background of a changing climate, and second in a ‘counterfactual’ world, where the background is held fixed over the past century. We describe the methodology in detail, and present results for the European 2003 heatwave and the Russian 2010 heatwave as a proof of concept. These show that the conditional attribution can be performed with a high signal-to-noise ratio on daily timescales and at local spatial scales. Our methodology is thus potentially highly useful for realistic stress testing of resilience strategies for climate impacts, when coupled to an impact model.

Linda van Garderen et al.

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Short summary
The storyline approach to extreme event attribution takes the event as given (and mainly due to weather variability) and asks how it was affected by known aspects of climate change. We show how this approach can be implemented within a global climate model, illustrated by application to the European 2003 and Russian 2010 heatwaves which show that such a conditional attribution features a high signal-to-noise ratio on daily timescales and at local spatial scales.
The storyline approach to extreme event attribution takes the event as given (and mainly due to...
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