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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jun 2020

10 Jun 2020

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This preprint is currently under review for the journal ESD.

Simulating compound weather extremes responsible for critical crop failure with stochastic weather generators

Peter Pfleiderer1,2,3, Aglaé Jézéquel4,5, Juliette Legrand6, Natacha Legrix7,8, Iason Markantonis10, Edoardo Vignotto9, and Pascal Yiou6 Peter Pfleiderer et al.
  • 1Climate Analytics, Berlin, Germany
  • 2Humboldt University, Berlin, Germany
  • 3Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 4LMD/IPSL, ENS, PSL Université, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS, Paris, France
  • 5Ecole des Ponts, Marne-la-Vallée, France
  • 6Laboratoire des Sciences du Climat et de l’Environnement, UMR8212 CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 7Climate and Environmental Physics, Physics Institute, University of Bern, Bern, 3012, Switzerland
  • 8Oeschger Centre for Climate Change Research, University of Bern, Bern, 3012, Switzerland
  • 9Research Center for Statistics, University of Geneva, Geneva, Switzerland
  • 10National Centre of Scientific Research "Demokritos", INRASTES Department, Aghia Paraskevi, Greece

Abstract. In 2016, northern France experienced an unprecedented wheat crop loss. This extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn-winter and an abnormally high precipitation during the spring season. The cause of this event is not fully understood yet and none of the most used crop forecast models were able to predict the event (Ben-Ari et al., 2018). Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss.

This work is motivated by two main questions: were the 2016 meteorological conditions the most extreme under current climate? and what would be the worst case meteorological scenario that would lead to the worst crop loss? To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue-based importance sampling algorithm that was recently introduced into this field of research (Yiou and Jézéquel, 2020). This algorithm is a modification of a stochastic weather generator (SWG) that gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This approach is inspired from importance sampling of complex systems (Ragone et al., 2017). This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way.

This paper explains how a SWG for extreme winter temperature and spring precipitation can be constructed in order to generate large samples of such extremes. We show that, with some adjustments, both types of weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method.

We find that the number of cold days in late autumn 2015 was close to the plausible maximum. But our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the crop loss of 2016 relation to climate variability is not fully understood yet, these results indicate that similar events with higher 20 impacts could be possible in present-day climate conditions.

Peter Pfleiderer et al.

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Peter Pfleiderer et al.

Model code and software

analogues_of_2016_crop_failure_in_France P. Pfleiderer, A. Jézéquel, J. Legrand, N. Legrix, I. Markantonis, E. Vignotto, and P. Yiou

Peter Pfleiderer et al.


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Latest update: 30 Sep 2020
Publications Copernicus
Short summary
In 2016, northern France experienced an unprecedented wheat crop loss. This crop loss was likely due to an extremely warm December 2015 and abnormally high precipitation during the following spring season. Using stochastic weather generators we investigate how severe the metrological conditions leading to the crop loss could be in current climate conditions. We find that December temperatures were close to the plausible maximum but that considerably wetter springs would be possible.
In 2016, northern France experienced an unprecedented wheat crop loss. This crop loss was likely...