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

• Main Authors: P. Pfleiderer, A. Jézéquel, J. Legrand, N. Legrix, I. Markantonis, E. Vignotto, P. Yiou
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-02-01
• Series: Earth System Dynamics
• Online Access: https://esd.copernicus.org/articles/12/103/2021/esd-12-103-2021.pdf

<p>In 2016, northern France experienced an unprecedented wheat crop loss. The cause of this event is not yet fully understood, and none of the most used crop forecast models were able to predict the event <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx3">Ben-Ari et al.</a>, <a href="#bib1.bibx3">2018</a>)</span>. However, this extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn–winter and abnormally high precipitation during the spring season. Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss.</p> <p>This work is motivated by the question of whether the 2016 meteorological conditions were the most extreme possible conditions under current climate, and what the worst-case meteorological scenario would be with respect to warm winters followed by wet springs. 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 <span class="cit" id="xref_paren.2">(<a href="#bib1.bibx27">Yiou and Jézéquel</a>, <a href="#bib1.bibx27">2020</a>)</span>. 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 by importance sampling of complex systems <span class="cit" id="xref_paren.3">(<a href="#bib1.bibx21">Ragone et al.</a>, <a href="#bib1.bibx21">2017</a>)</span>. This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way.</p> <p>This paper explains how an 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.</p> <p>We find that the number of cold days in late autumn 2015 was close to the plausible minimum. However, our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the relation of crop loss in 2016 to climate variability is not yet fully understood, these results indicate that similar events with higher impacts could be possible in present-day climate conditions.</p>