Abstract
<p>In 2016, northern France experienced an unprecedented wheat crop loss. This extreme event was likely due to particular meteorological conditions, i.e.  too few cold days in late autumn 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).  </p><p>This work is motivated by two main questions: were the 2016 meteorological conditions the most extreme we could imagine under current climate? and what would be the worst case scenario we could expect that could lead to even 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, 2019). This algorithm is a modification of a stochastic weather generator (SWG), which gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). 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 is the first application of SWGs to simulate warm winters and wet springs. We show that with some adjustments both (new) weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. </p><p>While the number of cold days in late autumn 2015 was close to the plausible maximum, 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 is not fully understood yet, these results indicate that similar events with higher impacts could be possible.</p>
Highlights
We find that the 2016 April–July period was a 1-in-17year event, while the majority of our stochastic weather generators (SWG) simulations are 1-in-10 000-year events
If crop yields responds to the number of cold days in winter and to the precipitation rate in spring, as shown in Ben-Ari et al (2018), we have shown here that in the current climate an even worse crop loss event would be possible
This paper is a proof of concept for the importance sampling for a simulation of a compound event that would have an impact on crop yield
Summary
France is one of the highest wheat producers and exporters in the world thanks to yields that are roughly twice as high as the world average (FAO, 2013). Given the prominent role of wheat production in France, crop failures can impact the national economy. Classical crop yield forecasting models, based on a combination of expert knowledge and data-driven methods (Müller et al, 2019; MacDonald and Hall, 1980), could not anticipate this unprecedented event because it was outside their training range. To overcome these limitations Ben-Ari et al (2018) developed a logistic model that links the meteorological conditions in the year preceding the harvest with the probability of a crop failure
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