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

5Citations
Citations of this article
28Readers
Mendeley users who have this article in their library.

Abstract

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 , ). 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. 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 , ). 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 , ). 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 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. 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.

References Powered by Scopus

The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation

3832Citations
N/AReaders
Get full text

A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006

2067Citations
N/AReaders
Get full text

A typology of compound weather and climate events

773Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts

109Citations
N/AReaders
Get full text

Climate risk to agriculture: A synthesis to define different types of critical moments

26Citations
N/AReaders
Get full text

Compound climate events and extremes in the midlatitudes: Dynamics, simulation, and statistical characterization

21Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Pfleiderer, P., Jezequel, A., Legrand, J., Legrix, N., Markantonis, I., Vignotto, E., & Yiou, P. (2021). Simulating compound weather extremes responsible for critical crop failure with stochastic weather generators. Earth System Dynamics, 12(1), 103–120. https://doi.org/10.5194/esd-12-103-2021

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 8

57%

Researcher 5

36%

Lecturer / Post doc 1

7%

Readers' Discipline

Tooltip

Earth and Planetary Sciences 6

46%

Environmental Science 4

31%

Engineering 2

15%

Chemistry 1

8%

Save time finding and organizing research with Mendeley

Sign up for free