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Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.
The co-occurrence of warm spells and droughts can lead to detrimental socio-economic and ecological impacts, largely surpassing the impacts of either warm spells or droughts alone. We quantify changes in the number of compound warm spells and droughts from 1979 to 2018 in the Mediterranean Basin using the ERA5 data set. We analyse two types of compound events: 1) warm season compound events, which are extreme in absolute terms in the warm season from May to October and 2) year-round deseasonalised compound events, which are extreme in relative terms respective to the time of the year. The number of compound events increases significantly and especially warm spells are increasing strongly – with an annual growth rates of 3.9 (3.5) % for warm season (deseasonalised) compound events and 4.6 (4.4) % for warm spells –, whereas for droughts the change is more ambiguous depending on the applied definition. Therefore, the rise in the number of compound events is primarily driven by temperature changes and not the lack of precipitation. The months July and August show the highest increases in warm season compound events, whereas the highest increases of deseasonalised compound events occur in spring and early summer. This increase in deseasonalised compound events can potentially have a significant impact on the functioning of Mediterranean ecosystems as this is the peak phase of ecosystem productivity and a vital phenophase.