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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/esd-2020-47
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2020-47
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Jul 2020

27 Jul 2020

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This preprint is currently under review for the journal ESD.

Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

Johannes Vogel1,2,, Pauline Rivoire3,4,, Cristina Deidda5, Leila Rahimi5,6, Christoph Alexander Sauter7, Elisabeth Tschumi3,8, Karin van der Wiel9, Tianyi Zhang10, and Jakob Zscheischler3,8 Johannes Vogel et al.
  • 1Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
  • 2Institute of Ecology, Technical University of Berlin, Berlin, Germany
  • 3Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 4Institute of Geography, University of Bern, Bern, Switzerland
  • 5Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Italy
  • 6Department of Water Engineering, University of Tabriz, Iran
  • 7Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, United Kingdom
  • 8Climate and Environmental Physics, University of Bern, Bern, Switzerland
  • 9Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 10Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • These authors contributed equally to this work.

Abstract. 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 understanding and forecasting. In this study we investigate whether key meteorological drivers of extreme impacts can be identified using 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 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 the logistic Lasso regression to predict 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 both between 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.

Johannes Vogel et al.

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Short summary
We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on Lasso regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
We present a statistical approach for automatically identifying multiple drivers of extreme...
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