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Substantial differences in crop yield sensitivities between models call for functionality-based model evaluation
  • +18
  • Christoph Müller,
  • Jonas Jägermeyr,
  • James A. Franke,
  • Alex C Ruane,
  • Juraj Balkovič,
  • Philippe Ciais,
  • Marie Dury,
  • Pete Falloon,
  • Christian Folberth,
  • Tobias Hank,
  • Munir Hoffmann,
  • Roberto Izaurralde,
  • Ingrid Jacquemin,
  • Nikolay Khabarov,
  • Wenfeng Liu,
  • Stefan Olin,
  • Thomas Pugh,
  • Xuhui Wang,
  • Karina Emmanuelle Williams,
  • Florian Zabel,
  • Joshua W Elliott
Christoph Müller
Potsdam Institute for Climate Impact Research

Corresponding Author:cmueller@pik-potsdam.de

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Jonas Jägermeyr
Columbia University
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James A. Franke
University of Chicago
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Alex C Ruane
Climate Impacts Group, NASA GISS, USA
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Juraj Balkovič
International Institute for Applied Systems Analysis
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Philippe Ciais
Laboratory for Climate Sciences and the Environment (LSCE)
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Marie Dury
University of Liège
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Pete Falloon
Met Office
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Christian Folberth
International Institute for Applied System Analysis
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Tobias Hank
Department of Geography Ludwig-Maximilians-Universität München (LMU Munich)
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Munir Hoffmann
University of Göttingen
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Roberto Izaurralde
Department of Geographical Sciences, University of Maryland
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Ingrid Jacquemin
University of Liège
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Nikolay Khabarov
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Wenfeng Liu
China Agricultural University
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Stefan Olin
Lund University
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Thomas Pugh
Lund University
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Xuhui Wang
Peking University
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Karina Emmanuelle Williams
Met Office
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Florian Zabel
Ludwig-Maximilians-Universit¨at (LMU), Munich, Germany
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Joshua W Elliott
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Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analysed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models’ sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.
10 May 2023Submitted to ESS Open Archive
13 May 2023Published in ESS Open Archive