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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-113
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-113
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 24 Jun 2020

Submitted as: methods for assessment of models | 24 Jun 2020

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This preprint is currently under review for the journal GMD.

Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences

Bruno Ringeval1, Christoph Müller2, Thomas A.M. Pugh3, Nathaniel D. Mueller4, Philippe Ciais5, Christian Folberth6, Wenfeng Liu5, Philippe Debaeke7, and Sylvain Pellerin1 Bruno Ringeval et al.
  • 1ISPA, Bordeaux Sciences Agro, INRAE, 33140, Villenave d’Ornon, France
  • 2Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
  • 3School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University ofBirmingham, Birmingham, UK
  • 4Department of Earth System Science, University of California, Irvine, CA, USA
  • 5Laboratoire de Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
  • 6Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
  • 7AGIR, University of Toulouse, INRAE, 31326, Castanet-Tolosan, France

Abstract. How Global Gridded Crop Models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Inter-comparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for Simple Mechanistic Model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer-Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs, so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed in time fraction of net primary productivity allocated to the grain (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow to capture the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale.

Bruno Ringeval et al.

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Bruno Ringeval et al.

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Process-based emulator to explain the differences in simulated potential yield between Global Gridded Crop Models B. Ringeval https://doi.org/10.15454/9EIJWU

Bruno Ringeval et al.

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Short summary
We assess how and why Global Gridded Crop Models (GGCMs) differ in their simulation of potential yield. We build an emulator of GGCMs based on generic formalism, and fit its parameters against aboveground biomass and yield at harvest simulated by 8 GGCMs. Despite huge differences between GGCMs, we show that the calibration of few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model would help to improve the global simulation of potential yield.
We assess how and why Global Gridded Crop Models (GGCMs) differ in their simulation of potential...
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