Global gridded crop model evaluation : benchmarking, skills, deficiencies and implications
Müller, Christoph (Postdam-Institut fUr Klimafolgenforschung)
Elliott, Joshua (University of Chicago)
Chryssanthacopoulos, James (University of Chicago)
Arneth, Almut (Karlsruhe Institute of Technology)
Balkovic, Juraj (International Institute for Applied Systems Analysis (Àustria))
Ciais, Philippe (Laboratoire des Sciences du Climat et de l'Environnement)
Deryng, Delphine (University of Chicago)
Folberth, Christian (International Institute for Applied Systems Analysis (Àustria))
Glotter, Michael (University of Chicago. Department of the Geophysical Sciences)
Hoek, Steven (Alterra Wageningen University. Earth Observation and Environmental Informatics)
Iizumi, Toshichika (Institute for Agro-Environmental Sciences (Tsukuba))
Izaurralde, Roberto C. (University of Maryland. Department of Geographical Sciences)
Jones, Curtis (University of Maryland. Department of Geographical Sciences)
Khabarov, Nikolay (International Institute for Applied Systems Analysis (Àustria))
Lawrence, Peter (National Center for Atmospheric Research (Boulder, USA))
Liu, Wenfeng (Swiss Federal Institute of Aquatic Science and Technology)
Olin, Stefan (Lunds universitet)
Pugh, Thomas A. M. (Karlsruhe Institute of Technology)
Ray, Deepak K. (University of Minnesota. Institute on the Environment)
Reddy, Ashwan (University of Maryland. Department of Geographical Sciences)
Rosenzweig, Cynthia (Columbia University. Center for Climate Systems Research)
Ruane, Alex C. (Columbia University. Center for Climate Systems Research)
Sakurai, Gen (Institute for Agro-Environmental Sciences (Tsukuba))
Schmid, Erwin (Universität für Bodenkultur Wien)
Song, Carol X. (Purdue University. Rosen Center for Advanced Computing)
Wang, Xuhui (Laboratoire des Sciences du Climat et de l'Environnement)
Wit, Allard de (Alterra Wageningen University. Earth Observation and Environmental Informatics)
Yang, Hong (Swiss Federal Institute of Aquatic Science and Technology)

Data:	2017
Resum:	Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0. 888 for maize, 0. 673 for wheat and 0. 643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
Ajuts:	European Commission 603542 European Commission 610028
Nota:	Paper contact with cynthia festin: festin@iiasa.ac.at
Nota:	Agraïments: We acknowledge the support and data provision by the Agricultural Intercomparison and Improvement Project (AgMIP). This work was completed in part with resources provided by the University of Chicago Research Computing Center. C. Müller acknowledges financial support from the MACMIT project (01LN1317A) funded through the German Federal Ministry of Education and Research (BMBF).
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	Geoscientific model development, Vol. 10 (Oct. 2017) , ISSN 1991-959X

DOI: 10.5194/gmd-10-1403-2017

20 p, 4.1 MB

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