eprintid: 13960 rev_number: 20 eprint_status: archive userid: 5 dir: disk0/00/01/39/60 datestamp: 2016-11-17 10:22:27 lastmod: 2021-08-27 17:28:02 status_changed: 2016-11-17 10:22:27 type: article metadata_visibility: show creators_name: Müller, C. creators_name: Elliott, J. creators_name: Chryssanthacopoulos, J. creators_name: Arneth, A. creators_name: Balkovic, J. creators_name: Ciais, P. creators_name: Deryng, D. creators_name: Folberth, C. creators_name: Glotter, M. creators_name: Hoek, S. creators_name: Iizumi, T. creators_name: Izaurralde, R.C. creators_name: Jones, C. creators_name: Khabarov, N. creators_name: Lawrence, P. creators_name: Liu, W. creators_name: Olin, S. creators_name: Pugh, T. creators_name: Ray, D. creators_name: Reddy, A. creators_name: Rosenzweig, C. creators_name: Ruane, A.C. creators_name: Sakurai, G. creators_name: Schmid, E. creators_name: Skalsky, R. creators_name: Song, C.X. creators_name: Wang, X. creators_name: de Wit, A. creators_name: Yang, H. creators_id: 1977 creators_id: 2033 creators_id: 1850 creators_id: 1654 creators_id: 1986 creators_orcid: 0000-0003-2955-4931 creators_orcid: 0000-0002-6738-5238 creators_orcid: 0000-0001-5372-4668 creators_orcid: 0000-0002-0983-6897 title: Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications ispublished: pub divisions: prog_esm abstract: Crop models are increasingly used to simulate crop yields at the global scale, but there so far is no general framework on how to assess model performance. We here 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 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 producer 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 also other modeling groups can test their model performance against the reference data and the GGCMI benchmark. date: 2017-04 date_type: published publisher: European Geosciences Union id_number: 10.5194/gmd-2016-207 creators_browse_id: 23 creators_browse_id: 92 creators_browse_id: 151 creators_browse_id: 1542 creators_browse_id: 287 full_text_status: public publication: Geoscientific Model Development Discussions volume: 10 pagerange: 1403-1422 refereed: TRUE issn: 1991-962X projects: Land use change: assessing the net climate forcing, and options for climate change mitigation and adaptation (LUC4C, FP7 603542) projects: Effects of phosphorus limitations on Life, Earth system and Society (IMBALANCE-P, FP7 610028) coversheets_dirty: FALSE fp7_project: yes fp7_project_id: info:eu-repo/grantAgreement/EC/FP7/603542/EU/Land use change: assessing the net climate forcing, and options for climate change mitigation and adaptation/LUC4C; info:eu-repo/grantAgreement/EC/FP7/610028/EU//IMBALANCE-P fp7_type: info:eu-repo/semantics/article access_rights: info:eu-repo/semantics/openAccess citation: Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J. ORCID: https://orcid.org/0000-0003-2955-4931 , Ciais, P., Deryng, D., Folberth, C. ORCID: https://orcid.org/0000-0002-6738-5238 , et al. (2017). Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications. Geoscientific Model Development Discussions 10 1403-1422. 10.5194/gmd-2016-207 . document_url: https://pure.iiasa.ac.at/id/eprint/13960/1/gmd-10-1403-2017.pdf