The Global Gridded Crop Model Intercomparison phase 1 simulation data set

Research output: Contribution to journalArticle

Authors

  • Christoph Mueller
  • Joshua Elliott
  • David Kelly
  • Almut Arneth
  • Philippe Ciais
  • Delphine Deryng
  • Christian Folberth
  • Steven Hoek
  • Toshichika Iizumi
  • Roberto C. Izaurralde
  • Curtis Jones
  • Nikolay Khabarov
  • Peter Lawrence
  • Wenfeng Liu
  • Stefan Olin
  • Ashwan Reddy
  • Cynthia Rosenzweig
  • Alex Ruane
  • Gen Sakurai
  • Erwin Schmid
  • Ratislav Skalsky
  • Xuhui Wang
  • Allard de Wit
  • Hong Yang

Colleges, School and Institutes

External organisations

  • Potsdam Institute for Climate Impact Research
  • University of Chicago and ANL Computation Institute, Chicago, Illinois, 60637, USA

Abstract

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

Details

Original languageEnglish
JournalScientific Data
Volume6
Issue number50
Publication statusPublished - 8 May 2019