Strong regional influence of climatic forcing datasets on global crop model ensembles

Research output: Contribution to journalArticlepeer-review

Authors

  • Alex C. Ruane
  • Meridel Phillips
  • Christoph Müller
  • Joshua Elliott
  • Jonas Jägermeyr
  • Almut Arneth
  • Juraj Balkovic
  • Delphine Deryng
  • Christian Folberth
  • Toshichika Iizumi
  • Roberto C. Izaurralde
  • Nikolay Khabarov
  • Peter Lawrence
  • Wenfeng Liu
  • Stefan Olin
  • Cynthia Rosenzweig
  • Gen Sakurai
  • Erwin Schmid
  • Benjamin Sultan
  • Xuhui Wang
  • Allard de Wit
  • Hong Yang

Colleges, School and Institutes

External organisations

  • NASA Goddard Institute for Space Studies
  • Center for Climate Systems Research
  • Member of the Leibniz Association
  • Northwestern University, Chicago, IL.
  • Karlsruhe Institute of Technology, Campus North
  • Comenius University
  • Ecosystem Services and Management Program
  • Humboldt-Universitat zu Berlin
  • National Agriculture and Food Research Organization, NARO
  • University of Maryland, College Park, Maryland 20742 USA
  • National Center for Atmospheric Research
  • China Agricultural University
  • Lund University
  • BOKU Vienna
  • Institute of Research for Development
  • Université Paris-Saclay
  • Peking University
  • Wageningen University and Research Centre
  • Swiss Federal Institute of Aquatic Science and Technology
  • University of Basel

Abstract

We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.

Bibliographic note

Funding Information: Funding: Support for this study was provided by NASA NNX16AK38G (INCA), and the NASA Earth Sciences Directorate/GISS Climate Impacts Group funding. DD acknowledges the HPC Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia for running PEGASUS. TAMP and AA acknowledge support from European Union FP7 Grant LUC4C (Grant 603542), and the Helmholtz Association in its ATMO Programme. ES acknowledges financial support from the UncertProp project (KR16AC0K13332) funded by the Austrian Climate and Energy Fund within the Austrian Climate Research Programme. TI acknowledges the Environment Research and Technology Development Fund (JPMEERF20202005) of the Environmental Restoration and Conservation Agency. Helpful initial analyses and insights were provided by Monica Morales, Nicholas Hudson, and Kevin Schwarzwald at the Columbia University Center for Climate Systems Research/NASA GISS. Figure preparation assisted by Maria Dombrov. Publisher Copyright: © 2020 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

Details

Original languageEnglish
Article number108313
JournalAgricultural and Forest Meteorology
Volume300
Publication statusPublished - 15 Apr 2021

Keywords

  • Agricultural Model Intercomparison and Improvement Project (AgMIP), Agroclimate, Climate Impacts, Climatic Forcing Datasets, Crop production, Global Gridded Crop Model Intercomparison (GGCMI)