TY - JOUR
T1 - Strong regional influence of climatic forcing datasets on global crop model ensembles
AU - Ruane, Alex C.
AU - Phillips, Meridel
AU - Müller, Christoph
AU - Elliott, Joshua
AU - Jägermeyr, Jonas
AU - Arneth, Almut
AU - Balkovic, Juraj
AU - Deryng, Delphine
AU - Folberth, Christian
AU - Iizumi, Toshichika
AU - Izaurralde, Roberto C.
AU - Khabarov, Nikolay
AU - Lawrence, Peter
AU - Liu, Wenfeng
AU - Olin, Stefan
AU - Pugh, Thomas A.M.
AU - Rosenzweig, Cynthia
AU - Sakurai, Gen
AU - Schmid, Erwin
AU - Sultan, Benjamin
AU - Wang, Xuhui
AU - de Wit, Allard
AU - Yang, Hong
PY - 2021/4/15
Y1 - 2021/4/15
N2 - 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.
AB - 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.
KW - Agricultural Model Intercomparison and Improvement Project (AgMIP)
KW - Agroclimate
KW - Climate Impacts
KW - Climatic Forcing Datasets
KW - Crop production
KW - Global Gridded Crop Model Intercomparison (GGCMI)
U2 - 10.1016/j.agrformet.2020.108313
DO - 10.1016/j.agrformet.2020.108313
M3 - Article
AN - SCOPUS:85099622128
VL - 300
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
SN - 0168-1923
M1 - 108313
ER -