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

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 (+11 others)
2021 Agricultural and Forest Meteorology  
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
more » ... certainty 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
doi:10.1016/j.agrformet.2020.108313 fatcat:s67onagp6jdz3isiab4ej5mhx4