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

Urheber*innen

Ruane,  Alex C.
External Organizations;

Phillips,  Meridell
External Organizations;

/persons/resource/Christoph.Mueller

Müller,  Christoph
Potsdam Institute for Climate Impact Research;

Elliott,  Joshua
External Organizations;

/persons/resource/jonasjae

Jägermeyr,  Jonas
Potsdam Institute for Climate Impact Research;

Arneth,  Almuth
External Organizations;

Balkovic,  Juraj
External Organizations;

Deryng,  Delphine
External Organizations;

Folberth,  Christian
External Organizations;

Iizumi,  Toshichika
External Organizations;

Izaurralde,  Robert C.
External Organizations;

Khabarov,  Nikolay
External Organizations;

Lawrence,  Peter
External Organizations;

Liu,  Wenfeng
External Organizations;

Olin,  Stefan
External Organizations;

Pugh,  Thomas A. M.
External Organizations;

Rosenzweig,  Cynthia
External Organizations;

Sakurai,  Gen
External Organizations;

Schmid,  Erwin
External Organizations;

Sultan,  Benjamin
External Organizations;

Wang,  Xuhui
External Organizations;

de Wit,  Allard
External Organizations;

Yang,  Hong
External Organizations;

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Zitation

Ruane, A. C., Phillips, M., Müller, C., Elliott, J., Jägermeyr, J., Arneth, A., Balkovic, J., Deryng, D., Folberth, C., Iizumi, T., Izaurralde, R. C., Khabarov, N., Lawrence, P., Liu, W., Olin, S., Pugh, T. A. M., Rosenzweig, C., Sakurai, G., Schmid, E., Sultan, B., Wang, X., de Wit, A., Yang, H. (2021): Strong regional influence of climatic forcing datasets on global crop model ensembles. - Agricultural and Forest Meteorology, 300, 108313.
https://doi.org/10.1016/j.agrformet.2020.108313


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_25122
Zusammenfassung
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.