Publication Abstracts

Ruane et al. 2021

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

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, soybean and rice. 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 and crop systems 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. 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. The use of multi-CFD-multi-GGCM ensembles (up to 91 members) also shows benefits over the use of smaller subset of models (although bigger is not always better). Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). 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.

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BibTeX Citation

  author={Ruane, A. C. and Phillips, M. and Müller, C. and Elliott, J. and Jägermeyr, J. and Arneth, A. and Balkovic, J. and Deryng, D. and Folberth, C. and Iizumi, T. and Izaurralde, R. C. and Khabarov, N. and Lawrence, P. and Liu, W. and Olin, S. and Pugh, T. A. M. and Rosenzweig, C. and Sakurai, G. and Schmid, E. and Sultan, B. and Wang, X. and de Wit, A. and Yang, H.},
  title={Strong regional influence of climatic forcing datasets on global crop model ensembles},
  journal={Agric. Forest Meteorol.},

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RIS Citation

ID  - ru00400o
AU  - Ruane, A. C.
AU  - Phillips, M.
AU  - Müller, C.
AU  - Elliott, J.
AU  - Jägermeyr, J.
AU  - Arneth, A.
AU  - Balkovic, J.
AU  - Deryng, D.
AU  - Folberth, C.
AU  - Iizumi, T.
AU  - Izaurralde, R. C.
AU  - Khabarov, N.
AU  - Lawrence, P.
AU  - Liu, W.
AU  - Olin, S.
AU  - Pugh, T. A. M.
AU  - Rosenzweig, C.
AU  - Sakurai, G.
AU  - Schmid, E.
AU  - Sultan, B.
AU  - Wang, X.
AU  - de Wit, A.
AU  - Yang, H.
PY  - 2021
TI  - Strong regional influence of climatic forcing datasets on global crop model ensembles
JA  - Agric. Forest Meteorol.
VL  - 300
SP  - 108313
DO  - 10.1016/j.agrformet.2020.108313
ER  -

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