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

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Strong regional influence of climatic forcing datasets on global crop model ensembles. / Ruane, Alex C.; Phillips, Meridel; Müller, Christoph; Elliott, Joshua; Jägermeyr, Jonas; Arneth, Almut; Balkovic, Juraj; Deryng, Delphine; Folberth, Christian; Iizumi, Toshichika; Izaurralde, Roberto C.; Khabarov, Nikolay; Lawrence, Peter; Liu, Wenfeng; Olin, Stefan; Pugh, Thomas A.M.; Rosenzweig, Cynthia; Sakurai, Gen; Schmid, Erwin; Sultan, Benjamin; Wang, Xuhui; de Wit, Allard; Yang, Hong.

In: Agricultural and Forest Meteorology, Vol. 300, 108313, 15.04.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Ruane, AC, Phillips, M, Müller, C, Elliott, J, Jägermeyr, J, Arneth, A, Balkovic, J, Deryng, D, Folberth, C, Iizumi, T, Izaurralde, RC, Khabarov, N, Lawrence, P, Liu, W, Olin, S, Pugh, TAM, 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, vol. 300, 108313. https://doi.org/10.1016/j.agrformet.2020.108313

APA

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., ... 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

Vancouver

Author

Ruane, Alex C. ; Phillips, Meridel ; Müller, Christoph ; Elliott, Joshua ; Jägermeyr, Jonas ; Arneth, Almut ; Balkovic, Juraj ; Deryng, Delphine ; Folberth, Christian ; Iizumi, Toshichika ; Izaurralde, Roberto C. ; Khabarov, Nikolay ; Lawrence, Peter ; Liu, Wenfeng ; Olin, Stefan ; Pugh, Thomas A.M. ; Rosenzweig, Cynthia ; Sakurai, Gen ; Schmid, Erwin ; Sultan, Benjamin ; Wang, Xuhui ; de Wit, Allard ; Yang, Hong. / Strong regional influence of climatic forcing datasets on global crop model ensembles. In: Agricultural and Forest Meteorology. 2021 ; Vol. 300.

Bibtex

@article{f392f0bc6a2446229a87d876156074b6,
title = "Strong regional influence of climatic forcing datasets on global crop model ensembles",
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.",
keywords = "Agricultural Model Intercomparison and Improvement Project (AgMIP), Agroclimate, Climate Impacts, Climatic Forcing Datasets, Crop production, Global Gridded Crop Model Intercomparison (GGCMI)",
author = "Ruane, {Alex C.} and Meridel Phillips and Christoph M{\"u}ller and Joshua Elliott and Jonas J{\"a}germeyr and Almut Arneth and Juraj Balkovic and Delphine Deryng and Christian Folberth and Toshichika Iizumi and Izaurralde, {Roberto C.} and Nikolay Khabarov and Peter Lawrence and Wenfeng Liu and Stefan Olin and Pugh, {Thomas A.M.} and Cynthia Rosenzweig and Gen Sakurai and Erwin Schmid and Benjamin Sultan and Xuhui Wang and {de Wit}, Allard and Hong Yang",
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.",
year = "2021",
month = apr,
day = "15",
doi = "10.1016/j.agrformet.2020.108313",
language = "English",
volume = "300",
journal = "Agricultural and Forest Meteorology",
issn = "0168-1923",
publisher = "Elsevier",

}

RIS

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

N1 - 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.

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)

UR - http://www.scopus.com/inward/record.url?scp=85099622128&partnerID=8YFLogxK

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 -