Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Research output: Contribution to journalArticlepeer-review

Standard

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. / Müller, Christoph; Franke, James; Jägermeyr, Jonas; Ruane, Alex C.; Elliott, Joshua; Moyer, Elisabeth; Heinke, Jens; Falloon, Pete D.; Folberth, Christian; Francois, Louis; Hank, Tobias; Izaurralde, R. César; Jacquemin, Ingrid; Liu, Wenfeng; Olin, Stefan; Pugh, Thomas A.M.; Williams, Karina; Zabel, Florian.

In: Environmental Research Letters, Vol. 16, No. 3, 034040, 03.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Müller, C, Franke, J, Jägermeyr, J, Ruane, AC, Elliott, J, Moyer, E, Heinke, J, Falloon, PD, Folberth, C, Francois, L, Hank, T, Izaurralde, RC, Jacquemin, I, Liu, W, Olin, S, Pugh, TAM, Williams, K & Zabel, F 2021, 'Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios', Environmental Research Letters, vol. 16, no. 3, 034040. https://doi.org/10.1088/1748-9326/abd8fc

APA

Müller, C., Franke, J., Jägermeyr, J., Ruane, A. C., Elliott, J., Moyer, E., Heinke, J., Falloon, P. D., Folberth, C., Francois, L., Hank, T., Izaurralde, R. C., Jacquemin, I., Liu, W., Olin, S., Pugh, T. A. M., Williams, K., & Zabel, F. (2021). Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environmental Research Letters, 16(3), [034040]. https://doi.org/10.1088/1748-9326/abd8fc

Vancouver

Author

Müller, Christoph ; Franke, James ; Jägermeyr, Jonas ; Ruane, Alex C. ; Elliott, Joshua ; Moyer, Elisabeth ; Heinke, Jens ; Falloon, Pete D. ; Folberth, Christian ; Francois, Louis ; Hank, Tobias ; Izaurralde, R. César ; Jacquemin, Ingrid ; Liu, Wenfeng ; Olin, Stefan ; Pugh, Thomas A.M. ; Williams, Karina ; Zabel, Florian. / Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. In: Environmental Research Letters. 2021 ; Vol. 16, No. 3.

Bibtex

@article{0751d77ca75c4c4e99b2aa83304a8e4b,
title = "Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios",
abstract = "Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.",
keywords = "AgMIP, Agriculture, Climate change, CMIP, Crop modeling, Uncertainty",
author = "Christoph M{\"u}ller and James Franke and Jonas J{\"a}germeyr and Ruane, {Alex C.} and Joshua Elliott and Elisabeth Moyer and Jens Heinke and Falloon, {Pete D.} and Christian Folberth and Louis Francois and Tobias Hank and Izaurralde, {R. C{\'e}sar} and Ingrid Jacquemin and Wenfeng Liu and Stefan Olin and Pugh, {Thomas A.M.} and Karina Williams and Florian Zabel",
note = "Funding Information: CMIP5 model output data provided by the WHOI CMIP5 Community Storage Server, Woods Hole Oceanographic Institution, Woods Hole, MA, USA from their website at http://cmip5.whoi.edu/. J A F was supported by the NSF NRT program (Grant No. DGE-1735359) and the NSF Graduate Research Fellowship Program (Grant No. DGE-1746045). RDCEP is funded by NSF through the Decision Making Under Uncertainty program (Grant No. SES-1463644). JJ received support from the Open Philanthropy Project and the NASA Co-op. ACR was supported by NASA GISS Climate Impacts Group funding from the NASA Earth Sciences Directorate. PF and KW were supported by the Newton Fund through the Met Office program Climate Science for Service Partnership Brazil (CSSP Brazil). The publication of this article was partially funded by the Open Access Fund of the Leibniz Association.",
year = "2021",
month = mar,
doi = "10.1088/1748-9326/abd8fc",
language = "English",
volume = "16",
journal = "Environmental Research Letters",
issn = "1748-9326",
publisher = "IOP Publishing",
number = "3",

}

RIS

TY - JOUR

T1 - Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

AU - Müller, Christoph

AU - Franke, James

AU - Jägermeyr, Jonas

AU - Ruane, Alex C.

AU - Elliott, Joshua

AU - Moyer, Elisabeth

AU - Heinke, Jens

AU - Falloon, Pete D.

AU - Folberth, Christian

AU - Francois, Louis

AU - Hank, Tobias

AU - Izaurralde, R. César

AU - Jacquemin, Ingrid

AU - Liu, Wenfeng

AU - Olin, Stefan

AU - Pugh, Thomas A.M.

AU - Williams, Karina

AU - Zabel, Florian

N1 - Funding Information: CMIP5 model output data provided by the WHOI CMIP5 Community Storage Server, Woods Hole Oceanographic Institution, Woods Hole, MA, USA from their website at http://cmip5.whoi.edu/. J A F was supported by the NSF NRT program (Grant No. DGE-1735359) and the NSF Graduate Research Fellowship Program (Grant No. DGE-1746045). RDCEP is funded by NSF through the Decision Making Under Uncertainty program (Grant No. SES-1463644). JJ received support from the Open Philanthropy Project and the NASA Co-op. ACR was supported by NASA GISS Climate Impacts Group funding from the NASA Earth Sciences Directorate. PF and KW were supported by the Newton Fund through the Met Office program Climate Science for Service Partnership Brazil (CSSP Brazil). The publication of this article was partially funded by the Open Access Fund of the Leibniz Association.

PY - 2021/3

Y1 - 2021/3

N2 - Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

AB - Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

KW - AgMIP

KW - Agriculture

KW - Climate change

KW - CMIP

KW - Crop modeling

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85102407712&partnerID=8YFLogxK

U2 - 10.1088/1748-9326/abd8fc

DO - 10.1088/1748-9326/abd8fc

M3 - Article

AN - SCOPUS:85102407712

VL - 16

JO - Environmental Research Letters

JF - Environmental Research Letters

SN - 1748-9326

IS - 3

M1 - 034040

ER -