[en] 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.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Müller, Christoph
Franke, James
Jägermeyr, Jonas
Ruane, Alex C.
Elliott, Joshua
Moyer, Elisabeth
Heinke, Jens
Falloon, Pete D.
Folberth, Christian
François, Louis ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Modélisation du climat et des cycles biogéochimiques
Hank, Tobias
Izaurralde, R. César
Jacquemin, Ingrid ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Modélisation du climat et des cycles biogéochimiques
Akinsanola A A, Kooperman G J, Reed K A, Pendergrass A G and Hannah W M 2020 Projected changes in seasonal precipitation extremes over the United States in CMIP6 simulations Environ. Res. Lett. 15 104078
Almazroui M, Saeed F, Saeed S, Nazrul Islam M, Ismail M, Klutse N A B and Siddiqui M H 2020 Projected change in temperature and precipitation over Africa from CMIP6 Earth Syst. Environ. 4 455-75
Asseng S et al 2013 Uncertainty in simulating wheat yields under climate change Nat. Clim. Change 3 827-32
Boote K J, Jones J W, White J W, Asseng S and Lizaso J I 2013 Putting mechanisms into crop production models Plant. Cell Environ. 36 1658-72
Deryng D et al 2016 Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity Nat. Clim. Change 6 786-90
Dury M, Hambuckers A, Warnant P, Henrot A, Favre E, Ouberdous M and Francois L 2011 Responses of European forest ecosystems to 21st century climate: assessing changes in interannual variability and fire intensity iForest Biogeosci. For. 4 82-99
Elliott J et al 2015 The global gridded crop model intercomparison: data and modeling protocols for phase 1 (v1.0) Geosci. Model. Dev. 8 261-77
Elliott J, Kelly D, Chryssanthacopoulos J, Glotter M, Jhunjhnuwala K, Best N, Wilde M and Foster I 2014 The parallel system for integrating impact models and sectors (pSIMS) Environ. Model. Softw. 62 509-16
Eyring V, Bony S, Meehl G A, Senior C A, Stevens B, Stouffer R J and Taylor K E 2016 Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization Geosci. Model. Dev. 9 1937-58
Fan X, Miao C, Duan Q, Shen C and Wu Y 2020 The performance of CMIP6 versus CMIP5 in simulating temperature extremes over the global land surface J. Geophys. Res. Atmos. 125 e2020JD033031
FAO 2001 Food Balance Sheets: A Handbook (Rome: FAO)
Folberth C et al 2019 Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble PloS One 14 e0221862
Folberth C, Gaiser T, Abbaspour K C, Schulin R and Yang H 2012 Regionalization of a large-scale crop growth model for sub-Saharan Africa: model setup, evaluation, and estimation of maize yields Agric. Ecosyst. Environ. 151 21-33
Franke J A et al 2020a The GGCMI phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0) Geosci. Model. Dev. 13 2315-36
Franke J A et al 2020b The GGCMI phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0) Geosci. Model. Dev. 13 3995-4018
Freund M B, Brown J R, Henley B J, Karoly D J and Brown J N 2020 Warming patterns affect El Niño diversity in CMIP5 and CMIP6 models J. Clim. 33 8237-60
Frieler K et al 2017 Assessing the impacts of 1.5 ◦C global warming-simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) Geosci. Model. Dev. 10 4321-45
Hank T B, Bach H and Mauser W 2015 Using a remote sensing-supported hydro-agroecological model for field-scale simulation of heterogeneous crop growth and yield: application for wheat in central Europe Remote Sens. 7 3934-65
Hawkins E and Sutton R 2009 The potential to narrow uncertainty in regional climate predictions Bull. Am. Meteorol. Soc. 90 1095-107
Izaurralde R C, Williams J R, Mcgill W B, Rosenberg N J and Jakas M C Q 2006 Simulating soil C dynamics with EPIC: model description and testing against long-term data Ecol. Modell. 192 362-84
Jägermeyr J et al Climate change signal in agriculture emerges earlier in new generation of projections In preparation
Jiang D, Hu D, Tian Z and Lang X 2020 Differences between CMIP6 and CMIP5 models in simulating climate over China and the East Asian monsoon Adv. Atmos. Sci. 37 1102-18
Jones J W et al 2017 Brief history of agricultural systems modeling Agric. Syst. 155 240-54
Kimball B A et al 2019 Simulation of maize evapotranspiration: an inter-comparison among 29 maize models Agric. For. Meteorol. 271 264-84
Liu W, Yang H, Folberth C, Wang X, Luo Q and Schulin R 2016 Global investigation of impacts of PET methods on simulating crop-water relations for maize Agric. For. Meteorol. 221 164-75
Maiorano A et al 2017 Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles Field Crops Res. 202 5-20
Mcsweeney C F and Jones R G 2016 How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP? Clim. Serv. 1 24-29
Meehl G A, Senior C A, Eyring V, Flato G, Lamarque J-F, Stouffer R J, Taylor K E and Schlund M 2020 Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models Sci. Adv. 6 eaba1981
Minoli S et al 2019 Global response patterns of major rainfed crops to adaptation by maintaining current growing periods and irrigation Earth's Future 7 1464-80
Mueller N D, Gerber J S, Johnston M, Ray D K, Ramankutty N and Foley J A 2012 Closing yield gaps through nutrient and water management Nature 490 254-7
Müller C et al 2017 Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications Geosci. Model. Dev. 10 1403-22
Müller C and Robertson R 2014 Projecting future crop productivity for global economic modeling Agric. Econ. 45 37-50
Nelson G C et al 2014 Climate change effects on agriculture: economic responses to biophysical shocks Proc. Natl Acad. Sci. 111 3274-9
O'Neill B C et al 2016 The Scenario Model Intercomparison Project (scenariomip) for CMIP6 Geosci. Model. Dev. 9 3461-82
Olin S, Schurgers G, Lindeskog M, Wårlind D, Smith B, Bodin P, Holmér J and Arneth A 2015 Modelling the response of yields and tissue C: N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe Biogeosciences 12 2489-515
Palosuo T et al 2011 Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models Eur. J. Agron. 35 103-14
Portmann F T, Siebert S and Döll P 2010 MIRCA2000-global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling Glob. Biogeochem. Cycles 24 Gb1011
Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N and Rafaj P 2011 RCP 8.5-a scenario of comparatively high greenhouse gas emissions Clim. Change 109 33-57
Rosenzweig C et al 2013 The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies Agric. For. Meteorol. 170 166-82
Rosenzweig C et al 2014 Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison Proc. Natl Acad. Sci. 111 3268-73
Rosenzweig C et al 2018 Coordinating AgMIP data and models across global and regional scales for 1.5 ◦C and 2.0 ◦C assessments Phil. Trans. R. Soc. A 376 20160455
Rötter R P, Carter T R, Olesen J E and Porter J R 2011 Crop-climate models need an overhaul Nat. Clim. Change 1 175-7
Ruane A C et al 2018 Biophysical and economic implications for agriculture of +1.5◦ and +2.0 ◦C global warming using AgMIP coordinated global and regional assessments Clim. Res. 76 17-39
Ruane A C, Goldberg R and Chryssanthacopoulos J 2015 Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation Agric. For. Meteorol. 200 233-48
Schauberger B, Rolinski S and Müller C 2016 A network-based approach for semi-quantitative knowledge mining and its application to yield variability Environ. Res. Lett. 11 123001
Taylor K E, Stouffer R J and Meehl G A 2012 An overview of CMIP5 and the experiment design Bull. Am. Meteorol. Soc. 93 485-98
Tokarska K B, Stolpe M B, Sippel S, Fischer E M, Smith C J, Lehner F and Knutti R 2020 Past warming trend constrains future warming in CMIP6 models Sci. Adv. 6 eaaz9549
Toreti A et al 2020 Narrowing uncertainties in the effects of elevated CO2 on crops Nat. Food 1 775-82
van Vuuren D et al 2011 The representative concentration pathways: an overview Clim. Change 109 5-31
von Bloh W, Schaphoff S, Müller C, Rolinski S, Waha K and Zaehle S 2018 Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJml (version 5.0) Geosci. Model. Dev. 11 2789-812
Wang X et al 2020 Emergent constraint on crop yield response to warmer temperature from field experiments Nat. Sustain. 908-16
Williams K, Gornall J, Harper A, Wiltshire A, Hemming D, Quaife T, Arkebauer T and Scoby D 2017 Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska Geosci. Model. Dev. 10 1291-320
Wirsenius S 2000 Human use of land and organic materials Department of Physical Resource Theory (Göteborg: Chalmers University of Technology and Göteborg University) p 255
Wyser K, van Noije T, Yang S, von Hardenberg J, O'Donnell D and Döscher R 2020 On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6 Geosci. Model. Dev. 13 3465-74
Xin X, Wu T, Zhang J, Yao J and Fang Y 2020 Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon Int. J. Climatol. 40 6423-40
Zhu P, Zhuang Q, Archontoulis S V, Bernacchi C and Müller C 2019 Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration Glob. Change Biol. 25 2470-84
Zhu Y Y and Yang S 2020 Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5 Adv. Clim. Change Res. 11 239-51