Page 1
Assessing agricultural risks of climate change in the
21st century in a global gridded crop
model intercomparison
Cynthia Rosenzweig
a,b,1
, Joshua Elliott
b,c
, Delphine Deryng
d
, Alex C. Ruane
a,b
, Christoph Müller
e
, Almut Arneth
f
,
Kenneth J. Boote
g
, Christian Folberth
h
, Michael Glotter
i
, Nikolay Khabarov
j
, Kathleen Neumann
k,l
, Franziska Piontek
e
,
Thomas A. M. Pugh
f
, Erwin Schmid
m
, Elke Stehfest
k
, Hong Yang
h
, and James W. Jones
g
a
National Aeronautics and Space Administration Goddard Institute for Space Studies, New York, NY 10025;
b
Columbia University Center for Climate Systems
Research, New York, NY 10025;
c
University of Chicago Computation Institute, Chicago, IL 60637;
d
Tyndall Centre and School of Environmental Sciences,
University of East Anglia, Norwich NR4 7TJ, UK;
e
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany;
f
Institute of Meteorology and
Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany;
g
Agricultural and
Biological Engineering Department, University of Florida, Gainesville, FL 32611;
h
EAWAG
–
Swiss Federal Institute of Aquatic Science and Technology 8600
Dübendorf, Switzerland;
i
Department of the Geophysical Sciences, University of Chicago, Chicago, IL 60637;
j
Ecosystems Services and Management Program
(ESM), International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria;
k
Planbureau voor de Leefomgeving (Netherlands
Environmental Assessment Agency), 3720 AH, Bilthoven, The Netherlands;
l
Wageningen University, 6700 AK, Wageningen, The Netherlands; and
m
University
of Natural Resources and Life Sciences, 1180 Vienna, Austria
Edited by Hans Joachim Schellnhuber, Potsdam Institute for Climate Impact Research, Potsdam, Germany, and approved June 4, 2013 (received for review
January 31, 2013)
Here we present the results from an intercomparison of multiple
global gridded crop models (GGCMs) within the framework of the
Agricultural Model Intercomparison and Improvement Project and
the Inter-Sectoral Impacts Model Intercomparison Project. Results
indicate strong negative effects of climate change, especially at
higher levels of warming and at low latitudes; models that include
explicit nitrogen stress project more severe impacts. Across seven
GGCMs,
fi
ve global climate models, and four representative con-
centration pathways, model agreement on direction of yield
changes is found in many major agricultural regions at both low
and high latitudes; however, reducing uncertainty in sign of re-
sponse in mid-latitude regions remains a challenge. Uncertainties
related to the representation of carbon dioxide, nitrogen, and high
temperature effects demonstrated here show that further re-
search is urgently needed to better understand effects of climate
change on agricultural production and to devise targeted adapta-
tion strategies.
food security
|
AgMIP
|
ISI-MIP
|
climate impacts
|
agriculture
T
he magnitude, rate, and pattern of climate change impacts on
agricultural productivity have been studied for approximately
two decades. To evaluate these impacts, researchers use bio-
physical process-based models (e.g., refs. 1
–
5), agro-ecosystem
models (e.g., ref. 6), and statistical analyses of historical data
(e.g., refs. 7 and 8). Although these and other methods have been
widely used to forecast potential impacts of climate change on
future agricultural productivity, the protocols used in previous
assessments have varied to such an extent that they constrain cross-
study syntheses and limit the ability to devise relevant adaptation
options (9, 10). In this project we have brought together seven
global gridded crop models (GGCMs) for a coordinated set of
simulations of global crop yields under evolving climate conditions.
This GGCM intercomparison was coordinated by the Agricul-
tural Model Intercomparison and Improvement Project (AgMIP;
11) as part of the Inter-Sectoral Impact Model Intercomparison
Project (ISI-MIP; 12). In order to facilitate analyses across models
and sectors, all global models are driven with consistent bias-
corrected climate forcings derived from the Coupled Model
Intercomparison Project Phase 5 (CMIP5) archive (13). The objec-
tives are to (
i
) establish the range of uncertainties of climate
change impacts on crop productivity worldwide, (
ii
) determine key
differences in current approaches used by crop modeling groups in
global analyses, and (
iii
) propose improvements in GGCMs and in
the methodologies for future intercomparisons to produce more
reliable assessments.
We examine the basic patterns of response to climate across
crops, latitudes, time periods, regional temperatures, and atmo-
spheric carbon dioxide concentrations [CO
2
]. In anticipation of
the wider scienti
fi
c community using these model outputs and the
expanded application of GGCMs, we introduce these models and
present guidelines for their practical application. Related studies
in this special issue focus on crop water demand and the fresh-
water supply for irrigation (14), the application of the crop model
results as part of wider intersectoral analyses (15), and the in-
tegration of crop-climate impact assessments with agro-economic
models (16).
1. Global Gridded Crop Models
Details of the seven global crop models used in this study are
provided in
SI Appendix
, Tables S1
–
S6
. These include the En-
vironmental Policy Integrated Climate Model [EPIC (17
–
20);
originally the Erosion Productivity Impact Calculator (17)], the
Geographic Information System (GIS)-based Environmental
Policy Integrated Climate Model (GEPIC; 18
–
21), the Global
AgroEcological Zone Model in the Integrated Model to Assess
the Global Environment (GAEZ-IMAGE; 22, 23), the Lund-
Potsdam-Jena managed Land Dynamic Global Vegetation and
Water Balance Model (LPJmL; 24
–
26), the Lund-Potsdam-Jena
Signi
fi
cance
Agriculture is arguably the sector most affected by climate
change, but assessments differ and are thus dif
fi
cult to com-
pare. We provide a globally consistent, protocol-based, multi-
model climate change assessment for major crops with explicit
characterization of uncertainty. Results with multimodel agree-
ment indicate strong negative effects from climate change, es-
pecially at higher levels of warming and at low latitudes where
developing countries are concentrated. Simulations that con-
sider explicit nitrogen stress result in much more severe impacts
from climate change, with implications for adaptation planning.
Author contributions: C.R., J.E., D.D., A.C.R., C.M., K.J.B., M.G., F.P., and J.W.J. designed
research; J.E., D.D., C.M., A.A., C.F., M.G., N.K., K.N., T.A.M.P., E. Schmid, E. Stehfest, and
H.Y. performed research; J.E., D.D., A.C.R., and C.M. contributed analytic tools; C.R., J.E.,
D.D., A.C.R., C.M., and K.J.B. analyzed data; and C.R., J.E., D.D., A.C.R., and C.M. wrote
the paper.
The authors declare no con
fl
ict of interest.
This article is a PNAS Direct Submission.
1
To whom correspondence should be addressed. E-mail: cynthia.rosenzweig@nasa.gov.
This article contains supporting information online at
www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1222463110/-/DCSupplemental
.
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General Ecosystem Simulator with Managed Land (LPJ-GUESS;
24, 27, 28), the parallel Decision Support System for Agro-tech-
nology Transfer [pDSSAT; 29, 30; using the Crop Environment
Resource Synthesis (CERES) models for maize, wheat, and rice
and the Crop Template approach (CROPGRO) for soybean], and
the Predicting Ecosystem Goods And Services Using Scenarios
model (PEGASUS; 31).
These models differ in regard to model type, inclusion and
parameterization of soil and crop processes, management inputs,
and outputs. These dissimilarities must be taken into account in
interpreting the results of the intercomparison and in the use of
results in other analyses (
SI Appendix
, Table S1
). Examples in-
clude the biological and environmental stresses affecting crops in
each model and the treatment of how increasing [CO
2
] affects
plant growth and yield. GAEZ-IMAGE, LPJ-GUESS, and
LPJmL focus on water and temperature responses, whereas the
other four models also consider stresses related to nitrogen de-
fi
ciency and severe heat during various stages of development. In
addition to these, pDSSAT considers oxygen stress, PEGASUS
considers phosphorus and potassium stresses, and EPIC and
GEPIC both consider oxygen, phosphorus, bulk density, and
aluminum stresses.
2. Comparison with Intergovernmental Panel on Climate
Change Fourth Assessment Report Results
A relevant question is to what extent
fi
ndings of this substantial
effort of coordinated GGCM modeling are different from what
was reported in the Intergovernmental Panel on Climate Change
Fourth Assessment Report (IPCC AR4; 32) (Fig. 1). Crop
modeling results in the IPCC AR4 showed that small bene
fi
cial
impacts on rainfed crop yields may be found in mid- and high-
latitude regions with moderate-to-medium local increases in
temperature (1
–
3 °C) along with associated [CO
2
] increase and
rainfall changes (
fi
gure 5.2 in ref. 32; reproduced as orange dots
and quadratic
fi
t in Fig. 1). In low-latitude regions, even moderate
temperature increases (1 to 2 °C) were found to have negative
yield impacts for major cereals, because the climate of many
tropical agricultural regions is already quite close to the high-
temperature thresholds for suitable production of these cereals
(33, 34). Furthermore, increases in tropical temperatures can lead
to greater evaporative demand and thus water stress on crops.
We
fi
nd that general patterns of the GGCM results are simi-
lar, especially among those models that simulate nitrogen stress
on crops and include fertilizer application rates based on ob-
servational databases (red line in Fig. 1). GGCMs without ni-
trogen stress tend to be more optimistic in yield response (green
line in Fig. 1). The 15
–
85% range of all GGCM results (indicated
by the shaded envelope) suggests that climate impacts on trop-
ical croplands are generally more negative than the mid- and
high-latitude impacts. There is considerable variation in response
within these broader latitudinal bands, with mid- and high-latitude
crop yields spanning both positive and negative responses, es-
pecially at high levels of temperature change (which are also
associated with higher [CO
2
]). The GGCM results generally dis-
play a wider range of uncertainty compared to the AR4 results,
re
fl
ecting the much broader geographical coverage, projected
temperature, and diversity of crop models represented in the
current study.
3. GGCM Structural Differences
A major source of uncertainties in projected climate impacts
across the globe is the result of variations in GGCM approaches,
assumptions, and structures. Documentation of these differences
is fundamental to at least partially constraining them and to im-
proving analyses of ensemble crop projections.
3.1 Model Types.
The seven GGCMs may be grouped into three
types according to their original purpose, structure, and pro-
cesses: site-based crop models (EPIC, GEPIC, and pDSSAT),
agro-ecosystem models (LPJ-GUESS, LPJmL, and PEGASUS),
and agro-ecological zone models (GAEZ-IMAGE) (
SI Appen-
dix
, Fig. S1
). A critical question is whether two models from the
same lineage, such as EPIC and GEPIC, and LPJ-GUESS and
LPJmL are truly independent. For instance, in the case of EPIC
and GEPIC, the same model version is used (0810), but with dif-
ferent parameterizations and assumptions about soil and manage-
ment input data that are re
fl
ected in the variations in their results.
Site-based models were developed to simulate processes at the
fi
eld scale, and include dynamic interactions among crop, soil,
atmosphere, and management components (2, 20, 30). These
models are often calibrated and validated with data from agro-
nomic
fi
eld experiments. The versions of the site-based models
used in this study have been developed to run simulations on
global grids, as has been done using DSSAT (29, 35
–
37).
Agro-ecosystem models were primarily developed to simulate
carbon and nitrogen dynamics, surface energy balance, and soil
water balance. The LPJmL and LPJ-GUESS models are dy-
namic global vegetation models that simulate the full global
carbon and water cycles. Vegetation dynamics and agricultural
modules were originally introduced to improve the simulations of
these cycles. PEGASUS is a simple global vegetation model
designed to test how agroecosystems respond to climate change
and to evaluate potential bene
fi
ts of various farming adaptation
options at the global scale.
0
2
4
6
8
60
40
20
0
20
40
60
0
2
4
6
8
60
40
20
0
20
40
60
0
2
4
6
8
60
40
20
0
20
40
60
0
2
4
6
8
60
40
20
0
20
40
60
Regional mean temperature change °C
Maize, low latitude
Wheat, low latitude
Rice, low latitude
Soy, low latitude
0
2
4
6
8
60
40
20
0
20
40
60
Yield
c
hange
0
2
4
6
8
60
40
20
0
20
40
60
Yield
change
0
2
4
6
8
60
40
20
0
20
40
60
Yield
change
0
2
4
6
8
60
40
20
0
20
40
60
Regional mean temperature change °C
Yield
change
Maize, mid- to high-latitude
Wheat, mid- to high-latitude
Rice, mid- to high-latitude
Soy, mid- to high-latitude
IPCC AR4
GGCMs without explicit N stress
GGCMs with explicit N stress
Fig. 1.
Mean relative yield change (%) from reference period (1980
–
2010)
compared to local mean temperature change (°C) in 20 top food-producing
regions for each crop and latitudinal band. Results shown for the 7 GGCMs (6
for rice) for all GCM combinations of RCP8.5 compared to results from IPCC
AR4 (represented as orange dots and quadratic
fi
t; 36). Quadratic least-
squares
fi
ts are used to estimate the general response for the GGCMs with
explicit nitrogen stress (EPIC, GEPIC, pDSSAT, and PEGASUS; red line) and for
those without (GAEZ-IMAGE, LPJ-GUESS, and LPJmL; green line). The 15
–
85% range of all models for each ¼°C band is represented in gray. Limits of
local temperature changes re
fl
ect differences in projected warming in cur-
rent areas of cultivation.
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The agro-ecological zone methodology (used here by GAEZ-
IMAGE) was developed to assess agricultural resources and
potential at regional and global scales and has been embedded
into integrated assessment models for global environmental
change (6, 23).
3.2 Model Processes.
Crop processes simulated in all or some of
the GGCMs include leaf area development, light interception
and utilization, yield formation, crop phenology, root distribu-
tion responsiveness to water availability at soil depth, water and
heat stress, soil
–
crop
–
atmosphere water cycle dynamics, evapo-
transpiration, soil carbon and nitrogen cycling, and the effect of
[CO
2
] (
SI Appendix
, Table S1
). All of the GGCMs explicitly
simulate the effects of temperature and water on crop growth;
fewer models simulate, for example, the effects of speci
fi
c heat
stress at critical stages of crop development or the effects of
water-logging on root function. GGCMs differ as to their simu-
lation of some processes in individual crops, such as which
models simulate rice phenology as sensitive to day length as well
as temperature.
Thus the GGCMs vary in their interactive responses to in-
creasing [CO
2
], rising temperature, and changes in water avail-
ability, which are the core characteristics of projected climate
changes in agricultural regions around the world (32). How the
GGCMs handle these factors and their interactions with nutrient
availability (especially N) has signi
fi
cant impacts on the results (41).
This GGCM intercomparison focuses on long-term yield levels
affected by inputs (climate, [CO
2
], water, nutrients) rather than
on short-term shocks. The effects of pests and diseases are not
included explicitly; pest vulnerability may be implicitly included
through calibration to observed yields in some of the models.
LPJmL and PEGASUS, for instance, re
fl
ect the level of farming
intensi
fi
cation and technological inputs (such as the use of
pesticides). However this method does not allow for estimation
of how the effects of pests and diseases may change under
changing climate conditions, an important area for future model
development.
Climate change in
fl
uences on short-term temperature extremes,
monsoon dynamics, and the frequency and intensity of pre-
cipitation may also play a substantial role in the nature of future
agricultural impacts. GCMs do not fully resolve these features,
and the representation of corresponding stresses remains an active
area of GGCM development.
3.3 Model Inputs.
A key contrast among the GGCMs is in nutrient
response in regard to underlying soil properties and to nutrients
applied (nitrogen, phosphorus, and potassium), amount, and
timing. Disparities in the resulting nutrient stress may affect the
sensitivity of yields to climate change because climate stresses and
bene
fi
ts may also interact with (or be overwhelmed by) nutrient
stresses. Alternate approaches in the GGCMs
’
fertilization and
nutrient schemes therefore need to be taken into account in
interpreting crop yield responses to [CO
2
] and other variables.
GGCM differences in the simulation of water availability and
the application of irrigation also have a direct effect on climate
sensitivity in irrigated regions. While the GGCMs deviate in how
water availability is determined, the effects of these deviations
were reduced by testing two irrigation scenarios: 1) no irrigation,
and 2) full irrigation (assuming water is available to fully irrigate
crops) (see
SI Appendix
). In GEPIC, full irrigation was set as a
complete elimination of water stress of crops. In other GGCMs,
full irrigation does not necessarily eliminate water stress com-
pletely, as irrigation events are triggered by model-speci
fi
c soil
moisture thresholds (rainfed and irrigated production responses
are shown in
Fig. S5
). In some cases, the ability of the crop plant
to transpire water may not be suf
fi
cient to satisfy the atmospheric
demand (i.e., stomata may close despite full irrigation).
3.4 Model Procedures.
An important disparity in GGCM outputs is
whether the models calculate actual or potential yields as the
primary output. The GAEZ-IMAGE and LPJ-GUESS results
represent potential yields, unlimited by nutrient or management
constraints and without calibration of growth parameters to repro-
duce historical yields. They are best suited to studies that are de-
signed to advance scienti
fi
c understanding of the plant-atmosphere
processes being represented and their sensitivity to climatic stresses,
rather than for economic forecasts or sensitivity to soil edaphic
conditions. LPJmL is similar to LPJ-GUESS in that nitrogen
stress is not explicitly represented; however, growth parameters
in the model are calibrated so that simulations over the historical
period reproduce realistic average yield patterns (see
SI Appendix
for details). GEPIC, PEGASUS, and pDSSAT used historical
patterns of fertilizer application rates, while EPIC used stan-
dardized low-, moderate-, and high-input management systems
with thresholds that trigger fertilizer and irrigation automatically.
All four of these models explicitly represent nitrogen stress. The
issue of actual vs. potential yields is further complicated by the
presence of numerous other
“
yield gap
”
factors, including varia-
tions in cultivars and farmer management, as well as soil char-
acteristics, pests, diseases, and weeds (38).
4. Current and Future Yield Simulations
4.1 Simulation of Current Crop Yields.
The seven GGCMs largely
reproduce relative patterns of current crop yields (39) at multi-
national regional scales but are dissimilar in the levels of their
base yields (maize: Fig. 2; wheat, rice, and soybean results in
SI
Appendix
, Figs. S2
–
S4
). PEGASUS displayed the largest regional
variation in simulated yields, whereas GAEZ-IMAGE displayed
the least. Each model has regions where crop yield simu-
lations vary markedly from the patterns observed in the ref-
erence period.
LPJmL and LPJ-GUESS vary in reproducing current maize
yields, even though they both have a common base model, as do
EPIC and GEPIC. Each of these two GGCM pairs vary in pa-
rameter settings, assumptions, inputs (e.g., management, fertil-
izer), processes (e.g., carbon allocation), and model procedures
EPIC
GEPIC
pDSSAT
M3-Observations
LPJ-GUESS
LPJmL
PEGASUS
IMAGE
Baseline Maize Yield (t/ha)
>10
5
0
A
B
C
D
E
F
G
H
Fig. 2.
Average reference period (1980
–
2010) GGCM maize yield (
A
–
F
,
H
),
rescaled to a common global average to make the spatial patterns more
apparent, and historical yield M3 observation set (
G
) (39). Note that because
some models are calibrated and others are not and because some models
simulate potential rather than actual yields, it is not advisable to compare
the absolute yields in the ensemble with observations.
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(e.g., calibration) that are re
fl
ected in the wide variations in their
results (
SI Appendix
).
4.2 Global Relative Yield Changes by Crop.
Despite the differences
among models in their assumed inputs and simulations of ab-
solute yields, relative yield changes provide a more consistent set
of results for comparison across models and with previously re-
ported climate change impact results. When taken as a multi-GGCM
and multi-GCM ensemble, global results for relative changes in the
major crops under representative concentration pathway 8.5
(RCP8.5; 42) with CO
2
effects show broad agreement with results
and regional patterns seen in previous studies (Fig. 3,
Upper
).
End-of-century (2070
–
2099) maize yield changes with CO
2
effects for RCP8.5 show substantial impacts and broad agree-
ment among GGCMs, at least as to the sign of the effect. Results
for maize and wheat indicate high-latitude increases and low-lat-
itude decreases with general agreement among models. However,
the quality, depth, and hydraulic properties of soils for agricultural
production at high latitudes merit further investigation. Results for
rice and soybean are consistent in the mid- and high-latitude
regions showing yield increases, but show less agreement among
models in the tropical regions where median changes are small.
Generally, the tropics are subject to more severe (or less bene
fi
-
cial) climate change impacts whereby CO
2
fertilization does not
compensate for increases in water demand and shortening of
already-short growing periods for annual C
3
crops.
When the results are grouped by GGCMs with and without
explicit nitrogen fertilization (
Lower Left
and
Lower Right
in
Fig. 3; red and green lines in Fig. 1), results are substantially
more negative with explicit nitrogen fertilization than without.
The GGCMs with explicit nitrogen fertilization may capture
enhanced dynamics of crop growth and yield interactions with
CO
2
fertilization; experiments show lower CO
2
enhancement of
yield under nitrogen limitation (41). Further work is needed to
understand how these interactions affect the GGCM results and
identify how variations in crop model parameter values also
affect simulated yields (e.g., ref. 43).
4.3 Sensitivity of Yield Response to CO
2
.
Projections of global rel-
ative yield changes under RCP8.5 differ substantially among
GGCMs but also between simulations with and without CO
2
effects for maize, wheat, rice, and soybean (Fig. 4). By the end of
the 21st century, most GGCMs show a range of approximately
±
10% yield change across the
fi
ve GCM scenarios when CO
2
effects are included (GCMs cause nearly double that range for
PEGASUS and only half that range for GAEZ-IMAGE). Rel-
ative global average model response to climate is more similar
and much more negative across tropical and midlatitude bands
once CO
2
effects are removed, indicating that crop model pa-
rameterization of CO
2
effects remains a crucial area of research.
Relative yield changes with and without CO
2
effects are much
closer in C
4
maize than in the C
3
crops.
Maize
Rice
Soy
Wheat
Maize
Maize
Wheat
Wheat
Rice
Rice
Soy
Soy
All GGCMs
GGCMs with explicit N stress
GGCMs without explicit N stress
<-50
>50
0
%
Fig. 3.
Median yield changes (%) for RCP8.5 (2070
–
2099 in comparison to 1980
–
2010 baseline) with CO
2
effects over all
fi
ve GCMs x seven GGCMs (6 GGCMs for rice)
for rainfed maize (35 ensemble members), wheat (35 ensemble members), rice (30 ensemble members), and soy (35 ensemble members). Hatching indicates areas
where more than 70% of the ensemble members agree on the directionality of the impact factor. Gray areas indicate historical areas with little to no yield capacity.
The bottom 8 panels show the corresponding yield change patterns over all
fi
ve GCMs x four GGCMs with nitrogen stress (20 ensemble members from EPIC, GEPIC,
pDSSAT, and PEGASUS; except for rice which has 15) (
Left
); and 3 GGCMs without nitrogen stress (15 ensemble members from GAEZ-IMAGE, LPJ-GUESS, and LPJmL).
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In near decades, relative yield changes display a lower range,
both with and without CO
2
effects, but after the 2050s that range
widens considerably. LPJ-GUESS, a potential yield model that
allows for nutrient-unlimited yield increases, consistently dis-
plays the highest relative changes with CO
2
effects for all crops.
The projected yield changes both with and without CO
2
effects
for PEGASUS (an ecosystem model) are more negative than the
LPJ ecosystem models (note that PEGASUS does not simulate
rice), which is likely due to its utilization of radiation use ef
fi
-
ciency (RUE) instead of leaf-level photosynthesis (40) for CO
2
effects and the inclusion of explicit heat stress. RUE-based
models simulate a universal saturating response to CO
2
and af-
fect water ef
fi
ciency via adjustment of canopy conductance. In
the leaf-level models, stomatal opening controls both photo-
synthesis (CO
2
availability) and transpiration. Recently, Free-Air
CO
2
Enrichment (FACE) experiment results (40) are being used
more intensively to calibrate and test crop models in AgMIP.
4.4 Quantifying Uncertainty from GCMs and RCPs.
GCMs and RCPs
contribute substantially to the uncertainties of the results (Fig.
5). Uncertainty is higher for soybean and rice than for maize and
wheat, because they have more concentrated production areas
and are therefore more sensitive to regional differences in GCM
projections. Uncertainties are greater in the later decades of the
century, where GCM inputs and GGCM results can lead to
uncertainties several times larger in the highest RCP8.5 than in
the lowest RCP2.6. Uncertainty is higher for all crops when CO
2
effects are included, especially in soybean (which is not directly
limited by nitrogen) and in the end of the century when [CO
2
] is
highest. Note that the RCP nomenclature is misleading for earlier
decades, because RCP4.5 actually has slightly higher [CO
2
] than
RCP6.0 until
∼
2060 (42).
5. Discussion and Conclusions
The models used in this GGCM intercomparison are tools to
analyze the response of crops to climate change, and to better
understand risks and opportunities in regard to food production
and food security. For this information to be useful for decision
makers, it needs to include analysis of sources of uncertainty due
to multiple greenhouse gas emissions pathways, climate models,
and crop impact models (44). The work presented here begins to
characterize the uncertainty cascade for GGCM simulations, in-
cluding greenhouse gas emission scenarios, global climate simu-
lations, variations in structure and implementation in crop models,
and assumptions about agricultural management, in a framework
that can be compared across sectors.
Because of such variations in model structure, processes, inputs,
assumptions, parameterizations, and outputs, the ensemble results
from the GGCM intercomparison need to be used with care and
may not be appropriate for certain studies (see recommendations
on data use in
SI Appendix
). Although the experimental design
and climate change scenarios were meant to harmonize simu-
lations to facilitate full comparability, several differences remain
that affect the GGCMs
’
response to climate change and their
utility for different types of assessments, including economic
analyses. Particularly important are the parameterization of CO
2
effects, handling of fertilizer applications, simulation of actual vs.
potential yields, and the extent of calibration. AgMIP is addressing
these in continuing work.
Given these important caveats, we can conclude that the re-
sults from the GGCMs used in this study show general agree-
ment with previous results, especially for those models that include
nitrogen stress (e.g., 6, 32, 45). They indicate negative impacts on
major crops in many agricultural regions at higher levels of
warming. The inclusion of ecosystem-based models in this analysis
has increased the range of uncertainty (previous analyses primarily
used site-based models). Relative global average model response
to climate is more similar once CO
2
effects are removed, indicating
that model parameterization of CO
2
effects (on both photosyn-
thesis and transpiration) remains a vital area of research.
There is ample reason to be concerned in regard to climate
change and crop production. Many regions throughout the world
are projected to experience climate change-induced reductions
Fig. 4.
Relative change (%) in RCP8.5 decadal mean production for each
GGCM (based on current agricultural lands and irrigation distribution) from
ensemble median for all GCM combinations with (solid) and without (dashed)
CO
2
effects for maize, wheat, rice, and soy; bars show range of all GCM com-
binations with CO
2
effects. GEPIC, GAEZ-IMAGE, and LPJ-GUESS only contrib-
uted one GCM without CO
2
effects.
Maize
Wheat
Rice
Soy
With CO
2
effects
Without
CO
2
effects
A
B
Fig. 5.
Absolute deviation of decadal average production changes from
ensemble median yield changes (as fraction of 1980
–
2010 reference period
mean production) for all GCM
×
GGCM combinations in RCP2.6 (dark blue),
RCP4.5 (light blue), RCP6.0 (orange), and RCP8.5 (red) for maize, wheat, rice,
and soy with (
Upper
) and without (
Lower
) CO
2
effects. Simulations in
A
with
CO
2
effects included
fi
ve GCMs and seven GGCMs (35 members), whereas
GAEZ-IMAGE, GEPIC, and LPJ-GUESS ran only a single GCM without CO
2
effects, resulting in 23 members in
B
.
3272
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www.pnas.org/cgi/doi/10.1073/pnas.1222463110
Rosenzweig et al.
Page 6
in crop yields in the climate scenario
–
crop model ensemble tested
here, and additional challenges are mounting (e.g., pests, water
supply, and soil degradation). The 2012 drought in the United States
led to a reduction of maize yields of up to 25% (which is moderate
compared with the impacts projected here for some regions at
higher levels of temperature increase), but US maize exports
declined by an even greater percentage (46). Although some high-
latitude regions may become more climatically viable for crops,
further study is needed to determine whether soil quality is suf-
fi
cient for sustained agricultural production in these locations.
AgMIP is dedicated to exploring the underlying mechanisms
behind GGCM differences and to quantifying uncertainties in
climate change impact assessments. AgMIP further endeavors to
improve agricultural models and expand the community of trans-
disciplinary modelers, thus supporting effective adaptation and
mitigation decisions in agriculture at both global and regional scales.
Materials and Methods
Critical sources of uncertainty for climate change impacts on agricultural pro-
ductivity are identi
fi
ed and characterized, including contrasts in results arising
from a range of global crop models, global climate models, and RCPs (42).
SI
Appendix
provides a full description of materials and methods.
Simulations are driven using 20 climate scenarios from the Coupled Model
Intercomparison Project Phase 5 archive with
fi
ve GCMs and four RCPs, each
bias-corrected at daily resolution based on the historical Water and Global
Change forcing dataset derived from the European Centre for Medium-
Range Weather Forecasts 40 Year Re-analysis (13). The reference period used
throughout this analysis is 1980
–
2010. All models submitted simulations with
CO
2
effects for
fi
ve GCMs for maize, wheat, and soybean (35 members). All
models except PEGASUS simulated
fi
ve GCMs for rice (30 members). All
models simulated the Hadley Centre Global Environment Model (HadGEM)
climate model without CO
2
effects, but only LPJmL, pDSSAT, PEGASUS, and
EPIC simulated the other four GCMs (23 members for maize and wheat
without CO
2
effects, and 18 members for rice).
ACKNOWLEDGMENTS.
We thank the AgMIP research community and the
ISI-MIP team for their contributions to this effort; the US Department of
Agriculture and the United Kingdom Department for International De-
velopment for their support of AgMIP; the World Climate Research
Programme
’
s Working Group on Coupled Modelling, which is responsible
for CMIP; the climate modeling groups for producing and making available
their model output; and two anonymous reviewers for their helpful com-
ments. At the Columbia Center for Climate Systems Research, we thank Erik
Mencos-Contreras and Shari Lifson for research and graphics assistance. For
CMIP the US Department of Energy
’
s Program for Climate Model Diagnosis
and Intercomparison provides coordinating support and led development of
software infrastructure in partnership with the Global Organization for
Earth System Science Portals. The research leading to these results has re-
ceived funding from the European Union
’
s Seventh Framework Programme
FP7/2007-2013 under Grant Agreement 266992.
1. Brisson N, et al. (2003) An overview of the crop model STICS.
Eur J Agron
18(3-4):
309
–
332.
2. Jones JW, et al. (2011) Use of crop models for climate-agricultural decisions.
Hand-
book of Climate Change and Agroecosystems. ICP Series on Climate Change Impacts,
Adaptation, and Mitigation
, eds Hillel D, Rosenzweig C (Imperial College Press, Lon-
don), Vol 1, pp 131
–
157.
3. Keating BA, et al. (2003) An overview of APSIM, a model designed for farming sys-
tems simulation.
Eur J Agron
18(3-4):267
–
288.
4. van Ittersum MK, Donatelli M (2003) Modeling cropping systems: Science, software
and applications.
Eur J Agron
16:309
–
332.
5. Challinor AJ, Wheeler TR, Craufurd PQ, Slingo JM, Grimes DLF (2004) Design and optimi-
zation of a large-area process-based model for annual crops.
Agr For Meteorol
124:99
–
120.
6. Fischer G, Shah M, van Velthuizen H, Nachtergaele FO (2002)
Global Agro-Ecological
Assessment for Agriculture in the 21st Century: Methodology and Results. (Research
Report RR-02-02).
(International Institute for Applied Systems Analysis and Food and
Agriculture Organization of the United Nations, Laxenburg, Austria).
7. Schlenker W, Hanemann WM, Fisher AC (2006) The impact of global warming on U.S.
agriculture: An econometric analysis of optimal growing conditions.
Rev Econ Stat
88(1):113
–
125.
8. Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield re-
sponses to climate change.
Agr For Meteorol
150(11):1443
–
1452.
9. White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simu-
lating impacts of climate change on crop production.
Field Crops Res
124(3):357
–
368.
10. Rötter RP, Carter TR, Olesen JE, Porter JR (2011) Crop-climate models need an over-
haul.
Nat Climate Change
1(4):175
–
177.
11. Rosenzweig C, et al. (2013) The Agricultural Model Intercomparison and Improve-
ment Project (AgMIP): Protocols and pilot studies.
Agric For Meteorol
170:166
–
182.
12. Warszawski L, et al. (2014) The Inter-Sectoral Impact Model Intercomparison Project
(ISI
–
MIP): Project framework.
Proc Natl Acad Sci USA
111:3228
–
3232.
13. Hempel S, Frieler K, Warszawski L, Shewe J, Piontek F (2013) A trend-preserving bias
correction
–
the ISI-MIP approach.
Earth Syst Dynam
4:219
–
236.
14. Elliott J, et al. (2014) Constraints and potentials of future irrigation water availability
on global agricultural production under climate change.
Proc Natl Acad Sci USA
111:3239
–
3244.
15. Piontek F, et al. (2014) Multisectoral climate impact hotspots in a warming world.
Proc
Natl Acad Sci USA
111:3233
–
3238.
16. Nelson GC, et al. (2014) Climate change effects on agriculture: Economic responses to
biophysical shocks.
Proc Natl Acad Sci USA
111:3274
–
3279.
17. Williams JR, et al. (1990)
EPIC-Erosion/Productivity Impact Calculator. Technical Bul-
letin Number 1768
(US Department of Agriculture Agricultural Research Service,
Spring
fi
eld, VA).
18. Williams JR (1995) The EPIC.
Computer Models of Watershed Hydrology
, Chapter 25,
ed Singh VP (Water Resources Publications, Littleton, CO), pp 909
–
1000.
19. Gassman PW, et al. (2004) Historical development and applications of the EPIC and APEX
models. ASAE/CSAE meeting paper no. 042097. Available at www.card.iastate.edu.
20. Izaurralde RC, et al. (2006) Simulating soil C dynamics with EPIC: Model description
and testing against long-term data.
Ecol Modell
192(3-4):362
–
384.
21. Liu J, et al. (2007) GEPIC
—
modeling wheat yield and crop water productivity with
high resolution on a global scale.
Agr Syst
94(2):478
–
493.
22. Leemans R, Solomon AM (1993) Modeling the potential change in yield and distri-
bution of the earth
’
s crops under a warmed climate.
Clim Res
3:79
–
96.
23. Bouwman AF, Kram T, Klein T, Goldewijk K, eds (2006)
Integrated Modelling of
Global Environmental Change. An Overview of IMAGE 2.4
(PBL Netherlands Envi-
ronmental Assessment Agency, The Hague).
24. Bondeau A, et al. (2007) Modelling the role of agriculture for the 20th century global
terrestrial carbon balance.
Glob Change Biol
13(3):679
–
706.
25. Fader M, et al. (2010) Virtual water content of temperate cereals and maize: Present
and potential future patterns.
J Hydrol (Amst)
384(3-4):218
–
231.
26. Waha K, et al. (2012) Climate-driven simulation of global crop sowing dates.
Glob Ecol
Biogeogr
21(2):247
–
259.
27. Smith B, et al. (2001) Representation of vegetation dynamics in the modeling of
terrestrial ecosystems: Comparing two contrasting approaches within European cli-
mate space.
Glob Ecol Biogeogr
10(6):621
–
637.
28. Lindeskog M, et al. (2013) Implications of accounting for land use in simulations of eco-
system services and carbon cycling in Africa.
Earth System Dynamics Discussions
4:235
–
278.
29. Elliott J, Glotter M, Best N, Kelly D, Wilde M, Foster I (2013) The parallel system for
integrating impact models and sectors (pSIMS).
Proceedings of the Conference on
Extreme Science and Engineering Discovery Environment: Gateway to Discovery
(XSEDE
’
13), Association for Computing Machinery, 21:1
–
8.
30. Jones JW, et al. (2003) The DSSAT cropping system model.
Eur J Agron
18(3-4):235
–
265.
31. Deryng D, Sacks WJ, Barford CC, Ramankutty N (2011) Simulating the effects of climate and
agricultural management practices on global crop yield.
Global Biogeochem Cycles
25(2).
32. Easterling WE, et al. (2007) Food,
fi
bre and forest products.
Climate Change 2007:
Impacts, Adaptation and Vulnerability: Contribution of Working Group II to the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change
, eds
Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (Cambridge Univ
Press, Cambridge, UK) pp 273
–
313.
33. Peng S, et al. (2004) Rice yields decline with higher night temperature from global
warming.
Proc Natl Acad Sci USA
101(27):9971
–
9975.
34. Hat
fi
eld JL, et al. (2011) Climate impacts on agriculture: Implications for crop pro-
duction.
Agron J
103(2):351
–
370.
35. Nelson GC, et al. (2009)
Climate Change: Impact on Agriculture and Costs of Adap-
tation
(International Food Policy Research Institute, Washington, DC).
36. Nelson GC, et al. (2010)
Food Security, Farming, and Climate Change to 2050: Scenarios,
Results, Policy Options
(International Food Policy Research Institute, Washington, DC).
37. Jones PG, Thornton PK (2003) The potential impacts of climate change on maize
production in Africa and Latin America in 2055.
Glob Environ Change
13(1):51
–
59.
38. Lobell DB, Cassman KG, Field CB (2009) Crop yield gaps: Their importance, magni-
tudes, and causes.
Annu Rev Env Resour
34:179
–
204.
39. Monfreda C, Ramankutty N, Foley JA (2008) Farming the planet: 2. Geographic dis-
tribution of crop areas, yields, physiological types, and net primary production in the
year 2000.
Global Biogeochem Cycles
22(1):19.
40. Farquhar GD, Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic
CO
2
assimilation in leaves of C3 species.
Planta
149:78
–
90.
41. Kimball BA (2010) Lessons from FACE: CO
2
Effects and Interactions with Water, Ni-
trogen, and Temperature. The Handbook of Climate Change and Agroecosystems,
eds Hillel D, Rosenzweig C (Imperial College Press, Singapore), pp 87
–
107.
42. Moss RH, et al. (2010) The next generation of scenarios for climate change research
and assessment.
Nature
463(7282):747
–
756.
43. Iizumi T, et al. (2011) Probabilistic evaluation of climate change impacts on paddy rice
productivity in Japan.
Clim Change
107:391
–
415.
44. Müller C (2011) Harvesting from uncertainties.
Nature Climate Change
1:253
–
254.
45. Rosenzweig C, Parry ML (1994) Potential impact of climate change on world food
supply.
Nature
367:133
–
138.
46. USDA (2012) Feed outlook. Capehart, Allen, and Bond. A Report from the Economic
Research Service. December 13, 2012. Available at http://www.ers.usda.gov/publications/
fds-feed-outlook. Accessed January 15, 2013.
Rosenzweig et al.
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|
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|
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AGRICULTUR
AL
SCIENCES
SUSTAIN
ABILITY
SCIENC
E
SPECIAL
FEATURE