Page 1
Geosci. Model Dev., 13, 2315–2336, 2020
https://doi.org/10.5194/gmd-13-2315-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
The GGCMI Phase 2 experiment: global gridded crop model
simulations under uniform changes in CO
2
, temperature,
water, and nitrogen levels (protocol version 1.0)
James A. Franke
1,2
, Christoph Müller
3
, Joshua Elliott
2,4
, Alex C. Ruane
5
, Jonas Jägermeyr
2,3,4,5
, Juraj Balkovic
6,7
,
Philippe Ciais
8,9
, Marie Dury
10
, Pete D. Falloon
11
, Christian Folberth
6
, Louis François
10
, Tobias Hank
12
,
Munir Hoffmann
13,22
, R. Cesar Izaurralde
14,15
, Ingrid Jacquemin
10
, Curtis Jones
14
, Nikolay Khabarov
6
,
Marian Koch
13
, Michelle Li
2,16
, Wenfeng Liu
8,17
, Stefan Olin
18
, Meridel Phillips
5,19
, Thomas A. M. Pugh
20,21
,
Ashwan Reddy
14
, Xuhui Wang
8,9
, Karina Williams
11,23
, Florian Zabel
12
, and Elisabeth J. Moyer
1,2
1
Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
2
Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
3
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
4
Department of Computer Science, University of Chicago, Chicago, IL, USA
5
NASA Goddard Institute for Space Studies, New York, NY, USA
6
Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
7
Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovak Republic
8
Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
9
Sino-French Institute of Earth System Sciences, College of Urban and Env. Sciences, Peking University, Beijing, China
10
Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES,
Institut d’Astrophysique et de Géophysique, University of Liège, Liège, Belgium
11
Met Office Hadley Centre, Exeter, UK
12
Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
13
Georg-August-University Göttingen, Tropical Plant Production and Agricultural Systems Modeling, Göttingen, Germany
14
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
15
Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA
16
Department of Statistics, University of Chicago, Chicago, IL, USA
17
EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
18
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
19
Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA
20
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
21
Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
22
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
23
Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
Correspondence:
Christoph Müller (cmueller@pik-potsdam.de)
Received: 23 August 2019 – Discussion started: 25 October 2019
Revised: 14 March 2020 – Accepted: 19 March 2020 – Published: 18 May 2020
Published by Copernicus Publications on behalf of the European Geosciences Union.
Page 2
2316
J. A. Franke et al.: The GGCMI Phase 2 experiment
Abstract.
Concerns about food security under climate
change motivate efforts to better understand future changes
in crop yields. Process-based crop models, which represent
plant physiological and soil processes, are necessary tools for
this purpose since they allow representing future climate and
management conditions not sampled in the historical record
and new locations to which cultivation may shift. However,
process-based crop models differ in many critical details, and
their responses to different interacting factors remain only
poorly understood. The Global Gridded Crop Model Inter-
comparison (GGCMI) Phase 2 experiment, an activity of
the Agricultural Model Intercomparison and Improvement
Project (AgMIP), is designed to provide a systematic param-
eter sweep focused on climate change factors and their in-
teraction with overall soil fertility, to allow both evaluating
model behavior and emulating model responses in impact as-
sessment tools. In this paper we describe the GGCMI Phase 2
experimental protocol and its simulation data archive. A total
of 12 crop models simulate five crops with systematic uni-
form perturbations of historical climate, varying CO
2
, tem-
perature, water supply, and applied nitrogen (“CTWN”) for
rainfed and irrigated agriculture, and a second set of simula-
tions represents a type of adaptation by allowing the adjust-
ment of growing season length. We present some crop yield
results to illustrate general characteristics of the simulations
and potential uses of the GGCMI Phase 2 archive. For ex-
ample, in cases without adaptation, modeled yields show ro-
bust decreases to warmer temperatures in almost all regions,
with a nonlinear dependence that means yields in warmer
baseline locations have greater temperature sensitivity. Inter-
model uncertainty is qualitatively similar across all the four
input dimensions but is largest in high-latitude regions where
crops may be grown in the future.
1
Introduction
Understanding crop yield response to a changing climate is
critically important, especially as the global food produc-
tion system will face pressure from increased demand over
the next century (Foley et al., 2005; Bodirsky et al., 2015).
Climate-related reductions in supply could therefore have
severe socioeconomic consequences (e.g., Stevanovi´c et al.,
2016; Wiebe et al., 2015). Multiple studies using different
crop or climate models concur in projecting sharp yield re-
ductions on currently cultivated cropland under business-
as-usual climate scenarios, although their yield projections
show considerable spread (e.g., Rosenzweig et al., 2014;
Schauberger et al., 2017; Porter et al., 2014, and references
therein). Although forecasts of future yields reductions can
be made with simple statistical models based on regressions
in historical weather data, process-based models, which sim-
ulate the effect of temperature, water and nutrient availabil-
ity, and atmospheric CO
2
concentration on the process of
photosynthesis and the biology and phenology of individual
crops, play a critical role in assessing the impacts of climate
change.
Process-based models are necessary for understanding
crop yields in novel conditions not included in historical
data, including higher CO
2
levels, out-of-sample combina-
tions of rainfall and temperature, cultivation in areas where
crops are not currently grown, and differing management
practices (e.g., Pugh et al., 2016; Roberts et al., 2017; Mi-
noli et al., 2019). Process-based models have therefore been
widely used in studies on future food security (Wheeler and
Von Braun, 2013; Elliott et al., 2014a; Frieler et al., 2017),
options for climate mitigation (Müller et al., 2015) and adap-
tation (Challinor et al., 2018), and future sustainable devel-
opment (Humpenöder et al., 2018; Jägermeyr et al., 2017).
They are a necessity for global gridded simulations, which al-
low understanding the global dynamics of agricultural trade,
because global market mechanisms can strongly modulate
the economic impacts of regional yield changes (Stevanovi´c
et al., 2016; Hasegawa et al., 2018). Global simulations are
especially necessary in studying agricultural effects of cli-
mate change (Müller et al., 2017), since systematic climate
assessments must account for cultivation area changes and
crop selection switching (Rosenzweig et al., 2018; Ruane
et al., 2018) and must consider inter-regional differences
(e.g., Nelson et al., 2014; Wiebe et al., 2015).
Modeling crop responses, however, continues to be chal-
lenging, as crop growth is a function of complex interac-
tions between climate inputs, soil, and management prac-
tices (Boote et al., 2013; Rötter et al., 2011). Models tend to
agree broadly in major response patterns, including a reason-
able representation of the spatial pattern in historical yields
of major crops and projections of shifts in yield under fu-
ture climate scenarios (e.g., Elliott et al., 2015; Müller et al.,
2017). But process-based models still struggle with some im-
portant details, including reproducing historical year-to-year
variability in many regions (e.g., Müller et al., 2017; Jäger-
meyr and Frieler, 2018), reproducing historical yields when
driven by reanalysis weather (e.g., Glotter et al., 2014), and
low sensitivity to extreme events (e.g., Glotter et al., 2015;
Schewe et al., 2019). Global models pose additional chal-
lenges due to variable input data quality and limited ability
for model calibration. Long-term projections therefore retain
considerable uncertainty (Wolf and Oijen, 2002; Jagtap and
Jones, 2002; Iizumi et al., 2010; Angulo et al., 2013; Asseng
et al., 2013, 2015).
Model intercomparison projects such as the Agricultural
Model Intercomparison and Improvement Project (AgMIP;
Rosenzweig et al., 2013) are crucial in quantifying uncer-
tainties in model projections (Rosenzweig et al., 2014). In-
tercomparison projects have also been used to develop proto-
cols for evaluating overall model performance (Elliott et al.,
2015; Müller et al., 2017) and to assess the representation
of individual physical mechanisms such as water stress and
CO
2
fertilization (e.g., Schauberger et al., 2017). However, to
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J. A. Franke et al.: The GGCMI Phase 2 experiment
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date, few such projects have systematically sampled critical
factors that may interact strongly in affecting crop yields. A
number of modeling exercises in the last 5 years have begun
to use systematic parameter sweeps in crop model evalua-
tion and emulation (e.g., Ruane et al., 2014; Makowski et al.,
2015; Pirttioja et al., 2015; Fronzek et al., 2018; Snyder et al.,
2019; Ruiz-Ramos et al., 2018), but all involve limited sites
and most also limited crops and scenarios.
The Global Gridded
Crop Model Intercomparison
(GGCMI) Phase 2 experiment is the first global gridded
crop model intercomparison involving a systematic parame-
ter sweep across critical interacting factors. GGCMI Phase 2
is an activity of AgMIP, and a continuation of a multi-model
comparison exercise begun in 2014. The initial GGCMI
Phase 1 (Elliott et al., 2015; Müller et al., 2017) compared
harmonized yield simulations over the historical period, with
primary goals of model evaluation and understanding sources
of uncertainty (including model parameterization, weather
inputs, and cultivation areas); see also Folberth et al. (2019)
and Porwollik et al. (2017) for more information. GGCMI
Phase 2 compares simulations across a set of inputs with uni-
form perturbations to historical climatology, including CO
2
,
temperature, precipitation, and applied nitrogen, as well as
adaptation to shifting growing seasons (collectively referred
to as “CTWN-A”). The CTWN-A experiment is inspired
by AgMIP’s Coordinated Climate-Crop Modeling Project
(C3MP; see Ruane et al., 2014; McDermid et al., 2015) and
contributes to the AgMIP Coordinated Global and Regional
Assessments (CGRA; see Ruane et al., 2018; Rosenzweig
et al., 2018).
In this paper, we describe the GGCMI Phase 2 model ex-
periments and present initial summary results. In the sections
that follow, we describe the experimental goals and proto-
cols; the different process-based models included in the in-
tercomparison; the levels of participation by the individual
models. We then provide an assessment of model fidelity
based on observed yields at the country level, and show some
selected examples of the simulation output dataset to illus-
trate model responses across the input dimensions.
2
Simulation objectives and protocol
2.1
Goals
The guiding scientific rationale of GGCMI Phase 2 is to pro-
vide a comprehensive, systematic evaluation of the response
of process-based crop models to critical interacting factors,
including CO
2
, temperature, water, and applied nitrogen un-
der two contrasting assumptions on growing season adapta-
tion (CTWN-A). The dataset is designed to allow researchers
to
–
enhance understanding of models sensitivity to climate
and nitrogen drivers;
–
study the interactions between climate variables and ni-
trogen inputs in driving modeled yield impacts;
–
characterize differences in crop responses to climate
change across the Earth’s climate regions;
–
provide a dataset that allows statistical emulation of
crop model responses for downstream modelers; and
–
explore the potential effects on future yield changes of
adaptations in growing season length.
2.2
Modeling protocol
The GGCMI Phase 1 intercomparison was a relatively lim-
ited computational exercise, requiring yield simulations for
19 crops across a total of 310 model years of historical sce-
narios, and had the participation of 14 modeling groups. The
GGCMI Phase 2 protocol is substantially larger, involving
over 1400 individual 30-year global scenarios, or over 42 000
model years; 12 modeling groups nevertheless participated.
To reduce the computational load, the GGCMI Phase 2 pro-
tocol reduces the number crops to five (maize, rice, soybean,
spring wheat, and winter wheat). The reduced set of crops
includes the three major global cereals and the major legume
and accounts for over 50 % of human calories in 2016: nearly
3.5 billion tons or 32 % of total global crop production by
weight (FAO, 2018). This set of major crops has the advan-
tage of historical yield data globally available at subnational
scale (Ray et al., 2012; Iizumi et al., 2014), and has been fre-
quently used in subsequent analyses (e.g., Müller et al., 2017;
Porwollik et al., 2017).
The Phase 2 protocol involves a suite of uniform pertur-
bations from a historical climate time series. The baseline
climate scenario for GGCMI Phase 2 is one of the weather
products used in Phase 1, daily climate inputs for 1980–2010
from the 0.5
◦
NASA AgMERRA (“agricultural”-modified
Modern Era Retrospective analysis for Research and Appli-
cations) gridded reanalysis product. AgMERRA is specif-
ically designed for agricultural modeling, with satellite-
corrected precipitation (Ruane et al., 2015). The experimen-
tal protocol consists of 9 levels for water supply perturba-
tions, 7 for temperature, 4 for CO
2
, and 3 for applied nitro-
gen, for a total of 756 simulations (Table 1), 672 for rainfed
agriculture and an additional 84 for irrigated (
W
∞
). Values of
climate variable perturbations are selected to represent rea-
sonable ranges for changes over the medium term (to 2100)
under business-as-usual emissions. Values for nitrogen appli-
cation levels are intended to cover a wide range of potentials.
The resulting GGCMI Phase 2 dataset captures the distribu-
tion of crop model responses over a wide range of potential
future climate and management conditions.
The protocol samples over all possible permutations of in-
dividual perturbations; i.e., all values are applied across all
crops and regions, so that the protocol includes many combi-
nations that are not realistic. For example, we simulate high
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J. A. Franke et al.: The GGCMI Phase 2 experiment
Figure 1. (a–e)
Cultivated areas for maize, rice, and soybean from the MIRCA2000 (Monthly Irrigated and Rainfed Crop Areas around the
year 2000) dataset (Portmann et al., 2010). Blue indicates grid cells with more that 20 000 ha (10 % of the equatorial grid cell), and the gray
contour shows grid cells with more that 10 ha cultivated. Areas for winter and spring wheat areas are adapted from MIRCA2000 and two
other sources; see text for details. For irrigated crops, see Fig. S1 in the Supplement.
(f–j)
Number of models providing simulations for each
grid cell. All models provide the minimum areal coverage of the GGCMI Phase 2 protocol, but some provide extra coverage at high latitudes
or in arid or otherwise unsuitable areas.
N application to soybeans, which are N fixers and need lit-
tle fertilizer. This choice also means that CO
2
changes are
applied independently of changes in climate variables, so
that higher CO
2
is not associated with higher temperatures
or other particular climate changes. The purpose of the ex-
periment is not to produce individual scenarios that represent
realistic future states but to sample over a wide range of pa-
rameter space to enable understanding the factors that drive
agricultural changes.
While all CTWN perturbations are applied uniformly
across the historical time series, they are applied in different
ways. CO
2
and nitrogen levels are specified as discrete val-
ues applied uniformly over all grid cells. Temperature pertur-
bations are applied as absolute offsets from the daily mean,
minimum, and maximum temperature time series for each
grid cell, and water perturbations are applied as fractional
changes to daily precipitation. The irrigated scenario (
W
∞
)
is a particular case of water supply levels, in which crops are
assumed to have no water constraints. That is, all crop wa-
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J. A. Franke et al.: The GGCMI Phase 2 experiment
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Table 1.
GGCMI Phase 2 input parameter levels for each dimen-
sion. Temperature and precipitation values indicate the perturba-
tions from the historical climatology. Irrigated (
W
∞
) simulations
assume the maximum beneficial levels of water. Bold font indicates
the “baseline” or historical level for each dimension. One model
provided simulations at the
T
+
5 level.
Input variable
Simulation input values
Unit
CO
2
(
C
)
360
, 510, 660, 810
ppm
Temperature (
T
)
−
1,
0
, 1, 2, 3, 4, 6
◦
C
Precipitation (
W
)
−
50,
−
30,
−
20,
−
10,
0
,
%
10, 20, 30, (and
W
∞
)
Applied nitrogen (
N
)
10, 60,
200
kg ha
−
1
Adaptation (
A
)
A0: none
, A1: new cultivar
–
to maintain original growing
season length
ter requirements are fulfilled regardless of local water sup-
ply limitations. To facilitate comparison, irrigated simula-
tions use the same growing seasons as all other simulations,
even though in reality irrigated growing seasons may be dif-
ferent (Portmann et al., 2010), and both irrigated and rainfed
cases are simulated with near-global coverage.
The uniform perturbations of the GGCMI Phase 2 proto-
col require some care in interpretation. Temperature and pre-
cipitation perturbations should be considered as differences
from historical climatology within the growing season only.
That is, a
T
+
1 simulation represents a 1
◦
C warmer grow-
ing season, not a 1
◦
C warmer annual mean temperature. (The
distinction is important because in climate projections, win-
ters generally warm more than summers; e.g., Haugen et al.,
2018.) In the GGCMI Phase 2 protocol, temperature and pre-
cipitation perturbations are applied uniformly in space, but
future changes in temperature and precipitation will not be
spatially or temporally uniform. In a realistic climate pro-
jection, higher latitudes generally warm more strongly than
lower latitudes (e.g., Hansen et al., 1997), and the northern
high latitudes warm more quickly than the southern ones. A
GGCMI Phase 2 simulation therefore represents a possible
future state that could occur in each grid cell but not one that
would in reality occur simultaneously in all grid cells across
the globe. The GGCMI Phase 2 simulations are intended to
be used for climate impact assessment not directly but in-
stead as a “training set” for statistical emulation of each crop
model. Once an emulator is constructed from the outputs de-
scribed here, it can be driven with growing-season climate
anomalies from any climate model projection. The GGCMI
Phase 2 protocol does not involve any simulated changes in
climate variability, but Franke et al. (2020) demonstrate that
these effects are relatively minor and that GGCMI Phase 2
emulators can effectively reproduce crop model yields under
realistic future climate scenarios.
The area simulated in the GGCMI Phase 2 protocol ex-
tends considerably outside currently cultivated areas, be-
cause cultivation may shift under climate change. Figure 1
shows both the present-day cultivated area of rainfed crops
(left) and model coverage (right). (Figs. S1–S2 for cur-
rently cultivated area for irrigated crops; model coverage
is the same.) Each model covers all currently cultivated ar-
eas and much of the uncultivated land area, run at 0.5
◦
spatial resolution. To reduce the computational burden, the
protocol requires simulation over only 80 % of Earth land
surface area, omitting areas assumed to remain non-arable
even under an extreme climate change, including Greenland,
far-northern Canada, Siberia, Antarctica, the Gobi and Sa-
hara deserts, and Central Australia. The protocol also al-
lows omitting regions judged unsuitable for cropland for
non-climatic reasons. Selection criterion involve a combi-
nation of soil suitability indices at 10 arcmin resolution and
excludes those 0.5
◦
grid cells in which at least 90 % of the
area is masked as unsuitable according to any single index,
and which do not contain any currently cultivated cropland.
Currently cultivated areas are provided by the MIRCA2000
(Monthly Irrigated and Rainfed Crop Areas around the year
2000) data product (Portmann et al., 2010). Soil suitability
indices measure excess salt, oxygen availability, rooting con-
ditions, toxicities, and workability, and are provided by the
IIASA (International Institute for Applied Systems Analysis)
Global Agro-Ecological Zone model (GAEZ; FAO/IIASA,
2011). The procedure follows that proposed by Pugh et al.
(2016). All modeling groups simulate the minimum required
coverage, but some provide simulations that extend into
masked zones, including, e.g., the Sahara and central Aus-
tralia (Fig. 1, right).
2.3
Harmonization between models
The 12 models included in GGCMI Phase 2 are all process-
based crop models that are widely used in impacts assess-
ments (Table 3). Although some models share a common
base (e.g., the LPJ or EPIC families of models), they have
subsequently developed independently. Wherever possible,
the GGCMI Phase 2 protocol harmonizes inputs, but differ-
ences in model structure mean that several key factors cannot
be fully standardized across the experiment. These include
soil treatment (which affects soil organic matter and carry-
over effects of soil moisture across growing years) and base-
line climate inputs.
While 10 of the 12 models participating in GGCMI Phase
2 use the AgMERRA historical daily climate data product,
two models require subdaily input data and thus use differ-
ent baseline climate inputs: Processes of Radiation, Mass and
Energy Transfer (PROMET) uses ERA-Interim reanalysis
(Dee et al., 2011), and the Joint UK Land Environment Simu-
lator (JULES) uses a bias-corrected version of ERA-Interim,
the 3 h WFDEI (WATCH Forcing Data ERA-Interim) (Wee-
don et al., 2014), specifically the WFDEI version with pre-
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J. A. Franke et al.: The GGCMI Phase 2 experiment
cipitation bias-corrected against the Climate Research Unit
(CRU) TS3.101/TS3.21 precipitation totals (Harris et al.,
2014). The data products show some differences (Figs. S3–
S4, which compare data products over currently cultivated
areas for each crop). For example, for maize-growing areas,
ERA-Interim daily precipitation is biased high from that in
AgMERRA by 7 % (
<
1
σ
), while mean daily precipitation
in WFDEI is only 3 % higher. Precipitation differences are
largest in wheat areas, where ERA-Interim is substantially
wetter (
+
60 mm yr
−
1
or 10 %). Temperature differences are
largest for rice, with ERA-Interim 1
◦
C cooler than Ag-
MERRA, and smaller for other crops, e.g., maize with ERA-
Interim 0.45
◦
C cooler and WFDEI 0.1
◦
C warmer. These dif-
ferences are relatively small compared to the perturbations
tested in the protocol.
Planting dates and growing season lengths are standard-
ized across models, following the procedure described in El-
liott et al. (2015) for the “fullharm” setting. (The exception
is that Phase 2, unlike Phase 1, uses identical growing sea-
sons for rainfed and irrigated cases, to allow for direct com-
parison of simulations along the
W
dimension.) This harmo-
nization is important because the parametrization of growing
seasons can have strong effects on simulated yields (Müller
et al., 2017; Jägermeyr and Frieler, 2018). In all the GGCMI
Phase 2 crop models, sowing dates are prescribed directly,
but the length of the growing season is a product of crop
phenology, which is driven mostly by phenological param-
eters and temperature. Modelers were therefore asked to ad-
just their phenological parameters so that the average grow-
ing season length of the baseline scenario (
C
=
360,
T
=
0,
W
=
0) matched the harmonization target. (The one excep-
tion to this harmonization protocol involves CARbon Assim-
ilation In the Biosphere (CARAIB), whose team kept their
own growing season specifications rather than tuning to stan-
dard lengths.) Two aspects of the procedure should be noted.
First, the target growing seasons used in GGCMI Phase 2 are
crop and location specific. For example, present-day maize
is sown in March in Spain, in July in Indonesia, and in De-
cember in Namibia (Portmann et al., 2010). Second, because
temperature varies between years in the 30-year baseline cli-
matology, realized growing season length will still vary in
individual years even after harmonization.
The dependence of harvest dates on climate parameters
means that growing seasons will alter under climate change
in a model with phenological parameters tuned to match
target growing seasons in the baseline climate. In general,
warmer future scenarios produce shorter growing seasons.
We denote simulations that allow these future changes as
“A0” experiments, where 0 denotes “no adaptation”. The
GGCMI Phase 2 protocol includes a second set of experi-
ments, “A1”, that assume that future cultivars are modified to
adjust to changes along the
T
dimension in the CTWN ex-
periment. For these simulations, modelers adjust phenologi-
cal parameters for each temperature scenario to hold grow-
ing season length approximately constant. (CARAIB sim-
ulations follow the same principle, fixing growing season
length at their baseline levels.) That is, the A1 simulations
require running a model with seven different choices of cul-
tivar parameters, one per warming level. Parameter settings
for
T
=
0 are identical in both A0 and A1. The A1 simula-
tions roughly capture the case in which adaptive crop cul-
tivar choice ensures that crops reach maturity at roughly the
same time as in the current temperature regime. This assump-
tion is simplistic, and does not reflect realistic opportunities
and limitations to adaptation (Vadez et al., 2012; Challinor
et al., 2018) but provides some insight into how crop modi-
fications could alter projected impacts on yields and is suffi-
ciently easy to implement in a large model intercomparison
project as GGCMI.
Growing seasons for maize, rice, and soybean are taken
from the SAGE (Center for Sustainability and the Global En-
vironment, University of Wisconsin) crop calendar (Sacks
et al., 2010), gap filled with the MIRCA2000 crop calen-
dar (Portmann et al., 2010) and, if no SAGE or MIRCA2000
data are available, with simulated Lund–Potsdam–Jena man-
aged Land (LPJmL) growing seasons (Waha et al., 2012) and
are identical to those used in GGCMI Phase 1 (Elliott et al.,
2015). In GGCMI Phase 2, we separately treat spring and
winter wheat and so must define different growing seasons
for each. As for the other crops, we use the SAGE crop cal-
endar, which separately specifies spring and winter wheat, as
the primary source for 69 % of grid cells. In the remaining
areas where no SAGE information is available, we turn to,
in order of preference, the MIRCA2000 crop calendar (Port-
mann et al., 2010) and to simulated LPJmL growing seasons
(Waha et al., 2012). These datasets each provide several op-
tions for wheat growing season for each grid cell but do not
label them as spring or winter wheat. We assign a growing
season to each wheat type for each location based on its base-
line climate conditions. A growing season is assigned to win-
ter wheat if all of the following hold, and to spring wheat
otherwise:
–
The monthly mean temperature is below freezing point
(
<
0
◦
C) at most for 5 months per year (i.e., winter is
not too long).
–
The coldest 3 months of a year are below 10
◦
C (i.e.,
there is a winter).
–
The season start date fits the criteria that
–
if in the Northern Hemisphere, it is after the
warmest or before the coldest month of the year (as
winter is around the end/beginning of the calendar
year);
–
if in the Southern Hemisphere, it is after the
warmest and before the coldest month of the year
(as winter is in the middle of the calendar year).
Nitrogen (N) application is standardized in timing across
models. N fertilizer is applied in two doses, as is often the
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J. A. Franke et al.: The GGCMI Phase 2 experiment
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norm in actual practice, to reduce losses to the environment.
In the GGCMI Phase 2 protocol, half of the total fertilizer
input is applied at sowing and the other half on day 40 af-
ter sowing, for all crops except for winter wheat. For winter
wheat, in practice the application date for the second N fer-
tilizer application varies according to local temperature, be-
cause the length of winter dormancy can vary strongly. In the
GGCMI Phase 2 protocol, the second fertilization date for
winter wheat must lie at least 40 d after planting and – if not
contradicting the distance to planting – no later than 50 d be-
fore maturity. If those limits permit, the second fertilization
is set to the middle day of the first month after sowing that
has average temperatures above 8
◦
C.
All stresses in models are disabled other than those related
to nitrogen, temperature, and water. For example, model re-
sponses to alkalinity, salinity, and non-nitrogen nutrients are
all disabled. No other external N inputs are permitted –
that is, there is no atmospheric deposition of nitrogen – but
some models allow additional release of plant-available ni-
trogen through mineralization in soils. In LPJmL, the Lund–
Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS),
and Agricultural Production Systems sIMulator (APSIM),
soil mineralization is a part of model treatments of soil or-
ganic matter and cannot be disabled. Some additional differ-
ences in model structure mean that several key factors are not
standardized across the experiment. For example, carryover
effects across growing years including residue management
and soil moisture are treated differently across models.
2.4
Output data products
All models in GGCMI Phase 2 provide 30-year time series of
annual crop yields for each scenario, 0.5
◦
grid cell and crop,
in units of t ha
−
1
yr
−
1
. They also provide all available vari-
ables of the following six: total aboveground biomass yield;
the dates of planting, anthesis and maturity; applied irriga-
tion water in irrigated scenarios; and total evapotranspira-
tion. We term these seven variables the “mandatory” outputs,
but note that some models do not compute all of them; e.g.,
CARAIB does not compute the anthesis date. Besides these
seven “mandatory” data products (Table 2, bold), the proto-
col requests any or all of 18 “optional” additional output vari-
ables (Table 2, plain text). Participating modeling groups pro-
vided between 3 (PEPIC) and 18 (APSIM-UGOE) of these
optional variables.
All output data are supplied as NetCDF version 4 files,
each containing values for one variable in a 30-year time
series associated with a single scenario, for all grid cells.
Filenames follow the naming conventions of GGCMI Phase
1 (Elliott et al., 2015), which themselves are derived from
those of ISIMIP (Frieler et al., 2017). Filenames are spec-
ified as [model]_[climate]_hist_fullharm_[variable]_[crop]
_global_annual_[start-year]_[end-
year]_[C]]_[T]_[W]_[N]_[A].nc4 Here, [model] is the
crop model name; [climate] is the original climate input
dataset (typically AgMERRA); [variable] is the output
variable (of those in Table 2); [crop] is the crop abbrevi-
ation (“mai” for maize, “ric” for rice, “soy” for soybean,
“swh” for spring wheat, and “wwh” for winter wheat); and
[start-year] and [end-year] specify the first and last years
recorded on file.
[
C
]
,
[
T
]
,
[
W
]
,
[
N
]
, and
[
A
]
indicate the
CTWN-A settings, each represented with the respective
uppercase letter and the number indicating the level (e.g.,
“C360_T0_W0_N200”; see Table 1). Except for the CTWN-
A letters, the entire filename needs to be in small caps.
All filenames include the identifiers
global
and
annual
to
distinguish them as global, annual model output, following
the updated ISIMIP file naming convention (Frieler et al.,
2017).
Output data are provided on a regular geographic grid,
identical for all models. Grid cell centers span latitudes from
−
89
.
75 to 89.75
◦
and longitudes from
−
179
.
75 to 179.75
◦
.
Missing values where no crop growth has been simulated are
distinguished from crop failures: a crop failure is reported
as zero yield but non-simulated areas (including ocean grid
cells) have yields reported as “missing values” (defined as
1
×
10
20
in the NetCDF files). Following NetCDF standards,
latitude, longitude, and time are included as separate vari-
ables in ascending order, with units “degrees north”, “degrees
east”, and “growing seasons since 1 January 1980 00:00:00”.
Following GGCMI Phase 1 standards, the first entry in
each file describes the first complete cropping cycle simu-
lated from the given climate input time series. In the Ag-
MERRA time series used for GGCMI Phase 2, the first year
provided is 1980 but the date of the first entry can vary by
crop and location. In the Northern Hemisphere, for summer
crops like maize (sown in spring 1980 and harvested in fall
1980), the first harvest record would be of 1980, but for win-
ter wheat (sown in fall 1980 and harvested in spring 1981)
the first harvest record would be of 1981. Output files report
the sequence of growing periods rather than calendar years.
While there is generally one sowing event per calendar year
(since simulations with harmonized growing seasons do not
permit double cropping), in some cases harvest events may
skip or repeat within a calendar year. For example, because
soybeans in North Carolina are typically harvested well into
December, some calendar years may include no harvest (if
it is not completed until after 31 December) or two harvests
(one in January and one 11 months later in the following De-
cember).
3
Models contributing
The 12 models participating in GGCMI Phase 2 are listed in
Table 3. Models differ substantially in structure and param-
eterization and can be separated into two broad categories:
site-based (field-scale) models and global ecosystem mod-
els. The six site-based models are APSIM, pDSSAT, and
the EPIC family of models; the six ecosystem models are
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J. A. Franke et al.: The GGCMI Phase 2 experiment
Table 2.
Output variables, naming convention, and units in the GGCMI Phase 2 protocol. Items in bold text are the mandatory minimum
requirements (if model capacities allow for these outputs). Other variables are optionally provided depending on availability and participating
modeling groups provided between 3 (PEPIC) and 18 (APSIM-UGOE) of these optional variables.
Variable
Variable name
Units
Yield
yield
_
<
crop
>
t ha
−
1
yr
−
1
(dry matter)
Total above ground biomass yield
biom
_
<
crop
>
t ha
−
1
yr
−
1
(dry matter)
Actual planting date
plant-day
_
<
crop
>
day of year
Anthesis date
b
anth-day
_
<
crop
>
days from planting
Maturity date
maty-day
_
<
crop
>
days from planting
Applied irrigation water
pirrww
_
<
crop
>
mm yr
−
1
Evapotranspiration (growing season sum)
etransp
_
<
crop
>
mm yr
−
1
(
W
∞
scenarios only
)
Transpiration (growing season sum)
transp_
<
crop
>
mm yr
−
1
Evaporation (growing season sum)
evap_
<
crop
>
mm yr
−
1
Runoff (total growing season sum, subsurface plus surface)
runoff_
<
crop
>
mm yr
−
1
Total available soil moisture in root zone
a
trzpah2o_
<
crop
>
mm yr
−
1
Total root biomass
rootm_
<
crop
>
t ha
−
1
yr
−
1
(dry matter)
Total reactive nitrogen (
N
r
) uptake (growing season sum)
tnrup_
<
crop
>
kg ha
−
1
yr
−
1
Total
N
r
inputs (growing season sum)
tnrin_
<
crop
>
kg ha
−
1
yr
−
1
Total
N
r
losses (growing season sum)
tnrloss_
<
crop
>
kg ha
−
1
yr
−
1
Gross primary production (GPP)
gpp_
<
crop
>
gC m
−
2
yr
−
1
Net primary production (NPP)
npp_
<
crop
>
gC m
−
2
yr
−
1
CO
2
response scaler on NPP
co2npp_
<
crop
>
−
{0...
∞
}
Water response scaler on NPP
h2onpp_
<
crop
>
−
{0..1}
Temperature response scaler on NPP
tnpp_
<
crop
>
−
{0..1}
N
r
response scaler on NPP
nrnpp_
<
crop
>
−
{0..1}
Other nutrient response scaler on NPP
ornpp_
<
crop
>
−
{0..1}
CO
2
response scaler on transpiration
co2trans_
<
crop
>
−
{0..1}
Maximum stress response scaler
maxstress_
<
crop
>
−
{0..1}
Maximum leaf area index (LAI)
laimax_
<
crop
>
m
2
m
−
2
a
Growing season sum, basis for computing average soil moisture.
b
Provided where possible.
LPJmL, LPJ-GUESS, PROMET, CARAIB, Organising Car-
bon and Hydrology In Dynamic Ecosystems (ORCHIDEE),
and JULES. Models employ a variety of approaches for the
core modules such as primary production or evapotranspira-
tion. For primary production, site-based models employ light
use efficiency approaches and ecosystem models use pho-
tosynthesis approaches. For evapotranspiration, most mod-
els use Priestley–Taylor, Penman–Monteith or Hargreaves
schemes, but JULES and PROMET utilize a land surface
model approach instead. Note that models that share a com-
mon genealogy may still use different schemes for evapotran-
spiration: for example, EPIC-TAMU uses Penman–Monteith
and EPIC-IIASA uses Hargreaves. To describe soils, most
models use either the Harmonized World Soil Database
(HWSD) from the FAO (FAO/IIASA/ISRIC/ISSCAS/JRC,
2012) or the ISRIC-WISE database (Batjes, 2005) or a
derivation thereof. Table S1 in the Supplement provides de-
tails on these model characteristics as well as on implemen-
tation, including spin-up, calibration other than growing sea-
son, residue management, and irrigation rules.
Table 3 also describes the simulation output contribution
of each model to the GGCMI Phase 2 archive. Not all mod-
eling groups provided simulations for the full protocol de-
scribed above. Given the substantial computational require-
ments, different participation tiers were specified to allow
submission of smaller subsets of the full protocol. These sub-
sets were designed as alternate samples across the four di-
mensions of the CTWN space, with
full
(12) and
low
(4) op-
tions for the
C
·
N
variables, and
full
(63),
reduced
(31), and
minimum
(9) options for
T
·
W
variables (described below).
All participating modeling groups provided identical cover-
age of the CTWN parameter space for different crops, but
most differed in CTWN coverage of A0 and A1 scenarios.
Since the adaptation dimension was defined as a secondary
priority for GGCMI Phase 2, some models provided a more
limited set of A1 scenarios. Of these, EPIC-IIASA, JULES,
and ORCHIDEE-crop provided no A1 scenarios.
The different participation levels are defined by combining
the
C
·
N
sets with the
T
·
W
sets:
– full
: all 756 A0 simulations (all 12
C
×
N
* all 63
T
×
W
);
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J. A. Franke et al.: The GGCMI Phase 2 experiment
2323
Table 3.
Models included in GGCMI Phase 2 and the number of CTWN-A simulations performed. The maximum number is 756 for A0
(no adaptation) experiments, and 648 for A1 (maintaining growing length) experiments, since T0 is not simulated under A1. “N dim”
indicates whether the models are able to represent varying nitrogen levels. Each model provides the same set of CTWN simulations across
all its modeled crops, but some models omit individual crops. (For example, APSIM-UGOE does not simulate winter wheat.) Bold numbers
indicate those models that provided all specified simulations (with or without varying nitrogen).
Winter
Spring
N
Sims per
Model (key citations)
Maize
Soybean
Rice
wheat
wheat
dim.
crop (A0/A1)
APSIM-UGOE, Keating et al. (2003),
Holzworth et al. (2014)
X
X
X
–
X
X
44/36
CARAIB, Dury et al. (2011),
Pirttioja et al. (2015)
X
X
X
X
X
–
252/216
EPIC-IIASA, Balkoviˇc et al. (2014)
X
X
X
X
X
X
39 / 0
EPIC-TAMU, Izaurralde et al. (2006)
X
X
X
X
X
X
756/648
JULES, Osborne et al. (2015),
Williams and Falloon (2015),
Williams et al. (2017)
X
X
X
–
X
–
252/0
GEPIC, Liu et al. (2007),
Folberth et al. (2012)
X
X
X
X
X
X
430/181
LPJ-GUESS, Lindeskog et al. (2013),
Olin et al. (2015)
X
–
–
X
X
X
756/648
LPJmL, von Bloh et al. (2018)
X
X
X
X
X
X
756/648
ORCHIDEE-crop, Wu et al. (2016)
X
–
X
X
–
X
33 / 0
pDSSAT, Elliott et al. (2014b),
Jones et al. (2003)
X
X
X
X
X
X
756/648
PEPIC, Liu et al. (2016a),
Liu et al. (2016b)
X
X
X
X
X
X
149/121
PROMET, Hank et al. (2015),
Mauser et al. (2015),
Zabel et al. (2019)
X
X
X
X
X
X
261/232
Totals
12
10
11
10
11
10
5240|3378
– high
: 362 simulations (all 12
C
×
N
combinations
·
re-
duced
T
×
W
set of 31 combinations);
– mid
: 124 simulations (
low
4
C
×
N
combinations
·
re-
duced
T
×
W
set of 31 combinations); and
– low
: 36 simulations (
low
4
C
×
N
combinations
·
mini-
mum
T
×
W
set of 9 combinations).
Of the 12 models submitting data, six followed the
full
pro-
tocol; these are marked with bold text in the last column of
Table 3. However, note that two of these models (CARAIB
and JULES) cannot represent nitrogen effects explicitly and
so do not sample over the nitrogen dimension. Two models
followed
high
with minor modifications (GEPIC adding an
additional
T
level and PROMET omitting the intermediate
N
level). One model (PEPIC) followed
mid
but included an ad-
ditional
C
level. Three models approximately followed
low
with APSIM-UGOE and EPIC-IIASA providing some ad-
ditional
T
×
W
levels and ORCHIDEE-crop omitting some
T
×
W
combinations.
The combinations of perturbation values in the
C
×
N
and
T
×
W
parameter spaces used in the various participation lev-
els are chosen to provide maximum coverage over plausible
future values. For the
C
×
N
space, we specify
–
full
as 12 pairs, with four
C
values (360, 510, 660,
810 ppm) and three
N
values (10, 60, 200 kg ha
−
1
yr
−
1
)
and
–
low
as
only
four
pairs:
C
360
_
N
10
,
C
360
_
N
200
,
C
660
_
N
60
, and
C
810
_
N
200
.
For the
T
×
W
space, we specify
–
full
as all seven
T
levels and nine
W
levels;
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J. A. Franke et al.: The GGCMI Phase 2 experiment
–
reduced
as 31 alternating combinations, with different
Ws for even Ts than for odd Ts. For even Ts (i.e.,
T
0
,
T
2
,
T
4
,
T
6
), we use
W
= −
50,
−
20, 0,
+
30
=
4
·
4
=
16
pairs. For odd Ts (i.e.,
T
−
1,
T
1,
T
3), we use
W
= −
30,
−
10,
+
10,
+
30, inf
=
3
·
5
=
15 pairs;
–
minimum
as nine combinations:
T
−
1
W
−
10
,
T
0
W
10
,
T
1
W
−
30
,
T
2
W
−
50
,
T
2
W
20
,
T
3
W
30
,
T
4
W
0
,
T
4
W
inf
,
T
6
W
−
20
.
4
Results
To illustrate the properties of the GGCMI Phase 2 model sim-
ulations, we provide an evaluation of model performance by
comparing model and historical yields, and show example
results that demonstrate the spread of model responses to cli-
mate and management inputs.
4.1
Evaluation of model performance
All models participating in GGCMI Phase 2 have be evalu-
ated against historical yields and site-specific experimental
data. Most models (9 of 12, all but CARAIB, JULES, and
PROMET) have been evaluated in their global setup in the
GGCMI Phase 1 evaluation exercise (Müller et al., 2017),
and many have used the GGCMI Phase 1 online tool to sim-
ilarly evaluate subsequent model versions (e.g., von Bloh
et al., 2018). Evaluating the performance of crop models in
the GGCMI Phase 2 archive is complicated by the artificial
nature of the protocol: the settings in the CTWN-A experi-
ment design do not reflect actual conditions in the real world.
The protocol includes one scenario of near-historical climate
inputs (
T
0
,
W
0
,
C
360
), but the prescribed uniform nitrogen
application levels do not reflect real-world fertilizer prac-
tices. Models also omit detailed calibrations to reflect the
performance of historical cultivars.
We provide a partial evaluation of the models’ skill in
reproducing crop yield characteristics using the methodol-
ogy of Müller et al. (2017), developed for GGCMI Phase
1. Müller et al. (2017) evaluate how well model crop yield
responses in a historical run capture real-world yield vari-
ations driven by year-to-year temperature and precipitation
variations. Following this approach, we compare yields in
the GGCMI Phase 2 baseline simulations with detrended his-
torical yields from the Food and Agriculture Organization
of the United Nations (FAO, 2018) by calculating the Pear-
son product moment correlation coefficient over 26 years of
yield. The procedure is sensitive to the detrending method
and the area mask used to aggregate yields; we use a 5-year
running mean removal and the MIRCA2000 cultivation area
mask for aggregation. In some cases, the model time series
are shifted by 1 year to account for discrepancies in FAO or
model year reporting. Because the GGCMI Phase 2 proto-
col imposes fixed, uniform nitrogen application levels that
are not realistic for individual countries, we evaluate control
runs for each model at multiple N levels whenever possible.
Overall, nine of the GGCMI Phase 2 models (Table 3) pro-
vide historical runs for all three nitrogen levels (10, 60, and
200 kg ha
−
1
yr
−
1
).
As expected due to the unrealistic features described
above, correlation coefficients for the GGCMI Phase 2 sim-
ulations are slightly lower than those found in the Phase 1
evaluation, but models show reasonable fidelity at capturing
year-to-year variation (Fig. 2). For example, global correla-
tion coefficients in Phase 1 and Phase 2, respectively, are 0.89
and 0.74 for maize, 0.67 and 0.64 for wheat, and 0.64 and
0.59 for soybeans. (Phase 1 values are from Figs. 1–4 and
6 in Müller et al., 2017.) Differences in fidelity between re-
gions and crops exceed differences between models; that is,
Fig. 2c–f show more color similarity in horizontal than ver-
tical bars. For example, maize in the United States is consis-
tently well simulated, while maize in Indonesia is problem-
atic (mean Pearson correlation coefficients of 0.68 and 0.18,
respectively). Note that in this methodology, simulations of
crops with low year-to-year variability such as irrigated rice
and wheat will tend to score more poorly than those with
higher variability. In some cases, especially in the develop-
ing world, low correlation coefficients may point to report-
ing problems in the FAO statistics and to real-world variabil-
ity caused by variations in management rather than weather
(Ray et al., 2012; Müller et al., 2017). No single model con-
sistently exhibits greater fidelity than others. Instead, each
model shows near best-in-class performance for at least one
location–crop combination. For example, pDSSAT is the best
model for maize in the US, LPJmL and GEPIC are best in
Germany, PROMET is best in Argentina, and PEPIC and
LPJ-GUESS are best in France.
4.2
Model crop yield responses under CTWN forcing
Crop models in the GGCMI Phase 2 ensemble show broadly
consistent responses to climate and management perturba-
tions in most regions, with a strong negative impact of in-
creased temperature in all but the coldest regions. Mapping
the distribution of baseline yields and yield changes shows
the geographic dependencies that underlie these results. Ab-
solute yield potentials show strong spatial variation, with
much of the Earth’s surface area unsuitable for any of these
crops (Fig. 3, left). Crop yield changes under climate pertur-
bations also show distinct geographic patterns (Fig. 3, right,
which shows fractional yield differences between the base-
line and
T
+
4 A0 scenarios). In general, models agree most
on yield response in regions where yield potentials are cur-
rently high and therefore where crops are currently grown.
In A0 simulations, models show robust decreases in yields at
low latitudes, and highly uncertain ensemble mean increases
at most high latitudes. Low-latitude yield reductions are due
in part to shortening of the growing season under warming
and in part to the direct effects of higher temperature. In A1
simulations, where growing seasons length does not change,
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J. A. Franke et al.: The GGCMI Phase 2 experiment
2325
Figure 2.
Assessment of crop model performance in GGCMI Phase 2, following the protocol of GGCMI Phase 1 (Müller et al., 2017).
(a,
b)
Example time series comparison between simulated crop yield and FAO country statistics (FAO, 2018) at the country level for two example
high production countries: US maize and rice in India, both for the 200 kg ha
−
1
nitrogen application level.
(c–f)
Heatmaps illustrating the
Pearson
r
correlation coefficient between the detrended simulated and observed country-level mean yields for the top 10 countries by
production for each crop, of those countries with continuous FAO data over 1981–2010. We show separate comparisons for simulations with
the three different nitrogen application levels, denoted 1, 2, and 3 for 10, 60, and 200 kg N ha
−
1
, respectively. Panels
(a, c, e)
show correlation
of ensemble mean yields with FAO data. Because FAO does not distinguish between wheat types, we sum simulated spring and winter wheat
for models that provide both (see Table 3). Note that differences by region and crop are stronger than difference between models; e.g.,
horizontal bars are more similar in color than vertical bars. Countries are ordered alphabetically, not by production quantity.
temperature-related reductions in yield are more muted (see
Fig. S14). In both A0 and A1 simulations, models show some
increases in high mountain regions that are currently cold
limited.
Projections of strong yield growth at higher latitudes
should be treated with caution, since the effects evident in
Fig. 3 are due in part to inaccuracies in model representa-
tions of present-day crop yields. For example, at latitudes
north of 45
◦
, the GGCMI Phase 2 models collectively sug-
gest strong (but uncertain) growth in soybean yields under
warmer conditions (Fig. 3g). However, model differences are
greater in the baseline than future simulations and greatest
in currently cultivated areas (Fig. 4). Both the mean pro-
jected growth and the inter-model spread are driven by three
models that show almost zero present-day potential soybean
yields across the entire high-latitude region, even in locations
where soybeans are currently grown (Fig. 4, left). PROMET,
for example, involves a stronger response to cold than other
models (e.g., LPJmL) with frost below
−
8
◦
C irreversibly
killing non-winter crops and prolonged periods of below-
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J. A. Franke et al.: The GGCMI Phase 2 experiment
Figure 3.
Illustration of the spatial patterns of baseline yields
(a–e)
and yield changes
(f–j)
in the GGCMI Phase 2 ensemble. The left
column shows multi-model climatological (30-year) median yields for the baseline scenario, with white stippling indicating areas where
these crops are not currently cultivated. Areas with less than 0.5 t ha
−
1
in the baseline are masked. Absence of cultivation aligns well with
the lowest yield contour (0–2 t ha
−
1
). The right column shows multi-model mean fractional yield changes in the
T
+
4
◦
C scenario relative to
the baseline scenario. Areas without stippling are those where models agree on changes: the multi-model mean fractional change exceeds the
standard deviation of changes in individual models. Stippling indicates areas of low confidence (
1 <
1
σ
). Some spatial structure in projected
changes at high latitudes may be due to differences in model coverage; see Fig. 1.
optimum temperatures also leading to complete crop fail-
ure. Over the high-latitude regions simulated by both models,
52 % of grid cells in PROMET report 0 yield in the present
climate vs. 11 % of cells in the
T
+
4 scenario, leading to a
strong yield gain in warmer future climates. In LPJmL out-
puts, the same high-latitude area is deemed suitable for cul-
tivation even in baseline climate, with crop failure rates of
4 % and 5 % in present and
T
+
4 cases, so that projected
yield changes are modest (Fig. 4). These spurious low base-
line yields result in very large fractional changes in the
T
+
4
warming scenario, when all models agree that conditions be-
come favorable for soybeans. Those models that most accu-
rately reproduce present-day high-latitude soybean yields of
1–2 t ha
−
1
(Ray et al., 2012) in fact show a slight decrease
in yield under a warming scenario (Fig. 4a). Apparent future
yield increases in the multi-model mean are driven by the
least realistic simulations.
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J. A. Franke et al.: The GGCMI Phase 2 experiment
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Figure 4.
Model probability densities for soybean yields at latitudes north of 45
◦
in historical and warming simulations in the A0 case. While
10 GGCMI Phase 2 models provide simulations (Table 3); we show eight representative models for clarity. Probability density functions are
estimated separately for locations with some current cultivation (
a
, approximately 2500 grid cells, unweighted by cultivated area) and for
uncultivated locations (
b
, approximately 1500 grid cells), for baseline historical (solid) and
T
+
4 (
◦
C) (dashed) simulations. Black line in
panel
(a)
shows actual yields from 1997 to 2003 derived from Ray et al. (2012). For historical simulations, models agree on low potential
yields in currently uncultivated areas
(b)
but disagree widely on yields in currently cultivated areas
(a)
. Color code groups models into
those with realistic yield distributions peaking at 1–2 t ha
−
1
(green), those with flatter distributions extending to unrealistically high values
(red), and those with predominantly zero yields (blue). “Green” models show slight decreases under
T
+
4 warming, “red” models moderate
increases, and “blue” models large increases.
The GGCMI Phase 2 exercise offers the opportunity to
examine and characterize not just crop response to a single
temperature change but nonlinearities in responses and in-
teractions between factors. We illustrate a few of these rela-
tionships in Figs. 5–6 using A0 simulations to capture max-
imum climate effects. We choose crops and factors whose
effects are reasonably well understood and show that these
are reproduced in models. It is expected, for instance, that
increases in precipitation should buffer the effects of warmer
temperatures and that CO
2
increases should reduce damage
to crops in scenarios where water is limited. Models gener-
ally confirm expected behavior but also provide insight into
unforeseen interactions. To show geographic effects, we di-
vide model responses in Figs. 5–6 by the primary Köppen–
Geiger climate regions (Rubel and Kottek, 2010), showing
the yield changes across all simulated grid cells in each re-
gion. In each panel, we examine relationships between two
factors, showing yield response against one for several sce-
narios of the other, in box plots that show the inter-model
spread. The responses highlighted here are qualitatively sim-
ilar across all crops included in this study (Figs. S5–S9 for
all simulated area and S10–S13 for cultivated area only).
For all crops, warming scenarios with precipitation held
constant produce yield decreases in most regions. These im-
pacts are robust for even moderate climate perturbations. For
rainfed maize, even a 1
◦
C temperature increase with other
factors held constant produces a median regional decline in
potential yield that exceeds the variance across models, in
all but the “cold-continental” regions (Fig. 5a). The remain-
ing areas (“warm temperate”, “equatorial”, and “arid”) ac-
count for nearly three-quarters of global maize production.
In the high-latitude “cold-continental” region, potential yield
changes are positive but highly uncertain, for the reasons
discussed previously; uncertainties are larger even for maize
than for soybeans. (Compare Fig. 5a and b.) Temperature ef-
fects are somewhat nonlinear, with the largest impacts for
maize in the warm “tropical” region (for soybeans, tempera-
ture effects are more complex; see Fig. S5). Precipitation ef-
fects on rainfed crops are more strongly nonlinear. The cur-
vature of the precipitation response can be seen by eye in
Fig. 5b: soybean yields are strongly negatively impacted by
reduced rainfall, peak under increased precipitation of 20 %,
and actually decline at higher precipitation levels.
As expected, precipitation and temperature effects inter-
act, with increases in precipitation buffering yield responses
to temperature. Increased rainfall mitigates the negative im-
pacts of warmer temperatures caused by increased evapotran-
spiration (e.g., Allen et al., 1998). For maize, the effect is
relatively modest outside the “arid” regions (Fig. 5a). Glob-
ally, a 4
◦
C temperature rise with no change in precipitation
results in median loss of
∼
13 % of rainfed maize, with all
models showing a negative response. With a 20 % increase in
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J. A. Franke et al.: The GGCMI Phase 2 experiment
Figure 5.
Illustration of the distribution of regional yield changes across the multi-model ensemble, split by Köppen–Geiger climate regions,
and with global response in rightmost panel. The
y
axis is the fractional change in the regional average climatological (30-year mean)
potential yield relative to the baseline. Box-and-whisker plots show distribution across models, with median marked; edges are first and third
quartiles and whiskers extend to 1
.
5
·
IQR. The figure shows all simulated grid cells for each model; see Figs. S10–S13 for only currently
cultivated land. We highlight responses to individual factors; note that results are not directly comparable to simulations of realistic projected
climate scenarios with identical global mean changes. Models generally agree outside high-latitude regions, with projected changes exceeding
inter-model variance.
(a)
Response of rainfed maize to applied uniform temperature perturbations, for three discrete precipitation perturbation
levels (
−
20 %, 0 %, and
+
20 %), with CO
2
and nitrogen held constant at baseline values (360 pmm and 200 kg ha
−
1
yr
−
1
). Outliers in the
tropics (strong negative impact of higher
T
) are the pDSSAT model; outliers in the arid region (strong positive impact of higher
P
) are
JULES.
(b)
Response of rainfed soybeans to applied uniform precipitation perturbations, for two discrete temperature levels. Cases with
reduced precipitation show greater inter-model spread than those with increased precipitation. At very large precipitation increases, yield
changes level out: benefits saturate once water availability is no longer limiting. Precipitation changes are more important in the arid region,
as expected. Note the large uncertainty in the cold continental region, also illustrated in Figs. 3 and 4.
precipitation, the median yield loss is
∼
8 %. For soybeans,
the equivalent values are
∼
11 % and 1 %, respectively. De-
creased rainfall, on the other hand, amplifies yield losses and
also increases inter-model variance. That is, models agree
that the response to decreased water availability is negative
in sign but disagree on its magnitude. Outside of arid regions,
the interaction effect itself shows little nonlinearity (i.e., re-
sponse slopes in Fig. 5a and b are roughly parallel). As ex-
pected, irrigated crops are more resilient to temperature in-
creases in all regions, especially so where water is already
limiting (other than winter wheat; Fig. S9).
Increased CO
2
boosts yields overall through the well-
known CO
2
fertilization effect (Fig. 6). The effect is
strongest for the C3 crops (wheat, soybeans, and rice), while
maize, a C4 grass, has a comparatively muted response. We
show irrigated rice and rainfed soy in Fig. 6 as representative
C3 crops. The effect of CO
2
on yields is nonlinear, as ex-
pected, with significant benefit from small increases but with
effects plateauing at higher concentrations (Fig. 6). CO
2
and
temperature effects show minimal interaction. This effect is
seen in Fig. 6a, which shows nearly parallel response slopes
at different CO
2
levels. That is, CO
2
fertilization does little to
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J. A. Franke et al.: The GGCMI Phase 2 experiment
2329
Figure 6.
Illustration of the distribution of regional yield changes across the multi-model ensemble, here for soybeans and rice for the A0
case. Conventions are as in Fig. 5.
(a)
Response of rainfed soybeans to atmospheric CO
2
, for three discrete precipitation perturbation levels
with temperature and nitrogen held constant at baseline values. Low outliers are the EPIC-TAMU model and the high outliers in the arid
region are the JULES model. Reduced precipitation tends to steepen the CO
2
response and increased precipitation tends to flatten it, as
expected. Reduced precipitation tends to increase the inter model spread, especially at the highest CO
2
levels.
(b)
Response of irrigated
rice for three discrete CO
2
levels, with nitrogen and precipitation held constant. CO
2
does not change the nature of temperature response
respective to baseline as the slopes at each CO
2
level are relatively constant.
change the nature of the temperature response. On the other
hand, CO
2
and precipitation effects interact strongly, as ex-
pected since higher CO
2
levels allow reduced stomatal con-
ductance and evapotranspiration losses, mitigating the effect
of reduced rainfall (e.g., McGrath and Lobell, 2013). This
interaction is seen in Fig. 6b as smaller yield losses from re-
duced rainfall when CO
2
levels are higher. For example, for
soy, raising CO
2
to 510 ppm actually outweighs the multi-
model median damages caused by a 20 % precipitation re-
duction in all climate regions. All crops show similar behav-
ior, but note that model uncertainties for wheat are substan-
tially higher than those for other crops. (Compare Fig. 6a for
soy and Fig. S7 for wheat.)
We show some additional cases in the Supplement. As
noted previously, the A1 adaptation simulations involve sig-
nificantly moderated temperature impacts relative to the A0
simulations shown here (Fig. S14). Figures S15 and S16
show the response in the nitrogen dimension and an irriga-
tion water demand response example.
5
Discussion and conclusions
The GGCMI Phase 2 experiment provides a database de-
signed to allow detailed study of crop yields in process-based
models under climate change. While previous crop model in-
tercomparison projects in the climate change context have
focused on simulations along realistic projected climate sce-
narios (e.g., Rosenzweig et al., 2014), the use of systematic
input parameter variations in GGCMI Phase 2, with up to
756 scenarios, allows not only comparing yield sensitivities
to changing climate and management inputs but also evaluat-
ing the complex interactions between important driving fac-
tors: CO
2
, temperature, water supply, and applied nitrogen.
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J. A. Franke et al.: The GGCMI Phase 2 experiment
The global extent of the experiment also allows identifying
geographic shifts in high potential yield locations. With 12
participating models and 31 simulation years per scenario,
the complete database constitutes over 150 000 years of grid-
ded global yield simulation output for each crop.
Preliminary results shown here highlight some of the in-
sights facilitated by the simulation exercise and lend con-
fidence in the models. In validation tests of historical sim-
ulations, year-to-year correlations in modeled and actual
country-level yields are similar to those of GGCMI Phase 1.
In simulations of scenarios with perturbed climate and man-
agement factors, models broadly agree on changes outside
the high latitudes, with the magnitude of changes at nearly
all perturbation levels exceeding the inter-model spread. At
high latitudes, differences between models may result from
differences in their assumed yields in current cold conditions.
In simulations with multiple perturbations, interactions be-
tween major yield drivers (e.g., temperature and precipitation
in Fig. 5, or precipitation and CO
2
in Fig. 6) generally follow
expectations and produce physically reasonable responses in
crop yields.
Users should however be aware of some limitations of
the GGCMI Phase 2 experiment that affect its potential ap-
plications. First, absolute yield values in the baseline sce-
nario, driven by 1981–2010 historical climate, will gener-
ally not match observed yields over this time period. In order
to match current yields, process-based models must be re-
tuned to account for the constant evolution of crop cultivar
genetics and management practice (e.g., Jones et al., 2017).
GGCMI Phase 2 is intended as a study of model responses
to changes in climatic conditions, which are assumed insen-
sitive to the adjustments needed to reproduce present-day
yields. The baseline scenario also includes no trend in CO
2
,
and no individual case involves realistic country-specific ni-
trogen application levels (Elliott et al., 2015).
The second major caveat is that no individual GGCMI
Phase 2 simulation is itself a realistic future yield projection.
The uniform applied offsets in temperature and precipitation
sample over potential changes, and do not individually cap-
ture the spatially heterogeneous warming and precipitation
changes expected in realistic climate projections. GGCMI
Phase 2 simulation results can be used for impacts projection
but only with the construction of an emulator of crop yield re-
sponse to climatological changes, which can then be driven
by arbitrary climate scenarios. Such emulators are shown to
accurately reproduce crop model output under realistic cli-
mate projections, even though the GGCMI Phase 2 experi-
ment does not sample over potential changes in the higher-
order moments in temperature and precipitation distributions
(Franke et al., 2020). Note that some factors that may af-
fect future climate-driven yield impacts cannot be captured
by the GGCMI Phase 2 models in any usage, since models
do not include representations of pests, diseases, and weeds.
Offline crop model simulations (i.e., with prescribed rather
than dynamically simulated atmospheric conditions) can also
not capture any feedbacks on the climate from land use, such
as irrigation impacts on humidity (e.g., Decker et al., 2017).
We expect that the GGCMI Phase 2 simulations will yield
multiple insights in future studies. Potential applications in-
clude, as mentioned, the construction of emulators and yield
response surfaces that can be used for both model diagnosis
and impacts assessment. Specific studies could include ana-
lyzing the drivers of temperature-related yield losses (which
may be due to both direct thermal effects or to shortening
growing seasons); the benefits of adaptation; interactions be-
tween CO
2
and water or other CTWN factors affecting yield;
changes in nitrogen use efficiency; geographic shifts in re-
gional production; and regional differences in yield sensitiv-
ities to CTWN-A factors. Emulators based on the dataset can
be used to identify hotspots of crop system vulnerability and
to conduct rapid assessment of new climate projections. In
general, the development of multi-model ensembles involv-
ing systematic parameter sweeps has large promise both for
increasing understanding of potential future crop responses
and for improving process-based crop models.
Code and data availability.
All input data are available via
zenodo
at
https://doi.org/10.5281/zenodo.3773827
(Müller
et al., 2020). For the minimum cropland mask, choose the
boolean_cropmask_ggcmi_phase2.nc4 file. Growing period data
for wheat are now divided up into winter and spring wheat, whereas
all other growing season data (maize, rice, soybean) are the same
as in Phase 1.
Supplement.
The supplement related to this article is available on-
line at: https://doi.org/10.5194/gmd-13-2315-2020-supplement.
Author contributions.
JE, CM, and ACR designed the research.
CM, JJ, JB, PC, MD, PF, CF, LF, MH, RCI, IJ, CJ, NK, MK,
WL, SO, MP, TAMP, AR, XW, KW, and FZ performed the sim-
ulations. JF, JJ, CM, and EM performed the analysis, and JF, EM,
and CM prepared the manuscript. All authors contributed to editing
the manuscript.
Competing interests.
The authors declare that they have no conflict
of interest.
Acknowledgements.
This research was performed as part of the
Center for Robust Decision-making on Climate and Energy Policy
(RDCEP) at the University of Chicago. This is paper number 35 of
the Birmingham Institute of Forest Research. Computing resources
were provided by the University of Chicago Research Computing
Center (RCC). We thank two anonymous reviewers for their helpful
comments.
Geosci. Model Dev., 13, 2315–2336, 2020
https://doi.org/10.5194/gmd-13-2315-2020
Page 17
J. A. Franke et al.: The GGCMI Phase 2 experiment
2331
Table 4.
DOIs for model data outputs. All model output data can be found at https://doi.org/10.5281/zenodo/XX, where XX is the value
listed in the table.
Model
Maize
Soybean
Rice
Winter wheat
Spring wheat
APSIM-UGOE
2582531
2582535
2582533
2582537
2582539
CARAIB
2582522
2582508
2582504
2582516
2582499
EPIC-IIASA
2582453
2582461
2582457
2582463
2582465
EPIC-TAMU
2582349
2582367
2582352
2582392
2582418
JULES
2582543
2582547
2582545
–
2582551
GEPIC
2582247
2582258
2582251
2582260
2582263
LPJ-GUESS
2581625
–
–
2581638
2581640
LPJmL
2581356
2581498
2581436
2581565
2581606
ORCHIDEE-crop
2582441
–
2582445
2582449
–
pDSSAT
2582111
2582147
2582127
2582163
2582178
PEPIC
2582341
2582433
2582343
2582439
2582455
PROMET
2582467
2582488
2582479
2582490
2582492
Financial support.
RDCEP is funded by NSF (grant no. SES-
1463644) through the Decision Making Under Uncertainty pro-
gram. James Franke was supported by the NSF NRT program (grant
no. DGE-1735359) and the NSF Graduate Research Fellowship
Program (grant no. DGE-1746045). Christoph Müller was sup-
ported by the MACMIT project (grant no. 01LN1317A) funded
through the German Federal Ministry of Education and Research
(BMBF). Alex C. Ruane was supported by NASA NNX16AK38G
(INCA) and the NASA Earth Sciences Directorate/GISS Climate
Impacts Group. Christian Folberth was supported by the European
Research Council Synergy (grant no. ERC-2013-SynG-610028)
Imbalance-P. Pete Falloon and Karina Williams were supported by
the Newton Fund through the Met Office program Climate Science
for Service Partnership Brazil (CSSP Brazil). Karina Williams was
supported by the IMPREX research project supported by the Eu-
ropean Commission under the Horizon 2020 Framework program
(grant no. 641811). Stefan Olin acknowledges support from the
Swedish strong research areas BECC and MERGE, together with
support from LUCCI (Lund University Centre for studies of Carbon
Cycle and Climate Interactions). R. Cesar Izaurralde acknowledges
support from the Texas Agrilife Research and Extension, Texas A
& M University.
Review statement.
This paper was edited by David Lawrence and
reviewed by two anonymous referees.
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