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CO2-Induced Sahel Greening in Three CMIP5 Earth System Models
SEBASTIAN BATHIANY
Max Planck Institute for Meteorology, Hamburg, Germany
MARTIN CLAUSSEN
Max Planck Institute for Meteorology, and Centrum f€
ur Erdsystemforschung und Nachhaltigkeit,
Universit€
at Hamburg, Hamburg, Germany
VICTOR BROVKIN
Max Planck Institute for Meteorology, Hamburg, Germany
(Manuscript received 29 August 2013, in final form 25 June 2014)
ABSTRACT
The existence and productivity of vegetation is the basis for food and energy supply in the Sahel. Past changes
in climate and vegetation abundance have raised the question whether the region could become greener in the
future as a result of higher CO2 levels. By analyzing three Earth system models (ESMs) from phase 5 of the
Coupled Model Intercomparison Project (CMIP5) with dynamic vegetation, the authors demonstrate why an
answer to this question remains elusive in contrast to more robust projections of vegetation cover in the extratropics. First, it depends on the location and the time scale whether vegetation expands or retreats. Until the
end of the twenty-first century, the three models agree on a substantial greening in the central and eastern Sahel
due to increased CO2 levels. This trend is reversed thereafter, and vegetation retreats in particular in the western
Sahel because the beneficial effect of CO2 fertilization is short lived compared to climate change. Second, the
vegetation cover changes are driven by different processes in different models (most importantly, precipitation
change and CO2 fertilization). As these processes tend to oppose each other, the greening and browning trends
are not a reliable result despite the apparent model agreement. The authors also find that the effect of vegetation
dynamics on the surface energy balance crucially depends on the location. In contrast to the results of many
previous studies, the Sahel appears as a hotspot where the physiological effects of CO2 can exert a cooling
because vegetation structure and distribution overcompensate for the decreased stomatal conductance.
1. Introduction
The Sahel is a climatic transition region where the
supply of water, food, and even energy crucially depends
on climate. Poverty, low development, and political
conflicts tend to enforce this dependency and may even
aggravate it in a future climate (Scheffran et al. 2012).
The large population of the Sahel is therefore very
vulnerable to climate variability (as the drought of the
1970s–80s demonstrated) and to anthropogenic climate
change.
Corresponding author address: Sebastian Bathiany, Max Planck
Institute for Meteorology, Bundesstraße 53, 20146 Hamburg,
Germany.
E-mail: sebastian.bathiany@zmaw.de
DOI: 10.1175/JCLI-D-13-00528.1
Ó 2014 American Meteorological Society
Reconstructions of vegetation abundance and climate
show that northern Africa was much wetter and greener
at the beginning of the mid-Holocene (Jolly et al. 1998a,b;
Prentice et al. 2000; Lézine et al. 2011). In this period,
the different Earth orbit enhanced the summer insolation in the Northern Hemisphere and thus the West
African summer monsoon (Kutzbach 1981). Petit-Maire
(1990) therefore posed the question ‘‘Will greenhouse
green the Sahara?’’ and outlined a future between the
two historical cases of an expanded desert during the last
ice age and the green Sahara during the early Holocene.
However, such analogies are of limited use for predictions, as the spatial pattern and the rate of change are
very different for natural and anthropogenic forcing
(Petit-Maire 1990; Claussen et al. 2003). It is therefore
unclear how natural vegetation in northern Africa will
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respond to the anthropogenic forcing scenarios of the
next centuries. Trends in recent satellite observations are
spatially inhomogeneous (de Jong et al. 2012), depend on
the chosen time period (Anyamba and Tucker 2005;
Fensholt and Rasmussen 2011), and are difficult to attribute (Fensholt and Rasmussen 2011).
Presumably, the most important drivers of future vegetation distribution in the Sahel are changes in land use,
CO2 concentration (because of its effect on vegetation
physiology), and precipitation (caused by the radiative
effect of CO2). In this study, we only investigate the potential natural vegetation dynamics resulting from CO2
concentration changes. These resulting changes in the
vegetation composition and distribution are therefore not
affected by any land-use change scenario but rather
provide the precondition for future land use.
Changes in precipitation can occur for different reasons, which are difficult to separate in observations.
First, a larger land–sea temperature contrast is expected to intensify the West African monsoon (WAM)
(Monerie et al. 2012; Skinner et al. 2012). However, the
effect of the radiative forcing of CO2 over land on surface evaporation (Giannini 2010), atmospheric stability,
and the distribution of moisture (Chou and Neelin 2004;
Chou et al. 2009) must also be considered. Second,
aerosols have been found to affect the monsoon system
in model simulations (Ackerley et al. 2011; Kim et al.
2008). Third, there is evidence for an influence of SSTs
in all major tropical and subtropical oceans (Giannini
et al. 2003; Lu and Delworth 2005; Herceg et al. 2007;
Cook 2008; Mohino et al. 2011; Patricola and Cook
2010). For example, in the Atlantic, an enhanced interhemispheric SST gradient with warmer water in the
North Atlantic is believed to induce a northward shift
of the intertropical convergence zone (ITCZ), which
then affects Sahelian rainfall (Hoerling et al. 2006;
Cook 2008), presumably as a result of an intensified
West African westerly jet (WAWJ) (Pu and Dickinson
2012). There is evidence that, in the twentieth century,
changes in SSTs have been the dominating driver of the
observed decadal variability in Sahelian rainfall (Joly
et al. 2007; Biasutti et al. 2008). Vegetation dynamics
have probably enhanced this decadal component (Zeng
et al. 1999). However, the quantitative attribution to
SSTs in different ocean basins and the superimposed
global warming trend is unclear (Rodríguez-Fonseca
et al. 2011). In the future, it is probable that changes in
SSTs will be less important than in the twentieth century, because the radiative forcing over land increases
(Patricola and Cook 2010, 2011; Monerie et al. 2012) and
may even become the dominant driver of Sahelian rainfall changes (Haarsma et al. 2005; Biasutti et al. 2008;
Giannini 2010).
VOLUME 27
For the productivity of vegetation, other aspects of the
terrestrial moisture balance must also be considered. In
particular, changes in evapotranspiration may offset
precipitation trends. Furthermore, other environmental
conditions will affect productivity and vegetation composition. In this regard, physiological effects of CO2
become important, and these effects differ between the
two relevant photosynthetic pathways: C3 and C4. First,
higher levels of CO2 tend to enhance the carboxylation
efficiency of Rubisco in C3 plants. Second, elevated atmospheric CO2 levels have been observed to decrease
stomatal conductance (Field et al. 1995; Long et al. 2004;
Ainsworth and Long 2005; Ainsworth and Rogers 2007;
Leakey et al. 2009; Norby and Zak 2011). Therefore,
they can result in decreased transpiration and increased
soil moisture. This increased water-use efficiency can
further enhance productivity in C3 as well as C4 plants
(Long et al. 2004; Ainsworth and Rogers 2007; Leakey
et al. 2009). The sum of both mechanisms is known as
CO2 fertilization. Consequently, the potential increase in
net primary productivity (NPP) may allow an expansion
of vegetation in arid regions (Mahowald 2007; Donohue
et al. 2013). As we focus on land cover changes, we only
refer to CO2 fertilization because of its impact on vegetation distribution.
In contrast, we more generally refer to ‘‘physiological
effects’’ to address any climatic changes arising from stomatal closure, not restricted to that caused by CO2 fertilization. Model results show that the reduced transpiration
and reduced low-level cloud cover due to CO2-induced
stomatal closure tend to warm the surface, especially over
tropical forests (Doutriaux-Boucher et al. 2009; Andrews
et al. 2011). This ‘‘physiological forcing’’ has been found to
contribute more than 10% to the CO2-induced warming
over land (Sellers et al. 1996; Cox et al. 1999; Boucher et al.
2009; O’ishi et al. 2009; Cao et al. 2010). The physiological
forcing generally refers to changes in stomatal closure assuming a fixed leaf area. However, increases in leaf area
index (LAI) may occur as a result of the increased productivity and an altered vegetation distribution and composition. These changes can enhance precipitation because
of the low albedo of vegetation (Otterman 1974; Charney
1975; Charney et al. 1975; Claussen 1997) and organic soils
(Claussen 2009; Vamborg et al. 2011). Also, the expansion
of vegetation into desert areas tends to increase evapotranspiration (Charney et al. 1977; Claussen 1997). It has
been argued that such structural changes (LAI and vegetation distribution) could overcompensate for the effect of
stomatal conductance change (Betts et al. 1997; Leipprand
and Gerten 2006). Among other important environmental
conditions besides CO2 are temperature and nutrient
availability, although changes in the latter are not considered in the simulations we analyze.
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TABLE 1. Summary of the CMIP5 models analyzed in this study.
Modeling center
Institute ID
Model name
Max Planck Institute for
Meteorology
Japan Agency for
Marine-Earth Science
and Technology,
Atmosphere and Ocean
Research Institute
(The University of Tokyo),
and National Institute
for Environmental Studies
Met Office Hadley Centre
MPI-M
MPI-ESM-LR
MIROC
MIROC-ESM
MOHC
HadGEM2-ES and
HadGEM2-A
With regard to these interactions, it seems most appropriate to incorporate all substantial drivers of vegetation changes as well as the important interactions
between vegetation and climate in order to assess the
possible future vegetation distribution. In this study, we
follow such an integrated view by analyzing results from
three Earth system models (ESMs) from phase 5 of the
Coupled Model Intercomparison Project (CMIP5) that
include a dynamic vegetation model. However, it is not
our intention to aim at a robust projection of Sahelian
vegetation cover. In contrast, we address the main caveats that prevent a reliable projection, even if more
models and simulations were available. These caveats
involve the dependency of the different processes on
location and the time horizon, as well as the large process and modeling uncertainty. As we will show, the
three models provide a useful set in order to demonstrate these caveats.
2. Models and experiments
a. Models
In this study we analyze simulations performed
by the ESMs MPI-ESM-LR (Giorgetta et al. 2013),
HadGEM2-ES (Collins et al. 2011; Martin et al. 2011),
and MIROC-ESM (Watanabe et al. 2011). These
models (expansions and summaries for the CMIP5
models used in this study are provided in Table 1) include comprehensive descriptions of atmosphere and
ocean circulation as well as terrestrial vegetation processes on time scales from minutes (stomatal conductance and photosynthesis) to centuries (changes in
vegetation distribution). We do not analyze output
from other CMIP5 models with dynamic vegetation
because either the experiments we analyze have not
Expansion
Max Planck Institute Earth System Model,
low resolution
Model for Interdisciplinary Research on
Climate (MIROC), Earth
System Model
Hadley Centre Global Environment Model,
version 2–Earth System and Hadley Centre
Global Environment Model, version
2–Atmosphere only version of ES
been performed with them or they were applied with
prescribed vegetation cover fractions.
The Max Planck Institute for Meteorology (MPI)
ESM in low resolution (MPI-ESM-LR) includes the
general circulation models ECHAM6 (Stevens et al.
2013; Roeckner et al. 2003) for the atmosphere with
a horizontal resolution of T63 (approximately 1.88); the
MPI Ocean Model (MPI-OM) (Jungclaus et al. 2013) for
the ocean and sea ice (approximately 1.58); and the
Hamburg Model of the Ocean Carbon Cycle, version 5
(HAMOCC5) ocean biogeochemistry model (Ilyina et al.
2013; Maier-Reimer et al. 2005). Land–atmosphere exchange is represented by the Jena Scheme for Biosphere–
Atmosphere Coupling in Hamburg (JSBACH) terrestrial
vegetation model (Raddatz et al. 2007). Its photosynthesis
schemes are based on Farquhar et al. (1980) for C3 plants
and on Collatz et al. (1992) for C4 plants. In the experiments analyzed in this study, eight natural and four
anthropogenic plant functional types (PFTs) are distinguished by JSBACH. A dynamic vegetation scheme
(Brovkin et al. 2009) determines their cover fractions as
well as the area fraction of total vegetation cover. The
distribution of woody versus grass cover is determined
by light competition and disturbances caused by fire and
windthrow. Competition between different tree or grass
types is based on the productivity of the individual types.
Similarly, vegetation can establish wherever the environmental conditions allow a positive productivity (Reick
et al. 2013). The preindustrial global vegetation distribution compares reasonably to the Vegetation Continuous Fields dataset (Hansen et al. 2007) derived from
the Moderate Resolution Imaging Spectroradiometer
(MODIS) (Fig. 1; see also Figs. 1 and 2 in Brovkin et al.
2013a). North Africa is covered by too much vegetation,
especially in its western part. However, the LAI of this
vegetation is small (less than 1) as a result of the dry
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FIG. 1. Preindustrial distribution of vegetation in the three Earth system models (in % surface coverage) and
present-day distribution in the VCF dataset from MODIS observations. The MODIS data have been converted to
the resolution of MPI-ESM-LR. As the original data have a much higher resolution, small islands appear upscaled to
the larger gridbox size.
climate. MPI-ESM-LR shows the strongest land carbonconcentration feedback (impact of CO2 concentration on
land uptake) of all CMIP5 ESMs compared in Arora
et al. (2013). The direct effect of CO2 on vegetation dynamics is therefore also expected to be large.
HadGEM2-ES comprises atmosphere, ocean and sea
ice dynamics, terrestrial hydrology, and a terrestrial and
marine carbon cycle. The horizontal resolution is approximately 1.258 latitude 3 1.8758 longitude. The interaction
between land surface and atmosphere is calculated by
the Met Office Surface Exchange Scheme, version 2
(MOSES2) (Essery et al. 2003). Leaf-level photosynthesis
is based on Collatz’s models for C3 (Collatz et al. 1991) and
C4 plants (Collatz et al. 1992). Vegetation distribution and
composition are calculated by the Top-Down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) dynamic vegetation model (Cox 2001),
which distinguishes five different plant types. Competition
is based on NPP and height. The produced biomass is first
used to increase the local carbon pools; in case of sufficient productivity, it is invested in the expansion of cover
fractions. The deficiencies in the tropical vegetation distribution are too much forest cover as well as a toosouthern Sahara–Sahel boundary (Fig. 1; see also Figs. 11
and 15 in Collins et al. 2011). HadGEM2-ES shows an
above-average land carbon–concentration feedback
when compared to other ESMs (Arora et al. 2013).
Like the other two models, MIROC-ESM includes
general circulation models of atmosphere (resolution:
T42; approximately 2.88) and ocean. It also includes
an ocean ecosystem model and the spatially explicit
individual-based Dynamic Global Vegetation Model
(SEIB-DGVM) terrestrial ecosystem model (Sato et al.
2007). SEIB calculates interactions between individual
trees of a sample plot at each grid point. Its parameters
have been tuned to match observations of forest structure and dynamics. A uniform grass layer is assumed to
exist under the tree canopy. Vegetation is classified into
13 PFTs (11 trees and 2 grasses). Photosynthesis is calculated daily from temperature and the availability of
light, CO2, humidity, and soil moisture based on empirical relationships. Stomatal conductance is calculated
following Ball et al. (1987) and Leuning (1995). Like
MPI-ESM-LR and HadGEM2-ES, MIROC-ESM distinguishes different plant functional types, but, in contrast to the other two models, MIROC-ESM does not
apply a tiling approach to describe the effect of vegetation
dynamics. Instead, a representative patch with individual
trees and an underlying grass cover is calculated and extrapolated to the gridbox size (Sato et al. 2007). The
global distribution of trees partly depends on empirical
relations with climate (e.g., because of establishment
processes). A desert fraction is not directly defined within
the model but classified as the area of natural vegetation
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TABLE 2. Summary of the CMIP5 experiments analyzed in this study.
CMIP5
experiment
Expansion
Length
RCP8.5
Representative
concentration
pathway 8.5
MPI-ESM-LR:
2006–2300
HadGEM2-ES:
2006–2299
L2A8.5
XXXXX
piControl
Preindustrial
control
1pctCO2
(RADPHYS)
1 percent per
year CO2
MPI-ESM-LR and
MIROC-ESM:
2006–2100
HadGEM2-ES:
2006–99
MPI-ESM-LR:
1000 yr
HadGEM2-ES:
576 yr
MIROC-ESM:
630 yr
140 yr
esmFdbk1
(RAD)
ESM feedback 1
140 yr
esmFixClim1
(PHYS)
ESM fixed
climate 1
140 yr
sstClim
Control SST
climatology
30 yr
sstClim4xCO2
SST climatology
with 4 3 CO2
forcing
30 yr
Vegetation
treatment
Number of
realizations
prescribed CO2 change
and land-use
change until 2100,
no change in land
use between 2100
and 2300
CO2 change according
to RCP8.5; land
use fixed at year 2006
conditions
dynamic, except
in areas
of land use
MPI-ESM-LR: 1
HadGEM2-ES: 1
MIROC-ESM: 0
dynamic, except
in areas
of land use
MPI-ESM-LR: 2
HadGEM2-ES: 4
MIROC-ESM: 1
no change in
anthropogenic
forcings
dynamic
All: 1
1% CO2 increase per year,
radiative and
physiological effects
active; no land-use
change
1% CO2 increase per year,
radiative effect only;
no land-use change
1% CO2 increase per year,
physiological
effect only; no land-use
change
no change in anthropogenic
forcings, SSTs
and sea ice prescribed
from piControl
as sstClim, but with
instantaneously
quadrupled CO2
dynamic
All: 1
dynamic
All: 1
dynamic
All: 1
vegetation
distribution
fixed, LAI
interactive
vegetation
distribution
fixed, LAI
interactive
MPI-ESM-LR: 1
HadGEM2-A: 1
MIROC-ESM: 0
Forcings
with an annual maximum LAI less than 0.2 (Sato et al.
2007) or with an annual NPP of 0 (T. Hajima 2013,
personal communication). Although the CMIP5 vegetation cover fraction is therefore not exactly the same
property as in the other two models, it is well comparable
at the desert margins, where changes in the quantities of
LAI, vegetation cover, and productivity are closely related. This is due to the common concept in all three
models that an increased productivity drives the expansion of vegetation into a desert. Because of wet biases in
MIROC-ESM, the global preindustrial distribution of
deserts differs substantially from observations in the
Northern Hemisphere, where no deserts other than the
Sahara exist (Fig. 1). However, the position of the Sahara’s
southern margin is captured reasonably. MIROC-ESM
has a comparatively small land carbon–concentration
feedback (Hajima et al. 2012; Arora et al. 2013).
MPI-ESM-LR: 1
HadGEM2-A: 1
MIROC-ESM: 0
Since the models differ in the location and movement of
the transition region between the Sahara and the tropical
African forest, we do not show multimodel averages. Instead, we will follow a model-based definition of the Sahel
as this transition region and define the desert boundary as
the 80% vegetation cover line in each model.
b. Experiments
All simulations we analyze in this study (summaries and expansions of the CMIP5 experiments analyzed in this study are provided in Table 2) have been
conducted within the latest Coupled Model Intercomparison Project and follow the setup described in
Taylor et al. (2012). The models were forced by the
radiative effects, biogeochemical effects, or the combination of both effects of increasing atmospheric CO2
concentration. To this end, CO2 concentrations are
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FIG. 2. Changes of vegetation cover (in %) in the RCP8.5 and L2A8.5 scenarios in three ESMs. The L2A8.5 maps show the difference
between the last and first 10 yr of the experiment, averaged over all available realizations (Table 2). The RCP8.5 maps show the difference
between averages over the last 10 yr and 2100–09. The black contours mark the boundary of 80% vegetation cover in the first 10 yr of each
time period. Black rectangles mark the four areas referred to in Fig. 4 and in the text.
prescribed in all experiments and mostly follow an
idealized trajectory.
In RCP8.5, the CO2 concentration shows an accelerating increase until the year 2100 (when a radiative
forcing of approximately 8.5 W m22 is reached), followed by a stabilization period with a decelerating increase. In the year 2250, the CO2 concentration reaches
its final level of almost 2000 ppm. CO2 concentration
and radiative forcing both remain constant after the
year 2250 (see Meinshausen et al. 2011, Figs. 4 and 5). In
RCP8.5 we analyze the period 2100–2300 (extended
RCP8.5) where the fractions of managed land were kept
constant at year 2100 values. Before 2100, we analyze
simulations with the identical CO2 forcing where land
use was fixed to the year 2006. These simulations are
called L2A8.5 and were conducted within the LandUse and Climate, Identification of Robust Impacts
(LUCID) project (Pitman et al. 2009; Brovkin et al.
2013b). For MIROC-ESM, the extended RCP8.5 scenario was not available. Our combination of L2A8.5
and RCP8.5 and the fact that we do not analyze the
differences between them allows us to exclude any
anthropogenic land-use changes (which occur in
RCP8.5 before 2100).
In the idealized CMIP5 scenarios called 1pctCO2
(in the following: RADPHYS, to be consistent with
similar studies before CMIP5), esmFdbk1 (RAD),
and FixClim1 (PHYS), CO2 is increased by 1% each
year until CO2 concentration has quadrupled after 140
years. In RAD, the CO2 change only affects radiation,
while the terrestrial vegetation sees preindustrial CO2.
In PHYS, the physiological effects of CO2 are considered, but radiation is calculated from preindustrial
CO2. In RADPHYS, both effects of CO2 are active.
These experiments have been used to analyze feedbacks in the carbon cycle and their nonlinearity (Arora
et al. 2013) but less so for the analysis of biogeophysical
effects.
Finally, we analyze a preindustrial control simulation
with climatological SSTs and sea ice (sstClim), and
a similar simulation with quadrupled CO2. In these experiments, both effects of CO2, radiative and physiological, are active. However, because of the fixed ocean
state, the forcings do not affect SSTs.
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FIG. 3. Preindustrial composition of vegetation in terms of land cover fractions (in %) of C3 plants, C4 plants, woody plants (trees and
shrubs), and nonwoody plants (natural grass, pasture and crops) in the three Earth system models.
3. Results
a. Comparison of vegetation cover changes
Figure 2 shows the changes of vegetation cover fraction
for the three ESMs in response to the L2A8.5 and
RCP8.5 concentration scenarios. In general, HadGEM2ES and MPI-ESM-LR agree that there is a greening in
the northern extratropics. In these areas, the increases in
precipitation, warming, and CO2 fertilization all tend to
enhance the establishment of vegetation. Therefore, this
extratropical greening agrees with expectations. MIROCESM shows no further greening in the extratropics (apart
from outer Greenland) because the vegetation cover for
present day is already 100% in these areas. As in the other
two models, the LAI increases on most parts of the extratropical land.
Compared to the greening in the extratropics, vegetation changes are less robust and inhomogeneous in
the tropics. However, MIROC-ESM and MPI-ESM-LR
agree that there is a greening of the Sahel in response to
increased atmospheric CO2 levels until 2100. A greening
also occurs in parts of the central Sahel in HadGEM2-ES
but not in the west. Interestingly, this zonal contrast also
occurs in MPI-ESM-LR, but after 2100: In case of the
very high CO2 concentration in RCP8.5 toward the year
2300, the initial greening continues in the east but reverses
in the western Sahel and Sahara. In contrast, HadGEM2ES shows a southward expansion of the desert at all
longitudes after 2100. In MIROC and HadGEM2-ES the
vegetation changes can mostly be attributed to C4 grass,
which grows near the desert margin; in MPI-ESM-LR, C3
trees and C4 grass types are both affected because of the
more heterogeneous vegetation composition (Fig. 3).
To attribute the obtained changes to the influences of
CO2 fertilization and precipitation, we investigate how the
evolution of precipitation compares to the evolution of
vegetation cover in different areas. We thereby distinguish
the western and central to eastern Sahel as indicated by
the black rectangles in Fig. 2. As preindustrial vegetation cover in MPI-ESM-LR already extends more to the
north, the largest changes also occur in more northern
areas, as in HadGEM2-ES, where they are confined to
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FIG. 4. Temporal evolution of vegetation cover and precipitation in the four areas indicated in Fig. 2: northeast,
northwest, southeast, and southwest. The ensemble means of the L2A85 simulations (which end at the black vertical
line in 2100) are shown in dark colors, and the extended RCP8.5 simulation is shownin light colors (green shows
vegetation cover, and blue shows precipitation). Note that the evolution of vegetation cover before 2100 does not
differ qualitatively between the RCP and L2A scenarios.
a comparatively thin latitudinal band. We therefore
consider the two northern boxes for MPI-ESM-LR and
MIROC-ESM and the two southern boxes for HadGEM2ES to create time series of the spatial and annual means
(Fig. 4). In addition, we analyze idealized experiments
with separated forcings in section 3b.
MPI-ESM-LR and HadGEM2-ES show a similar
pattern of precipitation change, most importantly a
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progressive drying in the west and a wettening in the
southeast of northern Africa. In the long term, vegetation cover roughly follows the trend in precipitation
(Fig. 4). However, in MPI-ESM-LR, vegetation cover
in the western Sahel increases until somewhat after 2100
in RCP8.5, whereas precipitation decreases. In this initial
phase, CO2 fertilization appears to be the driver of the
Sahel greening (also apparent in the northeastern box
until 2230). Another exception occurs in HadGEM2-ES
in the southeastern box where vegetation cover decreases
despite the increase in precipitation (and CO2) after 2100
as a result of the high temperature at certain grid points
(an effect to be discussed in section 3b).
MPI-ESM-LR and HadGEM2-ES agree on the trends
and their geographical pattern but disagree on the timing
because CO2 fertilization is stronger in MPI-ESM-LR. In
contrast, the greening in MIROC-ESM very closely follows annual-mean precipitation, which increases substantially over northern Africa. As explained in section 2a,
the dynamics of vegetation cover directly result from LAI
changes in MIROC-ESM, while a much longer time scale
is involved in the other two models. Therefore, the interannual variability is much larger in MIROC-ESM.
As changes in precipitation are such an important
driver of vegetation dynamics in the Sahel, it is necessary
to expand the view to results from other models at this
point. Compared to the ensemble of CMIP5 and also
previous CMIP3 models, the tendency of a drying in the
western and a wettening of the more eastern Sahel like in
HadGEM2-ES and MPI-ESM-LR can also be seen in
other models. The reason for these tendencies is a shift
in the seasonal distribution of rain: The majority of the
coupled climate models show a delayed rainy season,
which leads to a drying in spring (Biasutti and Sobel 2009).
In the western Sahel, the spring drying dominates the
change in annual rainfall, while in the center and east it is
overcompensated for by a wetter autumn (see Figs. 2b and
5 in Biasutti 2013). Consequently, Fontaine et al. (2011)
identified a northern shift of the area of moisture flux
convergence in the east and center of the African monsoon region in most CMIP3 models under the A1B scenario and no shift or a southern shift in the west. Monerie
et al. (2012) attributed the changes in the eastern parts to
an intensification of the low-level monsoon flow and the
western drying to increased subsidence and moisture flux
divergence in higher atmospheric levels. However, as the
relevant regional physical processes are not well understood and very crudely represented in global models
(Cook 2008; Patricola and Cook 2010; Druyan 2011; Ruti
et al. 2011; Fasullo 2012; Knutti and Sedlacek 2013;
Roehrig et al. 2013), the overall response of Sahelian
rainfall to greenhouse forcing nonetheless remains highly
uncertain. Although the large increase in North African
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precipitation in MIROC is exceptional, it should therefore not automatically be regarded as less realistic (Cook
2008). Starting with the next section, we will also examine
another source of uncertainties: the effect of CO2 on
physiological processes.
b. Separation of radiative and physiological
effects of CO2
To isolate the impacts of physiological effects and
radiative forcing on vegetation distribution, we analyze
the idealized scenarios RADPHYS, RAD, and PHYS
(Table 2). The analysis follows the logic of a factor
separation (Stein and Alpert 1993) with physiological
effects and radiative forcing as two (conceptually) independent factors. Figure 5 shows the differences in
African vegetation cover compared to the preindustrial
simulation for each of the three experiments as well as
the synergy of the effects. The synergy represents the
nonlinearity of the system. In case of vegetation cover, it
can be interpreted as the effect of CO2 fertilization on
the climate change impact or, alternatively, the effect of
climate change on CO2 fertilization (Claussen et al.
2013). It becomes obvious that radiative and physiological effects tend to oppose each other in their impact
on vegetation cover in the dry subtropical areas. This
contrast is most apparent in MPI-ESM-LR (Fig. 5, top).
While a decrease in precipitation in all subtropical areas
of the region leads to a decreased vegetation cover in
RAD, the CO2 fertilization acts to green the Sahel and
the northwestern Sahara as well as the Arabian Peninsula and the Middle East. In the Sahel, the CO2 fertilization is the dominating effect, as can be seen in
RADPHYS. The vegetation expansion in PHYS consists of C3 as well as C4 plants, as their photosynthesis
is modeled in a very similar way and because climate
change in PHYS is small.
A similar contrast between RAD and PHYS is
obtained in HadGEM2-ES (Fig. 5, middle), although the
changes in vegetation cover are not as uniform and the
net effect depends on the region. In the western Sahel,
the climate change dominates the vegetation response
and leads to a retreat of vegetation. In the rest of the
Sahel, the CO2 fertilization dominates and causes a
greening. The distribution of C3 plants thereby expands,
and the belt of C4 plants (Fig. 3) shifts farther north while
soil moisture increases south of 138N. Although precipitation increases in most parts of the central Sahel,
climate change acts to decrease vegetation cover, with the
exception of a small region to the east of Lake Chad. The
reason for this vegetation retreat is probably the very high
temperatures (around 338C), which tend to decrease productivity. Figure 6d illustrates that the vegetation retreat in
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VOLUME 27
FIG. 5. Differences in vegetation cover (in %) from preindustrial conditions for three idealized scenarios with 1% CO2 increase per year.
Each scenario is averaged over the last 30 yr (years 111–140). RAD stands for the experiment with radiative effects only (CMIP5 name:
esmFdbk1), PHYS stands for physiological effects only (esmFixClim1), and RADPHYS stands for the combination of both (1pctCO2).
The synergy is calculated as RADPHYS 2 RAD 2 PHYS. The black contours mark the boundary of 80% vegetation cover in the
preindustrial control simulations (Fig. 1).
areas with increased precipitation is largest where the
temperature increase is large and where temperature was
already high in the preindustrial climate. Although climate
and vegetation are not in equilibrium with each other and
with CO2 at the end of RAD, it is suggestive that the
changes cluster in different quadrants in Fig. 6. This effect
also explains the vegetation retreat in the RCP8.5 simulation (Fig. 4; HadGEM2-ES, SE). Changes in the seasonal
distribution of precipitation could play an additional role.
Hence, MPI-ESM-LR and HadGEM2-ES agree that
radiative forcing and CO2 fertilization are similarly important on the time scale of 100 yr and that a fast increase
in CO2 would green large parts of the Sahel because of an
enhanced productivity. However, the longer the climate
system can respond to the radiative forcing, the more
likely it is that the climate change will counteract this
greening, as we showed in section 3a. It is also striking
that the synergy of the effects is of a similar magnitude to
the individual effects. In MPI-ESM-LR and HadGEM2ES, a north–south pattern appears with negative values in
the north and positive values in the south. As vegetation
cover is limited to values between 0% and 100%, the
dependence of vegetation cover on environmental conditions is inevitably nonlinear. Therefore, the synergy as
a measure of nonlinearity is potentially large in all areas
where vegetation cover comes close to these limits. In
particular, the desert regions under preindustrial forcings
cannot lose any vegetation when exposed to drying.
However, if they become greener as a result of fertilization, the negative climate impact can take effect. At the
other end of the scale, where vegetation cover is close to
100%, fertilization can only take little effect, whereas
a concurrent drying will allow the fertilization mechanism
to become active by keeping vegetation cover high.
Therefore, the synergy pattern only reflects the initial
vegetation distribution at the desert boundaries.
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BATHIANY ET AL.
7173
FIG. 6. Relation between (a)–(c) anomalies in vegetation cover and precipitation and between (d) vegetation cover
and 2-m air temperature in RAD (difference of last 30 yr from mean preindustrial conditions) in different models.
Each cross represents a land grid point within the zone of the desert margin. This zone corresponds to the two
northern rectangles in Fig. 2 (138–248N) in case of MPI-ESM-LR and MIROC-ESM and to the two southern rectangles (58–138N) in case of HadGEM2-ES. In (d) only grid points with a precipitation increase are shown and the size
of each cross depends on the preindustrial mean temperature. These preindustrial temperatures range from 16.68C
(smallest crosses) to approximately 30.48C (largest crosses). Red crosses mark the spatial means of the changes (mean
of black crosses in each figure); zero lines are indicated in red.
The results presented so far are not confirmed by the
third model under analysis: Fig. 5 (bottom) demonstrates
that the greening in MIROC-ESM is almost completely
due to climate change: namely, the large increase in
precipitation by up to 250 mm over northern Africa. In
contrast, the impact of CO2 fertilization is hardly distinguishable from the pattern of natural variability. Therefore, it becomes obvious that the three models yield
similar results because of very different reasons. Figure 6
illustrates these model differences: In MPI-ESM-LR and
MIROC-ESM, vegetation cover changes in RAD are
both driven by precipitation changes but of different
signs. In HadGEM2-ES, the vegetation decrease in RAD
cannot be explained with mean precipitation changes
alone (Figs. 6c,d). A hypothetical climate model with
a climate response to radiative forcing similar to MPIESM-LR or HadGEM2-ES but a response to CO2 fertilization as weak as MIROC-ESM would show a reduced
vegetation growth rather than a greening. In this regard,
the multimodel result can hide potential uncertainties. As
the processes in the three models are represented so
differently, the future development of natural vegetation
in the Sahel remains highly uncertain.
c. Impacts of physiological effects on climate
So far, we have discussed climatic and physiological
effects separately. This view neglects that there is also
a climatic change in the experiment without radiative
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VOLUME 27
FIG. 7. Factor separation for annual-mean precipitation in MPI-ESM-LR. As in Fig. 5,
RADPHYS, RAD, and PHYS show the difference of the last 30 yr from preindustrial conditions. Changes at white grid points are not statistically significant at the 5% level.
forcing (PHYS) because of decreased stomatal conductance and increases in LAI and vegetation cover.
PHYS therefore allows us to investigate the potential
effects of desert greening on climate in MPI-ESM-LR
and HadGEM2-ES. We do not analyze results from
MIROC-ESM further because the physiological effect
on climate in this model is indiscernible from the natural
variability. Compared to the preindustrial simulation,
the last 30 yr of PHYS show a global warming of 0.25 K
in MPI-ESM-LR and 0.65 K in HadGEM2-ES and reduced evapotranspiration, relative humidity, and cloud
cover over land. Globally, this hydrological response is
in line with some previous studies (Cox et al. 1999; Betts
et al. 2007; Boucher et al. 2009; Cao et al. 2010; Pu and
Dickinson 2012).
However, there are regional exceptions to these
global changes. First, despite the decrease in stomatal
conductance, the vegetation enhances precipitation in
the African subtropics, especially in western Africa.
Applying the factor separation method to precipitation
changes reveals that this effect is of discernible magnitude
when compared to the effect of radiative forcing (Fig. 7):
While there are locations with significantly positive, significantly negative, and insignificant precipitation changes
in the experiment with combined effects, the contributions of the pure radiative and physiological effects alone
are much clearer. While radiative forcing tends to dry the
complete Sahel in RAD, physiological effects significantly
increase precipitation.
Second, there is a very pronounced tendency of the
Sahel not to warm like the rest of the continent (Fig. 8,
bottom). To our knowledge, no previous experiment
on physiological impacts of CO2 on climate resulted in a
temperature pattern as pronounced as in MPI-ESM-LR
and HadGEM2-ES (information and model expansions
for previous studies are provided in Table 3): Betts et al.
(1997) and Bounoua et al. (2010) obtained substantial
effects only in the extratropics, while in Kergoat et al.
(2002), Bala et al. (2006), Notaro et al. (2007), and O’ishi
et al. (2009) the evapotranspiration change did not
dominate the temperature response in the Sahel. In
contrast, by forcing the Lund–Potsdam–Jena (LPJ)
15 SEPTEMBER 2014
BATHIANY ET AL.
FIG. 8. Impact of the physiological effect on climate in MPI-ESM-LR and HadGEM2-ES. All
maps show the last 30 yr in PHYS minus preindustrial mean conditions, as in Figs. 5 and 7. Shown
are the surface fluxes of latent heat, net shortwave radiation, their difference, and surface air
temperature. LH is defined positive when directed upward. For MPI-ESM-LR, the black rectangle encloses the area analyzed in Fig. 9. Changes at white grid points are not statistically significant at the 5% level.
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TABLE 3. Previous studies on physiological effects of CO2 with implications for the Sahel climate. Specified are the models used in these studies, whether these models allowed for
interactive changes in LAI and vegetation cover, and the resulting effects on the Sahel climate compared to the rest of the world.
Study
Bala et al. 2006
Betts et al. 1997
Bounoua et al. 2010
Leipprand and
Gerten 2006
Notaro et al. 2007
Integrated Climate and Carbon model (INCCA)
[Community Climate Model, version 3.2
(CCM3.2)/Parallel Ocean Program (POP)/Integrated
Biosphere Simulator (IBIS)]
Hadley Centre general circulation model with Sheffield
University vegetation model
Colorado State University General Circulation Model
(GCM)/Simple Biosphere Model, version 2 (SiB2)
Unnamed vegetation model, forced with Action de
Recherche Petite Echelle Grande Echelle (ARPEGE)
Lund–Potsdam–Jena (LPJ) (driven by observed climate)
Cover
change
Effect on Sahel
climate
SRES A2,
prolonged
to 4 3 CO2
yes
yes
Albedo decrease warms Sahel
2 3 CO2
yes
no
Only small changes in tropics
2 3 CO2
yes
no
Only small changes in Africa
2 3 CO2
yes
no
Effects on ET cancel
2 3 CO2
yes
yes
yes
yes
yes
yes
Structural effect increases
transpiration in arid regions
In dry regions, ET does not
decrease as elsewhere
Albedo decrease warms
semiarid tropics
More soil evaporation cools
eastern Sahel
Warming, as everywhere
O’ishi et al. 2009
MIROC-LPJ
1% increase per
year to 4 3 CO2
4 3 CO2
Cao et al. 2010
NCAR (CCSM3)
2 3 CO2
no
no
Gopalakrishnan
et al. 2011
Pu and Dickinson
2012
NCAR [Community Land Model (CLM) 3.5 driven
by observed climate]
NCAR (CCSM4)
various fixed
levels of CO2
2 3 CO2
no
no
yes/no
no
Andrews et al. 2011
Hadley Centre Coupled Model, version 3, low
resolution with carbon cycle (HadCM3LC)
HadCM3
HadCM3LC
4 3 CO2
not specified
not specified
Sahel remains cool because of
increased ET, especially with
LAI increase
Warming, as everywhere
IS92a
2 3 CO2
no
not specified
no
not specified
No exception in Sahel
Warming, as everywhere
Hadley Centre Slab Climate Model, version 3 (HadSM3)
2 3 CO2
not specified
no
Colder Sahel when normalized
with zonal mean ocean
temperature
Boucher et al. 2009
Doutriaux-Boucher
et al. 2009
Joshi and Gregory
2008
Fast Ocean Atmosphere Model (FOAM)/LPJ
LAI
change
PHYS scenario
JOURNAL OF CLIMATE
Kergoat et al. 2002
Model
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BATHIANY ET AL.
vegetation model with observed atmospheric data,
Leipprand and Gerten (2006) obtain an increased
evapotranspiration (although no maps are shown in
their study).
When comparing the CMIP5 temperature anomalies
to the changes of vegetation cover in Figs. 2 and 5, it is
apparent that the cool stripe of the Sahel coincides with
the preindustrial border of vegetation cover. South of
this transition region, the decrease of stomatal conductance leads to a warming. In previously bare regions north
of the cool belt, the lower albedo of the surface increases
temperature because of more absorbed shortwave radiation. As the latent heat flux (LH) and net shortwave
radiation (SWnet) are the terms with the largest changes,
their superposition coincides well with the pattern of
temperature anomalies (Fig. 8).
In the case of MPI-ESM-LR, the physiological effects
of CO2 on the surface energy balance in different regions becomes obvious in scatter diagrams because the
transition region is wider than in HadGEM2-ES. Considering all grid points in the four boxes of Fig. 2 where
the preindustrial vegetation cover is above 80%, the
anomalies of temperature and latent heat flux are
closely related (Fig. 9a). No such relation exists between
temperature and absorbed shortwave radiation. When
only considering grid points where preindustrial vegetation cover is below 80%, the opposite is true. Figures
9e–h reveal why this occurs by showing changes in LAI
on the horizontal axis. In the vegetated areas with small
changes in LAI, the reduced stomatal conductance
dominates and decreases LH. However, the larger the
increase in LAI, the more positive the anomaly becomes. As the bare ground fraction in these initially
vegetated areas is low, an increased LAI has comparatively little influence on the surface albedo and thus
SWnet. In the areas with an initially substantial fraction
of desert, the expansion of vegetation into these bare
regions causes an albedo decrease. Although LH also
tends to increase with increasing LAI in these areas, the
absorbed shortwave radiation dominates the surface
energy balance.
However, the latent heat-induced cooling in the Sahel
can be caused not only by the local vegetation cover
change, but also by remote effects. For example, the
climate change in the extratropics can lead to a shift in
circulation and thus a precipitation increase in the Sahel.
Even without any change in local LAI, the increase in
precipitation may lead to latent heat cooling. This effect
may explain the results by Cao et al. (2010) and Pu and
Dickinson (2012), who applied different versions of the
National Center for Atmospheric Research (NCAR)
Community Climate System Model (CCSM) where no
LAI change was permitted (Table 3). Consequently, an
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offline experiment with prescribed atmospheric conditions
with the NCAR land surface model CLM3.5 does not
show any increase in evapotranspiration (Gopalakrishnan
et al. 2011).
The mechanism of precipitation-induced cooling also
becomes apparent in HadGEM2 (called HadGEM2-A in
this setup) in two sstClim experiments where both effects
of CO2, radiative and physiological, are active (Table 2). As
the temperature contrast between land and ocean is artificially increased in sstClim4xCO2 because of the fixed
SSTs, there is a massive increase in precipitation over
sub-Saharan Africa (Fig. 10). This precipitation increase
causes regionally very different responses in latent heat
flux: Where forest coverage is high, the stomatal response
causes a decrease in LH. In the subtropics, where LH is
most limited by precipitation, an increase occurs that is
large enough to decrease surface air temperature in some
locations despite the very large longwave heating. A
similar effect on Sahel hydrology is obtained by Andrews
et al. (2012), who show that a 2 3 CO2 experiment with
fixed SSTs leads to an exceptional increase in relative
humidity in the Sahel (see their Fig. 4e). In HadGEM2,
the evaporative cooling occurs despite negligible changes
in the vegetation’s condition: All plant functional types
are fixed to the same distribution in sstClim4xCO2 and
sstClim, and LAI shows only very small differences.
In MPI-ESM-LR, a large LAI increase occurs between
the equator and approximately 178N, as the phenology of
raingreen plant types in JSBACH is sensitive to soil
moisture and NPP. Therefore, the remote and local
causes of the evaporative cooling in sstClim4xCO2 cannot be strictly separated in MPI-ESM-LR.
Although an attribution of the cool Sahel to the local
vegetation change in the PHYS experiment is also not
strictly possible, we argue that this local effect is probably
more important than remotely induced changes. In the case
of HadGEM2-ES, Boucher et al. (2009) obtained a
warming that was largest over the forest areas but not
a particularly cool Sahel when they prescribed LAI in a
previous model version. A similar response was obtained
by Doutriaux-Boucher et al. (2009) and Andrews et al.
(2011), who only analyze the first 5 yr after an instantaneous CO2 increase. Assuming that the differences to
the CMIP5 results presented in Fig. 8 and the latter three
studies are not due to model differences other than the
effect of changes in LAI or vegetation cover, the CMIP5
results suggest that vegetation dynamics are of particular
importance in the Sahel. However, the northward shift of
the ITCZ over the Atlantic in PHYS implies that vegetation dynamics in other regions may also have an effect.
Whether an increase in precipitation at a particular grid
point is induced by the local vegetation or by the vegetation cover changes elsewhere is therefore hard to separate
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FIG. 9. Relation between anomalies in latent heat flux, surface net shortwave energy, and (a)–(d)
surface air temperature and (e)–(h) leaf area index in PHYS (difference from preindustrial conditions) in MPI-ESM-LR. Each cross represents a land grid point within the black rectangle in Fig. 8
(identical to the four boxes in Fig. 2), but separated into areas where preindustrial vegetation cover
is (left) above 80% and (right) below 80%. Red crosses mark the spatial means of the changes
(mean of black crosses); zero lines are indicated in red.
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BATHIANY ET AL.
7179
FIG. 10. Time-mean differences between sstClim4xCO2 and sstClim in (left) MPI-ESM-LR
and (right) HadGEM2-A. (top) Precipitation, (middle) latent heat flux (positive when directed
from land to atmosphere), and (bottom) surface air temperature. Changes at white grid points
are not statistically significant at the 5% level.
in HadGEM2-ES. In case of MPI-ESM-LR, it is obvious
from Figs. 5, 8, and 9 that latent cooling is strongest in
regions where vegetation cover change is largest (under
the precondition that initial vegetation cover is already
large). Finally, as we know that CO2 fertilization is large in
MPI-ESM-LR and, as precipitation changes are stronger
over land than over the ocean, we expect the vegetation
expansion to be caused by the local effect rather than by
teleconnections. We therefore conclude that vegetation
dynamics and structural changes are of particular importance in the Sahel in MPI-ESM-LR.
4. Conclusions
We have shown that three CMIP5 ESMs with dynamic vegetation indicate a Sahel greening due to increased CO2 until 2100. However, the reason for this
change differs among the models. In MIROC-ESM, the
greening is the result of an increased precipitation in
North Africa. In MPI-ESM-LR, it is the result of CO2
fertilization. In HadGEM2-ES, there is only little vegetation cover change in the first decades, while a subsequent decrease in rainfall and the large temperatures
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initiate a retreat of vegetation in case of the very high
CO2 levels of the RCP8.5 scenario. In MPI-ESM-LR
and HadGEM2-ES, there is a tendency of a vegetation
retreat in the west of North Africa. In MPI-ESM-LR this
retreat does not start until 2100, when climate change
proceeds while the fertilization effect levels off because
of the stabilized CO2 concentration. The dependency of
vegetation anomalies on the time horizon would probably be even more problematic in a scenario with a CO2
concentration that is reduced after a large peak (implying very low emissions or even anthropogenic carbon
removal activities). Under such conditions, the fertilization effect would decrease with the concentrations,
while the committed climate change from the highemission period would also act to reduce the vegetation’s
productivity. The impact of CO2 would then tend to
resemble the RAD scenario.
As there are several important drivers of vegetation
changes in the Sahel that can oppose each other and are
insufficiently understood and modeled, the three models
only represent three trajectories that do not represent
the full spectrum of possibilities. In particular, improved
parameterizations of surface and boundary layer processes, tropical convection, and mesoscale systems are
crucial to better represent Sahelian rainfall in climate
models (Pohl and Douville 2011; Ruti et al. 2011; Fasullo
2012; Rowell 2012; Roehrig et al. 2013). Considering the
large spatial gradients and the importance of small-scale
features and land–atmosphere coupling of the Sahel
region, in combination with the scarcity of observations,
progress may remain slow, and the application of regional models may be of particular benefit (Cook 2008;
Patricola and Cook 2010).
As many global models project rather small changes
in annual rainfall (Biasutti 2013), it seems likely that
physiological effects of CO2 will be of particular importance and a further source of uncertainty considering
the low level of understanding of these effects on long
time scales (Field et al. 1995; Long et al. 2004; Norby and
Zak 2011; Reich and Hobbie 2013). The idealized factor
separation experiments of CMIP5 indicate that CO2
fertilization is one of the dominating drivers of vegetation cover changes in MPI-ESM-LR and HadGEM2ES, which is similar to earlier results performed with
FOAM-LPJ by Notaro et al. (2007).
It also seems clear that such structural vegetation
changes affect the Sahelian climate, although the relative
importance of changes in albedo and evapotranspiration
is inconsistent among models. As several studies showed
similar effects on both aspects of the energy balance, we
consider it likely that previous experiments could not
reveal the spatial separation of mechanisms because of
their lower resolution. However, the balance of effects
VOLUME 27
depends on the physical properties of plants, represented
as constant parameters of plant functional types (PFTs)
in current models. Also, the translation of changes of
productivity on a leaf level and short time scales into the
dynamics of vegetation distribution and composition over
centuries remains a major uncertainty. To isolate the
climatic effect of local vegetation changes from teleconnections, it may be of value in the future to perform
additional simulations with an offline land model where
precipitation is prescribed (similar to Gopalakrishnan
et al. 2011), to compare simulations with and without
structural vegetation changes (as, e.g., in Betts et al.
1997), or to allow dynamic vegetation only in some
regions of the world but not in others.
The CMIP5 results also have implications for previously used concepts of analysis: First, it is a common
practice to analyze the difference between RADPHYS
and RAD experiments to determine the physiological
effects (Andrews et al. 2011; Betts et al. 1997; Bounoua
et al. 2010; Levis et al. 1999, 2000; Doutriaux-Boucher
et al. 2009; Joshi and Gregory 2008; O’ishi et al. 2009).
The substantial synergy effect implies that this difference is not identical to the pure PHYS effect. While the
chosen approach may make no difference for fast effects
such as stomata closure, it leads to differences whenever
large changes in vegetation cover occur. Differences will
appear mainly in those quantities that are much affected
by vegetation cover. Of course, the question of which
approach is more appropriate depends on the aim of the
analysis. To determine the additional contribution of
physiology on climate change, analyzing RADPHYS 2
RAD appears to be an appropriate approach.
Second, the concept of climate forcings (in terms of
W m22) and feedbacks (in terms of W m22 K21) is questioned when physiological effects are important. The fast
processes of nonradiative forcing (stomatal closure and
subsequent changes in cloud cover) are usually added to
radiatively induced adjustments: for example, adjustments of stratospheric temperature, lapse rate, and cloud
cover (Andrews and Forster 2008; Andrews et al. 2012).
However, the slow structural responses of vegetation to
enhanced CO2 are not related to temperature-driven
feedbacks (Bony et al. 2006) and must conceptually also
be regarded as forcing adjustments that are so slow that
they cannot be separated from climate feedbacks. As
CO2 contributes most to the anthropogenic radiative
forcing and, as climate change and CO2-driven vegetation
dynamics evolve on similar time scales, it can be argued
that CO2-induced vegetation cover changes can conceptually be formulated as a climate feedback (Cao et al.
2010). However, the CMIP5 results indicate the limits of
this assumption, as changes in LAI and vegetation distribution are faster than the warming of the deep ocean.
15 SEPTEMBER 2014
BATHIANY ET AL.
Third, it has been speculated whether land–atmosphere
feedbacks in North Africa can cause abrupt transitions
(Claussen et al. 1999; deMenocal et al. 2000) or even
multiple equilibria (Brovkin et al. 1998). Although the
feedback appears to be too small to allow for such nonlinear effects in current ESMs, CO2 fertilization may
make the region more susceptible to precipitation changes
than in the past.
Acknowledgments. We acknowledge financial support
by the Cluster of Excellence Integrated Climate System
Analysis and Prediction (CliSAP) DFG EXC 177/2, as well
as by the European Commission’s 7th Framework Programme under Grant Agreement 282672 (EMBRACE
project). We also acknowledge the World Climate
Research Programme’s Working Group on Coupled
Modelling, which is responsible for CMIP, and we thank
the climate modeling groups (listed in Table 1) for
producing and making available their model output.
For CMIP, the U.S. 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. We are
particularly grateful for the technical information and
data support by Tomohiro Hajima, who provided the
MIROC-ESM data for esmFixClim1 and esmFdbk1,
and by Chris D. Jones, who provided LAI data from
HadGEM2-A. Finally, the Doctor is gratefully acknowledged for rebooting the universe.
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