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Sensitivity of Twentieth-Century Sahel Rainfall to Sulfate Aerosol and CO2 Forcing
DUNCAN ACKERLEY,* BEN B. B. BOOTH,1 SYLVIA H. E. KNIGHT,# ELEANOR J. HIGHWOOD,*
DAVID J. FRAME,@ MYLES R. ALLEN,& AND DAVID P. ROWELL1
* Department of Meteorology, University of Reading, Reading, United Kingdom
1
Met Office Hadley Centre, Exeter, United Kingdom
#
Department of Earth Sciences, The Open University, Milton Keynes, United Kingdom
@
Smith School of Enterprise and the Environment, University of Oxford, Oxford, United Kingdom
&
Department of Physics, University of Oxford, Oxford, United Kingdom
(Manuscript received 12 December 2010, in final form 6 April 2011)
ABSTRACT
A full understanding of the causes of the severe drought seen in the Sahel in the latter part of the twentiethcentury remains elusive some 25 yr after the height of the event. Previous studies have suggested that this
drying trend may be explained by either decadal modes of natural variability or by human-driven emissions
(primarily aerosols), but these studies lacked a sufficiently large number of models to attribute one cause over
the other. In this paper, signatures of both aerosol and greenhouse gas changes on Sahel rainfall are illustrated. These idealized responses are used to interpret the results of historical Sahel rainfall changes from two
very large ensembles of fully coupled climate models, which both sample uncertainties arising from internal
variability and model formulation. The sizes of these ensembles enable the relative role of human-driven
changes and natural variability on historic Sahel rainfall to be assessed. The paper demonstrates that historic
aerosol changes are likely to explain most of the underlying 1940–80 drying signal and a notable proportion of
the more pronounced 1950–80 drying.
1. Introduction
The Sahel region of Africa experienced a severe reduction in wet season rainfall (July–September) from
the 1970s to the 1990s, from which there has been a
partial recovery. Numerous studies have found that the
direct cause of the drought largely arises from changes in
sea surface temperature (SST) patterns, with all ocean
basins being important. The role of Atlantic SSTs was
revealed first, with a tendency for dry Sahel years to be
associated with anomalously cool SSTs in the northern (tropical) Atlantic and anomalously warm SSTs in
the South Atlantic, particularly including the Gulf of
Guinea (e.g., Lamb 1978; Vizy and Cook 2001; Lau et al.
2006). Warmer SSTs in the Indian Ocean have also been
linked with drought over the Sahel (see, e.g., Palmer
1986; Hagos and Cook 2008; Lu 2009), along with a
large-scale SST gradient into the western Pacific (Rowell
2001). ENSO events are influential on interannual time
Corresponding author address: Duncan Ackerley, National Institute of Water and Atmospheric Research Ltd., Private Bag 14901,
Wellington, New Zealand.
E-mail: [email protected]
DOI: 10.1175/JCLI-D-11-00019.1
Ó 2011 American Meteorological Society
scales (e.g., Folland et al. 1991; Janicot et al. 1996; Rowell
2001), and more recently a contribution to drought of
decadal variability in the Mediterranean has been confirmed (Rowell 2003; Jung et al. 2006). These anomalous
regional SST patterns also contribute to a global ‘‘interhemispheric thermal contrast’’ pattern of SSTs that has
been linked with Sahel drought, particularly on decadal
time scales (e.g., Folland et al. 1986).
A key question, however, is whether these SST patterns are due to natural variability or anthropogenic
forcing. This remains an issue of some debate. Sutton
and Hodson (2005), Hoerling et al. (2006), and Ting et al.
(2009) suggest that Atlantic SST changes (and hence the
drought) were primarily of natural origin, whereas Held
et al. (2005), Biasutti and Giannini (2006), Lau et al.
(2006), and Kawase et al. (2010) suggest that observed
precipitation changes were due to a combination of external anthropogenic forcing and internal (natural) variability. Held et al. (2005) analyzed initial condition
ensembles of two versions of a coupled model, and concluded that the drying trend over the Sahel is partly anthropogenically forced (aerosol loadings and greenhouse
gas emissions) and partly due to internal variability of the
ocean–atmosphere climate system. Biasutti and Giannini
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(2006) use 19 models from the third Coupled Model
Intercomparison Project (CMIP3) archive, and reach
a similar conclusion, but also estimate that the fraction
of the 1930–99 drying trend that was externally forced
(primarily by anthropogenic activity) was ‘‘at least
30%.’’ Lau et al. (2006) analyzed the same 19 models,
but focused on the period of drought from the 1970s to
1990s, and speculated that the increase in both volcanic
and anthropogenic aerosols may have been responsible.
Sulfate aerosol, derived from anthropogenic activity
and concentrated mainly in the Northern Hemisphere
(NH), increased rapidly from 1940 to 1990 and could well
have contributed significantly to the cross-equatorial SST
gradient, which substantially altered low-latitude circulation and rainfall (Hulme and Kelly 1993; Williams et al.
2001; Rotstayn and Lohmann 2002; Baines and Folland
2007). However, the cross-equatorial SST changes have
been attributed to natural modes of ocean variability by
Sutton and Hodson (2005), Hoerling et al. (2006), and
Ting et al. (2009), suggesting that anthropogenic activity
alone is unlikely to be the sole cause of the Sahel drying.
Another process that has been discussed as playing a role
in the historical drought is a direct local radiative forcing from dust aerosol, which may have amplified the
SST-induced drying in the Sahel (Yoshioka et al. 2007).
However, there is currently no multimodel agreement
on the sign of the dust impact. This, and other processes
(such as carbonaceous aerosol and changing land use),
are beyond the scope of this study.
Resolving this discussion on the possible causes of the
recent SST variability (that has influenced Sahel precipitation) is important, not only for attributing the prolonged drought of recent decades, but also for improving
predictions of future anthropogenic change in this region
(see Cook 2008). One possible explanation for the lack of
scientific consensus may be due to the limited ensemble
sizes of available models to attribute the natural versus
forced contributions. Another may be that many of the
currently available CMIP3 models do not yet represent
the aerosol indirect effects very well [which have been
shown to be important in Williams et al. (2001)].
Sulfate aerosols influence climate directly (via the direct radiative effect) where aerosol particles act to scatter
or absorb radiation. Aerosol particles also influence the
microphysical properties of cloud and induce an indirect
radiative effect. There are two indirect radiative effects;
the ability of aerosol to change cloud albedo by increasing
or decreasing the number of cloud droplets, which can
subsequently scatter more or less solar radiation, making
the cloud more or less reflective [‘‘the first indirect effect,’’ assuming a constant cloud liquid water content; see
Twomey (1977)], as well as changing the precipitation
characteristics of clouds and increasing cloud lifetime
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[‘‘the second indirect effect’’; Albrecht (1989)]. Cooling
of the climate system is caused by both the direct and
indirect radiative effects of sulfate aerosol, which also
contribute to the ‘‘global dimming’’ phenomenon (see
Stanhill and Cohen 2001; Romanou et al. 2007). However, modeling work by Jones et al. (2001) suggests that
the first indirect effect may well have a greater impact on
climate than the direct effect. The first indirect effect is
represented in the model by
CDNC 5 3:75 3 108 [1 2 exp(22:5 3 1029 NCCN)],
(1)
where CDNC is the cloud droplet number concentration
and NCCN is the number concentration of sulfate aerosol particles (cloud condensation nuclei). Since the relationship in Eq. (1) is nonlinear, the amount of sulfate in
the initial (or control) state is important in governing the
effects of changes in atmospheric sulfate concentrations
on the surface air temperature. Jones et al. (2001) compared these relative aerosol effects in general circulation
model (GCM) simulations. They found that by reducing the natural dimethyl sulfide (DMS) emissions in the
preindustrial simulation by 50%, the indirect radiative
forcing (for present-day relative to preindustrial sulfate
concentrations) became 25% stronger. Therefore, the
lower the initial concentrations of aerosol present, the
more susceptible a cloud is to being influenced by an increase in aerosol concentration and the stronger its influence on climate. The effects of cloud susceptibility can
be seen in ‘‘ship track’’ observations by Ackerman et al.
(1995).
Despite the strong cooling influence of sulfate aerosols
on surface air temperatures, confirmation of their influence on Sahel rainfall by changing hemispheric temperatures is difficult without a large ensemble of models.
The ensemble size of the studies cited earlier limits their
estimates of the relative importance of different forcings,
due to problems in detecting the forced signal against the
background of internal variability. The climateprediction.
net experiment (Stainforth et al. 2005) uses a distributed
computing platform to perform tens of thousands of
climate simulations on the home computers of volunteers worldwide. Therefore, the results from the climate
prediction.net project are uniquely placed to separate the
signal of the sulfate aerosol and greenhouse gas forcing
from the effects of natural (and model internal) variability. Furthermore, the wide variety of model climatologies that are created by the ‘‘perturbed physics’’
approach employed by the climateprediction.net experiment also goes some way to improving on the limited
availability of models in earlier studies.
This study concentrates on the impacts of sulfate aerosol
contrasted with the response to greenhouse gas forcing.
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TABLE 1. The slab-ocean experiments where PI refers to a ‘‘preindustrial’’ control run (CO2 concentrations of 280 ppm and natural
sulfur emissions) and PD refers to a ‘‘present day’’ control run (CO2 concentrations of 346 ppm and sulfur emissions equivalent to those of
the mid-1980s). The term FUT denotes future forcings such as doubled PD CO2 concentrations, 2050 anthropogenic SO2 emissions, and
both of these forcings together.
Expt (number of
ensemble members)
Atmospheric CO2
concentration (ppm)
PDSO2 (2)
PDCO2 (1)
PDall (1)
FUTSO2 (243)
280
346
346
346
FUTCO2 (243)
FUTall (243)
692
692
Two types of experiment are analyzed. In section 2, we
investigate the response of a slab ocean GCM to changes
in greenhouse gas and sulfur dioxide (SO2) emissions.
These provide a conceptual understanding of the relative
roles of both aerosols and greenhouse gases in Sahel
rainfall, and are used to study climate anomalies in both
the present and future climates. The results from these
relatively simple experiments are then used to gain insight
into the twentieth-century ensemble experiments with
a fully coupled ocean–atmosphere GCM, described in
section 3. Here, we make use of two sets of ensemble
simulations of the twentieth century to explore relationships between changes in aerosol concentration, and
impacts on Sahel rainfall using two ensembles of fully
coupled ocean–atmosphere GCMs. The first ensemble
represents the transient climate response to historical
changes in greenhouse gases and sulfate aerosols as
well as volcanic and solar forcing. The second ensemble
makes use of an ensemble with identical forcings except that the sulfur emissions correspond to a period
shifted 70 yr into the future. The combination of all
ensembles uniquely enables us to explore the dependence of Sahel drying on sulfate aerosol concentrations
(over and above model internal variability) and responses
to other forcing mechanisms, such as an increase in
greenhouse gases. The conclusions from this study are
given in section 4.
2. Slab-ocean GCM experiments
a. Sulfur cycle background and experiments
In these experiments, the third Hadley Centre Atmospheric Model is coupled to both a slab ocean model
(HadSM3) and a fully interactive sulfur cycle scheme,
as documented in Jones et al. (2001). However, in this
study there was no representation of the second indirect
effect [unlike in Jones et al. (2001)] since HadSM3 did not
have a prognostic ice–cloud scheme. Two gaseous sulfur
Sulfur emissions
Control run used
1980s anthropogenic SO2
DMS and volcanic SO2 only
1980s anthropogenic SO2
2050 anthropogenic SO2
Emissions (IPCC SRES A2)
1980s anthropogenic SO2
2050 anthropogenic SO2
Emissions (IPCC SRES A2)
PI
PI
PI
PD
PD
PD
species are emitted: SO2 and dimethyl sulfide. Sulfur dioxide is derived from anthropogenic activity as well as
volcanic emissions within the model, and DMS, emitted
from all ocean basins, represents the sulfur produced
from the metabolic processes of phytoplankton. DMS is
emitted from the surface of the model, whereas anthropogenic SO2 emissions occur from both the surface and
between levels 3–5 in the model (to represent ‘‘high-level
chimney stack’’ emissions). Volcanic SO2 is emitted into
the model between the levels at 870 and 422 hPa.
To form sulfate aerosol, the sulfur gases need to be
oxidized. This occurs in cloud-free regions via dry oxidation or within cloud and precipitation droplets via
aqueous oxidation. These oxidation processes lead to
the formation of three modes of sulfate aerosol, which
are Aitken mode (median radius, r 5 0.024 mm), accumulation mode (r 5 0.095 mm), and dissolved sulfate.
Both gaseous sulfur species, as well as the sulfate aerosol, interact with the model meteorology (such as cloud
and precipitation) and are transported throughout the
domain using the model’s tracer advection scheme. All
sulfur species are subjected to removal via dry and wet
deposition (‘‘scavenging’’), except DMS, which is treated
as insoluble. The balance between emissions, conversion, and deposition leads to an atmospheric loading
of sulfate known as the ‘‘sulfate burden,’’ which will be
used in the rest of this paper [for more detailed information on the climateprediction.net sulfur cycle experiment, see Ackerley et al. (2009)].
An ensemble of 15-yr experiments was run with
HadSM3 within the climateprediction.net distributed
computing framework. Also, a smaller ensemble of
30-yr experiments was run using the computing resource
at the Rutherford Appleton Laboratory (RAL). Table 1
contains the boundary condition forcings applied to each
phase of the experiment, with the numbers in parentheses denoting the number of model runs attained. The
control runs (to which the subsequent forcing experiments
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The PDall and PDCO2 experiments consisted of only
one model integration, and PDSO2 consisted of two integrations with different initial conditions (see Table 1).
Each of the FUT experiments had a combination of perturbations to sulfur cycle physics and initial conditions to
provide an ensemble of 243 members [more details can
be found in Ackerley et al. (2009)].
The sulfate burdens in PI, PD, and 2050 (FUTSO2 )
can be seen in Fig. 1. With natural sulfur emissions only
(Fig. 1a), sulfate loadings are generally lowest close to
the poles and increase toward the tropics with most of
the sulfate being derived from DMS. There are several
areas where the sulfate burden becomes very high locally
(such as close to Central America and the Mediterranean), which are associated with volcanic SO2 emissions. When anthropogenic SO2 emissions are included
(Fig. 1b), the Northern Hemisphere (NH) loading of
sulfate becomes much higher than that in the Southern
Hemisphere (SH), which is associated with fossil fuel
combustion in the NH midlatitudes. When anthropogenic emissions are increased to those projected for
2050 following the Intergovernmental Panel on Climate
Change’s (IPCC) Special Report on Emissions Scenarios
(SRES) scenario A2 (Fig. 1c), there is still a strong NH
loading; however, the belt of high sulfate loading has
shifted toward the equator and is centered more over
southern and southeastern Asia, and the Middle East.
The changes in zonal mean sulfate burden for PDSO2
(relative to PI) and FUTSO2 (relative to PD) are plotted
in Fig. 2. The strong increases in NH sulfate loading and
slight increase in the SH loading for PDSO2 can be seen
clearly in Fig. 2a. The zonal mean changes in sulfate
burden are smaller for FUTSO2 (relative to PD) than for
PDSO2 (relative to PI) and indicate a slight decrease in
sulfate loading north of 408N. The largest increases are
in the NH subtropics (for FUTSO2 relative to PD) and the
increases extend across the equator to the SH subtropics.
However, in both PD and PDSO2 it is the asymmetrical
nature of the hemispheric, anthropogenic sulfate loading
that may lead to a reduction of precipitation in the Sahel.
that precipitation in the Sahel may be influenced by the
changes in interhemispheric temperatures and so we
start by analyzing the global and hemispheric 1.5-m
surface air temperature (SAT) responses to each of the
boundary condition forcings (see Table 1), which are
given in Table 2.
The results in Table 2 highlight the global mean cooling effects of sulfates and the warming influence of increased CO2 (when perturbed individually). The global
mean temperature (GMT) responses in the combined
forcing experiments are (approximately) a linear addition
of the responses to each individual forcing, which suggests
there is little nonlinear interaction resulting from perturbing CO2 and sulfate concentrations simultaneously,
in these experiments.
Also included in Table 2 are the hemispheric changes
in SAT, which highlight the inhomogeneous response
of SAT to a sulfate and/or CO2 forcing. In PDSO2, the
global mean sulfate cooling is dominated by the response in the NH (also see Williams et al. 2001; Feichter
et al. 2004; Kristjánsson et al. 2005), which cools by 1 K
compared to 0.3 K in the SH. A similar and opposite
response also occurs in PDSO2, which displays more
warming in the NH than in the SH although the interhemispheric temperature difference is not as large as in
PDSO2 (see Table 2). The combined forcing experiment
(PDall) has approximately no global mean change in SAT
but the NH cools by 0.3 K and the SH warms by 0.2 K,
which is similar to the effect seen in PDSO2 (NH cooling
more than the SH).
The results are very different in the FUT experiments
however. The sulfate cooling in FUTSO2 is much smaller
than in PDSO2 and is similar between the NH and SH,
whereas the warming in the FUTCO2 experiment is
much stronger in the NH than the in SH. The strong
NH CO2 warming relative to the SH is also apparent
in FUTall but is reduced slightly due to the presence
of increased anthropogenic SO2 emissions. The strong
NH warming in these runs (despite the globally uniform CO2 concentrations used in this study) is due to
a combination of the sea ice–albedo feedback mechanism (see Curry et al. 1995) and strong heating of the
NH continental interiors as discussed in Sutton et al.
(2007).
The results in this section clearly show that the responses to sulfate and CO2 forcing differ between the
NH and the SH, which will have implications for the
seasonal migration of the intertropical convergence zone
(ITCZ) and may therefore influence Sahel rainfall.
b. Global and hemispheric surface air temperatures
c. Zonal mean surface air temperature response
Previous work, such as that by Ting et al. (2009),
Hoerling et al. (2006), and Held et al. (2005), indicated
To show the asymmetrical properties of the surface
air temperature response to each of the sulfate and/or
in Table 1 are compared) consisted of one of the following
features:
1) a preindustrial (PI) atmosphere with CO2 concentrations set to 280 ppm and natural sulfur emissions
(volcanic SO2 and DMS) only or
2) a present day atmosphere (PDall) with CO2 concentrations set to 346 ppm and both natural and anthropogenic sulfur emissions.
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FIG. 1. Annual and ensemble mean sulfate burdens (mgSO m22 ) for (a) natural sulfur
4
emissions only (PI), (b) present-day anthropogenic SO2 emissions and natural sulfur emissions
(PD), and (c) predicted 2050 anthropogenic SO2 emissions using the IPCC SRES A2 and
natural sulfur emissions (FUTSO ).
2
CO2 forcings further (introduced in Table 2), the zonal,
annual ensemble mean temperature responses for Hadley
Centre–Climate Research Unit historical temperature
data (HadCRU; Brohan et al. 2006) and each experiment are shown in Fig. 3. Figures 3a and 3b show the
changes in zonal SAT from the HadCRU dataset, for
the period 1860–1980, globally and in the Atlantic basin, respectively. The HadCRU data are not distributed
uniformly and so do not provide a direct comparison to
the model data but both plots (Figs. 3a and 3b) clearly
show a preferential warming of the SH relative to the
NH (south of approximately 508N).
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TABLE 2. The change in global, NH, and SH surface air temperatures (DGMT, DNHT, and DSHT, respectively) for each of the
forcing experiments given in Table 1 relative to their respective
control runs (K).
FIG. 2. The zonal, annual, and ensemble mean changes in sulfate
burden (mgSO4 m22 ) for (a) PDSO2 relative to PI and (b) FUTSO2
relative to PD (see Table 1 for details on the PI and PD control runs).
Having identified the larger warming in the SH compared to the NH in HadCRU, we now consider the
model SAT changes for each of the forcing experiments
globally and within the Atlantic basin. For PDCO2 (Figs.
3c and 3d, solid lines), the zonal mean SAT increases
at all latitudes but shows a slightly higher increase in
the NH, centered around 508N whereas the zonal mean
change in SAT over the Atlantic is reasonably uniform
between 6508. The opposite is true for PDSO2 (Fig. 3c,
dotted–dashed lines) where there is strong cooling of
the NH due to the presence of anthropogenic sulfate
aerosol. Unlike in PDCO2, PDSO2 has a stronger cooling
in the NH than the SH over the Atlantic (Fig. 3d). In
PDall (Fig. 3c, dashed lines), the SH (NH) zonal mean
changes in SAT are generally positive (negative) and
there is also a warming (cooling) at most latitudes in the
SH (NH) Atlantic basin (Fig. 3d). So, despite the warming influence of increased CO2, the NH cools relative to
the SH with the presence of sulfate aerosol and more
closely resembles the HadCRU data in Figs. 3a and 3b.
Expt
DGMT
DNHT
DSHT
Control run
used
PDSO
2
PDCO
2
PDall
FUTSO
2
FUTCO
2
FUTall
20.6
0.7
0.0
20.23
3.3
3.0
21.0
0.8
20.3
20.25
3.9
3.6
20.3
0.5
0.2
20.20
2.7
2.4
PI
PI
PI
PD
PD
PD
The zonal mean surface air temperature responses to
the FUT experiments globally, and in the Atlantic basin,
can be seen in Figs. 3e and 3f. The strong warming of
the NH due to the sea ice–albedo feedback mechanism
and the hotter continental interiors can clearly be seen
in FUTCO2 and also in FUTall, despite the presence of
increased anthropogenic sulfates in the NH for the latter
experiment. The effects of increasing sulfates in FUTSO2
have very little impact on the zonal mean SAT, either
globally (Fig. 3e) or in the Atlantic basin (Fig. 3f), despite the stronger increase in the NH sulfate burden than
in the SH shown in Fig. 2b. The weak response to the
increased sulfate loading can be attributed to the slight
reduction in the sulfate burden in the NH extratropics
and the nonlinear relationship between CDNC and CCN
given in Eq. (1). To demonstrate the nonlinear response,
we introduce the ‘‘normalized SAT response’’ (NTR)
as the change in SAT divided by the change in sulfate
loading. The NTR for PDSO2 is 20:26 K(mgSO4 m2 2 )21 ,
and for FUTSO2 the NTR is 20:14 K(mgSO4 m22 )21 . The
values of NTR indicate that the cooling ability of the
sulfate in the FUTSO2 experiment is less than that in
PDSO2 as the initial sulfate loadings were much lower
in the PI control run (1:28 mgSO4 m22 ) than in the PD
(ensemble mean 5 4:07 mgSO4 m22 ) control run.
The present-day to future hemispheric temperature
response therefore is dominated by the CO2-induced
warming whereas the preindustrial to present-day response is dominated by the asymmetrical influence of
anthropogenic aerosol cooling the NH relative to the
SH. The surface air temperature responses observed in
this section will help explain the precipitation response
to each of the imposed forcings.
d. Precipitation response
Figure 4 shows the zonal annual mean precipitation
responses to each of the forcing experiments given in
Table 1. The effects of CO2 warming in PDCO2 cause an
increase in the NH tropical precipitation and a slight
decrease in the SH tropical precipitation (in the zonal
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FIG. 3. The zonal mean change in SAT (K) from HadCRU between 1860 and 1980 for the average (a) along all
meridians and (b) for the Atlantic basin only. The model zonal mean surface air temperature responses (K) for (c) PD
relative to PI globally, (d) PD relative to PI in the Atlantic basin, (e) FUT relative to PD globally, and (f) FUT
relative to PD in the Atlantic basin. The ensemble mean is taken for experiments consisting of more than one run
(also see Table 1).
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FIG. 4. The zonal, annual, and ensemble mean changes in precipitation (mm day21) for (a) PD relative to PI and (b) FUT relative
to PD (see Table 1 for details on the PI and PD control runs).
mean, Fig. 4a). Including anthropogenic sulfates in PDSO2
and PDall causes strong drying in the NH tropics and
a moistening in the SH tropics (see Fig. 4a). Studies
by Rotstayn et al. (2000), Feichter et al. (2004), and
Kristjánsson et al. (2005) have also observed similar
effects of NH aerosol loading on tropical precipitation
and support the results in this study, indicating that the
strong cooling in the NH causes a southward shift in the
model ITCZ.
The opposite is true in the FUTCO2 and FUTall
experiments, which both display a strong increase in
NH tropical precipitation with a smaller decrease in the
SH tropical precipitation (Fig. 4b), relative to the NH.
There is also a very small drying (moistening) of the NH
(SH) tropics in the FUTSO2 experiment, which agrees
with the slight preferential cooling of the NH compared
to the SH (see Table 2) and reduces the moistening of
the NH tropics for FUTall relative to FUTCO2. The results from the FUTCO2, FUTall, and PDCO2 runs are indicative of a northward shift in the ITCZ associated with
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CO2-induced warming, which agrees with the review of
climate models by Houghton et al. (2001) and is also
discussed in Zhang et al. (2007). However, due to the
convective nature of precipitation in the tropics, the
zonal mean precipitation response may not be representative of precipitation changes in the Sahel, which
have also been shown to be influenced by local atmospheric processes (see Cook and Vizy 2006; Hagos and
Cook 2008), and so we now look at the Sahel region
itself to see if the zonal mean changes seen in Fig. 4 apply
there.
The Sahel is defined here to be a rectangle between
108–188N and 208W–228E (Ruosteenoja et al. 2003) and
we focus on the June–July–August (JJA) period when
the precipitation is high in this region. The mean change
in precipitation associated with each of the forcing experiments in Table 1, relative to their respective control runs in JJA, can be seen in Fig. 5. The hemispheric
impacts of increasing the sulfate loading alone from
PI in PDSO2 (Fig. 5a) acts to decrease the precipitation
throughout the Sahel, which can also be seen in Fig. 5c
when CO2 concentrations are also increased to presentday levels (PDall). However, there is little change in
precipitation throughout the Sahel when CO2 alone is
increased from PI to PD (PDCO2 (Fig. 5b)), which indicates that sulfate aerosol is having the dominant effect on
precipitation in this region, in these experiments.
When considering the ‘‘future’’ forcing scenarios
(FUT experiments in Table 1), the precipitation responses are very different. The drying associated with
the sulfate cooling in FUTSO2 (Fig. 5d) is closer to the
equator, with very little change in Sahel rainfall. However, the effects of doubling CO2 concentrations with
(FUTall; see Fig. 5f) and without (FUTCO2; see Fig. 5e)
changes in anthropogenic SO 2 emissions cause strong
increases in precipitation throughout most of the
Sahel and hint at a possible rainfall recovery relative
to PDall .
The results of the simple thermodynamic slab-ocean
model demonstrate the differing effects on tropical and
Sahel precipitation and give a conceptual background
as to how the Hadley Centre’s climate model responds
to each forcing. The response of these slab models can
be seen as a form of idealized climate response, where
both the response time scale and the influence of the
full ocean-driven natural variability can be neglected.
When exploring the implications of drivers of historic
Sahel drying however, we need to account for these
processes. In the next section we use large ensembles of
fully coupled ocean–atmosphere GCMs, which enable
the forced climate responses to be analyzed against the
background of the ocean-driven variability and response
time scale.
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FIG. 5. The JJA mean precipitation response (mm day21) to (a) PDSO2, (b) PDCO , (c) PDall, (d) FUTSO , (e) FUTCO2
2
2
and (f) FUTall, relative to each respective control run given in Table 1. The ensemble mean is taken for experiments consisting of more than one run (also see Table 1).
3. Transient GCM experiments
The results of the slab model experiments have provided a useful conceptual platform to help us understand
the effects of sulfate and CO2 forcing on precipitation in
the Sahel. However, the slab model uses a rather simple
representation of the ocean and gives no information
on how the time-evolving climate drivers impact the
transient climate response. In this section we make
use of a fully coupled ocean—atmosphere GCM to
explore some of these transient effects. Utilizing the
very large third climate configuration of the Met Office
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Unified Model (HadCM3L) ensemble generated within
climateprediction.net (see Frame et al. 2009) enables us to
look for a signal of the sulfate aerosol and CO2 forcings
on Sahel precipitation against the background of large
model variability (which refers to imposed natural forcing, model internal variability, and differences in model
parameter settings).
a. Method
Several thousand simulations of the HadCM3L coupled ocean–atmosphere GCM were completed by volunteers on home computers (Frame et al. 2009). Each of
the distributed models had a different combination of
model parameters (Stainforth et al. 2005), forcing fields
[well-mixed greenhouse gases, anthropogenic sulfate,
and solar and volcanic aerosol; see Frame et al. (2009),
but all with zero DMS emissions], and initial conditions.
There were two ensemble experiments run:
1) standard transient runs [STR, 1920–2000 greenhouse
gas concentrations, sulfur emissions, and natural
forcings from Nakićenović et al. (2000)], where the
simulations have SO2 emissions specified according
to observations and a total of 1566 simulations are
included, which have been selected such that they
include the same ranges of model parameter values
and forcing fields (solar or volcanic) as in the AATR
ensemble (described below), and
2) altered aerosol transient runs [AATR, 1920–2000
greenhouse concentrations and natural forcings with
IPCC SRES A1B scenario 1990–2070 sulfur emissions, from Nakićenović et al. (2000)], where the
simulations have SO2 emissions appropriate for
70 yr ahead of the actual model time (see Fig. 6a)
and extend on into the SRES A1B scenario for the
future; these runs contain 519 models, which simulated 1920–2000 greenhouse gas and natural forcings with future SO2 emissions.
In this section, the focus will be on July–September
(JAS) precipitation, as this accounts for 70% of the
annual total in the Sahel and has changed significantly in
the period of interest (Lau et al. 2006). For comparisons
with observations, we use the CRU’s 0.58 3 0.58 precipitation dataset (New et al. 2000). We discard results
prior to 1940 as there is some evidence that the models
were still spinning up. Due to the distributed computing
nature of this experiment, we were able to include data
from runs that did not have a full dataset uploaded from
the volunteer. To account for any missing model data,
a ‘‘climatology’’ was calculated for each run, as the parameter combinations led to some simulations being
systematically wetter (or drier) than others. From this
a precipitation anomaly can then be calculated relative
FIG. 6. (a) The total anthropogenic SO2 emissions for the STR
(red) and AATR (black) ensembles. (b) The global mean radiative
forcing relative to 1920–40, diagnosed for the standard model
configuration for STR (red) and AATR (black). Solely to illustrate
the relationships between the annual and decadal radiative forcing
estimates, values are also shown (blue) for an alternative model
(HadCM3) relative to 1860–80. (c) The implied hemispheric difference in radiative forcing (NH 2 SH) for STR (red) and AATR
(black) for each decade.
to the climatology of each ensemble member, which can
then be combined with available model data to produce
an ensemble of anomalies.
From the slab experiment we would expect models
with high initial sulfate loadings to show a smallermagnitude Sahel precipitation response than in those
with low initial sulfate loadings.
b. Results
Figure 6a shows the total sulfur emissions in the
AATR (black lines) and STR (red lines) runs. The STR
ensemble starts with much lower sulfur emissions than in
AATR (approximately a factor of 5) and emissions
rapidly increased, between 1940 to 1980, by a factor of 5.
With AATR, sulfur emissions are offset 70 yr into the
future so that sulfur emissions for the same period in
AATR correspond to 2010–50, while all other climate
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ACKERLEY ET AL.
forcings are identical to STR. For comparison with the
slab experiments, note that for the 1940–80 period, the
AATR ensemble uses SO2 emissions, which lie closer to
the FUTSO2 experiment.
The impacts of the different sulfur emissions in STR
and AATR on the radiative forcing were estimated (similar to Forster and Taylor 2006) for the standard model
configuration of STR and AATR using the following
equation:
Q 5 lDT 1 DTOA
(2)
At a particular point in time, changes to the radiative
balance of the earth’s climate (represented by Q, the
radiative forcing, in W m22) are related to the realized
global temperature response (DT, K) to these changes
and the flux imbalance at the top-of-the-atmosphere
(DTOA, W m22). These radiative changes, Q, may arise
from changes in atmospheric composition (e.g., aerosols
and greenhouse gases) or incoming solar energy. When
these changes are first applied, the surface temperature
will not have had time to respond and hence Q will equal
DTOA. As the climate system equilibrates with any radiative forcing, DTOA approaches zero and lDT approaches Q. The magnitude of the DT response will be
dependent on the blackbody response to the radiative
change and the combined effects of the climate feedback
processes represented by l.
Using diagnosed, modeled DTOA and DT, and a
l value representative of the standard model configuration (l 5 1.1 W m22 K21), the temporal estimate of
the radiative forcing of the standard transient simulation
can be found. Forcing estimates calculated in this way
can be seen as indicative of forcing across the ensemble;
though some variation in magnitude could be expected
[intermodel forcing (Q) differences are expected to be
smaller than differences in the magnitude of climate
responses (l) to these forcings]. This approach has become widely used since it was first demonstrated by
Forster and Taylor (2006). The radiative forcing implied
for both experiments, estimated using this method, is
shown Fig. 6b. The estimates are decadal only (a number of the required diagnostics are only available for
decadal means). To illustrate how decadal radiative forcing relates to the annual mean forcing [using Eq. (2)], we
have also shown decadal and annual values for a higherresolution (HadCM3) configuration of this model (as used
in Collins et al. 2006) forced by the same emissions as
STR, plus the inclusion of background DMS. This shows
for example how the radiative forcing associated with
individual large explosive eruptions (such as the Agung
eruption in 1963) can have significant impacts on the decadal estimates (as can also be seen in STR and AATR).
The impacts of the different aerosol loadings on the
radiative forcings in the STR and AATR ensembles are
clearly evident (Fig. 6). The STR ensemble has a more
negative global forcing relative to AATR, which is consistent with the inferred response from the more rapid
increase in sulfur emissions from relatively clean background levels. This estimate corroborates the slab experiment findings presented in section 2.
Also, from the findings of the slab experiment, we
would expect the interhemispherical forcing difference
(NH – SH) to be weaker in AATR than STR, due to the
higher initial sulfate loading in AATR. The hemispheric
forcing can be estimated using the following two equations:
QNH 5 lDTNH 1 DTOANH ,
(3)
where QNH, DTNH, and TOANH are the Northern
Hemispheric radiative forcing, surface temperature
change, and change in the top-of-the-atmosphere radiative balance, respectively. Similarly for the Southern Hemisphere (SH),
QSH 5 lDTSH 1 DTOASH .
(4)
To do this, we need to make a few assumptions. We
assume that there is no net flux of energy between
hemispheres and that the l global parameter is representative for the two hemispheres. We do not expect the
first of these assumptions to hold in practice as energy is
likely to flow from the warmer hemisphere, and hence
the values derived are likely to underestimate the true
hemispheric forcing somewhat. Nevertheless, the contrasting hemispheric forcing estimates derived using
this method are likely to be indicative of the sign of the
hemispheric difference (even if they may underestimate
the magnitude).
The differences in the hemispheric radiative forcing
calculated in this way are illustrated in Fig. 6c. The
hemispheric difference in forcing is significantly more
pronounced in the STR ensemble relative to AATR.
This ties in with the results of the slab model experiments
(section 2) and the discussion of the sulfur emission differences shown above. The consequences of this hemispheric forcing difference are likely to lead to a weaker
warming trend in the Northern Hemisphere relative to
the Southern Hemisphere, which is expected to weaken
the northern migration of the ITCZ, particularly in STR.
The precipitation trend in an individual model will
depend on both the forced response and on the natural
internal variability represented within the model. Although we may expect the difference in hemispheric
warming to lead to a drying signal in the Sahel (on the
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basis of the slab simulations), this signal in any full GCM
may be more than offset (or enhanced) by natural variability (predominately driven by ocean processes). Particularly in a region such as the Sahel, which is prone to
high coupled internal variability, it is often difficult if
not impossible to tease apart the relative impacts of the
forced response from a natural mode of variability. The
value of the ensemble presented within this section is
that we can look at the precipitation response across
a large number of models. Looking at the characteristics of the ensemble as a whole enables us to identify
the influence of common drivers over the impact of internal variability.
Looking first at the time period during which the historical SO2 emissions have increased at the most consistent rate (1940–80; see Fig. 6a), we investigate how the
range of trends across the model ensembles compares
to the observed rainfall changes. The distribution of
modeled precipitation changes during this period is illustrated in Fig. 7a. The impacts of modeled process
uncertainty and, perhaps more importantly, the internal
climate variability are evident in the widths of the two
model distributions.
Differences that arise from the aerosol representation
within these two ensembles are evident from the difference in the mean ensemble responses. The vast majority, although not all, of the STR ensemble produced
a drying signal during this period. In contrast the AATR
ensemble produces both drying and moistening responses, but no net signal emerges across the ensemble.
The observed trend based on the CRU data (Fig. 7c) is
also plotted in this same figure. Although a small fraction of the AATR simulations reproduce the changes,
it is the large number of STR simulations that lie close
to the CRU trend. Indeed, the trend in the median
STR ensemble response lies close to the observed response, suggesting that much of the long-term (1940–80)
change in rainfall can be explained by forced anthropogenic aerosol changes rather than a mode of internal
variability.
The largest period of rainfall change, however, occurred as a subset of this longer period. The 1950s were
moister in the Sahel, and the drying trend to the 1980s
was more dramatic (Fig. 7c). Figure 7b illustrates the
rainfall trends simulated by the ensembles for the 1950–
80 period. As was the case earlier, the drying trend is
evident across most of the STR ensemble, again in contrast to AATR. The shorter time period for the trends
(30 yr compared to 40 in Fig. 7a) results in broader
model distributions due to the larger variability on this
shorter time scale. Also marked in Fig. 7b are the observed rainfall changes. Unlike the longer 1940–80 time
period, the CRU observations lie in the tails of both
VOLUME 24
distributions, with only a small fraction of models from
either ensemble reproducing the observed trend. This
suggests that a relationship between aerosols and rainfall
change found in the shorter 1950–80 period is not as
straightforward as the longer underlying 1940–80 change.
We can now say that the observed aerosol changes are
likely to have contributed to the drying from the 1950s
to the 1980s although there remains a substantial fraction of the observed drying that is not explained by the
forced response within this modeling framework.
There are a number of possible interpretations for
the observational trend lying in the tails of the histogram
of modeled trends. Perhaps the simplest of which is
that the 1950–80 drying trend was a rare event. In other
words, a mode (or modes) of natural variability within
the climate system conspired to enhance the 1950–80
drying signal. There is well-established existing literature that supports a role for natural processes to drive
the Sahel variability. Knight et al. (2006) and Ting et al.
(2009), for example, argue that the Sahel rainfall changes
respond to the Atlantic multidecadal oscillation (AMO)
through changes in Atlantic SSTs. If we assume that the
model captures the correct processes, then the interpretation that a rare mode of natural variability also
contributed to the drying trend is consistent with the
small fraction of modeled trends agreeing with the observed drying (Fig. 7b).
An alternative interpretation, however, is that the
model does not capture the full strength of the precipitation response to the underlying climate drivers. It
has previously been suggested by Lau et al. (2006) and
Biasutti and Giannini (2006) that HadCM3 (the higher
ocean resolution version of this model) reproduces only
a weak Sahel response to SST anomalies in the Indian
and Atlantic Oceans. Indeed, a weak SST response appears to be a general limitation of current climate
models driven by observed SST changes (Scaife et al.
2009). The implication of this would be that the model
may underestimate the magnitude of the rainfall changes
as a response to either natural variability in SSTs or from
SST changes resulting from the forced response. If the
model is underrepresenting the rainfall impacts from
natural variability, then this would suggest that the
width of the distributions of trends (in Figs. 7a and 7b) is
too small and hence natural variability is more likely to
explain the Sahel 1950–80 drying. If the model is underrepresenting the rainfall impacts from anthropogenically forced SSTs, then this might explain why the
ensemble underestimates the 1950–80 drying signal. If
we accept this interpretation, a similar experiment with
a model, which was able to capture stronger coupling,
may conclude that the 1950–80 drying trend is much
more consistent with the forced response. We are not
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ACKERLEY ET AL.
5011
FIG. 7. The relative frequencies of the mean precipitation trends in the AATR (black) and
STR (red) ensembles for the period are shown for (a) 1940–80 and (b) 1950–80. The horizontal
axis indicates the decadal trend in mean rainfall (mm month21) for JAS for the region 108–188N,
208W–228E. The observational estimate for the same period and region is marked by the blue
cross. (c) Also plotted is the observational (CRU) estimate (black diamonds) for the mean
precipitation change over the same region (mm month21). The 1940–80 and 1950–80 observational trends indicated by the blue crosses in (a) and (b) is overlaid in blue.
able to resolve this issue here, and this remains a question for the future literature.
A further possibility is that the model may fail to fully
capture the strength of some important local feedbacks.
These could operate on any time scale from days to years,
and might include, for example, dust aerosol emissions
(Yoshioka et al. (2007)), soil moisture feedbacks, or
vegetation feedbacks (Los et al. (2006)). If this is the case,
then improved modeling of these processes would also
have the effects of broadening the distributions of trends
(due to enhanced natural variability) and enhancing the
response to anthropogenically forced SST changes.
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4. Conclusions
Simulations with a slab-ocean model have shown that
NH tropical precipitation is sensitive to both greenhouse
gas and sulfate aerosol forcing. Increases in greenhouse
gases cause an increase in precipitation and increases
in aerosol loading cause a reduction. We suggest that a
hemispherical asymmetry in temperature change leads
to a shift in the seasonal migration of the ITCZ.
When the sulfate and greenhouse gas forcings are
combined, tropical precipitation depends both on the
relative magnitude of the imposed forcings and on the
location of the ITCZ. For the sorts of combinations of
aerosol and greenhouse gas forcings characteristic of the
second half of the twentieth century (such as the 1980s),
a reduction in precipitation is seen. The model suggests
that, historically, aerosol changes dominate. In contrast,
when simulating the change between 1980 and 2050, the
future Sahel rainfall increases because the future change
is influenced more by greenhouse gas concentrations.
The slab model simulations presented here enable us
to identify the model response to changes in atmospheric
composition within a framework that minimizes atmospheric variability by explicitly neglecting major modes
of ocean internal variability (and using multiyear averages). This enables us to identify hemispheric warming
differences as a driver of ITCZ position and seasonal
rainfall as an important physical mechanism (within this
model structure). The slab experiment presented here
provides a conceptual framework against which we can
understand the modeled Sahel precipitation changes in
ensembles of fully coupled ocean–atmosphere GCMs.
In light of the slab model results, it is not surprising
that, in a large fully coupled ocean–atmosphere GCM
ensemble simulation (STR) of past climate, we see a fall
in Sahel precipitation. This reduction may be attributed
to hemispheric forcing differences associated with large
increases in historic NH, anthropogenic SO2 emissions
over the period, ameliorated by the effect of a rise in
greenhouse gas emissions. This drying signal is not evident in a second large ensemble (AATR) with identical
climate forcings apart from the sulfur emissions.
The differences between historical rainfall trends in the
two ensembles illustrate the important role that aerosol
changes are likely to have played in the Sahel drought.
Previous studies have also suggested that aerosol
changes may have played an important role in governing
Sahel precipitation, but the small number of models
available in these studies and the large variability inherent in this region has made it difficult to link the
observed changes either to variability or the forced response. In this paper we have shown that representing
the historical aerosol changes within the models drives
VOLUME 24
a consistent drying trend across most of the ensemble.
The absence of this signal in the ensemble with the altered sulfur emissions emphasizes the importance of the
historical aerosol changes as part of our understanding
of the observed drying trend.
The unique perturbed physics, boundary forcing, and
initial condition ensemble approach used here allow us
to confirm that historical SO2 emissions are likely to
explain most of the 1940–80 rainfall changes and a significant proportion of the more pronounced 1950–80
drying. The use of a perturbed-physics ensemble means
that our conclusions are not reliant on any single version
of the Hadley Centre climate model, but they do, of
course, rely on the underlying model structure. If this
structure correctly represents the relevant processes in
the real world, it is very unlikely that the observed
drought was entirely due to natural climate variability.
Acknowledgments. This work was funded by the Natural Environmental Research Council Grant NER/T/S/
2001/00871 and E-Science Grant NER/S/G/2003/1193,
and enabled by the climateprediction.net team, particularly Tolu Aina, Carl Christensen, and Nick Faull. The
work undertaken by Ben Booth and David Rowell was
funded by the Joint DECC, Defra, and MoD Integrated
Climate Program (GA01101 CBC/2B/0417_Annex C5),
and (for David Rowell) the DFID Climate Science
Research Programme. We are also extremely grateful
for collaboration with the BBC, to all the volunteers
who donated computer time to this project, and the
Rutherford Appleton Laboratory for use of their supercomputing capabilities. Christopher Knight helped
with statistical analyses and Mark New supplied the
CRU data.
REFERENCES
Ackerley, D., E. J. Highwood, and D. J. Frame, 2009: Quantifying the
effects of perturbing the physics of an interactive sulfur scheme
using an ensemble of GCMs on the climateprediction.net platform. J. Geophys. Res., 114, D01203, doi:10.1029/2008JD010532.
Ackerman, A. S., O. B. Toon, and P. V. Hobbs, 1995: Numerical
modeling of ship tracks produced by injections of cloud condensation nuclei into marine stratiform clouds. J. Geophys.
Res., 100 (D24), 7121–7133.
Albrecht, B., 1989: Aerosols, cloud microphysics and fractional
cloudiness. Science, 245, 1227–1230.
Baines, P. G., and C. K. Folland, 2007: Evidence for a rapid
global climate shift across the late 1960s. J. Climate, 20,
2721–2744.
Biasutti, M., and A. Giannini, 2006: Robust Sahel drying in response to late 20th century forcings. Geophys. Res. Lett., 33,
L11706, doi:10.1029/2006GL026067.
Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones,
2006: Uncertainty estimates in regional and global observed
temperature changes: A new dataset from 1850. J. Geophys.
Res., 111, D12106, doi:10.1029/2005JD006548.
1 OCTOBER 2011
ACKERLEY ET AL.
Collins, M., B. B. B. Booth, G. R. Harris, J. M. Murphy, D. M. H.
Sexton, and M. J. Webb, 2006: Towards quantifying uncertainty in transient climate change. Climate Dyn., 27, 127–147,
doi:10.1007/s00382-006-0121-0.
Cook, K. H., 2008: The mysteries of Sahel droughts. Nat. Geosci., 1,
647–648.
——, and E. K. Vizy, 2006: Coupled model simulations of the West
African monsoon system: Twentieth- and twenty-first-century
simulations. J. Climate, 19, 3681–3703.
Curry, J. A., J. L. Schramm, and E. E. Ebert, 1995: Sea ice–albedo
climate feedback mechanism. J. Climate, 8, 240–247.
Feichter, J., E. Roeckner, U. Lohmann, and B. Liepert, 2004:
Nonlinear aspects of the climate response to greenhouse gas
and aerosol forcing. J. Climate, 17, 2384–2398.
Folland, C. K., T. N. Palmer, and D. E. Parker, 1986: Sahel rainfall
and worldwide sea temperatures, 1901–85. Nature, 320, 602–607.
——, J. Owen, M. N. Ward, and A. Colman, 1991: Prediction
of seasonal rainfall in the Sahel region using empirical and
dynamical methods. J. Forecast., 10, 21–56, doi:10.1002/
for.3980100104.
Forster, P. M. F., and K. E. Taylor, 2006: Climate forcings and
climate sensitivities diagnosed from coupled climate model
integrations. J. Climate, 19, 6181–6194.
Frame, D. J., and Coauthors, 2009: The climateprediction.net BBC
climate change experiment: Design of the coupled model ensemble. Philos. Trans. Roy. Soc., 367A, 855–870, doi:10.1098/
rsta.2008.0240.
Hagos, S. M., and K. H. Cook, 2008: Ocean warming and latetwentieth-century Sahel drought and recovery. J. Climate, 21,
3797–3814.
Held, I. M., T. L. Delworth, J. Lu, K. L. Findell, and T. R. Knutson,
2005: Simulation of Sahel drought in the 20th and 21st centuries.
Proc. Natl. Acad. Sci. USA, 102, doi:10.1073/pnas.0509057102.
Hoerling, M. J., J. Hurrell, J. Eischeid, and A. Phillips, 2006: Detection and attribution of twentieth-century northern and
southern African rainfall change. J. Climate, 19, 3989–4008.
Houghton, J. T., Y. Ding, D. Griggs, M. Noguer, P. J. van der
Linden, X. Dai, K. Maskell, and C. A. Johnson, Eds., 2001:
Climate Change 2001: The Scientific Basis. Cambridge University Press, 892 pp.
Hulme, M., and M. Kelly, 1993: Exploring the links between
desertification and climate change. Environment, 5, 5–11,
39–45.
Janicot, S., V. Moron, and B. Fontaine, 1996: Sahel droughts and
ENSO dynamics. Geophys. Res. Lett., 23, 515–518.
Jones, A., D. Roberts, M. Woodage, and C. Johnson, 2001: Indirect
sulphate aerosol forcing in a climate model with an interactive
sulphur cycle. J. Geophys. Res., 106 (D17), 20 293–20 310.
Jung, T., L. Ferranti, and A. M. Tompkins, 2006: Response to the
summer of 2003 Mediterranean SST anomalies over Europe
and Africa. J. Climate, 19, 5439–5454.
Kawase, H., M. Abe, Y. Yamada, T. Takemura, T. Yokohata, and
T. Nozawa, 2010: Physical mechanism of long-term drying
trend over tropical North Africa. Geophys. Res. Lett., 37,
L09706, doi:10.1029/2010GL043038.
Knight, J. R., C. K. Folland, and A. A. Scaife, 2006: Climate impacts of the Atlantic Mulitdecadal Oscillation. Geophys. Res.
Lett., 33, L17706, doi:10.1029/2006GL026242.
Kristjánsson, J. E., T. Iversen, A. Kirkevåg, and Ø. Seland, 2005:
Response of the climate system to aerosol direct and indirect
forcing: Role of cloud feedbacks. J. Geophys. Res., 110, D24206,
doi:10.1029/2005JD006299.
5013
Lamb, P., 1978: Large-scale tropical Atlantic surface circulation
patterns associated with sub-Saharan weather anomalies. Tellus, 30, 240–251.
Lau, K. M., S. S. P. Shen, K.-M. Kim, and H. Wang, 2006: A multimodel study of the twentieth-century simulations of Sahel
drought from the 1970s to 1990s. J. Geophys. Res., 111, D07111,
doi:10.1029/2005JD006281.
Los, S. O., G. P. Weedon, P. R. J. North, J. D. Kaduk, C. M. Taylor,
and P. M. Cox, 2006: An observation-based estimate of the
strength of rainfall–vegetation interactions in the Sahel. Geophys. Res. Lett., 33, L16402, doi:10.1029/2006GL027065.
Lu, J., 2009: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought. Climate Dyn., 33, 445–460,
doi:10.1007/s00382-009-0596-6.
Nakićenović, N., and Coauthors, 2000: Special Report on Emissions
Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, 599 pp.
New, M., M. Hulme, and P. Jones, 2000: Representing twentiethcentury space–time climate variability. Part II: Development
of 1901–96 monthly grids of terrestrial surface climate. J. Climate, 13, 2217–2238.
Palmer, T., 1986: Influence of the Atlantic, Pacific and Indian Oceans
on Sahel rainfall. Nature, 322, 251–253.
Romanou, A., B. Liepert, G. A. Schmidt, W. B. Rossow, R. A.
Ruedy, and Y. Zhang, 2007: Twentieth-century changes in
surface solar irradiance in simulations and observations. Geophys. Res. Lett., 34, L05713, doi:10.1029/2006GL028356.
Rotstayn, L. D., and U. Lohmann, 2002: Tropical rainfall trends
and the indirect aerosol effect. J. Climate, 15, 2103–2116.
——, B. F. Ryan, and J. E. Penner, 2000: Precipitation changes in
a GCM resulting from the indirect effects of anthropogenic
aerosols. Geophys. Res. Lett., 27, 3045–3048.
Rowell, D., 2001: Teleconnections between the tropical Pacific and
the Sahel. Quart. J. Roy. Meteor. Soc., 127A, 1683–1706.
——, 2003: The impact of Mediterranean SSTs on the Sahelian
rainfall season. J. Climate, 16, 849–862.
Ruosteenoja, K., T. R. Carter, K. Jylha, and H. Tuomenvirta, 2003:
Future climate in world regions: An intercomparison of
model-based projections for the new IPCC emissions scenarios. The Finnish Environment 644, Finnish Environment
Institute, 83 pp.
Scaife, A. A., and Coauthors, 2009: The CLIVAR C20C project:
Selected twentieth century climate events. Climate Dyn., 33,
603–614, doi:10.1007/s00382-008-0451-1.
Stainforth, D. A., and Coauthors, 2005: Uncertainty in predictions
of the climate response to rising levels of greenhouse gases.
Nature, 433, 403–406.
Stanhill, G., and S. Cohen, 2001: Global dimming: A review of the
evidence for a widespread and significant reduction in global
radiation with a discussion of its probable causes and possible
agricultural consequences. Agric. For. Meteor., 107, 255–278.
Sutton, R. T., and D. Hodson, 2005: Atlantic Ocean forcing of
North American and European summer climate. Science, 309,
115–118, doi:10.1126/science.1109496.
——, B. Dong, and J. M. Gregory, 2007: Land/sea warming ratio in
response to climate change: IPCC AR4 model results and
comparisons with observations. Geophys. Res. Lett., 34, L02701,
doi:10.1029/2006GL028164.
Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century SST trends in the North Atlantic.
J. Climate, 22, 1469–1481.
5014
JOURNAL OF CLIMATE
Twomey, S. A., 1977: The influence of pollution on the shortwave
albedo of clouds. J. Atmos. Sci., 34, 1149–1152.
Vizy, E., and K. Cook, 2001: Mechanisms by which Gulf of
Guinea and eastern North Atlantic sea surface temperature anomalies can influence African rainfall. J. Climate, 14,
795–821.
Williams, K. D., A. Jones, D. L. Roberts, C. A. Senior, and M. J.
Woodage, 2001: The response of the climate system to the
indirect effects of anthropogenic sulfate aerosol. Climate Dyn.,
17, 845–856.
VOLUME 24
Yoshioka, M., N. M. Mahowald, A. J. Conley, W. D. Collins, D. W.
Fillmore, C. S. Zender, and D. B. Coleman, 2007: Impact of
desert dust radiative forcing on Sahel precipitation: Relative
importance of dust compared to sea surface temperature
variations, vegetation changes, and greenhouse gas warming.
J. Climate, 20, 1445–1467.
Zhang, Z. B., F. W. Zwiers, G. C. Hegerl, F. H. Lambert, N. P.
Gillet, S. Solomon, P. A. Stott, and T. Nozawa, 2007: Detection of human influence on twentieth-century precipitation
trends. Nature, 448, 461–465.