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Anthropogenic Aerosols Dominate Forced Multidecadal Sahel Precipitation Change throughDistinct Atmospheric and Oceanic Drivers
HARUKI HIRASAWA AND PAUL J. KUSHNER
Department of Physics, University of Toronto, Toronto, Ontario, Canada

MICHAEL SIGMOND AND JOHN FYFE
Canadian Centre for Climate Change Modelling and Analysis, Environment and Climate Change Canada, Victoria, British
Columbia, Canada

CLARA DESER
National Center for Atmospheric Research, Boulder, Colorado(Manuscript received 1 November 2019, in final form 24 August 2020)
ABSTRACT: Sahel precipitation has undergone substantial multidecadal time scale changes during the twentieth century
that have had severe impacts on the region’s population. Using initial-condition large ensembles (LE) of coupled generalcirculation model (GCM) simulations from two institutions, forced multidecadal variability is found in which Sahel precipitation declines from the 1950s to 1970s and then recovers from the 1970s to 2000s. This forced variability has similar
timing to, but considerably smaller magnitude than, observed Sahel precipitation variability. Isolating the response using
single forcing simulations within the LEs reveals that anthropogenic aerosols (AA) are the primary driver of this forced
variability. The roles of the direct-atmospheric and the ocean-mediated atmospheric responses to AA forcing are determinedwith the atmosphere–land GCM (AGCM) components of the LE coupled GCMs. The direct-atmospheric response arises
from changes to aerosol and precursor emissions with unchanged oceanic boundary conditions while the ocean-mediatedresponse arises from changes to AA-forced sea surface temperatures and sea ice concentrations diagnosed from the AAforced LE. In the AGCMs studied here, the direct-atmospheric response dominates the AA-forced 1970s 2 1950s Saheldrying. On the other hand, the 2000s 2 1970s wetting is mainly driven by the ocean-mediated effect, with some direct
atmospheric contribution. Although the responses show differences, there is qualitative agreement between the AGCMs
regarding the roles of the direct-atmospheric and ocean-mediated responses. Since these effects often compete and show
nonlinearity, the model dependence of these effects and their role in the net aerosol-forced response of Sahel precipitationneed to be carefully accounted for in future model analysis.
KEYWORDS: Africa; Monsoons; Aerosols; Atmosphere-ocean interaction; General circulation models; Multidecadal
variability

1. Introduction
Emissions of anthropogenic aerosols (AAs) and theirchemical precursors have undergone spatially and temporally
complex variations that present an intriguing challenge forunderstanding anthropogenic influence on multidecadal climate variability. One climate variability signal that has been
linked to AA forcing is twentieth-century multidecadal variability of Sahel precipitation, which featured a wet period in
the 1950s, drought conditions that peaked in the 1980s, and amoderate recovery thereafter toward the present day (e.g., Daiet al. 2004; Rodríguez-Fonseca et al. 2015). The coincident
timing of Sahel precipitation with variations of sulfur dioxide
emissions from North America and Europe suggests a

Supplemental information related to this paper is available atthe Journals Online website: https://doi.org/10.1175/JCLI-D-190829.s1.
Corresponding author: Haruki Hirasawa, hirasawa@physics.
utoronto.ca

potential causal link between these precipitation changes andAA forcing. Indeed, the drying effect of AA forcing on the
Sahel has been well established in general circulation models(GCMs) (Rotstayn and Lohmann 2002; Held et al. 2005;
Kawase et al. 2010; Ackerley et al. 2011; Westervelt et al. 2017;
Undorf et al. 2018; Hua et al. 2019). For example, Undorf et al.
(2018) detected a signature of AA forcing on twentiethcentury West African precipitation variability using an ensemble of CMIP5 models, but also found that the model
responses needed to be scaled up to match observations. Uncertainty
remains regarding the extent of anthropogenic influence on
Sahel precipitation due to the inability of GCMs to capture theregion’s climatology and variability (Giannini et al. 2008; Biasutti2019) and due to uncertainty in AA forcing and its forced impact
on the climate (Myhre et al. 2013).
Proposed mechanisms of climate change can be generally be
categorized into two broad pathways: 1) direct-atmosphericpathways that comprise rapid atmospheric responses to radiative and cloud impacts of an external forcing and 2) oceanmediated pathways that comprise atmospheric responses toocean surface anomalies due to internal and externally forced

DOI: 10.1175/JCLI-D-19-0829.1
Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).
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10188JOURNAL OF CLIMATE

variability. [We prefer this ‘‘direct-atmospheric’’/‘‘ocean-mediated’’terminology to the ambiguous ‘‘fast’’/‘‘slow’’ terminology (e.g.,Andrews et al. 2009; Biasutti 2013; Li et al. 2018) since rates ofclimate response to external forcing can range from weeks tomillennia.]
Determining the roles of direct-atmospheric versus oceanmediated mechanisms has been central to understanding thedrivers of the late-twentieth-century Sahel drought. Charney(1975) hypothesized that the observed Sahel drought was a
direct-atmospheric response to increasing surface albedo fromovergrazing in the region. Although land cover changes areimportant for understanding climate changes in the region,such as the Green Sahara period (e.g., Braconnot et al. 1999;
Pausata et al. 2016), overgrazing is insufficient to account for
the twentieth-century drought (Biasutti 2019). Further work
instead found that Sahel precipitation variability, and thetwentieth-century drought in particular, is predominantly a
response to large-scale variations in global sea surface temperature (SST) (Lamb 1978; Folland et al. 1986; Palmer 1986), aview that has come to be widely accepted in the field (Giannini
et al. 2008; Rodríguez-Fonseca et al. 2015; Biasutti 2019).
Decomposing Sahel climate change into its directatmospheric and ocean-mediated components highlights thecomplexities of the region’s response to external radiative
forcing. The two components of the response to greenhousegas (GHG) forcing tend to have opposing effects on Sahel
precipitation, exacerbating uncertainties in projected futurechanges for the region (Biasutti 2013; Gaetani et al. 2017).
Furthermore, Dong and Sutton (2015) found that much of the
precipitation recovery since the 1980s is due to the directatmospheric response to GHGs. In the case of AA forcing, theSahel precipitation response to increasing AA forcing is often
interpreted as a primarily ocean-mediated response. AA
forcing is thought to cause Sahel drying by cooling the North
Atlantic relative to the South Atlantic, shifting the intertropical convergence zone southward (Ackerley et al. 2011; Mohino
et al. 2011; Hwang et al. 2013; Wang 2015; Hua et al. 2019;
Bonfils et al. 2020), or by cooling the North Atlantic relative tothe global tropical ocean, reducing the West African monsoon’s ability to supply sufficient heat and moisture to meet thethreshold for convection (Giannini et al. 2013; Giannini and
Kaplan 2019). However, studies of atmosphere–land GCM(AGCM) simulations have also found that there is a directatmospheric Sahel drying response to AAs even in the absenceof an ocean-mediated response (Dong et al. 2014; Richardson
et al. 2016). Furthermore, studies of the Asian monsoon havealso found that AA forcing causes direct-atmospheric andocean-mediated responses that have different, and in placescompeting, roles in regional precipitation changes (Ganguly
et al. 2012; Li et al. 2018). Thus, there is a need to study thesecomponents of the response together to assess their relative
contributions to the AA-forced effect on past Sahel drought.
We seek here to robustly characterize the climate responseof Sahel precipitation to AA forcing since the 1950s, and to
identify the roles of direct-atmospheric and ocean-mediatedresponses in the coupled response. We take a two-part approach. First, we examine historical climate variability in initialcondition large ensembles (LEs) from two independently

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developed coupled GCMs. Subsets of these large ensembles
are forced by different combinations of external forcing agents.
Using different combinations of radiative forcing allows us to
identify the effects of individual forcing agents; using LEs allows us to do so in a way that separates forced signals frominternal variability; and using independent GCMs allows us to
examine whether forced signals so obtained are robust between the models. In particular, we aim to isolate the role of
AAs in the externally forced response of Sahel precipitation in
the historic period. Second, we conduct AGCM experiments
focused on three epochs in the latter half of the twentiethcentury, with the AGCM components of the coupled GCMs
used for the LEs. We choose three epochs that enable us topartially resolve the increase and subsequent decline of AA
emissions from regions like North America and Europe. The
AA-forced SST and sea ice concentration (SIC) anomalies
from the LEs are used to perturb oceanic boundary conditions
for the AGCM experiments. The use of LEs is critical, as thepatterned SST response to AA forcing can be relatively weak
and thus obscured by internal variability (Oudar et al. 2018).
These experiments allow us to investigate the roles of thedirect-atmospheric and ocean-mediated responses in AAforced multidecadal variability of Sahel precipitation sincethe 1950s. Here again, we will be able to compare the AGCM
responses in two separate models to get a sense of the robustness to model formulation.
Section 2 provides details on the large ensemble coupled
GCM and the AGCM simulations analyzed in this study. In
section 3, we analyze the LE simulations and the coupled climate response to AA forcing in the Sahel. In section 4, weanalyze the AGCM simulations and the resultant directatmospheric and ocean-mediated responses. Concluding remarks are made in section 5.

2. Methods
a. Coupled general circulation model large ensemble
simulations
We use large initial condition ensembles (LE) of coupled
ocean–atmosphere GCM simulations from the National
Center for Atmospheric Research–Department of Energy
Community Earth System Model 1 (NCAR–DOE CESM1)(Kay et al. 2015) and the Canadian Centre for Climate
Modeling and Analysis Canadian Earth System Model 2(CCCma CanESM2) (Kirchmeier-Young et al. 2017; Kushneret al. 2018). Aerosol levels in both models are set by prescribing emissions of aerosols (e.g., black carbon) and aerosol
precursors (e.g., sulfur dioxide) of anthropogenic origin. The
simulations used in this study are summarized in Table 1.
In addition to the original historical forcing LE simulations,
there are LE simulations in both models that are configured toisolate the effects of AA forcing. From the CESM1 LE weanalyze 35 all-forcing (ALL) and 20 all-but-aerosol forcing(XAER) simulations that are carried out at a nominal 18 resolution (Deser et al. 2020). In the XAER simulation, external
forcings follow their historical trajectory except for industrial
AA and their precursor emissions, which are held fixed to 1920
conditions (Deser et al. 2020). Notably, biomass burning (BMB)

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HIRASAWA ET AL.
TABLE 1. A summary of the coupled ocean–atmosphere GCM simulations used in this study.

Model

Simulation
name

Anthropogenic
aerosol emissions

Biomass burningemissions

Other external
forcings

N

Years

CESM1
CESM1
CESM1
CESM1
CanESM2
CanESM2

ALL
XAER
XBMB
AER
ALL
AER

Historical
1920
Historical
Historical
Historical
Historical

Historical
Historical
1920
Historical
Historical
Historical

Historical
Historical
Historical
Pre-industrial
Historical
Pre-industrial

35
20
15
3
50
50

1920–2080
1920–2080
1920–2029
1850–2005
1950–2020
1950–2020

aerosol emissions are not grouped with AA in the XAER
simulations. Thus, we also analyze a set of 15 all-but-biomassburning aerosol (XBMB) simulations from the CESM1 LE toassess the impact of BMB aerosols. These simulations areanalogous to the XAER simulations, but for BMB aerosol
emissions. The impact of AA forcing is determined by taking
the difference of the ensemble mean of the ALL minus the
XAER simulations. To obtain the ensemble spread of ‘‘aerosol
only’’ simulations for CESM1, individual AER ensemble
members are calculated as
AERi 5 (XAERi 2 XAERem ) 1 (ALLem 2 XAERem ),where subscript i denotes an individual ensemble member andsubscript ‘‘em’’ denotes the ensemble mean (Deser et al. 2020).
From the CanESM2 LE we analyze 50 all forcing (ALL)simulations and 50 AA-only simulations (AER) that are carried out at T63 resolution (;28 resolution). In contrast to the
XAER simulations, in the AER simulations AAs and theirprecursor emissions vary along their historical trajectories
while all other external forcings are set to preindustrial conditions. Unlike the CESM1 LE, BMB aerosol emissions areincluded with industrial AAs in the CanESM2 LE AER
simulations.
Both the ALL and the XAER LEs in CESM1 were initialized using round-off level atmospheric perturbations from asingle all-forcing historical simulation at 1920. The latter was inturn initialized at 1850 from a long 1850 control integration(Kay et al. 2015; Pendergrass et al. 2019; Deser et al. 2020).
As a result, all the simulations in the CESM1 ALL and XAER
ensembles have the same ocean conditions at their initialization in 1920. The ALL and AER CanESM2 LE simulations are
initialized by using atmospheric perturbations to branch 10
simulations at 1950 from each simulation in two smaller fivemember ALL and AER forcing ensembles. These original
simulations are initialized at 1850 by randomly sampling
ocean–atmosphere states from a preindustrial control simulation (Kirchmeier-Young et al. 2017; Kushner et al. 2018).
If the forced responses to different external forcings wereapproximately additive, the two simulation protocols for isolating the response to AA would be approximately equivalent.
We partially test for nonadditivity using a small three-member
ensemble of historical AER simulations in CESM1. Note thatthe emission changes differ in the two CESM1 ensembles, as
BMB aerosols are included in the small aerosol-only CESM1
ensemble (Table 1). Additionally, the three-member ensemble

was conducted in the CMIP5 CESM1-CAM5 version of
CESM1, which differs slightly from the CESM1-LE version(Kay et al. 2015).
Precipitation variability in the LEs are evaluated against the
Global Precipitation Climatology Centre precipitation analysis(Schneider et al. 2015), the Climate Research Unit Time series(CRU TS) (University of East Anglia Climatic Research Unit
2013), the University of Delaware Terrestrial Precipitation(U. Delaware) (Willmott and Matsuura 2001), and the National
Oceanic and Atmospheric Administration Precipitation
Reconstruction over Land (PREC/L) (Chen et al. 2002).
Further analysis is conducted comparing a set of transient
AGCM simulations from CAM5 to CESM1 and CanAM4 to
CanESM2 in which SST/SIC conditions are set to their historical observed conditions and external forcing agents varyalong their historical trajectories. For CanAM4 we analyze
four AMIP simulations carried out from 1950 to 2009 (vonSalzen et al. 2013). For CAM5 we analyze 50 C20C1 simulations carried out from 1959 to 2012 (Stone et al. 2018). Notethat aerosol levels are prescribed by concentration in the
CAM5 C20C1 simulations rather than by emissions as in
CESM1 and the CAM5 AGCM simulations described below.
Both CESM1 and CanESM2 include prognostic aerosol
schemes that differ in their representation of aerosol processes
such as formation, deposition, and cloud interactions. CESM1
uses the Modal Aerosol Model version 3 (Liu et al. 2012) andincludes representations of both the aerosol–cloud albedo(Twomey 1977) and aerosol–cloud lifetime effects (Neale et al.
2012). CanESM2 uses a bulk aerosol model and only represents the aerosol-cloud albedo effect (von Salzen et al. 2013).
This results in differing aerosol effective radiative forcings(ERFs) with CESM1 at 21.37 W m 22 and CanESM2
at 20.84 W m22 (Zelinka et al. 2014). It has been found that
CESM1 overestimates the enhancement of cloud liquid water
path in response to aerosol perturbations from naturally occurring tropospheric volcanic aerosols (Malavelle et al. 2017),
which suggests the model may overestimate the negative radiative forcing due to the aerosol–cloud lifetime effect (Tollet al. 2019). CESM1-CAM5 and CanESM2 were identified inMonerie et al. (2017) as having similar patterns of Sahel-precipitation response to projected GHG forcing. However, as
GHG radiative forcing largely affects the longwave and the spatial
patterns of the GHG and AA forcings differs, this finding does notnecessarily apply to the historical response to AA forcing.
We focus our analysis and additional simulations on three
periods during the late twentieth century: 1950–59 (1950s),

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TABLE 2. Descriptions of the CAM5 and CanAM4 AA and precursor emission and SST/SIC conditions in the AGCM simulations. The
2000s climatological SST/SIC were obtained from the HadISST dataset.
Years
Expt
1
2
3
4
5
6
7

Anthropogenic
aerosol emissions

SST/sea ice

CAM5

CanAM4

2000s
1970s
2000s
1970s
1950s
2000s
1950s

2000s
2000s
2000s 1 (1970s–2000s SST/sea ice from LENS)
2000s 1 (1970s–2000s SST/sea ice from LENS)
2000s
2000s 1 (1950s–2000s SST/sea ice from LENS)
2000s 1 (1950s–2000s SST/sea ice from LENS)

100
100
50
50
100
50
50

100
100
100
100
100
100
100

1970–79 (1970s), and 2000–09 (2000s). The 1950–59 period is
the initial decade of the CanESM2 LE simulations andrepresents a time of relatively weak AA forcing and high Sahel
precipitation. The 1970–79 period marks the peak of sulfur
dioxide emissions in North America and Europe (NA/EU),and thus strong sulfate AA forcing in those regions (Smithet al. 2011). Sulfur dioxide emissions in these regions subsequently declined due to air quality regulations toward the nearpresent-day 2000–09 period while emissions from Asia continued to increase. Thus, we study two distinct regimes in thehistory of sulfur dioxide emissions (and thus sulfate aerosol
burdens): 1) the 1970s minus the 1950s (drying period), which
is a period of drying in the Sahel region along with strong increases in AA-related emissions from NA/EU and moderateincreases from Asia; 2) and the 2000s minus the 1970s (recovery
period), which is a period of precipitation recovery in the Sahelalong with a decline in emissions from NA/EU and continued
emissions increases from Asia.

b. Atmospheric general circulation model simulation
To separate the roles of the direct-atmospheric response andthe ocean-mediated response in the total coupled response to AA
forcing we perform a set of ‘‘time-slice’’ AGCM experiments
using the atmospheric and land components of the LE models: theCommunity Atmosphere Model version 5 (CAM5) for CESM1(Neale et al. 2012) and the Canadian Atmosphere Model version
4 (CanAM4) for CanESM2 (von Salzen et al. 2013). Time-slice
AGCM simulations use boundary conditions that vary seasonally,
but do not vary from year to year. The simulation descriptions are
summarized in Table 2. Similar protocols have been used in
Ganguly et al. (2012) and Li et al. (2018) to study the Asianmonsoon response to AA forcing. The simulations described here
provide two advantages over previous work:
They provide more information about the multidecadal
evolution of the precipitation signal by comparing three
decades rather than the effect of change between two epochs
such as the preindustrial to present-day change.
d They utilize initial condition large ensembles to filter internal climate variability in SST and sea ice. Thus, by applying
these anomalies as perturbations to the atmosphere componentsof the coupled models, it is possible to obtain model-consistentestimates of the direct-atmospheric and ocean-mediated responses such that the AGCM simulations approximate the response from their respective coupled LE simulations.
d

The control simulation (Exp 1) is set to seasonally varying
observed conditions averaged over 2000–09 globally, with the
SST and sea ice concentration climatology calculated using the
Hadley Centre Global Sea Ice and Sea Surface Temperaturedataset (Rayner et al. 2003). AA emissions are averaged over
2000–09 with data pre-2005 using historical emissions and post2005 using RCP8.5 projected emissions. Other forcing conditions,such as GHG and land use, are set to year 2000 conditions.
We then perturb the boundary conditions away from thiscontrol simulation and obtain the response of the 2000s 2 1970s(2000s 2 1950s) by changing aerosol emissions in Exp 2 (Exp 5),
perturbing the SST/SIC conditions using AA-forced LE anomalies in Exp 3 (Exp 6), or applying both changes in Exp 4 (Exp 7).
The response for the 1970s 2 1950s is then calculated as thedifference between the 2000s 2 1950s and 2000s 2 1970s responses and thus is noisier than the 2000s 2 1970s response.
In the AGCM simulations, the direct-atmospheric response
is obtained by modifying the aerosol and aerosol precursor
emissions to the levels of the target decade. For CAM5, we donot modify BMB aerosol emissions as they are not included
with industrial AA emissions in the CESM1 LE XAER simulations. For CanAM4, BMB aerosol emissions are included asthey are also included in the CanESM2 LE AER simulations.
The boundary conditions for the AGCM simulations thatprovide the ocean-mediated response are obtained by calculating the ensemble and time mean SST and SIC anomalies due
to AA forcing in the coupled LEs for each month of the year
for the 2000s minus the target decade, then subtracting theseanomalies from the observed climatology. The SST and SIC
fields are then adjusted in sea ice regions to ensure physicalconsistency between the two fields (Hurrell et al. 2008). Sea ice
thickness is not modified in these simulations.
By using the anomalies from each set of LE simulations as
perturbations in their respective atmosphere components, we
obtain a combined direct-atmospheric plus ocean-mediatedresponse that is, in principle, equivalent to the total coupled
response of the LE simulations. However, the match between
the AGCM and coupled simulations will be imperfect due tomissing feedbacks from the lack of ocean–atmosphere coupling in the AGCM experiments, the use of time-slice rather
than transient forcing, and the nonadditive or state-dependent
effects introduced by the construction of the SST/SIC boundary forcing (in which perturbations are applied to the observed
SST/SIC climatology rather than the model climatology, which

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HIRASAWA ET AL.

would provide a cleaner comparison to the coupled results).
The ability of the AGCM experiments to replicate the LE
response is evaluated in section 4a.
The two boundary condition perturbations are applied
separately and combined in both models for the 2000s minus
1950s and 2000s minus 1970s giving a total of seven time-slice
simulations (including the control) for each model which
are each at least 50 years in length (Table 2). The directatmospheric and ocean-mediated responses can be calculated
in two ways (Table S1 in the online supplemental material).
For example, if we consider the direct-atmospheric response
for the 2000s 2 1970s, it can be calculated as EXP 1 minus EXP
2 in which we change AA emissions with a background ofcontrol SST/SIC (CTRL SST), as well as EXP 3 minus EXP 4 inwhich we change AA emissions with a background of SST/SIC
perturbed with the AA-forced anomalies (PERT SST). Thus,
we obtain the direct-atmospheric response with two different
SST/SIC background conditions. Similarly, the ocean-mediatedresponse can be obtained with control (CTRL EMIS) or perturbed(PERT EMIS) AA emissions. We evaluate the nonlinearity in the
AGCM responses by comparing different combinations of simulations in section 4. However, for Figs. 6–11 (shown later) both
combinations are averaged together in order to maximize thestatistical sampling of the responses. The full list of combinations
used is listed in Table S1.

c. Moisture budget analysis
Analysis of the atmospheric moisture budget is conductedfollowing the moisture convergence (MC) method of Li andTing (2017), wherein a detailed derivation of the following
equations can be found. The steady state balance of atmospheric moisture is approximated as
D(P 2 E) ’ dMC 1 dED 5 dTH 1 dDY 1 dED,
where precipitation P minus evaporation E anomaly is approximated as the sum of a mean flow (dMC) and an eddy(dED) component of the moisture convergence change, withthe mean flow component further divided into the mean flow
thermodynamic (dTH) and dynamic (dDY) terms. These twoterms are calculated asdTH 1 dDY 5

K
K
21
1
=  å uk,C dqk Dpk 2=  å duk qk,C Dpk ,grw k51
grw
k51

with gravitational acceleration g, density of water rw, verticalpressure level k, total vertical levels K, pressure thickness oflevel k Dpk, horizontal wind vector at level k uk, and specifichumidity at level k qk. All variables represent the climatological average of monthly mean values, ()C indicates the timeaveraged value in the control simulation and the d symbol indicates the response calculated as d() 5 ()F 2 ()C, where ()F is
the time-averaged value in the perturbed simulation.
In these equations, responses to different forcings in thelong-term mean of the time-slice AGCM simulations areinterpreted as anomalies with respect to a control climatology.
The mean flow thermodynamic term (dTH) represents thecontribution of anomalous specific humidity (dq) being

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advected by the climatological winds (uC) and the mean flow
dynamic term (dDY) represents the contribution of anomalous
winds (du) advecting the climatological specific humidity (qC).
We calculate dTH and dDY and assume the residual between
these two terms and the P 2 E is dominated by the dED term,
thus neglecting the quadratic term associated with covarying u
and q anomalies. That is, products of the response are neglected in this simple budget.

3. Coupled GCM large ensemble responses
July–September (JAS) precipitation over the Sahel undergoes significant forced multidecadal changes over the latetwentieth century in CESM1 (Fig. 1) and CanESM2 (Fig. 2)
LEs. In both models, the ensemble mean of the ALL forcing
simulations exhibits a JAS drying across much of Northern
Hemisphere Africa in the drying period (1970s 2 1950s)(Figs. 1a and 2a) and a wetting over the Sahel and drying overthe Guinea coast in the recovery period (2000s 2 1970s)(Figs. 1c and 2c). Comparison of the ALL forcing ensembles to
the AA forcing simulations shows a close correspondence between the response to ALL and AA forcings. This indicatesthat AA forcing is the predominant cause of the 1970s 2 1950s
forced drying (Figs. 1b and 2b) and a substantial contributor to
the 2000s 2 1970s forced recovery (Figs. 1d and 2d). The ALL
forcing response sees larger increases in precipitation compared to the AA forcing response in the recovery period inboth models, suggesting that strengthening GHG forcing
contributes to the recovery of precipitation, as discussed inprevious studies (e.g., Dong and Sutton 2015; Giannini and
Kaplan 2019). This GHG effect on the recovery is stronger in
CanESM2 (Fig. 2c vs Fig. 2d) than in CESM1 (Fig. 1c vs Fig1d), especially in the Sahel. Both models simulate similar
patterns of precipitation response to AA forcing over the region, although the local maximum of the CanESM2 response
occurs south of the Sahel. Analysis of the signal to noise of the
precipitation change suggests that the minimum ensemble size
required to detect a statistically significant response to AA
forcing using the t test requires as many as 20 ensemble
members over much of the Sahel for the 1970s 2 1950s(Fig. S1), reinforcing the need for LEs to identify forced signals
on multidecadal time scales.
The transient behavior of this climate signal is shown in
Fig. 3, which displays the 11-yr moving average of JAS precipitation averaged over the Sahel (108–208N, 208W–358E excluding ocean grid points; see the blue boxes in Figs. 1a,b and2a,b). There is close correspondence of the ALL and AER
ensemble means in both CESM1 (Fig. 3a) and CanESM2(Fig. 3b) with a good degree of agreement between the two
coupled GCMs in the timing and amplitude of the forced
variability. CanESM2 has relatively small intraensemble variability compared to CESM1 in this region, which is in part due
to the largest precipitation anomalies occurring south of the
Sahel in CanESM2 (Fig. 2). This in turn may be due to thelatitudinal peak of climatological precipitation over Africa
occurring further south in CanESM2 relative to CESM1(Fig. S2). The dominance of AA forcing in the externallyforced variability seen here is consistent with the analysis ofUndorf et al. (2018), who detected an AA-forced signal in West

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10192JOURNAL OF CLIMATEVOLUME 33

FIG. 1. Ensemble mean JAS precipitation anomalies from the CESM1 large ensemble simulations for the (top)1970s 2 1950s and (bottom) 2000s 2 1970s in (a),(c) CESM1 ALL and (b),(d) ALL-XAER. Stippling surrounded
by a thin red contour indicates grid points whose responses are statistically significant at the 95% level. The blue box
indicates the averaging region used in Figs. 3 and 12, which includes notches on the western edge as a result ofmasking out ocean grid boxes.

African monsoon precipitation in the CMIP5 multimodel ensemble. The CanESM2 ALL and AER ensemble means diverge somewhat in the 2000s, suggesting an increased wetting
effect due to GHG. Nonetheless, AA forcing has the largest
contribution to the recovery in both models, in contrast to thefindings of Dong and Sutton (2015) who saw a dominant role ofthe direct-atmospheric response to GHG in the recovery ofSahel precipitation in their AGCM simulations. In CESM1, theAA forcing response drives a 20.14 mm day21 drying for the
1970s 2 1950s and a 0.13 mm day21 wetting for the 2000s 2 1970s.

FIG. 2. As in Fig. 1, but for the CanESM2 large ensemble’s (a),(c) ALL and (b),(d) AER simulations.

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FIG. 3. The 11-yr central moving average Sahel regional average July–September (JAS) precipitation anomalies
relative to the 1950–99 mean (108–208N and 208W–358E, excluding ocean grid points) from the (a) CESM1 and(b) CanESM2 large ensembles. The ensemble means of the LEs are shown in solid red (all forcing response) andblue (AA forcing response) lines where the AA forcing response is calculated as the ensemble mean of ALLXAER for CESM1 and ensemble mean of AER CanESM2. Shading indicates the 5th–95th percentile ranges ofeach large ensemble. The ensemble mean of a smaller three-member ensemble of AA-only simulations in CESM1CMIP5 (cyan) is plotted in (a). The 11-yr central moving average observed JAS precipitation anomalies from CRU
TS (dashed), NOAA PREC/L (dash-dotted), GPCC (solid), and U. Delaware (dotted) are overlaid on the modeled
anomalies in black.

As a fraction of the ALL forcing response, these are 118% and102% of the forced response in their respective periods. In
CanESM2, the AA forcing response drives a 20.11 mm day21
drying for the 1970s 2 1950s and a 0.17 mm day21 wetting for the
2000s 2 1970s, which are 85% and 96% of the ALL-forcing response in their respective periods.
Calculating the minimum precipitation year in the 11-yr
rolling averaged JAS precipitation time series for individual
ALL ensemble members between 1950 and 2020, we find theaverage year of peak drying conditions among members of theALL forcing LEs is 1977 for both CESM1 and CanESM2 withstandard deviations of 12 and 10 years, respectively. Thus, theobserved peak drought timing of ;1986 (with some spreadamong the observational datasets) is consistent with the internal variability of the LEs. However, a comparison to theobserved precipitation records shows that both CESM1 and
CanESM2 substantially underestimate the magnitude of themultidecadal variability (black curves in Fig. 3), as the precipitation anomalies for all four observation datasets fall outside the range given by the ensembles. The inability of the
CESM1 LE to capture this multidecadal variability is also
shown by McKinnon and Deser (2018), who found that the
CESM1 LE underestimates the variability of 50-yr JJA precipitation trends throughout sub-Saharan Africa relative to an
observational LE derived estimate of internal variability. Thisis a deficiency that is common among CMIP5 models (Biasutti
2013; Undorf et al. 2018), although some coupled models areable to produce larger precipitation trends (Held et al. 2005;
Ackerley et al. 2011).
Analyses of AMIP simulations which are forced by historicalobserved SSTs and sea ice have found that some models areable to capture multidecadal variability of a similar magnitude
to observations (Giannini et al. 2003; Lu and Delworth 2005;
Held et al. 2005; Caminade and Terray 2010; Hoerling et al.

2006). However, the ability to reproduce the observed response varies among AGCMs. For example, an analysis ofmultiple AGCMs by Scaife et al. (2009) found that only oneof the models they analyzed could reproduce the magnitude of
the more severe 1950–80 drying trend. We are able to update
some of this analysis with simulations carried out for the
AGCMs we studied here. Comparing the Sahel precipitation
time series for CAM5 C20C1 (Fig. S3a) and CanAM4 AMIP
(Fig. S3b) simulations to the ALL LE simulations, we find that,compared to the LE, CAM5 C20C1 sees larger multidecadal
variability that better matches the observed variability while
CanAM4 AMIP sees relatively little change in its decadal
variability. Thus, it appears that CESM1 underestimates theobserved variability in large part because it does not capture
historical coupled ocean–atmosphere SST variability, while for
CanESM2 there is a lack of sensitivity of Sahel precipitation to
SST anomalies even in the absence of possible errors in SSTvariability.
As mentioned in section 2, for CESM1 we can partiallycheck the additivity assumption that the responses to AER areequivalent to the ALL responses minus the XAER responses.
Figure 3a shows the three-member AER ensemble along with
the ALL minus XAER ensemble. The two protocols have
similar Sahel precipitation trajectories, suggesting that there isreasonable additivity between the response to AA forcing andother external forcings such as GHG for this variable and region. In addition, we find that the regional drying does notappear to be amplified in the ALL simulations (where GHG
and AA are both present) relative to the AER simulations in
CESM1 and CanESM2. Thus, these models do not show direct
evidence for enhanced drying in response to combined GHG
and AA forcing as proposed by Giannini and Kaplan (2019).
Figure 4 displays the JAS mean sulfate (SO4) burden
anomalies from the CESM1 and CanESM2 LEs. The 1970s 2 1950s

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10194JOURNAL OF CLIMATEVOLUME 33

FIG. 4. JAS mean sulfate burden anomalies for the (top) 1970s 2 1950s and (bottom) 2000s 2 1970s from (a),(c)CESM1 ALL–XAER LE and (b),(d) CanESM2 AER LE.

period is characterized by widespread increases in sulfate, especially in the North Hemisphere near and downwind ofEuropean and North American industrial regions (Figs. 4a,b).
There is a local increase of sulfate over the Sahel during thisperiod due in part to emissions being transported from Europe.
The sulfate anomalies are transported farther in CanESM2,
resulting in greater anomalies in the North Atlantic, North
Pacific, and Arctic relative to CESM1. For the 2000s 2 1970s,
there are decreases in sulfate burdens in much of the North
Hemisphere extratropics associated with declining North
American and European emissions. Continued increases occur
in East Asia, South Asia, and the Middle East. Locally, there
is a decline in concentrations over the Sahel which is likely due
to decreasing transport from Europe, offset by increasing local
emissions and transport from the Middle East.
Black carbon (BC) burdens increase locally over the Sahel inboth periods (Fig. S4), with much larger increases in CanESM2
because of the inclusion of BMB emissions. BC burdens increase over Asia in both periods and decrease over eastern
North America and western Europe in both periods. The effectof BMB emissions is evaluated using the CESM1 LE XBMB
simulations. BMB changes result in large BC burden increases
over central Africa in the drying period (Fig. S5b). However,unlike CanESM2, there is no decline in BC burdens in therecovery period (Fig. S5d), indicating there is some discrepancy in emissions between the models. In addition, we see increasing sulfate burdens from central Africa in the drying
period (Fig. S5a) and declining burdens over the Europe andNorthern Africa in the recovery period (Fig. S5c), althoughthese BMB anomalies are an order of magnitude smaller than
the anomalies due to industrial emissions in Fig. 4. BMB
aerosol changes result in increased precipitation in the Guinea
region in both periods (Figs. S6a,c), with a small contribution
to the aerosol-forced drying and recovery of Sahel precipitation. Thus, industrial AA emissions are the main drivers ofAA-forced Sahel precipitation change in CESM1, with BMB

emissions playing a minor role in amplifying the overall
aerosol-forced signal in the Sahel.
Figure 5 displays the JAS mean AA-forced SST and SIC
responses from the LEs in the drying and recovery periods.
These are representative of the boundary conditions used toforce the AGCM simulations described in section 2b (Table 2;
see also Table S1). Both CESM1 and CanESM2 have broadly
similar SST anomalies, although CESM1 sees larger anomalies
due in part to the larger aerosol ERF in the model (Zelinka
et al. 2014). In the drying period, there is widespread coolingthat is strongest in the Northern Hemisphere. In the recovery
period, the SST anomalies change sign in the North
Hemisphere, with both models showing warming in the North
Atlantic and the midlatitude and subpolar North Pacific.
However, there is continued cooling in the Indian Ocean andtropical west Pacific, downwind of Asian regions that undergo
continued AA emission increases. Arctic SIC responses follow
the Northern Hemisphere SST response, with an increase in
Arctic SIC in the drying period and a decline in the recovery
period. In CESM1 we see an increase in SIC in parts of the
Arctic during the recovery period. This appears to be an artifact of subtracting anomalies from the ALL and XAER simulations, which both lose sea ice but do so in different regions
due to their differing sea ice covers. Changes to Antarctic SIC
and Southern Ocean SST are small as AA forcing is weak inthis region.
The AA-forced multidecadal SST change in the North
Atlantic in the LEs (Fig. 5) results in a reversal of the hemispheric asymmetry of SST anomalies between the two periods.
Based on previous arguments (e.g., Ackerley et al. 2011), thisreversal of the hemispheric asymmetry of SST anomalies bothin the Atlantic and globally would appear to be responsible forthe reversal of AA-forced Sahel precipitation anomalies.
However, past studies in coupled models could not explicitly
establish a causal link between the hemispheric asymmetry and
Sahel precipitation as this ocean-mediated effect was not

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FIG. 5. As in Fig. 4, but for the AA-forced SST anomalies. Sea ice concentration anomalies are displayed in purple
contours in 2.5% intervals.

isolated from the direct-atmospheric effect. Thus, we seek tobetter understand the mechanisms underlying the coupled
GCM responses by conducting AGCM experiments to separate the influence of SSTs from the influence of the aerosols
themselves. Indeed, a key takeaway from the AGCM experiments discussed below is that the direct-atmospheric response
to AA forcing plays a substantial and at times competing rolewith the ocean-mediated response. In particular, it is the directatmospheric response rather than the ocean-mediated responsethat mainly drives the 1970s 2 1950s drying in CAM5 and
CanAM4.

4. Direct-atmospheric and ocean-mediated responses
a. Evaluation of AGCM simulations
We hypothesize that the total AA response (directatmospheric plus ocean-mediated response) in our prescribed
SST/sea ice AGCM simulations is close to the coupled response to AA forcing from the LE simulations as the atmosphere in both cases experience approximately the same
perturbations. To evaluate the ability of the AGCM simulations to reproduce the coupled LE response in light of potential
errors due to the AGCM protocol and fixed-SST conditions,the two sets of simulations are compared for the 1970s 2 1950s
in Fig. 6 and the 2000s 2 1970s in Fig. S7. We find that for the
1970s – 1950s the CAM5 simulations (Fig. 6c) can reasonably
reproduce the JAS African precipitation response from the
CESM1 LE (Fig. 6a), while the CanAM4 (Fig. 6d) simulations
only capture part of the drying seen in the CanESM2 LE(Fig. 6b). The spatial correlation between the LE and AGCM
simulations over global land (African land) is 0.43 (0.42) for
CAM5 and 0.35 (0.14) for CanAM4. The agreement between
the LE and AGCM improves for the 2000s 2 1970s (Fig. S7),
with spatial correlations for global land (African land) being
0.68 (0.68) for CAM5 and 0.51 (0.63) for CanAM4. This may be
because the 1970s 2 1950s response is noisier as it is calculated

as the difference of two experiments. Although there are discrepancies in the pattern of response, the regionally averaged
Sahel precipitation anomalies in the AGCM simulations are
consistent with those in the coupled LE simulations, as we
discuss below (section 4d; Fig. 12).
Thus, while there are errors arising from the idealized natureof the AGCM simulations, the CAM5 simulations can capture
the broad characteristics of the response. However, theCanAM4 simulations can only do so in the recovery period.
Furthermore, CanAM4 is unable to reproduce historical Sahel
precipitation variability given observed SST change, suggesting Sahel climate in this model is insufficiently sensitive to SST
forcing (Fig. S3b). Thus, the subsequent discussion first centerson the CAM5 AGCM simulations, which provide the clearest
signals that are most consistent with the coupled model response. We then return to the CanAM4 simulations, which
provide intriguing points of comparison with CAM5.

b. CAM5 results
The CAM5 experiments reveal that distinct mechanisms
drive the precipitation changes during the drying and recovery
periods in the Sahel (Fig. 7). For the 1970s 2 1950s, the drying
seen across much of NH Africa in the total response is dominated by the direct-atmospheric response (Fig. 7b), with aweak ocean-mediated wetting over the Sahel (Fig. 7c). Thispattern of direct-atmospheric drying is consistent with otherstudies of the direct-atmospheric response to sulfate aerosol
forcing (Dong et al. 2014; Richardson et al. 2016). However,the dominance of the direct-atmospheric response contrasts
with the view that AA-forced Sahel drying is a primarily oceanmediated response caused by a southward shift of the ITCZ in
response to hemispherically asymmetric SST anomalies (e.g.,
Biasutti and Giannini 2006; Ackerley et al. 2011; Hua et al.
2019). One potential explanation for the lack of oceanmediated precipitation response in this period may be cancelling effects from SST anomalies in different basins. In CESM1Brought to you by University of Colorado Libraries | Unauthenticated | Downloaded 07/06/21 08:35 PM UTC

10196JOURNAL OF CLIMATEVOLUME 33

FIG. 6. Comparison of the AA-forced 1970s 2 1950s JAS precipitation anomalies for the coupled LE (a) CESM1
and (b) CanESM2 compared to the total (direct-atmospheric 1 ocean-mediated) response in their respective atmosphere models: (c) CAM5 and (d) CanAM4. Note that (a) and (b) are the same as Figs. 1b and 2b, respectively.
Stippling and thin red contour indicates responses that are significant at the 95% level using a point-wise t test. The
blue boxes indicate the averaging region used in Fig. 12.

for this period (Fig. 5a), the aerosol radiative cooling gives
rise to patterned cooling of comparable strength in the
North Atlantic, the tropical Atlantic and Pacific, and theIndian Ocean. Previous literature suggests that such regional
responses could cause interfering responses in the Sahel: coolNorth Atlantic and Mediterranean SSTs are associated withdrying in the Sahel while cool Indian Ocean, cool tropicalAtlantic, and warm east tropical Pacific SSTs are associatedwith wetting in the Sahel (Giannini et al. 2008; Mohino et al.
2011; Dyer et al. 2017).
A simplified picture of this cancellation can be evaluatedusing the North Atlantic Relative Index (NARI), which isdefined as the spatial average of SSTs in the subtropical North
Atlantic (108–408N, 758–158W) minus tropical ocean (208S–208N)

FIG. 7. (left) Total, (center) direct-atmospheric, and (right) ocean-mediated components of the JAS precipitation response to AA
forcing in CAM5 for the (a)–(c) 1970s 2 1950s and (d)–(f) 2000s 2 1970s. The stippling and the thin red contours indicate responses thatare significant at the 95% level using a point-wise t test. The blue boxes indicate the averaging region used in Fig. 12.

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FIG. 8. As in Fig. 7, but for JAS vertical velocity v zonally averaged over Africa (158W–358E). Black contours indicate the climatologicalvertical velocity in intervals of 0.1 Pa s21.

(Giannini et al. 2013). This index is positively correlated with
Sahel precipitation in both observations and coupled model
simulations (Giannini et al. 2013; Giannini and Kaplan 2019).
The CESM1 AER LE SSTs have a NARI anomaly of 20.09 K
for the 1970s and 1950s, which would be expected to cause
ocean-mediated drying in the Sahel. The weak wetting thus
indicates that the remaining contributions from the patterned
SST response have an additional wetting effect on the Sahelthat is not accounted for. Nevertheless, the NARI change suggests that the AA-forced response mediated through North
Atlantic cooling (20.238C) may be partially countered by thecooling in the global tropical oceans (20.148C).
For the 2000s 2 1970s, the CAM5 ocean-mediated response(Fig. 7f) causes increased precipitation across much of Africa,
particularly in the western Sahel and Congo basin, with directatmospheric drying on the Guinea Coast and weak wetting
over the eastern Sahel (Fig. 7e). Thus, the increasing Sahel
precipitation appears to be mostly an ocean-mediated response, with an additional contribution or cancellation fromthe direct-atmospheric response depending on the region. In
this period, the SST anomalies across the basins tend to be
those that are associated with increased precipitation in the
Sahel, with warmer North Atlantic SST and cooler Indian
Ocean, tropical Atlantic, and east tropical Pacific SST (Fig. 5c).
These anomalies result in a positive NARI anomaly of 0.18 K
for the 2000s 2 1970s, which aligns with the ocean-mediatedresponse we see in the AGCM results. The direct-atmosphericdrying response of the coastal West Africa occurs in spite ofrelatively small changes in sulfate burden in the region andlarge declines in AA emissions from North America andEurope and may be a remote impact of increasing AA emissions from Asia, similar to that reported by Dong et al. (2014).
Thus, what appears in the total response to be a northward shiftof precipitation over Africa extending from the shift in thetropical Atlantic ITCZ (Figs. 1c and 7d) actually results from a

combination of opposing but spatially offset direct-atmosphericand ocean-mediated responses.
The CAM5 direct-atmospheric and ocean-mediated responses of zonally averaged vertical velocity over Africa alignwith the precipitation responses in the model (Fig. 8). The
direct-atmospheric response causes a weakening of tropical
upwelling for both periods (Figs. 8b,e). In the recovery period,
the downwelling anomaly shifts south, resulting in weak zonally averaged changes in the Sahel north of 158N and upwellingin the Sahara north of 208N. This change in the response aligns
with the southward shift of the drying anomaly and weakwetting in the northern Sahel and Sahara. The ocean-mediatedresponse causes weak upwelling anomalies over the Sahelduring the drying period (Fig. 8c). In the recovery period, theupwelling anomalies strengthen above the Sahel (Fig. 8f).
These responses are consistent with competing and latitudinally separated direct-atmospheric and ocean-mediated precipitation responses remarked on above. Thus, the changing
total response over the Sahel between the two periods is theresult of the strengthening of the ocean-mediated upwelling
combined with the weakening of the direct-atmospheric downwelling north of 158N.
That the direct-atmospheric response drives subsidence forthe drying period hearkens back to the model of Charney(1975) in which subsidence occurs as a response increasing
net albedo in desert regions. Although this was originally discussed in the context of surface albedo change, one could alsointerpret this change in net albedo as a change in shortwave
forcing by aerosols over the Sahara and Sahel. On the other
hand, there is continued direct-atmospheric drying for the recovery period extending from coastal regions of West Africa
into the western Sahel in spite of the relatively small local
changes in sulfate burden. Thus, a view of the direct-atmospheric
response as wholly driven by local perturbations to shortwave
radiation in the Sahel is incomplete, as there can be dynamicallyBrought to you by University of Colorado Libraries | Unauthenticated | Downloaded 07/06/21 08:35 PM UTC

10198JOURNAL OF CLIMATEVOLUME 33

FIG. 9. Correlation coefficient between the P 2 E anomaly field to the mean flow total (dMC), dynamic (dDY),
and thermodynamic (dTH) moisture convergence anomalies over NH Africa (08–208N, 208W–358E) for the (top)1970s 2 1950s and (bottom) 2000s 2 1970s in (a),(c) CAM5 and (b),(d) CanAM4. For each case, the total (TOT;
green), direct-atmospheric (DIR ATM; orange), and ocean-mediated (OCN MED; blue) response bar plots areshown with the total (dMC), dynamic (dDY), and thermodynamic (dTH) mean flow moisture convergence from left
to right.

driven drying due to teleconnected circulation responses toremote emissions (Dong et al. 2014).
Further analysis of the AGCM response is conducted using
moisture convergence analysis to decompose the P 2 E change
into mean flow dynamic (dDY) and thermodynamic (dTH)components (section 2c). Over the Guinea Coast and Sahelregion south of 158N, the total mean flow component explains
most of the change in P 2 E, while the eddy component plays amore pronounced role north of 158N (Fig. S8 for CAM5;
Fig. S9 for CanAM4). To determine the relative roles of the
dynamic and thermodynamic moisture convergence, we
calculate the spatial correlation coefficient between P 2 E
and the mean flow moisture convergence components over
NH Africa (08–208N, 158W–358E) (red boxes in Figs. S10 andS11). We find that for CAM5 (Figs. 9a,c), dDY explains mostof the P 2 E change for the total, direct-atmospheric, andocean-mediated responses in both periods, with dTH accounting
for a relatively small portion of the change. These results
suggest that AA-forced precipitation changes over continental
Africa are largely dominated by changes to circulation in theregion, which aligns with similar analysis performed forchanges to Asian monsoon circulation (Li et al. 2018). This is
expected in the case of the direct-atmospheric response as the
fixed SSTs cause little change in moisture supply. On the other

hand, the small thermodynamic component of the oceanmediated response indicates that increasing moisture supply
from warming SSTs in the North Atlantic and Mediterranean
during the recovery period play a relatively minor role indriving the precipitation increase.

c. CanAM4 results
Although the precipitation response in CanAM4 is noisier
and weaker than in CAM5 (Fig. 10), aspects of directatmospheric and ocean-mediated responses appear to bequalitatively similar. The generally weak signals reflect
CanAM4’s smaller sensitivity to SST anomalies as demonstrated by its response to observed SST changes (Fig. S3b) as
well as weaker AA ERF and local feedbacks, such as smallerchanges in the interactive dust emissions in CanAM4 (notshown), which act as a positive feedback on Sahel precipitation
anomalies (e.g., Pausata et al. 2016). For the 1970s 2 1950s,
there is drying in the Sahel (Fig. 10a) that is caused by a directatmospheric drying (Fig. 10b), which is partially canceled byocean-mediated wetting (Fig. 10c). For the 2000s 2 1970s,
there is an increase in precipitation over the Sahel in the total
response (Fig. 10d) that is dominated by the ocean-mediatedresponse (Fig. 10f), but also has a contribution from a directatmospheric wetting response (Fig. 10e).

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FIG. 10. As in Fig. 7, but for CanAM4 precipitation. Note that the color scale is halved relative to the CAM5 response in Fig. 7.

The moderate drying period ocean-mediated wetting inthese runs is contrary to our expectations from the 20.08-K
NARI anomaly in the CanESM2 AER LE, which we expect tocorrespond to Sahel drying, although again we see cancellation
between North Atlantic (20.258C) and global tropical(20.178C) SST anomalies. On the other hand, the 10.08-K
anomaly for the recovery period aligns with the oceanmediated wetting in the AGCM results. CanAM4 resembles
CAM5 in that drying seen for the drying period is driven by the
direct-atmospheric response, and the wetting seen for the recovery period is driven by the ocean-mediated response.
However, the models differ in that CanAM4 sees a larger
relative contribution from direct-atmospheric wetting in therecovery period.
In the drying period CanAM4, like CAM5, shows a directatmospheric downwelling response (Fig. 11b) and an ocean-

mediated upwelling response (Fig. 11c) in the ITCZ. In therecovery period, the direct-atmospheric vertical velocity response is diminished and reverses sign (Fig. 11e) while there
continues to be an ocean-mediated upwelling response(Fig. 11f). Thus, the models have qualitative agreement on thesign of the circulation response to the AA-forced directatmospheric and ocean-mediated responses, except in the caseof the recovery period direct-atmospheric response.
Analysis of the P 2 E response using moisture convergenceshows that, as in CAM5, the dynamic component dominates
over the thermodynamic component (Figs. 9b,d). However, we
see that the total mean flow moisture convergence has a lower
correlation with the P 2 E for some of the responses, withcorrelation coefficient of less than 0.5 for the directatmospheric response for the 1970s 2 1950s. Again, this may
be due to the noisy nature of the precipitation response as the

FIG. 11. As in Fig. 8, but for the CanAM4 response. Note that the color scale is halved relative to the CAM5 response in Fig. 8.

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FIG. 12. Boxplots showing the distribution of means obtained via bootstrap resampling (n 5 105) of regionally averaged Sahel (108–208N
and 208W–358E, excluding ocean grid points) precipitation change in the (a),(c) CAM5 and (b),(d) CanAM4 simulations for the (top)1970s 2 1950s and (bottom) 2000s 2 1970s. The boxes display the interquartile range and the whiskers display the 5th–95th percentile
range of bootstrapped means. The fraction of bootstrapped means above or below 0 (i.e., the p value) is displayed next to the whiskers ofeach box. The coupled LE response to all and AA forcing is shown on the far left of each panel in red and green, respectively. These arefollowed by the total, direct-atmospheric (DIR ATM), and ocean-mediated (OCN MED) responses in green, orange, and blue from left toright. The darker shaded boxes show the differences shown in Figs. 7–11 and are the means of their two respective lighter shaded boxes.
Lighter shaded boxes display the two methods for calculating each of the responses using different combinations of AGCM simulations as
described in Table S1 and are used to assess nonlinearity in the responses.

correlation tends to be lower for the experiments with weaker
responses.

d. Analysis of regional mean precipitation anomalies
To synthesize the experiments considered here we assess theregionally averaged Sahel precipitation anomalies in Fig. 12.
This figure displays the distribution of epoch differences(1970s 2 1950s and 2000s 2 1970s) of regional mean Sahel
precipitation in the LE and AGCM simulations obtained via
bootstrap resampling of the years in the simulations, with thewhiskers showing the 5th–95th percentile ranges of the bootstrapped means. As we assume each year is independent of theothers in the bootstrap resampling, this likely underestimates
the spread in the sample means. The information in Fig. 12 isarranged as follows, from left to right: ALL (red) and AER(green) responses from the coupled LE simulations; total(green) responses from the AGCM experiments; directatmospheric (orange) responses from the AGCM experiments; and ocean-mediated (blue) responses from the
AGCM experiments. Each of the AGCM responses iscomputed using two different methods (see Table S1;lighter boxes) as well as the average of the two methods(darker boxes). By comparing the bootstrap distributions
for the different methods for calculating each response, we

can then assess potential nonlinearities found in the directatmospheric and ocean-mediated responses.
We find that the 5th–95th percentile ranges (whiskers in
Fig. 12) of all-forcing and AA-forcing LE responses overlap for
CESM1 and CanESM5, for both periods, consistent with Fig. 3.
Furthermore, the AGCM 5th–95th percentile ranges of thetotal responses encompass the mean coupled LE AA-forced
responses for both periods and both models. Thus, the AGCMs
appear to be consistent with the corresponding LEs in the regional average. The range of the bootstrapped mean total
AGCM anomalies suggests that internal variability is partially
responsible for the discrepancy in the anomaly patterns in the
AGCMs versus the LEs.
In CAM5, the 1970s 2 1950s direct atmospheric and total
drying signals are statistically significant while the weak oceanmediated wetting is not (Fig. 12a). However, there are notablenonlinearities in the responses. We evaluate the nonlinearity
by comparing the sum of the CTRL SST direct-atmosphericand CTRL EMIS ocean-mediated responses (labeled as the‘‘CTRL SUM’’ response in the ‘‘TOTAL’’ column in Fig. 12)
to the response when the two components are applied simultaneously (AVERAGE TOTAL response) (Fig. S10). For thisperiod, the CTRL SUM total response has a weaker drying
throughout the Sahel relative to the AVERAGE total response

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(Fig. S10c). As a result, the regional average CTRL SUM
total response is near zero and falls outside the 5th–95th percentile range of the AVERAGE total response. Thus, there are
nonlinear interactions between the direct-atmospheric andocean-mediated responses that result in statistically significant(p 5 0.028) differences between the regionally averaged
AVERAGE and CTRL SUM total responses (Fig. 12a). This isdue to weaker CTRL SST direct-atmospheric (‘‘DIR ATM’’column) drying compared to PERT SST and stronger, statistically significant CTRL EMIS ocean-mediated (‘‘OCN MED’’column) wetting compared to the weak drying seen in the
PERT EMIS case. Thus, while the nonlinearity significantly
affects the magnitude of the responses, the interpretation of
the relative roles of the responses remains the same, with thedirect-atmospheric response driving drying in either case.
In the 2000s 2 1970s, the CAM5 ocean-mediated wetting issignificant while the direct-atmospheric and total responses are
not significant (Fig. 12c) due to opposite signed responses in
the northern versus southern Sahel (Figs. 7d,e). Comparing the
CTRL SUM and AVERAGE total response we find the latterhas more recovery period wetting in the eastern Sahel(Figs. S10d–f), that passes our statistical significance criterion
in some grid boxes. However, the nonlinearity is not significant
in the regional average for this period (Fig. 12c; p 5 0.18), although we see that using different combinations of experiments can yield differing statistical inferences, with the
AVERAGE and PERT EMIS ocean-mediated responses
having significant wetting signals while the CTRL EMIS response is not significant.
In CanAM4 for the Sahel averaged, there is a significant
1970s 2 1950s direct-atmospheric drying, a near-zero oceanmediated response, and a total drying response that is marginally not significant (Fig. 12b). In the 2000s 2 1970s, there isan ocean-mediated wetting that is significant and a weakerdirect-atmospheric wetting response that is not, which combined show a significant total wetting signal (Fig. 12d). There islittle statistically significant nonlinearity in CanAM4 for either
period (Figs. S11c,f), although the magnitude of the nonlinearity is larger for the drying period than the recovery period,
again as the former is the residual of two experiments and isthus noisier than the latter. The nonlinearities are not significant (Figs. 12b,d) in the Sahel regional average. However, as inCAM5, certain statistical inferences differ depending on themethod of calculating the response. Namely, the drying period
CTRL SUM total and CTRL SST direct-atmospheric responses are significant though their average changes are not.

5. Conclusions
By using the single forcing simulations from the CESM1 and
CanESM2 LEs to isolate externally forced Sahel precipitation
variability from internal variability, we find that it is AA
forcing rather than GHG forcing that dominates the externallyforced component of both the Sahel drought and recovery in
the late twentieth century in both models. However, while theforced precipitation variability in the LEs has similar timing tothat of the observed Sahel precipitation variability, the LEs
substantially underestimate the magnitude of the variability.
Thus, it is difficult to determine the relative roles of external

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forcing and internal variability in these models. Nonetheless,the single forcing LE simulations prove particularly importantfor deciphering the influence of AA forcing on regional climate, due to the spatially nonuniform and temporally nonmonotonic changes in aerosol and precursor emissions. As aresult, AA forcing can have impacts on multidecadal timescales that might be difficult to robustly identify with smaller
ensembles.
Using AGCM experiments designed to capture multidecadal AA-forced changes in the coupled GCMs, we find thatthe direct-atmospheric and ocean-mediated responses havedistinct, and at times competing, roles in driving multidecadal
AA-forced Sahel climate variability. The CAM5 AGCM
simulations are able to reproduce the coupled LE response,while the CanAM4 AGCM simulations can only do so for therecovery period. In the drying period (1970s 2 1950s), AAforced Sahel drying is largely caused by the direct-atmospheric
response with a weak ocean-mediated wetting response. Thisapparently contrasts with the hypothesis that AA-forced Sahel
precipitation variability is predominantly a response to hemispheric asymmetries in SST anomalies (Held et al. 2005;
Ackerley et al. 2011; Mohino et al. 2011; Wang 2015). Although
we cannot determine why the CAM5 ocean-mediated component of the response is so weak, we hypothesize that it may
be a result of competing influences from different ocean basins.
If this is indeed the case, this aspect of the response is likely to
be sensitive to model differences in the pattern and strength of
AA forcing and the corresponding SST response. In the recovery period (2000s 2 1970s), the ocean-mediated response is
the main driver of the increase in Sahel precipitation with thedirect-atmospheric response causing drying in the southern
Sahel and wetting in the northern Sahel in CAM5 and having
moderate wetting effect in CanAM4. The two models havequalitatively more consistent direct-atmospheric and oceanmediated vertical velocity responses in the two periods, but
CAM5 has stronger and more coherent responses.
The AGCM protocol used here has notable limitations including possible errors arising from the lack of atmosphere–
ocean coupling and the fact that the 1970s 2 1950s responses
are calculated as the residual of two sets of experiments andthus are noisier and likely less robust than the 2000s 2 1970s.
Furthermore, when separating the ocean-mediated and directatmospheric effects we find that there are nonlinear effects
indicating that forced Sahel precipitation response is sensitive
to background SST and emission conditions. The differences
between the responses in the experiments described here hints
at wider intermodel uncertainty regarding the relative roles of
direct-atmospheric and ocean-mediated responses in determining the regional climate response to anthropogenic aerosol
forcing. As these effects can compete, their model dependencecompounds uncertainty in the Sahel precipitation response toaerosols, mirroring the uncertainty resulting from the opposing
responses to direct-atmospheric GHG forcing versus SST
warming (Biasutti 2013; Gaetani et al. 2017). Furthermore, thediffering multidecadal variability of CAM5 and CanAM4 in
AMIP-style experiments indicates that there are substantiallydifferent sensitivities to SST variability between the models.
Such analysis reinforces the utility of longer-term AMIP-style

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10202JOURNAL OF CLIMATE

simulations as an avenue for understanding coupled GCM
errors in comparison to observations, such as those produced
for the C20C1 project (e.g., Stone et al. 2018). Nevertheless,the qualitative agreement between the models in spite of thesedifferences suggests the relative roles of the direct-atmosphericand ocean-mediated effects over the two periods examined
here may be relatively robust.
The experiments described here suggest that the AA-forced
response of Sahel precipitation cannot be solely understood interms of SST anomalies feeding back on the atmosphere.
Instead, the direct-atmospheric response to the forcing also
plays a key role in the forced response. Due to the potential
role of the direct-atmospheric response to AA forcing, correlations between multidecadal SST variability and Sahel precipitation derived from observations and coupled ocean–atmosphere
GCM simulations may not necessarily imply a direct causal link.
Rather, they may covary in part due to common influence fromexternal forcing.
Because AA and precursor emissions are expected to decline into the future (Gidden et al. 2019), we expect further
AA-forced increases in Sahel precipitation. Indeed, analysis of
the CESM1 XAER large ensemble suggests that AAs willcontinue to have a major contribution to Sahel precipitation
increases up to the mid-twenty-first century (not shown).
However, the ALL and AER simulations in CanESM2 begin
to diverge in the 2000s (Fig. 3b), indicating that GHG dominate future changes in that model. Thus, it appears that the
CAM5 and CanAM4 disagree regarding the magnitude of theAA effect on the Sahel into the future. The direct-atmospheric
response to AA will likely contribute to the continued recovery
of Sahel precipitation as emissions from Asia and Africa begin
to decline. As long as emission reductions in Asia lag behind
reductions in Europe and North America, there will be future
AA-forced SST warming that is strongest in the North Atlantic
and Pacific with weaker warming in the Indian and tropical
Pacific, resulting in an increase in the NARI. Thus, we might
expect further near-term ocean-mediated wetting of the Sahelin response to AA forcing.
The contrasting roles of direct-atmospheric and oceanmediated responses for the drying and recovery periods emphasizes the importance of considering the transient responsewhen studying the influence of AA on regional climate.
Assessing the full preindustrial to present-day response cangive an incomplete picture of the effect of AA on Sahel precipitation by obscuring aspects of the transient response suchas the timing of direct-atmospheric and ocean-mediated influences on regional climate. Analysis of transient AGCM simulations from CMIP6 endorsed MIPs such as AerChemMIP(Collins et al. 2017) will provide an opportunity to gain further
insight into the role of direct-atmospheric and ocean-mediatedresponses in driving the regional climate response to AA forcing.
Acknowledgments. We thank Thomas Oudar for his helpful
input to the study, the staff at SciNet for their assistance incompleting our simulations, and the Journal of Climate reviewers and editor for their valuable discussion on the manuscript. This work was supported by funding from Environment
and Climate Change Canada and the National Science and

VOLUME 33

Engineering Research Council of Canada. Computations were
performed on the Niagara supercomputer at the SciNet HPC
Consortium. SciNet is funded by the Canada Foundation for
Innovation; the Government of Ontario; Ontario Research
Fund–Research Excellence; and the University of Toronto.
This material is based in part on work supported by the
National Center for Atmospheric Research, which is a major
facility sponsored by the National Science Foundation (NSF)
under Cooperative Agreement 1852977. CESM1 LE data are
available from ESG at http://www.cesm.ucar.edu/projects/
community-projects/LENS/data-sets.html?ref5hp. The CESM
project is supported primarily by the NSF. CanESM2 LE data
are available from the Environment Canada and Climate
Change Canada at https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c. U. Delaware, PREC/L,
and GPCC precipitation data provided by the NOAA/OAR/ESRL
PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/.
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