Fig. 10. Compares the fast Sahelian precipitation response to forcing in
AMIP simulations (purple, as in Figure 5c) to the estimated fast
component of the precipitation MMMs in coupled CMIP6 simulations
(precipitation – 0.87*NARI; the difference between the colored and
light blue curves in the left column of Figure 9) forced with ALL (a,
blue), AA (b, magenta), NAT (c, brown), and GHG (d, green). Similar to
Figure 2, the colored shaded areas denote the bootstrapping confidence
interval of this difference, and the yellow shaded areas, which
represent the magnitude of noise in the fast MMMs, are the confidence
intervals of the MMM of randomized bootstrapped differences between
precipitation and 0.87*NARI in piC simulations. Panel (a) additionally
shows a 20-year running mean of the sum of the AA, NAT, and GHG fast
MMMs (burgundy dashed curve). The label shows the number of institutions
used for each CMIP6 MMM (N), the correlation of the fast MMM with the
AMIP fast response (r), and the standardized root mean squared error of
the CMIP6 MMM with observations (sRMSE).
Though NARI in the GHG simulations differs between CMIP5 and CMIP6, most
of the difference in simulated forced precipitation between CMIP5 and
CMIP6 is not mediated by a linear relationship with NARI, and can be
attributed to the fact that the GHG- and AA-induced drying in CMIP5 is
replaced with AA- and GHG-induced wetting in CMIP6. Whether the
GHG-induced drying in CMIP5 is a fast response to forcing or a response
mediated by SST in ocean basins other than the Atlantic cannot be firmly
established by this analysis, but we offer our perspective below.
5. Discussion
Using SST (and specifically NARI) as a mediator, we have established
that the failure of CMIP coupled models to simulate observed Sahel
rainfall stems from their inability to simulate observed SST, especially
NA, and that the differences in simulation of Sahel rainfall between
CMIP5 and CMIP6 stem from differences in mechanisms not mediated by a
linear teleconnection with NARI. (Let’s denote the difference between
simulated precipitation and scaled NARI as PnonNARI). We
initially suggested that PnonNARI provides a good
measure of the fast (non-SST-related) response to forcing because of the
prominence of the NARI-Sahel teleconnection in observations and
AMIP-style simulations of the 20th century. But
without examining further mediators, we cannot decisively rule out the
possibility that PnonNARI captures teleconnections with
other ocean basins or nonlinearities in the NARI teleconnection. Which
explanation is most likely?
The PnonNARI indices in CMIP5 and CMIP6 are nearly
opposite. If we assume that both represent a fast response to forcing,
we need to conclude that increasing GHG (or reducing AA) lead to fast
wetting in CMIP6, but drying in CMIP5.
The interpretation of PnonNARI in CMIP6 as a fast
response is more consistent with theory. First, increasing rainfall is
consistent with theory linking reduced aerosol concentrations to fast
surface warming and decreasing optical depth of the atmosphere
(Allen and Ingram 2002;
Rosenfeld et al. 2008), although a couple
highly non-linear simulations suggest the fast precipitation response of
the Sahel to changing AA in the 20th century was
drying whether AA forcing was increasing or decreasing
(Hirasawa et al. 2020). Second, it is
generally accepted that the fast response of the Sahel to GHG is wetting
(e.g. Biasutti 2013;
Gaetani et al. 2017;
Giannini 2010;
Haarsma et al. 2005). The good match in
the estimated fast response between coupled CMIP6 simulations and the
amip-hist simulations increases our confidence that the deviations from
the NARI-mediated slow response to forcing in CMIP6 really reflect a
fast response to forcing. The same cannot be said for CMIP5.
We noted in Section 4.c that NARI only explains 36% of simulated
SST-forced variability in the amip-piF simulations, leaving room for the
influence of other ocean basins or SST indices on Sahel precipitation.
Indeed, this is consistent with GK19: while they argue that NARI is the
primary indicator for 20th century Sahel rainfall,
they also argue that p1, which is approximately (NA+GT)/2 and is
intended to capture the effects of uniform global warming, plays a
secondary—but important—role in the 20th century
and a dominant role in the future. In CMIP5, PnonNARImay capture not the fast responses to forcing, but slow drying in
response to uniform global warming, consistent with previous literature
(e.g. Gaetani et al. 2017). In this read,
the differences in simulation of Sahel rainfall between CMIP5 and CMIP6
are due to a combination of changes in the fast response to forcing and
the influence of SST patterns not captured by NARI.
6. Summary and Conclusions
In this paper, we decompose simulated Sahelian precipitation into (1)
teleconnections with SST, (2) fast, atmospheric- and land-mediated
responses to forcing, (3) atmospheric noise, (4) forced SST variability,
and (5) internal SST variability, in order to determine why the
5th and 6th generations of CMIP
differ in their simulation of Sahel rainfall, and why both ensembles are
inconsistent with observed Sahel precipitation variability.
CMIP6 atmospheric simulations forced with observed SST alone capture
observed Sahel precipitation quite well (r=0.6), and, in combination
with atmospheric white noise, are able to reproduce the power of
observed low-frequency variability. This is a welcome improvement from
previous generations of climate models. Including radiative forcing
alongside observed SST barely changes simulated precipitation,
suggesting that the fast response is small and plays a secondary role to
SST-forced precipitation variability. We summarize the Sahel
teleconnections with global SST as a linear relationship with an index
of the warming of the North Atlantic relative to the global Tropics
(NARI), which explains about 36% of the simulated precipitation
response to observed SST. The simulated NARI teleconnection is measured
as \(0.87\pm 0.26\frac{\text{mm}}{day*C}\), consistent with the
strength of the observed teleconnection. We conclude that the observed
SST history and simulated teleconnections in atmospheric simulations are
together necessary and sufficient to capture the timing and magnitude of
the low-frequency droughts and pluvials in 20thcentury Sahel rainfall.
In coupled simulations, the NARI-Sahel teleconnection is consistent with
AMIP simulations, but NARI’s variability – which mostly comes from
North Atlantic SST (NA) – differs from the observed. In simulations, AA
cause a cooling trend and GHG cause a warming trend with magnitudes
comprable to the observed, but no combination of forcing agents produces
a decadal-scale oscillation in NA in either CMIP5 or CMIP6, and only
three CMIP6 models (out of 25 CMIP5 and 30 CMIP6 models) are able to
generate internal SST variability commensurate to the residual (the
difference between total and radiatively forced) low-frequency
variability. How do we reconcile our results with those claiming that
the observed Atlantic Multidecadal Variability (AMV) is externally
forced (mainly by AA; Bellomo et al. 2018;
Booth et al. 2012;
Hirasawa et al. 2020;
Hua et al. 2019;
Murphy et al. 2017)? The discrepancy can
be explained because these studies examine only one or two models
(Booth et al. 2012;
Hirasawa et al. 2020) or subtract a
linear trend from simulated NA before comparing to observations
(Bellomo et al. 2018;
Hua et al. 2019;
Murphy et al. 2017), thus inducing
low-frequency variability in the simulated monotonic decreasing step
function. Moreover, a prominent role for internal variability cannot yet
be dismissed, as suggested by Yan et al.
(2018), who, consistent with our analysis, find that most models do not
capture observed AMOC variability. The NARI-mediated slow response to
external radiative forcing is to dry the Sahel slightly in the 60s and
to wet it immediately afterwards; this does not, in isolation, explain
the timing or magnitude of the observed drought or recovery.
Furthermore, forced NARI variability is small in the first half of the
century. We are led to conclude that either the pattern of the simulated
SST response to forcing in coupled models is incorrect or the Sahelian
precipitation response to internal SST variability overshadowed the
response to external radiative forcing in the 20thcentury, at least up to the mid-1960s.
While we can ascribe the deficiency of 20th century
Sahel rainfall simulations in both CMIP5 and CMIP6 coupled models to
their simulations of SST, NARI is not the main explanation for the
differences in forced Sahel rainfall between the two ensembles, since it
is quite similar in CMIP5 and CMIP6 ALL simulations. The difference,
rather, is in PnonNARI: the component of Sahel rainfall
that comes either from the influence of other SST patterns or from the
fast response to forcing. CMIP6 underperforms relative to CMIP5 because
PnonNARI includes substantial fast wetting responses to
increasing GHG and decreasing AA, comparable in magnitude to the
NARI-related component. In contrast, PnonNARI in CMIP5
is drying, likely in response to uniform SST warming. Sahel drying in
response to uniform warming is strong in models that simulate a deeper
ascent profile, but weak otherwise (Hill et al 2017), so it is possible
that newer parameterizations and higher resolution have changed the
sensitivity to this forcing in the latest generation of models.
This work has shown that, while there has been progress in the
simulation of the Sahel’s response to global SST, much remains uncertain
in the simulation of the pathways of Sahel multi-decadal variability,
especially in the amplitude and timing of forced and natural SST
anomalies in the Atlantic and in the fast and slow response of rainfall
to GHG forcing. Differing mechanisms can lead to similar time evolutions
in observations and simulations; to avoid this pitfall, future work
should focus on evaluating in more detail the hypothesized pathways of
the Sahel response to anthropogenic emissions and oceanic internal
variability in order to further categorize model performance and improve
predictions of the future.
Acknowledgments.
We acknowledge the World Climate Research Programme’s Working Group on
Coupled Modelling, which is responsible for CMIP, and we thank the
climate modeling groups for producing and making available their model
output (listed in Tables S1-S3 of this paper). For CMIP the U.S.
Department of Energy’s Program for Climate Model Diagnosis and
Intercomparison provides coordinating support and led development of
software infrastructure in partnership with the Global Organization for
Earth System Science Portals. The CMIP6 data used in this study are
available in Google cloud storage
(https://console.cloud.google.com/storage/browser/cmip6) as a
result of a grant to the Pangeo project (https://pangeo.io/). We
thank Haibo Liu for preparing the CMIP5 data for use, and Naomi
Henderson for transferring needed CMIP6 simulations to the cloud, and
for aiding in data access and general technical support throughout the
project. We additionally thank Alessandra Giannini for her guidance
throughout the project. This research was supported by the U.S. National
Science Foundation Grant AGS-1612904.
Data Availability Statement.
Observational data from the Global Precipitation Climatology Center
(GPCC, Becker et al. 2013) and the
National Oceanic and Atmospheric Administration’s (NOAA) Extended
Reconstructed Sea Surface Temperature, Version 5
(ERSSTv5, Huang et al. 2017) are freely
available online
(see https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html and
https://www.ncei.noaa.gov/products/extended-reconstructed-sst,
respectively). CMIP5 (CMIP5, Taylor et al.
2012) and CMIP6 (Eyring et al. 2016)
model data is freely available through the Earth System Grid
(see https://esgf-node.llnl.gov/projects/esgf-llnl/).
REFERENCES
Ackerley, D., B. B. B. Booth, S. H. E.
Knight, E. J. Highwood, D. J. Frame, M. R. Allen, and D. P. Rowell,
2011: Sensitivity of twentieth-century Sahel rainfall to sulfate aerosol
and CO2 forcing. J. Climate , 24, 4999-5014,
https://doi.org/10.1175/JCLI-D-11-00019.1.
Adam, O., T. Bischoff, and T.
Schneider, 2016: Seasonal and interannual variations of the energy flux
equator and ITCZ. Part II: Zonally varying shifts of the ITCZ. J.
Climate , 29, 7281-7293,
https://doi.org/10.1175/JCLI-D-15-0710.1.
Allen, M. R., and W. J. Ingram, 2002:
Constraints on future changes in climate and the hydrologic cycle.Nature , 419, 228-232,
https://doi.org/10.1038/nature01092.
Becker, A., P. Finger, A.
Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese,
2013: A description of the global land-surface precipitation data
products of the Global Precipitation Climatology Centre with sample
applications including centennial (trend) analysis from 1901–present.Earth Syst. Sci. Data , 5, 71-99,
https://doi.org/10.5194/essd-5-71-2013.
Bellomo, K., L. N. Murphy, M. A. Cane,
A. C. Clement, and L. M. Polvani, 2018: Historical forcings as main
drivers of the Atlantic multidecadal variability in the CESM large
ensemble. Climate Dynam. , 50, 3687-3698,
https://doi.org/10.1007/s00382-017-3834-3.
Biasutti, M., 2013: Forced Sahel
rainfall trends in the CMIP5 archive. J. Geophys. Res.-Atmos. ,118, 1613-1623, https://doi.org/10.1002/jgrd.50206.
——, 2019: Rainfall trends in the
African Sahel: Characteristics, processes, and causes. Wiley
Interdisciplinary Reviews: Climate Change , 10 ,
https://doi.org/10.1002/wcc.591.
Biasutti, M., and A. Giannini, 2006:
Robust Sahel drying in response to late 20th century forcings.Geophys. Res. Lett. , 33 ,
https://doi.org/10.1029/2006GL026067.
Biasutti, M., I. M. Held, A. H. Sobel,
and A. Giannini, 2008: SST forcings and Sahel rainfall variability in
simulations of the twentieth and twenty-first centuries. J.
Climate , 21, 3471-3486,
https://doi.org/10.1175/2007JCLI1896.1.
Biasutti, M., and Coauthors, 2018:
Global energetics and local physics as drivers of past, present and
future monsoons. Nature Geoscience , 11, 392-400,
https://doi.org/10.1038/s41561-018-0137-1.
Bonfils, C. J., B. D. Santer, J. C.
Fyfe, K. Marvel, T. J. Phillips, and S. R. Zimmerman, 2020: Human
influence on joint changes in temperature, rainfall and continental
aridity. Nat. Clim. Change , 10, 726-731,
https://doi.org/10.1038/s41558-020-0821-1.
Booth, B. B., N. J. Dunstone, P. R.
Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols implicated as a
prime driver of twentieth-century North Atlantic climate variability.Nature , 484, 228-232,
https://doi.org/10.1038/nature10946.
Camberlin, P., S. Janicot, and I.
Poccard, 2001: Seasonality and atmospheric dynamics of the
teleconnection between African rainfall and tropical sea‐surface
temperature: Atlantic vs. ENSO. International Journal of
Climatology: A Journal of the Royal Meteorological Society ,21, 973-1005, https://doi.org/10.1002/joc.673.
Caminade, C., and L. Terray, 2010:
Twentieth century Sahel rainfall variability as simulated by the ARPEGE
AGCM, and future changes. Climate Dynam. , 35, 75-94,
https://doi.org/10.1007/s00382-009-0545-4.
Chang, C.-Y., J. Chiang, M. Wehner,
A. Friedman, and R. Ruedy, 2011: Sulfate aerosol control of tropical
Atlantic climate over the twentieth century. J. Climate ,24, 2540-2555, https://doi.org/10.1175/2010JCLI4065.1.
Dong, B., and R. Sutton, 2015:
Dominant role of greenhouse-gas forcing in the recovery of Sahel
rainfall. Nat. Clim. Change , 5, 757-761,
https://doi.org/10.1038/nclimate2664.
Donohoe, A., J. Marshall, D.
Ferreira, and D. Mcgee, 2013: The relationship between ITCZ location and
cross-equatorial atmospheric heat transport: From the seasonal cycle to
the Last Glacial Maximum. J. Climate , 26, 3597-3618,
https://doi.org/10.1175/JCLI-D-12-00467.1.
Eade, R., D. Stephenson, A. Scaife,
and D. Smith, 2021: Quantifying the rarity of extreme multi-decadal
trends: how unusual was the late twentieth century trend in the North
Atlantic Oscillation? Climate Dynam. , 1-14,
https://doi.org/10.1007/s00382-021-05978-4.
Eyring, V., S. Bony, G. A. Meehl, C.
A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview
of the Coupled Model Intercomparison Project Phase 6 (CMIP6)
experimental design and organization. Geosci. Model Dev. ,9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Folland, C. K., T. N. Palmer, and D.
E. Parker, 1986: Sahel rainfall and worldwide sea temperatures,
1901–85. Nature , 320, 602-607,
https://doi.org/10.1038/320602a0.
Gaetani, M., and Coauthors, 2017:
West African monsoon dynamics and precipitation: the competition between
global SST warming and CO 2 increase in CMIP5 idealized simulations.Climate Dynam. , 48, 1353-1373,
https://doi.org/10.1007/s00382-016-3146-z.
Giannini, A., 2010: Mechanisms of
climate change in the semiarid African Sahel: The local view. J.
Climate , 23, 743-756,
https://doi.org/10.1175/2009JCLI3123.1.
Giannini, A., and A. Kaplan, 2019:
The role of aerosols and greenhouse gases in Sahel drought and recovery.Climatic Change , 152, 449-466,
https://doi.org/10.1007/s10584-018-2341-9.
Giannini, A., R. Saravanan, and P.
Chang, 2003: Oceanic forcing of Sahel rainfall on interannual to
interdecadal time scales. Science , 302, 1027-1030,
https://doi.org/10.1126/science.1089357.
Giannini, A., M. Biasutti, I. M.
Held, and A. H. Sobel, 2008: A global perspective on African climate.Climatic Change , 90, 359-383,
https://doi.org/10.1007/s10584-008-9396-y.
Giannini, A., S. Salack, T. Lodoun,
A. Ali, A. Gaye, and O. Ndiaye, 2013: A unifying view of climate change
in the Sahel linking intra-seasonal, interannual and longer time scales.Environ. Res. Lett. , 8, 024010,
https://doi.org/10.1088/1748-9326/8/2/024010.
Haarsma, R. J., F. M. Selten, S. L.
Weber, and M. Kliphuis, 2005: Sahel rainfall variability and response to
greenhouse warming. Geophys. Res. Lett. , 32 ,
https://doi.org/10.1029/2005GL023232.
Han, Z., F. Luo, S. Li, Y. Gao, T.
Furevik, and L. Svendsen, 2016: Simulation by CMIP5 models of the
Atlantic multidecadal oscillation and its climate impacts.Advances in Atmospheric Sciences , 33, 1329-1342,
https://doi.org/10.1007/s00376-016-5270-4.
Haywood, J. M., A. Jones, N.
Bellouin, and D. Stephenson, 2013: Asymmetric forcing from stratospheric
aerosols impacts Sahelian rainfall. Nat. Clim. Change ,3, 660-665, https://doi.org/10.1038/nclimate1857.
Held, I. M., T. L. Delworth, J. Lu,
K. u. Findell, and T. Knutson, 2005: Simulation of Sahel drought in the
20th and 21st centuries. Proc. Nat. Acad. Sci. , 102,17891-17896, https://doi.org/10.1073/pnas.0509057102.
Herman, R. J., A. Giannini, M.
Biasutti, and Y. Kushnir, 2020: The effects of anthropogenic and
volcanic aerosols and greenhouse gases on twentieth century Sahel
precipitation. Scientific reports , 10, 1-11,
https://doi.org/10.1038/s41598-020-68356-w.
Hill, S. A., 2019: Theories for past
and future monsoon rainfall changes. Current Climate Change
Reports , 5, 160-171,
https://doi.org/10.1007/s40641-019-00137-8.
Hirasawa, H., P. J. Kushner, M.
Sigmond, J. Fyfe, and C. Deser, 2020: Anthropogenic Aerosols Dominate
Forced Multidecadal Sahel Precipitation Change through Distinct
Atmospheric and Oceanic Drivers. J. Climate , 33,10187-10204, https://doi.org/10.1175/JCLI-D-19-0829.1.
Hoerling, M., 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,
https://doi.org/10.1175/JCLI3842.1.
Hua, W., A. Dai, L. Zhou, M. Qin, and
H. Chen, 2019: An Externally Forced Decadal Rainfall Seesaw Pattern Over
the Sahel and Southeast Amazon. Geophys. Res. Lett. , 46,923-932, https://doi.org/10.1029/2018GL081406.
Huang, B., and Coauthors, 2017:
Extended reconstructed sea surface temperature, version 5 (ERSSTv5):
upgrades, validations, and intercomparisons. J. Climate ,30, 8179-8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
Hwang, Y. T., D. M. Frierson, and S.
M. Kang, 2013: Anthropogenic sulfate aerosol and the southward shift of
tropical precipitation in the late 20th century. Geophys. Res.
Lett. , 40, 2845-2850.
Iles, C. E., and G. C. Hegerl, 2014:
The global precipitation response to volcanic eruptions in the CMIP5
models. Environ. Res. Lett. , 9 ,
https://doi.org/10.1088/1748-9326/9/10/104012.
Kang, S. M., D. M. Frierson, and I.
M. Held, 2009: The tropical response to extratropical thermal forcing in
an idealized GCM: The importance of radiative feedbacks and convective
parameterization. J. Atmos. Sci. , 66, 2812-2827,
https://doi.org/10.1175/2009JAS2924.1.
Kang, S. M., I. M. Held, D. M.
Frierson, and M. Zhao, 2008: The response of the ITCZ to extratropical
thermal forcing: Idealized slab-ocean experiments with a GCM. J.
Climate , 21, 3521-3532,
https://doi.org/10.1175/2007JCLI2146.1.
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 , https://doi.org/10.1029/2010GL043038.
Klimont, Z., S. J. Smith, and J.
Cofala, 2013: The last decade of global anthropogenic sulfur dioxide:
2000–2011 emissions. Environ. Res. Lett. , 8 ,
https://doi.org/10.1088/1748-9326/8/1/014003.
Knight, J. R., C. K. Folland, and A.
A. Scaife, 2006: Climate impacts of the Atlantic multidecadal
oscillation. Geophys. Res. Lett. , 33 ,
https://doi.org/10.1029/2006GL026242.
Knight, J. R., R. J. Allan, C. K.
Folland, M. Vellinga, and M. E. Mann, 2005: A signature of persistent
natural thermohaline circulation cycles in observed climate.Geophys. Res. Lett. , 32 ,
https://doi.org/10.1029/2005GL024233.
Knutson, T. R., and S. Manabe, 1995:
Time-mean response over the tropical Pacific to increased C02 in a
coupled ocean-atmosphere model. J. Climate , 8,2181-2199,
https://doi.org/10.1175/1520-0442(1995)008<2181:TMROTT>2.0.CO;2.
Kucharski, F., N. Zeng, and E.
Kalnay, 2013: A further assessment of vegetation feedback on decadal
Sahel rainfall variability. Climate Dynam. , 40,1453-1466, DOI 10.1007/s00382-012-1397-x.
Losada, T., B. Rodriguez‐Fonseca, E.
Mohino, J. Bader, S. Janicot, and C. R. Mechoso, 2012: Tropical SST and
Sahel rainfall: A non‐stationary relationship. Geophys. Res.
Lett. , 39 , https://doi.org/10.1029/2012GL052423.
Lu, J., 2009: The dynamics of the
Indian Ocean sea surface temperature forcing of Sahel drought.Climate Dynam. , 33, 445-460,
https://doi.org/10.1007/s00382-009-0596-6.
Marvel, K., M. Biasutti, and C.
Bonfils, 2020: Fingerprints of external forcings on Sahel rainfall:
aerosols, greenhouse gases, and model-observation discrepancies.Environ. Res. Lett. , 15 ,
https://doi.org/10.1088/1748-9326/ab858e.
Menary, M. B., and Coauthors, 2020:
Aerosol‐forced AMOC changes in CMIP6 historical simulations.Geophys. Res. Lett. , 47 ,
https://doi.org/10.1029/2020GL088166.
Murphy, L. N., K. Bellomo, M. Cane,
and A. Clement, 2017: The role of historical forcings in simulating the
observed Atlantic multidecadal oscillation. Geophys. Res. Lett. ,44, 2472-2480, https://doi.org/10.1002/2016GL071337.
Neelin, J., C. Chou, and H. Su, 2003:
Tropical drought regions in global warming and El Nino teleconnections.Geophys. Res. Lett. , 30 ,
https://doi.org/10.1029/2003GL018625.
Okonkwo, C., and Coauthors, 2015:
Combined effect of El Niño southern oscillation and Atlantic
multidecadal oscillation on Lake Chad level variability. Cogent
Geoscience , 1 ,
https://doi.org/10.1080/23312041.2015.1117829.
Palmer, T., 1986: Influence of the
Atlantic, Pacific and Indian oceans on Sahel rainfall. Nature ,322, 251-253, https://doi.org/10.1038/322251a0.
Parhi, P., A. Giannini, P. Gentine,
and U. Lall, 2016: Resolving contrasting regional rainfall responses to
El Niño over tropical Africa. J. Climate , 29, 1461-1476,
https://doi.org/10.1175/JCLI-D-15-0071.1.
Park, J.-y., J. Bader, and D. Matei,
2016: Anthropogenic Mediterranean warming essential driver for present
and future Sahel rainfall. Nat. Clim. Change , 6,941-945, https://doi.org/10.1038/nclimate3065.
Pearl, J., M. Glymour, and N. P.
Jewell, 2016: Causal inference in statistics: A primer. John
Wiley & Sons, 136 pp.
Polson, D., M. Bollasina, G. Hegerl,
and L. Wilcox, 2014: Decreased monsoon precipitation in the Northern
Hemisphere due to anthropogenic aerosols. Geophys. Res. Lett. ,41, 6023-6029, https://doi.org/10.1002/2014GL060811.
Pomposi, C., Y. Kushnir, and A.
Giannini, 2015: Moisture budget analysis of SST-driven decadal Sahel
precipitation variability in the twentieth century. Climate
Dynam. , 44, 3303-3321,
https://doi.org/10.1007/s00382-014-2382-3.
Pomposi, C., A. Giannini, Y. Kushnir,
and D. E. Lee, 2016: Understanding Pacific Ocean influence on
interannual precipitation variability in the Sahel. Geophys. Res.
Lett. , 43, 9234-9242,
https://doi.org/10.1002/2016GL069980.
Qin, M., A. Dai, and W. Hua, 2020:
Quantifying contributions of internal variability and external forcing
to Atlantic multidecadal variability since 1870. Geophys. Res.
Lett. , 47 , https://doi.org/10.1029/2020GL089504.
Rahmstorf, S., J. E. Box, G. Feulner,
M. E. Mann, A. Robinson, S. Rutherford, and E. J. Schaffernicht, 2015:
Exceptional twentieth-century slowdown in Atlantic Ocean overturning
circulation. Nat. Clim. Change , 5, 475-480,
https://doi.org/10.1038/nclimate2554.
Rodríguez-Fonseca, B., and Coauthors,
2015: Variability and predictability of West African droughts: A review
on the role of sea surface temperature anomalies. J. Climate ,28, 4034-4060, https://doi.org/10.1175/JCLI-D-14-00130.1.
Rosenfeld, D., and Coauthors, 2008:
Flood or drought: How do aerosols affect precipitation? Science ,321, 1309-1313, https://doi.org/10.1126/science.1160606.
Rotstayn, L. D., and U. Lohmann,
2002: Tropical rainfall trends and the indirect aerosol effect. J.
Climate , 15, 2103-2116,
https://doi.org/10.1175/1520-0442(2002)015<2103:TRTATI>2.0.CO;2.
Scaife, A., and Coauthors, 2009: The
CLIVAR C20C project: selected twentieth century climate events.Climate Dynam. , 33, 603-614,
https://doi.org/10.1007/s00382-008-0451-1.
Schneider, T., T. Bischoff, and G. H.
Haug, 2014: Migrations and dynamics of the intertropical convergence
zone. Nature , 513, 45-53,
https://doi.org/10.1038/nature13636.
Smith, S. J., J. v. Aardenne, Z.
Klimont, R. J. Andres, A. Volke, and S. Delgado Arias, 2011:
Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos. Chem.
Phys. , 11, 1101-1116,
https://doi.org/10.5194/acp-11-1101-2011.
Sobel, A. H., I. M. Held, and C. S.
Bretherton, 2002: The ENSO signal in tropical tropospheric temperature.J. Climate , 15, 2702-2706,
https://doi.org/10.1175/1520-0442(2002)015<2702:TESITT>2.0.CO;2.
Sutton, R. T., and D. L. Hodson,
2005: Atlantic Ocean forcing of North American and European summer
climate. Science , 309, 115-118,
https://doi.org/10.1126/science.1109496.
Taylor, K. E., R. J. Stouffer, and G.
A. Meehl, 2012: An overview of CMIP5 and the experiment design.Bull. Am. Meteorol. Soc. , 93, 485-498,
https://doi.org/10.1175/BAMS-D-11-00094.1.
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,
https://doi.org/10.1175/2008JCLI2561.1.
Undorf, S., D. Polson, M. Bollasina,
Y. Ming, A. Schurer, and G. Hegerl, 2018: Detectable impact of local and
remote anthropogenic aerosols on the 20th century changes of West
African and South Asian monsoon precipitation. J. Geophys.
Res.-Atmos. , 123, 4871-4889,
https://doi.org/10.1029/2017JD027711.
Vellinga, M., and Coauthors, 2016:
Sahel decadal rainfall variability and the role of model horizontal
resolution. Geophys. Res. Lett. , 43, 326-333,
https://doi.org/10.1002/2015GL066690.
Westervelt, D., and Coauthors, 2017:
Multimodel precipitation responses to removal of US sulfur dioxide
emissions. J. Geophys. Res.-Atmos. , 122, 5024-5038,
https://doi.org/10.1002/2017JD026756.
Yan, X., R. Zhang, and T. R. Knutson,
2018: Underestimated AMOC Variability and Implications for AMV and
Predictability in CMIP Models. Geophys. Res. Lett. , 45,4319-4328, https://doi.org/10.1029/2018GL077378.
——, 2019: A multivariate AMV
index and associated discrepancies between observed and CMIP5 externally
forced AMV. Geophys. Res. Lett. , 46, 4421-4431,
https://doi.org/10.1029/2019GL082787.
Zhang, R., 2017: On the persistence
and coherence of subpolar sea surface temperature and salinity anomalies
associated with the Atlantic multidecadal variability. Geophys.
Res. Lett. , 44, 7865-7875,
https://doi.org/10.1002/2017GL074342.
Zhang, R., and T. L. Delworth, 2006:
Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and
Atlantic hurricanes. Geophys. Res. Lett. , 33 ,
https://doi.org/10.1029/2006GL026267.
Zhang, R., R. Sutton, G. Danabasoglu,
T. L. Delworth, W. M. Kim, J. Robson, and S. G. Yeager, 2016: Comment on
“The Atlantic Multidecadal Oscillation without a role for ocean
circulation”. Science , 352, 1527-1527,
https://doi.org/10.1126/science.aaf1660.
Zhang, R., and Coauthors, 2013: Have
aerosols caused the observed Atlantic multidecadal variability? J.
Atmos. Sci. , 70, 1135-1144,
https://doi.org/10.1175/JAS-D-12-0331.1.