# The role of aerosols and greenhouse gases in Sahel drought and recovery

## Abstract

We exploit the multi-model ensemble produced by phase 5 of the Coupled Model Intercomparison Project (CMIP5) to synthesize current understanding of external forcing of Sahel rainfall change, past and future, through the lens of oceanic influence. The CMIP5 multi-model mean simulates the twentieth century evolution of Sahel rainfall, including the mid-century decline toward the driest years in the early 1980s and the partial recovery since. We exploit a physical argument linking anthropogenic emissions to the change in the temperature of the sub-tropical North Atlantic Ocean relative to the global tropical oceans to demonstrate indirect attribution of late twentieth century Sahel drought to the unique combination of aerosols and greenhouse gases that characterized the post-World War II period. The subsequent reduction in aerosol emissions around the North Atlantic that resulted from environmental legislation to curb acid rain, occurring as global tropical warming continued unabated, is consistent with the current partial recovery and with projections of future wetting. Singular Value Decomposition (SVD) applied to the above-mentioned sea surface temperature (SST) indices provides a succinct description of oceanic influence on Sahel rainfall and reveals the near-orthogonality in the influence of emissions between twentieth and twenty-first centuries: the independent effects of aerosols and greenhouse gases project on the difference of SST indices and explain past variation, while the dominance of greenhouse gases projects on their sum and explains future projection. This result challenges the assumption that because anthropogenic warming had a hand in past Sahel drought, continued warming will result in further drying. In fact, the twenty-first century dominance of greenhouse gases, unchallenged by aerosols, results in projections consistent with warming-induced strengthening of the monsoon, a response that has gained in coherence in CMIP5 compared to prior multi-model exercises.

## 1 Introduction

The Sahel witnessed an outstanding climatic shift with the abrupt onset of drought in the late 1960s. The magnitude and spatial extent of the downward trend in precipitation over the twentieth century (Greene et al. 2009) and the persistence of years of deficient rainfall through the 1970s and 1980s (Lamb 1982; Nicholson 1983; Dai et al. 2004; Ali and Lebel 2009) led scientists to hypothesize that Sahelian drought may be a sign of anthropogenic change. Some pointed to the local pressure of rapid population growth on vegetation cover and the consequent impact of an increase in albedo on the atmospheric energy budget (Charney 1975). Others linked drought to the nascent preoccupation with the impact of global anthropogenic emissions, of greenhouse gases (GHGs), and other pollutants, on the general circulation of the atmosphere (Bryson 1973).

We reconcile the just-mentioned arguments about the role of anthropogenic emissions in Sahel drought, and more recent arguments attributing global precipitation changes to aerosols, natural and anthropogenic (Robock and Liu 1994; Gillett et al. 2004; Liepert et al. 2004; Lambert et al. 2004; Zhang et al. 2007; Haywood et al. 2013) with demonstration that the dominant cause of Sahel drought lies in changes in the surface temperatures of the global oceans (Folland et al. 1986; Giannini et al. 2003). We do so by linking the unique post-World War II combination of aerosol and greenhouse gas emissions to the patterns of sea surface temperature (SST) known to affect Sahel rainfall.

On interannual to millennial time scales, Sahel rainfall variations have been ascribed to interhemispheric differences in SST, whether global, restricted to the Atlantic, or to the tropical Atlantic Ocean (Lough 1986; Shanahan et al. 2009; Park et al. 2015; Lindzen and Nigam 1987; Kang et al. 2008; Schneider et al. 2014). Recent work attempting to explain the disagreement in the sign of projected twenty-first century rainfall change (Biasutti and Giannini 2006) with differences in projected SST change added the explicit consideration of oceanic warming, resulting in a linear regression model that describes Sahel rainfall as a function of two regional SST indices: the difference of tropical North and tropical South Atlantic SST averages and the average of tropical Indo-Pacific sector SSTs (Biasutti et al. 2008)*.* This regression model was further distilled into a single predictor, the North Atlantic Relative Index (NARI; Giannini et al. 2013), computed as the difference between SST averages over the sub-tropical North Atlantic (10°N–40°N, 75°W–15°W) and the global tropical oceans (20°S–20°N). Here, we denote these as NA and GT, respectively, so that NARI = NA − GT. In this difference, the tropical SST average (GT) captures global vertical stability (Chou and Neelin 2004): the stabilization effect of warming SSTs is communicated vertically through deep convection and is spread laterally by upper-atmosphere wave dynamics, given the weak temperature gradient constraint (Chiang and Sobel 2002). The sub-tropical North Atlantic SST average (NA) counters stabilization through moisture supply (Giannini et al. 2008; Seth et al. 2013). To the extent that Sahel rainfall variations on different time scales are the result of the underlying oceanic forcing’s preferred time scales of variation, our physical interpretation of NARI follows the paradigm that convection is in quasi-equilibrium with its environment (Emanuel et al. 1994). This assumption eliminates the need to consider time scales of variation separately, as long as these are longer than the time scales of convective adjustment (including the adjustment in the coupled ocean-atmosphere system described in Chiang and Sobel (2002)). In addition, it should be noted that a “relative index” effectively detrends variations (Vecchi et al. 2008), in the sense that insofar as both SST time series express GHG-induced warming, taking their difference largely removes it. In observations, NARI explains ~ 50% of the variance in twentieth century Sahel rainfall variability, all time scales included (Giannini et al. 2013).

In the “CMIP5 simulation of Sahel rainfall” section, we show that the CMIP5 multi-model mean reproduces the twentieth century evolution of Sahel rainfall, rendering discussion of its attribution to external forcing possible. In the “Climate change in the Sahel through the lens of oceanic influence” section, we propose an explanation that reconciles studies of variability, which typically seek to relate oceanic forcing and regional rainfall response, with studies of change, which typically seek to attribute regional response to external forcing, natural or anthropogenic, by developing an argument for indirect attribution of Sahel rainfall, through the influence of anthropogenic emissions on sea surface temperatures. In the “Transformation of predictors” sub-section, we exploit Singular Value Decomposition (SVD) to describe the linearly independent linear combinations of the two SST predictors defined above, i.e., the averages of sub-tropical North Atlantic and global tropical SSTs. In the “Linear regressions of Sahel rainfall” sub-section, we use the resulting singular vectors in bivariate linear regressions that describe their influence on Sahel rainfall. We present explicit formulas for these SVD-based predictors in the bivariate case; although, as noted by Preisendorfer (1988), such formulas had been available since a publication by Pearson (1901), their presentation in terms of non-dimensional parameters, introduced here, helps to visualize the underlying relationships and facilitates the interpretation of results. In particular, we show that when the ratio of standard deviations of the original predictors is close to one, the bivariate SVD rotation results in the sum and difference of these original predictor time series. In our case, this means that NARI, the difference of NA and GT, which we defined above based on a physical argument, is nearly proportional to one of the SVD predictors and is exhaustively complemented by the other, which is nearly proportional to the sum of the same SST indices, and represents global warming. The derivation of all necessary formulas is relegated to the Appendix. In the “Conclusions: Past is not prologue” section, we conclude with a synthesis that finds coherence in attributing past Sahel drought to anthropogenic emissions, while lending credence to projections of a wetter future.

## 2 CMIP5 simulation of Sahel rainfall

The multi-model ensemble produced by the Coupled Model Intercomparison Project in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), referred to as CMIP5 (Taylor et al. 2012), reproduces the observed twentieth century evolution of Sahel rainfall and projects wetter end of twenty-first century conditions more coherently than its CMIP3 predecessor (e.g., compare Biasutti and Giannini (2006) with Biasutti (2013)).

We restrict our analysis to 29 models participating in CMIP5, because only these have all the thermodynamic and dynamical variables necessary to evaluate their moisture budget (the subject of a parallel study). The models used are named in the tables in Supplementary Material. We focus on ensemble means, because we are interested in attribution of observed twentieth century Sahel rainfall variability, i.e., in describing its externally forced component, whether the external forcing is natural (i.e., due to variations in incoming top of the atmosphere insolation and spikes in aerosol concentration from volcanic eruptions) or anthropogenic (most notably, emissions of aerosols and GHGs from fossil fuel burning). Single-model ensemble sizes range between 1 and 10 in the historical simulations of the twentieth century and in the RCP8.5 scenario simulations of the twenty-first century. (In the case of the pre-Industrial control, typically, one simulation is run per model, albeit of varying length.) For each model and type of simulation, when more than one is available, we average realizations in the model’s ensemble mean. We then compute the multi-model mean as the average of the single models’ ensemble mean, a procedure which further suppresses the manifestation of internal variability.

## 3 Climate change in the Sahel through the lens of oceanic influence

*m*-dimensional case (Singular Value Decomposition of an m-dimensional predictors’ space) and for linear regression (Use of time-dimensional singular vectors as predictors), as well as the derivation of the explicit form for the parameters of SVD in the bivariate case (Singular Value Decomposition of a two-dimensional predictors’ space; derivation of Eqs. (1)–(5)).

Predictors’ space characteristics for the multi-model means from CMIP5 simulations and for twentieth century observations

Century | CMIP5 simulation | σ GT | σ NA | | |
| °C | °C |
% |
% |
---|---|---|---|---|---|---|---|---|---|---|

19th | PI control | 0.03234 | 0.04195 | 0.369 | 0.771 | 62.75 | 0.0020 | 0.0008 | 71.90 | 28.10 |

20th | Historical | 0.13150 | 0.12475 | 0.955 | 1.054 | 43.42 | 0.0321 | 0.0007 | 97.74 | 2.26 |

21st | RCP8.5 | 0.86711 | 0.83118 | 0.999 | 1.043 | 43.79 | 1.4422 | 0.0005 | 99.96 | 0.04 |

1901–1999 | Observations | 0.19565 | 0.22748 | 0.619 | 0.860 | 51.86 | 0.0734 | 0.0166 | 81.52 | 18.48 |

Regression coefficients and skill for Sahel rainfall in CMIP5 multi-model means and in twentieth century observations

Century | CMIP5 simulation | | | Correlation coefficient of |
---|---|---|---|---|

19th | PI-control | 0.054 | 0.312 | 0.317 |

20th | Historical | −0.320 | 0.477 | 0.574 |

21st | RCP8.5 | 0.783 | 0.171 | 0.801 |

1901–1999 | Observations | −0.182 | 0.607 | 0.633 |

### 3.1 Transformation of predictors

We relate differences between our predictors’ covariance structures to differences in the external forcing applied to the pre-Industrial control and twentieth and twenty-first century simulations. The latter two types of simulations are run with time-varying external forcing, natural and anthropogenic: variations in each realization of a single model’s ensemble are a combination of internal variability and externally forced change, and their ensemble mean is taken to filter out internal variability. The former are run with constant external forcing, including CO_{2} concentrations held fixed at pre-Industrial levels (280 ppm), the intent being to provide a truthful estimation of each model’s internal variability. We repeat analyses on each model’s first 100 years of the pre-Industrial control simulation (typically, there is only one such simulation per model, of variable length) and on each model’s ensemble mean (with ensemble sizes varying between one and ten) for the twentieth and twenty-first century simulations.

*x*

_{1}= GT and

*x*

_{2}= NA—standardized and mutually uncorrelated SVD-based predictors

*p*

_{1},

*p*

_{2}are obtained from the original predictors

*x*

_{1},

*x*

_{2}by orthogonal rotation in the

*x*

_{1}

*x*

_{2}plane followed by rescaling:

*ϕ*and squared rescaling coefficient

*λ*

_{1},

*λ*

_{2}are given by:

*k*=

*σ*

_{1}/

*σ*

_{2}is the ratio of standard deviations σ

_{1}and σ

_{2}of

*x*

_{1}and

*x*

_{2}, respectively, and

*ρ*is their correlation coefficient. Incidentally,

*k*and

*ρ*are the two non-dimensional parameters, characterizing the variance-covariance matrix of

*x*

_{1}and

*x*

_{2}up to a constant factor; to represent the same in terms of

*p*

_{1}and

*p*

_{2}, one such parameter is

*ϕ*, while the relative value of either of

*λ*

_{1}or

*λ*

_{2}where

*λ*

_{1}>

*λ*

_{2}, can be another:

Derivation of Eqs. (1)–(5) is given in “Singular Value Decomposition of a two-dimensional predictors’ space; derivation of Eqs. (1)–(5)”.

Figure 2 illustrates these relationships in twentieth century observations (see the last row of Table 1 for the values of relevant parameters). The observed values of the original predictors are the coordinates of the color triangles on the (*x*_{1}, *x*_{2}) plane, with the corresponding standardized values of Sahel rainfall shown by color. The thick segments shown on the *x*_{1}, *x*_{2} axes indicate sample standard deviations, *σ*_{1} and *σ*_{2}, of these original predictors. Their values are approximately 0.20 and 0.23 °C, respectively, and their ratio is *k* = *σ*_{1}/*σ*_{2} = 0.86. The correlation coefficient of the original predictors is *ρ* = 0.62. SVD-based predictors *p*_{1}, *p*_{2}, whose corresponding axes are shown by dash lines, are obtained by orthogonal rotation of the original predictors *x*_{1}, *x*_{2} with rotation angle about ϕ = 52°, indicated by the arc connecting the *x*_{1} and *p*_{1} axes. The thick segments on the *p*_{1} and *p*_{2} axes show standard deviations (in our notation, *λ*_{1}^{1/2} = 0.27 °C and *λ*_{2}^{1/2} = 0.13 °C) of the data projections on these axes. The variance of projections is maximized for *p*_{1}, among all possible directions on the *x*_{1}, *x*_{2} plane, therefore *λ*_{1} > *λ*_{2}, necessarily. Also by construction, the in-sample correlation coefficient between *p*_{1} and *p*_{2} is zero; since there are only two predictors, *p*_{2}, being the “last” of them, minimizes the variance among all directions on the *x*_{1}, *x*_{2} plane. Therefore, *p*_{2} only explains *λ*_{2}/(*λ*_{1} + *λ*_{2}) = 18.5% of variance in the sample (while *p*_{1} explains the other 81.5%). These features of the data and predictors’ pairs are illustrated by the shape of “concentration ellipses” (von Storch and Zwiers 1999; see their example 2.8.12, p. 43). The 95% concentration ellipse is shown by the dotted line in Fig. 2; its major semiaxes are 2.45*λ*_{1}^{1/2} and 2.45*λ*_{2}^{1/2}. It should be noted that *λ*_{1} and *λ*_{2} do not necessarily determine the relative importance of the predictors *p*_{1} and *p*_{2} in modeling other variables: as color changes among triangles in Fig. 2 demonstrate, *p*_{2} is much more important than *p*_{1} in describing Sahel rainfall changes over 1901–1999. This is confirmed by the calculation of the regression coefficients in the last row of Table 2.

*k*, of standard deviations of global tropical and sub-tropical North Atlantic SSTs is also close to 1 in the twentieth and twenty-first centuries, and the correlation coefficient of the SST indices,

*ρ*, is positive (Table 1), the angle

*ϕ*in Eq. (3) is close to 45°. That the diagonal is the direction along which is expressed the greatest covariance between

*x*

_{1}= GT and

*x*

_{2}= NA can also be clearly seen in Fig. 3. The remaining variance, orthogonal to it by construction, is larger in the twentieth (blue dots in Fig. 3) than in the twenty-first century (red squares). Since sin

*ϕ*= cos

*ϕ*for

*ϕ*= 45

^{o},

*p*

_{1}and

*p*

_{2}, respectively, in Eqs. (1) and (2), become nearly proportional to the sum and difference of the original predictors, and specifically,

*p*

_{2}approximates NARI. The time series of

*p*

_{1}are depicted in the middle panels of Fig. 1: indeed,

*p*

_{1}represents a “global warming” mode, evident in both sub-tropical North Atlantic and global tropical ocean temperatures.

*p*

_{2}(~NARI) explains more variance in the twentieth than in the twenty-first century. Its standardized form is depicted in the bottom panels of Fig. 1. In the middle and bottom left-hand side panels, thick red lines represent the principal components in observations (Kaplan et al. 1998).

Our original predictors, GT and NA, are highly correlated in the twentieth and twenty-first century simulations, less so in the pre-Industrial control simulations (Fig. 3 and Table 1). As a consequence, the percent of variance explained by the leading mode, *λ*_{1} in Eq. (4), increases in the simulations with time-dependent external forcing, as compared to those with constant forcing. The correlation further increases from twentieth to twenty-first century and consequently so does the relative variance of the leading mode, *λ*_{1}/(*λ*_{1} + *λ*_{2}) = 1 − *λ*_{2}/(*λ*_{1} + *λ*_{2}), see Eq. (5). This behavior represents one prominent way in which external forcing expresses itself: it forces a positive correlation between the two predictors, through their common, GHG-induced warming. In addition, the relative cooling of the North Atlantic associated with aerosols contributes to the co-variability of indices with a second, preferred direction along which variance is expressed, which by construction is orthogonal to the first. This direction expresses more variance, visible in the scatter plots in Fig. 3, in the twentieth (blue dots) than in the twenty-first century (red squares).

### 3.2 Linear regressions of Sahel rainfall

*p*

_{1}, i.e., the “sum” or “warming” mode, while the residual mode,

*p*

_{2}, i.e., the “difference” or NARI, is completely devoid of any significant trend. We model standardized Sahel rainfall

*y*, our predictand, by linear regression on

*p*

_{1}and

*p*

_{2}(see Section “Use of time-dimensional singular vectors as predictors” in the Appendix):

Regression coefficients *a* and *b* and the correlation coefficients between simulated and predicted Sahel rainfall for the multi-model mean and for observations are reported in Table 2. Since the SVD-based predictor time series are standardized and mutually uncorrelated, and predictand time series are standardized, the regression coefficients, *a* and *b*, are precisely the correlation coefficients of the predictand and corresponding predictors. In the twentieth century, correlation of the multi-model mean simulation of Sahel rainfall with its SST-based regression is 0.574 and correlation in observations is 0.633. Both terms in the regression contribute, but *p*_{2} contributes more (since *b > a*): late twentieth century Sahel drought is explained by the absence of North Atlantic warming, relative to warming of the global tropical oceans. The (positive) sign of *b*, the regression coefficient multiplying *p*_{2} (NARI), is consistent across centuries. However, its magnitude and explanatory power vis à vis Sahel rainfall are drastically diminished in the twenty-first century. Over the twenty-first century, the correlation between the simulated Sahel rainfall and its in-sample prediction is 0.801. The multi-model ensemble achieves this high a correlation essentially through a positive correlation between Sahel rainfall and warming, captured by *p*_{1}. This happens because the coherence among models in projections of twenty-first century precipitation change has increased considerably from CMIP3 to CMIP5 (Biasutti 2013): a majority of models in CMIP5 project a future increase in Sahel rainfal to go along with warming (Schewe and Levermann 2017), in analogy to paleoclimate reconstructions of the “Green Sahara”, when summer insolation was stronger than now (de Menocal et al. 2000). Among the models with a twenty-first century correlation coefficient between *p*_{1} and Sahel rainfall larger than 0.45 (thus explaining ~ 20% of the variance with the single predictor *p*_{1}), seven of nine project future wetting (Table S3 in Supplementary Materials). In sum, regression models between centuries are nearly orthogonal: *p*_{2}, analogous to the difference of predictors or NARI, explains a larger fraction of twentieth century variation, while *p*_{1}, analogous to the sum of predictors or warming, alone explains the externally forced twenty-first century change.

## 4 Conclusions: Past is not prologue

Our analysis of the multi-model mean by design emphasizes the externally forced response and leads us to propose the following indirect attribution argument for Sahelian climate change, past and future. In the second half of the twentieth century, as global dimming (Stanhill and Cohen 2001; Liepert 2002) opposed global warming in the northern hemisphere, a unique combination of anthropogenic emissions contributed to the late twentieth century drying of the Sahel through their effect on sea surface temperatures: aerosols by cooling the North Atlantic and greenhouse gases by warming the tropical oceans, especially the Indian Ocean. Warming of the global tropical oceans “upped the ante” for deep convection (Chou and Neelin 2004; Held et al. 2005), while the absence of warming in the North Atlantic reduced the moisture supply to the monsoon and thus its potential to meet the “upped ante” and to trigger vertical instability (Giannini et al. 2008). Our argument is distinct from others previously proposed, which attributed late twentieth century Sahel drought solely to aerosols, whether through cooling of the North Atlantic or of the entire northern hemisphere (Rotstayn and Lohmann 2002; Kawase et al. 2010; Ackerley et al. 2011; Booth et al. 2012; Hwang et al. 2013*;* Park et al. 2015; Wang et al. 2016), in three ways. First of all, we argue for an *indirect effect* of anthropogenic emissions, i.e., mediated by sea surface temperatures. Linking emissions to Sahel rainfall through SSTs has the added advantage that it allows the synthesis in a single physical explanation of *natural* variability and *anthropogenic* change. Secondly, our argument is not about the role of interhemispheric gradients in SST shifting the latitudinal location of the Intertropical Convergence Zone and by extension rainfall in the Sahel. Rather, it is about *quasi-equilibrium in convection* as the world warms. The observed late twentieth century drying of the Sahel was more profound—longer lasting and of greater amplitude—than, e.g., the early twentieth century drought around 1910, despite the North Atlantic being cooler during the early period. This observation justifies the search for additional factors in drought, which we identify in greenhouse gas-induced tropical warming, which in the twentieth century occurred simultaneously with North Atlantic cooling. Therefore, thirdly, we argue for the *combined drying effect* of the two contrasting anthropogenic influences in twentieth century: drought was not caused by aerosols alone or by the cooling of the North Atlantic alone. Neither could it have been caused by greenhouse gases alone, as is made evident in simulations which include only the latter forcing (Biasutti 2013; Dong and Sutton 2015; Gaetani et al. 2017; Hill et al. 2017). Rather, drought resulted from the combination of aerosols and greenhouse gases. One influence cooled and reduced the moisture supply, while the other, warming, raised the threshold for convection—a double jeopardy. In the twenty-first century, external influence is dominated by GHG-induced warming, of both the North Atlantic and global tropical oceans. NARI, their difference, tends to zero, leaving all response to external forcing to be explained by their sum.

SVD rotation of the original predictors to their weighted sum and difference and the relative sizes of their regression coefficients (Table 2) expose the near-orthogonality of the twentieth and twenty-first centuries in the response of SSTs and rainfall to external forcing. The oceans’ translation of external forcing of predominantly anthropogenic nature leads to contrasting but not inconsistent outcomes in the past and the future. Late twentieth century drying and twenty-first century projections of wetting are consistent with different balances in the influences of aerosol and greenhouse gas emissions on regional energy and moisture budgets, underlined by one coherent physical explanation rooted in the dynamics of quasi-equilibrium in convection. The fact that emissions need to be considered holistically, not additively, explains how it is possible that while anthropogenic warming had a role in past drought, continued warming in the twenty-first century gives rise to a strengthening of the monsoon. As aerosol emissions have abated, especially around the North Atlantic, it is the combination of warmings of both North Atlantic and global tropical oceans that explains the strengthening of the monsoon: the upped ante can now be met. This situation is consistent with understanding of monsoon response to warming on paleoclimate time scales: when the ocean warms enough to contribute to an “upped ante” with increased moisture supply but not enough to shift the favored locus of convection from land to ocean (Chou et al. 2001), it reinforces the monsoon, as is the case for West Africa (Braconnot et al. 2012). With the usual caveats related to uncertainty in multi-model mean projections, that further warming may result in wetting of the Sahel is a conclusion worth reinforcing, in equal parts because it has gained in coherence between CMIP3 and CMIP5 (Biasutti 2013), and because the recovery of the rains is being experienced on the ground, in a fashion that is remarkably consistent with expectation from anthropogenic warming (West et al. 2008; Lodoun et al. 2013).

## Notes

### Acknowledgements

This article is dedicated to the memory of professor A. Ben Mohamed. The authors acknowledge the technical support of Naomi Henderson and Haibo Liu, in the Ocean and Climate Physics division of LDEO. Through partial processing and local storage, they facilitated the access to the CMIP simulations for the IRI & LDEO community. The authors also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modeling groups listed in the tables in Supplementary Material for producing and making available their model output. They acknowledge the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, in partnership with the Global Organization for Earth System Science Portals, for their coordination to support and develop the software infrastructure to distribute CMIP simulations. AG was supported by National Science Foundation grant AGS-0955372 (CAREER), by National Aeronautics and Space Administration grant NNX16AN29G (SERVIR), and by Columbia University’s World Project ACToday. AK was partially supported by Office of Naval Research Multidisciplinary University Research Initiatives grant N00014-12-1-0911 and by National Oceanic and Atmospheric Administration award NA17OAR4310156.

## Supplementary material

## References

- Ackerley D, Booth BBB, Knight SHE, Highwood EJ, Frame DJ, Allen MR, Rowell DP (2011) Sensitivity of 20th century Sahel rainfall to sulfate aerosol and CO2 forcing. J Climate 24:4999–5014Google Scholar
- Ali A, Lebel T (2009) The Sahelian standardized rainfall index revisited. Int J Climatol 29:1705–1714CrossRefGoogle Scholar
- Becker A, Finger P, Meyer-Christoffer A, Rudolf B, Schamm K, Schneider U, Ziese M (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 CrossRefGoogle Scholar
- Biasutti M (2013) Forced Sahel rainfall trends in the CMIP5 archive. J Geophys Res 118:1613–1623Google Scholar
- Biasutti M, Giannini A (2006) Robust Sahel drying in response to late 20
^{th}century forcings. Geophys Res Lett 11:L11706. https://doi.org/10.1029/2006GL026067 CrossRefGoogle Scholar - Biasutti M, Held IM, Sobel AH, Giannini A (2008) SST forcings and Sahel rainfall variability in simulations of 20
^{th}and 21^{st}centuries. J Clim 21:3471–3486CrossRefGoogle Scholar - Booth BBB, Dunstone NJ, Halloran PR, Andrews T, Bellouin N (2012) Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484:228–232CrossRefGoogle Scholar
- Braconnot P, Harrison S, Kageyama M, Masson-Delmotte V, Abe-Ouchi A, Otto- Bliesner B, Zhao Y (2012) Evaluation of climate models using palaeoclimatic data. Nat Clim Chang 2:417–424CrossRefGoogle Scholar
- Bryson RA (1973) Drought in Sahelia who or what is to blame? Ecologist 3:366–371Google Scholar
- Charney JG (1975) Dynamics of deserts and drought in the Sahel. Q J Roy Meteor Soc 101:193–202CrossRefGoogle Scholar
- Chiang JCH, Sobel AH (2002) Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J Clim 15:2616–2631CrossRefGoogle Scholar
- Chou C, Neelin JD (2004) Mechanisms of global warming impacts on regional tropical precipitation. J Clim 17(13):2688–2701CrossRefGoogle Scholar
- Chou C, Neelin JD, Su H (2001) Ocean-atmosphere feedbacks in an idealized monsoon. Q J R Meteorol Soc 127:1869–1891CrossRefGoogle Scholar
- Dai A, Lamb PJ, Trenberth KE, Hulme M, Jones PD, Xie P (2004) The recent Sahel drought is real. Int J Climatol 24:1323–1331CrossRefGoogle Scholar
- de Menocal P, Ortiz J, Guilderson T, Adkins J, Sarnthein M, Baker L, Yarusinskya M (2000) Abrupt onset and termination of the African Humid Period: rapid climate responses to gradual insolation forcing. Quat Sci Rev 19:347–361CrossRefGoogle Scholar
- Dong B, Sutton R (2015) Dominant role of greenhouse-gas forcing in the recovery of Sahel rainfall. Nat Clim Chang 5:757–760CrossRefGoogle Scholar
- Emanuel KA, Neelin JD, Bretherton CS (1994) On large-scale circulations in convecting atmospheres. Q J R Meteorol Soc 120:1111–1143CrossRefGoogle Scholar
- Folland CK, Palmer TN, Parker DE (1986) Sahel rainfall and worldwide sea temperatures, 1901-85. Nature 320:602–607CrossRefGoogle Scholar
- Gaetani M, Flamant C, Bastin S, Janicot S, Lavaysse C, Hourdin F, Braconnot P, Bony S (2017) West African monsoon dynamics and precipitation: the competition between global SST warming and CO
_{2}increase in CMIP5 idealized simulations. Clim Dyn 48:1353–1373CrossRefGoogle Scholar - Giannini A, Biasutti M, Held IM, Sobel AH (2008) A global perspective on African climate. Clim Chang 90:359–383. https://doi.org/10.1007/s10584-008-9396-y CrossRefGoogle Scholar
- Giannini A, Salack S, Lodoun T, Ali A, Gaye AT, Ndiaye O (2013) A unifying view of climate change in the Sahel linking intra-seasonal, interannual and longer time scales. Environ Res Lett 8:024010CrossRefGoogle Scholar
- Giannini A, Saravanan R, Chang P (2003) Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science 302:1027–1030. https://doi.org/10.1126/science.1089357 CrossRefGoogle Scholar
- Gillett NP, Weaver AJ, Zwiers FW, Wehner MF (2004) Detection of volcanic influence on global precipitation. Geophys Res Lett 31:L12217. https://doi.org/10.1029/2004GL020044 CrossRefGoogle Scholar
- Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, Baltimore, MD, U.S.A., p 694Google Scholar
- Greene AM, Giannini A, Zebiak SE (2009) Drought return times in the Sahel: a question of attribution. Geophys Res Lett 36:L12701. https://doi.org/10.1029/2009GL038868 CrossRefGoogle Scholar
- Haywood JM, Jones A, Bellouin N, Stephenson D (2013) Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nature Climate Change, 3:660–665. https://doi.org/10.1038/nclimate1857
- Held IM, Delworth TL, Lu J, Findell K, Knutson TR (2005) Simulation of Sahel drought in the 20
^{th}and 21^{st}centuries. Proc Natl Acad Sci 102:17891–17896. https://doi.org/10.1073/pnas.0509057102 CrossRefGoogle Scholar - Hill SA, Ming Y, Held IM, Zhao M (2017) A moist static energy budget-based analysis of the Sahel rainfall response to uniform oceanic warming. J Clim 30:5637–5660CrossRefGoogle Scholar
- Hwang Y-T, Frierson DMW, Kang SM (2013) Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20
^{th}century. Geophys Res Lett 40:2845–2850. https://doi.org/10.1002/grl.50502 CrossRefGoogle Scholar - Kang SM, Held IM, Frierson DMW, Zhao M (2008) The response of the ITCZ to extratropical thermal forcing: idealized slab-ocean experiments with a GCM. J Clim 21(14):3521–3532CrossRefGoogle Scholar
- Kaplan A, Cane MA, Kushnir Y, Clement AC, Blumenthal MB, Rajagopalan B (1998) Analyses of global sea surface temperature 1856-1991. J Geophys Res 103:18,567–18,589CrossRefGoogle Scholar
- Kawase H, Abe M, Yamada Y, Takemura T, Yokohata T, Nozawa T (2010) Physical mechanism of long-term drying trend over tropical North Africa. Geophys Res Lett 37:L09706Google Scholar
- Lamb PJ (1982) Persistence of Subsaharan drought. Nature 299:46–48CrossRefGoogle Scholar
- Lambert FH, Stott PA, Allen MR, Palmer MA (2004) Detection and attribution of changes in 20
^{th}century land precipitation. Geophys Res Lett 31:L10203. https://doi.org/10.1029/2004GL019545 CrossRefGoogle Scholar - Liepert BG 2002 Observed reductions in surface solar radiation in the United States and worldwide from 1961 to 1990. Geophys Res Lett doi: https://doi.org/10.1029/2002GL014910
- Liepert BG, Feichter J, Lohmann U, Roeckner E (2004) Can aerosols spin down the water cycle in a warmer and moister world? Geophys Res Lett 31:L06207. https://doi.org/10.1029/2003GL019060 CrossRefGoogle Scholar
- Lindzen RS, Nigam S (1987) On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J Atmos Sci 44:2418–2436CrossRefGoogle Scholar
- Lodoun T, Giannini A, Traoré PS, Somé L, Sanon M, Vaksmann M, Millogo-Rasolodimby J (2013) Changes in the character of precipitation in Burkina Faso associated with late-20
^{th}century drought and recovery in the Sahel. Environ Dev 5:96–108CrossRefGoogle Scholar - Lough JM (1986) Tropical Atlantic Sea surface temperatures and rainfall variations in sub-Saharan Africa. Mon Weather Rev 114:561–570CrossRefGoogle Scholar
- Nicholson SE (1983) Sub-Saharan rainfall in the years 1976-1980: evidence of continued drought. Mon Weather Rev 111:1646–1654CrossRefGoogle Scholar
- Park J-Y, Bader J, Matei D (2015) Northern-hemispheric differential warming is the key to understanding the discrepancies in the projected Sahel rainfall. Nat Commun 6:5985. https://doi.org/10.1038/ncomms6985 CrossRefGoogle Scholar
- Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag, 6th Series 2:559–572CrossRefGoogle Scholar
- Preisendorfer RW (1988)
*Principal component analysis in meteorology and oceanography*, posthumously compiled. In: Mobley CD (ed) Developments in atmospheric science, vol 17. Elsevier, Amsterdam, New York, pp xviii–xv425Google Scholar - Robock A, Liu Y (1994) The volcanic signal in Goddard Institute for Space Studies three-dimensional model simulations. J Clim 7:44–55CrossRefGoogle Scholar
- Rotstayn L, Lohmann U (2002) Tropical rainfall trends and the indirect aerosol effect. J Clim 15:2103–2116CrossRefGoogle Scholar
- Schewe J, Levermann A (2017) Non-linear intensification of Sahel rainfall as a possible dynamic response to future warming. Earth System Dynamics 8:495–505CrossRefGoogle Scholar
- Schneider T, Bischoff T, Haug GH (2014) Migrations and dynamics of the intertropical convergence zone. Nature 513:45–53. https://doi.org/10.1038/nature13636 CrossRefGoogle Scholar
- Seth A, Rauscher SA, Biasutti M, Giannini A, Camargo SJ, Rojas M (2013) CMIP5 projected changes in the annual cycle of precipitation in monsoon regions. J Clim 26:7328–7351CrossRefGoogle Scholar
- Shanahan TM, Overpeck JT, Anchukaitis KJ, Beck JW, Cole JE, Dettman DL, Peck JA, Scholz CA, King JW (2009) Atlantic forcing of persistent drought in West Africa. Science 324:377–380CrossRefGoogle Scholar
- Stanhill G, Cohen S (2001) Global dimming: a review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agric For Meteorol 107:255–278CrossRefGoogle Scholar
- Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Amer Meteor Soc 93:485–498CrossRefGoogle Scholar
- Vecchi GA, Swanson KL, Soden BJ (2008) Whither hurricane activity? Science 322:687–689CrossRefGoogle Scholar
- von Storch H and Zwiers FW 1999 Statistical analysis in climate research. Cambridge University Press pp. 484Google Scholar
- Wang H, Xie S-P, Tokinaga H, Liu Q, Kosaka Y (2016) Detecting cross-equatorial wind change as a fingerprint of climate response to anthropogenic aerosol forcing. Geophys Res Lett 43:3444–3450. https://doi.org/10.1002/2016GL068521 CrossRefGoogle Scholar
- West CT, Roncoli C, Ouattara F (2008) Local perceptions and regional climate trends of the Central Plateau of Burkina Faso. Land Degrad Dev 19:289–304CrossRefGoogle Scholar
- Zhang X, Zwiers FW, Hegerl GC, Gillett NP, Solomon S, Stott PA, Nozawa T (2007) Detection of human influence on twentieth-century precipitation trends. Nature 448:461–466. https://doi.org/10.1038/nature06025 CrossRefGoogle Scholar

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