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The past, present and future of African dust


African dust emission and transport exhibits variability on diurnal1 to decadal2 timescales and is known to influence processes such as Amazon productivity3, Atlantic climate modes4, regional atmospheric composition and radiative balance5 and precipitation in the Sahel6. To elucidate the role of African dust in the climate system, it is necessary to understand the factors governing its emission and transport. However, African dust is correlated with seemingly disparate atmospheric phenomena, including the El Niño/Southern Oscillation7,8, the North Atlantic Oscillation9, the meridional position of the intertropical convergence zone10,11, Sahelian rainfall8 and surface temperatures over the Sahara Desert12, all of which obfuscate the connection between dust and climate. Here we show that the surface wind field responsible for most of the variability in North African dust emission reflects the topography of the Sahara, owing to orographic acceleration of the surface flow. As such, the correlations between dust and various climate phenomena probably arise from the projection of the winds associated with these phenomena onto an orographically controlled pattern of wind variability. A 161-year time series of dust from 1851 to 2011, created by projecting this wind field pattern onto surface winds from a historical reanalysis13, suggests that the highest concentrations of dust occurred from the 1910s to the 1940s and the 1970s to the 1980s, and that there have been three periods of persistent anomalously low dust concentrations—in the 1860s, 1950s and 2000s. Projections of the wind pattern onto climate models give a statistically significant downward trend in African dust emission and transport as greenhouse gas concentrations increase over the twenty-first century, potentially associated with a slow-down of the tropical circulation. Such a dust feedback, which is not represented in climate models, may be of benefit to human and ecosystem health in West Africa via improved air quality14 and increased rainfall6. This feedback may also enhance warming of the tropical North Atlantic15, which would make the basin more suitable for hurricane formation and growth16.

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Figure 1: Second EOF/PC of North African 10-m winds.
Figure 2: North African orography and dust source regions.
Figure 3: Estimates of North Atlantic dust.


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This work was supported by the Agence Nationale de la Recherche (ANR) grant ANR-10-LABX-18-01 of the national Programme Investissements d’Avenir provided by the Laboratoire d’excellence Institut Pierre Simon Laplace (L-IPSL). We appreciate comments from our colleagues.

Author information




A.T.E. carried out the main analysis and wrote the manuscript. F.G. analysed wind speed data from weather stations. All authors designed the study, discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Amato T. Evan.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 First three EOFs and PCs of 10-m winds over North Africa from ERA-I.

Shown is the spatial structure of the first three EOFs from the eigenanalysis of monthly mean 10-m winds from ERA-I (ac), and the corresponding PC time series in units of standard deviation (df). Descriptions of arrows, shading and time series are identical to that for Fig. 1. See Methods for details of the eigenanalysis. Source data

Extended Data Figure 2 Comparison of monthly mean wind speeds and dust uplift potential.

Shown are scatter plots (blue filled circles) of mean monthly DUP (ordinate axis) and wind speeds (abscissa axis) for the North African synoptic stations at Djanet, Algeria (a), Tamanrasset, Algeria (b) and Agadez, Niger (c). Also shown for reference are the least-squares best-fit lines (red). The correlation coefficient r and statistical significance P are indicated in each plot. Source data

Extended Data Figure 3 Topography affecting surface winds across the Sahara.

Shown are surface elevations along each transect in Fig. 2: the Atlas to the Ahaggar mountains (a), the Ahaggar to the Tibesti mountains (b), the Ahaggar mountains to the Aïr massif (c) and the Tibesti mountains to the Ennedi plateau (d). These topographic features accelerate the surface flow and give rise to the spatial structure of the second EOF and PC pair (Fig. 1a). The titles indicate the major topographic features bounding each transect. Note the different horizontal lengths (abscissa) and heights (ordinate) for each plot. Source data

Extended Data Figure 4 Non-stationary correlations between CIRES-20CR PC2 and climate indices.

Plotted is the correlation coefficient between the annual mean CIRES-20CR PC2 and the Jones North Atlantic Oscillation, Niño 3.4, the Sahel-averaged Palmer Drought Severity index (PDSI), the latitude of the intertropical convergence zone, and the 925–700 hPa thickness of the Saharan Heat Low (ZSHL). All correlation coefficients are for the preceding 31-year period (for example, the value of 0.6 for the Jones North Atlantic Oscillation in 1940 indicates that the correlation coefficient between the Jones North Atlantic Oscillation and the CIRES-20CR PC2 is 0.6 for the period 1910–1940). We indicate statistically significant correlations (P < 0.05) with a filled circle, although here the P value is not calculated using effective degrees of freedom (as is the case elsewhere in this Letter). Source data

Extended Data Figure 5 CMIP5 RCP 4.5 and RCP 8.5 twenty-first-century trends in PC2.

a, RCP 8.5. b, RCP 4.5. Shown are the PC2 linear trends (circles), 95% confidence intervals (error bars) and the multimodel mean trend (blue dashed line) for RCP 8.5 (top) and 4.5 (bottom) simulations. All trends are in units of dust optical depth per 100 years. Source data

Extended Data Figure 6 CMIP5 RCP 8.5 twenty-first-century trends in estimated and modelled dust.

Plotted is the CMIP5 models’ twentieth-century trends in dust mass path for the RCP 8.5 experiments (abscissa) versus the twentieth-century trends calculated from the PC2 time series (ordinate). Included here are only CMIP5 models for which dust mass path and 10-m wind data are available. The red line is the least-squares best-fit line (slope is not statistically different from zero) and the dashed line is the one-to-one line. All trends are in units of standard deviation per 100 years. Source data

Extended Data Figure 7 EOF/PC pairs for various reanalyses.

Shown are the first three EOFs (top rows) and corresponding PC time series (bottom rows) calculated from the CIRES-20CR, ERA-20CR, MERRA, NCEP2 and NNRP data sets. Percentages of the variance of the data set explained by each EOF/PC pair are shown. Source data

Extended Data Figure 8 Wind speed comparisons between observations and reanalyses.

Shown are the r2 values from the correlation between monthly mean surface winds from station data and monthly mean 10-m winds from reanalyses for five stations in the Sahara over the period 2000–2013. In all cases the r2 values for ERA-I are greater than 0.5 and higher than those for the other reanalyses. Source data

Extended Data Figure 9 Modelled and reanalysis time series of historical dust.

Plotted is the PC2 time series from the CIRES-20CR, identical to that shown in Fig. 3a, and the CMIP5 multimodel mean PC2 time series from the historical forcing simulations. Both annul mean time series have been smoothed with an 11-point running mean filter to highlight decadal scale variability. Source data

Extended Data Table 1 CMIP5 models used in this study

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Evan, A., Flamant, C., Gaetani, M. et al. The past, present and future of African dust. Nature 531, 493–495 (2016).

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