Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Frequency of extreme Sahelian storms tripled since 1982 in satellite observations


The hydrological cycle is expected to intensify under global warming1, with studies reporting more frequent extreme rain events in many regions of the world2,3,4, and predicting increases in future flood frequency5. Such early, predominantly mid-latitude observations are essential because of shortcomings within climate models in their depiction of convective rainfall6,7. A globally important group of intense storms—mesoscale convective systems (MCSs)8—poses a particular challenge, because they organize dynamically on spatial scales that cannot be resolved by conventional climate models. Here, we use 35 years of satellite observations from the West African Sahel to reveal a persistent increase in the frequency of the most intense MCSs. Sahelian storms are some of the most powerful on the planet9, and rain gauges in this region have recorded a rise in ‘extreme’17 daily rainfall totals. We find that intense MCS frequency is only weakly related to the multidecadal recovery of Sahel annual rainfall, but is highly correlated with global land temperatures. Analysis of trends across Africa reveals that MCS intensification is limited to a narrow band south of the Sahara desert. During this period, wet-season Sahelian temperatures have not risen, ruling out the possibility that rainfall has intensified in response to locally warmer conditions. On the other hand, the meridional temperature gradient spanning the Sahel has increased in recent decades, consistent with anthropogenic forcing driving enhanced Saharan warming10. We argue that Saharan warming intensifies convection within Sahelian MCSs through increased wind shear and changes to the Saharan air layer. The meridional gradient is projected to strengthen throughout the twenty-first century, suggesting that the Sahel will experience particularly marked increases in extreme rain. The remarkably rapid intensification of Sahelian MCSs since the 1980s sheds new light on the response of organized tropical convection to global warming, and challenges conventional projections made by general circulation models.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Trends in MCS and rainfall characteristics across the Sahel.
Figure 2: Temperature trends (for June to September) in observations and models.
Figure 3: Evolution of observed precipitable water, measured at GPS stations.

Similar content being viewed by others


  1. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002)

    ADS  CAS  Google Scholar 

  2. Westra, S. et al. Future changes to the intensity and frequency of short-duration extreme rainfall. Rev. Geophys. 52, 522–555 (2014)

    Article  ADS  Google Scholar 

  3. Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470, 378–381 (2011)

    Article  ADS  CAS  Google Scholar 

  4. Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A. & Maher, N. More extreme precipitation in the world’s dry and wet regions. Nat. Clim. Chang. 6, 508–513 (2016)

    Article  ADS  Google Scholar 

  5. Kundzewicz, Z. W. et al. Flood risk and climate change: global and regional perspectives. Hydrol. Sci. J. 59, 1–28 (2014)

    Article  Google Scholar 

  6. Kendon, E. J. et al. Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Chang. 4, 570–576 (2014)

    Article  ADS  Google Scholar 

  7. O’Gorman, P. A. Precipitation extremes under climate change. Curr. Clim. Change Rep. 1, 49–59 (2015)

    Article  Google Scholar 

  8. Houze, R. A. Mesoscale convective systems. Rev. Geophys. 42, RG4003 (2004)

    Article  ADS  Google Scholar 

  9. Zipser, E. J., Liu, C., Cecil, D. J., Nesbitt, S. W. & Yorty, D. P. Where are the most intense thunderstorms on Earth? Bull. Am. Meteorol. Soc. 87, 1057–1071 (2006)

    Article  ADS  Google Scholar 

  10. Biasutti, M., Held, I. M., Sobel, A. H. & Giannini, A. SST forcings and Sahel rainfall variability in simulations of the twentieth and twenty-first centuries. J. Clim. 21, 3471–3486 (2008)

    Article  ADS  Google Scholar 

  11. O’Gorman, P. A. Sensitivity of tropical precipitation extremes to climate change. Nat. Geosci. 5, 697–700 (2012)

    Article  ADS  Google Scholar 

  12. Singleton, A. & Toumi, R. Super-Clausius–Clapeyron scaling of rainfall in a model squall line. Q. J. R. Meteorol. Soc. 139, 334–339 (2013)

    Article  ADS  Google Scholar 

  13. Berg, P., Moseley, C. & Haerter, J. O. Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci. (2013)

  14. Muller, C. Impact of convective organization on the response of tropical precipitation extremes to warming. J. Clim. 26, 5028–5043 (2013)

    Article  ADS  Google Scholar 

  15. Mathon, V., Laurent, H. & Lebel, T. Mesoscale convective system rainfall in the Sahel. J. Appl. Meteorol. 41, 1081–1092 (2002)

    Article  ADS  Google Scholar 

  16. Futyan, J. M. & Del Genio, A. D. Deep convective system evolution over Africa and the tropical Atlantic. J. Clim. 20, 5041–5060 (2007)

    Article  ADS  Google Scholar 

  17. Panthou, G., Vischel, T. & Lebel, T. Recent trends in the regime of extreme rainfall in the Central Sahel. Int. J. Climatol. 34, 3998–4006 (2014)

    Article  Google Scholar 

  18. Giannini, A., Saravanan, R. & Chang, P. Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science 302, 1027–1030 (2003)

    Article  ADS  CAS  Google Scholar 

  19. Dong, B. & Sutton, R. Dominant role of greenhouse-gas forcing in the recovery of Sahel rainfall. Nat. Clim. Chang. 5, 757–760 (2015)

    Article  ADS  CAS  Google Scholar 

  20. Evan, A. T ., Flamant, C ., Gaetani, M . & Guichard, F. The past, present and future of African dust. Nature 531, 493–495 (2016)

    Article  ADS  CAS  Google Scholar 

  21. Dardel, C. et al. Re-greening Sahel: 30 years of remote sensing data and field observations (Mali, Niger). Remote Sens. Environ. 140, 350–364 (2014)

    Article  ADS  Google Scholar 

  22. Cook, K. H. & Vizy, E. K. Detection and analysis of an amplified warming of the Sahara Desert. J. Clim. 28, 6560–6580 (2015)

    Article  ADS  Google Scholar 

  23. Rotunno, R., Klemp, J. B. & Weisman, M. L. A theory for strong, long-lived squall lines. J. Atmos. Sci. 45, 463–485 (1988)

    Article  ADS  Google Scholar 

  24. Alfaro, D. A. Low-tropospheric shear in the structure of squall lines: impacts on latent heating under layer-lifting ascent. J. Atmos. Sci. 74, 229–248 (2017)

    Article  ADS  Google Scholar 

  25. Barnes, G. M. & Sieckman, K. The environment of fast- and slow-moving tropical mesoscale convective cloud lines. Mon. Weath. Rev. 112, 1782–1794 (1984)

    Article  ADS  Google Scholar 

  26. Roca, R., Lafore, J.-P., Piriou, C. & Redelsperger, J.-L. Extratropical dry-air intrusions into the West African monsoon midtroposphere: an important factor for the convective activity over the Sahel. J. Atmos. Sci. 62, 390–407 (2005)

    Article  ADS  Google Scholar 

  27. Skinner, C. B. & Diffenbaugh, N. S. Projected changes in African easterly wave intensity and track in response to greenhouse forcing. Proc. Natl Acad. Sci. USA 111, 6882–6887 (2014)

    Article  ADS  CAS  Google Scholar 

  28. Seidel, D. J. & Randel, W. J. Variability and trends in the global tropopause estimated from radiosonde data. J. Geophys. Res. D 111, D21101 (2006)

    Article  ADS  Google Scholar 

  29. Parker, D. J., Thorncroft, C. D., Burton, R. R. & Diongue-Niang, A. Analysis of the African easterly jet, using aircraft observations from the JET2000 experiment. Q. J. R. Meteorol. Soc. 131, 1461–1482 (2005)

    Article  ADS  Google Scholar 

  30. Del Genio, A. D. Representing the sensitivity of convective cloud systems to tropospheric humidity in general circulation models. Surv. Geophys. 33, 637–656 (2012)

    Article  ADS  Google Scholar 

  31. Knapp, K. R. et al. Globally gridded satellite observations for climate studies. Bull. Am. Meteorol. Soc. 92, 893–907 (2011)

    Article  ADS  Google Scholar 

  32. Knapp, K. R. Calibration assessment of ISCCP geostationary infrared observations using HIRS. J. Atmos. Ocean. Technol. 25, 183–195 (2008)

    Article  ADS  Google Scholar 

  33. Bock, O. et al. West African Monsoon observed with ground-based GPS receivers during African Monsoon Multidisciplinary Analysis (AMMA). J. Geophys. Res. D 113, D21105 (2008)

    Article  ADS  Google Scholar 

  34. Holloway, C. E. & Neelin, J. D. Temporal relations of column water vapor and tropical precipitation. J. Atmos. Sci. 67, 1091–1105 (2010)

    Article  ADS  Google Scholar 

  35. Rohde, R. et al. A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinfor. Geostat Overview (2013)

  36. Lawrimore, J. H. et al. An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res. D 116, D19121 (2011)

    Article  ADS  Google Scholar 

  37. Jones, P. D. et al. Hemispheric and large-scale land-surface air temperature variations: an extensive revision and an update to 2010. J. Geophys. Res. D 117, D05127 (2012)

    ADS  Google Scholar 

  38. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011)

    Article  ADS  Google Scholar 

  39. Christy, J. R., Spencer, R. W. & Braswell, W. D. MSU tropospheric temperatures: dataset construction and radiosonde comparisons. J. Atmos. Ocean. Technol. 17, 1153–1170 (2000)

    Article  ADS  Google Scholar 

  40. Durre, I., Vose, R. S. & Wuertz, D. B. Overview of the integrated global radiosonde archive. J. Clim. 19, 53–68 (2006)

    Article  ADS  Google Scholar 

  41. Free, M. et al. Radiosonde atmospheric temperature products for assessing climate (RATPAC): a new data set of large-area anomaly time series. J. Geophys. Res. D 110, D22101 (2005)

    Article  ADS  Google Scholar 

  42. Sherwood, S. C., Meyer, C. L., Allen, R. J. & Titchner, H. A. Robust tropospheric warming revealed by iteratively homogenized radiosonde data. J. Clim. 21, 5336–5352 (2008)

    Article  ADS  Google Scholar 

  43. Thorne, P. W. et al. Revisiting radiosonde upper air temperatures from 1958 to 2002. J. Geophys. Res. D 110, D18105 (2005)

    Article  ADS  Google Scholar 

  44. Haimberger, L., Tavolato, C. & Sperka, S. Toward elimination of the warm bias in historic radiosonde temperature records—some new results from a comprehensive intercomparison of upper-air data. J. Clim. 21, 4587–4606 (2008)

    Article  ADS  Google Scholar 

  45. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2011)

    Article  Google Scholar 

Download references


The research leading to these results received funding from the UK’s National Environment Research Council (NERC)/Department for International Development (DFID) Future Climate For Africa programme, under the AMMA-2050 project (grant numbers NE/M020428/1, NE/M019969/1, NE/M019950/1, NE/M020126/1 and NE/M019934/1). D.J.P. is supported by a Royal Society Wolfson Research Merit Award. We thank K. Knapp, J. Marsham and D. Kniveton for helpful comments; J. Griffin for assistance in preparing the figures; the providers of key data sets used here (Eumetsat; the US National Oceanic and Atmospheric Administration (NOAA); NASA; the European Centre for Mid-Range Weather Forecasts (ECMWF); the meteorological services of Mali, Burkina Faso, Niger and Benin; the World Climate Research Programme’s Working Group on Coupled Modelling); and the centres that provided modelling data.

Author information

Authors and Affiliations



This study was conceived by C.M.T. and T.V.; C.M.T., D.B., F.G., D.J.P., T.V. and S.J. designed the research; C.M.T., D.B., F.G., P.P.H., C.K. and G.P. analysed the data; and O.B. provided expertise on the GPS analysis. C.M.T. wrote the manuscript, which all authors commented on.

Corresponding author

Correspondence to Christopher M. Taylor.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

RReviewer Information Nature thanks C. Liu and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Rainfall and temperature time series 1950–2015.

a, Annual mean rainfall (in mm), and b, contribution of extreme rain events to the annual rainfall total (as a percentage), from the daily rainfall data set in the Central Sahel. c, Global land mean temperatures for JJAS from the Climatic Research Unit (CRU) data set ( Note that data for 2015 were not included in the CRU data. Five-year running means are shown as a red line.

Extended Data Figure 2 Comparison between observations of extreme rainfall and measures of cold cloud from MSG data.

a, b, In the bottom graphs, daily rainfall (in mm; values of less than 10 mm are excluded), recorded at 19 Sahelian gauges in 2004–2015, is plotted against the logarithm of the maximum area of contiguous cold cloud (a) and the minimum local brightness temperature (in °C) for MCSs greater than 25,000 km2 (b). c, Mean MCS temperature is plotted as a function of the maximum rainfall rate from TRMM precipitation radar, based on 1,640 coincident overpasses. Each point is shaded according to its normalized kernel density. The bars in the upper plots show the percentage contribution of equally populated quintiles to the total number of extreme events (greater than 38 mm for a and b; greater than 30 mm per hour for c).

Extended Data Figure 3 Diurnal cycle of MCS properties.

ah, Diurnal mean MCS frequencies (red line; left panels) and 90th centile cold-cloud areas (red line; right panels), for temperature thresholds of −40 °C (a, b), −60 °C (c, d), −70 °C (e, f), and −75 °C (g, h). Also shown are the trends (black lines) in these quantities, expressed in terms of the linear regression gradient multiplied by the length of the data set (35 years). Trends that are significant (according to a two-tailed t-test) at the 99.5% (or 95%) are denoted by a circle (or a plus sign).

Extended Data Figure 4 Mean temperature of MCSs identified from GridSat data using a temperature threshold of −40 °C.

Temperatures (in °C) are presented as JJAS averages, sampled at four different times of day. Linear trends are shown as dashed lines, and the values of associated correlation coefficients (r) are quoted.

Extended Data Figure 5 Trends in MCS cloud cover at 1800 utc.

aj, Shaded pixels denote significant trends in MCS cloud cover (P < 0.05) over the full annual cycle (a, b), March to May (c, d), June to August (e, f), September to November (g, h), and December to February (i, j), using data from 1982–2015. The left-hand column uses a temperature threshold of −60 °C; the threshold for the right-hand column is −70 °C. Trends are expressed as the percentage change per decade relative to the long-term mean (shown as contours).

Extended Data Figure 6 Trends in temperature at 2 metres.

ae, Temperature trends (°C per decade) from a, station data within the Berkeley Earth data set; b, MERRA-2 reanalysis; c, GHCN station data; d, the CMIP5 ensemble mean for ‘all forcings minus natural forcings’; and e, the gridded station data of CRUTEM4. Trends significant at the 95% level are shown as circles with black edges in panels a and c; with plus signs in e; and enclosed by a blue contour in b.

Extended Data Figure 7 Event-based correlations between pre-MCS atmospheric variables and observed MCS mean temperature on arrival at Niamey.

aj, The pre-MCS variables (x-axes) are from ERA-Interim. All linear regressions are significant at the 99.55% level according to a two-tailed t-test, with the exception of c, h and i, which are significant at the 95% level.

Extended Data Figure 8 Trends and correlations with MCS intensity of zonal and annual mean variables from ERA-Interim.

ai, Correlation coefficients (shaded where significant at P < 0.05) showing the relationship of atmospheric variables with af, observed Sahelian MCS (−40 °C threshold) mean temperature, and gi, year. In af, contours depict mean values of: a, potential temperature (in K); b, zonal wind (in m s−1); c, meridional wind (in m s−1); d, specific humidity (in g kg−1); e, relative humidity (as a percentage); and f, variance of meridional wind (in m2 s−2). In gi, the contours show the trends in: g, potential temperature (in K per decade); h, zonal wind (in m s−1 decade−1); and i, meridional wind (in m s−1 decade−1). Dotted lines depict the latitudinal limits of the Sahel. Note that negative correlations with MCS temperature (that is, positive correlations with intensity; af), and positive correlations with time (gi) are shaded red for ease of comparison. The colour bars show correlation coefficients.

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taylor, C., Belušić, D., Guichard, F. et al. Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature 544, 475–478 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing