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Frequency of extreme Sahelian storms tripled since 1982 in satellite observations

Nature volume 544, pages 475–478 (2017)Cite this article

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Abstract

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.

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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.

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Acknowledgements

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

  1. Centre for Ecology and Hydrology, Wallingford, OX10 8BB, UK

    Christopher M. Taylor, Danijel Belušić, Phil P. Harris & Cornelia Klein

  2. National Centre for Earth Observation, Wallingford, OX10 8BB, UK

    Christopher M. Taylor & Phil P. Harris

  3. Swedish Meteorological and Hydrological Institute, Norrköping, 601 76, SE, Sweden

    Danijel Belušić

  4. Centre National de Recherches Météorologique (CNRM), UMR 3589, Centre National de la Recherche Scientifique (CNRS) & Météo-France, Toulouse Cedex, 31057, France

    Françoise Guichard

  5. School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK

    Douglas J. Parker

  6. Univ. Grenoble Alpes, l’Institut de Recherche pour le Développement (IRD), CNRS, Institute of Engineering Univ. Grenoble Alpes (G-INP), Institut des Géosciences de l’Environnement (IGE), Grenoble, F-38000, France

    Théo Vischel & Gérémy Panthou

  7. Institut national de l’information géographique et forestière (IGN) Laboratoire de Recherche en Géodésie (LAREG), Université Paris Diderot, Sorbonne Paris Cité, 75205, Paris, France

    Olivier Bock

  8. UMR7159, Laboratoire d’Océanographie et du Climat: Expérimentations et Approches Numérique (LOCEAN), Sorbonne Universités, l’Université Pierre et Marie Curie (UPMC)-CNRS-l’Institut de Recherche pour le Développement (IRD)-Muséum National d’Histoire Naturelle (MNHN), Paris, 75252, France

    Serge Janicot

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  1. Christopher M. Taylor
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Contributions

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.

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Correspondence to Christopher M. Taylor.

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

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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 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/). 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.

a–h, 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.

a–j, 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.

a–e, 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.

a–j, 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.

a–i, Correlation coefficients (shaded where significant at P < 0.05) showing the relationship of atmospheric variables with a–f, observed Sahelian MCS (−40 °C threshold) mean temperature, and g–i, year. In a–f, 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 g–i, 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; a–f), and positive correlations with time (g–i) are shaded red for ease of comparison. The colour bars show correlation coefficients.

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Taylor, C., Belušić, D., Guichard, F. et al. Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature 544, 475–478 (2017). https://doi.org/10.1038/nature22069

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