• Ackerley, D., , 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, 49995014, doi:10.1175/JCLI-D-11-00019.1.

    • Search Google Scholar
    • Export Citation
  • AMMA, 2010: The International Science Plan 2010–2020. African Monsoon Multidisciplinary Analyses, 136 pp. [Available online at www.amma-international.org/IMG/pdf/ISP2_v2.pdf.]

  • Bader, J., , and M. Latif, 2003: The impact of decadal scale Indian Ocean SST anomalies on Sahelian rainfall and the North Atlantic Oscillation. Geophys. Res. Lett., 30, 2169, doi:10.1029/2003GL018426.

    • Search Google Scholar
    • Export Citation
  • Bader, J., , and M. Latif, 2011: The 1983 drought in the West Sahel: A case study. Climate Dyn., 36, 463472, doi:10.1007/s00382-009-0700-y.

    • Search Google Scholar
    • Export Citation
  • Batté, L., , and M. Déqué, 2011: Seasonal predictions of precipitation over Africa using coupled ocean–atmosphere general circulation models: Skill of the ENSEMBLES project multimodel ensemble forecasts. Tellus, 63A, 283299, doi:10.1111/j.1600-0870.2010.00493.x.

    • Search Google Scholar
    • Export Citation
  • Biasutti, M., 2011: Atmospheric science: A man-made drought. Nat. Climate Change, 1, 197198, doi:10.1038/nclimate1151.

  • Biasutti, M., 2013: Forced Sahel rainfall trends in the CMIP5 archive. J. Geophys. Res., 118, 1613–1623, doi:10.1002/jgrd.50206.

  • Biasutti, M., , and A. Giannini, 2006: Robust Sahel drying in response to late 20th century forcings. Geophys. Res. Lett., 33, L11706, doi:10.1029/2006GL026067.

    • Search Google Scholar
    • Export Citation
  • Biasutti, M., , and A. H. Sobel, 2009: Delayed Sahel rainfall and global seasonal cycle in a warmer climate. Geophys. Res. Lett., 36, L23707, doi:10.1029/2009GL041303.

    • Search Google Scholar
    • Export Citation
  • Biasutti, M., , A. H. Sobel, , and Y. Kushnir, 2006: AGCM precipitation biases in the tropical Atlantic. J. Climate, 19, 935958, doi:10.1175/JCLI3673.1.

    • Search Google Scholar
    • Export Citation
  • 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, 34713486, doi:10.1175/2007JCLI1896.1.

    • Search Google Scholar
    • Export Citation
  • Boone, A., , I. Poccard-Leclerq, , Y. Xue, , J. Feng, , and P. de Rosnay, 2010: Evaluation of the WAMME model surface fluxes using results from the AMMA land-surface model intercomparison project. Climate Dyn., 35, 127142, doi:10.1007/s00382-009-0653-1.

    • Search Google Scholar
    • Export Citation
  • 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, 228232, doi:10.1038/nature10946.

    • Search Google Scholar
    • Export Citation
  • Brooks, G. E., 1998: Climate and history in West Africa. Transformations in Africa: Essays on Africa’s Later Past, G. Connah, Ed., University Press, 139–159.

  • 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. Int. J. Climatol., 21, 9731005, doi:10.1002/joc.673.

    • Search Google Scholar
    • Export Citation
  • Caminade, C., , and L. Terray, 2010: Twentieth century Sahel rainfall variability as simulated by the ARPEGE AGCM, and future changes. Climate Dyn., 35, 7594, doi:10.1007/s00382-009-0545-4.

    • Search Google Scholar
    • Export Citation
  • Caminade, C., and et al. , 2011: Mapping Rift Valley fever and malaria risk over West Africa using climatic indicators. Atmos. Sci. Lett., 12, 96103, doi:10.1002/asl.296.

    • Search Google Scholar
    • Export Citation
  • Caminade, C., and et al. , 2014: Impact of climate change on global malaria distribution. Proc. Natl. Acad. Sci. USA,111, 3286–3291, doi:10.1073/pnas.1302089111.

  • Carton, J. A., , X. Cao, , B. S. Giese, , and A. M. Da Silva, 1996: Decadal and interannual SST variability in the tropical Atlantic. Ocean. J. Phys. Oceanogr., 26, 11651175, doi:10.1175/1520-0485(1996)026<1165:DAISVI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cash, B. A., , X. Rodó, , J. Ballester, , M. J. Bouma, , A. Baeza, , R. Dhiman, , and M. Pascual, 2013: Malaria epidemics and the influence of the tropical South Atlantic on the Indian monsoon. Nat. Climate Change, 3, 502507, doi:10.1038/nclimate1834.

    • Search Google Scholar
    • Export Citation
  • Charney, J. G., 1975: Dynamics of deserts and drought in the Sahel. Quart. J. Roy. Meteor. Soc., 101, 193202, doi:10.1002/qj.49710142802.

    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., , and A. H. Sobel, 2002: Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate, 15, 26162631, doi:10.1175/1520-0442(2002)015<2616:TTTVCB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chin, M., and et al. , 2014: Multi-decadal aerosol variations from 1980 to 2009: A perspective from observations and a global model. Atmos. Chem. Phys., 14, 36573690, doi:10.5194/acp-14-3657-2014.

    • Search Google Scholar
    • Export Citation
  • Chou, C., , and C.-W. Lan, 2012: Changes in the annual range of precipitation under global warming. J. Climate, 25, 222235, doi:10.1175/JCLI-D-11-00097.1.

    • Search Google Scholar
    • Export Citation
  • Chou, C., , J. C. H. Chiang, , C.-W. Lan, , C.-H. Chung, , Y.-C. Liao, , and C.-J. Lee, 2013: Increase in the range between wet and dry season precipitation. Nat. Geosci., 6, 263267, doi:10.1038/ngeo1744.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., 2008: The mysteries of Sahel droughts. Nat. Geosci., 1, 647648, doi:10.1038/ngeo320.

  • Davey, M. K., and et al. , 2001: STOIC: A study of coupled model climatology and variability in tropical ocean regions. Climate Dyn., 18, 403420, doi:10.1007/s00382-001-0188-6.

    • Search Google Scholar
    • Export Citation
  • Dezfuli, A. K., , and S. E. Nicholson, 2011: A note on long-term variations of the African easterly jet. Int. J. Climatol., 31, 20492054, doi:10.1002/joc.2209.

    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., , A. Weisheimer, , T. N. Palmer, , J. M. Murphy, , and D. Smith, 2010: Forecast quality assessment of the ENSEMBLES seasonal-to-decadal Stream 2 hindcasts. ECMWF Tech. Memo. 621, 45 pp.

  • Doblas-Reyes, F. J., and et al. , 2013: Initialized near-term regional climate change prediction. Nature Commun.,4, 1715, doi:10.1038/ncomms2704.

  • Douville, H., 2002: Influence of soil moisture on the Asian and African monsoon. Part II: Interannual variability. J. Climate, 15, 701720, doi:10.1175/1520-0442(2002)015<0701:IOSMOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Douville, H., , S. Conil, , S. Tyteca, , and A. Voldoire, 2007: Soil moisture memory and West African monsoon predictability: Artefact or reality? Climate Dyn., 28, 723742, doi:10.1007/s00382-006-0207-8.

    • Search Google Scholar
    • Export Citation
  • Druyan, L. M., 2011: Studies of 21st-century precipitation trends over West Africa. Int. J. Climatol., 31, 14151424, doi:10.1002/joc.2180.

    • Search Google Scholar
    • Export Citation
  • Druyan, L. M., and et al. , 2010: The WAMME regional model intercomparison study. Climate Dyn., 35, 175192, doi:10.1007/s00382-009-0676-7.

    • Search Google Scholar
    • Export Citation
  • Eltahir, E. A. B., , and C. Gong, 1996: Dynamics of wet and dry years in West Africa. J. Climate, 9, 10301042, doi:10.1175/1520-0442(1996)009<1030:DOWADY>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ermert, V., , A. H. Fink, , and H. Paeth, 2013: The potential effects of climate change on malaria transmission in Africa using bias-corrected regionalised climate projections and a simple malaria seasonality model. Climatic Change, 120, 741754, doi:10.1007/s10584-013-0851-z.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., , T. N. Palmer, , and D. E. Parker, 1986: Sahel rainfall and worldwide sea temperatures, 1901–85. Nature, 320, 602607, doi:10.1038/320602a0.

    • Search Google Scholar
    • Export Citation
  • Fontaine, B., , and S. Janicot, 1996: Sea surface temperature fields associated with West African rainfall anomaly types. J. Climate, 9, 29352940, doi:10.1175/1520-0442(1996)009<2935:SSTFAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fontaine, B., and et al. , 2010: Impacts of warm and cold situations in the Mediterranean basins on the West African monsoon: Observed connection patterns (1979–2006) and climate simulations. Climate Dyn., 35, 95114, doi:10.1007/s00382-009-0599-3.

    • Search Google Scholar
    • Export Citation
  • Fontaine, B., , M. Gaetani, , A. Ullmann, , and P. Roucou, 2011a: Time evolution of observed July–September sea surface temperature-Sahel climate teleconnection with removed quasi-global effect (1900–2008). J. Geophys. Res.,116, D04105, doi:10.1029/2010JD014843.

  • Fontaine, B., , P.-A. Monerie, , M. Gaetani, , and P. Roucou, 2011b: Climate adjustments over the African-Indian monsoon regions accompanying Mediterranean Sea thermal variability. J. Geophys. Res.,116, D23122, doi:10.1029/2011JD016273.

  • Frich, P., , L. V. Alexander, , P. Della-Marta, , B. Gleason, , M. Haylock, , A. M. G. Klein Tank, , and T. Peterson, 2002: Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Res., 19, 193212, doi:10.3354/cr019193.

    • Search Google Scholar
    • Export Citation
  • Gaetani, M., , and E. Mohino, 2013: Decadal prediction of the Sahelian precipitation in CMIP5 simulations. J. Climate, 26, 77087719, doi:10.1175/JCLI-D-12-00635.1.

    • Search Google Scholar
    • Export Citation
  • Gaetani, M., , B. Fontaine, , P. Roucou, , and M. Baldi, 2010: Influence of the Mediterranean Sea on the West African monsoon: Intraseasonal variability in numerical simulations. J. Geophys. Res., 115, D24115, doi:10.1029/2010JD014436.

    • Search Google Scholar
    • Export Citation
  • García-Serrano, J., , F. J. Doblas-Reyes, , R. J. Haarsma, , and I. Polo, 2013: Decadal prediction of the dominant West African monsoon rainfall modes. J. Geophys. Res., 118, 52605279, doi:10.1002/jgrd.50465.

    • Search Google Scholar
    • Export Citation
  • García-Serrano, J., , V. Guemas, , and F. J. Doblas-Reyes, 2015: Added-value from initialization in predictions of Atlantic multi-decadal variability. Climate Dyn., doi:10.1007/s00382-014-2370-7, in press.

    • Search Google Scholar
    • Export Citation
  • Giannini, A., 2010: Mechanisms of climate change in the semi-arid African Sahel: The local view. J. Climate, 23, 743756, doi:10.1175/2009JCLI3123.1.

    • Search Google Scholar
    • Export Citation
  • Giannini, A., , R. Saravanan, , and P. Chang, 2003: Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales. Science, 302, 10271030, doi:10.1126/science.1089357.

    • Search Google Scholar
    • Export Citation
  • Giannini, A., , S. Salack, , T. Lodoun, , A. Ali, , A. T. 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, doi:10.1088/1748-9326/8/2/024010.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., and et al. , 2010: Providing seasonal-to-interannual climate information for risk management and decision-making. Proc. Environ. Sci., 1, 81101, doi:10.1016/j.proenv.2010.09.007.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., and et al. , 2013: A verification framework for interannual-to-decadal prediction experiments. Climate Dyn., 40, 245272, doi:10.1007/s00382-012-1481-2.

    • Search Google Scholar
    • Export Citation
  • Goutorbe, J. P., and et al. , 1997: An overview of HAPEX-Sahel: A study in climate and desertification. J. Hydrol., 188–189, 417, doi:10.1016/S0022-1694(96)03308-2.

    • Search Google Scholar
    • Export Citation
  • Graham, R. J., and et al. , 2011: Long-range forecasting and the Global Framework for Climate Services. Climate Res., 47, 47–55, doi:10.3354/cr00963.

    • Search Google Scholar
    • Export Citation
  • Gu, G., , and R. F. Adler, 2006: Interannual rainfall variability in the tropical Atlantic region. J. Geophys. Res., 111, D02106, doi:10.1029/2005JD005944.

    • Search Google Scholar
    • Export Citation
  • Guemas, V., , J. García-Serrano, , A. Mariotti, , F. J. Doblas-Reyes, , and L.-P. Caron, 2015: Prospects for decadal climate prediction in the Mediterranean region. Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.2379, in press.

    • Search Google Scholar
    • Export Citation
  • 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, L17702, doi:10.1029/2005GL023232.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , and K. Cook, 2008: Ocean warming and late-twentieth-century Sahel drought and recovery. J. Climate, 21, 37973814, doi:10.1175/2008JCLI2055.1.

    • Search Google Scholar
    • Export Citation
  • Harris, I., , P. D. Jones, , T. J. Osborn, , and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 Dataset. Int. J. Climatol., 34, 623642, doi:10.1002/joc.3711.

    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., 1984: Interannual variability and annual cycle: Mechanisms of circulation and climate in the tropical Atlantic sector. Mon. Wea. Rev., 112, 10971107, doi:10.1175/1520-0493(1984)112<1097:IVAACM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., , and P. J. Lamb, 1977: Some aspects of circulation and climate over the eastern equatorial Atlantic. Mon. Wea. Rev., 105, 10191023, doi:10.1175/1520-0493(1977)105<1019:SAOCAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., , and D. Polzin, 2011: Long-term variations of circulation in the tropical Atlantic sector and Sahel rainfall. Int. J. Climatol., 31, 649655, doi:10.1002/joc.2116.

    • Search Google Scholar
    • Export Citation
  • Haywood, J. M., , A. Jones, , N. Bellouin, , and D. Stephenson, 2013: Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nat. Climate Change, 3, 660665, doi:10.1038/nclimate1857.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, doi:10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , T. L. Delworth, , J. Lu, , K. L. Findell, , and T. R. Knutson, 2005: Simulation of Sahel drought in the 20th and 21st centuries. Proc. Natl. Acad. Sci. USA, 102, 17 89117 896, doi:10.1073/pnas.0509057102.

    • Search Google Scholar
    • Export Citation
  • Hernández-Díaz, L., , R. Laprise, , L. Sushama, , A. Martynov, , K. Winger, , and B. Dugas, 2013: Climate Dyn., 40, 14151433, doi:10.1007/s00382-012-1387-z.

    • Search Google Scholar
    • Export Citation
  • Hewitt, C. D., 2004: ENSEMBLES-based predictions of climate changes and their impacts. Eos, Trans. Amer. Geophys. Union,85, 566, doi:10.1029/2004EO520005.

  • Hoerling, M., , J. W. Hurrell, , J. Eischeid, , and A. S. Phillips, 2006: Detection and attribution of twentieth-century northern and southern African rainfall change. J. Climate, 19, 39894008, doi:10.1175/JCLI3842.1.

    • Search Google Scholar
    • Export Citation
  • Hoerling, M., , J. Eisheid, , and J. Perlwitz, 2010: Regional precipitation trends: Distinguishing natural variability from anthropogenic forcing. J. Climate, 23, 21312145, doi:10.1175/2009JCLI3420.1.

    • Search Google Scholar
    • Export Citation
  • Horel, J. D., , V. E. Kousky, , and M. T. Kagano, 1986: Atmospheric conditions in the Atlantic sector during 1983 and 1984. Nature, 322, 248251, doi:10.1038/322248a0.

    • Search Google Scholar
    • Export Citation
  • Hoshen, M. B., , and A. P. Morse, 2004: A weather-driven model of malaria transmission. Malar. J., 3, 32, doi:10.1186/1475-2875-3-32.

  • Huffman, G. J., and et al. , 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Hwang, Y.-T., , D. M. W. 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, 28452850, doi:10.1002/grl.50502.

    • Search Google Scholar
    • Export Citation
  • Ingram, K. T., , M. C. Roncoli, , and P. H. Kirshen, 2002: Opportunities and constraints for farmers of West Africa to use seasonal precipitation forecasts with Burkina Faso as a case study. Agric. Syst., 74, 331349, doi:10.1016/S0308-521X(02)00044-6.

    • Search Google Scholar
    • Export Citation
  • Janicot, S., , V. Moron, , and B. Fontaine, 1996: ENSO dynamics and Sahel droughts. Geophys. Res. Lett., 23, 515518, doi:10.1029/96GL00246.

    • Search Google Scholar
    • Export Citation
  • Janicot, S., , A. Harzallah, , B. Fontaine, , and V. Moron, 1998: West African monsoon dynamics and eastern equatorial Atlantic and Pacific SST anomalies (1970–88). J. Climate, 11, 18741882, doi:10.1175/1520-0442-11.8.1874.

    • Search Google Scholar
    • Export Citation
  • Janicot, S., , S. Trzaska, , and I. Poccard, 2001: Summer Sahel–ENSO teleconnection and decadal time scale SST variations. Climate Dyn., 18, 303320, doi:10.1007/s003820100172.

    • Search Google Scholar
    • Export Citation
  • Joly, M., , and A. Voldoire, 2009: Influence of ENSO on the West African monsoon: Temporal aspects and atmospheric processes. J. Climate, 22, 31933210, doi:10.1175/2008JCLI2450.1.

    • Search Google Scholar
    • Export Citation
  • Joly, M., , and A. Voldoire, 2010: Role of the Gulf of Guinea in the inter-annual variability of the West African monsoon: What do we learn from CMIP3 coupled simulations? Int. J. Climatol., 30, 18431856.

    • Search Google Scholar
    • Export Citation
  • Joly, M., , A. Voldoire, , H. Douville, , P. Terray, , and J. F. Royer, 2007: African monsoon teleconnections with tropical SSTs: Validation and evolution in a set of IPCC4 simulations. Climate Dyn., 29, 1–20, doi:10.1007/s00382-006-0215-8.

    • Search Google Scholar
    • Export Citation
  • Jung, T., , L. Ferranti, , and A. M. Tompkins, 2006: Response to the summer of 2003 Mediterranean SST anomalies over Europe and Africa (2006). J. Climate, 19, 54395454, doi:10.1175/JCLI3916.1.

    • Search Google Scholar
    • Export Citation
  • Kang, H.-S., , Y. Xue, , and G. J. Collatz, 2007: Assessment of satellite-derived leaf area index datasets using a general circulation model: Seasonal variability. J. Climate, 20, 9931015, doi:10.1175/JCLI4054.1.

    • Search Google Scholar
    • Export Citation
  • Kang, S. M., , D. M. W. 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, 28122827, doi:10.1175/2009JAS2924.1.

    • Search Google Scholar
    • Export Citation
  • 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, L09706, doi:10.1029/2010GL043038.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., , and H. F. Diaz, 1989: Global climatic anomalies associated with extremes in the Southern Oscillation. J. Climate, 2, 10691090, doi:10.1175/1520-0442(1989)002<1069:GCAAWE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kim, K.-M., , W. K.-M. Lau, , Y. C. Sud, , and G. K. Walker, 2010: Influence of aerosol-radiative forcings on the diurnal and seasonal cycles of rainfall over West Africa and eastern Atlantic Ocean using GCM simulations. Climate Dyn., 35, 115126, doi:10.1007/s00382-010-0750-1.

    • Search Google Scholar
    • Export Citation
  • Knight, J. R., 2009: The Atlantic multidecadal oscillation inferred from the forced climate response in coupled general circulation models. J. Climate, 22, 16101625, doi:10.1175/2008JCLI2628.1.

    • Search Google Scholar
    • Export Citation
  • 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, L20708, doi:10.1029/2005GL024233.

    • Search Google Scholar
    • Export Citation
  • Knight, J. R., , C. K. Folland, , and A. A. Scaife, 2006: Climate impacts of the Atlantic multidecadal oscillation. Geophys. Res. Lett., 33, L17706, doi:10.1029/2006GL026242.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and et al. , 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590610, doi:10.1175/JHM510.1.

    • Search Google Scholar
    • Export Citation
  • Kucharski, F., , N. Zeng, , and E. Kalnay, 2013: A further assessment of vegetation feedback on decadal Sahel rainfall variability. Climate Dyn., 40, 14531466, doi:10.1007/s00382-012-1397-x.

    • Search Google Scholar
    • Export Citation
  • Lamb, P. J., 1978: Large-scale tropical Atlantic surface circulation patterns associated with sub-Saharan weather anomalies. Tellus, 30, 240251, doi:10.1111/j.2153-3490.1978.tb00839.x.

    • Search Google Scholar
    • Export Citation
  • Lau, K. M., , and K. M. Kim, 2007: Cooling of the Atlantic by Saharan dust. Geophys. Res. Lett., 34, L23811, doi:10.1029/2007GL031538.

  • Lau, K. M., , S. S. P. Shen, , K.-M. Kim, , and H. Wang, 2006: A multimodel study of the twentieth-century simulations of Sahel drought from the 1970s to 1990s. J. Geophys. Res., 111, D07111, doi:10.1029/2005JD006281.

    • Search Google Scholar
    • Export Citation
  • Laval, K., , and L. Picon, 1986: Effect of a change of the surface albedo of the Sahel on climate. J. Atmos. Sci., 43, 2418–2429, doi:10.1175/1520-0469(1986)043<2418:EOACOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Le Barbe, L. L., , T. Lebel, , and D. Tapsoba, 2002: Rainfall variability in West Africa during the years 1950–90. J. Climate, 15, 187202, doi:10.1175/1520-0442(2002)015<0187:RVIWAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, W.-P., , Y. Xue, , and I. Poccard, 2007: Numerical investigation of the impact of vegetation indices on the variability of West African summer monsoon. J. Meteor. Soc. Japan, 85A, 363383, doi:10.2151/jmsj.85A.363.

    • Search Google Scholar
    • Export Citation
  • Losada, T., , B. Rodríguez-Fonseca, , S. Janicot, , S. Gervois, , F. Chauvin, , and P. Ruti, 2010: A multi-model approach to the Atlantic equatorial mode: Impact on the West African monsoon. Climate Dyn., 35, 2943, doi:10.1007/s00382-009-0625-5.

    • Search Google Scholar
    • Export Citation
  • Losada, T., , B. Rodríguez-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, L12705, doi:10.1029/2012GL052423.

    • Search Google Scholar
    • Export Citation
  • Lu, J., 2009: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought. Climate Dyn., 33, 445460, doi:10.1007/s00382-009-0596-6.

    • Search Google Scholar
    • Export Citation
  • Lu, J., , and T. L. Delworth, 2005: Oceanic forcing of the late 20th century Sahel drought. Geophys. Res. Lett., 32, L22706, doi:10.1029/2005GL023316.

    • Search Google Scholar
    • Export Citation
  • Ma, H., , H. Xiao, , C. Mechoso, , and Y. Xue, 2013: Sensitivity of global tropical climate to land surface processes: Mean state and interannual variability. J. Climate, 26, 1818–1837, doi:10.1175/JCLI-D-12-00142.1.

    • Search Google Scholar
    • Export Citation
  • MacLeod, D. A., , C. Caminade, , and A. P. Morse, 2012: Useful decadal climate prediction at regional scales? A look at the ENSEMBLES stream 2 decadal hindcasts. Environ. Res. Lett.,7, 044012, doi:10.1088/1748-9326/7/4/044012.

  • Mahajan, S., , K. J. Evans, , J. E. Truesdale, , J. J. Hack, , and J.-L. Lamarque, 2012: Interannual tropospheric aerosol variability in the late twentieth century and its impact on tropical Atlantic and West African climate by direct and semidirect effects. J. Climate, 25, 80315056, doi:10.1175/JCLI-D-12-00029.1.

    • Search Google Scholar
    • Export Citation
  • Mantua, N. J., , S. R. Hare, , Y. Zhang, , J. M. Wallace, , and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 10691079, doi:10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Marteau, R., , B. Sultan, , V. Moron, , A. Alhassane, , C. Baron, , and S. B. Traoré, 2011: The onset of the rainy season and farmers’ sowing strategy for pearl millet cultivation in southwest Niger. Agric. For. Meteor., 151, 13561369, doi:10.1016/j.agrformet.2011.05.018.

    • Search Google Scholar
    • Export Citation
  • Martin, E. R., , and C. D. Thorncroft, 2014a: The impact of the AMO on the West African monsoon annual cycle. Quart. J. Roy. Meteor. Soc.,140, 31–46, doi:10.1002/qj.2107.

  • Martin, E. R., , and C. D. Thorncroft, 2014b: Sahel rainfall in multimodel CMIP5 decadal hindcasts. Geophys. Res. Lett., 41, 21692175, doi:10.1002/2014GL059338.

    • Search Google Scholar
    • Export Citation
  • Martin, E. R., , C. D. Thorncroft, , and B. B. B. Booth, 2014: The multidecadal Atlantic SST–Sahel rainfall teleconnection in CMIP5 simulations. J. Climate, 27, 784806, doi:10.1175/JCLI-D-13-00242.1.

    • Search Google Scholar
    • Export Citation
  • Mechoso, C. R., and et al. , 1995: The seasonal cycle over the tropical Pacific in coupled ocean–atmosphere general circulation models. Mon. Wea. Rev., 123, 28252838, doi:10.1175/1520-0493(1995)123<2825:TSCOTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and et al. , 2009: Decadal prediction: Can it be skillful? Bull. Amer. Meteor. Soc., 90, 14671485, doi:10.1175/2009BAMS2778.1.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and et al. , 2014: Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc., 95, 243267, doi:10.1175/BAMS-D-12-00241.1.

    • Search Google Scholar
    • Export Citation
  • Mohino, E., , S. Janicot, , and J. Bader, 2011a: Sahelian rainfall and decadal to multidecadal SST variability. Climate Dyn., 37, 419440, doi:10.1007/s00382-010-0867-2.

    • Search Google Scholar
    • Export Citation
  • Mohino, E., , B. Rodríguez-Fonseca, , T. Losada, , S. Gervois, , S. Janicot, , J. Bader, , P. Ruti, , and F. Chauvin, 2011b: Changes in the interannual SST-forced signals on West African rainfall: AGCM intercomparison. Climate Dyn., 37, 17071725, doi:10.1007/s00382-011-1093-2.

    • Search Google Scholar
    • Export Citation
  • Mohino, E., , B. Rodríguez-Fonseca, , C. R. Mechoso, , S. Gervois, , P. Ruti, , and F. Chauvin, 2011c: Impacts of the tropical Pacific/Indian Oceans on the seasonal cycle of the West African monsoon. J. Climate, 24, 38783891, doi:10.1175/2011JCLI3988.1.

    • Search Google Scholar
    • Export Citation
  • Monerie, P. A., , P. Roucou, , and B. Fontaine, 2013: Mid-century effects of climate change on African monsoon dynamics using the A1B emission scenario. Int. J. Climatol., 33, 881896, doi:10.1002/joc.3476.

    • Search Google Scholar
    • Export Citation
  • Murphy, J., and et al. , 2010: Towards prediction of decadal climate variability and change. Procedia Environ. Sci., 1, 287304, doi:10.1016/j.proenv.2010.09.018.

    • Search Google Scholar
    • Export Citation
  • Ndiaye, O., , W. M. Neil, , and W. M. Thiaw, 2011: Predictability of seasonal Sahel rainfall using GCMs and lead-time improvements through the use of a coupled model. J. Climate,24, 1931–1949, doi:10.1175/2010JCLI3557.1.

  • Neelin, J. D., , C. Chou, , and H. Su, 2003: Tropical drought regions in global warming and El Niño teleconnections. Geophys. Res. Lett., 30, 2275, doi:10.1029/2003GL018625.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., 1978: Climatic variations in the Sahel and other African regions during the past five centuries. J. Arid Environ., 1, 324.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., 1979: The methodology of historical climate reconstruction and its application to Africa. J. Afr. Hist., 20, 3149, doi:10.1017/S0021853700016704.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., 2000: Land surface processes and Sahel climate. Rev. Geophys., 38, 117139, doi:10.1029/1999RG900014.

  • Nicholson, S. E., 2013: The West African Sahel: A review of recent studies on the rainfall regime and its interannual variability. ISRN Meteor., 2013, 43521, doi:10.1155/2013/453521.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., , A. K. Dezfuli, , and D. Klotter, 2012: A two-century precipitation dataset for the continent of Africa. Bull. Amer. Meteor. Soc., 93, 12191231, doi:10.1175/BAMS-D-11-00212.1.

    • Search Google Scholar
    • Export Citation
  • Nikulin, G., and et al. , 2012: Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. J. Climate, 25, 60576078, doi:10.1175/JCLI-D-11-00375.1.

    • Search Google Scholar
    • Export Citation
  • Nnamchi, H. C., , and J. Li, 2011: Influence of the South Atlantic Ocean dipole on West African summer precipitation. J. Climate, 24, 11841197, doi:10.1175/2010JCLI3668.1.

    • Search Google Scholar
    • Export Citation
  • Notaro, M., 2008: Statistical identification of global hotspots in soil moisture feedbacks among IPCC AR4 models. J. Geophys. Res., 113, D09101, doi:10.1029/2007JD00919.

    • Search Google Scholar
    • Export Citation
  • Paeth, H., , K. Born, , R. Girmes, , R. Podzun, , and D. Jacob, 2009: Regional climate change in tropical and Sahel due to greenhouse forcing and land use changes. J. Climate, 22, 114132, doi:10.1175/2008JCLI2390.1.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., 1986: Influence of the Atlantic, Pacific, and Indian Oceans on Sahel rainfall. Nature, 322, 251253, doi:10.1038/322251a0.

    • Search Google Scholar
    • Export Citation
  • Patricola, C. M., , and K. H. Cook, 2011: Sub-Saharan northern African climate at the end of the twenty-first century: Forcing factors and climate change processes. Climate Dyn., 37, 11651188, doi:10.1007/s00382-010-0907-y.

    • Search Google Scholar
    • Export Citation
  • Peyrillé, P., , and J. P. Lafore, 2007: An idealized two-dimensional framework to study the West African monsoon. Part II: Large-scale advection and the diurnal cycle. J. Atmos. Sci., 64, 27832803, doi:10.1175/JAS4052.1.

    • Search Google Scholar
    • Export Citation
  • Peyrillé, P., , J. P. Lafore, , and J. L. Redelsperger, 2007: An idealized two-dimensional framework to study the West African monsoon. Part I: Validation and key controlling factors. J. Atmos. Sci., 64, 27652782, doi:10.1175/JAS3919.1.

    • Search Google Scholar
    • Export Citation
  • Philippon, N., , and B. Fontaine, 2002: The relationship between the Sahelian and previous 2nd Guinean rainy seasons: a monsoon regulation by soil wetness? Ann. Geophys., 20, 575582, doi:10.5194/angeo-20-575-2002.

    • Search Google Scholar
    • Export Citation
  • Philippon, N., , F. J. Doblas-Reyes, , and P. M. Ruti, 2010: Skill, reproducibility and potential predictability of the West African monsoon in coupled GCMS. Climate Dyn., 35, 53–74, 10.1007/s00382-010-0856-5.

    • Search Google Scholar
    • Export Citation
  • Polo, I., , B. Rodríguez-Fonseca, , T. Losada, , and J. García-Serrano, 2008: Tropical Atlantic variability modes (1979–2002). Part I: Time-evolving SST modes related to West African rainfall. J. Climate, 21, 64576475, doi:10.1175/2008JCLI2607.1.

    • Search Google Scholar
    • Export Citation
  • Polo, I., , A. Ullmann, , P. Roucou, , and B. Fontaine, 2011: Weather regimes in the Euro-Atlantic and Mediterranean sector and relationship with West African rainfall over the period 1989–2008 from a self-organizing maps approach. J. Climate, 24, 34233432, doi:10.1175/2011JCLI3622.1.

    • Search Google Scholar
    • Export Citation
  • Reason, C. J. C., , and M. Rouault, 2006: Sea surface temperature variability in the tropical southeast Atlantic Ocean and West African rainfall. Geophys. Res. Lett.,33, L21705, doi:10.1029/2006GL027145.

  • Redelsperger, J.-L., , C. D. Thorncroft, , A. Diedhiou, , T. Lebel, , D. J. Parker, , and J. Polcher, 2006: African Monsoon Multidisciplinary Analysis: An international research project and field campaign. Bull. Amer. Meteor. Soc., 87, 17391746, doi:10.1175/BAMS-87-12-1739.

    • Search Google Scholar
    • Export Citation
  • Richter, I., , S.-P. Xie, , S. K. Behera, , T. Doi, , and Y. Masumoto, 2014: Equatorial Atlantic variability and its relation to mean state biases in CMIP5. Climate Dyn., 42, 171188, doi:10.1007/s00382-012-1624-5.

    • Search Google Scholar
    • Export Citation
  • Rodríguez-Fonseca, B., , I. Polo, , J. Garcia-Serrano, , L. Losada, , E. Mohino, , C. R. Mechoso, , and F. Kucharski, 2009: Is the Atlantic Niño dynamically affecting the Pacific ENSO in recent decades? Geophys. Res. Lett., 36, L20705, doi:10.1029/2009GL040048.

    • Search Google Scholar
    • Export Citation
  • Rodríguez-Fonseca, B., and et al. , 2011: Interannual and decadal SST-forced responses of the West African monsoon. Atmos. Sci. Lett., 12, 6774, doi:10.1002/asl.308.

    • Search Google Scholar
    • Export Citation
  • Roehrig, R., , D. Bouniol, , F. Guichard, , F. Hourdin, , and J. L. Redelsperger, 2013: The present and future of the West African monsoon: A process-oriented assessment of CMIP5 simulations along the AMMA transect. J. Climate, 26, 64716505, doi:10.1175/JCLI-D-12-00505.1.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., , and M. S. Halpert, 1989: Precipitation patterns associated with the high index phase of the Southern Oscillation. J. Climate, 2, 268284, doi:10.1175/1520-0442(1989)002<0268:PPAWTH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., , and U. Lohmann, 2002: Tropical rainfall trends and the indirect aerosol effect. J. Climate, 15, 21032116, doi:10.1175/1520-0442(2002)015<2103:TRTATI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., 1996: Reply to comments by Y. C. Sud and W. K.-M. Lau on ‘Variability of summer rainfall over tropical North Africa (1906–92): Observations and modelling’ by D. P. Rowell, C. K. Folland, K. Maskell and M. N. Ward (April 4, 1995, 121, 669–704): Further analysis of simulated interdecadal and interannual variability of summer rainfall over tropical North Africa. Quart. J. Roy. Meteor. Soc., 122, 10071013, doi:10.1002/qj.49712253213.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., 2001: Teleconnections between the tropical Pacific and the Sahel. Quart. J. Roy. Meteor. Soc., 127, 16831706, doi:10.1002/qj.49712757512.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., 2003: The impact of Mediterranean SSTs on the Sahelian rainfall season. J. Climate, 16, 849862, doi:10.1175/1520-0442(2003)016<0849:TIOMSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., 2013: Simulating SST teleconnections to Africa: What is the state of the art? J. Climate, 26, 53975418, doi:10.1175/JCLI-D-12-00761.1.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., , C. K. Folland, , K. Maskell, , J. A. Owen, , and M. N. Ward, 1992: Modelling the influence of global sea surface temperatures on the variability and predictability of seasonal Sahel rainfall. Geophys. Res. Lett., 19, 905908, doi:10.1029/92GL00939.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., , C. K. Folland, , K. Maskell, , and M. N. Ward, 1995: Variability of summer rainfall over tropical North Africa (1906–92): Observations and modelling. Quart. J. Roy. Meteor. Soc., 121, 669704, doi:10.1002/qj.49712152311.

    • Search Google Scholar
    • Export Citation
  • Ruiz-Barradas, A., , J. A. Carton, , and S. Nigam, 2000: Structure of interannual-to-decadal climate variability in the tropical Atlantic sector. J. Climate, 13, 32853297, doi:10.1175/1520-0442(2000)013<3285:SOITDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ruti, P., and et al. , 2011: The West African climate system: A review of the AMMA model inter-comparison initiatives. Atmos. Sci. Lett., 12, 116122, doi:10.1002/asl.305.

    • Search Google Scholar
    • Export Citation
  • Salack, S., , A. Giannini, , M. Diakhaté, , A. T. Gaye, , and B. Muller, 2014: Oceanic influence on the sub-seasonal to interannual timing and frequency of extreme dry spells over the West African Sahel. Climate Dyn., 42, 189201, doi:10.1007/s00382-013-1673-4.

    • Search Google Scholar
    • Export Citation
  • Scaife, A., and et al. , 2009: The CLIVAR C20C Project: Selected 20th century climate events. Climate Dyn., 33, 603–614, doi:10.1007/s00382-008-0451-1.

    • Search Google Scholar
    • Export Citation
  • Servain, J., , A. J. Busalacchi, , M. J. McPhaden, , A. D. Moura, , G. Reverdin, , M. Vianna, , and S. E. Zebiak, 1998: A Pilot Research Moored Array in the Tropical Atlantic (PIRATA). Bull. Amer. Meteor. Soc., 79, 20192032, doi:10.1175/1520-0477(1998)079<2019:APRMAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seth, A., , S. A. Rauscher, , M. Rojas, , A. Giannini, , and S. J. Camargo, 2011: Enhanced spring convective barrier for monsoons in a warmer world? Climatic Change, 104, 403414, doi:10.1007/s10584-010-9973-8.

    • Search Google Scholar
    • Export Citation
  • Seth, A., , S. A. Rauscher, , M. Biasutti, , A. Giannini, , S. J. Camargo, , and M. Rojas, 2013: CMIP5 projected changes in the annual cycle of precipitation in monsoon regions. J. Climate, 26, 73287351, doi:10.1175/JCLI-D-12-00726.1.

    • Search Google Scholar
    • Export Citation
  • Shanahan, T. M., and et al. , 2009: Atlantic forcing of Persistent drought in West Africa. Science, 324, 377380, doi:10.1126/science.1166352.

    • Search Google Scholar
    • Export Citation
  • Shinoda, M., , and R. Kawamura, 1994: Tropical rainbelt, circulation, and sea surface temperatures associated with the Sahelian rainfall trend. J. Meteor. Soc. Japan, 72, 341357.

    • Search Google Scholar
    • Export Citation
  • Shinoda, M., , and Y. Yamaguchi, 2003: Influence of soil moisture anomaly on temperature in the Sahel: A comparison between wet and dry decades. J. Hydrometeor., 4, 437447, doi:10.1175/1525-7541(2003)4<437:IOSMAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Smith, D. M., , A. A. Scaife, , and B. P. Kirtman, 2012: What is the current state of scientific knowledge with regard to seasonal and decadal forecasting? Environ. Res. Lett.,7, 015602, doi:10.1088/1748-9326/7/1/015602.

  • Sud, Y. C., , and A. Molud, 1988: A GCM simulation study of the influence of Saharan evapotranspiration and surface-albedo anomalies on July circulation and rainfall. Mon. Wea. Rev., 116, 23882400, doi:10.1175/1520-0493(1988)116<2388:AGSSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sultan, B., , and S. Janicot, 2003: The West African monsoon dynamics. Part II: The “preonset” and “onset” of the summer monsoon. J. Climate, 16, 34073427, doi:10.1175/1520-0442(2003)016<3407:TWAMDP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taylor, C. M., , E. F. Lambin, , N. Stephenne, , R. J. Harding, , and R. L. H. Essery, 2002: The influence of land use change on climate in the Sahel. J. Climate, 15, 36153629, doi:10.1175/1520-0442(2002)015<3615:TIOLUC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., , R. J. Stouffer, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Ting, M., , Y. Kushnir, , R. Seager, , and C. Li, 2009: Forced and internal 20th century SST trends in the North Atlantic. J. Climate, 22, 14691481, doi:10.1175/2008JCLI2561.1.

    • Search Google Scholar
    • Export Citation
  • Ting, M., , Y. Kushnir, , R. Seager, , and C. Li, 2011: Robust features of Atlantic multi-decadal variability and its climate impacts. Geophys. Res. Lett., 38, L17705, doi:10.1029/2011GL048712.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., 2006: Filtering of GCM simulated Sahel precipitation. Geophys. Res. Lett., 33, L01804, doi:10.1029/2005GL024923.

  • Tippett, M. K., , and A. Giannini, 2006: Potentially predicable components of African summer rainfall in an SST-forced GCM simulation. J. Climate, 19, 31333144, doi:10.1175/JCLI3779.1.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., , and V. Ermert, 2013: A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology. Malar. J., 12, 65, doi:10.1186/1475-2875-12-65.

    • Search Google Scholar
    • Export Citation
  • Traore, A. K., 2011: Etude and modélisation de l’influence des processus couplés surface-atmosphere sur la variabilité des pluies et du climat ouest africain. Ph.D. thesis, University Pierre et Marie Curie, 169 pp.

  • Trenberth, K. E., and et al. , 2007: Observations: Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 235–336.

  • Trzaska, S., , A. W. Robertson, , J. Farrara, , and C. R. Mechoso, 2007: South Atlantic variability arising from air–sea coupling: Local mechanisms and tropical–subtropical interactions. J. Climate, 20, 33453365, doi:10.1175/JCLI4114.1.

    • Search Google Scholar
    • Export Citation
  • van den Hurk, B. J. J. M., , and E. van Meijgaard, 2010: Diagnosing land–atmosphere interaction from a regional climate model simulation over West Africa. J. Hydrometeor., 11, 467481, doi:10.1175/2009JHM1173.1.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G. J., , F. J. Doblas-Reyes, , B. Wouters, , and W. Hazeleger, 2012: Decadal prediction skill in a multi-model ensemble. Climate Dyn., 38, 12631280, doi:10.1007/s00382-012-1313-4.

    • Search Google Scholar
    • Export Citation
  • Vellinga, M., , A. Arribas, , and R. Graham, 2013: Seasonal forecasts for regional onset of the West African monsoon. Climate Dyn., 40, 30473070, doi:10.1007/s00382-012-1520-z.

    • Search Google Scholar
    • Export Citation
  • Venegas, S. A., , L. A. Mysak, , and D. N. Straub, 1997: Atmosphere–ocean coupled variability in the South Atlantic. J. Climate, 10, 29042920, doi:10.1175/1520-0442(1997)010<2904:AOCVIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vera, C., and et al. , 2010: Needs assessment for climate information on decadal timescales and longer. Procedia Environ. Sci., 1, 275286, doi:10.1016/j.proenv.2010.09.017.

    • Search Google Scholar
    • Export Citation
  • Vizy, E. K., , and K. H. Cook, 2001: Mechanisms by which Gulf of Guinea and eastern North Atlantic sea surface temperature anomalies can influence African rainfall. J. Climate, 14, 795821, doi:10.1175/1520-0442(2001)014<0795:MBWGOG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vizy, E. K., , and K. H. Cook, 2002: Development and application of a mesoscale climate model for the tropics: Influence of sea surface temperature anomalies on the West African monsoon. J. Geophys. Res., 107 (D3), 4023, doi:10.1029/2001JD000686.

    • Search Google Scholar
    • Export Citation
  • Vizy, E. K., , and K. H. Cook, 2012: Mid-twenty-first-century changes in extreme events over northern and tropical Africa. J. Climate, 25, 57485767, doi:10.1175/JCLI-D-11-00693.1.

    • Search Google Scholar
    • Export Citation
  • Vizy, E. K., , K. H. Cook, , J. Crétat, , and N. Neupane, 2013: Projections of a wetter Sahel in the twenty-first century from global and regional models. J. Climate, 26, 4664–4687, doi:10.1175/JCLI-D-12-00533.1.

    • Search Google Scholar
    • Export Citation
  • Wagner, R. G., , and A. M. da Silva, 1994: Surface conditions associated with anomalous rainfall in the Guinea coastal region. Int. J. Climatol., 14, 179199, doi:10.1002/joc.3370140205.

    • Search Google Scholar
    • Export Citation
  • Wang, C., 2002: Atlantic climate variability and its associated atmospheric circulation cells. J. Climate, 15, 15161536, doi:10.1175/1520-0442(2002)015<1516:ACVAIA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, C., , S. Dong, , A. T. Evan, , G. R. Foltz, , and S. K. Lee, 2012: Multidecadal covariability of North Atlantic sea surface temperature, African dust, Sahel rainfall, and Atlantic hurricanes. J. Climate, 25, 54045415, doi:10.1175/JCLI-D-11-00413.1.

    • Search Google Scholar
    • Export Citation
  • Wang, G. L., , E. A. B. Eltahir, , J. A. Foley, , D. Pollard, , and S. Levis, 2004: Decadal variability of rainfall in the Sahel: Results from the coupled GENESIS-IBIS atmosphere–biosphere model. Climate Dyn., 22, 625637, doi:10.1007/s00382-004-0411-3.

    • Search Google Scholar
    • Export Citation
  • Ward, M. N., 1998: Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales. J. Climate, 11, 31673191, doi:10.1175/1520-0442(1998)011<3167:DASLTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Washington, R., and et al. , 2012: FENNEC—The Saharan Climate System. CLIVAR Exchanges, No. 60, International CLIVAR Project Office, Southampton, United Kingdom, 31–33.

  • Xue, Y., 1997: Biosphere feedback on regional climate in tropical North Africa. Quart. J. Roy. Meteor. Soc.,123B, 1483–1515, doi:10.1002/qj.49712354203.

  • Xue, Y., , and M. D. Fennessy, 2002: Under what conditions does land cover change impact regional climate? Global Desertification: Do Humans Cause Deserts? J. F. Reynolds and D. M. Stafford Smith, Eds., Dahlem University Press, 59–74.

  • Xue, Y., , K. N. Liou, , and A. Kasahara, 1990: Investigation of the biogeophysical feedback on the African climate using a two-dimensional model. J. Climate, 3, 337352, doi:10.1175/1520-0442(1990)003<0337:IOBFOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., and et al. , 2004a: The Sahelian climate. Vegetation, Water, Humans and the Climate, P. Kabat et al., Eds., Springer-Verlag, 59–77.

  • Xue, Y., , H.-M. H. Juang, , W. Li, , S. Prince, , R. DeFries, , Y. Jiao, , and R. Vasic, 2004b: Role of land surface processes in monsoon development: East Asia and West Africa. J. Geophys. Res., 109, D03105, doi:10.1029/2003JD003556.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., , F. De Sales, , R. Vasic, , C. R. Mechoso, , S. D. Prince, , and A. Arakawa, 2010a: Global and seasonal assessment of interactions between climate and vegetation biophysical processes: A GCM study with different land–vegetation representations. J. Climate, 23, 14111433, doi:10.1175/2009JCLI3054.1.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., and et al. , 2010b: Intercomparison and analyses of the climatology of the West African monsoon in the West African Monsoon Modeling and Evaluation project (WAMME) first model intercomparison experiment. Climate Dyn., 35, 327, doi:10.1007/s00382-010-0778-2.

    • Search Google Scholar
    • Export Citation
  • Yoshioka, M., , N. M. Mahowald, , A. J. Conley, , W. D. Collins, , D. W. Fillmore, , C. S. Zender, , and D. B. Coleman, 2007: Impact of desert dust radiative forcing on Sahel precipitation: Relative importance of dust compared to sea surface temperature variations, vegetation changes, and greenhouse gas warming. J. Climate, 20, 14451467, doi:10.1175/JCLI4056.1.

    • Search Google Scholar
    • Export Citation
  • Zebiak, S. E., 1993: Air–sea interaction in the equatorial Atlantic region. J. Climate, 6, 15671586, doi:10.1175/1520-0442(1993)006<1567:AIITEA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zeng, N., , J. D. Neelin, , K.-M. Lau, , and C. J. Tucker, 1999: Enhancement of interdecadal climate variability in the Sahel by vegetation interaction. Science, 286, 15371540, doi:10.1126/science.286.5444.1537.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., , and T. L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett., 33, L17712, doi:10.1029/2006GL026267.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., and et al. , 2013: Have aerosols caused the observed Atlantic multidecadal variability? J. Atmos. Sci., 70, 11351144, doi:10.1175/JAS-D-12-0331.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., , J. M. Wallace, , and D. S. Battisti, 1997: ENSO-like interdecadal variability: 1900–93. J. Climate, 10, 10041020, doi:10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Mean precipitation climatology (mm day−1) for 1998–2012 from TRMM 3B42 V6 product (Huffman et al. 2007): (a) daily values and (b) with a 10-day smoothing applied. Values are averaged from 12°W to 6°E to avoid the precipitation maximum over the Cameroon highlands, where seasonal variations are different from those in regions to the west.

  • View in gallery

    Evolution of the number of papers published in relation to “Sahelian rainfall” from the 1950s (5 papers in the 1950s and 154 in the 1960s). In the last 15 years, the papers sum 63 840. From 1950 to 1997 data are plotted as averages over each decade; from 1998 onward data are yearly plotted. Source: Google scholar (http://scholar.google.com). Before 1990 units are in number of papers per decade, and after 1990 the units are number of papers per year. The dates of some of the most remarkable international projects studying the Sahelian climate variability are marked in the figure.

  • View in gallery

    (top) Ensemble of AGCM response to Atlantic Niño SST for four AMMA AGCMs in JAS with respect to the control run: (a) precipitation (mm day−1) and (b) sea level pressure (SLP; hPa) and surface winds (m s−1). Figure from results computed in Losada et al. (2010). (bottom) Leading mode of (c) tropical Atlantic SST from February–May (FMAM) to September–December (SOND) and (d) summer June–September (JJAS) precipitation over West Africa during the period 1979–2003. Only summer (left) SST and (right) precipitation (std dev in mm) patterns have been shown. Red colors are positive (negative) for the SST (precipitation) fields. Figure modified from Polo et al. (2008).

  • View in gallery

    JAS warm minus cold Mediterranean Sea composites: (a) GPCC precipitation (mm) and (b) ERA-Interim specific humidity (contours; g kg−1) and moisture transport at 950 hPa (arrows; reference arrow is 10 m s−1 g kg−1). Values in colored regions and black arrows indicate 90% significance regarding a Student’s t test. Warm and cold events are identified on the basis of a standardized Mediterranean SST index (30°–40°N, 5°W–35°E) during the period 1991–2010. Warm and cold SST years correspond to 1994, 1999, and 2003 and 1993, 1996, 1997, and 2007 during which the index is >1 and <−1, respectively. The 1991–2010 period is chosen in order to exclude the turning of the Sahel precipitation trend in the 1980s and all the data are detrended.

  • View in gallery

    (a),(b) Composite of anomalous observed rainfall (mm day−1) in years 1983, 1987, and 1997 (“warm” Pacific years) minus 1985, 1988, and 1999 (“cold” Pacific years); (c),(d) linear component of the anomalous rainfall (mm day−1) simulated by the four models’ ensemble mean obtained in the sensitivity experiments; (e),(f) anomalous observed sea surface temperature (K) used in the sensitivity experiments, obtained as a composite of warm Pacific years minus cold ones; (g),(h) as in (c),(d), but for the velocity potential (106 m2 s−1) at 200 hPa and divergent wind (m s−1); (i),(j) as in (c),(d), but for the streamfunction (106 m2 s−1) at 200 hPa and rotational wind (m s−1), all showing the average for (left) May and June and (right) July and August. Gray contours mark where the simulated anomalies are larger in magnitude than the intermodel standard deviation. Warm (cold) Pacific years were chosen as those in which the SST expansion coefficient for the first mode of covariability between Indo-Pacific SSTs and West African rainfall was above (below) the threshold of +1 (−1) std dev of the index. Figure modified from Mohino et al. (2011c).

  • View in gallery

    A 20-yr sliding window running correlation between the Niño-3 and Sahelian rainfall indices (middle grey line), the Atl3 and Sahelian rainfall indices (bottom black line), and the Atl3 and Guinea rainfall indices (top continuous line) during June–September. Modified from Losada et al. (2012). Atl3 is defined as the SST area averaged over the region 3°N–3°S, 20°W–0°. Niño-3 is defined as the SST area averaged over the region 5°N–5°S, 150°–90°W. The Guinea index is defined as the rainfall area averaged over the region 8°–4°N, 20°W–10°E and the Sahelian index is defined as the rainfall area averaged over the region 20°–10°N, 20°W–10°E. Dots indicate the 20-yr windows in which the correlation is significant at 95% of confidence level.

  • View in gallery

    Seasonal (July–September) anomalies of rainfall over the Sahel (10°–20°N, 10°W–10°E) calculated from the CRU time series, version 3.1 (TS3.1), dataset (Harris et al. 2014) (black solid line), an average of 16 AGCM models forced with observed SSTs (dark gray solid line), CanAM4 (light gray solid line), and IPSL-CM5A-MR (light gray dotted line). Data have been smoothed with a 5-yr running mean to focus on decadal time scales. Dashed dark gray lines show the one standard deviation of the 16 CMIP5 AMIP models. Simulated data were obtained from the CMIP5 database. AMIP simulations in CMIP5 start in 1979. Only two models started in 1950s, which are plotted separately in the figure.

  • View in gallery

    Land surface: reductions of simulation error (mm day−1) by introducing the VBP over different regions in different seasons [modified from Xue et al. (2010a)].

  • View in gallery

    Average time–latitude propagation of zonally averaged (10°W–10°E) rainfall over the West African monsoon region for observations and for six state-of-the-art coupled models from ENSEMBLES. The onset date for each model is shown as a vertical black line. For reference, the average onset date in observations is 29 June. [From Vellinga et al. (2013)]

  • View in gallery

    Decadal prediction: anomaly correlation coefficient (ACC) between the Sahelian precipitation index (10°–20°N, 15°W–15°E) from the CRU dataset and decadal hindcasts (solid), historical experiments (dashed), and decadal-forcing residuals (dotted). ACC is computed for the multimodel ensemble mean. Dots indicate the 95% significant positive correlations; significance is estimated using a Monte Carlo test with 200 permutations. The decadal-forcing residuals are analyzed to obtain an evaluation of the initialization weight in the decadal experiments [see Gaetani and Mohino (2013) for details].

  • View in gallery

    Future projections: summer (JAS) rainfall changes (mm day−1) as simulated by the (a) CMIP3 and (b) CMIP5 multimodel ensemble means. Changes are calculated between the SRES A1B scenario and historical experiments for CMIP3 and the RCP4.5 scenario and the historical experiments for CMIP5 (2065–95 vs 1970–99). The stippling indicates grid boxes where 50% of the models simulate either positive or negative changes. (c) JAS Sahel rainfall indices for the CMIP3 and CMIP5 multimodel ensembles and the CRU TS3.1 observations (see the box for the domain). The time series are in units of mean JAS rainfall where the mean is calculated for the period 1901–99. The one standard deviation spread is shown by the orange envelope for the CMIP5 ensemble and by the blue dotted lines for the CMIP3 ensemble. All indices have been low-pass filtered using an 8-yr running average [modified from Biasutti (2013)].

  • View in gallery

    Projection of the anomalous SSTs onto the grey bottom correlation line in Fig. 6. Only 95% significant regions under a Monte Carlo test are represented.

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Variability and Predictability of West African Droughts: A Review on the Role of Sea Surface Temperature Anomalies

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  • 1 Departamento de Física de la Tierra, Astronomía y Astrofísica-I, Facultad de Ciencias Físicas, and Instituto de Geociencias, CSIC, and Universidad Complutense de Madrid, Madrid, Spain
  • | 2 Departamento de Física de la Tierra, Astronomía y Astrofísica-I, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain
  • | 3 Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
  • | 4 School of Environmental Sciences, Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
  • | 5 Lamont-Doherty Earth Observatory, Columbia University, New York, New York
  • | 6 Consiglio Nazionale delle Ricerche, Istituto di Biometeorologia, Rome, Italy
  • | 7 Institut Català de Ciències del Clima, Barcelona, Spain
  • | 8 Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas
  • | 9 NCAS-Climate, Department of Meteorology, University of Reading, Reading, United Kingdom
  • | 10 Instituto de Ciencias Ambientales, Universidad de Castilla-La Mancha, Toledo, Spain
  • | 11 Center for Climate Systems Research, Columbia University, and NASA Goddard Institute for Space Studies, New York, New York
  • | 12 Centre de Recherches de Climatologie, CNRS/Université de Bourgogne, Dijon, France
  • | 13 Max Planck Institute for Meteorology, Hamburg, Germany
  • | 14 Institut Català de Ciències del Clima, and Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
  • | 15 International Research Institute for Climate and Society, Columbia University, New York, New York
  • | 16 IRD, LOCEAN/IPSL, UPMC, Paris, France
  • | 17 Met Office Hadley Center, Exeter, United Kingdom
  • | 18 Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 19 Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
  • | 20 Centre National de Recherches Météorologiques/Groupe d’Etude de l’Atmosphère Météorologique, Météo-France, CNRS, Toulouse, France
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Abstract

The Sahel experienced a severe drought during the 1970s and 1980s after wet periods in the 1950s and 1960s. Although rainfall partially recovered since the 1990s, the drought had devastating impacts on society. Most studies agree that this dry period resulted primarily from remote effects of sea surface temperature (SST) anomalies amplified by local land surface–atmosphere interactions. This paper reviews advances made during the last decade to better understand the impact of global SST variability on West African rainfall at interannual to decadal time scales. At interannual time scales, a warming of the equatorial Atlantic and Pacific/Indian Oceans results in rainfall reduction over the Sahel, and positive SST anomalies over the Mediterranean Sea tend to be associated with increased rainfall. At decadal time scales, warming over the tropics leads to drought over the Sahel, whereas warming over the North Atlantic promotes increased rainfall. Prediction systems have evolved from seasonal to decadal forecasting. The agreement among future projections has improved from CMIP3 to CMIP5, with a general tendency for slightly wetter conditions over the central part of the Sahel, drier conditions over the western part, and a delay in the monsoon onset. The role of the Indian Ocean, the stationarity of teleconnections, the determination of the leader ocean basin in driving decadal variability, the anthropogenic role, the reduction of the model rainfall spread, and the improvement of some model components are among the most important remaining questions that continue to be the focus of current international projects.

Corresponding author address: Belén Rodríguez-Fonseca, Departamento de Física de la Tierra, Astronomía y Astrofísica I, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Ciudad Universitaria, Plaza Ciencias, 1, 28040 Madrid, Spain. E-mail: brfonsec@ucm.es

This article is included in the GDIS Drought Worldwide Special Collection.

Abstract

The Sahel experienced a severe drought during the 1970s and 1980s after wet periods in the 1950s and 1960s. Although rainfall partially recovered since the 1990s, the drought had devastating impacts on society. Most studies agree that this dry period resulted primarily from remote effects of sea surface temperature (SST) anomalies amplified by local land surface–atmosphere interactions. This paper reviews advances made during the last decade to better understand the impact of global SST variability on West African rainfall at interannual to decadal time scales. At interannual time scales, a warming of the equatorial Atlantic and Pacific/Indian Oceans results in rainfall reduction over the Sahel, and positive SST anomalies over the Mediterranean Sea tend to be associated with increased rainfall. At decadal time scales, warming over the tropics leads to drought over the Sahel, whereas warming over the North Atlantic promotes increased rainfall. Prediction systems have evolved from seasonal to decadal forecasting. The agreement among future projections has improved from CMIP3 to CMIP5, with a general tendency for slightly wetter conditions over the central part of the Sahel, drier conditions over the western part, and a delay in the monsoon onset. The role of the Indian Ocean, the stationarity of teleconnections, the determination of the leader ocean basin in driving decadal variability, the anthropogenic role, the reduction of the model rainfall spread, and the improvement of some model components are among the most important remaining questions that continue to be the focus of current international projects.

Corresponding author address: Belén Rodríguez-Fonseca, Departamento de Física de la Tierra, Astronomía y Astrofísica I, Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Ciudad Universitaria, Plaza Ciencias, 1, 28040 Madrid, Spain. E-mail: brfonsec@ucm.es

This article is included in the GDIS Drought Worldwide Special Collection.

1. Introduction

West Africa is the westernmost region of the northern tropical African continent. The region is primarily characterized by distributions of rainfall and vegetation that are primarily zonal with strong north–south gradients, and it is considered as an entity in the meteorological context (Nicholson 2013). A monsoon season [the West African monsoon (WAM)] occurs every year, lasting from four to five months (May–September) near the Guinean coast and three months [July–September (JAS)] in the Sahel. Enhanced precipitation is associated with the seasonal northward migration of the intertropical convergence zone (ITCZ), where the northeasterly harmattan winds converge with the moisture-laden flow from the colder eastern equatorial Atlantic Ocean. Nicholson (2013) recently conducted an extensive review of rainfall variability over the Sahel and documented novel features of the region’s storm circulations. Figure 1 (from Huffman et al. 2007) shows a latitude–time plot that illustrates the seasonal cycle of rainfall in West Africa. Rainfall rates increase along the Guinean coast of Africa (approximately 4°–6°N) throughout May, and precipitation remains high in this region through June. In the early summer [7 July, according to climatology compiled with the Tropical Rainfall Measuring Mission (TRMM)], rainfall decreases along the coast of the Gulf of Guinea and the rainfall maximum becomes established over the Sahel (about 10°–15°N). This sudden jump in latitude of the precipitation maximum represents the onset of the West African monsoon (Le Barbe et al. 2002; Sultan and Janicot 2003).

Fig. 1.
Fig. 1.

Mean precipitation climatology (mm day−1) for 1998–2012 from TRMM 3B42 V6 product (Huffman et al. 2007): (a) daily values and (b) with a 10-day smoothing applied. Values are averaged from 12°W to 6°E to avoid the precipitation maximum over the Cameroon highlands, where seasonal variations are different from those in regions to the west.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

The number of scientific papers motivated by different aspects of Sahel rainfall has increased exponentially since the 1950s (see Fig. 2), from around 150 to more than 5000 entries in the period from January to May 2013. Droughts are major natural disasters for the largely rain-fed agriculture of most African countries. Particularly in the Sahel, a weak rainy season can create dramatic situations for millions of people (according to the International Federation of Red Cross and Red Crescent Societies; http://www.ifrc.org). The Sahel drought during the 1970s and 1980s was the most significant climate event at the continental scale during the twentieth century, and is arguably among the largest climatic changes worldwide (Trenberth et al. 2007). The event was associated with changes in the intensity, spatial distribution, and temporal evolution of the WAM and associated circulation features, such as the trade winds, African easterly jet (AEJ), and tropical easterly jet (TEJ) (Le Barbe et al. 2002; Sultan and Janicot 2003; Xue et al. 2004a; Dezfuli and Nicholson 2011).

Fig. 2.
Fig. 2.

Evolution of the number of papers published in relation to “Sahelian rainfall” from the 1950s (5 papers in the 1950s and 154 in the 1960s). In the last 15 years, the papers sum 63 840. From 1950 to 1997 data are plotted as averages over each decade; from 1998 onward data are yearly plotted. Source: Google scholar (http://scholar.google.com). Before 1990 units are in number of papers per decade, and after 1990 the units are number of papers per year. The dates of some of the most remarkable international projects studying the Sahelian climate variability are marked in the figure.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

The rainy season in the Sahel has large interannual and decadal variations. A substantial part of this variability is due to the influence of slowly varying climate subcomponents, such as sea surface temperatures (SSTs) and land surface conditions. The importance of oceanic influences at interannual and decadal time scales has been supported by the results of several studies (Folland et al. 1986; Palmer 1986; Rowell et al. 1992; Ward 1998; Camberlin et al. 2001; Giannini et al. 2003; Lu and Delworth 2005; Cook 2008; Caminade and Terray 2010; Losada et al. 2010; Rodríguez-Fonseca et al. 2011; Mohino et al. 2011a; Rowell 2013; Nicholson 2013). Other studies have addressed the effects of land–atmosphere interactions (Xue, 1997; Zeng et al. 1999; Nicholson 2000; Giannini et al. 2003; Yoshioka et al. 2007) and aerosol–radiative forcings (Kim et al. 2010). These effects can potentially interact with each other. For example, the variability of land surface conditions can affect the circulation over the ocean, which in turn can modify the SSTs and indirectly affect conditions over land (Ma et al. 2013).

The existence of significant impacts on WAM rainfall of slowly varying climate subcomponents indicates the potential for useful long-range forecasts (Vellinga et al. 2013; Gaetani and Mohino 2013; García-Serrano et al. 2013). To realize this potential with climate models, these must successfully reproduce the important characteristics of the WAM precipitation and circulation. Despite continuous model improvements in the models, a skillful simulation and prediction of the WAM, including its variability at different time and spatial scales and its association with external forcings, remains a daunting task.

The present paper surveys the literature on drought in West Africa and the Sahel with particular emphasis on recent work on these subjects. The text discusses the dynamical mechanisms linking anomalies in West African rainfall with those in SST over the World Ocean, the time dependence of these relationships, their predictability, and future projections. It is appropriate to acknowledge that many results presented in the following were obtained under the sponsorship of coordinated international research projects (see Fig. 2). The African Monsoon Multidisciplinary Analysis program (AMMA; http://amma-international.org/) has coordinated an ambitious program aimed to improve the knowledge and understanding of the WAM’s variability and predictability on daily-to-decadal time scales, including climate change (Redelsperger et al. 2006; AMMA 2010; Ruti et al. 2011). The West African Monsoon Modeling and Evaluation (WAMME; Druyan et al. 2010) is a community initiative designed to evaluate the performance of state-of-the-art GCMs and regional climate models (RCMs) in reproducing WAM precipitation, drought scenarios, and their relevant processes. WAMME applies recently available observational and assimilation data for model evaluation and improvement (Boone et al. 2010; Xue et al. 2010b). The Coordinated Regional Climate Downscaling Experiment in Africa (CORDEX-Africa) has led to a coordinated evaluation of RCM skill, spread and uncertainties for simulating the West African monsoon mean climate and, to a lesser extent, its simulated onset and variability (Nikulin et al. 2012; Hernández-Díaz et al. 2013). The European Commission Seventh Framework Programme (EC FP7) Quantifying Weather and Climate Impacts on Health in Developing Countries (QWECI) project has aimed to understand, on a more fundamental level, the climate drivers of the vector-borne diseases of malaria, Rift Valley fever, and certain tick-borne diseases, all of which have major human and livestock health and economic implications in Africa (Cash et al. 2013; Tompkins and Ermert 2013; Ermert et al. 2013; Caminade et al. 2014). Some of these international projects have focused on the impact of SST anomalies on the WAM at interannual and decadal time scales. The collective findings from research sponsored by these projects have contributed significantly to progress, particularly in cases that occurred in the last few decades. Finally, phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) have addressed outstanding scientific questions in the Intergovernmental Panel on Climate Change (IPCC) Fourth and Fifth Assessment Reports (AR4 and AR5) process, improving understanding of climate and providing estimates of future climate change that will be useful to those considering its possible consequences.

The text is organized as follows. We start in section 2 and 3 by surveying the state-of-the-art knowledge of the SST influence on Sahel rainfall at interannual to decadal time scales, at which the variability of the ocean is the main driver of that in the atmosphere. Next, we summarize the progress in seasonal (section 5) to decadal predictability (section 6) and its skill in West Africa, following with an update of the future projections. A final section will summarize the most remarkable results, remaining questions, modeling issues, and future directions (section 8).

2. SST influence at interannual time scales

This section is dedicated to review recent findings on the influence of SST anomalies in different ocean basins (the Atlantic, Pacific, and Indian Oceans and the Mediterranean Sea) on the WAM precipitation at interannual time scales.

a. Influence of the tropical Atlantic Ocean

Since the early papers by Hastenrath and Lamb (1977), Lamb (1978), and Hastenrath (1984), many others have documented the tropical Atlantic influence on West African rainfall. This influence unfolds at different time scales: the variability in the equatorial and southern sectors affects that in interannual time scales, while that in the northern sector affects that in decadal time scales (Hastenrath and Polzin 2011).

At interannual time scales, the leading mode of tropical Atlantic variability is the Atlantic Niño, also known as the equatorial mode or zonal mode (Zebiak 1993; Carton et al. 1996). This coupled atmosphere–ocean mode is characterized by a warming (cooling) of the equatorial Atlantic during the boreal spring–summer in association with a relaxation (strengthening) of the trades and a deepening (shallowing) of the eastern equatorial thermocline. Several works using different methodologies have concluded that events of positive SST anomalies (Atlantic Niños) originate a dipole pattern of precipitation anomalies consisting of positive values along the coast of Guinea and negative ones over the Sahel (Horel et al. 1986; Wagner and da Silva 1994; Fontaine and Janicot 1996; Ward 1998). For a warming in the Atlantic, most AGCMs show how this dipole is the result of a weakening in the sea level pressure gradient between ocean and land and hence a weaker ITCZ shift, which is translated into more rainfall over Guinea and less rainfall over Sahel (Fig. 3, top). In the last decades, major international field programs, such as PIRATA (Servain et al. 1998), have sampled the tropical Atlantic and gathered important in situ data. Moreover, unprecedented information is been provided by instruments on board satellites. The resulting availability of homogeneous data in time and space has allowed for a better analyses of covariability between SSTs in the tropical Atlantic and rainfall in West Africa (Ruiz-Barradas et al. 2000; Vizy and Cook 2002; Giannini et al. 2003; Gu and Adler 2006; Reason and Rouault 2006; Polo et al. 2008). On the basis of data from the end of the 1970s, Polo et al. (2008) demonstrated that an Atlantic Niño can be associated with positive rainfall anomalies over the coast and negligible ones over the Sahel, as it could be expected from the dipolar structure in precipitation anomalies reported in previous studies (Fig. 3, bottom). Polo et al. (2008) also found that SST anomalies along the equator in the northern summer were preceded in spring by anomalies along the Benguela coast of Angola in association with the Atlantic Niño, putting forward the potential predictability of the West African rainfall linked to the variability in the southern tropical Atlantic. Also, in this work, the presence of anomalies with different signs in the Pacific and Indian Ocean raised a hitherto unexplored possibility: the influence of these tropical oceans could interfere with the direct influence of the tropical Atlantic (Mohino et al. 2011b; Losada et al. 2012; Rodríguez-Fonseca et al. 2009, 2011).

Fig. 3.
Fig. 3.

(top) Ensemble of AGCM response to Atlantic Niño SST for four AMMA AGCMs in JAS with respect to the control run: (a) precipitation (mm day−1) and (b) sea level pressure (SLP; hPa) and surface winds (m s−1). Figure from results computed in Losada et al. (2010). (bottom) Leading mode of (c) tropical Atlantic SST from February–May (FMAM) to September–December (SOND) and (d) summer June–September (JJAS) precipitation over West Africa during the period 1979–2003. Only summer (left) SST and (right) precipitation (std dev in mm) patterns have been shown. Red colors are positive (negative) for the SST (precipitation) fields. Figure modified from Polo et al. (2008).

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

Most current state-of-the-art atmospheric GCMs (AGCMs) are able to capture the links between SST anomalies and anomalous precipitation over West Africa using either observed global SSTs (Mohino et al. 2011b) or observed SSTs over the tropical Atlantic with climatology elsewhere (Vizy and Cook 2001; Wang 2002; Losada et al. 2010). Nevertheless, most coupled atmosphere–ocean GCMs of the current generation have important systematic errors in the tropical Pacific and Atlantic Oceans as they obtain too weak trades, a spurious ITCZ south of the equator, and too warm SSTs in the eastern part of the basins (Mechoso et al. 1995; Davey et al. 2001; Richter et al. 2014; Biasutti et al. 2006). Such errors compromise the successful representation of the WAM and its response to the equatorial mode (Joly and Voldoire 2010; Rowell 2013).

Complementary to works examining the impacts of SST anomalies in the tropical Atlantic, Nnamchi and Li (2011) highlighted the importance of anomalies in the subtropical South Atlantic together with temperature anomalies with the opposite sign at the equator forming a South Atlantic Ocean dipole. This configuration of SST anomalies would impact rainfall over the Gulf of Guinea coast via the so-called Lindzen–Nigam process (i.e., by enhancing anomalous divergence with opposite sign at both centers of the dipole). Venegas et al. (1997) and Trzaska et al. (2007) also discussed the relative importance of the South Atlantic in the tropical Atlantic variability.

b. Influence of the Mediterranean Sea

The finding of relationships between Mediterranean climate variability and WAM dynamics motivated a new line of research in the last decade. Rowell (2003) showed that positive SST anomalies in the Mediterranean Sea tend to be associated with similarly positive precipitation anomalies in the Sahel (Fig. 4a). He demonstrated that increased evaporation over the positive SST anomalies leads to increased moisture content in the lower troposphere, which is advected southward into the Sahel by the low-level flow across the eastern Sahara, resulting in stronger moisture convergence and precipitation over the Sahel. Figure 4b illustrates these features using recent observed and reanalysis data. By means of AGCM sensitivity experiments, Rowell (2003) showed that such increase in rainfall is then amplified by positive feedback mechanisms, such as 1) a more intense moisture inflow from the tropical Atlantic triggered by enhanced convective heating, 2) a reduced outflow of moisture from the midlevel African easterly jet, 3) an enhanced hydrological cycle, and 4) a larger rainfall contribution by African easterly waves.

Fig. 4.
Fig. 4.

JAS warm minus cold Mediterranean Sea composites: (a) GPCC precipitation (mm) and (b) ERA-Interim specific humidity (contours; g kg−1) and moisture transport at 950 hPa (arrows; reference arrow is 10 m s−1 g kg−1). Values in colored regions and black arrows indicate 90% significance regarding a Student’s t test. Warm and cold events are identified on the basis of a standardized Mediterranean SST index (30°–40°N, 5°W–35°E) during the period 1991–2010. Warm and cold SST years correspond to 1994, 1999, and 2003 and 1993, 1996, 1997, and 2007 during which the index is >1 and <−1, respectively. The 1991–2010 period is chosen in order to exclude the turning of the Sahel precipitation trend in the 1980s and all the data are detrended.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

More recently, a number of empirical and numerical studies have provided further support to the links between anomalies in Mediterranean SSTs and WAM precipitation (Jung et al. 2006; Fontaine et al. 2011b; Polo et al. 2011). Peyrillé et al. (2007) and Peyrillé and Lafore (2007) described the local circulations and mechanisms favoring the northward migration of the monsoon rainbelt. Fontaine et al. (2010) provided evidence that warm events over the Mediterranean Sea are associated with an intensified WAM, stronger low-level moisture advection, and a more northward location of ascending motions in West Africa. Moreover, they found that SST variations in the western Mediterranean Sea are associated with others in deep convection over the Gulf of Guinea, while those in the eastern Mediterranean Sea affect the atmospheric circulation over the North African subcontinent. Thus, anomalous eastern Mediterranean Sea warm conditions are linked to a northward migration of the monsoon system accompanied by enhanced southwesterly flow and weakened northeasterly climatological wind. Gaetani et al. (2010) explored these relationships at subseasonal time scales, showing that rainfall anomalies in the Sahel are concentrated in July–August when the monsoon circulation is fully developed inland, so that the effect of the northerly moisture transport from the Mediterranean Sea is maximized during that period.

c. Influence of the Pacific Ocean

At interannual time scales, a warming in the tropical Pacific tends to be associated with increased precipitation over the Gulf of Guinea and decreased precipitation over the Sahel (Folland et al. 1986; Rowell et al. 1995; Janicot et al. 1996, 1998, 2001; Ward 1998; Camberlin et al. 2001; Rowell 2001; Joly et al. 2007). Not all analyses before the 2000s, however, were supportive of this negative link (Kiladis and Diaz 1989; Ropelewski and Halpert 1989; Shinoda and Kawamura 1994).

In the framework of AMMA, experiments on the sensitivity of WAM rainfall to SST anomalies in the Pacific were performed for the period 1979–2002. In general, the results showed that positive SST anomalies in the Pacific SST have negative effects on Sahel rainfall (Mohino et al. 2011c). Figure 5 shows composites of rainfall differences between years with both warmer and colder than average SSTs in the Pacific Ocean (Mohino et al. 2011c). The relationships between SST anomalies in the Pacific and Indian Oceans with West African rainfall vary strongly with season, being different for May–June (Fig. 5, left) and for July–August (Fig. 5, right). In late spring, anomalous subsidence develops over both the Maritime Continent and the equatorial Atlantic in association with enhanced equatorial heating in the Pacific. Rowell (2001) interprets this feature as a stationary Kelvin wave response originating in the east Pacific. Precipitation increases over continental West Africa in association with stronger zonal convergence of moisture. In addition, precipitation decreases over the Gulf of Guinea. During the monsoon peak (July and August), the SST anomalies move westward over the equatorial Pacific and the two regions of subsidence during the previous months merge over West Africa, weakening the monsoon and thus rainfall over the Sahel.

Fig. 5.
Fig. 5.

(a),(b) Composite of anomalous observed rainfall (mm day−1) in years 1983, 1987, and 1997 (“warm” Pacific years) minus 1985, 1988, and 1999 (“cold” Pacific years); (c),(d) linear component of the anomalous rainfall (mm day−1) simulated by the four models’ ensemble mean obtained in the sensitivity experiments; (e),(f) anomalous observed sea surface temperature (K) used in the sensitivity experiments, obtained as a composite of warm Pacific years minus cold ones; (g),(h) as in (c),(d), but for the velocity potential (106 m2 s−1) at 200 hPa and divergent wind (m s−1); (i),(j) as in (c),(d), but for the streamfunction (106 m2 s−1) at 200 hPa and rotational wind (m s−1), all showing the average for (left) May and June and (right) July and August. Gray contours mark where the simulated anomalies are larger in magnitude than the intermodel standard deviation. Warm (cold) Pacific years were chosen as those in which the SST expansion coefficient for the first mode of covariability between Indo-Pacific SSTs and West African rainfall was above (below) the threshold of +1 (−1) std dev of the index. Figure modified from Mohino et al. (2011c).

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

It has been shown using observational data that links between anomalies in WAM rainfall and Pacific SSTs occur during the developing phase of an El Niño–Southern Oscillation (ENSO) event. That is, the anomaly in WAM appears in boreal summer before the peak of ENSO in autumn–winter. CGCMs have difficulties in capturing the temporal aspects of these connections, as shown by Joly and Voldoire (2009) for CMIP3 models. These model difficulties were attributed to shortcomings in the simulation of ENSO locking to the seasonal cycle and the associated atmospheric teleconnections.

d. Influence of the Indian Ocean

The Indian Ocean has important impacts on the Sahel at both decadal and interannual time scales. At interannual time scales, Biasutti et al. (2008) found substantial negative correlations between Sahel precipitation and Indian Ocean SSTs in observations and in CGCM simulations performed in the framework of CMIP3. Bader and Latif (2011) argued that the Indian Ocean SSTs were the main forcing for the drought over the western Sahel in 1983, presenting evidence that the dry conditions that persisted over the western Sahel in that year were mainly forced by positive SST anomalies in the Indian Ocean, which probably remained from the strong 1982/83 El Niño event. Except for 1983, however, SST anomalies in the Indian Ocean at interannual time scales are generally weak in comparison to the other basins. Thus, the impact of Indian Ocean anomalies on Sahel rainfall could be masked by that of other basins. Palmer (1986) analyzed the AGCM response to SST anomalies in individual ocean basins that correspond to those in the SST pattern for the global tropics used in Folland et al. (1986). The AGCM experiments showed that the Indian Ocean warming produces a slight rainfall enhancement over the western Sahel, but that the concomitant anomalies in the Atlantic and Pacific Oceans are responsible for the precipitation reduction in the west Sahel. Fontaine et al. (2011a) showed that positive SST differences between the Indian Ocean and the eastern Mediterranean Sea are synchronous with in-phase rainfall deficits over the whole Sudan Sahel. In addition, Rowell (2001) suggested that the impact of the Indian Ocean acts through changes in the large-scale gradient in SSTs from the west Pacific to the Indian Ocean. AGCM experiments showed that these changes lead to a stationary equatorial Rossby wave response over the Indian Ocean, causing anomalous subsidence over the Sahel.

e. Modulation of the interannual variability by lower-frequency variations

Studies performed in the last decades have argued that the impact of SST anomalies in some basins appear to be different depending on the decades considered. We will refer to this property as “nonstationarity.”

The links between SST anomalies in the equatorial Atlantic and rainfall over the Gulf of Guinea are persistent features, but those with the Sahel rainfall seem to wane after the 1970s and at the beginning of the twentieth century. In these periods, the observations suggest that the influence of the Atlantic SST anomalies on the Sahel rainfall is balanced by concomitant SST anomalies with opposite signs in the Pacific and Indian Oceans (see Fig. 6). Joly and Voldoire (2010), Rodríguez-Fonseca et al. (2011), and Mohino et al. (2011b) have suggested that the counteracting effect of the Pacific could explain the absence of the dipolar relation with the Guinean rainfall reported before the 1970s. This hypothesis has been recently validated by the sensitivity experiments performed by Losada et al. (2012) with different AGCMs with prescribed SSTs in single or multiple ocean basins.

Fig. 6.
Fig. 6.

A 20-yr sliding window running correlation between the Niño-3 and Sahelian rainfall indices (middle grey line), the Atl3 and Sahelian rainfall indices (bottom black line), and the Atl3 and Guinea rainfall indices (top continuous line) during June–September. Modified from Losada et al. (2012). Atl3 is defined as the SST area averaged over the region 3°N–3°S, 20°W–0°. Niño-3 is defined as the SST area averaged over the region 5°N–5°S, 150°–90°W. The Guinea index is defined as the rainfall area averaged over the region 8°–4°N, 20°W–10°E and the Sahelian index is defined as the rainfall area averaged over the region 20°–10°N, 20°W–10°E. Dots indicate the 20-yr windows in which the correlation is significant at 95% of confidence level.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

A similar tendency to nonstationarity appears in the impact of SST anomalies in the Mediterranean Sea. After removing the global SST trend during the twentieth century, Fontaine et al. (2011a) indicated that SST anomalies in the eastern Mediterranean Sea and Indian Ocean have important effects on WAM rainfall, both in terms of intensity and time stability, with a growing importance of the Mediterranean Sea at the end of the twentieth century.

Broadly, two explanations have been offered for the nonstationarity of impacts. First, nonstationarity might be simply an artifact of sampling errors because some variation in the correlation between two variables can be expected by chance even during a stationary period of climate. Rowell (2013) addressed this hypothesis by applying a bootstrap resampling technique to SST teleconnections to African rainfall. He found no reason to reject the hypothesis that nonstationarity and multidecadal variability in the strength of these teleconnections might arise only from sampling variability, at least for the specific SST and rainfall in the regions considered. Internal atmospheric variability could also be relevant even at these longer time scales. For instance, Traore (2011) showed that running a AGCM over one to several centuries with fixed SST patterns (either the 1955–65 “moist” SST pattern or 1975–85 “dry” pattern) can provide decadal-scale rainfall anomalies up to 20% of the difference between the “moist” and “wet” long-term rainfall averages. Second, nonstationary teleconnections could be due to the slowly varying oceans and their multidecadal modes, as will be discussed later. These can alter the mean state of the atmosphere and/or the magnitude of SST variability in a way that during some decades the Atlantic influence interferes with that from the Pacific, whereas in other decades the Atlantic influence acts in isolation from the rest of the tropical basins (Mohino et al. 2011b).

3. Variability at decadal time scales

The first hypothesis on the prolonged Sahelian drought was that a decrease in vegetation cover led to increased albedo and, consequently, to enhanced subsidence in the region (Charney 1975). Later studies, however, identified the crucial role of SSTs over the world ocean in driving decadal Sahelian rainfall variability at decadal time scales. Using observed SSTs as boundary conditions, most AGCMs are able to reproduce the twentieth-century drying trend in West Africa and the Sahel drought of the 1970–80s and subsequent rainfall recovery (Rowell 1996; Giannini et al. 2003; Bader and Latif 2003; Lu and Delworth 2005; Haarsma et al. 2005; Hoerling et al. 2006, 2010; Lu 2009; Tippett 2006; Tippett and Giannini 2006; Caminade and Terray 2010; Mohino et al. 2011a; see also Fig. 8). The AGCM simulated impacts of SST anomalies on Sahel rainfall at decadal time scales, however, are weaker in comparison to the observations. This might be partially as a result of many reasons, such as shortcomings in the representation of vegetation–land–atmosphere interactions (Zeng et al. 1999; Giannini et al. 2003; Wang et al. 2004; Kucharski et al. 2013), changes in surface albedo (Kucharski et al. 2013), feedback with desert dust (Yoshioka et al. 2007; Wang et al. 2012; Mahajan et al. 2012; Chin et al. 2014), and the atmosphere’s internal variability (Traore 2011). Analyses carried out by the International Climate of the Twentieth Century Project (C20C) confirmed that SST variability is one, but not the only, driver of past Sahel droughts (Scaife et al. 2009). In this section we discuss separately the role of SST anomalies and land surface processes on the Sahel drought.

a. Role of SST anomalies

Decreased Sahel rainfall at decadal time scales has been associated with the warming of the tropical SSTs (Giannini et al. 2003, 2013; Lu and Delworth 2005; Mohino et al. 2011a). Hagos and Cook (2008) suggested that the combination of the SST anomalies over the tropical Indian and Atlantic Ocean basins was responsible for the 1980s Sahel drought and subsequent recovery in the 1990s. Caminade and Terray (2010) highlighted the role of the warming of the Pacific basin in reducing Sahel rainfall, while other works suggested that the drought was driven by the warming trend of the Indian Ocean basin during the late twentieth century (Bader and Latif 2003; Giannini et al. 2003; Tippett and Giannini 2006; Lu 2009). This warming of the tropical Indian Ocean and/or Pacific basin would induce enhanced convection locally and the propagation of Kelvin and Rossby waves that would communicate the tropospheric warming to West Africa (Chiang and Sobel 2002; Lu 2009). This would stabilize the region, leading to decreased rainfall over the Sahel through the so-called upped-ante mechanism (Neelin et al. 2003; Lu 2009; Caminade and Terray 2010). Lu and Delworth (2005) also suggested that the Pacific and Indian Ocean warming could lead to an anomalous Walker-type overturning cell with increased dry air subsidence over the Sahel and subsequent drought.

Alternatively, Sahel drought at decadal time scales has been related to the impacts of an interhemispheric dipole pattern of SST anomalies that is global, but most pronounced in the Atlantic basin (Folland et al. 1986; Palmer 1986). The differential heating of the Northern and Southern Hemisphere leads to a meridional shift of the ITCZ (Zhang and Delworth 2006; Kang et al. 2009; Hwang et al. 2013) and anomalous rainfall in the Sahel (Hoerling et al. 2006; Knight et al. 2006; Ting et al. 2009, 2011; Mohino et al. 2011a). Mechanistically, the movement of the rainbelt in the Sahel might occur through changes in the moisture content of the monsoon flow (Giannini et al. 2013) or circulation changes linked to a strengthening of the Saharan heat low (Martin and Thorncroft 2014a).

The relative importance of SST anomalies in different basins on Sahel rainfall has been greatly debated (e.g., Hoerling et al. 2006; Giannini et al. 2003; Bader and Latif 2003), partly because the sensitivity to each basin appears to be highly model dependent (Scaife et al. 2009; Biasutti et al. 2008) and partly because the details of the warming pattern matter greatly (Hagos and Cook 2008). Yet, recent work by Giannini et al. (2013) suggests that these differences might be reconciled by assuming that Sahel rainfall responds to relative changes between SST anomalies in the northern Atlantic and the entire tropics.

The broad agreement of the scientific community on SST anomalies being crucial drivers of Sahel rainfall decadal variability during the twentieth century has spurred a lively debate on whether this variability might be due to external forcings (anthropogenic and natural emissions) or to internal climate processes (natural variability modes) or, more likely, to a combination of the two. Recently, Mohino et al. (2011a) interpreted the pattern of SST variations linked to SST rainfall changes as a combination of three different modes of SST decadal variability: the response to the global warming trend, the positive phase of the interdecadal Pacific oscillation (Zhang et al. 1997), also known as the Pacific decadal oscillation (Mantua et al. 1997), and the negative phase of the Atlantic multidecadal oscillation (AMO; Knight et al. 2005), with all three leading to reductions in Sahel precipitation. Some works suggests that the observed AMO is an internal mode of variability not explained by external forcings (Knight et al. 2005; Knight 2009; Ting et al. 2009; 2011), while others point to the role of anthropogenic aerosols in cooling the North Atlantic more than the South Atlantic (Rotstayn and Lohmann 2002; Biasutti and Giannini 2006; Kawase et al. 2010; Ackerley et al. 2011; Biasutti 2011). Recently, Booth et al. (2012) showed that their CGCM experiments could simulate most of the observed twentieth-century SST variability in the North Atlantic, and that this variability was highly dependent on the indirect effect of aerosols. However, Zhang et al. (2013) call into question these results as there are multiple inconsistencies between the previous experiments and key aspects of observed variability within and without the North Atlantic.

Hoerling et al. (2006) and Lau et al. (2006) analyzed historical simulations of CGCMs participating in the CMIP3. Overall, the models failed to simulate the mid-twentieth-century Sahel drought and recent recovery (Hoerling et al. 2010) with the correct magnitude and timing, suggesting that anthropogenic forcings played little or no role in driving the drought. On the other hand, Held et al. (2005) showed that the GFDL CM2.0 and CM2.1 coupled models could very well reproduce the observed drying trend during the second half of the twentieth century. Ackerley et al. (2011) suggested that although the aerosols contributed to the intense decline in the rainfall over the Sahel in the 1950–80 period, a fraction of this drying could be related to either the effect of an internal mode (AMO) or climate model deficiencies. Biasutti and Giannini (2006) estimated from historical CMIP3 simulations that anthropogenic forcing, especially by aerosols, may have contributed a third of the long-term twentieth-century drying, and that estimate has been confirmed by CMIP5 models (Biasutti 2013). Recently, Hwang et al. (2013) posited that both the drought and subsequent recovery in the Sahel are greatly influenced by the increase and subsequent reduction of sulfate emissions in Europe and North America. However, neither generation of CMIP climate models reproduces the magnitude of the observed rainfall variability at decadal to multidecadal time scales over the twentieth century. Dismissing a strong role of natural variability in driving droughts at time scales less than a century might thus be premature (Biasutti 2011). This point of view is strengthened by paleoclimate and historical studies, which have highlighted how recent droughts in the Sahel are not unprecedented (Shanahan et al. 2009; Brooks 1998; Nicholson 1978, 1979; Nicholson et al. 2012). Shanahan et al. (2009) highlighted the existence of intervals of severe drought lasting for periods ranging from decades to centuries over Ghana over the last 3000 years and they related these megadroughts to the AMO. Prolonged drought episodes lasting from 1100 to 1500 might have partly contributed to the collapse of the Malian empire (Brooks 1998); multidecadal droughts were also reported in Senegambia from 1710 to 1750 and from 1770 to 1780 followed by reported mass starvation from 1790 to 1840 (Nicholson 1978, 1979, 2013). The Sahel drying observed during the second half of the twentieth century appears to be neither unusual nor extreme from a paleoclimate perspective or considering the long-term historical context.

Analyses of simulations by recent generations of CGCMs have provided additional insight on the WAM and its variability (Fig. 7). Control runs, historical simulations, future projections, and seasonal-to-decadal predictions have been analyzed. Rowell (2013) has recently assessed the capability of Sahel models to represent WAM teleconnections. His work suggests that some teleconnections tend to be poorly reproduced (e.g., those with the equatorial Atlantic SSTs and the Pacific ENSO), while others seem to be captured by most models (e.g., the link between the Mediterranean SSTs and Sahel rainfall).

Fig. 7.
Fig. 7.

Seasonal (July–September) anomalies of rainfall over the Sahel (10°–20°N, 10°W–10°E) calculated from the CRU time series, version 3.1 (TS3.1), dataset (Harris et al. 2014) (black solid line), an average of 16 AGCM models forced with observed SSTs (dark gray solid line), CanAM4 (light gray solid line), and IPSL-CM5A-MR (light gray dotted line). Data have been smoothed with a 5-yr running mean to focus on decadal time scales. Dashed dark gray lines show the one standard deviation of the 16 CMIP5 AMIP models. Simulated data were obtained from the CMIP5 database. AMIP simulations in CMIP5 start in 1979. Only two models started in 1950s, which are plotted separately in the figure.

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

b. Role of land surface processes

Pioneer projects as Hydrological Atmospheric Pilot Experiment (HAPEX) in the Sahel (Goutorbe et al. 1997) made a contribution to understanding the land surface processes in relation to the WAM variability. Since then, several studies have addressed the role of land surface processes in driving Sahel drought. Land surface parameterizations in the atmospheric models used in these studies cover a wide range of complexity (e.g., Laval and Picon 1986; Sud and Molud 1988; Xue et al. 1990; Eltahir and Gong 1996). Nevertheless, the results on sensitivity to land conditions in Sahel were quite consistent across models. Increases in albedo (soil moisture) produce negative (positive) feedbacks on rainfall, even the magnitude of the impacts is within a narrow range (Xue and Fennessy 2002). A proper evaluation of the surface feedback to climate can be obtained only when all relevant components of the surface energy and water balances are taken into account.

The impact of land degradation upon regional climate seasonal variability and drought events over the Sahel has been explored using a GCM coupled to a biophysical model (Xue et al. 2004a). According to the results, the primary effect of degrading savanna and shrub conditions in the Sahel is reduced evaporation. This is partially due to reduced net radiation because of higher albedo, but more importantly to lower leaf area index (LAI) and surface roughness length, and to higher stomatal resistance. The reduction in evaporation results in less convection and lower latent heating rates in the troposphere, in association with a relative subsidence, which in turn weakens the monsoon flow and reduces moisture flux convergence and lowers rainfall.

It has also been found that the Sahel, along with a few other regions that are mostly semiarid, has the largest soil moisture/climate coupling strength in the world. This result was obtained in the Global Land–Atmosphere Coupling Experiment (GLACE; Koster et al. 2006). Two sets of boreal summer (June–August) simulations were performed with multiple GCMs. In one, moisture varied during the simulations, while in the other set the same geographically varying time series of subsurface soil moisture was prescribed. The soil moisture–atmosphere coupling strength in each model was then estimated with a statistical method. The high soil moisture–climate coupling strength in the Sahel is consistent with a number of soil moisture feedback studies (e.g., Douville 2002; Philippon and Fontaine 2002). Results based on CMIP3 simulations also show that North Africa is another region with strong soil moisture feedbacks (Notaro 2008). A few modeling studies have shown that the root-zone soil moisture might not act as a memory of rainfall anomalies for the following rainy season and therefore might not affect the persistence of the drought (Shinoda and Yamaguchi 2003; Douville, et al. 2007; van den Hurk and van Meijgaard 2010).

The impact of the vegetation biophysical processes (VBP; i.e., land surface processes relevant to climate interactions associated with vegetation) on the WAM has also been investigated by Xue et al. (2004b, 2010b), who analyzed the results from two AGCMs coupled to three different land models with varying degrees of physically based complexity in the representation of VBP. The criterion to assess the importance of VBP effects was based on the simulation skill in reproducing the observed global precipitation under the assumption that their inclusion would improve precipitation simulations. Figure 8 shows the reduction in absolute seasonal mean bias of 5-yr mean simulated precipitation (or improved prediction skill) due to VBP processes. Accordingly, West Africa has the largest VBP impact in the world, especially in summer (see also Ma et al. 2013). Using remotely sensed LAI datasets, compared with using LAI based on a few ground surveys, in the boundary conditions of a GCM produced substantial improvements in the near-surface climate in West Africa (Kang et al. 2007; Li et al. 2007).

Fig. 8.
Fig. 8.

Land surface: reductions of simulation error (mm day−1) by introducing the VBP over different regions in different seasons [modified from Xue et al. (2010a)].

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

The numerical experiments described so far in this section prescribe land conditions. Other experiments have also been performed allowing for two-way vegetation–climate interactions (i.e., land surface conditions are not specified but predicted). Using a simple dynamic vegetation model coupled with the Quasi-Equilibrium Tropical Circulation Model (QTCM), Zeng et al. (1999) showed that the best reproduction of the observed interannual precipitation variability over the Sahel during the past half century was obtained when interactions among vegetation, soil, and ocean components were all included. Wang et al. (2004) investigated the impact of large-scale oceanic forcing and local vegetation feedback on the variability of Sahel rainfall using a global biosphere–atmosphere model. When vegetation was dynamic, the model realistically reproduced the multidecadal-scale fluctuation of rainfall in the Sahel region. However, when the vegetation was kept static, the rainfall regime was characterized by fluctuations at much shorter time scales. This suggests that vegetation dynamics acts as a mechanism for the persistence of the regional climate. Kucharski et al. (2013) obtained a similar result and showed that about 60% of the observed Sahel drought could be reproduced if vegetation dynamics were included in their AGCM ensemble simulations, whereas only 30% could be reproduced if vegetation feedbacks were static. Furthermore, Kucharski et al. (2013) demonstrated that the dominant positive feedback mechanism for the vegetation impact on the Sahel drought is the albedo feedback, in accordance with early work by Charney (1975).

Although land use changes might not have been the main driver of the 1980s Sahel drought, their impacts over West Africa could increase on the coming years (Taylor et al. 2002). In fact, the work by Paeth et al. (2009) suggests that in climate change scenarios land use changes could become primarily responsible for the simulated climate response over West Africa.

4. Seasonal forecasting of drought in Africa

During the last decade seasonal forecasting has matured from a research activity to a fully operational service, with many centers using initialized state-of-the-art coupled models. There are currently 12 WMO Global Producing Centers (GPCs) for long-range forecasting that routinely issue operational forecasts for rainy season totals (Graham et al. 2011). The forecasts are freely available for national meteorological services, regional climate centers, and global product centers via www.wmolc.org.

Over the African region, the skill of these long-range forecasting systems to predict seasonal total rainfall is high enough to make them useful for planning purposes. An example of the practical use of long-range forecasts and postprocessing techniques is the Prévisions Saisonnière en Afrique de l’Ouest (PRESAO; Fig. 9, top) forum, which meets annually to produce a consensus forecast for the West African region. To make this forecast, output from GPC dynamical systems and statistical models are combined and a spatial bias correction is made using canonical correlation analysis.

Fig. 9.
Fig. 9.

Average time–latitude propagation of zonally averaged (10°W–10°E) rainfall over the West African monsoon region for observations and for six state-of-the-art coupled models from ENSEMBLES. The onset date for each model is shown as a vertical black line. For reference, the average onset date in observations is 29 June. [From Vellinga et al. (2013)]

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

These improvements in forecasting seasonal averages have been achieved thanks to the continuous research completed in the last 10 years. For example, Philippon et al. (2010) found that the correlation between observations and a five-model ensemble mean from the ENSEMBLES Sixth Framework Programme (FP6) project (Hewitt 2004) was 0.55 for the Guinean rainfall variability mode. Batté and Déqué (2011) also used the ENSEMBLES dataset to analyze the skill of the multimodel system not only for the WAM but also the South African winter rainy season and the greater Horn of Africa long and short rains. They found that the multimodel ensemble improved the spread-to-skill ratio and average skill score over the use of a single model by as much as 10% when measured as potential economic value. Philippon et al. (2010) and Ndiaye et al. (2011) found that skill can be enhanced by predicting modes of variability using model output statistics (MOS) instead of using direct model output and therefore such enhancements are used to produce forecasts.

Despite these advances, there are still some remaining scientific questions such as large biases in tropical Atlantic SSTs in coupled models. Also, from a user’s point of view, even more critical than the total seasonal rainfall is the ability to predict the temporal distribution of rain throughout the season (Salack et al. 2014), which determines the optimal planting and harvesting time (Marteau et al. 2011). This has created a clear demand for information such as the onset of rainy seasons in many parts of Africa (Ingram et al. 2002; Graham et al. 2011).

Vellinga et al. (2013) analyzed the links between SST and precipitation over the WAM region using dynamical forecasting systems from the ENSEMBLES project. Confirming the results found by Salack et al. (2014) in observations, Vellinga et al. (2013) showed that the forecast skill in coupled models was related to tropical Atlantic SSTs in June. The ability of models to forecast the timing of the monsoon onset was found to be useful, with relative operating characteristics (ROC) scores of 0.6–0.8 at 3-months lead time.

A crucial point from the user perspective is that useful levels of skill are found even in some models with large mean rainfall biases over the region. This is important because, although there are still major model problems that need to be solved (such as biases in tropical Atlantic SSTs and total precipitation), the forecast systems are starting to be able to provide relevant probabilistic information to a crucial user question: Will the onset of the WAM be earlier or later than average this year? Figure 9 (from Vellinga et al. 2013) shows a major advance toward a meaningful answer to such a question, since only 10 years ago there was no forecasting system able to do it with a direct and relevant application to users.

5. Decadal prediction of the WAM

The new field of decadal climate prediction aims to provide climate information on time scales from a few years to a few decades into the future, which is recognized as a key planning horizon (Goddard et al. 2010; Vera et al. 2010; Smith et al. 2012). Decadal predictions are certainly of increasing scientific interest because they potentially represent a benefit to society through improvements in the development of climate services and adaptation strategies. They are recognized as a major part of the CMIP5 experimental design (Taylor et al. 2012).

Decadal predictions explore the benefits of initializing climate models. The initialization tries to provide the forecast system with contemporaneous information on, for instance, the state of the upper-ocean heat content, to achieve forecast quality beyond that provided by simulations based only on externally forced signals such as the standard CMIP3 and CMIP5 climate change scenario experiments. The objectives of decadal prediction include capturing low-frequency internal variability and correcting the model response to climate change forcings and commitment (Meehl et al. 2009, 2014; Murphy et al. 2010; Goddard et al. 2013; Doblas-Reyes et al. 2013).

As has been indicated in previous sections, WAM variability at decadal time scales results from a combination of internal and externally forced components (Biasutti 2011; Mohino et al. 2011a), including anthropogenic aerosols (e.g., Biasutti and Giannini 2006) and greenhouse gases (e.g., Biasutti et al. 2008). All the evidence points to the WAM as a good test bed for assessing the feasibility of decadal prediction.

Using the first coordinated experiment to explore the feasibility of decadal prediction (ENSEMBLES; Doblas-Reyes et al. 2010), van Oldenborgh et al. (2012) and MacLeod et al. (2012) found no significant skill in point-wise precipitation predictions over West Africa for 4-yr forecast averages. Predictions of precipitation trends did not have skill either. MacLeod et al. (2012) further suggested that the spread of precipitation hindcasts over land is such that the potential for predictability may not be sufficient to be useful. However, García-Serrano et al. (2013) have shown that the ENSEMBLES forecast systems are reliable, in a probabilistic sense, when recapturing the Sahelian precipitation variability. Instead, multiyear forecast quality assessment of the dominant WAM precipitation regimes suggests that the Guinean rainfall is not skillful.

From the most recent generation of climate models (i.e., CMIP5), Goddard et al. (2013), Gaetani and Mohino (2013), and Doblas-Reyes et al. (2013) found positive pointwise precipitation correlation over West Africa. However, this finding is model dependent, and the conditional biases that exist in the predictions may actually make the information less accurate than climatological averages (Goddard et al. 2013). A proper simulation of the remote SST influences on the Sahel appears to be key for predictability at decadal–multidecadal time scales (Martin and Thorncroft 2014b). The Sahelian rainfall skill is likely associated with a significant skill in the AMO prediction (García-Serrano et al. 2015; Guemas et al. 2015), although the relative SST difference between the subtropical North Atlantic and the tropics (Giannini et al. 2013) and SST variability in the Mediterranean Sea (e.g., Fontaine et al. 2010) have also been shown to be important for providing skill (Martin and Thorncroft 2014b). Results from CMIP5 (Goddard et al. 2013; Gaetani and Mohino 2013) show a nonnegligible contribution to the Sahelian rainfall skill from the external radiative forcing (Fig. 10), whereas initialized hindcasts outperform empirical predictions based on persistence at longer lead times (Gaetani and Mohino 2013; Martin et al. 2014).

Fig. 10.
Fig. 10.

Decadal prediction: anomaly correlation coefficient (ACC) between the Sahelian precipitation index (10°–20°N, 15°W–15°E) from the CRU dataset and decadal hindcasts (solid), historical experiments (dashed), and decadal-forcing residuals (dotted). ACC is computed for the multimodel ensemble mean. Dots indicate the 95% significant positive correlations; significance is estimated using a Monte Carlo test with 200 permutations. The decadal-forcing residuals are analyzed to obtain an evaluation of the initialization weight in the decadal experiments [see Gaetani and Mohino (2013) for details].

Citation: Journal of Climate 28, 10; 10.1175/JCLI-D-14-00130.1

The good performance of the ENSEMBLES (García-Serrano et al. 2013) and CMIP5 (Gaetani and Mohino 2013) decadal forecast systems in reproducing the relationship between the Sahel precipitation and its associated SST patterns encourages the promotion of improved decadal prediction systems in the future where the problems of SST biases are properly dealt with. This is only achievable in a context of a model development strategy across all communities that use climate models,