Climate Dynamics

, Volume 48, Issue 3–4, pp 1353–1373 | Cite as

West African monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations

  • Marco GaetaniEmail author
  • Cyrille Flamant
  • Sophie Bastin
  • Serge Janicot
  • Christophe Lavaysse
  • Frederic Hourdin
  • Pascale Braconnot
  • Sandrine Bony


Climate variability associated with the West African monsoon (WAM) has important environmental and socio-economic impacts in the region. However, state-of-the-art climate models still struggle in producing reliable climate predictions. An important cause of this low predictive skill is the sensitivity of climate models to different forcings. In this study, the mechanisms linking the WAM dynamics to the CO2 forcing are investigated, by comparing the effect of the CO2 direct radiative effect with its indirect effect mediated by the global sea surface warming. The July-to-September WAM variability is studied in climate simulations extracted from the Coupled Model Intercomparison Project Phase 5 archive, driven by prescribed sea surface temperature (SST). The individual roles of global SST warming and CO2 atmospheric concentration increase are investigated through idealized experiments simulating a 4 K warmer SST and a quadrupled CO2 concentration, respectively. Results show opposite and competing responses in the WAM dynamics and precipitation. A dry response (−0.6 mm/day) to the SST warming is simulated in the Sahel, with dryer conditions over western Sahel (−0.8 mm/day). Conversely, the CO2 increase produces wet conditions (+0.5 mm/day) in the Sahel, with the strongest response over central-eastern Sahel (+0.7 mm/day). The associated responses in the atmospheric dynamics are also analysed, showing that the SST warming affects the Sahelian precipitation through modifications in the global tropical atmospheric dynamics, reducing the importance of the regional drivers, while the CO2 increase reinforces the coupling between precipitation and regional dynamics. A general agreement in model responses demonstrates the robustness of the identified mechanisms linking the WAM dynamics to the CO2 direct and indirect forcing, and indicates that these primary mechanisms are captured by climate models. Results also suggest that the spread in future projections may be caused by unbalanced model responses to the CO2 direct and indirect forcing.


West Africa Sahel Monsoon Precipitation Sahara CO2 SST Global warming Climate modelling CMIP5 



This work benefitted from the support of the Agence Nationale de la Recherche (ANR) Grant ANR-10-LABX-18-01 of the national Programme Investissements d’Avenir. Funding for this work was also provided by Laboratoire d’excellence Institut Pierre Simon Laplace (L-IPSL). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Authors thank A. Evan for useful discussions, and two anonymous reviewers for their insightful comments that have greatly improved the quality of the paper.

Supplementary material

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Supplementary material 1 (PDF 1326 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Marco Gaetani
    • 1
    • 2
    Email author
  • Cyrille Flamant
    • 1
  • Sophie Bastin
    • 1
  • Serge Janicot
    • 3
  • Christophe Lavaysse
    • 4
  • Frederic Hourdin
    • 2
  • Pascale Braconnot
    • 5
  • Sandrine Bony
    • 2
  1. 1.Laboratoire Atmosphères, Milieux, Observations Spatiales, LATMOS-IPSLUPMC Univ. Paris 06 Sorbonne Universités, UVSQ, CNRSParisFrance
  2. 2.Laboratoire de Météorologie Dynamique, LMD-IPSLUPMC Univ. Paris 06 Sorbonne Universités, Ecole Normale Supérieur, Ecole Polytechnique, CNRSParisFrance
  3. 3.Laboratoire d’Océanographie et du Climat: Expérimentation et Approches Numériques, LOCEAN-IPSLUPMC Univ. Paris 06 Sorbonne Universités, IRD, MNHM, CNRSParisFrance
  4. 4.Joint Research CentreEuropean CommissionIspraItaly
  5. 5.Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSLUVSQ, CEA, CNRSGif-Sur-YvetteFrance

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