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Climate Dynamics

, Volume 26, Issue 5, pp 531–547 | Cite as

West African monsoon response to greenhouse gas and sulphate aerosol forcing under two emission scenarios

  • C. CaminadeEmail author
  • L. Terray
  • E. Maisonnave
Article

Abstract

The impact of increased greenhouse gases (GHG) and aerosols concentrations upon the West African monsoon (WAM) is investigated for the late twenty-first century period using the Météo-France ARPEGE-IFS high-resolution atmospheric model. Perturbed (2070–2100) and current (1961–2000) climates are compared using the model in time-slice mode. The model is forced by global sea surface temperatures provided by two transient scenarios performed with low-resolution coupled models and by two GHG evolution scenarios, SRES-A2 and SRES-B2. Comparing to reanalysis and observed data sets, the model is able to reproduce a realistic seasonal cycle of WAM despite a clear underestimation of the African Easterly Jet (AEJ) during the boreal summer. Mean temperature change indicates a global warming over the continent (stronger over North and South Africa). Simulated precipitation change at the end of the twenty-first century shows an increase in precipitation over Sudan-Sahel linked to a strong positive feedback with surface evaporation. Along Guinea Gulf coast, rainfall regimes are driven by large-scale moisture advection. Moreover, results show a mean precipitation decrease (increase) in the most (less) enhanced GHG atmosphere over this region. Modification of the seasonal hydrological cycle consists in a rain increase during the monsoon onset. There is a significant increase in rainfall variance over the Sahel, which extends over the Guinea coast region in the moderate emission scenario. Enhanced precipitation over Sahel is linked to large-scale circulation changes, namely a weakening of the AEJ and an intensification of the Tropical Easterly Jet.

Keywords

West Africa Monsoon Inter Tropical Convergence Zone Monsoon Flow Tropical Atlantic Ocean Guinea Coast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.CERFACSToulouse-CedexFrance

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