West African monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations
- 553 Downloads
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.
KeywordsWest 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.
- Fontaine B, Garcia-Serrano J, Roucou P, Rodriguez-Fonseca B, Losada T, Chauvin F, Gervois S, Sijikumar S, Ruti P, Janicot S (2010) Impacts of warm and cold situations in the mediterranean basins on the West African monsoon: observed connection patterns (1979–2006) and climate simulations. Clim Dyn 35:95–114. doi: 10.1007/s00382-009-0599-3 CrossRefGoogle Scholar
- IPCC (2014) Climate change 2014: synthesis report. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, SwitzerlandGoogle Scholar
- Kandji ST, Verchot L, Mackensen J (2006) Climate change and variability in the Sahel region : impacts and adaptation strategies in the agricultural sector. UNEP & ICRAFGoogle Scholar
- Nicholson SE, Some B, McCollum J, Nelkin E, Klotter D, Berte Y, Diallo BM, Gaye I, Kpabeba G, Ndiaye O, Noukpozounkou JN, Tanu MM, Thiam A, Toure AA, Traore AK (2003) Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part I: validation of GPCC rainfall product and pre-TRMM satellite and blended products. J Appl Meteorol 42:1337–1354. doi: 10.1175/1520-0450(2003)042<1337:VOTAOR>2.0.CO;2 CrossRefGoogle Scholar
- Rodríguez-Fonseca B, Mohino E, Mechoso CR, Caminade C, Biasutti M, Gaetani M, Garcia-Serrano J, Vizy EK, Cook K, Xue Y, Polo I, Losada T, Druyan L, Fontaine B, Bader J, Doblas-Reyes FJ, Goddard L, Janicot S, Arribas A, Lau W, Colman A, Vellinga M, Rowell DP, Kucharski F, Voldoire A (2015) Variability and predictability of West African droughts: a review on the role of sea surface temperature anomalies. J Clim 28:4034–4060. doi: 10.1175/JCLI-D-14-00130.1 CrossRefGoogle Scholar
- Santer BD, Taylor KE, Gleckler PJ, Bonfils C, Barnett TP, Pierce DW, Wigley TML, Mears C, Wentz FJ, Bruggemann W, Gillett NP, Klein SA, Solomon S, Stott PA, Wehner MF (2009) Incorporating model quality information in climate change detection and attribution studies. Proc Natl Acad Sci USA 106:14778–14783. doi: 10.1073/pnas.0901736106 CrossRefGoogle Scholar
- Trenberth KE, Jones PD, Ambenje P, Bojariu R, Easterling D, Klein Tank A, Parker D, Rahimzadeh F, Renwick JA, Rusticucci M, Soden B, Zhai P (2007) Observations: surface and atmospheric climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar