Theoretical and Applied Climatology

, Volume 123, Issue 1, pp 369–386

Daily characteristics of West African summer monsoon precipitation in CORDEX simulations

  • Nana Ama Browne Klutse
  • Mouhamadou Bamba Sylla
  • Ismaila Diallo
  • Abdoulaye Sarr
  • Alessandro Dosio
  • Arona Diedhiou
  • Andre Kamga
  • Benjamin Lamptey
  • Abdou Ali
  • Emiola O. Gbobaniyi
  • Kwadwo Owusu
  • Christopher Lennard
  • Bruce Hewitson
  • Grigory Nikulin
  • Hans-Jürgen Panitz
  • Matthias Büchner
Original Paper

DOI: 10.1007/s00704-014-1352-3

Cite this article as:
Klutse, N.A.B., Sylla, M.B., Diallo, I. et al. Theor Appl Climatol (2016) 123: 369. doi:10.1007/s00704-014-1352-3

Abstract

We analyze and intercompare the performance of a set of ten regional climate models (RCMs) along with the ensemble mean of their statistics in simulating daily precipitation characteristics during the West African monsoon (WAM) period (June–July–August–September). The experiments are conducted within the framework of the COordinated Regional Downscaling Experiments for the African domain. We find that the RCMs exhibit substantial differences that are associated with a wide range of estimates of higher-order statistics, such as intensity, frequency, and daily extremes mostly driven by the convective scheme employed. For instance, a number of the RCMs simulate a similar number of wet days compared to observations but greater rainfall intensity, especially in oceanic regions adjacent to the Guinea Highlands because of a larger number of heavy precipitation events. Other models exhibit a higher wet-day frequency but much lower rainfall intensity over West Africa due to the occurrence of less frequent heavy rainfall events. This indicates the existence of large uncertainties related to the simulation of daily rainfall characteristics by the RCMs. The ensemble mean of the indices substantially improves the RCMs’ simulated frequency and intensity of precipitation events, moderately outperforms that of the 95th percentile, and provides mixed benefits for the dry and wet spells. Although the ensemble mean improved results cannot be generalized, such an approach produces encouraging results and can help, to some extent, to improve the robustness of the response of the WAM daily precipitation to the anthropogenic greenhouse gas warming.

Supplementary material

704_2014_1352_MOESM1_ESM.docx (15 kb)
ESM 1(DOCX 14 kb)
704_2014_1352_MOESM2_ESM.docx (15 kb)
ESM 2(DOCX 14 kb)

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Nana Ama Browne Klutse
    • 1
  • Mouhamadou Bamba Sylla
    • 2
    • 3
  • Ismaila Diallo
    • 4
    • 5
  • Abdoulaye Sarr
    • 6
  • Alessandro Dosio
    • 7
  • Arona Diedhiou
    • 8
  • Andre Kamga
    • 9
  • Benjamin Lamptey
    • 9
  • Abdou Ali
    • 10
  • Emiola O. Gbobaniyi
    • 11
  • Kwadwo Owusu
    • 12
  • Christopher Lennard
    • 13
  • Bruce Hewitson
    • 13
  • Grigory Nikulin
    • 14
  • Hans-Jürgen Panitz
    • 15
  • Matthias Büchner
    • 16
  1. 1.Ghana Atomic Energy CommissionGhana Space Science and Technology InstituteAccraGhana
  2. 2.West African Science Service Center on Climate Change and Adapted Landuse (WASCAL), WASCAL Competence CenterOuagadougouBurkina Faso
  3. 3.Department of Civil Engineering and Environmental Science, Seaver College of Science and EngineeringLoyola Marymount UniversityLos AngelesUSA
  4. 4.Laboratoire de Physique de l’Atmosphère et de l’Océan Siméon Fongang (LPAO-SF)Université Cheikh Anta Diop, Ecole Supérieure Polytechnique (UCAD-ESP)DakarSenegal
  5. 5.Earth System Physics SectionAbdus Salam International Center for Theoretical PhysicsTriesteItaly
  6. 6.Nationale de la Météorologie du Sénégal (ANACIM)Dakar-YoffSénégal
  7. 7.European Commission Joint Research CentreInstitute for Environment and SustainabilityIspraItaly
  8. 8.Institut de Recherche pour le Développement, IRD/LTHEGrenobleFrance
  9. 9.ACMADNiameyNiger
  10. 10.AGRHYMETNiameyNiger
  11. 11.Federal University of TechnologyAkureNigeria
  12. 12.Geography DepartmentUniversity of GhanaAccraGhana
  13. 13.Climate System Analysis GroupUniversity of Cape TownCape TownSouth Africa
  14. 14.Rossby CentreSwedish Meteorological and Hydrological InstituteNorrköpingSweden
  15. 15.Institut für Meteorologie und Klimaforschung Karlsruher Institut für TechnologieKarlsruheGermany
  16. 16.Potsdam Institute for Climate Impact ResearchPotsdamGermany

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