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Process-oriented assessment of RCA4 regional climate model projections over the Congo Basin under \(1.5 \ ^{\circ }{\text {C}}\) and \(2 \ ^{\circ }{\text {C}}\) global warming levels: influence of regional moisture fluxes

  • Alain T. TamoffoEmail author
  • Wilfran Moufouma-Okia
  • Alessandro Dosio
  • Rachel James
  • Wilfried M. Pokam
  • Derbetini A. Vondou
  • Thierry C. Fotso-Nguemo
  • Guy Merlin Guenang
  • Pierre H. Kamsu-Tamo
  • Grigory Nikulin
  • Georges-Noel Longandjo
  • Christopher J. Lennard
  • Jean-Pierre Bell
  • Roland R. Takong
  • Andreas Haensler
  • Lucie A. Djiotang Tchotchou
  • Robert Nouayou
Article

Abstract

Understanding the processes responsible for precipitation and its future change is important to develop plausible and sustainable climate change adaptation strategies, especially in regions with few available observed data like Congo Basin (CB). This paper investigates the atmospheric circulation processes associated with climate model biases in CB rainfall, and explores drivers of projected rainfall changes. Here we use an ensemble of simulations from the Swedish Regional Climate Model (RCM) RCA4, driven by eight General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), for the \(1.5 \ ^{\circ }{\text {C}}\) and \(2\ ^{\circ }{\text {C}}\) global warming levels (GWLs), and under the representative concentration pathways (RCPs) 4.5 and 8.5. RCA4 captures reasonably well the observed patterns of CB rainfall seasonality, but shows dry biases independent of seasons and large scale driving atmospheric conditions. While simulations mimic observed peaks in transition seasons (March–May and September–November), the rain-belt is misplaced southward (northward) in December–February (June–August), reducing the latitudinal extent of rainfall. Moreover, ERA-Interim reanalysis driven RCM simulation and RCM–GCM combinations show similar results, indicating the dominance of systematic biases. Modelled dry biases are associated with dry upper-tropospheric layers, resulting from a western outflow stronger than the eastern inflow and related to the northern component of African Easterly Jet. From the analysis of the climate change signal, we found that regional scale responses to anthropogenic forcings vary across GWLs and seasons. Changes of rainfall and moisture divergence are correlated, with values higher in March–May than in September–November, and larger for global warming of \(2.0 \ ^{\circ }{\text {C}}\) than at \(1.5 \ ^{\circ }{\text {C}}\). There is an increase of zonal moisture divergence fluxes in upper atmospheric layers (\(> 700\,{\text {hPa}}\)) under RCP8.5 compared to RCP4.5. Moreover, it is found that additional warming of \(0.5 \ ^{\circ }{\text {C}}\) will change the hydrological cycle and water availability in the CB, with potential to cause challenges to water resource management, agriculture, hydro-power generation, sanitation and ecosystems.

Keywords

Congo Basin rainfall biases RCA4 CMIP5 Moisture convergence Global warming levels RCPs 

Notes

Acknowledgements

The constructive comments and suggestions of the editor and two anonymous reviewers led to several key improvements of the first version of the manuscript. The first author wish to express their gratitude to very fruitful discussions with F. Guichard (CNRM). The authors would like to acknowledge support from the Swedish Government through the Swedish International Development Cooperation Agency (SIDA). This work is partially supported by the International Joint Laboratory’s research “Dynamics of Land Ecosystems in Central Africa: A Context of Global Changes” (IJL DYCOCA/LMI DYCOFAC). GNL acknowledges support by PREFACE project (EU FP7/2007-2013 under grant agreement no. 603521); National Research Foundation SARChI Chair in Ocean-Atmosphere-Land modelling and ACCESS project. We also acknowledge logistical support from the CORDEX International Project Office, the Swedish Meteorological Institute and the Climate System Analysis Group at the University of Cape Town. We are grateful to all the modeling groups that performed the simulations and made their data available.

Supplementary material

382_2019_4751_MOESM1_ESM.pdf (42 mb)
Supplementary material 1 (pdf 43035 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory for Environmental Modelling and Atmospheric Physics (LEMAP), Department of PhysicsUniversity of Yaounde 1YaoundeCameroon
  2. 2.2LMI DYCOFAC (IRD, University of Yaounde 1, IRGM), IRDYaoundéCameroun
  3. 3.Universite Paris Saclay, Intergovernmental Panel on Climate Change (IPCC) Working Group 1 (WG1) Technical Support Unit (TSU)Saint AubinFrance
  4. 4.European Commission, Joint Research Centre (JRC)IspraItaly
  5. 5.Environmental Change InstituteUniversity of OxfordOxfordUK
  6. 6.Department of Physics, Higher Teacher Training CollegeUniversity of Yaounde 1YaoundeCameroon
  7. 7.Climate Change Research Laboratory (CCRL)National Institute of CartographyYaoundeCameroon
  8. 8.Laboratory of Mechanics and Modeling of Physical Systems, Department of Physics, Faculty of ScienceUniversity of DschangDschangCameroon
  9. 9.Climate Prediction Center, National Centers for Environmental PredictionsNational Oceanic and Atmospheric AdministrationCollege ParkUSA
  10. 10.Cooperative Programs for the Advancement of Earth System ScienceUniversity Corporation for Atmospheric ResearchBoulderUSA
  11. 11.Rossby CentreSwedish Meteorological and Hydrological InstituteNorrköpingSweden
  12. 12.Department of Oceanography, Nansen-Tutu Center for Environmental Marine ResearchUniversity of Cape TownCape TownSouth Africa
  13. 13.Department of Environmental and Geographical ScienceUniversity of Cape TownCape TownSouth Africa
  14. 14.CEPAMOQ, Faculty of ScienceUniversity of DoualaDoualaCameroon
  15. 15.Helmholtz-Zentrum Geesthacht, Climate Service Center GermanyHamburgGermany
  16. 16.Laboratory of Geophysics and Geoexploration, Department of PhysicsUniversity of Yaounde 1YaoundeCameroon

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