Modeling Earth Systems and Environment

, Volume 5, Issue 4, pp 1815–1838 | Cite as

Capability of CAM5.1 in simulating maximum air temperature patterns over West Africa during boreal spring

  • Kamoru A. LawalEmail author
  • Babatunde J. Abiodun
  • Dáithí A. Stone
  • Eniola Olaniyan
  • Michael F. Wehner
Original Article


This study classifies maximum air temperature patterns over West Africa into six groups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets. Using Self Organizing Map algorithm to classify the spatial patterns of maximum air temperature during boreal spring, the study reveals the temperature patterns that CAM can simulate well and those the model struggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature pattern group (which features positive temperatures anomalies over the Sahel) and Node 2 (which features near-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds (horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperature patterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. An ensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that of ensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the study may be used to improve the perfomance of CAM over West Africa, thereby strengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa.


Boreal spring CAM Ensemble mean Maximum air temperature Patterns West Africa 



This work was supported with grants from the Water Research Commission (WRC, South Africa – Project K5/2067/1) and the U.K. Research and Innovation as part of the Global Challenges Research Fund, African SWIFT program, grant number NE/P021077/1 ( work packages R2 and R5). Computation supports were provided by the Climate Sciences Analysis Group (CSAG, University of Cape Town) and the Centre for High Performance Computing (CHPC, South Africa). DAS and MFW were supported by the Director, Office of Science, Office of Biological and Environ-mental Research of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The International CLIVAR C20C+ D&A project and the simulations of the CAM5.1 model used resources of the National Energy Research Scientific Computing Center (NERSC), also supported by the Office of Science of the U.S. Department of Energy, under Contract No. DE-AC02-05CH11231. The CAM5.1 data are available at We thank all anonymous reviewers whose comments improved the quality of this manuscript.


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Authors and Affiliations

  1. 1.African Climate and Development InitiativeUniversity of Cape TownCape TownSouth Africa
  2. 2.Nigerian Meteorological AgencyAbujaNigeria
  3. 3.Climate System Analysis GroupUniversity of Cape TownCape TownSouth Africa
  4. 4.Computational Chemistry, Materials and Climate GroupLawrence Berkeley National LaboratoryBerkeleyUSA
  5. 5.Federal University of TechnologyAkureNigeria

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