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This paper briefly presents the West African Monsoon (WAM) Modeling and Evaluation Project (WAMME) and evaluates WAMME general circulation models' (GCM) performances in simulating variability of WAM precipitation, surface temperature, and major circulation features at seasonal and intraseasonal scales in the first WAMME experiment. The analyses indicate that models with specified sea surface temperature generally have reasonable simulations of the pattern of spatial distribution of WAM seasonal mean precipitation and surface temperature as well as the averaged zonal wind in latitudeheight cross-section and low level circulation. But there are large differences among models in simulating spatial correlation, intensity, and variance of precipitation compared with observations. Furthermore, the majority of models fail 123Clim Dyn (2010) 35:3-27 DOI 10.1007 to produce proper intensities of the African Easterly Jet (AEJ) and the tropical easterly jet. AMMA Land Surface Model Intercomparison Project (ALMIP) data are used to analyze the association between simulated surface processes and the WAM and to investigate the WAM mechanism. It has been identified that the spatial distributions of surface sensible heat flux, surface temperature, and moisture convergence are closely associated with the simulated spatial distribution of precipitation; while surface latent heat flux is closely associated with the AEJ and contributes to divergence in AEJ simulation. Common empirical orthogonal functions (CEOF) analysis is applied to characterize the WAM precipitation evolution and has identified a major WAM precipitation mode and two temperature modes (Sahara mode and Sahel mode). Results indicate that the WAMME models produce reasonable temporal evolutions of major CEOF modes but have deficiencies/ uncertainties in producing variances explained by major modes. Furthermore, the CEOF analysis shows that WAM precipitation evolution is closely related to the enhanced Sahara mode and the weakened Sahel mode, supporting the evidence revealed in the analysis using ALMIP data. An analysis of variability of CEOF modes suggests that the Sahara mode leads the WAM evolution, and divergence in simulating this mode contributes to discrepancies in the precipitation simulation.

Atmospheric flow patterns are examined over the South Atlantic Ocean where a maximum of tropospheric ozone has been observed just west of southern Africa. We investigate the flow climatology during October and perform a case study for 6 days during October 1989. Analyses from the European Center for Medium‐Range Weather Forecasting are employed, and a high‐resolution global spectral model is used to prepare forecasts during the period. Horizontal and vertical motions are examined and used to prepare three‐dimensional backward trajectories from the region of greatest ozone. An initially zonally symmetric distribution of ozone is treated as a passive tracer and advected by three‐dimensional flows forecast by the global model. Results from the passive tracer simulation indicate that three‐dimensional advection alone can produce a maximum of tropospheric ozone in the observed location. In addition, the trajectories suggest that by‐products of biomass burning could be transported to the area of maximum ozone. Low‐level flow from commonly observed regions of burning in Africa streams westward to the area of interest. Over Brazil, if the burning by‐products are carried into the upper troposphere by convective process, they then could be transported eastward to the ozone feature in approximately 5 days. There is considerable subsidence over the tropical southern Atlantic, such that stratospheric influences also are a factor in producing the ozone maximum. Both planetary‐scale and transient synoptic‐scale circulation features play major roles in the various transport processes that influence the region. In summary, the observed tropospheric ozone maximum appears to be caused by a complex set of horizontal and vertical advections, transport from regions of biomass burning, and stratospheric influences.

A potentially operational forecast system for 3-month total precipitation for three sections of the African continent has been developed at NOAA's Climate Prediction Center using the statistical method of canonical correlation analysis (CCA). The levels and sources of predictive skills have been explored at lead times of up to 1 year, using a cross-validation design. The predictor field is quasi-global sea surface temperature (SST). Four consecutive 3-month predictor periods are used to detect evolving as well as steady-state SST conditions. Low to modest forecast skills are found for most regions and seasons of the year. However, moderate skills (correlation ú 0.5) are found for parts of northern tropical Africa (the Sahel) for lead times of up to several seasons