Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Climate System Analysis Group, University of Cape Town, Cape Town, South Africa; CSIR Natural Resources and the Environment, Climate Studies, Modelling and Environmental Health, Pretoria, South Africa
Tozuka, T., Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan; Abiodun, B.J., Climate System Analysis Group, University of Cape Town, Cape Town, South Africa; Engelbrecht, F.A., CSIR Natural Resources and the Environment, Climate Studies, Modelling and Environmental Health, Pretoria, South Africa
This study examines southern African summer rainfall and tropical temperate troughs (TTTs) simulated with three versions of an atmospheric general circulation model differing only in the convection scheme. All three versions provide realistic simulations of key aspects of the summer (November-February) rainfall, such as the spatial distribution of total rainfall and the percentage of rainfall associated with TTTs. However, one version has a large bias in the onset of the rainy season. Results from self-organizing map (SOM) analysis on simulated daily precipitation data reveals that this is because the occurrence of TTTs is underestimated in November. This model bias is not related to westerly wind shear that provides favorable conditions for the development of TTTs. Rather, it is related to excessive upper level convergence and associated subsidence over southern Africa. Furthermore, the model versions are shown to be successful in capturing the observed drier (wetter) conditions over the southern African region during El Niño (La Niña) years. The SOM analysis reveals that nodes associated with TTTs in the southern (northern) part of the domain are observed less (more) often during El Niño years, while nodes associated with TTTs occur more frequently during La Niña years. Also, nodes associated with dry conditions over southern Africa are more (less) frequently observed during El Niño (La Niña) years. The models tend to perform better for La Niña events, because they are more successful in representing the observed frequency of different synoptic patterns. © 2013 Springer-Verlag Berlin Heidelberg.