Impacts of convection schemes on simulating tropical-temperate troughs over southern 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.
KeywordsTropical-temperate trough El Niño-Southern Oscillation Southern Africa Convection scheme Atmospheric general circulation model
Constructive comments from two anonymous reviewers helped us to improve our manuscript. The AGCM was run on the supercomputers of Information Technology Center, the University of Tokyo under the cooperative research with Atmosphere and Ocean Research Institute, the University of Tokyo. The SOM_PAK software was provided by the Neural Network Research Centre at the Helsinki University of Technology and is available at http://www.cis.hut.fi/research/som_pak. The present research is supported by Japan Science and Technology Agency and Japan International Cooperation Agency through Science and Technology Research Partnership for Sustainable Development (SATREPS). The first author is supported by the Japan Society for Promotion of Science through Grant-in-Aid for Exploratory Research 24654150. The second and third authors acknowledge the support from the Applied Centre for Climate and Earth System Studies (ACCESS, South Africa), and National Research Foundation (NRF, South Africa) in performing this research.
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