MENVSC (Climatology) / Department of Geography and Geo-Information Sciences / Subtropical anticyclones locate and modulate weather and climate over subtropical belts for both the Northern and Southern Hemispheres. This study investigates the spatio-temporal variability of the Mascarene High over the South Indian Ocean on (anomalous) weather and climate over southern Africa at intraseasonal, seasonal, interannual, multidecadal and event time-scales. The Mascarene High is located 25-35°S, 40-110°E, playing a vital role in day-to-day weather and climate patterns conditions over southern Africa. Spatio-temporal characteristics of the Mascarene High investigated in this study span the period 1985-2014 and 2071-2100, using NCEP-NCAR reanalysis datasets for present-day climate observations and the Conformal-Cubic Atmospheric Model (CCAM) for future projections. The Mascarene High is analysed using mean sea level pressure (MSLP) extracted from ECMWF ERA-interim monthly reanalysis data. The Mascarene High is also subjected to Principal Components Analysis, depicting eastern displacements of the weather system to be dominant for weather and climate fluctuations over southern Africa. The Mascarene High migrates south (north) during austral summer (winter) and is centred over the eastern Indian Ocean in summer in connection with the Indian Ocean Subtropical Dipole. Event scale analysis is also employed for investigating Mascarene High blocking and induced anomalous weather. Mascarene High blocking leads to anomalous rainfall events over southern Africa associated with tropical cyclones, cut-off lows and cloud bands. There is also a vital geographical variability of the Mascarene High development, distribution and movement in the South Indian Ocean at the different time-scales. Projections of the Mascarene High indicate a shift in mean location as a result of future expansion and intensification. This projected expansion and intensification is expected to shift tropical cyclone trajectories equatorward, with the baroclinic structure of cold fronts expected to shift poleward affecting changes in the weather and climate of southern Africa. This finding is important as it projects changes in weather and climate conditions over southern Africa in a changing climate due to increased greenhouse gas emissions.

MENVSC / Department of Geography and Geo-Information Sciences / The process of cloud formation and distribution in the atmospheric circulation system is very important yet not easy to comprehend and forecast. Clouds affect the climate system by controlling the amount of solar radiation, precipitation and other climatic variables. Parameterised induced General Circulation Model (GCMs) are unable to represent clouds and aerosol particles explicitly and their influence on the climate and are thought to be responsible for most of the uncertainty in climate predictions. Therefore, the aim of the study is to investigate the climate of South Africa as simulated by Super Parameterised Community Atmosphere Model (SPCAM) for the period of 1987-2016. Community Atmosphere Model (CAM) and SPCAM datasets used in the study were obtained from Colorado State University (CSU), whilst dynamic and thermodynamic fields were obtained from the NCEP reanalysis ll. The simulations were compared against rainfall and temperature observations obtained from the South African Weather Service (SAWS) database. The accuracy of the model output from CAM and SPCAM was tested in simulating rainfall and temperature at seasonal timescales using the Root Mean Square Error (RMSE). It was found that CAM overestimates rainfall over the interior of the subcontinent during December - February (DJF) season whilst SPCAM showed a high performance in depicting summer rainfall particularly in the central and eastern parts of South Africa. During June – August (JJA), both configurations (CAM and SPCAM) had a dry bias with simulating winter rainfall over the south Western Cape region in cases of little rainfall in the observations. CAM was also found to underestimate temperatures during DJF with SPCAM results closer to the reanalysis. The study further analyzed inter-annual variability of rainfall and temperature for different homogenous regions across the whole of South Africa using both configurations. It was found that SPCAM had a higher skill than CAM in simulating inter-annual variability of rainfall and temperature over the summer rainfall regions of South Africa for the period of 1987 to 2016. SPCAM also showed reasonable skill simulating (mean sea level pressure, geopotential height, omega etc) in contrast to the standard CAM for all seasons at the low and middle levels (850 hPa and 500 hPa). The study also focused on major El Niño Southern Oscillation (ENSO) events and found that SPCAM tended to compare better in general with the observations. Although both versions of the model still feature substantial biases in simulating South African climate variables (rainfall, temperature, etc), the magnitude of the biases are generally smaller in the super parameterized CAM than the default CAM, suggesting that the implementation of the super parameterization in CAM improves the model performance and therefore seasonal climate prediction. / NRF