The mixing mechanism of South African cumulus congestus clouds is investigated using a combined observational and modeling approach. The experimental data consists of aircraft measurements collected in developing cloud towers near Nelspruit on three case study days. The observations are analyzed to determine the source regions of the entrained air. The mixing processes are simulated using both axially and slab symmetric cumulus models with very high spatial and temporal resolutions. The simulated clouds have a structured organization with small scale features such as in-cloud downdrafts. The mixing processes are examined by analyzing the time variation of dynamic and thermodynamic quantities along computed parcel trajectories. / Both observations and simulations indicate that most of the entrainment occurs at the cloud top. Evaporative cooling causes downdrafts that transport highly diluted air from the cloud top down to lower levels. The trajectory analysis shows that the penetrative downdrafts are also affected by pressure perturbations. / In the presence of wind shear the downshear sides of the clouds become more diluted, cooler and have stronger downdrafts. The asymmetric organisation is attributed to turbulent exchange of horizontal momentum at the cloud top.

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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