Abstract:
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.
