ABSTRACT:The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied.

ABSTRACT:The aim of this study is to investigate the internal rainfall variability of a nested model system and of a regional climate model (RCM). Four solutions obtained through perturbing the wind fields at initialization for the ECHAM4.5 atmospheric general circulation model (AGCM) are used to force the RegCM3 RCM over South Africa. To determine the amount of variability introduced by non-linearities in an RCM, four additional RegCM3 simulations are made through initializing the RegCM3 on different days but using a single realization from the ECHAM4.5. The simulations are made for one dry and one wet, El Niño-Southern Oscillation (ENSO) associated summer season defined as December to February. The rainfall variability associated with non-linearities in the nested system is high to an extent that ensemble members produce anomalies that have opposite signs in the same season. However, the sign of the ensemble average anomaly generally corresponds with the observed anomaly. The internal rainfall variability of the RCM is small when seasonal totals are analysed while with the daily rainfall totals the variability is larger. The number of events that fall into the three rainfall categories (i.e. below-normal, normal and above-normal) for the RegCM3 ensemble members are close to one another however the timing of the events is different. The results suggest that in seasonal operational forecasting making ensemble members associated with the internal variability of an RCM is not necessary because the information obtained from the ensemble members is almost similar.

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