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ABSTRACT This study evaluates the capability of regional climate models ( RCMs) in simulating extreme rainfall events over Southern Africa, and in reproducing the characteristics of widespread extreme rainfall events ( WERE) in the Western Cape (South Africa). We obtained simulation datasets of nine RCMs from the Co-ordinated Regional Downscaling Experiment ( CORDEX) and compared them with observation datasets from the Global Precipitation Climatology Project ( GPCP) and Tropical Rainfall Measuring Mission ( TRMM) as well as with the reanalysis dataset ( ERAINT) that forced the simulations. A self-organising map was used to classify the WEREs into 12 nodes. The contribution of each RCM to each node was compared with the contributions of GPCP, TRMM and ERAINT. Using ERAINT dataset, we analysed the synoptic-scale atmospheric condition associated with each node. The results show that, in simulating the spatial distribution of the extreme rainfall event over Southern Africa, only four RCMs perform better than the forcing reanalysis ( ERAINT) while two RCMs perform worse than the reanalysis. All the RCMs underestimate the threshold of extreme rainfall over Western Cape, poorly simulate the inter-annual variability of the WEREs ( r ≤ 0.3), but correctly reproduce the maximum frequency of the WEREs in Autumn (March-May). The WEREs in the Western Cape may be broadly grouped into four synoptic rainfall patterns. The first pattern links WEREs with tropical rainfall activities (tropical-temperate troughs); the second pattern shows isolated WEREs; the third and fourth patterns link WEREs with rainfall activities in the mid-latitudes (frontal systems) and over the Agulhas Current, respectively. While most RCMs overestimate the frequency of the first pattern, they all underestimate the frequency of the second pattern but simulate the frequencies of the third and fourth patterns well. The results of this study should help in improving the RCM simulations over Southern Africa and in downscaling impacts of climate change on extreme rainfall events over the region. [ABSTRACT FROM AUTHOR]

Observed trends in seasonal and annual total rainfall, number of rain days and daily maximum and minimum temperature were calculated for a number of stations in South Africa for the period 1960-2010. Statistically significant decreases in rainfall and the number of rain days are shown over the central and northeastern parts of the country in the autumn months and significant increases in the number of rain days around the southern Drakensberg are evident in spring and summer. Maximum temperatures have increased significantly throughout the country for all seasons and increases in minimum temperatures are shown for most of the country. A notable exception is the central interior, where minimum temperatures have decreased significantly. Regionally aggregated trends for six water management zones covering the entire country are not evident for total rainfall, but there are some significant trends for the number of rain days. Temperature in these zones has increased significantly for most seasons, with the exception of the central interior. Comparison of the observed trends with statistically downscaled global climate model simulations reveals that the models do not represent the observed rainfall changes nor the cooling trend of minimum temperature in the central interior. Although this result does not rule out the possibility of attributing observed local changes in rainfall to anthropogenically forced global change, it does have major implications for attribution studies. It also raises the question of whether an alternative statistical downscaling method or dynamical downscaling through the use of a regional climate model might better represent regional and local climatic processes and their links to global change. [ABSTRACT FROM AUTHOR]

Monthly-mean precipitation, mean (T), maximum (T) and minimum (T) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for T and T than for T, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations. [ABSTRACT FROM AUTHOR]