The resent increase in availability of high-performance computing (HPC) resources in South Africa allowed the development of an Ocean-Atmosphere coupled general circulation model (OAGCM). The ECHAM4.5-MOM3-SA is the first OAGCM to be developed in Africa for seasonal climate prediction. This model employs an initialization strategy that is different from previous versions of the model that coupled the same atmosphere and ocean models. Evaluation of hindcasts performed with the model revealed that the OAGCM is successful in capturing the development and maturity of ElNiño and La-Niña episodes up to 8 months ahead. A model intercomparison also indicated that the ECHAM4.5-MOM3-SA has skill levels for the Niño-3.4 region SST comparable with other coupled models administered by international centres. Further analysis of the coupled model revealed that La-Niña events are more skillfully discriminated than El-Niño events. However, as is typical for OAGCM the model skill was generally found to decay faster during the spring barrier.The analysis also showed that the coupled model has useful skill up to several months lead-time when predicting the equatorial Indian Ocean Dipole (IOD) during the period spanning between the mid austral spring and the start of the summer seasons which reaches its peak in November. The weakness of the model in other seasons was mainly caused by the western segment of the dipole which eventually contaminates the Dipole Mode Index (DMI). The model is also able to forecast the anomalous upper air circulations, particularly in the equatorial belt, and surface air temperature in the southern African region as opposed to precipitation.
Forecasts of a Global Coupled Model for austral summer with a 1 month lead are downscaled to end-ofseason maize yields and accumulated streamflow over the Limpopo Province and adjacent districts in northeastern South Africa through application of an MOS (Model Output Statistics) approach applied over a 28 year period. Promising results, based on the hindcasts of the Global Models and historically observed yield and streamflow data, suggest potential for a commodity-orientated forecast system for application in agriculture in an operational environment. It also serves as a baseline study for inclusion of sophisticated crop or runoff models using GCM output data towards estimating potential yields and streamflows in the region.
South Africa is frequently subjected to severe droughts and dry spells during the rainy season. As such, rainfall is one of the most significant factors limiting dryland crop production in South Africa. The mid-summer period is particularly important for agriculture since a lack of rain during this period negatively affects crop yields. Dry spell frequency analyses are used to investigate the impacts of sub-seasonal rainfall variability on crop yield, since seasonal rainfall totals alone do not explain the relationship between rainfall and crop yields. This study investigated the spatial and temporal occurrences of the mid-summer dry spells based on magnitude, length and time of occurrence in the major maize growing areas of the summer rainfall region of South Africa. Three thresholds of 5 mm, 10 mm, and 15 mm total rainfall for a pentad were used for the analysis of dry spells. Dry spell analysis showed that dry pentads occur during mid-summer with differing intensity, duration and frequency across the summer rainfall region. Annual frequency of dry pentads for the mid-summer period ranged between 0 and 4 pentads for the 5 mm threshold and 1 to 7 for the 10 mm and 15 mm thresholds. The non-parametric Mann-Kendall trend analysis of the dry pentads indicates that there is no significant trend in the frequency of dry spells at a 95% confidence level. The initial and conditional probabilities of getting a dry spell using the Markov chain model also showed that there is a 32% to 80% probability that a single pentad will be dry using the 15 mm threshold. There is a 5% to 48% probability of experiencing two consecutive dry pentads and 1% to 29% probability of getting three consecutive dry pentads. The duration and intensity of dry spells, as well as the Markov chain probabilities, showed a decrease in dry spells from west to east of the maize-growing areas of the summer rainfall region of South Africa.
We propose how seasonal climate prediction with the use of an atmospheric general circulation model (AGCM) can be optimized. The AGCM predictive skill is extensively examined under various forecast strategies that mimic truly operational prediction. It is shown that the AGCM predictive skill is found to produce superior results given a suitable sea-surface temperatures (SSTs) as forcing and is subject to an initialization strategy that uses realistic atmosphere and soil moisture states. Evaluation of hindcasts performed with the model further revealed that the AGCM is able to forecast anomalous upper air atmospheric dynamics (circulation) over the tropics up to several months ahead. The AGCM probabilistic forecasts for rainfall and surface air temperatures during the austral summer season are also found to be informative and useful. The contribution of the predicted SST, which is based on a multi-model approach, is shown to be of significant importance for best AGCM results. The AGCM may also benefit from the initial condition interface in the AGCM's configuration which is implicitly considered in the analysis. Notwithstanding, the AGCM's predictive skill does not vary much whether the AGCM is initialized with realistic or climatological soil moisture which is presumably suggestive of the AGCM's internal weakness.
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