Understanding the impacts of ENSO patterns on droughts over southern Africa using SPEEDY
The El Niño Southern Oscillation (ENSO) is a major driver of southern Africa droughts, but the nonlinearity of ENSO variation inhibits accurate prediction of droughts. While studies have identified multiple patterns of ENSO, most drought predictions over southern Africa are still based on only two ENSO patterns. This study examines the relationship between southern African droughts and eight ENSO patterns: four El Niño SST conditions (EN1 - EN4) and four La Niña SST conditions (LN1 - LN4). In this study we analyzed multi-forcing ensemble simulations from SPEEDY (a general circulation model from the International Centre for Theoretical Physics) and used two drought indices (SPEI: Standardized Precipitation Evapotranspiration Index; SPI: Standardized Precipitation Index) to characterize drought. The capability of SPEEDY in reproducing southern Africa climate was evaluated by comparing the historical simulations (1979- 2008) with the Climate Research Unit (CRU) observation. To obtain the influence of ENSO patterns, we forced the SPEEDY simulations with SST of each ENSO pattern, analyzed the impacts on the simulated drought indices (SPEI and SPI), and studied the atmospheric dynamics that link each ENSO pattern to southern Africa droughts. The results show that SPEEDY generally captures the temporal and spatial distribution of climate variables over southern Africa well, although with warm and wet biases across the region. However, in most cases, these results are comparable with those from more complex atmospheric models. In agreement with previous studies, the results show that El Niño SST conditions weaken the Walker circulation and cause drier conditions over parts of southern Africa, whilst La Niña SST conditions strengthen the Walker Circulation and cause wetter conditions. However, the results show that the differences in the El Niño SST conditions (EN1 - EN4) alter the circulation, thereby influencing the spatial pattern and intensity of drought over the region. For instance, while EN2 induces the most severe drought in the tropical area, EN4 produces it in the southwestern region, because the two patterns feature different characteristics of anticyclonic moisture flux over southern Africa. The same is true of the La Niña SST conditions. Although, LN1 and LN4 show wet conditions across the southern part of the region, LN1 produces drought in the northern part, while LN4 induces it along the western coast. Hence, this study shows that accounting for the differences in El Niño (or La Niña) conditions may improve drought predictions in southern Africa.
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Department of Environmental and Geographical Science