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https://doi.org/10.1007/BF01025183

has been cited by the following article:

• TITLE: On the Co-Variability between Climate Indices and the Potential Spread of Seasonal Climate Simulations over South African Provinces

  AUTHORS: Kamoru A. Lawal, Dáithí A. Stone

  KEYWORDS: Climate Indices, Correlation Coefficient, Ensemble Spread, Seasonal Climate, South Africa

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.9 No.3, July 17, 2019

  ABSTRACT: This study investigates the co-variability between measures of the spreads in the ensembles of seasonal climate simulations and large scale climate indices. Spreads in the ensembles of seasonal simulations (of rainfall and near surface air temperature) from an atmospheric model, over South African provinces, are quantified with the de-trended anomalies of standard deviation (StdDev) and the distance between the 90th and 10th percentiles (RoP) of the simulations. Results indicate that, on seasonal time scales, measures of spread significantly co-vary with observed global sea surface temperatures (SST) far and near. This suggests that the climate factors controlling the degree to which the seasonal climate may be precisely forecast over the South African provinces may both be locally and remotely based. Results also indicate that all significant predictors of spread—El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Benguela Niño (BGN) and Southwest Indian Ocean Index (SWI), are of tropical origin; they co-vary significantly with measures of spread on seasonal time scales over a number of provinces and seasons, particularly during the rainfall onset and peak periods, as well as during the cold season. Nevertheless, responses of measures of spread to climate predictors are relatively small, either indicating that predictor-spread relationships are more complex in nature than can be represented by the traditionally simple climate indices, or indicating that controls on ensemble spread are weak. Therefore, there may be limits to the extent to which year-to-year variations in the predictability of seasonal climate over South Africa provinces might be understood.