Operational long-lead prediction of South African rainfall using canonical correlation analysis

Landman, Willem A.; Mason, Simon J.

A statistically based technique is used to study the variability and predictability of South African summer rainfall. The country is divided into homogeneous regions on the basis of the interannual rainfall variability. Canonical variates are then used to make 3‐month aggregate precipitation forecasts for October–November–December and January–February–March for South Africa from global‐scale sea‐surface temperatures. Four consecutive 3‐month mean periods of sea‐surface temperatures are used to incorporate evolutionary features as well as steady‐state conditions in the global oceans. Levels and possible origins of forecast skill are investigated for up to 5‐month lead‐times. Modest skill (correlation >0.5) is found over mainly the central and western interiors of the country, but the skill is poor over the north‐eastern regions. The most important contribution of the prediction skill comes from the equatorial Pacific Ocean, with weaker predictability from the equatorial Indian and Atlantic oceans. Sea‐surface temperatures in the Atlantic and Indian oceans have important influences on the atmospheric circulation and moisture fluxes over southern Africa, and therefore provide useful predictability, at least for the October–December rainfall. When forecasting South African rainfall, it is insufficient to consider only the El Niño–Southern Oscillation (ENSO) phenomenon, because it does not occur every year and because the sea‐surface temperatures of the adjacent oceans modify the ENSO forcing on South African rainfall. Unfortunately, the predictability during years not associated with the ENSO is weak.

Geographic Areas


  • thumnail for Landman_WA_SJ_Mason_1999_IJoC_19_1073.doc Landman_WA_SJ_Mason_1999_IJoC_19_1073.doc application/msword 195 KB Download File

Also Published In

More About This Work

Academic Units
International Research Institute for Climate and Society
Published Here
March 23, 2020