Climatic determinants of Benguela SST variability

Climatic determinants of summer sea surface temperature (SST) variations in the Benguela upwelling zone of the SE Atlantic are studied through statistical associations with field variables of SST and surface winds in the ocean basins surrounding SW Africa. Shelf SST indices are formulated from COADS monthly ship data for the southern and central Benguela for February and October respectively, months of active upwelling. Linear correlation maps are produced at lags −4 and 0 months and provide some unexpected results. February, southern area SST trends are downward over the period 1950–1988. Correlations between the shelf SST index and SST in the rest of the Benguela zone are positive and suggest that interannual signals are widespread in the longshore direction, but out of phase with the central ocean basins. Correlations between SST and meridional winds at 0 lag are −0.4 in the south and −0.8 in the central Benguela. This confirms the expected relationship between southerly wind and upwelling. The lower correlation in the south indicates that SST variability is often advectively forced whereas, in the central Benguela, SST variability is driven by local upwelling. ess the impact of an El Nin˜o on winds in the SE Atlantic, a general circulation model (GCM) simulation is performed. A fixed +2°C SST anomaly is imposed in the central Indian Ocean for a period of three years. Resultant surface layer winds in February are compared with climatology and inferences are made with regard to upwelling. Southerly wind stress is nearly doubled in the central Benguela. On the other hand, the Cape Town area obtains increased onshore flow from mid-latitude westerlies, and a reduction in upwelling is inferred. The GCM simulation is consistent with the observed pattern of an enhanced anticyclonic gyre in the SE Atlantic and cooler SST in the central Benguela. M and statistical results demonstrate regional response patterns with respect to coastal upwelling, and offer deterministic inputs to environmental long-range forecasts.