Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. Thus, predictive skill for regional oceanographic conditions is highly desirable. Recently, seasonal predictability of major coastal upwelling systems has been investigated, but with the exception of the southern part of the Canary Upwelling System: the Senegalo-Mauritanian Upwelling System (SMUS), which is of primary importance for the regional ecosystem and has crucial relevance for local populations. On this aspect, we analyse the seasonal sea surface temperature (SST) prediction in the SMUS, using the latest version of seasonal predictions from the Max Planck Institute for Meteorology Earth System Model High Resolution (MPI-ESM-HR). In the SMUS region, seasonal variations of SST are predictable 1 to 4 months in advance during boreal winter, consistent with variable wind forcing being the dominant driver of SST predictability particularly during the strong phases of the Atlantic Meridional Mode (AMM) and El Niño Southern Oscillation (ENSO) events. We find that the AMM plays a major role in enhancing the predictability of SST in the SMUS, while the remote influence from Pacific ENSO shows only a limited contribution to SST predictability. Predicting SST of coastal upwelling systems may have direct implications on fisheries management strategies.
Sylla et al. (Tue,) studied this question.
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