Abstract
The combined application of machine learning and satellite observations offers a new way for analysing complex ocean biological and physical processes. Here, an unsupervised machine learning approach, Self Organizing Maps (SOM), is applied to discover links between surface current variability and phytoplankton productivity during seasonal upwelling over the Agulhas Bank (South Africa), from 23 years (November-March 1997-2020) of daily satellite observations (surface current, sea surface temperature, chlorophyll-a). The SOM patterns extracted over this dynamically complex region, which is dominated by the Agulhas Current (AC), revealed 4 topologies/modes of the AC system. An AC flowing southwestward along the shelf edge is the dominant mode. An AC with a cyclonic meander near shelf is the second most frequent mode. An AC with a cyclonic meander off shelf and AC early retroflection modes are the least frequent. These AC topologies influence the circulation and the phytoplankton productivity on the shelf. Strong (weak) seasonal upwelling is seen in the AC early retroflection, the AC with a cyclonic meander near shelf modes and in part of the AC along the shelf edge mode (the AC with a cyclonic meander off shelf mode and in part the AC along the shelf edge mode). The more productive patterns are generally associated with a strong southwestward flow over the central bank caused by the AC intrusion to the east Bank or via an anticyclonic meander. The less productive situations can be related to a weaker southwest flow over the central bank, strong northeast flow on the eastern bank, and/or to a stronger northwest flow on the central bank. The SOM patterns show marked year-to-year variability. The high/low productivity events seem to be linked to the occurrence of extreme phases in climate variability modes (El Niño Southern Oscillation, Indian Ocean Dipole).