Dynamics of the Seychelles-Chagos Thermocline Ridge

The southwest tropical Indian Ocean (SWTIO) features a unique, seasonal upwelling of the thermocline also known as the Seychelles-Chagos Thermocline Ridge (SCTR). More recently, this ridge or “dome”-like feature in the thermocline depth at (55°E-65°E, 5°S-12°S) in the SWTIO has been linked to interannual variability in the semi-annual Indian Ocean monsoon seasons as well as the Madden-Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO). The SCTR is a region where the MJO is associated with strong SST variability. Normally more cyclones are found generated in this SCTR region when the thermocline is deeper, which has a positive relation to the arrival of a downwelling Rossby wave from the southeast tropical Indian Ocean. Previous studies have focused their efforts solely on sea surface temperature (SST) because they determined salinity variability to be low, but with the Soil Moisture and Ocean Salinity (SMOS), and Aquarius salinity missions new insight can be shed on the effects that the seasonal upwelling of the thermocline has on Sea Surface Salinity (SSS). Seasonal SSS anomalies these missions will reveal the magnitude of seasonal SSS variability, while Argo depth profiles will show the link between changes in subsurface salinity and temperature structure. A seasonal increase in SST and a decrease in SSS associated with the downwelling of the thermocline have also been shown to occasionally generate MJO events, an extremely important part of climate variability in the Indian ocean. Satellite derives salinity and Argo data can help link changes in surface and subsurface salinity structure to the generation of the important MJO events. This study uses satellite derived salinity from Soil Moisture and Ocean Salinity (SMOS), and Aquarius to see if these satellites can yield new information on seasonal and interannual surface variability. In this study barrier layer thickness (BLT) estimates will be derived from satellite measurements using a multilinear regression model (MRM). This study will help to improve monsoon modeling and forecasting, two areas that remain highly inaccurate after decades of research work.