The role of the El Niño–Southern Oscillation (ENSO) on the modulation of tropical cyclone activity over the Bay of Bengal (BoB) for the 1979–2011 period is examined. It is shown that Niño-3.4 sea surface temperature (SST) anomalies are negatively correlated with the BoB tropical cyclone activity to a statistically significant percentage by a lead time of 5 months. Composites of 10-m zonal winds exhibit greater variance during La Niña events, favoring the development of low-level cyclonic vorticity. Low vertical wind shear over the central and northern BoB also aids in the development of tropical cyclones during La Niña events. Increased relative humidity is the result of enhanced moisture transport and higher precipitable water under La Niña conditions. Furthermore, storm-relative composites of relative humidity show stronger moisture pulses over the BoB during La Niña. The enhanced moisture associated with tropical cyclogenesis likely aids in the development and strengthening of the systems. ENSO forces modulations in oceanic conditions as well. The observed negative (positive) SST anomalies during La Niña (El Niño) could be seen as the result of increased (decreased) net heat flux across the sea surface. Tropical cyclone activity varies between El Niño and La Niña as a result of anomalous wind and moisture patterns during each ENSO phase.

Monthly barrier layer thickness (BLT) estimates are derived from satellite measurements using a multilinear regression model (MRM) within the Indian Ocean. Sea surface salinity (SSS) from the recently launched Soil Moisture and Ocean Salinity (SMOS) and Aquarius SAC-D salinity missions are utilized to estimate the BLT. The MRM relates BLT to sea surface salinity (SSS), sea surface temperature (SST), and sea surface height anomalies (SSHA). Three regions where the BLT variability is most rigorous are selected to evaluate the performance of the MRM for 2012; the Southeast Arabian Sea (SEAS), Bay of Bengal (BoB), and Eastern Equatorial Indian Ocean (EEIO). The MRM derived BLT estimates are compared to gridded Argo and Hybrid Coordinate Ocean Model (HYCOM) BLTs. It is shown that different mechanisms are important for sustaining the BLT variability in each of the selected regions. Sensitivity tests show that SSS is the primary driver of the BLT within the MRM. Results suggest that salinity measurements obtained from Aquarius and SMOS can be useful for tracking and predicting the BLT in the Indian Ocean. Largest MRM errors occur along coastlines and near islands where land contamination skews the satellite SSS retrievals. The BLT evolution during 2012, as well as the advantages and disadvantages of the current model are discussed. BLT estimations using HYCOM simulations display large errors that are related to model layer structure and the selected BLT methodology.