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An attempt is made to understand the long-term variability of SST using NOAA optimum interpolation SST data for the period (1982–2011) in the Southern Ocean. This dataset has been used (i) to study the interannual variability in SST anomaly and (ii) to carry out regression analysis to compute linear trend in the annual averaged Southern Ocean SST. It is observed that summer season exhibits more variability than winter. Moreover, El Nino/La Nina events apparently play a critical role in the variability of Southern Ocean SST. Thus, higher SST anomalies were observed in El Nino years (e.g., 1983), while cooler anomalies were seen during La Nina years (e.g., 1985). In addition, the eastern and western sides of Antarctica experience episodes of warm and cold SST. Western parts of the Southern Ocean experienced higher anomalies during 1992, 1993, and 1994, while the eastern part experienced positive anomalies in 1997, 1998, 2002, and 2003. The paper also highlights the different regions of the Southern Ocean showing statistically significant positive/negative trends in the variability of interannual average SST. However, in general, the Southern Ocean as a whole is showing a weak interannual cooling trend in SST.

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The identification of sea-ice has frequently been cited as one of the most important tasks for deriving the sea-ice parameters and to avoid erroneous retrieval of wind vector over sea-ice infested oceans using space-borne scatterometer data. Discrimination between sea-ice and ocean is ambiguous under the high wind and/or thin/scattered ice conditions. The pre-launch technique developed for Oceansat-2, utilizes the dual-polarized QuikSCAT scatterometer data by using the spatio-temporal coherence properties of sea ice in addition to backscatter coefficient and the Active Polarization Ratio. Results were compared with the operational sea-ice products from National Snow and Ice Data Center. The threshold API value of −0.025 was found optimum for sea-ice and ocean discrimination. The overall sea-ice identification accuracy achieved was of the order of 95 per cent, ranging from 92.5% (during December in Southern Hemisphere) to 98% (during March in Northern Hemisphere). The applicability of the algorithm for both the Arctic as well as Antarctic makes it suitable for its operational use with the Oceansat-2 scatterometer data.