Ocean surface salinity in the tropical oceans using satellite derived OLR

Subrahmanyam Bulusu
University of South Carolina
Abstract:

The objective of this research work is to develop algorithms and estimate sea surface salinity (SSS) from satellite measurements of Outgoing Longwave Radiation (OLR) over the tropical oceans. This new technique was applied to the tropical Indian Ocean, with a set of algorithms based on the inter-relationships between OLR, the Effective Oceanic Layer (EOL), climatological SSS (World Ocean Atlas 2001; WOA01) and freshwater flux (P-E). The results for the tropical Indian Ocean showed higher correlation coefficients for the relationships between OLR vs. EOL, EOL vs. WOA98 SSS and OLR vs. P-E. The former relationship couples the atmospheric convection (OLR) and the geopotential thickness of the near-surface stratified layer (EOL). The estimated SSS at 2.5x2.5° grid on monthly scale was close to the WOA98 SSS with lesser differences (within ±0.5 psu) away from the coastal region. The estimated SSS also agrees reasonably with the observed SSS along the transindian zonal sections occupied during World Ocean Circulation Experiment (WOCE) and also other sections. The SSS differences brought out clearly the impact of the strong 1997-98 El Niño on the rainfall in the southeastern Indian Ocean and the eastern equatorial region, wherein the salinity anomalies (estimated SSS minus WOA98 SSS) were large and positive. Using 25 years (1979-present) of daily OLR data at 2.5x2.5° grid and the algorithms, the SSS was estimated in the tropical Indian Ocean. It was found that consideration of the mixing and advection processes using the Hybrid Coordinate Ocean Model (HYCOM) was necessary for the refinement of estimated SSS. This new technique of obtaining SSS from OLR would further be applied to the time series upper ocean temperature and salinity measurements in the tropical oceans, and the results would be discussed. This information would be useful for further studies in the other tropical Oceans aiming on coupled models, El Niño-Southern Oscillation forecast models and Ocean Global Circulation Models or regional scale circulation models.