Causes for intraseasonal sea surface salinity variability in the western tropical P acific O cean and its seasonality

2016 ◽  
Vol 121 (1) ◽  
pp. 85-103 ◽  
Author(s):  
Yuanlong Li ◽  
Weiqing Han
Keyword(s):  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

scholarly journals Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing

2020 ◽  
Vol 248 ◽  
pp. 111964 ◽  
Author(s):  
V.P. Akhil ◽  
J. Vialard ◽  
M. Lengaigne ◽  
M.G. Keerthi ◽  
J. Boutin ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

Observed year-to-year sea surface salinity variability in the Bay of Bengal during the 2009–2014 period

2014 ◽  
Vol 65 (2) ◽  
pp. 173-186 ◽  
Author(s):  
Akurathi Venkata Sai Chaitanya ◽  
Fabien Durand ◽  
Simi Mathew ◽  
Vissa Venkata Gopalakrishna ◽  
Fabrice Papa ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

scholarly journals Simulated Sea Surface Salinity Variability in the Tropical Indian Ocean

2010 ◽  
Vol 23 (24) ◽  
pp. 6542-6554 ◽  
Author(s):  
Rashmi Sharma ◽  
Neeraj Agarwal ◽  
Imran M. Momin ◽  
Sujit Basu ◽  
Vijay K. Agarwal
Keyword(s):  
Indian Ocean ◽  
Time Scales ◽  
General Circulation ◽  
Circulation Model ◽  
Equatorial Indian Ocean ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

Abstract A long-period (15 yr) simulation of sea surface salinity (SSS) obtained from a hindcast run of an ocean general circulation model (OGCM) forced by the NCEP–NCAR daily reanalysis product is analyzed in the tropical Indian Ocean (TIO). The objective of the study is twofold: assess the capability of the model to provide realistic simulations of SSS and characterize the SSS variability in view of upcoming satellite salinity missions. Model fields are evaluated in terms of mean, standard deviation, and characteristic temporal scales of SSS variability. Results show that the standard deviations range from 0.2 to 1.5 psu, with larger values in regions with strong seasonal transitions of surface currents (south of India) and along the coast in the Bay of Bengal (strong Kelvin-wave-induced currents). Comparison of simulated SSS with collocated SSS measurements from the National Oceanographic Data Center and Argo floats resulted in a high correlation of 0.85 and a root-mean-square error (RMSE) of 0.4 psu. The correlations are quite high (>0.75) up to a depth of 300 m. Daily simulations of SSS compare well with a Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoy in the eastern equatorial Indian Ocean (1.5°S, 90°E) with an RMSE of 0.3 psu and a correlation better than 0.6. Model SSS compares well with observations at all time scales (intraseasonal, seasonal, and interannual). The decorrelation scales computed from model and buoy SSS suggest that the proposed 10-day sampling of future salinity sensors would be able to resolve much of the salinity variability at time scales longer than intraseasonal. This inference is significant in view of satellite salinity sensors, such as Soil Moisture and Ocean Salinity (SMOS) and Aquarius.

scholarly journals A coral δ18O record of ENSO driven sea surface salinity variability in Fiji (south -western tropical Pacific)

2000 ◽  
Vol 27 (23) ◽  
pp. 3897-3900 ◽  
Author(s):  
Nolwenn Le Bec ◽  
Anne Julliet-Leclerc ◽  
Thierry Corrège ◽  
Dominique Blamart ◽  
Thierry Delcroix
Keyword(s):  
Tropical Pacific ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Western Tropical Pacific ◽  
Salinity Variability
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