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.

Sea surface salinity variability in the tropical Indian Ocean

2011 ◽  
Vol 115 (3) ◽  
pp. 944-956 ◽  
Author(s):  
Bulusu Subrahmanyam ◽  
V.S.N. Murty ◽  
David M. Heffner
Keyword(s):  
Indian Ocean ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Salinity Variability

scholarly journals A global eddying hindcast ocean simulation with OFES2

2020 ◽  
Vol 13 (7) ◽  
pp. 3319-3336 ◽  
Author(s):  
Hideharu Sasaki ◽  
Shinichiro Kida ◽  
Ryo Furue ◽  
Hidenori Aiki ◽  
Nobumasa Komori ◽  
...  
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Tidal Mixing ◽  
Surface Salinity ◽  
Previous Version ◽  
Spatiotemporal Scales ◽  
The Kuroshio

Abstract. A quasi-global eddying ocean hindcast simulation using a new version of our model, called OFES2 (Ocean General Circulation Model for the Earth Simulator version 2), was conducted to overcome several issues with unrealistic properties in its previous version, OFES. This paper describes the model and the simulated oceanic fields in OFES2 compared with OFES and also observed data. OFES2 includes a sea-ice model and a tidal mixing scheme, is forced by a newly created surface atmospheric dataset called JRA55-do, and simulated the oceanic fields from 1958 to 2016. We found several improvements in OFES2 over OFES: smaller biases in the global sea surface temperature and sea surface salinity as well as the water mass properties in the Indonesian and Arabian seas. The time series of the Niño3.4 and Indian Ocean Dipole (IOD) indexes are somewhat better in OFES2 than in OFES. Unlike the previous version, OFES2 reproduces more realistic anomalously low sea surface temperatures during a positive IOD event. One possible cause of these improvements in El Niño and IOD events is the replacement of the atmospheric dataset. On the other hand, several issues remained unrealistic, such as the pathways of the Kuroshio and Gulf Stream and the unrealistic spreading of salty Mediterranean overflow. Given the worldwide use of the previous version and the improvements presented here, the output from OFES2 will be useful in studying various oceanic phenomena with broad spatiotemporal scales.

A review of recent evaluation of satellite estimates sea surface salinity in the tropical Indian Ocean

2016 ◽  
Author(s):  
Imranali M. Momin ◽  
Ashis K. Mitra ◽  
D. K. Mahapatra ◽  
E. N. Rajagopal
Keyword(s):  
Indian Ocean ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Recent Evaluation

A sea surface salinity dipole mode in the tropical Indian Ocean

2016 ◽  
Vol 47 (7-8) ◽  
pp. 2573-2585 ◽  
Author(s):  
Yuhong Zhang ◽  
Yan Du ◽  
Tangdong Qu
Keyword(s):  
Indian Ocean ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Dipole Mode ◽  
Surface Salinity

scholarly journals Multiple Time Scale Variability of the Sea Surface Salinity Dipole Mode in the Tropical Indian Ocean

2017 ◽  
Vol 31 (1) ◽  
pp. 283-296 ◽  
Author(s):  
Yuhong Zhang ◽  
Yan Du ◽  
Ming Feng
Keyword(s):  
Indian Ocean ◽  
Western Australia ◽  
Time Scale ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Surface Salinity ◽  
Ocean Advection

Abstract In this study, multiple time scale variability of the salinity dipole mode in the tropical Indian Ocean (S-IOD) is revealed based on the 57-yr Ocean Reanalysis System 4 (ORAS4) sea surface salinity (SSS) reanalysis product and associated observations. On the interannual time scale, S-IOD is highly correlated with strong Indian Ocean dipole (IOD) and ENSO variability, with ocean advection forced by wind anomalies along the equator and precipitation anomalies in the southeastern tropical Indian Ocean (IO) dominating the SSS variations in the northern and southern poles of the S-IOD, respectively. S-IOD variability is also associated with the decadal modulation of the Indo-Pacific Walker circulation, with a stronger signature at its southern pole. Decadal variations of the equatorial IO winds and precipitations in the central IO force zonal ocean advection anomalies that contribute to the SSS variability in the northern pole of S-IOD on the decadal time scale. Meanwhile, oceanic dynamics dominates the SSS variability in the southern pole of S-IOD off Western Australia. Anomalous ocean advection transports the fresher water from low latitudes to the region off Western Australia, with additional contributions from the Indonesian Throughflow. Furthermore, the southern pole of S-IOD is associated with the thermocline variability originated from the tropical northwestern Pacific through the waveguide in the Indonesian Seas, forced by decadal Pacific climate variability. A deepening (shoaling) thermocline strengthens (weakens) the southward advection of surface freshwater into the southern pole of S-IOD and contributes to the high (low) SSS signatures off Western Australia.

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