Sea surface salinity variability in the tropical Indian Ocean

Subrahmanyam, Bulusu; Murty, V. S. N.; Heffner, David M.

doi:10.1016/j.rse.2010.12.004

Cited by 62 publications

(55 citation statements)

References 29 publications

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“…In contrast, the anomalously strong alongshore northeastward winds during the 2011 monsoon weakened the equatorward volume transport, when the coastal region received lower GR discharge, and supported overall contribution to the upwelling process due to anomalous offshore Ekman transport in August 2011. Subrahmanyam et al (2011) also noticed that the anomalous coastal circulation is equatorward during the nIOD event in 2005 and poleward during the pIOD event of 2006 indicating that upwelling is favoured during latter period. This is well supported from the studies of Behera et al (1999) and Saji and Yamagata (2003).…”

Section: Year To Year Variations In Indian Monsoon Rain Fall Gr Discmentioning

confidence: 86%

Impact of river discharge on the coastal water pH and pCO2 levels during the Indian Ocean Dipole (IOD) years in the western Bay of Bengal

Sarma

Paul

Vani

et al. 2015

Continental Shelf Research

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Section: Year To Year Variations In Indian Monsoon Rain Fall Gr Discmentioning

confidence: 86%

Impact of river discharge on the coastal water pH and pCO2 levels during the Indian Ocean Dipole (IOD) years in the western Bay of Bengal

Sarma

Paul

Vani

et al. 2015

Continental Shelf Research

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“…Among them, layer model experiments revealed that the IO circulations, including Indonesian Throughflow, redistributed the rainfall input and river discharges in the Bay of Bengal to adjacent ocean and induced changes in SST, mixed layer, barrier layer, and thermocline (Han and McCreary 2001;Han et al 2001;Perigaud et al 2003). Recent modeling and observational studies revealed that the SSS in the TIO has a significant year-to-year variability (Thompson et al 2006;Illig and Perigaud 2007;Vinayachandran and Nanjundiah 2009;Subrahmanyam et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Satellite and Argo Observed Surface Salinity Variations in the Tropical Indian Ocean and Their Association with the Indian Ocean Dipole Mode

Zhang

2015

Journal of Climate

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This study investigates sea surface salinity (SSS) variations in the tropical Indian Ocean (IO) using the Aquarius/Satelite de Aplicaciones Cientificas-D (SAC-D) and the Soil Moisture and Ocean Salinity (SMOS) satellite data and the Argo observations during July 2010-July 2014. Compared to the Argo observations, the satellite datasets generally provide SSS maps with higher space-time resolution, particularly in the regions where Argo floats are sparse. Both Aquarius and SMOS well captured the SSS variations associated with the Indian Ocean dipole (IOD) mode. Significant SSS changes occurred in the central equatorial IO, along the Java-Sumatra coast, and south of the equatorial IO, due to ocean circulation variations. During the negative IOD events in 2010, 2013, and 2014, westerly wind anomalies strengthened along the equator, weakening coastal upwelling off Java and Sumatra and decreasing SSS. South of the equatorial IO, an anomalous cyclonic gyre changed the tropical circulation, which favored the eastward high-salinity tongue along the equator and the westward low-saline tongue in the south. An upwelling Rossby wave favored the increase of SSS farther to the south. During the positive IOD events in 2011 and 2012, the above-mentioned processes reversed, although the decrease of SSS was weaker in magnitude.

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“…The well-known seasonal SSS variability (Rao and Sivakumar 2003; Subrahmanyam, Murty, and Heffner 2011) is detected by the satellite estimates at all the study locations. Closer agreement of satellite-derived V3 SSS with buoy observations can also be seen as compared with V2.…”

Section: Variability Of Sss In the Tiomentioning

confidence: 75%

“…Figure 1 shows the mean V3 SSS from ARGO and Aquarius V3 data sets and their difference. Both observations show similar large-scale SSS features (Rao and Sivakumar 2003;Sharma et al 2010;Subrahmanyam, Murty, and Heffner 2011), such as higher SSS over the northern AS (NAS) and lower SSS over the northern BOB. There is a gradual increase in SSS from the northern BOB to the southern BOB.…”

Section: Comparison Of Aquarius-derived Sss To Rama Buoys and Argo Obmentioning

confidence: 77%

“…Furthermore, in the presence of the barrier layer, the oceanic response to wind forcing also increases by trapping momentum in the mixed layer (Vialard and Delecluse 1998). Additionally, SSS over the TIO exhibits considerable variability at different spatial and temporal scales (Rao and Sivakumar 2003;Sharma et al 2010;Subrahmanyam, Murty, and Heffner 2011;Grunseich et al 2011). Sanilkumar et al (1994 analysed surface meteorological data and near-surface salinity structure in 1990 over the northern BOB during the monsoon trough boundary layer experiment and demonstrated a connection between the Indian monsoon and salinity.…”

Section: Introductionmentioning

confidence: 99%

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Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

Momin

Mitra

Prakash

et al. 2015

International Journal of Remote Sensing

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Sea surface salinity (SSS) is one of the key components of the Earth's global water cycle. Reliable information on SSS is very important for ocean modelling, data assimilation, and ocean and climate research applications. In this study, SSS variability in the tropical Indian Ocean (TIO) was analysed using the Aquarius instrument on board the SAC-D satellite and in situ observations from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys and Array for Real-Time Geostrophic Oceanography (ARGO) data sets for the period 2012-2013. Comparison of two recent versions (V2 and V3) of Aquarius-based SSS estimates to nine RAMA buoys on a daily timescale showed excellent mutual agreement. The systematic underestimation of SSS by satellite-based V2 products over the TIO shows a clear advantage for the new version product (V3). A larger root-meansquare error of the order of 0.50 psu in the satellite-based SSS was observed over the highly variable (larger standard deviation) Bay of Bengal region as compared with ARGO data sets. In the eastern equatorial Indian Ocean region, satellite-based SSS overestimated SSS below 34 psu and underestimated SSS of 34-35 psu as compared with ARGO data. However, the V3 SSS from Aquarius showed marginal improvement over V2 SSS. Monthly variation and fast Fourier analysis of the satellite-based SSS estimates are in reasonably good agreement with in situ observations which suggest that Aquarius is able to capture SSS variability in the TIO. The Aquarius-based V3 SSS showed a temporal autocorrelation of 0.6 over most parts of the TIO up to day 10, and decreased gradually with time. Overall analysis suggests that Aquarius-derived V3 SSS can detect variability in SSS satisfactorily in the TIO and is in reasonably good agreement with in situ observations.

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Sea surface salinity variability in the tropical Indian Ocean

Cited by 62 publications

References 29 publications

Impact of river discharge on the coastal water pH and pCO2 levels during the Indian Ocean Dipole (IOD) years in the western Bay of Bengal

Impact of river discharge on the coastal water pH and pCO2 levels during the Indian Ocean Dipole (IOD) years in the western Bay of Bengal

Satellite and Argo Observed Surface Salinity Variations in the Tropical Indian Ocean and Their Association with the Indian Ocean Dipole Mode

Variability of sea surface salinity in the tropical Indian Ocean as inferred from Aquarius and in situ data sets

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