Seasonal variability of sea surface salinity and salt budget of the mixed layer of the north Indian Ocean

2003 ◽  
Vol 108 (C1) ◽  
Author(s):  
R. R. Rao
Keyword(s):  
Indian Ocean ◽  
Mixed Layer ◽  
Seasonal Variability ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The North ◽  
Salt Budget

Towards long-term (2002-present) reconstruction of northern Indian Ocean Sea Surface Salinity based on AMSR-E and L-band Radiometer data

2021 ◽  
Author(s):  
Marie Montero ◽  
Nicolas Reul ◽  
Clément de Boyer Montégut ◽  
Jérôme Vialard ◽  
Jean Tournadre
Keyword(s):  
Indian Ocean ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Northern Indian Ocean ◽  
Surface Salinity ◽  
The North ◽  
X Band ◽  
L Band

<p>Salinity plays an important role in the oceanic circulation, because of its impact on pressure gradients and the upper ocean stability. This is particularly the case in the North Indian Ocean where freshwater inputs from monsoonal rain and rivers into the Bay of Bengal and strong evaporation in the Arabian Sea leads to high salinity contrasts, and a strong variability tied to the large monsoonal currents seasonal cycle.</p><p>In situ salinity data is however too sparse to allow a detailed study of the contrasted and variable Northern Indian Ocean Sea Surface Salinity (SSS). This situation has changed since the launch of SMOS in 2009, and the advent of L-band-based SSS remote sensing with a much higher spatio-temporal sampling. Here, we explore the capacity of C and X-band measurements, such as those of AMSR-E (May 2002-October 2011) to reconstruct Northern Indian Ocean SSS prior 2009. Previous studies have indeed demonstrated the ability of C- and X-band products to reconstruct SSS in high-contrast regions like river estuaries, especially at high Sea Surface Temperature (SST), like in the Northern Indian Ocean.</p><p> </p><p>We are currently focusing on the development of the algorithm to reconstruct salinity from the C- and X-band data of AMSR-E. The ESA Climate Change Initiative (CCI) SSS dataset build from a merge of SMOS, Aquarius and SMAP data, provides a reference SSS that is both used for training our algorithm and for validation, over the common AMSR-E and CCI period (January 2010 to October 2011).</p><p>Our first results are encouraging: spatial contrast between the low-SSS values close to estuaries and along the coast and higher SSS in the middle of the Bay of Bengal as well as some aspects of the seasonal cycle are reproduced. However, spurious signals linked to either radio frequency interferences still need to be filtered out and signals associated with other residual geophysical contributions (e.g. wind, atmospheric vapor content) need to be better estimated. The long-term goal of this work is to merge the C-, X-, and L-band data with in-situ measurements and thus provide a long-term reconstruction of monthly SSS in the north Indian Ocean with a ~50km resolution.</p>

Meridional Distribution of Surface CO2 along 67°E of the Indian Ocean

2020 ◽  
Author(s):  
Dong-Jin Kang ◽  
Sang-Hwa Choi ◽  
Daeyeon Kim ◽  
Gyeong-Mok Lee
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Seawater ◽  
Surface Salinity ◽  
The North ◽  
The Indian Ocean ◽  
North And South

<p>Surface seawater carbon dioxide was observed from 3 °S to 27 °S along 67 °E of the Indian Ocean in April 2018 and 2019. Partial pressure of CO<sub>2</sub>(pCO<sub>2</sub>) in the surface seawater and the atmosphere were observed every two minutes using an underway CO2 measurement system (General Oceanics Model 8050) installed on R/V Isabu. Surface water temperature and salinity were measured as well. The pCO<sub>2</sub> was measured using Li-7000 NDIR. Standard gases were measured every 8 hours in five classes with concentrations of 0 µatm, 202 µatm, 350 µatm, 447 µatm, and 359.87 µatm. The fCO<sub>2</sub> of atmosphere remained nearly constant at 387 ± 2 µatm, but the surface seawater fCO<sub>2</sub> peaked at about 3 °S and tended to decrease toward the north and south. The distribution of fCO<sub>2</sub> in surface seawater according to latitude tends to be very similar to that of sea surface temperature. In order to investigate the factors that control the distribution of fCO<sub>2</sub> in surface seawater, we analyzed the sea surface temperature, sea surface salinity, and other factors. The effects of salinity are insignificant, and the surface fCO<sub>2</sub> distribution is mainly controlled by sea surface temperature and other factors that can be represented mainly by biological activity and mixing.</p>

scholarly journals Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application

2020 ◽  
Vol 12 (3) ◽  
pp. 447 ◽  
Author(s):  
Viviane V. Menezes
Keyword(s):  
Red Sea ◽  
Ocean Circulation ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Hydrological Cycle ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The North

Sea-surface salinity (SSS) is an essential climate variable connected to Earth’s hydrological cycle and a dynamical component of ocean circulation, but its variability is not well-understood. Thanks to Argo floats, and the first decade of salinity remote sensing, this is changing. While satellites can retrieve salinity with some confidence, accuracy is regionally dependent and challenging within 500–1000 km offshore. The present work assesses the first four years of the National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) satellite in the North Indian Ocean. SMAP’s improved spatial resolution, better mitigation for radio-frequency interference, and land contamination make it particularly attractive to study coastal areas. Here, regions of interest are the Bay of Bengal, the Arabian Sea, and the extremely salty Red Sea (the last of which has not yet received attention). Six SMAP products, which include Levels 2 and 3 data, were statistically evaluated against in situ measurements collected by a variety of instruments. SMAP reproduced SSS well in both the Arabian Sea and the Bay of Bengal, and surprisingly well in the Red Sea. Correlations there were 0.81–0.93, and the root-mean-square difference was 0.38–0.67 for Level 3 data.

scholarly journals Sea surface salinity variability from a simplified mixed layer model of the global ocean

2007 ◽  
Vol 4 (1) ◽  
pp. 41-106 ◽  
Author(s):  
S. Michel ◽  
B. Chapron ◽  
J. Tournadre ◽  
N. Reul
Keyword(s):  
Mixed Layer ◽  
Seasonal Variability ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Freshwater Flux ◽  
Layer Model ◽  
Surface Salinity ◽  
Salinity Balance

Abstract. A bi-dimensional mixed layer model (MLM) of the global ocean is used to investigate the sea surface salinity (SSS) balance and variability at daily to seasonal scales. Thus a simulation over an average year is performed with daily climatological forcing fields. The forcing dataset combines air-sea fluxes from a meteorological model, geostrophic currents from satellite altimeters and in situ data for river run-offs, deep temperature and salinity. The model is based on the "slab mixed layer" formulation, which allows many simplifications in the vertical mixing representation, but requires an accurate estimate for the Mixed Layer Depth. Therefore, the model MLD is obtained from an original inversion technique, by adjusting the simulated temperature to input sea surface temperature (SST) data. The geographical distribution and seasonal variability of this "effective" MLD is validated against an in situ thermocline depth. This comparison proves the model results are consistent with observations, except at high latitudes and in some parts of the equatorial band. The salinity balance can then be analysed in all the remaining areas. The annual tendency and amplitude of each of the six processes included in the model are described, whilst providing some physical explanations. A map of the dominant process shows that freshwater flux controls SSS in most tropical areas, Ekman transport in Trades regions, geostrophic advection in equatorial jets, western boundary currents and the major part of subtropical gyres, while diapycnal mixing leads over the remaining subtropical areas and at higher latitudes. At a global scale, SSS variations are primarily caused by horizontal advection (46%), then vertical entrainment (24%), freshwater flux (22%) and lateral diffusion (8%). Finally, the simulated SSS variability is compared to an in situ climatology, in terms of distribution and seasonal variability. The overall agreement is satisfying, which confirms that the salinity balance is reliable. The simulation exhibits stronger gradients and higher variability, due to its fine resolution and high frequency forcing. Moreover, the SSS variability at daily scale can be investigated from the model, revealing patterns considerably different from the seasonal cycle. Within the perspective of the future satellite missions dedicated to SSS retrieval (SMOS and Aquarius/SAC-D), the MLM could be useful for determining calibration areas, as well as providing a first-guess estimate to inversion algorithms.

scholarly journals Sea-surface salinity variations in the Northern Caribbean Sea across the mid-Pleistocene transition

2010 ◽  
Vol 6 (3) ◽  
pp. 1229-1265
Author(s):  
S. Sepulcre ◽  
L. Vidal ◽  
K. Tachikawa ◽  
F. Rostek ◽  
E. Bard
Keyword(s):  
Temperature Gradient ◽  
Planktonic Foraminifera ◽  
Caribbean Sea ◽  
Meridional Overturning Circulation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Inter Tropical Convergence Zone ◽  
The North ◽  
The Difference

Abstract. This study aimed at documenting climate changes in tropical area in response to the Mid-Pleistocene Transition (MPT) by reconstructing past hydrologic variations in the Northern Caribbean Sea and its influence on the stability of the Atlantic Meridional Overturning Circulation (AMOC) during the last 940 kyr. Using core MD03-2628, we estimated past changes in sea surface salinity (SSS) using Δδ18O, the difference between the modern and the past δ18O of seawater (obtained by combining alkenone thermometer data with the δ18O of the planktonic foraminifera Globigerinoides ruber (white) and corrected for ice-sheet volume effects). Today, the lowest SSS values in the studied area are associated with the northernmost location of the Inter-Tropical Convergence Zone (ITCZ). The Δδ18O record exhibits glacial/interglacial cyclicity with higher values during all glacial periods spanning the last 940 kyr, indicating increased SSS. At a longer timescale, the Δδ18O exhibits a shift toward lower values for interglacial periods during the last 450 kyr, when compared to interglacial stages older than 650 kyr. A rise in SSS during glacial stages may be related to the southernmost location of the ITCZ, which is induced by a steeper interhemispheric temperature gradient and associated with reduced northward cross equatorial oceanic transport. Therefore, the results suggest a permanent link between the tropical salinity budget and the AMOC during the last 940 kyr. Following the MPT, lower salinities during the last five interglacial stages indicate a northernmost ITCZ location, forced by changes in the interhemispheric temperature gradient that is associated with the poleward position of Southern Oceanic Fronts that amplified the transport of heat and moisture to the North Atlantic. These processes may have contributed to amplification of the climate cycles that followed the MPT.

Assessing Sea Surface Salinity Derived by Aquarius in the Indian Ocean

2014 ◽  
Vol 11 (4) ◽  
pp. 719-722 ◽  
Author(s):  
Smitha Ratheesh ◽  
Rashmi Sharma ◽  
Rajesh Sikhakolli ◽  
Raj Kumar ◽  
Sujit Basu
Keyword(s):  
Indian Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The Indian Ocean

scholarly journals Study of the mixed layer depth variations within the north Indian Ocean using a 1-D model

2004 ◽  
Vol 109 (C8) ◽  
pp. n/a-n/a ◽  
Author(s):  
K. N. Babu ◽  
Rashmi Sharma ◽  
Neeraj Agarwal ◽  
Vijay K. Agarwal ◽  
R. A. Weller
Keyword(s):  
Indian Ocean ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
North Indian Ocean ◽  
Layer Depth ◽  
The North ◽  
D Model
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