scholarly journals Variability of Near-Surface Circulation and Sea Surface Salinity Observed from Lagrangian Drifters in the Northern Bay of Bengal During the Waning 2015 Southwest Monsoon

Oceanography ◽  
2016 ◽  
Vol 29 (2) ◽  
pp. 124-133 ◽  
Author(s):  
Verena Hormann ◽  
Luca Centurioni ◽  
Amala Mahadevan ◽  
Sebastian Essink ◽  
Eric D'Asaro ◽  
...  
Keyword(s):  
Bay Of Bengal ◽  
Southwest Monsoon ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Lagrangian Drifters ◽  
Surface Circulation ◽  
Northern Bay Of Bengal

Variability of Intraseasonal Oscillations and Synoptic Signals in Sea Surface Salinity in the Bay of Bengal

2019 ◽  
Vol 32 (20) ◽  
pp. 6703-6728 ◽  
Author(s):  
Corinne B. Trott ◽  
Bulusu Subrahmanyam ◽  
Heather L. Roman-Stork ◽  
V. S. N. Murty ◽  
C. Gnanaseelan
Keyword(s):  
Soil Moisture ◽  
River Runoff ◽  
Bay Of Bengal ◽  
Southwest Monsoon ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Intraseasonal Oscillations ◽  
Salinity Data

Abstract Intraseasonal oscillations (ISOs) significantly impact southwest monsoon precipitation and Bay of Bengal (BoB) variability. The response of ISOs in sea surface salinity (SSS) to those in the atmosphere is investigated in the BoB from 2005 to 2017. The three intraseasonal processes examined in this study are the 30–90-day and 10–20-day ISOs and 3–7-day synoptic weather signals. A variety of salinity data from NASA’s Soil Moisture Active Passive (SMAP) and the European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellite missions and from reanalysis using the Hybrid Coordinate Ocean Model (HYCOM) and operational analysis of Climate Forecast System version 2 (CFSv2) were utilized for the study. It is found that the 30–90-day ISO salinity signal propagates northward following the northward propagation of convection and precipitation ISOs. The 10–20-day ISO in SSS and precipitation deviate largely in the northern BoB wherein the river runoff largely impacts the SSS. The weather systems strongly impact the 3–7-day signal in SSS prior to and after the southwest monsoon. Overall, we find that satellite salinity products captured better the SSS signal of ISO due to inherent inclusion of river runoff and mixed layer processes. CFSv2, in particular, underestimates the SSS signal due to the misrepresentation of river runoff in the model. This study highlights the need to include realistic riverine freshwater influx for better model simulations, as accurate salinity simulation is mandatory for the representation of air–sea coupling in models.

Ocean Salinity Aspects of the Ningaloo Niño

2021 ◽  
pp. 1
Author(s):  
Yaru Guo ◽  
Yuanlong Li ◽  
Fan Wang ◽  
Yuntao Wei
Keyword(s):  
Fresh Water ◽  
Large Scale ◽  
Southern Oscillation ◽  
Regional Climate ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Ocean Salinity

AbstractNingaloo Niño – the interannually occurring warming episode in the southeast Indian Ocean (SEIO) – has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced fresh-water transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward fresh-water advection near the eastern boundary, which is critical in causing the strong freshening (> 0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with the El Niño-Southern Oscillation (0.57, 0.77, and 0.70 with Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (-0.27, -0.42, and -0.35) during 1993-2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.

Assessment of Aquarius sea surface salinity with Argo in the Bay of Bengal

2019 ◽  
Vol 40 (22) ◽  
pp. 8547-8565 ◽  
Author(s):  
Xinyu Lin ◽  
Yun Qiu ◽  
Jing Cha ◽  
Xiaogang Guo
Keyword(s):  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity

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

scholarly journals Effects of Tropical Cyclones on Sea Surface Salinity in the Bay of Bengal Based on SMAP and Argo Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2975
Author(s):  
Huabing Xu ◽  
Rongzhen Yu ◽  
Danling Tang ◽  
Yupeng Liu ◽  
Sufen Wang ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Vertical Mixing ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Ekman Pumping ◽  
Surface Salinity ◽  
Argo Data ◽  
Before And After ◽  
The Impact

This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

scholarly journals Response of the Salinity-Stratified Bay of Bengal to Cyclone Phailin

2019 ◽  
Vol 49 (5) ◽  
pp. 1121-1140 ◽  
Author(s):  
Dipanjan Chaudhuri ◽  
Debasis Sengupta ◽  
Eric D’Asaro ◽  
R. Venkatesan ◽  
M. Ravichandran
Keyword(s):  
Barrier Layer ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Density Stratification ◽  
Upper Ocean ◽  
Dimensional Model ◽  
Cyclone Track ◽  
Surface Salinity ◽  
Near Surface ◽  
One Dimensional

AbstractCyclone Phailin, which developed over the Bay of Bengal in October 2013, was one of the strongest tropical cyclones to make landfall in India. We study the response of the salinity-stratified north Bay of Bengal to Cyclone Phailin with the help of hourly observations from three open-ocean moorings 200 km from the cyclone track, a mooring close to the cyclone track, daily sea surface salinity (SSS) from Aquarius, and a one-dimensional model. Before the arrival of Phailin, moored observations showed a shallow layer of low-salinity water lying above a deep, warm “barrier” layer. As the winds strengthened, upper-ocean mixing due to enhanced vertical shear of storm-generated currents led to a rapid increase of near-surface salinity. Sea surface temperature (SST) cooled very little, however, because the prestorm subsurface ocean was warm. Aquarius SSS increased by 1.5–3 psu over an area of nearly one million square kilometers in the north Bay of Bengal. A one-dimensional model, with initial conditions and surface forcing based on moored observations, shows that cyclone winds rapidly eroded the shallow, salinity-dominated density stratification and mixed the upper ocean to 40–50-m depth, consistent with observations. Model sensitivity experiments indicate that changes in ocean mixed layer temperature in response to Cyclone Phailin are small. A nearly isothermal, salinity-stratified barrier layer in the prestorm upper ocean has two effects. First, near-surface density stratification reduces the depth of vertical mixing. Second, mixing is confined to the nearly isothermal layer, resulting in little or no SST cooling.

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 Upper-Ocean Response to the Super Tropical Cyclone Phailin (2013) over the Freshwater Region of the Bay of Bengal

2019 ◽  
Vol 49 (5) ◽  
pp. 1201-1228 ◽  
Author(s):  
Yun Qiu ◽  
Weiqing Han ◽  
Xinyu Lin ◽  
B. Jason West ◽  
Yuanlong Li ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Upper Ocean ◽  
Surface Salinity ◽  
Ocean Response ◽  
Upper Ocean Response ◽  
The Impact

AbstractThis study investigates the impact of salinity stratification on the upper-ocean response to a category 5 tropical cyclone, Phailin, that crossed the northern Bay of Bengal (BOB) from 8 to 13 October 2013. A drastic increase of up to 5.0 psu in sea surface salinity (SSS) was observed after Phailin’s passage, whereas a weak drop of below 0.5°C was observed in sea surface temperature (SST). Rightward biases were apparent in surface current and SSS but not evident in SST. Phailin-induced SST variations can be divided into the warming and cooling stages, corresponding to the existence of the thick barrier layer (BL) and temperature inversion before and erosion after Phailin’s passage, respectively. During the warming stage, SST increased due to strong entrainment of warmer water from the BL, which overcame the cooling induced by surface heat fluxes and horizontal advection. During the cooling stage, the entrainment and upwelling dominated the SST decrease. The preexistence of the BL, which reduced entrainment cooling by ~1.09°C day−1, significantly weakened the overall Phailin-induced SST cooling. The Hybrid Coordinate Ocean Model (HYCOM) experiments confirm the crucial roles of entrainment and upwelling in the Phailin-induced dramatic SSS increase and weak SST decrease. Analyses of upper-ocean stratification associated with 16 super TCs that occurred in the BOB during 1980–2015 show that intensifications of 13 TCs were associated with a thick isothermal layer, and 5 out of the 13 were associated with a thick BL. The calculation of TC intensity with and without considering subsurface temperature demonstrates the importance of large upper-ocean heat storage in TC growth.

scholarly journals New insights into SMOS Sea Surface Salinity retrievals in the Arctic Ocean.

2020 ◽  
Author(s):  
Alexandre Supply ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Nicolas Kolodziejczyk ◽  
Gilles Reverdin ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Near Surface ◽  
The Arctic Ocean ◽  
L Band

<p>Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band, providing the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, retrieving SSS at high latitudes with a reasonable accuracy remains challenging, in particular due to the low sensitivity of L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents and sea ice as well as the presence of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval are significant sources of uncertainty. We develop a novel correction methodology accordingly.</p><p>SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.26 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD = 1.86 pss), particularly in river plumes fresher by about 10 pss than surrounding waters. Furthermore, comparisons with in-situ measurements ranging from 1 to 11 m depths identify huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.</p>

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