scholarly journals A modeling study of processes controlling the Bay of Bengal sea surface salinity interannual variability

2016 ◽  
Vol 121 (12) ◽  
pp. 8471-8495 ◽  
Author(s):  
V. P. Akhil ◽  
M. Lengaigne ◽  
J. Vialard ◽  
F. Durand ◽  
M. G. Keerthi ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Modeling Study

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.

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 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.

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.

scholarly journals Estimation of sea surface salinity in the Bay of Bengal using Outgoing Longwave Radiation

2002 ◽  
Vol 29 (16) ◽  
pp. 22-1-22-4 ◽  
Author(s):  
V. S. N. Murty ◽  
Bulusu Subrahmanyam ◽  
M. S. S. Sarma ◽  
V. Tilvi ◽  
V. Ramesh Babu
Keyword(s):  
Bay Of Bengal ◽  
Outgoing Longwave Radiation ◽  
Longwave Radiation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity

scholarly journals Inter Annual and Seasonal Cycle of Satellite Derived Sea Surface Salinity in the Bay of Bengal

2021 ◽  
Author(s):  
Kandasamy Priyanka ◽  
Ranjit Kumar Sarangi ◽  
Ramalingam Shanthi ◽  
Durairaj Poornima ◽  
Ayyappan Saravanakumar
Keyword(s):  
Coastal Water ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Spatial And Temporal Variation ◽  
Surface Salinity ◽  
Decadal Scale ◽  
Salinity Data ◽  
Offshore Region

Abstract A spatial and temporal variation of sea surface salinity (SSS) is vital to understand the dynamics of the seasonal and inter-annual changes in the marine environment. In the present study, Soil Moisture Active-Passive (SMAP) derived daily SSS product and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) remote sensing reflectance (Rrs) based SSS images (Algorithm by Qing et al, 2013), applied in the coastal and offshore region of the Bay of Bengal (BoB). SMAP data validation with in situ data (offshore and coastal water, 10 and 15 points) showed good correlation at offshore water and less correlation at coastal water (R2 = 0.707/0.499, SEE = ± 0.291/±0.546, MNB = -0.0029/-0.0089 and RMSE = ± 0.092/±0.139) respectively. Similarly, MODIS-Aqua Rrs derived salinity data validated with in-situ SSS and observed the correlation as follows with R2 = 0.908/0.891, SEE = ± 2.395/±1.512, MNB = 0.0718/0.0361, RMSE = ± 0.760/±0.316 in offshore and coastal water respectively during April and August 2019. The salinity data observed in the range of 32 to 34.5psu. High SSS mean (35.6-35.8psu) observed during the spring inter-monsoon and low salinity (34.6-34.9psu) observed during winter monsoon phase as depicted from decadal scale interpretation. The present study inferred that MODIS aqua derived SSS is better than SMAP based SSS at coastal and offshore region of the western BoB, irrespective of their resolution and spectral differences. More data points based validation would provide the scope for further improvements and seasonal studies on salinity variability using ocean color sensors reflectance based datasets.

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