Observed Upper Ocean Seasonal and Intraseasonal Variability in the Andaman Sea

Ashin, K.; Girishkumar, M. S.; Suprit, K.; Thangaprakash, V. P.

doi:10.1029/2019jc014938

Cited by 17 publications

(4 citation statements)

References 64 publications

(147 reference statements)

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“…A similar validation analysis was conducted for the island region (Andaman), where a buoy is moored at a water depth of 3200 m and about 100 nautical miles away from the island coast. This region experiences intense monsoonal rains [64], [65] and high runoff from the continent [66]. The comparison between RM SSS and in-situ SSS indicated a slightly lower performance of the RM (MNB 0.12, RMSE 0.32, intercept 8.71, slope 0.73, and correlation coefficient 0.68) when compared to its SSS estimates in other regions.…”

Section: A Evaluation Of Smap Sssmentioning

confidence: 91%

“…The RM was used to analyse the spatial structure of SSS in BoB and AS for the year 2019. The northern BoB is characterized by low saline water from the discharges of Ganges and Irrawaddy river systems [64]. Precipitation due to southwest (June-August) and northeast (October-December) monsoons further leads to freshening of the bay [60].…”

Section: B Application Of the Rm In The Bob And Asmentioning

confidence: 99%

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Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

2022

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Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimatessuch as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI timeseries measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.

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Section: A Evaluation Of Smap Sssmentioning

confidence: 91%

Section: B Application Of the Rm In The Bob And Asmentioning

confidence: 99%

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

2022

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“…This includes measurement errors and uncertainty in the parametrization schemes, such as the estimate of penetrating shortwave radiation, which has considerable uncertainty when the MLD is shallow (Lotliker et al., 2016). The unresolved processes consist of the horizontal and vertical diffusivity (Ashin et al., 2019; Foltz & McPhaden, 2009; Girishkumar et al., 2020), and the eddy component of horizontal advection due to the smoothing of SST fields (Katsura et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

Importance of Seasonally Evolving Near‐Surface Salinity Stratification on Mixed Layer Heat Budget During Summer Monsoon Intraseasonal Oscillation in the Northern Bay of Bengal in 2019

Sherin,

Girishkumar,

Shivaprasad

et al. 2023

JGR Oceans

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The discharge of freshwater from major rivers into the northern Bay of Bengal (BoB) increases dramatically during the summer monsoon season, reaching a peak in August–September, and there is a corresponding increase in the vertical salinity gradient in the upper ocean. Here we study the impact of seasonally evolving near‐surface salinity stratification on the response of ocean mixed layer temperature (MLT) to Summer Monsoon Intraseasonal Oscillations (MISO), using accurate surface fluxes and high vertical resolution (∼2 m) hydrographic measurements from a mooring in the northern BoB (17.8°N, 89.5°E) during June–September 2019. Prominent MLT warming and cooling with a range of 1.5°C is observed between suppressed (clear skies, calm winds) and active (cloudy, windy) phases of MISO convection. However, the intraseasonal MLT response to the active phase of a late‐season MISO event is minimal compared to MISO events in early summer. We infer this is primarily due to the much smaller contribution from oceanic vertical processes (∼6 Wm−2) in late summer 2019, compared to their role in early summer (−15 to −55 Wm−2). During the active phase of the MISO event of late summer 2019, the combined effect of reduced entrainment and weak vertical temperature gradients associated with a barrier layer inhibits near‐surface cooling. Conversely, the near‐surface salinity stratification and the barrier layer are weak during MISO events in the early summer of 2019—these hydrographic conditions lead to enhanced MLT cooling in response to MISO, apparently through a freer turbulent exchange of cool thermocline water with the surface layer.

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“…Akhil et al (2016b) investigated the dominant processes that control the interannual variability of the surface salinity in the Bay of Bengal and noted Irrawaddy discharge as the most important factor contributing to the variability of the surface salinity in the Andaman Sea during the summer monsoon. Ashin et al (2019) studied the variability of the upper ocean in the Andaman Sea on seasonal and intraseasonal time-scales and highlighted the importance of the salinity in controlling the mixed-layer stratification. There have been many efforts of modeling surface salinity distribution in the Bay of Bengal and its response to forcing by sources of freshwater such as rivers and precipitation (Han and McCreary, 2001;Jensen, 2001;Howden and Murtugudde, 2001;Vinayachandran and Kurian, 2007;Benshila et al, 2014;Jana et al, 2015;Behara and Vinayachandran, 2016) but none of these studies focuses on the freshwater plume that arises due to discharge from IR river in the Andaman Sea.…”

Section: Introductionmentioning

confidence: 99%

The Irrawaddy River Jet in the Andaman Sea During the Summer Monsoon

Pargaonkar

Vinayachandran

2022

Front. Mar. Sci.

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The Irrawaddy (IR) is the largest river discharging into the Andaman Sea and plays an important role in the salinity distribution and the mixed layer physics of the Andaman Sea. This study presents the first report of the IR plume pathways in the Andaman Sea during the summer monsoon and the mechanisms behind them. An ocean circulation model is employed to conduct idealized experiments in which the freshwater forcing, due to rivers other than IR as well as precipitation, are ignored. Our simulations reveal that, during the summer monsoon, the discharge from Irrawaddy spreads as a freshwater jet oriented towards southeast and accumulates over the shelf at the eastern coast of the Andaman Sea. Climatology of Chlorophyll-a concentration measured by satellite and surface currents from global ocean model reanalysis indicates the presence of the Irrawaddy freshwater jet during the summer monsoon. The evolution of surface salinity and currents along the jet suggests that the IR freshwater traps momentum imparted by winds. The momentum balance in the Irrawaddy jet is between Coriolis and wind friction term, indicating that the freshwater jet is completely driven by winds during the summer monsoon. Surface distribution of wind friction term also shows that the northwest-southeast orientation of the Irrawaddy jet is due to the southwesterly orientation of the summer monsoon winds. Further experiments with three different wind forcing scenarios (no winds, winds over the equator only, and winds over the Bay of Bengal only) reveal that the flow of Irrawaddy jet during the summer monsoon is completely controlled by the local winds.

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Observed Upper Ocean Seasonal and Intraseasonal Variability in the Andaman Sea

Cited by 17 publications

References 64 publications

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

Importance of Seasonally Evolving Near‐Surface Salinity Stratification on Mixed Layer Heat Budget During Summer Monsoon Intraseasonal Oscillation in the Northern Bay of Bengal in 2019

The Irrawaddy River Jet in the Andaman Sea During the Summer Monsoon

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