A numerical study on the mixed layer depth variability and its influence on the sea surface temperature during 2013–2014 in the Bay of Bengal and Equatorial Indian Ocean

Sen, Radharani; Francis, P. A.; Chakraborty, Arun; Effy, John B.

doi:10.1007/s10236-021-01452-1

Cited by 3 publications

(4 citation statements)

References 50 publications

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“…Despite the dominance of the net surface heat flux in the growth of SST anomalies, Alexander and Penland (1996) previously showed that vertical entrainment of water from the base of the mixed layer strongly influences SST anomalies and the upper ocean heat budget by controlling the MLD. However, the changes in the surface heat fluxes from MLD variability during the MHW require a detailed mixed layer heat budget analysis with the resolution of vertical advection, vertical mixing, and entrainment from the base of the mixed layer (Vijith et al, 2020;Sen et al, 2021). We also note the magnitude of the residuals, likely due to biases in surface fluxes in the reanalysis products (Wijesekera et al, 2022) and, in parts, related to changes in horizontal and vertical mixing and entrainment (Sen Gupta et al, 2020;Vijith et al, 2020).…”

Section: Physical Mechanismmentioning

confidence: 91%

“…For a mechanistic understanding underlying the MHW event and its interaction with tropical cyclone Amphan, we performed a mixed layer heat budget analysis (Sen et al, 2021). In the mixed layer heat budget, the vertically integrated temperature of the mixed layer is equal to the sum of horizontal advection, air-sea heat flux exchange, and residual processes "R" which represent vertical advection, vertical diffusion, and entrainment at the base of the mixed layer.…”

Section: Methodsmentioning

confidence: 99%

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Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 2020

et al. 2022

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Interactions are diagnosed between a marine heatwave (MHW) event and tropical super cyclone Amphan in the Bay of Bengal. In May 2020, an MHW developed in the Bay of Bengal driven by coupled ocean-atmosphere processes which included shoaling of the mixed layer depth due to reduced wind speed, increased net surface shortwave radiation flux into the ocean, increased upper ocean stratification, and increased sub-surface warming. Ocean temperature, rather than salinity, dominated the stratification that contributed to the MHW development and the subsurface ocean warming that also increased tropical cyclone heat potential. The presence of this strong MHW with sea surface temperature anomalies >2.5°C in the western Bay of Bengal coincided with the cyclone track and facilitated the rapid intensification of tropical cyclone Amphan to a super cyclone in just 24 h. This rapid intensification of a short-lived tropical cyclone, with a lifespan of 5 days over the ocean, is unprecedented in the Bay of Bengal during the pre-monsoon period (March-May). As the cyclone approached landfall in northern India, the wind-induced mixing deepened the mixed layer, cooled the ocean's surface, and reduced sub-surface warming in the bay, resulting in the demise of the MHW. This study provides new perspectives on the interactions between MHWs and tropical cyclones that could aid in improving the current understanding of compound extreme events that have severe socio-economic consequences in affected countries.

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Section: Physical Mechanismmentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 2020

et al. 2022

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“…The combination of multidisciplinary ocean data to estimate MLD has been increasingly valued by researchers in the ocean field. Some new methods have also been proposed to estimate MLD using sea-surface parameters, which are usually based on dynamic models or statistical models [ 2 , 17 , 33 , 34 , 35 , 36 , 37 ]. For example, Courtois et al pointed out that the numerical model for judging MLD is usually based on the density difference with the ocean surface [ 17 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sen et al studied the variability of MLD using the Regional Ocean Modeling System. Due to the bias of salinity, they failed to accurately measure the barrier layer thickness and temperature inversion in the northern Bay of Bengal (BoB) [ 35 ]. Jeong et al used a multiple linear regression (MLR) model to reconstruct three-dimensional ocean thermal structures from satellite sea-surface measurements and detected decadal variation in the global MLD [ 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

Yue

et al. 2022

Sensors

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The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters.

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A numerical study on the role of atmospheric forcing on mixed layer depth variability in the Bay of Bengal using a regional ocean model

et al. 2021

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A numerical study on the mixed layer depth variability and its influence on the sea surface temperature during 2013–2014 in the Bay of Bengal and Equatorial Indian Ocean

Cited by 3 publications

References 50 publications

Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 2020

Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 2020

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

A numerical study on the role of atmospheric forcing on mixed layer depth variability in the Bay of Bengal using a regional ocean model

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