Articles | Volume 20, issue 5
https://doi.org/10.5194/os-20-1167-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-1167-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution numerical modelling of seasonal volume, freshwater, and heat transport along the Indian coast
Kunal Madkaiker
CORRESPONDING AUTHOR
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
Ambarukhana D. Rao
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
Sudheer Joseph
ARO-OMARS, Indian National Centre for Ocean Information Services (INCOIS), Ministry of Earth Sciences, Hyderabad, India
Related authors
No articles found.
Jennifer Veitch, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Mauro Cirano, Emanuela Clementi, Fraser Davidson, Ghada el Sarafy, Guilherme Franz, Patrick Hogan, Sudheer Joseph, Svitlana Liubartseva, Yasumasa Miyazawa, Heather Regan, and Katerina Spanoudaki
State Planet Discuss., https://doi.org/10.5194/sp-2024-22, https://doi.org/10.5194/sp-2024-22, 2024
Preprint under review for SP
Short summary
Short summary
Ocean forecast systems provide information about a future state of the ocean. This information is provided in the form of decision support tools, or downstream applications, that can be accessed by various stakeholders to support livelihoods, coastal resilience, as well as the good governance of the marine environment. This manuscript provides an overview of the various downstream applications of ocean forecast systems that are utilised around the world.
Mauro Cirano, Enrique Alvarez-Fanjul, Arthur Capet, Stefania Ciliberti, Emanuela Clementi, Boris Dewitte, Matias Dinápoli, Ghada El Serafy, Patrick Hogan, Sudheer Joseph, Yasumasa Miyazawa, Ivonne Montes, Diego Narvaez, Heather Regan, Claudia G. Simionato, Clemente A. S. Tanajura, Pramod Thupaki, Claudia Urbano-Latorre, and Jennifer Veitch
State Planet Discuss., https://doi.org/10.5194/sp-2024-26, https://doi.org/10.5194/sp-2024-26, 2024
Preprint under review for SP
Short summary
Short summary
Predicting the ocean state in support of human activities, environmental monitoring and policymaking across different regions worldwide is fundamental. The status of operational ocean forecasting systems (OOFS) in 8 key regions worldwide is provided. A discussion follows on the numerical strategy and available OOFS, pointing out the straightness and the ways forward to improve the essential ocean variables predictability from regional to coastal scales, products reliability and accuracy.
Lianne C. Harrison, Jennifer A. Graham, Piyali Chowdhury, Tiago A. M. Silva, Danja P. Hoehn, Alakes Samanta, Kunal Chakraborty, Sudheer Joseph, T. M. Balakrishnan Nair, and T. Srinivasa Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-3096, https://doi.org/10.5194/egusphere-2024-3096, 2024
Short summary
Short summary
Particle tracking models allow us to explore pathways of floating marine litter, source-to-sink, between countries. This study showed the influence of seasonality for dispersal in Bay of Bengal and how ocean current forcing impacts model performance. Most litter beached on the country of origin, but there was a greater spread shown between countries during the post-monsoon period (Oct–Jan). Results will inform future model developments as well as management of marine litter in the region.
Pawan Tiwari, Ambarukhana D. Rao, Smita Pandey, and Vimlesh Pant
EGUsphere, https://doi.org/10.5194/egusphere-2024-2985, https://doi.org/10.5194/egusphere-2024-2985, 2024
Short summary
Short summary
Concave coast act as funnel, concentrating storm waters and leading to higher storm surges (SS), while convex coast redistribute waters, reducing surges. Study uses the ADCIRC model to simulate peak surges (PS) for different cyclone tracks, showing how coastline geometry, landfall location, and cyclone angle influence PS. Cyclones passing near concave coasts without landfall can still cause high SS, highlighting vulnerability in these regions. This insight aids in assessing coastal flood risks.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
Short summary
Short summary
A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
Related subject area
Approach: Numerical Models | Properties and processes: Coastal and near-shore processes
Influence of river runoff and precipitation on the seasonal and interannual variability of sea surface salinity in the eastern North Tropical Atlantic
A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models
Mechanisms and intraseasonal variability in the South Vietnam Upwelling, South China Sea: the role of circulation, tides, and rivers
Dynamics of salt intrusion in complex estuarine networks; an idealised model applied to the Rhine-Meuse Delta
Application of Wave-current coupled Sediment Transport Models with Variable Grain Properties for Coastal Morphodynamics: A Case Study of the Changhua River, Hainan
Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective
Exploring the tidal response to bathymetry evolution and present-day sea level rise in a channel–shoal environment
Wave-resolving Voronoi model of Rouse number for sediment entrainment equilibrium
Influence of stratification and wind forcing on the dynamics of Lagrangian residual velocity in a periodically stratified estuary
Fjord circulation permits a persistent subsurface water mass in a long, deep mid-latitude inlet
Salt intrusion dynamics in a well-mixed sub-estuary connected to a partially to well-mixed main estuary
Transport dynamics in a complex coastal archipelago
Modeling the interannual variability in Maipo and Rapel river plumes off central Chile
Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm
Clovis Thouvenin-Masson, Jacqueline Boutin, Vincent Échevin, Alban Lazar, and Jean-Luc Vergely
Ocean Sci., 20, 1547–1566, https://doi.org/10.5194/os-20-1547-2024, https://doi.org/10.5194/os-20-1547-2024, 2024
Short summary
Short summary
We focus on understanding the impact of river runoff and precipitation on sea surface salinity (SSS) in the eastern North Tropical Atlantic (e-NTA) region off northwestern Africa. By analyzing regional simulations and observational data, we find that river flows significantly influence SSS variability, particularly after the rainy season. Our findings underscore that a main source of uncertainty representing SSS variability in this region is from river runoff estimates.
Stefan Hagemann, Thao Thi Nguyen, and Ha Thi Minh Ho-Hagemann
Ocean Sci., 20, 1457–1478, https://doi.org/10.5194/os-20-1457-2024, https://doi.org/10.5194/os-20-1457-2024, 2024
Short summary
Short summary
We have developed a methodology for the bias correction of simulated river runoff to force ocean models in which low, medium, and high discharges are corrected once separated at the coast. We show that the bias correction generally leads to an improved representation of river runoff in Europe. The methodology is suitable for model regions with a sufficiently high coverage of discharge observations, and it can be applied to river runoff based on climate hindcasts or climate change simulations.
Marine Herrmann, Thai To Duy, and Patrick Marsaleix
Ocean Sci., 20, 1013–1033, https://doi.org/10.5194/os-20-1013-2024, https://doi.org/10.5194/os-20-1013-2024, 2024
Short summary
Short summary
In summer, deep, cold waters rise to the surface along and off the Vietnamese coast. This upwelling of water lifts nutrients, inducing biological activity that is important for fishery resources. Strong tides occur on the shelf off the Mekong Delta. By increasing the mixing of ocean waters and modifying currents, they are a major factor in the development of upwelling on the shelf, accounting for ~75 % of its average summer intensity.
Bouke Biemond, Wouter Kranenburg, Ymkje Huismans, Huib E. de Swart, and Henk A. Dijkstra
EGUsphere, https://doi.org/10.5194/egusphere-2024-2322, https://doi.org/10.5194/egusphere-2024-2322, 2024
Short summary
Short summary
We study salinity in estuaries which consist of a network of channels. To this end, we develop a model which computes the flow and salinity in such systems. We use the model to quantify by which mechanisms salt is transported in estuarine networks, the response to changes in river discharge, and the impact of depth changes. Results e.g. show that when changing the depth of a channel, effects on salt intrusion in other channels in the network can be larger than the effect on the channel itself.
Yuxi Wu, Enjin Zhao, Xiwen Li, and Shiyou Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2154, https://doi.org/10.5194/egusphere-2024-2154, 2024
Short summary
Short summary
This study presents a comprehensive sand transport model to investigate sediment dynamics in the downstream Changhua River estuary of Hainan Island. It captures the intricate relationship between wave action, currents, and sediment transport. Verified against field measurements, the model exposes notable sediment deposition, significantly affected by coastal currents and geological structures. These insights provide strategies for sedimentation monitoring and control.
Mingyu Li, Alessandro Stocchino, Zhongya Cai, and Tingting Zu
Ocean Sci., 20, 931–944, https://doi.org/10.5194/os-20-931-2024, https://doi.org/10.5194/os-20-931-2024, 2024
Short summary
Short summary
In this study, we explored how water accumulates in a coastal estuary, a key factor affecting the estuary's environmental health and ecosystem. We revealed significant bottom accumulations influenced by plume fronts and velocity convergence, with notable seasonal variability. By analyzing trajectories, we identified subregions with distinct accumulation patterns and examined their interconnections, highlighting the substantial impact of tides and river discharge on these dynamics.
Robert Lepper, Leon Jänicke, Ingo Hache, Christian Jordan, and Frank Kösters
Ocean Sci., 20, 711–723, https://doi.org/10.5194/os-20-711-2024, https://doi.org/10.5194/os-20-711-2024, 2024
Short summary
Short summary
Most coastal environments are sheltered by tidal flats and salt marshes. These habitats are threatened from drowning under sea level rise. Contrary to expectation, recent analyses in the Wadden Sea showed that tidal flats can accrete faster than sea level rise. We found that this phenomenon was facilitated by the nonlinear link between tidal characteristics and coastal bathymetry evolution. This link caused local and regional tidal adaptation with sharp increase–decrease edges at the coast.
Johannes Lawen
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2404.10878, https://doi.org/https://doi.org/10.48550/arXiv.2404.10878, 2024
Short summary
Short summary
A new Voronoi mesh-borne coastal ocean model has been developed. Recent publications encouraged the development of models that work with different mesh types. Voronoi meshes exhibit less acute polygon angles and less numerical diffusion. The developed model is sufficiently generalized to work with any mesh type (Delaunay triangles, Voronoi, structured, mixed). The model is suitable for wave-resolving simulations for coastal developments to resolve intricate changes in erosion and deposition.
Fangjing Deng, Feiyu Jia, Rui Shi, Shuwen Zhang, Qiang Lian, Xiaolong Zong, and Zhaoyun Chen
Ocean Sci., 20, 499–519, https://doi.org/10.5194/os-20-499-2024, https://doi.org/10.5194/os-20-499-2024, 2024
Short summary
Short summary
Southwesterly winds impact cross-estuary flows by amplifying the eddy viscosity component during smaller tides. Moreover, they modify along-estuary gravitational circulation by diminishing both the barotropic and baroclinic components. Stratification results in contrasting sheared flows, distinguished by different dominant components compared to destratified conditions. Additionally, the eddy viscosity component is governed by various subcomponents in diverse stratified waters.
Laura Bianucci, Jennifer M. Jackson, Susan E. Allen, Maxim V. Krassovski, Ian J. W. Giesbrecht, and Wendy C. Callendar
Ocean Sci., 20, 293–306, https://doi.org/10.5194/os-20-293-2024, https://doi.org/10.5194/os-20-293-2024, 2024
Short summary
Short summary
While the deeper waters in the coastal ocean show signs of climate-change-induced warming and deoxygenation, some fjords can keep cool and oxygenated waters in the subsurface. We use a model to investigate how these subsurface waters created during winter can linger all summer in Bute Inlet, Canada. We found two main mechanisms that make this fjord retentive: the typical slow subsurface circulation in such a deep, long fjord and the further speed reduction when the cold waters are present.
Zhongyuan Lin, Guang Zhang, Huazhi Zou, and Wenping Gong
Ocean Sci., 20, 181–199, https://doi.org/10.5194/os-20-181-2024, https://doi.org/10.5194/os-20-181-2024, 2024
Short summary
Short summary
From 2021 to 2022, a particular sub-estuary (East River estuary) suffered greatly from an enhanced salt intrusion. We conducted observation analysis, numerical simulations, and analytical solution to unravel the underlying mechanisms. This study is of help in the investigation of salt dynamics in sub-estuaries connected to main estuaries and of implications for mitigating salt intrusion problems in the regions.
Elina Miettunen, Laura Tuomi, Antti Westerlund, Hedi Kanarik, and Kai Myrberg
Ocean Sci., 20, 69–83, https://doi.org/10.5194/os-20-69-2024, https://doi.org/10.5194/os-20-69-2024, 2024
Short summary
Short summary
We studied circulation and transports in the Archipelago Sea (in the Baltic Sea) with a high-resolution hydrodynamic model. Transport dynamics show different variabilities in the north and south, so no single transect can represent transport through the whole area in all cases. The net transport in the surface layer is southward and follows the alignment of the deeper channels. In the lower layer, the net transport is southward in the northern part of the area and northward in the southern part.
Julio Salcedo-Castro, Antonio Olita, Freddy Saavedra, Gonzalo S. Saldías, Raúl C. Cruz-Gómez, and Cristian D. De la Torre Martínez
Ocean Sci., 19, 1687–1703, https://doi.org/10.5194/os-19-1687-2023, https://doi.org/10.5194/os-19-1687-2023, 2023
Short summary
Short summary
Considering the relevance and impact of river discharges on the coastal environment, it is necessary to understand the processes associated with river plume dynamics in different regions and at different scales. Modeling studies focused on the eastern Pacific coast under the influence of the Humboldt Current are scarce. Here, we conduct for the first time an interannual modeling study of two river plumes off central Chile and discuss their characteristics.
Qiyan Ji, Lei Han, Lifang Jiang, Yuting Zhang, Minghong Xie, and Yu Liu
Ocean Sci., 19, 1561–1578, https://doi.org/10.5194/os-19-1561-2023, https://doi.org/10.5194/os-19-1561-2023, 2023
Short summary
Short summary
Accurate wave forecasts are essential to marine engineering safety. The research designs a model with combined signal decomposition and multiple neural network algorithms to predict wave parameters. The hybrid wave prediction model has good robustness and generalization ability. The contribution of the various algorithms to the model prediction skill was analyzed by the ablation experiments. This work provides a neoteric view of marine element forecasting based on artificial intelligence.
Cited articles
Akhil, V. P., Vialard, J., Lengaigne, M., Keerthi, M. G., Boutin, J., Vergely, J. L., and Papa, F.: Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing, Remote Sens. Environ., 248, 1–18, https://doi.org/10.1016/j.rse.2020.111964, 2020.
Amol, P., Shankar, D., Fernando, V., Mukherjee, A., Aparna, S. G., Fernandes, R., Michael, G. S., Khalap, S. T., Satelkar, N. P., Agarvadekar, Y., Gaonkar, M. G., Tari, A. P., Kankonkar, A., and Vernekar, S. P.: Observed intraseasonal and seasonal variability of the West India Coastal Current on the continental slope, J. Earth Syst. Sci., 123, 1045–1074, https://doi.org/10.1007/s12040-014-0449-5, 2014.
Amol, P., Vinayachandran, P. N., Shankar, D., Thushara, V., Vijith, V., Chatterjee, A., and Kankonkar, A.: Effect of freshwater advection and winds on the vertical structure of chlorophyll in the northern Bay of Bengal, Deep-Sea Res. Pt. II, 179, 1–18, https://doi.org/10.1016/j.dsr2.2019.07.010, 2020.
Anutaliya, A., Send, U., McClean, J. L., Sprintall, J., Rainville, L., Lee, C. M., Jinadasa, S. U. P., Wallcraft, A. J., and Metzger, E. J.: An undercurrent off the east coast of Sri Lanka, Ocean Sci., 13, 1035–1044, https://doi.org/10.5194/os-13-1035-2017, 2017.
Arora, A.: On the role of the Arabian Sea thermal variability in governing rainfall variability over the Western Ghats, J. Earth Syst. Sci., 130, 1–13, https://doi.org/10.1007/s12040-021-01615-0, 2021.
Behara, A. and Vinayachandran, P. N.: An OGCM study of the impact of rain and river water forcing on the Bay of Bengal, J. Geophys. Res.-Oceans, 121, 2425–2446, https://doi.org/10.1002/2015JC011325, 2016.
Behara, A., Vinayachandran, P. N., and Shankar, D.: Influence of Rainfall Over Eastern Arabian Sea on Its Salinity, J. Geophys. Res.-Oceans, 124, 5003–5020, https://doi.org/10.1029/2019JC014999, 2019.
Benshila, R., Durand, F., Masson, S., Bourdallé-Badie, R., de Boyer Montégut, C., Papa, F., and Madec, G.: The upper Bay of Bengal salinity structure in a high-resolution model, Ocean Model., 74, 14–39, https://doi.org/10.1016/j.ocemod.2013.12.001, 2014.
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Model., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9, 2002.
Bleck, R. and Boudra, D.: Wind-driven spin-up in eddy-resolving ocean models formulated in isopycnic and isobaric coordinates, J. Geophys. Res., 91, 7611, https://doi.org/10.1029/JC091iC06p07611, 1986.
Bleck, R. and Smith, L. T.: A wind-driven isopycnic coordinate model of the north and equatorial Atlantic Ocean: 1. Model development and supporting experiments, J. Geophys. Res., 95, 3273, https://doi.org/10.1029/JC095iC03p03273, 1990.
Boutin, J., Vergely, J.-L., Khvorostyanov, D., and Supply, A.: SMOS SSS L3 maps generated by CATDS CEC LOCEAN. debias V8.0, SEANOE [data set], https://doi.org/10.17882/52804, 2023.
Bower, A. S. and Furey, H. H.: Mesoscale eddies in the Gulf of Aden and their impact on the spreading of Red Sea Outflow Water, Prog. Oceanogr., 96, 14–39, https://doi.org/10.1016/j.pocean.2011.09.003, 2012.
Brandt, P., Stramma, L., Schott, F., Fischer, J., Dengler, M., and Quadfasel, D.: Annual Rossby waves in the Arabian Sea from TOPEX/POSEIDON altimeter and in situ data, Deep-Sea Res. Pt. II, 49, 1197–1210, https://doi.org/10.1016/S0967-0645(01)00166-7, 2002.
Campin, J.-M., Heimbach, P., Losch, M., Forget, G., edhill3, Adcroft, A., amolod, Menemenlis, D., dfer22, Jahn, O., Hill, C., Scott, J., stephdut, Mazloff, M., Fox-Kemper, B., antnguyen13, Doddridge, E., Fenty, I., Bates, M., Smith, T., AndrewEichmann-NOAA, mitllheisey, Wang, O., Lauderdale, J., Martin, T., Abernathey, R., samarkhatiwala, dngoldberg, hongandyan, and Deremble, B.: MITgcm/MITgcm: checkpoint68z (Version checkpoint68z), Zenodo [code], https://doi.org/10.5281/zenodo.13104130, 2024.
Carton, J. A., Chepurin, G. A., and Chen, L.: SODA3: A New Ocean Climate Reanalysis, J. Climate, 31, 6967–6983, https://doi.org/10.1175/JCLI-D-18-0149.1, 2018 (dat available at: https://www2.atmos.umd.edu/%7Eocean/index_files/soda3.12.2_mn_download_b.htm, last access: 25 October 2023).
Chaudhuri, A., Shankar, D., Aparna, S. G., Amol, P., Fernando, V., Kankonkar, A., Michael, G. S., Satelkar, N. P., Khalap, S. T., Tari, A. P., Gaonkar, M. G., Ghatkar, S., and Khedekar, R. R.: Observed variability of the West India Coastal Current on the continental slope from 2009–2018, J. Earth Syst. Sci., 129, 1–23, https://doi.org/10.1007/s12040-019-1322-3, 2020.
Chen, G., Wang, D., and Hou, Y.: The features and interannual variability mechanism of mesoscale eddies in the Bay of Bengal, Cont. Shelf Res., 47, 178–185, https://doi.org/10.1016/j.csr.2012.07.011, 2012.
Cheng, X., McCreary, J. P., Qiu, B., Qi, Y., Du, Y., and Chen, X.: Dynamics of Eddy Generation in the Central Bay of Bengal, J. Geophys. Res.-Oceans, 123, 6861–6875, https://doi.org/10.1029/2018JC014100, 2018.
Chin, T. M., Vazquez-Cuervo, J., and Armstrong, E. M.: A multi-scale high-resolution analysis of global sea surface temperature, Remote Sens. Environ., 200, 154–169, https://doi.org/10.1016/j.rse.2017.07.029, 2017 (data avilable at: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1, last access: 25 October 2023).
Chirokova, G. and Webster, P. J.: Interannual variability of Indian Ocean heat transport, J. Climate, 19, 1013–1031, https://doi.org/10.1175/JCLI3676.1, 2006.
Copernicus Marine Service: Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15 m, Marine Data Store [data set], https://doi.org/10.48670/mds-00327, 2024.
Cullen, K. E. and Shroyer, E. L.: Seasonality and interannual variability of the Sri Lanka dome, Deep-Sea Res. Pt. II, 168, 1–10, https://doi.org/10.1016/j.dsr2.2019.104642, 2019.
Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3, 660–687, https://doi.org/10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2, 2002.
Das, U., Vinayachandran, P. N., and Behara, A.: Formation of the southern Bay of Bengal cold pool, Clim. Dynam., 47, 2009–2023, https://doi.org/10.1007/s00382-015-2947-9, 2016.
Ding, R., Xuan, J., Zhang, T., Zhou, L., Zhou, F., Meng, Q., and Kang, I. S.: Eddy-induced heat transport in the South China sea, J. Phys. Oceanogr., 51, 2329–2349, https://doi.org/10.1175/JPO-D-20-0206.1, 2021.
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012.
Durand, F., Shankar, D., Birol, F., and Shenoi, S. S. C.: Spatiotemporal structure of the East India Coastal Current from satellite altimetry, J. Geophys. Res.-Oceans, 114, 1–18, https://doi.org/10.1029/2008JC004807, 2009.
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015.
Gangopadhyay, A., Bharat Raj, G. N., Chaudhuri, A. H., Babu, M. T., and Sengupta, D.: On the nature of meandering of the springtime western boundary current in the Bay of Bengal, Geophys. Res. Lett., 40, 2188–2193, https://doi.org/10.1002/grl.50412, 2013.
Garternicht, U. and Schott, F.: Heat fluxes of the Indian Ocean from a global eddy-resolving model, J. Geophys. Res.-Oceans, 102, 21147–21159, https://doi.org/10.1029/97JC01585, 1997.
Gopalakrishnan, G., Subramanian, A. C., Miller, A. J., Seo, H., and Sengupta, D.: Estimation and prediction of the upper ocean circulation in the Bay of Bengal, Deep-Sea Res. Pt. II, 172, 104721, https://doi.org/10.1016/j.dsr2.2019.104721, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023b.
Hormann, V., Centurioni, L. R., and Gordon, A. L.: Freshwater export pathways from the Bay of Bengal, Deep-Sea Res. Pt. II, 168, 1–12, https://doi.org/10.1016/j.dsr2.2019.104645, 2019.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., and Xie, P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD) Version 4.5, https://gpm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.5.pdf (last access: 25 October 2023), 2015 (data available at: http://apdrc.soest.hawaii.edu/datadoc/gpm_imerg_mon.php, last access: 25 October 2023).
Iskandarani, M., Levin, J. C., Choi, B.-J., and Haidvogel, D. B.: Comparison of advection schemes for high-order h–p finite element and finite volume methods, Ocean Model., 10, 233–252, https://doi.org/10.1016/j.ocemod.2004.09.005, 2005.
Jana, S., Gangopadhyay, A., and Chakraborty, A.: Impact of seasonal river input on the Bay of Bengal simulation, Cont. Shelf Res., 104, 45–62, https://doi.org/10.1016/j.csr.2015.05.001, 2015.
Jana, S., Gangopadhyay, A., Lermusiaux, P. F. J., Chakraborty, A., Sil, S., and Haley, P. J.: Sensitivity of the Bay of Bengal upper ocean to different winds and river input conditions, J. Marine Syst., 187, 206–222, https://doi.org/10.1016/j.jmarsys.2018.08.001, 2018.
Kara, A. B., Rochford, P. A., and Hurlburt, H. E.: An optimal definition for ocean mixed layer depth, J. Geophys. Res.-Oceans, 105, 16803–16821, https://doi.org/10.1029/2000JC900072, 2000.
Kara, A. B., Hurlburt, H. E., and Wallcraft, A. J.: Stability-Dependent Exchange Coefficients for Air–Sea Fluxes, J. Atmos. Ocean. Tech., 22, 1080–1094, https://doi.org/10.1175/JTECH1747.1, 2005.
Kumar, S. P. and Prasad, T. G.: Formation and spreading of Arabian Sea high-salinity water mass, J. Geophys. Res.-Oceans, 104, 1455–1464, https://doi.org/10.1029/1998jc900022, 1999.
Kurien, P., Ikeda, M., and Valsala, V. K.: Mesoscale variability along the east coast of India in spring as revealed from satellite data and OGCM simulations, J. Oceanogr., 66, 273–289, https://doi.org/10.1007/s10872-010-0024-x, 2010.
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363, https://doi.org/10.1029/94RG01872, 1994.
Lee, T. and Marotzke, J.: Seasonal cycles of meridional overturning and heat transport of the Indian Ocean, J. Phys. Oceanogr., 28, 923–943, https://doi.org/10.1175/1520-0485(1998)028<0923:SCOMOA>2.0.CO;2, 1998.
Lin, X., Qiu, Y., and Sun, D.: Thermohaline structures and heat/freshwater transports of mesoscale eddies in the Bay of Bengal observed by Argo and satellite data, Remote Sens.-Basel, 11, 1–21, https://doi.org/10.3390/rs11242989, 2019.
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K. W., Paver, C. R., and Smolyar, I. V.: World Ocean Atlas 2018, Vol. 1: Temperature, NOAA Atlas NESDIS 81, 1, https://www.ncei.noaa.gov/sites/default/files/2020-04/woa18_vol1.pdf (last access: 25 October 2023), 2018.
Luis, A. J. and Kawamura, H.: Wintertime wind forcing and sea surface cooling near the South India tip observed using NSCAT and AVHRR, Remote Sens. Environ., 73, 55–64, https://doi.org/10.1016/S0034-4257(00)00081-X, 2000.
Mahadevan, A.: The Impact of Submesoscale Physics on Primary Productivity of Plankton, Annu. Rev. Mar. Sci., 8, 161–184, https://doi.org/10.1146/annurev-marine-010814-015912, 2016.
Mahadevan, A., Paluszkiewicz, T., Ravichandran, M., Sengupta, D., and Tandon, A.: Introduction to the Special Issue on the Bay of Bengal: From Monsoons to Mixing, Oceanography, 29, 14–17, https://doi.org/10.5670/oceanog.2016.34, 2016.
Mallick, S. K., Agarwal, N., Sharma, R., Prasad, K. V. S. R., and Ramakrishna, S. S. V. S.: Thermodynamic Response of a High-Resolution Tropical Indian Ocean Model to TOGA COARE Bulk Air–Sea Flux Parameterization: Case Study for the Bay of Bengal (BoB), Pure Appl. Geophys., 177, 4025–4044, https://doi.org/10.1007/s00024-020-02448-6, 2020.
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers, J. Geophys. Res.-Oceans, 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997.
Mathew, S., Natesan, U., Latha, G., and Venkatesan, R.: Dynamics behind warming of the southeastern Arabian Sea and its interruption based on in situ measurements, Ocean Dynam., 68, 457–467, https://doi.org/10.1007/s10236-018-1130-3, 2018.
Mazloff, M. R., Heimbach, P., and Wunsch, C.: An eddy-permitting Southern Ocean state estimate, J. Phys. Oceanogr., 40, 880–899, https://doi.org/10.1175/2009JPO4236.1, 2010.
McDougall, T. J., Jackett, D. R., Wright, D. G., and Feistel, R.: Accurate and computationally efficient algorithms for potential temperature and density of seawater, J. Atmos. Ocean. Tech., 20, 730–741, https://doi.org/10.1175/1520-0426(2003)20<730:AACEAF>2.0.CO;2, 2003.
McPhaden, M. J., Meyers, G., Ando, K., Masumoto, Y., Murty, V. S. N., Ravichandran, M., Syamsudin, F., Vialard, J., Yu, L., and Yu, W.: RAMA: The research moored array for African-Asian-Australian monsoon analysis and prediction, B. Am. Meteorol. Soc., 90, 459–480, https://doi.org/10.1175/2008BAMS2608.1, 2009.
Menemenlis, D., Fukumori, I., and Lee, T.: Using Green's Functions to Calibrate an Ocean General Circulation Model, Mon. Weather Rev., 133, 1224–1240, https://doi.org/10.1175/MWR2912.1, 2005.
Mukherjee, A., Shankar, D., Fernando, V., Amol, P., Aparna, S. G., Fernandes, R., Michael, G. S., Khalap, S. T., Satelkar, N. P., Agarvadekar, Y., Gaonkar, M. G., Tari, A. P., Kankonkar, A., and Vernekar, S.: Observed seasonal and intraseasonal variability of the east india coastal current on the continental slope, J. Earth Syst. Sci., 123, 1197–1232, https://doi.org/10.1007/s12040-014-0471-7, 2014.
Mukhopadhyay, S., Shankar, D., Aparna, S. G., Mukherjee, A., Fernando, V., Kankonkar, A., Khalap, S., Satelkar, N. P., Gaonkar, M. G., Tari, A. P., Khedekar, R. R., and Ghatkar, S.: Observed variability of the East India Coastal Current on the continental slope during 2009–2018, J. Earth Syst. Sci., 129, 1–22, https://doi.org/10.1007/s12040-020-1346-8, 2020.
Murty, V. S. N., Sarma, Y. V. B., Rao, D. P., and Murty, C. S.: Water characteristics, mixing and circulation in the Bay of Bengal during southwest monsoon, J. Mar. Res., 50, 207–228, 1992.
Papa, F., Bala, S. K., Pandey, R. K., Durand, F., Gopalakrishna, V. V., Rahman, A., and Rossow, W. B.: Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal, J. Geophys. Res.-Oceans, 117, 1–13, https://doi.org/10.1029/2012JC008158, 2012.
Parampil, S. R., Gera, A., Ravichandran, M., and Sengupta, D.: Intraseasonal response of mixed layer temperature and salinity in the Bay of Bengal to heat and freshwater flux, J. Geophys. Res.-Oceans, 115, 1–17, https://doi.org/10.1029/2009JC005790, 2010.
Pinker, R. T., Bentamy, A., Grodsky, S. A., and Chen, W.: Annual and seasonal variability of net heat flux in the Northern Indian Ocean, Int. J. Remote Sens., 41, 6461–6483, https://doi.org/10.1080/01431161.2020.1746858, 2020.
Pottapinjara, V. and Joseph, S.: Evaluation of mixing schemes in the HYbrid Coordinate Ocean Model (HYCOM) in the tropical Indian Ocean, Ocean Dynam., 72, 341–359, https://doi.org/10.1007/s10236-022-01510-2, 2022 (data available at: https://incois.gov.in/portal/datainfo/drform.jsp, last access: 25 October 2023).
Pratik, K., Parekh, A., Karmakar, A., Chowdary, J. S., and Gnanaseelan, C.: Recent changes in the summer monsoon circulation and their impact on dynamics and thermodynamics of the Arabian Sea, Theor. Appl. Climatol., 136, 321–331, https://doi.org/10.1007/s00704-018-2493-6, 2019.
Rainville, L., Lee, C. M., Arulananthan, K., Jinadasa, S. U. P., Fernando, H. J. S., Priyadarshani, W. N. C., and Wijesekera, H.: Water Mass Exchanges between the Bay of Bengal and Arabian Sea from Multiyear Sampling with Autonomous Gliders, J. Phys. Oceanogr., 52, 2377–2396, https://doi.org/10.1175/JPO-D-21-0279.1, 2022.
Rao, R. R., Girish Kumar, M. S., Ravichandran, M., Rao, A. R., Gopalakrishna, V. V., and Thadathil, P.: Interannual variability of Kelvin wave propagation in the wave guides of the equatorial Indian Ocean, the coastal Bay of Bengal and the southeastern Arabian Sea during 1993–2006, Deep-Sea Res. Pt. I, 57, 1–13, https://doi.org/10.1016/j.dsr.2009.10.008, 2010.
Rio, M. H., Mulet, S., and Picot, N.: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. Lett., 41, 8918–8925, https://doi.org/10.1002/2014GL061773, 2014.
Schott, F., Reppin, J., Fischer, J., and Quadfasel, D.: Currents and transports of the Monsoon Current south of Sri Lanka, J. Geophys. Res., 99, 25127–25141, https://doi.org/10.1029/94jc02216, 1994.
Schott, F. A. and McCreary, J. P.: The monsoon circulation of the Indian Ocean, Prog. Oceanogr., 51, 1–123, https://doi.org/10.1016/S0079-6611(01)00083-0, 2001.
Sen, R., Pandey, S., Dandapat, S., Francis, P. A., and Chakraborty, A.: A numerical study on seasonal transport variability of the North Indian Ocean boundary currents using Regional Ocean Modeling System (ROMS), J. Oper. Oceanogr., 15, 32–51, https://doi.org/10.1080/1755876X.2020.1846266, 2022.
Shankar, D. and Shetye, S. R.: On the dynamics of the Lakshadweep high and low in the southeastern Arabian Sea, J. Geophys. Res.-Oceans, 102, 12551–12562, https://doi.org/10.1029/97JC00465, 1997.
Shankar, D., Vinayachandran, P. N., and Unnikrishnan, A. S.: The monsoon currents in the north Indian Ocean, Prog. Oceanogr., 52, 63–120, https://doi.org/10.1016/S0079-6611(02)00024-1, 2002.
Shenoi, S. S. C.: Differences in heat budgets of the near-surface Arabian Sea and Bay of Bengal: Implications for the summer monsoon, J. Geophys. Res., 107, 5-1–5-14, https://doi.org/10.1029/2000jc000679, 2002.
Shetye, S. R., Gouveia, A. D., Shenoi, S. S. C., Michael, G. S., Sundar, D., Almeida, A. M., and Santanam, K.: The coastal current off western India during the northeast monsoon, Deep-Sea Res., 38, 1517–1529, https://doi.org/10.1016/0198-0149(91)90087-V, 1991a.
Shetye, S. R., Shenoi, S. S. C., Gouveia, A. D., Michael, G. S., Sundar, D., and Nampoothiri, G.: Wind-driven coastal upwelling along the western boundary of the Bay of Bengal during the southwest monsoon, Cont. Shelf Res., 11, 1397–1408, https://doi.org/10.1016/0278-4343(91)90042-5, 1991b.
Shetye, S. R., Gouveia, A. D., Shankar, D., Shenoi, S. S. C., Vinayachandran, P. N., Sundar, D., Michael, G. S., and Nampoothiri, G.: Hydrography and circulation in the western Bay of Bengal during the northeast monsoon, J. Geophys. Res.-Oceans, 101, 14011–14025, https://doi.org/10.1029/95JC03307, 1996.
Shetye, S. R., Suresh, I., Shankar, D., Sundar, D., Jayakumar, S., Mehra, P., Prabhudesai, R. G., and Pednekar, P. S.: Observational evidence for remote forcing of the West India Coastal Current, J. Geophys. Res.-Oceans, 113, 1–10, https://doi.org/10.1029/2008JC004874, 2008.
Shi, W., Morrison, J. M., and Bryden, H. L.: Water, heat and freshwater flux out of the northern Indian Ocean in September–October 1995, Deep-Sea Res. Pt. II, 49, 1231–1252, https://doi.org/10.1016/S0967-0645(01)00154-0, 2002.
Srivastava, A., Dwivedi, S., and Mishra, A. K.: Intercomparison of High-Resolution Bay of Bengal Circulation Models Forced with Different Winds, Mar. Geod., 39, 271–289, https://doi.org/10.1080/01490419.2016.1173606, 2016.
Srivastava, A., Rao, S. A., and Ghosh, S.: Impact of Riverine Fresh Water on Indian Summer Monsoon: Coupling a Runoff Routing Model to a Global Seasonal Forecast Model, Frontiers in Climate, 4, 1–21, https://doi.org/10.3389/fclim.2022.902586, 2022.
Stammer, D., Wunsch, C., Giering, R., Eckert, C., Heimbach, P., Marotzke, J., Adcroft, A., Hill, C. N., and Marshall, J.: Volume, heat, and freshwater transports of the global ocean circulation 1993–2000, estimated from a general circulation model constrained by World Ocean Circulation Experiment (WOCE) data, J. Geophys. Res.-Oceans, 108, 7-1–7-23, https://doi.org/10.1029/2001jc001115, 2003.
Swapna, P., Jyoti, J., Krishnan, R., Sandeep, N., and Griffies, S. M.: Multidecadal Weakening of Indian Summer Monsoon Circulation Induces an Increasing Northern Indian Ocean Sea Level, Geophys. Res. Lett., 44, 10560–10572, https://doi.org/10.1002/2017GL074706, 2017.
Szekely, T., Gourrion, J., Pouliquen, S., and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation, Ocean Sci., 15, 1601–1614, https://doi.org/10.5194/os-15-1601-2019, 2019.
Szekely, T., Gourrion, J., Pouliquen, S., and Reverdin, G.: CORA, Coriolis Ocean Dataset for Reanalysis, SEANOE [data set], https://doi.org/10.17882/46219, 2024.
Tang, W., Fore, A., Yueh, S., Lee, T., Hayashi, A., Sanchez-Franks, A., Martinez, J., King, B., and Baranowski, D.: Validating SMAP SSS with in situ measurements, Remote Sens. Environ., 200, 326–340, https://doi.org/10.1016/j.rse.2017.08.021, 2017 (data available at: https://podaac.jpl.nasa.gov/dataset/SMAP_JPL_L3_SSS_CAP_MONTHLY_V5, last access: 25 October 2023).
UK Met Office: OSTIA L4 SST Analysis (GDS2), Ver. 2.0. PO.DAAC, CA, USA [data set], https://doi.org/10.5067/GHOST-4FK02, 2023.
Vinayachandran, P. N., Masumoto, Y., Mikawa, T., and Yamagata, T.: Intrusion of the southwest monsoon current into the Bay of Bengal, J. Geophys. Res.-Oceans, 104, 11077–11085, https://doi.org/10.1029/1999jc900035, 1999.
Wacongne, S. and Pacanowski, R.: Seasonal heat transport in a primitive equations model of the tropical Indian ocean, J. Phys. Oceanogr., 26, 2666–2699, https://doi.org/10.1175/1520-0485(1996)026<2666:SHTIAP>2.0.CO;2, 1996.
Weatherall, P., Tozer, B., Arndt, J. E., Bazhenova, E., Bringensparr, C., Castro, C., Dorschel, B., Ferrini, V., Hehemann, L., and Jakobsson, M.: The gebco_2020 grid – A continuous terrain model of the global oceans and land, British Oceanographic Data Centre, National Oceanography Centre, NERC, UK [data set], https://doi.org/10.5285/a29c5465-b138-234d-e053-6c86abc040b9, 2020.
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms for fluids, J. Comput. Phys., 31, 335–362, https://doi.org/10.1016/0021-9991(79)90051-2, 1979.
Zhang, Y. and Du, Y.: Seasonal variability of salinity budget and water exchange in the northern Indian Ocean from HYCOM assimilation, Chin. J. Oceanol. Limn., 30, 1082–1092, https://doi.org/10.1007/s00343-012-1284-7, 2012.
Zhang, Y., Du, Y., Jayarathna, W. N. D. S., Qiwei, S., Zhang, Y., Fengchao, Y., and Feng, M.: A prolonged high-salinity event in the northern Arabian sea during 2014–17, J. Phys. Oceanogr., 50, 849–865, https://doi.org/10.1175/JPO-D-19-0220.1, 2020.
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Antonov, J. I., Locarnini, R. A., Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K. W., Paver, C. R., and Smolyar, I. V.: World Ocean Atlas 2018, Vol. 2: Salinity, NOAA Atlas NESDIS, 82, https://doi.org/10.5285/a29c5465-b138-234d-e053-6c86abc040b9, 2019.
Short summary
Using a high-resolution model, we estimated the volume, freshwater, and heat transports along Indian coasts. Affected by coastal currents, transport along the eastern coast is highly seasonal, and the western coast is impacted by intraseasonal oscillations. Coastal currents and equatorial forcing determine the relation between NHT and net heat flux in dissipating heat in coastal waters. The north Indian Ocean functions as a heat source or sink based on seasonal flow of meridional heat transport.
Using a high-resolution model, we estimated the volume, freshwater, and heat transports along...