Articles | Volume 19, issue 6
https://doi.org/10.5194/os-19-1579-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-19-1579-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Nov 2023
Research article |
| 16 Nov 2023
| 16 Nov 2023
Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning
Yingjie Liu
CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Xiaofeng Li
CORRESPONDING AUTHOR
CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
Related authors
Qian Liu, Yingjie Liu, and Xiaofeng Li
Biogeosciences, 20, 4857–4874, https://doi.org/10.5194/bg-20-4857-2023, https://doi.org/10.5194/bg-20-4857-2023, 2023
Short summary
Short summary
In the Southern Ocean, abundant eddies behave opposite to our expectations. That is, anticyclonic (cyclonic) eddies are cold (warm). By investigating the variations of physical and biochemical parameters in eddies, we find that abnormal eddies have unique and significant effects on modulating the parameters. This study fills a gap in understanding the effects of abnormal eddies on physical and biochemical parameters in the Southern Ocean.
Yibin Ren, Xiaofeng Li, and Yunhe Wang
Geosci. Model Dev., 18, 2665–2678, https://doi.org/10.5194/gmd-18-2665-2025, https://doi.org/10.5194/gmd-18-2665-2025, 2025
Short summary
Short summary
This study developed a transformer-based deep learning model to predict the Arctic sea ice seasonally. By integrating the sea ice thickness data into the model, the spring prediction barrier of Arctic sea ice is optimized significantly. The model achieves better skills than the typical numerical model in predicting September’s sea ice extent seasonally. The sea ice thickness data play a key role in reducing the prediction errors of the Beaufort Sea, the East Siberian Sea and the Laptev Sea.
Xudong Zhang and Xiaofeng Li
Earth Syst. Sci. Data, 16, 5131–5144, https://doi.org/10.5194/essd-16-5131-2024, https://doi.org/10.5194/essd-16-5131-2024, 2024
Short summary
Short summary
Internal wave (IW) is an important ocean process and is frequently observed in the South China Sea (SCS). This study presents a detailed IW dataset for the northern SCS spanning from 2000 to 2022, with a spatial resolution of 250 m, comprising 3085 IW MODIS images. This dataset can enhance understanding of IW dynamics and serve as a valuable resource for studying ocean dynamics, validating numerical models, and advancing AI-driven model building, fostering further exploration into IW phenomena.
Le Gao, Yuan Guo, and Xiaofeng Li
Earth Syst. Sci. Data, 16, 4189–4207, https://doi.org/10.5194/essd-16-4189-2024, https://doi.org/10.5194/essd-16-4189-2024, 2024
Short summary
Short summary
Since 2008, the Yellow Sea has faced a significant ecological issue, the green tide, which has become one of the world's largest marine disasters. Satellite remote sensing plays a pivotal role in detecting this phenomenon. This study uses AI-based models to extract the daily green tide from MODIS and SAR images and integrates these daily data to introduce a continuous weekly dataset, which aids research in disaster simulation, forecasting, and prevention.
Qian Liu, Yingjie Liu, and Xiaofeng Li
Biogeosciences, 20, 4857–4874, https://doi.org/10.5194/bg-20-4857-2023, https://doi.org/10.5194/bg-20-4857-2023, 2023
Short summary
Short summary
In the Southern Ocean, abundant eddies behave opposite to our expectations. That is, anticyclonic (cyclonic) eddies are cold (warm). By investigating the variations of physical and biochemical parameters in eddies, we find that abnormal eddies have unique and significant effects on modulating the parameters. This study fills a gap in understanding the effects of abnormal eddies on physical and biochemical parameters in the Southern Ocean.
Related subject area
Approach: Remote Sensing | Properties and processes: Mesoscale to submesoscale dynamics
Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF)
Sargassum accumulation and transport by mesoscale eddies
Blending 2D topography images from the Surface Water and Ocean Topography (SWOT) mission into the altimeter constellation with the Level-3 multi-mission Data Unification and Altimeter Combination System (DUACS)
Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms
Integrating wide-swath altimetry data into Level-4 multi-mission maps
Monitoring the coastal–offshore water interactions in the Levantine Sea using ocean color and deep supervised learning
Multiple timescale variations in fronts in the Seto Inland Sea, Japan
MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion
Advances in Surface Water and Ocean Topography for Fine-Scale Eddy Identification from Altimeter Sea Surface Height Merging Maps
Enhanced resolution capability of SWOT sea surface height measurements and its application in monitoring ocean dynamics variability
Deep learning for the super resolution of Mediterranean sea surface temperature fields
Regional mapping of energetic short mesoscale ocean dynamics from altimetry: performances from real observations
Ocean 2D eddy energy fluxes from small mesoscale processes with SWOT
Aida Alvera-Azcárate, Dimitry Van der Zande, Alexander Barth, Antoine Dille, Joppe Massant, and Jean-Marie Beckers
Ocean Sci., 21, 787–805, https://doi.org/10.5194/os-21-787-2025, https://doi.org/10.5194/os-21-787-2025, 2025
Short summary
Short summary
This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.
Rosmery Sosa-Gutierrez, Julien Jouanno, and Leo Berline
EGUsphere, https://doi.org/10.5194/egusphere-2025-514, https://doi.org/10.5194/egusphere-2025-514, 2025
Short summary
Short summary
Since 2010, pelagic Sargassum blooms have increased in several tropical Atlantic regions, causing socioeconomic and ecosystem impacts. Offshore the structuration of Sargassum by the mesoscale dynamics may influence transport and growth. Sargassum, stays afloat, constantly interacting with currents, waves, winds, and mesoscale eddies. We find that anticyclonic and cyclonic effectively trap Sargassum throughout their propagation, with a greater tendency for cyclones to accumulate Sargassum.
Gerald Dibarboure, Cécile Anadon, Frédéric Briol, Emeline Cadier, Robin Chevrier, Antoine Delepoulle, Yannice Faugère, Alice Laloue, Rosemary Morrow, Nicolas Picot, Pierre Prandi, Marie-Isabelle Pujol, Matthias Raynal, Anaelle Tréboutte, and Clément Ubelmann
Ocean Sci., 21, 283–323, https://doi.org/10.5194/os-21-283-2025, https://doi.org/10.5194/os-21-283-2025, 2025
Short summary
Short summary
The Surface Water and Ocean Topography (SWOT) mission delivers unprecedented swath-altimetry products. In this paper, we describe how we extended the Level-3 algorithms to handle SWOT’s unique swath-altimeter data. We also illustrate and discuss the benefits, relevance, and limitations of Level-3 swath-altimeter products for various research domains.
Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli
Ocean Sci., 21, 199–216, https://doi.org/10.5194/os-21-199-2025, https://doi.org/10.5194/os-21-199-2025, 2025
Short summary
Short summary
Ocean surface currents are routinely derived from satellite observations of the sea level, allowing regional- to global-scale synoptic monitoring. In order to overcome the theoretical and instrumental limits of this methodology, we exploit the synergy of multi-sensor satellite observations. We rely on deep learning, physics-informed algorithms to predict ocean currents from sea surface height and sea surface temperature observations. Results are validated by means of in situ measurements.
Maxime Ballarotta, Clément Ubelmann, Valentin Bellemin-Laponnaz, Florian Le Guillou, Guillaume Meda, Cécile Anadon, Alice Laloue, Antoine Delepoulle, Yannice Faugère, Marie-Isabelle Pujol, Ronan Fablet, and Gérald Dibarboure
Ocean Sci., 21, 63–80, https://doi.org/10.5194/os-21-63-2025, https://doi.org/10.5194/os-21-63-2025, 2025
Short summary
Short summary
The Surface Water and Ocean Topography (SWOT) mission provides unprecedented swath altimetry data. This study examines SWOT's impact on mapping systems, showing a moderate effect with the current nadir altimetry constellation and a stronger impact with a reduced one. Integrating SWOT with dynamic mapping techniques improves the resolution of satellite-derived products, offering promising solutions for studying and monitoring sea-level variability at finer scales.
Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
Ocean Sci., 20, 1707–1720, https://doi.org/10.5194/os-20-1707-2024, https://doi.org/10.5194/os-20-1707-2024, 2024
Short summary
Short summary
Understanding the flow of the Levantine Sea surface current is not straightforward. We propose a study based on learning techniques to follow interactions between water near the shore and further out at sea. Our results show changes in the coastal currents past 33.8° E, with frequent instances of water breaking away along the Lebanese coast. These events happen quickly and sometimes lead to long-lasting eddies. This study underscores the need for direct observations to improve our knowledge.
Menghong Dong and Xinyu Guo
Ocean Sci., 20, 1527–1546, https://doi.org/10.5194/os-20-1527-2024, https://doi.org/10.5194/os-20-1527-2024, 2024
Short summary
Short summary
We employed a gradient-based algorithm to identify the position and intensity of the fronts in a coastal sea using sea surface temperature data, thereby quantifying their variations. Our study provides a comprehensive analysis of these fronts, elucidating their seasonal variability, intra-tidal dynamics, and the influence of winds on the fronts. By capturing the temporal and spatial dynamics of these fronts, our understanding of the complex oceanographic processes within this region is enhanced.
Edwin Goh, Alice Yepremyan, Jinbo Wang, and Brian Wilson
Ocean Sci., 20, 1309–1323, https://doi.org/10.5194/os-20-1309-2024, https://doi.org/10.5194/os-20-1309-2024, 2024
Short summary
Short summary
An AI model was used to fill in missing parts of sea temperature (SST) maps caused by cloud cover. We found masked autoencoders can recreate missing SSTs with less than 0.2 °C error, even when 80 % are missing. This is 5000 times faster than conventional methods tested on a single central processing unit. This can enhance our ability in monitoring global small-scale ocean fronts that affect heat, carbon, and nutrient exchange in the ocean. The method is promising for future research.
Xiaoya Zhang, Lei Liu, Jianfang Fei, Zhijin Li, Zexun Wei, Zhiwei Zhang, Xingliang Jiang, Zexin Dong, and Feng Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2773, https://doi.org/10.5194/egusphere-2024-2773, 2024
Short summary
Short summary
Our research evaluated the precision of mapping the ocean's surface with combined data from a couple of satellites, focusing on dynamic aspects revealed by sea level changes. Results show that 2DVAR, a new mapping product, aligns more closely and with less error with the most advanced satellite detailed observations than a widely used mapping product called AVISO. The results suggest that 2DVAR better detects minor ocean movements, making it more valuable and reliable for ocean dynamics study.
Yong Wang, Shengjun Zhang, and Yongjun Jia
EGUsphere, https://doi.org/10.5194/egusphere-2024-3005, https://doi.org/10.5194/egusphere-2024-3005, 2024
Short summary
Short summary
The present study explores the capabilities of four satellite missions in assessing the true resolution of the sea surface. A new weighted averaging method is introduced in the analysis of global sea surface height slope maps. The results show that SWOT significantly improves the accuracy and mesoscale resolution capability. Using the correlation method of mutual power spectra, we define a new parameter, ocean dynamics scale variability, and apply this parameter to the global ocean.
Claudia Fanelli, Daniele Ciani, Andrea Pisano, and Bruno Buongiorno Nardelli
Ocean Sci., 20, 1035–1050, https://doi.org/10.5194/os-20-1035-2024, https://doi.org/10.5194/os-20-1035-2024, 2024
Short summary
Short summary
Sea surface temperature (SST) is an essential variable to understanding the Earth's climate system, and its accurate monitoring from space is essential. Since satellite measurements are hindered by cloudy/rainy conditions, data gaps are present even in merged multi-sensor products. Since optimal interpolation techniques tend to smooth out small-scale features, we developed a deep learning model to enhance the effective resolution of gap-free SST images over the Mediterranean Sea to address this.
Florian Le Guillou, Lucile Gaultier, Maxime Ballarotta, Sammy Metref, Clément Ubelmann, Emmanuel Cosme, and Marie-Helène Rio
Ocean Sci., 19, 1517–1527, https://doi.org/10.5194/os-19-1517-2023, https://doi.org/10.5194/os-19-1517-2023, 2023
Short summary
Short summary
Altimetry provides sea surface height (SSH) data along one-dimensional tracks. For many applications, the tracks are interpolated in space and time to provide gridded SSH maps. The operational SSH gridded products filter out the small-scale signals measured on the tracks. This paper evaluates the performances of a recently implemented dynamical method to retrieve the small-scale signals from real SSH data. We show a net improvement in the quality of SSH maps when compared to independent data.
Elisa Carli, Rosemary Morrow, Oscar Vergara, Robin Chevrier, and Lionel Renault
Ocean Sci., 19, 1413–1435, https://doi.org/10.5194/os-19-1413-2023, https://doi.org/10.5194/os-19-1413-2023, 2023
Short summary
Short summary
Oceanic eddies are the structures carrying most of the energy in our oceans. They are key to climate regulation and nutrient transport. We prepare for the Surface Water and Ocean Topography mission, studying eddy dynamics in the region south of Africa, where the Indian and Atlantic oceans meet, using models and simulated satellite data. SWOT will provide insights into the structures smaller than what is currently observable, which appear to greatly contribute to eddy kinetic energy and strain.
Cited articles
Assassi, C., Morel, Y., Vandermeirsch, F., Chaigneau, A., Pegliasco, C., Morrow, R., Colas, F., Fleury, S., Carton, X., and Klein, P.: An index to distinguish surface- and subsurface-intensified vortices from surface observations, J. Phys. Oceanogr., 46, 2529–2552, https://doi.org/10.1175/JPO-D-15-0122.1, 2016.
Babu, M. T., Kumar, S. P., and Rao, D. P.: A subsurface cyclonic eddy in the Bay of Bengal, J. Mar. Res., 49, 403–410, 1991.
Badin, G., Tandon, A., and Mahadevan, A.: Lateral mixing in the pycnocline by baroclinic mixed layer eddies, J. Phys. Oceanogr., 41, 2080–2101, https://doi.org/10.1175/JPO-D-11-05.1, 2011.
Banse, K. and English, D.: Geographical differences in seasonality of CZCS-derived phytoplankton pigment in the Arabian Sea for 1978–1986, Deep-Sea Res. Pt. II, 47, 1623–1677, https://doi.org/10.1016/S0967-0645(99)00157-5, 2000.
Bartier, P. M. and Keller, C. P.: Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW), Comput. Geosci., 22, 795–799, https://doi.org/10.1016/0098-3004(96)00021-0, 1996.
Bashmachnikov, I., Boutov, D., and Dias, J.: Manifestation of two meddies in altimetry and sea-surface temperature, Ocean Sci., 9, 249–259, https://doi.org/10.5194/os-9-249-2013, 2013.
Bisson, K., Boss, E., Westberry, T., and Behrenfeld, M.: Evaluating satellite estimates of particulate backscatter in the global open ocean using autonomous profiling floats, Opt. Express, 27, 30191–30203, https://doi.org/10.1364/OE.27.030191, 2019.
Bôas, A. B. V., Sato, O. T., Chaigneau, A., and Castelão, G. P.: The signature of mesoscale eddies on the air-sea turbulent heat fluxes in the South Atlantic Ocean, Geophys. Res. Lett., 42, 1856–1862, https://doi.org/10.1002/2015GL063105, 2015.
Brock, J. C., McClain, C. R., Luther, M. E., and Hay, W. W.: The phytoplankton bloom in the northwestern Arabian Sea during the southwest monsoon of 1979, J. Geophys. Res.-Oceans, 96, 20623–20642, https://doi.org/10.1029/91JC01711, 1991.
Caballero, A., Pascual, A., Dibarboure, G., and Espino, M.: Sea level and Eddy Kinetic Energy variability in the Bay of Biscay, inferred from satellite altimeter data, J. Marine Syst., 72, 116–134, https://doi.org/10.1016/j.jmarsys.2007.03.011, 2008.
Chelton, D. B., Gaube, P., Schlax, M. G., Early, J. J., and Samelson, R. M.: The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll, Science, 334, 328–332, https://doi.org/10.1126/science.1208897, 2011a.
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, https://doi.org/10.1016/j.pocean.2011.01.002, 2011b.
Chen, G. and Han, G.: Contrasting short–lived with long–lived mesoscale eddies in the global ocean, J. Geophys. Res.-Oceans, 124, 3149–3167, https://doi.org/10.1029/2019JC014983, 2019.
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.
Chen, G., Han, G., and Yang, X.: On the intrinsic shape of oceanic eddies derived from satellite altimetry, Remote Sens. Environ., 228, 75–89, https://doi.org/10.1016/j.rse.2019.04.011, 2019.
Chen, G., Yang, J., and Han, G.: Eddy morphology: Egg-like shape, overall spinning, and oceanographic implications, Remote Sens. Environ., 257, 112348, https://doi.org/10.1016/j.rse.2021.112348, 2021.
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.
Cui, W., Yang, J., and Ma, Y.: A statistical analysis of mesoscale eddies in the Bay of Bengal from 22 year altimetry data, Acta Oceanol. Sin., 35, 16–27, https://doi.org/10.1007/s13131-016-0945-3, 2016.
Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., and Ayed, I. B.: HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation, IEEE T. Med. Imaging, 38, 1116–1126, https://doi.org/10.1109/TMI.2018.2878669, 2018.
Dong, D., Brandt, P., Chang, P., Schütte, F., Yang, X., Yan, J., and Zeng, J.: Mesoscale Eddies in the Northwestern Pacific Ocean: Three-Dimensional Eddy Structures and Heat/Salt Transports, J. Geophys. Res.-Oceans, 122, 9795–9813, https://doi.org/10.1002/2017jc013303, 2017.
Faghmous, J. H., Frenger, I., Yao, Y., Warmka, R., Lindell, A., and Kumar, V.: A daily global mesoscale ocean eddy dataset from satellite altimetry, Scientific Data, 2, 150028, https://doi.org/10.1038/sdata.2015.28, 2015.
Findlater, J.: A major low-level air current near the Indian Ocean during the northern summer, Q. J. Roy. Meteor. Soc., 95, 362–380, https://doi.org/10.1002/qj.49709540409, 1969.
Garfield, N., Collins, C. A., Paquette, R. G., and Carter, E.: Lagrangian exploration of the California Undercurrent, 1992–95, J. Phys. Oceanogr., 29, 560–583, 1999.
Gaube, P., Chelton, D. B., Strutton, P. G., and Behrenfeld, M. J.: Satellite observations of chlorophyll, phytoplankton biomass, and Ekman pumping in nonlinear mesoscale eddies, J. Geophys. Res.-Oceans, 118, 6349–6370, https://doi.org/10.1002/2013jc009027, 2013.
Gaube, P., McGillicuddy, D. J., Chelton, D. B., Behrenfeld, M. J., and Strutton, P. G.: Regional variations in the influence of mesoscale eddies on near-surface chlorophyll, J. Geophys. Res.-Oceans, 119, 8195–8220, https://doi.org/10.1002/2014JC010111, 2014.
Gaube, P., Chelton, D. B., Samelson, R. M., Schlax, M. G., and O'Neill, L. W.: Satellite observations of mesoscale eddy-Induced Ekman pumping, J. Phys. Oceanogr., 45, 104–132, https://doi.org/10.1175/jpo-d-14-0032.1, 2015.
Greaser, S. R., Subrahmanyam, B., Trott, C. B., and Roman-Stork, H. L.: Interactions between mesoscale eddies and synoptic oscillations in the Bay of Bengal during the strong monsoon of 2019, J. Geophys. Res.-Oceans, 125, e2020JC016772, https://doi.org/10.1029/2020JC016772, 2020.
Gulakaram, V. S., Vissa, N. K., and Bhaskaran, P. K.: Characteristics and vertical structure of oceanic mesoscale eddies in the Bay of Bengal, Dynam. Atmos. Oceans, 89, 101131, https://doi.org/10.1016/j.dynatmoce.2020.101131, 2020.
Haëntjens, N., Della Penna, A., Briggs, N., Karp-Boss, L., Gaube, P., Claustre, H., and Boss, E.: Detecting mesopelagic organisms using biogeochemical-Argo floats, Geophys. Res. Lett., 47, e2019GL086088, https://doi.org/10.1029/2019GL086088, 2020.
Ham, Y. G., Kim, J. H., and Luo, J. J.: Deep learning for multi-year ENSO forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019.
Holte, J. and Talley, L.: A New Algorithm for Finding Mixed Layer Depths with Applications to Argo Data and Subantarctic Mode Water Formation, J. Atmos. Ocean. Tech., 26, 1920–1939, https://doi.org/10.1175/2009jtecho543.1, 2009.
Hsu, P.-C., Lin, C.-C., Huang, S.-J., and Ho, C.-R.: Effects of Cold Eddy on Kuroshio Meander and Its Surface Properties, East of Taiwan, IEEE J. Sel. Top. Appl., 9, 5055–5063, https://doi.org/10.1109/jstars.2016.2524698, 2016.
Jiang, H., Song, Y., Mironov, A., Yang, Z., Xu, Y., and Liu, J.: Accurate mean wave period from SWIM instrument on-board CFOSAT, Remote Sens. Environ., 280, 113149, https://doi.org/10.1016/j.rse.2022.113149, 2022.
Karstensen, J., Schütte, F., Pietri, A., Krahmann, G., Fiedler, B., Grundle, D., Hauss, H., Körtzinger, A., Löscher, C. R., Testor, P., Vieira, N., and Visbeck, M.: Upwelling and isolation in oxygen-depleted anticyclonic modewater eddies and implications for nitrate cycling, Biogeosciences, 14, 2167–2181, https://doi.org/10.5194/bg-14-2167-2017, 2017.
Klemas, V. and Yan, X.-H.: Subsurface and deeper ocean remote sensing from satellites: An overview and new results, Prog. Oceanogr., 122, 1–9, https://doi.org/10.1016/j.pocean.2013.11.010, 2014.
Kumar, S. P., Madhupratap, M., Kumar, M. D., Muraleedharan, P., De Souza, S., Gauns, M., and Sarma, V.: High biological productivity in the central Arabian Sea during the summer monsoon driven by Ekman pumping and lateral advection, Curr. Sci. India, 81, 1633–1638, 2001.
Kumar, S. P., Muraleedharan, P. M., Prasad, T. G., Gauns, M., Ramaiah, N., de Souza, S. N., Sardesai, S., and Madhupratap, M.: Why is the Bay of Bengal less productive during summer monsoon compared to the Arabian Sea?, Geophys. Res. Lett., 29, 88-1–88-4, https://doi.org/10.1029/2002gl016013, 2002.
Lecun, Y., Bengio, Y., and Hinton, G. E.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Ledwell, J. R., McGillicuddy Jr., D. J., and Anderson, L. A.: Nutrient flux into an intense deep chlorophyll layer in a mode-water eddy, Deep-Sea Res. Pt. II, 55, 1139–1160, 2008.
Lee, C. M., Jones, B. H., Brink, K. H., and Fischer, A. S.: The upper-ocean response to monsoonal forcing in the Arabian Sea: seasonal and spatial variability, Deep-Sea Res. Pt. II, 47, 1177–1226, 2000.
Lehahn, Y., d'Ovidio, F., Levy, M., Amitai, Y., and Heifetz, E.: Long range transport of a quasi isolated chlorophyll patch by an Agulhas ring, Geophys. Res. Lett., 38, L16610, https://doi.org/10.1029/2011gl048588, 2011.
Liu, Y., Chen, G., Sun, M., Liu, S., and Tian, F.: A parallel SLA-based algorithm for global mesoscale eddy identification, J. Atmos. Ocean. Tech., 33, 2743–2754, https://doi.org/10.1175/JTECH-D-16-0033.1, 2016.
Liu, Y. and Li, X.: A deep learning-based surface- and subsurface-intensified eddy detection model, figshare [data set], https://doi.org/10.6084/m9.figshare.23599473, 2023a.
Liu, Y. and Li, X.: Eddy-induced Chlorophyll-a Variations in the Northern Indian Ocean, figshare [code], https://doi.org/10.6084/m9.figshare.23599683, 2023b.
Lu, W., Su, H., Yang, X., and Yan, X.-H.: Subsurface temperature estimation from remote sensing data using a clustering-neural network method, Remote Sens. Environ., 229, 213–222, https://doi.org/10.1016/j.rse.2019.04.009, 2019.
Maritorena, S., d'Andon, O. H. F., Mangin, A., and Siegel, D. A.: Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues, Remote Sens. Environ., 114, 1791–1804, https://doi.org/10.1016/j.rse.2010.04.002, 2010.
Mcdougall, T. J. and Barker, P. M.: Getting started with TEOS-10 and the Gibbs Seawater (GSW) Oceanographic Toolbox, SCOR/IAPSO WG, 127, 1–28, 2011.
McGillicuddy Jr., D. J.: Formation of Intrathermocline Lenses by Eddy-Wind Interaction, J. Phys. Oceanogr., 45, 606–612, https://doi.org/10.1175/jpo-d-14-0221.1, 2015.
McGillicuddy Jr., D. J., Anderson, L. A., Bates, N. R., Bibby, T., Buesseler, K. O., Carlson, C. A., Davis, C. S., Ewart, C., Falkowski, P. G., and Goldthwait, S. A.: Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms, Science, 316, 1021–1026, 2007.
Meunier, T., Tenreiro, M., Pallàs-Sanz, E., Ochoa, J., Ruiz-Angulo, A., Portela, E., Cusí, S., Damien, P., and Carton, X.: Intrathermocline eddies embedded within an anticyclonic vortex ring, Geophys. Res. Lett., 45, 7624–7633, https://doi.org/10.1029/2018GL077527, 2018.
Mignot, A., Claustre, H., Uitz, J., Poteau, A., d'Ortenzio, F., and Xing, X.: Understanding the seasonal dynamics of phytoplankton biomass and the deep chlorophyll maximum in oligotrophic environments: A Bio-Argo float investigation, Global Biogeochem. Cy., 28, 856–876, https://doi.org/10.1002/2013GB004781, 2014.
Paillet, J., Le Cann, B., Carton, X., Morel, Y., and Serpette, A.: Dynamics and evolution of a northern meddy, J. Phys. Oceanogr., 32, 55–79, 2002.
Piontkovski, S., Al-Azri, A., and Al-Hashmi, K.: Seasonal and interannual variability of chlorophyll a in the Gulf of Oman compared to the open Arabian Sea regions, Int. J. Remote Sens., 32, 7703–7715, https://doi.org/10.1080/01431161.2010.527393, 2011.
Prasad, T. G.: A comparison of mixed-layer dynamics between the Arabian Sea and Bay of Bengal: One-dimensional model results, J. Geophys. Res.-Oceans, 109, C03035, https://doi.org/10.1029/2003jc002000, 2004.
Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C., Ablain, M., and Picot, N.: DUACS DT2014: the new multi-mission altimeter data set reprocessed over 20 years, Ocean Sci., 12, 1067–1090, https://doi.org/10.5194/os-12-1067-2016, 2016.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G.: Daily high-resolution-blended analyses for sea surface temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1, 2007.
Roesler, C., Uitz, J., Claustre, H., Boss, E., Xing, X., Organelli, E., Briggs, N., Bricaud, A., Schmechtig, C., and Poteau, A.: Recommendations for obtaining unbiased chlorophyll estimates from in situ chlorophyll fluorometers: A global analysis of WET Labs ECO sensors, Limnol. Oceanogr., 15, 572–585, https://doi.org/10.1002/lom3.10185, 2017.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, Cham, Springer International Publishing, 234–241, https://doi.org/10.48550/arXiv.1505.04597, 2015.
Shafeeque, M., Balchand, A. N., Shah, P., George, G., B. R, S., Varghese, E., Joseph, A. K., Sathyendranath, S., and Platt, T.: Spatio-temporal variability of chlorophyll a in response to coastal upwelling and mesoscale eddies in the South Eastern Arabian Sea, Int. J. Remote Sens., 42, 4836–4863, https://doi.org/10.1080/01431161.2021.1899329, 2021.
Siegel, D. A., Peterson, P., McGillicuddy, D. J., Maritorena, S., and Nelson, N. B.: Bio-optical footprints created by mesoscale eddies in the Sargasso Sea, Geophys. Res. Lett., 38, L13608, https://doi.org/10.1029/2011gl047660, 2011.
Smitha, B., Sanjeevan, V., Padmakumar, K., Hussain, M. S., Salini, T., and Lix, J.: Role of mesoscale eddies in the sustenance of high biological productivity in North Eastern Arabian Sea during the winter-spring transition period, Sci. Total Environ., 809, 151173, https://doi.org/10.1016/j.scitotenv.2021.151173, 2022.
Somayajulu, Y. K., Murty, V. S. N., and Sarma, Y. V. B.: Seasonal and inter-annual variability of surface circulation in the Bay of Bengal from TOPEX/Poseidon altimetry, Deep-Sea Res. Pt. II, 50, 867–880, https://doi.org/10.1016/S0967-0645(02)00610-0, 2003.
Su, H., Wu, X., Yan, X.-H., and Kidwell, A.: Estimation of subsurface temperature anomaly in the Indian Ocean during recent global surface warming hiatus from satellite measurements: A support vector machine approach, Remote Sens. Environ., 160, 63–71, https://doi.org/10.1016/j.rse.2015.01.001, 2015.
Su, H., Zhang, T., Lin, M., Lu, W., and Yan, X.-H.: Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks, Remote Sens. Environ., 260, 112465, https://doi.org/10.1016/j.rse.2021.112465, 2021.
Su, J., Strutton, P. G., and Schallenberg, C.: The subsurface biological structure of Southern Ocean eddies revealed by BGC-Argo floats, J. Marine Syst., 220, 103569, https://doi.org/10.1016/j.jmarsys.2021.103569, 2021.
Sun, B., Liu, C., and Wang, F.: Global meridional eddy heat transport inferred from Argo and altimetry observations, Sci. Rep.-UK, 9, 1345, https://doi.org/10.1038/s41598-018-38069-2, 2019.
Thomas, L. N.: Formation of intrathermocline eddies at ocean fronts by wind-driven destruction of potential vorticity, Dynam. Atmos. Oceans, 45, 252–273, https://doi.org/10.1016/j.dynatmoce.2008.02.002, 2008.
Trott, C. B., Subrahmanyam, B., Chaigneau, A., and Delcroix, T.: Eddy tracking in the northwestern Indian Ocean during southwest monsoon regimes, Geophys. Res. Lett., 45, 6594–6603, https://doi.org/10.1029/2018gl078381, 2018.
Trott, C. B., Subrahmanyam, B., Chaigneau, A., and Roman-Stork, H. L.: Eddy-induced temperature and salinity variability in the Arabian Sea, Geophys. Res. Lett., 46, 2734–2742, https://doi.org/10.1029/2018GL081605, 2019.
Wang, Z.-F., Sun, L., Li, Q.-Y., and Cheng, H.: Two typical merging events of oceanic mesoscale anticyclonic eddies, Ocean Sci., 15, 1545–1559, https://doi.org/10.5194/os-15-1545-2019, 2019.
Yang, G., Wang, F., Li, Y., and Lin, P.: Mesoscale eddies in the northwestern subtropical Pacific Ocean: Statistical characteristics and three-dimensional structures, J. Geophys. Res.-Oceans, 118, 1906–1925, https://doi.org/10.1002/jgrc.20164, 2013.
Yang, X., Xu, G., Liu, Y., Sun, W., Xia, C., and Dong, C.: Multi-Source Data Analysis of Mesoscale Eddies and Their Effects on Surface Chlorophyll in the Bay of Bengal, Remote Sens.-Basel, 12, 3485, https://doi.org/10.3390/rs12213485, 2020.
Zhan, P., Guo, D., and Hoteit, I.: Eddy-induced transport and kinetic energy budget in the Arabian Sea, Geophys. Res. Lett., 47, e2020GL090490, https://doi.org/10.1029/2020GL090490, 2020.
Zhang, Z., Qiu, B., Klein, P., and Travis, S.: The influence of geostrophic strain on oceanic ageostrophic motion and surface chlorophyll, Nat. Commun., 10, 2838, https://doi.org/10.1038/s41467-019-10883-w, 2019.
Short summary
The study developed a deep learning model that effectively distinguishes between surface- and subsurface-intensified eddies in the northern Indian Ocean by integrating sea surface height and temperature data. The accurate distinction between these types of eddies provides valuable insights into their dynamics and their impact on marine ecosystems in the northern Indian Ocean and contributes to understanding the complex interactions between eddy dynamics and biogeochemical processes in the ocean.
The study developed a deep learning model that effectively distinguishes between surface- and...
