A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal

Wentao JIA; Xun GONG; Shanliang ZHU; Jifeng QI; Xianmei ZHOU; Hengkai YAO; Xiang GONG; Wenwu WANG

doi:10.1007/s00343-024-4122-9

Your Location：

Home >

Browse articles >

A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal

Physics | Updated：2025-08-07

- A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal
- A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal
- 海洋湖沼学报(英文) 2025年43卷第4期页码：1075-1092
- Affiliations：
  
  1.School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
  2.Institute for Advanced Marine Research, China University of Geosciences, Guangzhou 511462, China
  3.State Key Laboratory of Biogeology and Environmental Geology, Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China
  4.Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
  5.Qingdao Innovation Center of Artificial Intelligence Ocean Technology, Qingdao 266061, China
  6.Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  7.University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  zhushanliang@qust.edu.cn
  jfqi@qdio.ac.cn
- Funds：
  
  the Ministry of Science and Technology of the People’s Republic of China(2019YFE0125000);the National Natural Science Foundation of China(42376032)
- DOI：10.1007/s00343-024-4122-9
  中图分类号：
- 收稿：2024-05-09，
  
  纸质出版：2025-07-01
- Accepted：
Scan QR Code
A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal[J]. 海洋湖沼学报(英文), 2025,43(4):1075-1092.

JIA Wentao,GONG Xun,ZHU Shanliang,et al.A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal[J].Journal of Oceanology and Limnology,2025,43(04):1075-1092.
A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal[J]. 海洋湖沼学报(英文), 2025,43(4):1075-1092. DOI： 10.1007/s00343-024-4122-9.

JIA Wentao,GONG Xun,ZHU Shanliang,et al.A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal[J].Journal of Oceanology and Limnology,2025,43(04):1075-1092. DOI： 10.1007/s00343-024-4122-9.

摘要

Abstract

Variations in ocean mixed layer depth (MLD) show a significant impact on energy balance in the global climate systems and marine ecosystems. At present

the accuracy of modeling MLD

especially in the region with complex ocean dynamics

remains a challenge

thus calling for an emergency using artificial intelligence approach to improve the assessment of the MLD. A novel convolutional neural network model was developed based on a dual-attention module (DA-CNN) to estimate the MLD in the Bay of Bengal (BoB) by integrating multi-source remote sensing data and Argo gridded data. Compared with the original CNN model

the DA-CNN model exhibits superior performance with notable improvements in the annual average root mean square error (RMSE) and

values by 13.0% and 8.4%

respectively

while more accurately capturing the seasonal variations in MLD. Moreover

the results using the DA-CNN model show minimum RMSE and maximum

values

in comparison to the calculation by the random forest

artificial neural network model

and the hybrid coordinate ocean model. Accordingly

our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.

关键词

Keywords

references

Akhil V P , Vialard J , Lengaigne M et al . 2020 . Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing. Remote Sensing of Environment , 248 : 111964 , https://doi.org/10.1016/j.rse.2020.111964 https://doi.org/10.1016/j.rse.2020.111964 .

Atlas R , Hoffman R N , Ardizzone J et al . 2011 . A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications . Bulletin of the American Meteorological Society , 92 ( 2 ): 157 - 174 , https://doi.org/10.1175/2010BAMS2946.1 https://doi.org/10.1175/2010BAMS2946.1 .

Balaguru K , Chang P , Saravanan R et al . 2012 . Ocean barrier layers’ effect on tropical cyclone intensification . Proceedings of the National Academy of Sciences of the United States of America , 109 ( 36 ): 14343 - 14347 , https://doi.org/10.1073/pnas.1201364109 https://doi.org/10.1073/pnas.1201364109 .

Banzon V , Smith T M , Chin T M et al . 2016 . A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies . Earth System Science Data , 8 ( 1 ): 165 - 176 , https://doi.org/10.5194/essd-8-165-2016 https://doi.org/10.5194/essd-8-165-2016 .

Boutin J , Vergely J L , Marchand S et al . 2018 . New SMOS Sea Surface Salinity with reduced systematic errors and improved variability . Remote Sensing of Environment , 214 : 115 - 134 , https://doi.org/10.1016/j.rse.2018.05.022 https://doi.org/10.1016/j.rse.2018.05.022 .

Courtois P , Hu X M , Pennelly C et al . 2017 . Mixed layer depth calculation in deep convection regions in ocean numerical models . Ocean Modelling , 120 : 60 - 78 , https://doi.org/10.1016/j.ocemod.2017.10.007 https://doi.org/10.1016/j.ocemod.2017.10.007 .

Dall’Olmo G , Dingle J , Polimene L et al . 2016 . Substantial energy input to the mesopelagic ecosystem from the seasonal mixed-layer pump . Nature Geoscience , 9 ( 11 ): 820 - 823 , https://doi.org/10.1038/ngeo2818 https://doi.org/10.1038/ngeo2818 .

de Fommervault O P , Perez-Brunius P , Damien P et al . 2017 . Temporal variability of chlorophyll distribution in the Gulf of Mexico: bio-optical data from profiling floats . Biogeosciences , 14 ( 24 ): 5647 - 5662 , https://doi.org/10.5194/bg-14-5647-2017 https://doi.org/10.5194/bg-14-5647-2017 .

Diaz B P , Knowles B , Johns C T et al . 2021 . Seasonal mixed layer depth shapes phytoplankton physiology, viral production, and accumulation in the North Atlantic . Nature Communications , 12 ( 1 ): 6634 , https://doi.org/10.1038/s41467-021-26836-1 https://doi.org/10.1038/s41467-021-26836-1 .

Dickey T , Granata T , Marra J et al . 1993 . Seasonal variability of bio-optical and physical properties in the Sargasso Sea . Journal of Geophysical Research : Oceans , 98 ( C1 ): 865 - 898 , https://doi.org/10.1029/92JC01830 https://doi.org/10.1029/92JC01830 .

Du Y , Zhang Y H , Zhang L Y et al . 2020 . Thermocline warming induced extreme Indian Ocean dipole in 2019 . Geophysical Research Letters , 47 ( 18 ): e2020 GL 090079 , https://doi.org/10.1029/2020GL090079 https://doi.org/10.1029/2020GL090079 .

Duerr A E S , Dhanak M R , Van Zwieten J H . 2012 . Utilizing the hybrid coordinate ocean model for the assessment of Florida Current’s hydrokinetic renewable energy resource . Marine Technology Society Journal , 46 ( 5 ): 24 - 33 , https://doi.org/10.4031/MTSJ.46.5.2 https://doi.org/10.4031/MTSJ.46.5.2 .

Dwivedi S , Srivastava A , Mishra A K . 2018 . Upper ocean four-dimensional variational data assimilation in the Arabian Sea and Bay of Bengal . Marine Geodesy , 41 ( 3 ): 230 - 257 , https://doi.org/10.1080/01490419.2017.1405128 https://doi.org/10.1080/01490419.2017.1405128 .

Fiedler P C . 1988 . Surface manifestations of subsurface thermal structure in the California Current . Journal of Geophysical Research : Oceans , 93 ( C5 ): 4975 - 4983 , https://doi.org/10.1029/JC093iC05p04975 https://doi.org/10.1029/JC093iC05p04975 .

Foster D , Gagne D J , Whitt D B . 2021 . Probabilistic machine learning estimation of ocean mixed layer depth from dense satellite and sparse in situ observations . Journal of Advances in Modeling Earth Systems , 13 ( 12 ): e2021 MS 002474 , https://doi.org/10.1029/2021MS002474 https://doi.org/10.1029/2021MS002474 .

Fu J , Liu J , Tian H et al . 2019 . Dual attention network for scene segmentation . In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE , Long Beach, CA, USA . p. 3141 - 3149 , https://doi.org/10.1109/CVPR.2019.00326 https://doi.org/10.1109/CVPR.2019.00326 .

Gadgil S , Joseph P V , Joshi N V . 1984 . Ocean-atmosphere coupling over monsoon regions . Nature , 312 ( 5990 ): 141 - 143 , https://doi.org/10.1038/312141a0 https://doi.org/10.1038/312141a0 .

Goswami B B , Murtugudde R , An S I . 2022 . Role of the Bay of Bengal warming in the Indian summer monsoon rainfall trend . Climate Dynamics , 59 ( 5 ): 1733 - 1751 , https://doi.org/10.1007/s00382-021-06068-1 https://doi.org/10.1007/s00382-021-06068-1 .

Greaser S R , Subrahmanyam B , Trott C B et al . 2020 . Interactions between mesoscale eddies and synoptic oscillations in the Bay of Bengal during the strong monsoon of 2019 . Journal of Geophysical Research : Oceans , 125 ( 10 ): e2020 JC 016772 , https://doi.org/10.1029/2020JC016772 https://doi.org/10.1029/2020JC016772 .

Gu C , Qi J F , Zhao Y Z et al . 2022 . Estimation of the mixed layer depth in the Indian Ocean from surface parameters: a clustering-neural network method . Sensors , 22 ( 15 ): 5600 , https://doi.org/10.3390/s22155600 https://doi.org/10.3390/s22155600 .

Gueye M B , Niang A , Arnault S et al . 2014 . Neural approach to inverting complex system: application to ocean salinity profile estimation from surface parameters . Computers & Geosciences , 72 : 201 - 209 , https://doi.‍org/10.1016/j.cageo.2014.07.012 https://doi.‍org/10.1016/j.cageo.2014.07.012 . https://do 10.1016/j.cageo.2014.07.012 http://dx.doi.org/10.1016/j.cageo.2014.07.012

Hauser D , Tourain C , Hermozo L et al . 2021 . New observations from the SWIM radar on-board CFOSAT: instrument validation and ocean wave measurement assessment . IEEE Transactions on Geoscience and Remote Sensing , 59 ( 1 ): 5 - 26 , https://doi.org/10.1109/TGRS.2020.2994372 https://doi.org/10.1109/TGRS.2020.2994372 .

Helber R W , Kara A B , Richman J G et al . 2012 . Temperature versus salinity gradients below the ocean mixed layer . Journal of Geophysical Research : Oceans , 117 ( C5 ): C 05006 , https://doi.org/10.1029/2011JC007382 https://doi.org/10.1029/2011JC007382 .

Holte J , Talley L . 2009 . A new algorithm for finding mixed layer depths with applications to Argo data and Subantarctic Mode Water formation . Journal of Atmospheric and Oceanic Technology , 26 ( 9 ): 1920 - 1939 , https://doi.org/10.1175/2009JTECHO543.1 https://doi.org/10.1175/2009JTECHO543.1 .

Holte J , Talley L D , Gilson J et al . 2017 . An Argo mixed layer climatology and database . Geophysical Research Letters , 44 ( 11 ): 5618 - 5626 , https://doi.org/10.1002/2017GL073426 https://doi.org/10.1002/2017GL073426 .

Hosoda S , Ohira T , Sato K et al . 2010 . Improved description of global mixed-layer depth using Argo profiling floats . Journal of Oceanography , 66 ( 6 ): 773 - 787 , https://doi.org/10.1007/s10872-010-0063-3 https://doi.org/10.1007/s10872-010-0063-3 .

Howden S D , Murtugudde R . 2001 . Effects of river inputs into the Bay of Bengal . Journal of Geophysical Research : Oceans , 106 ( C9 ): 19825 - 19843 , https://doi.org/10.1029/2000JC000656 https://doi.org/10.1029/2000JC000656 .

Jang E , Im J , Park G H et al . 2017 . Estimation of fugacity of carbon dioxide in the East Sea using in situ measurements and Geostationary Ocean Color Imager satellite data . Remote Sensing , 9 ( 8 ): 821 , https://doi.org/10.3390/rs9080821 https://doi.org/10.3390/rs9080821 .

Jeong Y , Hwang J , Park J et al . 2019 . Reconstructed 3-D ocean temperature derived from remotely sensed sea surface measurements for mixed layer depth analysis . Remote Sensing , 11 ( 24 ): 3018 , https://doi.org/10.3390/rs11243018 https://doi.org/10.3390/rs11243018 .

Jiang F , Ma J T , Wang B S et al . 2021 . Ocean observation data prediction for Argo data quality control using deep bidirectional LSTM network . Security and Communication Networks , 2021 ( 1 ): 9812428 , https://doi.org/10.1155/2021/5665386 https://doi.org/10.1155/2021/5665386 .

Kara A B , Rochford P A , Hurlburt H E . 2003 . Mixed layer depth variability over the global ocean . Journal of Geophysical Research : Oceans , 108 ( C3 ): 3079 , https://doi.org/10.1029/2000jc000736 https://doi.org/10.1029/2000jc000736 .

Kara A B , Rochford P A , Hurlburt H E . 2000a . An optimal definition for ocean mixed layer depth . Journal of Geophysical Research : Oceans , 105 ( C7 ): 16803 - 16821 , https://doi.org/10.1029/2000JC900072 https://doi.org/10.1029/2000JC900072 .

Kara A B , Rochford P A , Hurlburt H E . 2000b . Mixed layer depth variability and barrier layer formation over the North Pacific Ocean . Journal of Geophysical Research : Oceans , 105 ( C7 ): 16783 - 16801 , https://doi.org/10.1029/2000JC900071 https://doi.org/10.1029/2000JC900071 .

Karras T , Laine S , Aila T . 2021 . A style-based generator architecture for generative adversarial networks . IEEE Transactions on Pattern Analysis and Machine Intelligence , 43 ( 12 ): 4217 - 4228 , https://doi.org/10.1109/TPAMI.2020.2970919 https://doi.org/10.1109/TPAMI.2020.2970919 .

Keerthi M G , Lengaigne M , Vialard J et al . 2013 . Interannual variability of the Tropical Indian Ocean mixed layer depth . Climate Dynamics , 40 ( 3-4 ): 743 - 759 , https://doi.org/10.1007/s00382-012-1295-2 https://doi.org/10.1007/s00382-012-1295-2 .

Kumari A , Kumar S P , Chakraborty A . 2018 . Seasonal and interannual variability in the barrier layer of the Bay of Bengal . Journal of Geophysical Research : Oceans , 123 ( 2 ): 1001 - 1015 , https://doi.org/10.1002/2017JC013213 https://doi.org/10.1002/2017JC013213 .

Lecun Y , Bottou L , Bengio Y et al . 1998 . Gradient-based learning applied to document recognition . Proceedings of the IEEE , 86 ( 11 ): 2278 - 2324 , https://doi.org/10.1109/5.726791 https://doi.org/10.1109/5.726791 .

Li H , Xu F H , Zhou W et al . 2017 . Development of a global gridded Argo data set with Barnes successive corrections . Journal of Geophysical Research : Oceans , 122 ( 2 ): 866 - 889 , https://doi.org/10.1002/2016JC012285 https://doi.org/10.1002/2016JC012285 .

Li X F , Clemente-Colón P , Friedman K S . 2000 . Estimating oceanic mixed-layer depth from internal wave evolution observed from Radarsat-1 SAR . Johns Hopkins APL Technical Digest , 21 ( 1 ): 130 - 135 .

Li X F , Zhou Y , Wang F . 2022 . Advanced information mining from ocean remote sensing imagery with deep learning. Journal of Remote Sensing , 2022 : 9849645 , https://doi.org/10.34133/2022/9849645 https://doi.org/10.34133/2022/9849645 .

Liu R , Lehman J , Molino P et al . 2018 . An intriguing failing of convolutional neural networks and the CoordConv solution . In: Proceedings of the 32nd International Conference on Neural Information Processing Systems . Curran Associates Inc. , New York, USA . p. 9628 - 9639 . https://do 10.1109/icinpro43533.2018.9096860 http://dx.doi.org/10.1109/icinpro43533.2018.9096860

Lorbacher K , Dommenget D , Niiler P P et al . 2006 . Ocean mixed layer depth: a subsurface proxy of ocean-atmosphere variability . Journal of Geophysical Research : Oceans , 111 ( C7 ): C 07010 , https://doi.org/10.1029/2003jc002157 https://doi.org/10.1029/2003jc002157 .

Masson S , Luo J J , Madec G et al . 2005 . Impact of barrier layer on winter-spring variability of the southeastern Arabian Sea . Geophysical Research Letters , 32 ( 7 ): L 07703 , https://doi.org/10.1029/2004GL021980 https://doi.org/10.1029/2004GL021980 .

Meng L S , Yan C , Zhuang W et al . 2022 . Reconstructing high-resolution ocean subsurface and interior temperature and salinity anomalies from satellite observations. IEEE Transactions on Geoscience and Remote Sensing , 60 : 4104114 , https://doi.org/10.1109/TGRS.2021.3109979 https://doi.org/10.1109/TGRS.2021.3109979 .

Mignot J , de Boyer Montégut C , Lazar A et al . 2007 . Control of salinity on the mixed layer depth in the world ocean: 2. Tropical areas . Journal of Geophysical Research : Oceans , 112 ( C10 ): C 10010 , https://doi.org/10.‍1029/2006JC003954 https://doi.org/10.‍1029/2006JC003954 .

Pailler K , Bourlès B , Gouriou Y . 1999 . The barrier layer in the western tropical Atlantic Ocean . Geophysical Research Letters , 26 ( 14 ): 2069 - 2072 , https://doi.org/10.1029/1999GL900492 https://doi.org/10.1029/1999GL900492 .

Pauthenet E , Bachelot L , Balem K et al . 2022 . Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks . Ocean Science , 18 ( 4 ): 1221 - 1244 , https://doi.org/10.5194/os-18-1221-2022 https://doi.org/10.5194/os-18-1221-2022 .

Polovina J J , Mitchum G T , Evans G T . 1995 . Decadal and basin-scale variation in mixed layer depth and the impact on biological production in the Central and North Pacific , 1960 - 88 . Deep Sea Research Part I : Oceanographic Research Papers , 42 ( 10 ): 1701 - 1716 , https://doi.org/10.1016/0967-0637(95)00075-H https://doi.org/10.1016/0967-0637(95)00075-H .

Qi J F , Xie B W , Li D L et al . 2023 . Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model. Frontiers in Marine Science , 10 : 1181182 , https://doi.‍org/10.3389/fmars.2023.1181182 https://doi.‍org/10.3389/fmars.2023.1181182 . https://do 10.3389/fmars.2023.1181182 http://dx.doi.org/10.3389/fmars.2023.1181182

Rao R R , Kumar M S G , Ravichandran M et al . 2010 . 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 Research Part I : Oceanographic Research Papers , 57 ( 1 ): 1 - 13 , https://doi.org/10.1016/j.dsr.2009‍.‍10.008 https://doi.org/10.1016/j.dsr.2009‍.‍10.008 .

Ratna S B , Cherchi A , Osborn T J et al . 2021 . The extreme positive Indian Ocean dipole of 2019 and associated Indian summer monsoon rainfall response . Geophysical Research Letters , 48 ( 2 ): e2020 GL 091497 , https://doi.org/10.1029/2020GL091497 https://doi.org/10.1029/2020GL091497 .

Ray S , Swain D , Ali M M et al . 2022 . Coastal upwelling in the western Bay of Bengal: role of local and remote windstress . Remote Sensing , 14 ( 19 ): 4703 , https://doi.org/10.3390/rs14194703 https://doi.org/10.3390/rs14194703 .

Ren Y B , Li X F , Yang X F et al . 2022 . Development of a dual-attention U-Net model for sea ice and open water classification on SAR images. IEEE Geoscience and Remote Sensing Letters , 19 : 4010205 , https://doi.org/10.1109/LGRS.2021.3058049 https://doi.org/10.1109/LGRS.2021.3058049 .

Rintoul S R , Trull T W . 2001 . Seasonal evolution of the mixed layer in the Subantarctic zone south of Australia . Journal of Geophysical Research : Oceans , 106 ( C12 ): 31447 - 31462 , https://doi.org/10.1029/2000JC000329 https://doi.org/10.1029/2000JC000329 .

Shi J , Tang C , Liu Q Y et al . 2022 . Role of mixed layer depth in the location and development of the Northeast Pacific warm blobs . Geophysical Research Letters , 49 ( 16 ): e2022 GL 098849 , https://doi.org/10.1029/2022GL098849 https://doi.org/10.1029/2022GL098849 .

Su H , Tang Z W , Qiu J L et al . 2024 . Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning . International Journal of Digital Earth , 17 ( 1 ): 2332374 , https://doi.org/10.1080/17538947.2024.2332374 https://doi.org/10.1080/17538947.2024.2332374 .

Su H , Wang A , Zhang T Y et al . 2021a . Super-resolution of subsurface temperature field from remote sensing observations based on machine learning. International Journal of Applied Earth Observation and Geoinformation , 102 : 102440 , https://doi.org/10.1016/j.jag.2021.102440 https://doi.org/10.1016/j.jag.2021.102440 .

Su H , Zhang T Y , Lin M J et al . 2021b . Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks. Remote Sensing of Environment , 260 : 112465 , https://doi.org/10.1016/j.rse.2021.112465 https://doi.org/10.1016/j.rse.2021.112465 .

Thomson R E , Fine I V . 2003 . Estimating mixed layer depth from oceanic profile data . Journal of Atmospheric and Oceanic Technology , 20 ( 2 ): 319 - 329 , https://doi.org/10.1175/1520-0426(2003)020<0319:emldfo>2.0.co;2. https://doi.org/10.1175/1520-0426(2003)020<0319:emldfo>2.0.co;2.

Vernieres G , Kovach R , Keppenne C et al . 2014 . The impact of the assimilation of Aquarius sea surface salinity data in the GEOS ocean data assimilation system . Journal of Geophysical Research : Oceans , 119 ( 10 ): 6974 - 6987 , https://doi.org/10.1002/2014JC010006 https://doi.org/10.1002/2014JC010006 .

Vinayachandran P N , Murty V S N , Ramesh Babu V . 2002 . Observations of barrier layer formation in the Bay of Bengal during summer monsoon . Journal of Geophysical Research : Oceans , 107 ( C12 ): SRF 19 - 1 -SRF 19 - 9 , https://doi.org/10.1029/2001JC000831 https://doi.org/10.1029/2001JC000831 .

Wang H Y , Song T Q , Zhu S L et al . 2021 . Subsurface temperature estimation from sea surface data using neural network models in the western Pacific Ocean . Mathematics , 9 ( 8 ): 852 , https://doi.org/10.3390/math9080852 https://doi.org/10.3390/math9080852 .

Wei H H , Subramanian A C , Karnauskas K B et al . 2023 . The role of in situ ocean data assimilation in ECMWF subseasonal forecasts of sea-surface temperature and mixed-layer depth over the tropical Pacific Ocean . Quarterly Journal of the Royal Meteorological Society , 149 ( 757 ): 3513 - 3524 , https://doi.org/10.1002/qj.4570 https://doi.org/10.1002/qj.4570 .

Wong A P S , Wijffels S E , Riser S C et al . 2020 . Argo data 1999 - 2019 : two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats. Frontiers in Marine Science, 7: 700 , https://doi.org/10.3389/fmars.2020.00700 https://doi.org/10.3389/fmars.2020.00700 .

Xue T F , Frenger I , Oschlies A et al . 2022 . Mixed layer depth promotes trophic amplification on a seasonal scale . Geophysical Research Letters , 49 ( 12 ): e2022 GL 098720 , https://doi.org/10.1029/2022GL098720 https://doi.org/10.1029/2022GL098720 .

Yamamoto A , Abe-Ouchi A , Shigemitsu M et al . 2015 . Global deep ocean oxygenation by enhanced ventilation in the southern Ocean under long-term global warming . Global Biogeochemical Cycles , 29 ( 10 ): 1801 - 1815 , https://doi.org/10.1002/2015GB005181 https://doi.org/10.1002/2015GB005181 .

Yang J L , Liu Q Y , Xie S P et al . 2007 . Impact of the Indian Ocean SST basin mode on the Asian summer monsoon . Geophysical Research Letters , 34 ( 2 ): L 02708 , https://doi.org/10.1029/2006GL028571 https://doi.org/10.1029/2006GL028571 .

Yu X L , Garabato A C N , Martin A P et al . 2019 . An annual cycle of submesoscale vertical flow and restratification in the upper ocean . Journal of Physical Oceanography , 49 ( 6 ): 1439 - 1461 , https://doi.org/10.1175/JPO-D-18-0253.1 https://doi.org/10.1175/JPO-D-18-0253.1 .

Yue W H , Xu Y S , Xiang L et al . 2024 . Prediction of 3-D ocean temperature based on self-attention and predictive RNN. IEEE Geoscience and Remote Sensing Letters , 21 : 1501305 , https://doi.org/10.1109/LGRS.2024.3358348 https://doi.org/10.1109/LGRS.2024.3358348 .

Zhao Y Z , Qi J F , Zhu S L et al . 2023 . Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model. Deep Sea Research Part I : Oceanographic Research Papers , 202 : 104179 , https://doi.org/10.1016/j.dsr.2023.104179 https://doi.org/10.1016/j.dsr.2023.104179 .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Assessment of spatial distribution, sources, and ecological risks of microplastics in the estuarine and coastal regions of the northern Bay of Bengal

Stock appraisal for Atlantic tripletail (Lobotes surinamensis; Bloch, 1790) in the Bay of Bengal, Bangladesh

Contrasting physical mechanisms of yellowfin tuna fluctuations between the western and eastern Indian Ocean

大涵道比风扇/增压级进气畸变数值模拟研究

基于模态频率的V型缺口梁裂纹扩展速率模型