Articles | Volume 15, issue 4
https://doi.org/10.5194/essd-15-1765-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-1765-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global Lagrangian eddy dataset based on satellite altimetry
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Ryan Abernathey
Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA
Related authors
Chengyan Liu, Zhaomin Wang, Dake Chen, Xianxian Han, Hengling Leng, Xi Liang, Liangjun Yan, Xiang Li, Craig Stevens, Andrew Hogg, Kazuya Kusahara, Kaihe Yamazaki, Kay Ohshima, Meng Zhou, Xiao Cheng, Dongxiao Wang, Changming Dong, Jiping Liu, Qinghua Yang, Xichen Li, Ruibo Lei, Minghu Ding, Zhaoru Zhang, Dujuan Kang, Di Qi, Tongya Liu, Jihai Dong, Lu An, Ru Chen, Tong Zhang, Xiaoming Hu, Bo Han, Haibo Bi, Qi Shu, Longjiang Mu, Shiming Xu, Hu Yang, Hailong Liu, Tingfeng Dou, Zhixuan Feng, Lei Zheng, Xueyuan Tang, Guitao Shi, Yongqing Cai, Bingrui Li, Yang Wu, Xia Lin, Wenjin Sun, Yu Liu, Kai Yu, Yu Zhang, Weizeng Shao, Xiaoyu Wang, Shaojun Zheng, Chengyi Yuan, Chunxia Zhou, Jian Liu, Yang Liu, Yue Xia, Xiaoyu Pan, Jiabao Zeng, Kechen Liu, Jiahao Fan, Chen Cheng, and Qi Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-6487, https://doi.org/10.5194/egusphere-2025-6487, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice shelves interact around Antarctica. This helps us understand their critical role in global climate and sea-level rise. Our model successfully captures essential features like major currents and seasonal ice changes. Despite some remaining biases, it provides a useful tool for predicting future changes in this vital and rapidly evolving region.
Han Zhang, Dake Chen, Tongya Liu, Di Tian, Min He, Qi Li, Guofei Wei, and Jian Liu
Earth Syst. Sci. Data, 16, 5665–5679, https://doi.org/10.5194/essd-16-5665-2024, https://doi.org/10.5194/essd-16-5665-2024, 2024
Short summary
Short summary
This paper provides a cross-shaped moored array dataset (MASCS 1.0) of observations that consist of five buoys and four moorings in the northern South China Sea from 2014 to 2015. The moored array is influenced by atmospheric forcings such as tropical cyclones and monsoon as well as oceanic tides and flows. The data reveal variations of the air–sea interface and the ocean itself, which are valuable for studies of air–sea interactions and ocean dynamics in the northern South China Sea.
Chengyan Liu, Zhaomin Wang, Dake Chen, Xianxian Han, Hengling Leng, Xi Liang, Liangjun Yan, Xiang Li, Craig Stevens, Andrew Hogg, Kazuya Kusahara, Kaihe Yamazaki, Kay Ohshima, Meng Zhou, Xiao Cheng, Dongxiao Wang, Changming Dong, Jiping Liu, Qinghua Yang, Xichen Li, Ruibo Lei, Minghu Ding, Zhaoru Zhang, Dujuan Kang, Di Qi, Tongya Liu, Jihai Dong, Lu An, Ru Chen, Tong Zhang, Xiaoming Hu, Bo Han, Haibo Bi, Qi Shu, Longjiang Mu, Shiming Xu, Hu Yang, Hailong Liu, Tingfeng Dou, Zhixuan Feng, Lei Zheng, Xueyuan Tang, Guitao Shi, Yongqing Cai, Bingrui Li, Yang Wu, Xia Lin, Wenjin Sun, Yu Liu, Kai Yu, Yu Zhang, Weizeng Shao, Xiaoyu Wang, Shaojun Zheng, Chengyi Yuan, Chunxia Zhou, Jian Liu, Yang Liu, Yue Xia, Xiaoyu Pan, Jiabao Zeng, Kechen Liu, Jiahao Fan, Chen Cheng, and Qi Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-6487, https://doi.org/10.5194/egusphere-2025-6487, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice shelves interact around Antarctica. This helps us understand their critical role in global climate and sea-level rise. Our model successfully captures essential features like major currents and seasonal ice changes. Despite some remaining biases, it provides a useful tool for predicting future changes in this vital and rapidly evolving region.
Han Zhang, Dake Chen, Tongya Liu, Di Tian, Min He, Qi Li, Guofei Wei, and Jian Liu
Earth Syst. Sci. Data, 16, 5665–5679, https://doi.org/10.5194/essd-16-5665-2024, https://doi.org/10.5194/essd-16-5665-2024, 2024
Short summary
Short summary
This paper provides a cross-shaped moored array dataset (MASCS 1.0) of observations that consist of five buoys and four moorings in the northern South China Sea from 2014 to 2015. The moored array is influenced by atmospheric forcings such as tropical cyclones and monsoon as well as oceanic tides and flows. The data reveal variations of the air–sea interface and the ocean itself, which are valuable for studies of air–sea interactions and ocean dynamics in the northern South China Sea.
Shanice T. Bailey, C. Spencer Jones, Ryan P. Abernathey, Arnold L. Gordon, and Xiaojun Yuan
Ocean Sci., 19, 381–402, https://doi.org/10.5194/os-19-381-2023, https://doi.org/10.5194/os-19-381-2023, 2023
Short summary
Short summary
This study explores the variability of water mass transformation within the Weddell Gyre (WG). The WG is the largest source of Antarctic Bottom Water (AABW). Changes to our climate can modify the mechanisms that transform waters to become AABW. In this study, we computed water mass transformation volume budgets by using three ocean models and a mathematical framework developed by Walin. Out of the three models, we found one to be most useful in studying the interannual variability of AABW.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
Short summary
Short summary
Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
Cited articles
Adcroft, A., Campin, J.-M., Doddridge, S. D., Evangelinos, C., Ferreira, D., Follows, M., Forget, G., Hill, H., Jahn, O., Klymak, J., Losch, M., Marshall, J., Maze, G., Mazloff, M., Menemenlis, D., Molod, A., and Scott, J.: MITgcm documentation, Release checkpoint67a-12-gbf23121, 19, https://buildmedia.readthedocs.org/media/pdf/mitgcm/latest/mitgcm.pdf (last access: 14 April 2023), 2018. a
Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faugère, Y., Delepoulle, A., Chelton, D., Dibarboure, G., and Picot, N.:
On the resolutions of ocean altimetry maps, Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019, 2019. a
Beron-Vera, F. J., Wang, Y., Olascoaga, M. J., Goni, G. J., and Haller, G.:
Objective detection of oceanic eddies and the Agulhas leakage, J. Phys. Oceanogr., 43, 1426–1438, 2013. a
Busecke, J. J. and Abernathey, R. P.:
Ocean mesoscale mixing linked to climate variability, Sci. Adv., 5, eaav5014, https://doi.org/10.1126/sciadv.aav5014, 2019. a
Chelton, D. B., DeSzoeke, R. A., Schlax, M. G., El Naggar, K., and Siwertz, N.:
Geographical variability of the first baroclinic Rossby radius of deformation, J. Phys. Oceanogr., 28, 433–460, 1998. a
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, 2011a. a
Chelton, D. B., Schlax, M. G., and Samelson, R. M.:
Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, 2011b. a
Dong, C., McWilliams, J. C., Liu, Y., and Chen, D.:
Global heat and salt transports by eddy movement, Nat. Commun., 5, 1–6, 2014. a
Dong, C., Liu, L., Nencioli, F., Bethel, B. J., Liu, Y., Xu, G., Ma, J., Ji, J., Sun, W., Shan, H., and Lin, X.:
The near-global ocean mesoscale eddy atmospheric-oceanic-biological interaction observational dataset, Sci. Data, 9, 1–13, 2022. a
d'Ovidio, F., Isern-Fontanet, J., López, C., Hernández-García, E., and García-Ladona, E.:
Comparison between Eulerian diagnostics and finite-size Lyapunov exponents computed from altimetry in the Algerian basin, Deep-Sea Res. Pt. I, 56, 15–31, 2009. a
Faghmous, J. H., Frenger, I., Yao, Y., Warmka, R., Lindell, A., and Kumar, V.:
A daily global mesoscale ocean eddy dataset from satellite altimetry, Sci. Data, 2, 1–16, 2015. a
Fu, L.-L., Chelton, D. B., Le Traon, P.-Y., and Morrow, R.:
Eddy dynamics from satellite altimetry, Oceanography, 23, 14–25, 2010. a
Gaube, P. and McGillicuddy Jr., D. J.:
The influence of Gulf Stream eddies and meanders on near-surface chlorophyll, Deep-Sea Res. Pt. I, 122, 1–16, 2017. a
Hausmann, U. and Czaja, A.:
The observed signature of mesoscale eddies in sea surface temperature and the associated heat transport, Deep-Sea Res. Pt. I, 70, 60–72, 2012. a
He, Q., Zhan, H., Cai, S., He, Y., Huang, G., and Zhan, W.:
A new assessment of mesoscale eddies in the South China Sea: Surface features, three-dimensional structures, and thermohaline transports, J. Geophys. Res.-Oceans, 123, 4906–4929, 2018. a
He, Y., Feng, M., Xie, J., He, Q., Liu, J., Xu, J., Chen, Z., Zhang, Y., and Cai, S.:
Revisit the vertical structure of the eddies and eddy-induced transport in the Leeuwin Current system, J. Geophys. Res.-Oceans, 126, e2020JC016556, https://doi.org/10.1029/2020JC016556, 2021. a, b
Hughes, C. W. and Miller, P. I.:
Rapid water transport by long-lasting modon eddy pairs in the southern midlatitude oceans, Geophys. Res. Lett., 44, 12–375, 2017. a
Killworth, P. D., Chelton, D. B., and de Szoeke, R. A.:
The speed of observed and theoretical long extratropical planetary waves, J. Phys. Oceanogr., 27, 1946–1966, 1997. a
Lacorata, G., Corrado, R., Falcini, F., and Santoleri, R.:
FSLE analysis and validation of Lagrangian simulations based on satellite-derived GlobCurrent velocity data, Remote Sens. Environ., 221, 136–143, 2019. a
Lagerloef, G. S., Mitchum, G. T., Lukas, R. B., and Niiler, P. P.:
Tropical Pacific near-surface currents estimated from altimeter, wind, and drifter data, J. Geophys. Res.-Oceans, 104, 23313–23326, 1999. a
Li, H., Xu, F., and Wang, G.:
Global mapping of mesoscale eddy vertical tilt, J. Geophys. Res.-Oceans, 127, e2022JC019131, https://doi.org/10.1029/2022JC019131, 2022. a, b
Li, J., Roughan, M., and Kerry, C.:
Drivers of ocean warming in the western boundary currents of the Southern Hemisphere, Nat. Clim. Change, 12, 901–909, 2022. a
Liu, T.: 180-day RCLVs, Vimeo [video], https://vimeo.com/773609039, last access: 14 April 2023. a
Liu, T. and Abernathey, R.:
A global Lagrangian eddy dataset based on satellite altimetry (GLED v1.0), Zenodo, https://doi.org/10.5281/zenodo.7349753, 2022. a, b, c
Liu, T., Ou, H.-W., Liu, X., and Chen, D.:
On the role of eddy mixing in the subtropical ocean circulation, Front. Marine Sci., 9, 353, https://doi.org/10.3389/fmars.2022.832992, 2022b. a
Mahadevan, A.:
The impact of submesoscale physics on primary productivity of plankton, Annu. Rev. Mar. Sci., 8, 161–184, 2016. a
McGillicuddy Jr., D. J.:
Mechanisms of physical-biological-biogeochemical interaction at the oceanic mesoscale, Annu. Rev. Mar. Sci., 8, 125–159, 2016. a
Nencioli, F., Dong, C., Dickey, T., Washburn, L., and McWilliams, J. C.:
A vector geometry–based eddy detection algorithm and its application to a high-resolution numerical model product and high-frequency radar surface velocities in the Southern California Bight, J. Atmos. Ocean. Tech., 27, 564–579, 2010. a
Pegliasco, C., Delepoulle, A., Mason, E., Morrow, R., Faugère, Y., and Dibarboure, G.:
META3.1exp: a new global mesoscale eddy trajectory atlas derived from altimetry, Earth Syst. Sci. Data, 14, 1087–1107, https://doi.org/10.5194/essd-14-1087-2022, 2022. a, b
Shadden, S. C., Lekien, F., and Marsden, J. E.:
Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows, Physica D, 212, 271–304, 2005. a
Wang, Y., Olascoaga, M. J., and Beron-Vera, F. J.:
Coherent water transport across the South Atlantic, Geophys. Res. Lett., 42, 4072–4079, 2015. a
Wang, Y., Beron-Vera, F. J., and Olascoaga, M. J.:
The life cycle of a coherent Lagrangian Agulhas ring, J. Geophys. Res.-Oceans, 121, 3944–3954, 2016. a
Whalen, C. B., MacKinnon, J. A., and Talley, L. D.:
Large-scale impacts of the mesoscale environment on mixing from wind-driven internal waves, Nat. Geosci., 11, 842–847, 2018. a
Zhang, W., Wolfe, C. L., and Abernathey, R.:
Role of Coherent Eddies in Potential Vorticity Transport in Two-layer Quasigeostrophic Turbulence, arXiv [preprint], arXiv:1911.01520, 2019. a, b, c
Short summary
Nearly all existing datasets of mesoscale eddies are based on the Eulerian method because of its operational simplicity. Using satellite observations and a Lagrangian method, we present a global Lagrangian eddy dataset (GLED v1.0). We conduct the statistical comparison between two types of eddies and the dataset validation. Our dataset offers relief from dilemma that the Eulerian eddy dataset is nearly the only option for studying mesoscale eddies.
Nearly all existing datasets of mesoscale eddies are based on the Eulerian method because of its...
Altmetrics
Final-revised paper
Preprint