the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Feb 2025
Integrating Global Ocean Profiles Data and Altimetry-Derived Eddies
Abstract. Satellite altimetry has revolutionized our understanding of ocean physics by providing global sea-surface height data. These measurements reveal the intricate dynamics of ocean mesoscale strain and vortices, and their interactions with multiple physical scales in the oceans. Although surface dynamics has been extensively studied, investigating the vertical structure of mesoscale eddies globally remains a computational challenge. In this study, we combine the comprehensive World Ocean Database (WOD) with a database of Eulerian mesoscale eddies (META3.2 DT). We pre-process and filter the WOD data, selecting quality controlled profiles at local depths greater than 100-m. By integrating WOD data with altimetry-derived mesoscale eddies, we aim to facilitate future studies on the role of mesoscale vortices in multiple processes, such as heat, mass and nutrient transport, and water-mass subduction. The analysis is performed using high-performance computing resources, with Python packages for parallel processing of the data and analysis of more than 4.2 million profiles with more than 35 million vortex observations. The dataset is available by download and by direct access through an OPeNDAP/HTTP server. Additionally, we provide the code for performing the vortex-profile matching, along with an example of use to facilitate future updates to the code and merged data. This dataset supports further research on eddy vertical structure, biogeochemical processes, and their role in climate systems across different regions and time periods.
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Status: final response (author comments only)
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CC1: 'RC Comment on essd-2025-40', Paola Picco, 12 Feb 2025
The authors created a new data-set which allows users to select Temperature, Salinity and other available water column properties profiles inside eddies detected by altimetry. This can be of great advantage for scientists involved in ocean dynamic studies, providing them with precious in-situ observations. The paper is well organised. State of the art, data sources and methods are properly described and well documented; references are exhaustive.
Statistics is used to describe and validate the data-set. Nevertheless, a few test-cases with selected relevant examples of the eddy along with the contained profiles would allow the reader to better appreciate the potentialities of the data-set and will corroborate the validation process.
Will be the data-set updated in the future?
Citation: https://doi.org/10.5194/essd-2025-40-CC1 -
AC1: 'Reply on CC1', Iury Simoes-Sousa, 26 Aug 2025
Thanks for the comments and suggestions!
We have now created and included in the repository an example of a composite analysis of a unique eddy that had many profiles while trapping an Argo float.
https://github.com/iuryt/vortex_profile_matching/blob/main/examples/unique_eddy.ipynb
Regarding updates, we are planning to create an NRT combined dataset in the future. The code is also available for other users to collaborate on future enhancements and requests. Feel free to create new issues on the GitHub repo.
https://github.com/iuryt/vortex_profile_matchingCitation: https://doi.org/10.5194/essd-2025-40-AC1
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AC1: 'Reply on CC1', Iury Simoes-Sousa, 26 Aug 2025
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RC1: 'Comment on essd-2025-40', Anonymous Referee #1, 31 Mar 2025
This dataset provides an integration of data from the World Ocean Database (subsurface ocean measurements) with data about the location of mesoscale eddies (as detected by satellite altimetry). This dataset is an exceptionally useful resource for amateur and experienced oceanographers alike, as it provides a well-documented manuscript description and high-quality data. I recommend publication of the manuscript and dataset in full in recognition of the novelty and usefulness of this product.
The manuscript is thorough, well-written, and provides useful figures that demonstrate potential use cases of the dataset. It provides useful background about mesoscale eddies while focusing in the majority of the text on the dataset's creation and validation.
Citation: https://doi.org/10.5194/essd-2025-40-RC1 -
AC2: 'Reply on RC1', Iury Simoes-Sousa, 26 Aug 2025
We thank the reviewer for the comments, and we are glad the manuscript and dataset are seen as useful and well-documented. This positive assessment supports our main goal to make this dataset broadly accessible and relevant to the oceanographic community.
Citation: https://doi.org/10.5194/essd-2025-40-AC2
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AC2: 'Reply on RC1', Iury Simoes-Sousa, 26 Aug 2025
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RC2: 'Comment on essd-2025-40', Anonymous Referee #2, 11 Jun 2025
Integrating Global Ocean Profiles Data and Altimetry-Derived Eddies
by Iury Simoes-Sousa, Cesar Rocha, Amit Tandon, and Andre Schmidt
--- Summary ---This paper describes a merged eddy-profile database constructed by co-referencing World Ocean Database T and S profiles to a database of eddies identified and tracked using satellite altimetry. Since much of the sub-surface variability of the ocean is linked to mesoscale eddies, the collection of profiles along with eddy parameters should enable new directions of analysis for understanding eddy stirring and transport, dynamics and geography of eddy formation processes, and attribution of subsurface structure.
The approach has some similarity to others in the literature, including (a) SatGEM (mapping subsurface fields using regional binned profile averages by SSH; Meijers et al, JTech 2011), (b) SSH-Argo linear regression (constructing fine-grid relationships between SSH and subsurface T and S; Uriel Zajaczkovski Ph.D. dissertation, Scripps, 2017), and (c) eddy composite structure from satellite SST referenced to tracked eddies in SSH (Gaube, JPO 2015). This "eddy+profile dataset" approach is complementary to those, particularly in that facilitates further investigation rather than presenting conclusions.
--- General Comments ---Overall, the manuscript is well constructed and well written, with compelling figures, and should need little revision. My comments are primarily suggestions about choices that would make the intended meaning clearer.
For example, I would suggest finding a different descriptor than "Eulerian" for the eddies tracked in SSH maps, since the most common use for Eulerian is to describe measurements fixed in space (e.g., moorings) rather than following the flow (e.g. drifters). Mapped SSH features are free to move around, even if they don't necessarily follow fluid trajectories. "SSH eddies" might be a better name. Or just "eddies" (following the nomenclature of Chelton et al, Prog Oceanogr. 2011) is fine too, as long as the methodology is clear. I realize the goal is to make a distinction from the Liu and Abernathey method which uses particle tracking in the time-varying SSH fields.
Plenty of useful detail is presented on both the prior datasets and the computational methods used to construct this eddy-profile database. Much of the WOD description is available elsewhere, but it doesn't hurt to include it here to remind potential users.
One point that could be made clearer in parts of the manuscript is the distinction between eddy observations on individual days and the identification of specific eddies with a lifetime over many days (e.g. the p.10 l.200 tallies of "cyclones" and "unique cyclones" seems to refer to single-day and full-lifetime numbers). A particular strength of this dataset appears to be that it has connected all profiles within the same eddy tracked over time (e.g. Fig 5), enabling study of an eddy's evolution over its lifetime.
However, since the algorithm connecting sequential observations into specific eddies is unique to the META3.2 DT eddy database (and presumably involves some choices that are made differently in other eddy-tracking algorithms), it would be useful to add some description of the algorithm here.
It's also a bit unclear exactly how much detail is available on each observation-eddy match. I'm guessing each profile location and the trajectory of each eddy center over time are all included (along with the eddy radius timeseries), so that composite eddy structures could be constructed. This is hinted at in Section 6 (Future directions), but a bit difficult to find stated explicitly.
--- Minor Comments ---Title: suggest delete "Data"
p.10 l.195. It is mentioned that the dataset is separated into monthly files. How are specific eddies connected from one file to the next? Is each assigned a unique identification number that is carried through the entire dataset? Also, what is the relationship between the monthly "files" and 120 "chunks" that are processed separately. Is there a direct relationship such as 3 months per chunk (given 360 months in the 30-year dataset)?
Fig.5. Caption should specify that it (a) is the number of profiles over the full lifetime of each eddy and (b) excludes eddies with no profiles in them.
p.15 l.285. Some letters referring to Fig 7 panels are swapped.
Citation: https://doi.org/10.5194/essd-2025-40-RC2 -
AC3: 'Reply on RC2', Iury Simoes-Sousa, 26 Aug 2025
General Comments:
We would like to thank the reviewer for the valuable comments and general suggestions. These recommendations definitely improved the quality of our work. We are also pleased that the reviewer found the manuscript to be well-presented and well-written, and that the figures were considered compelling. Below, we provide detailed responses to the main points raised in the general comments section. We also attached the latexdiff document to make it easier to track changes from the last version.
We agree that “Eulerian” is traditionally associated with fixed-point observations, such as moorings. However, in the context of mesoscale eddy detection, the term has been previously used in the literature (Abernathey and Haller, 2018), including in a publication in Earth System Science Data (Liu and Abernathey, 2023). This term seems appropriate to describe eddies identified from methods that do not take into account the evolution of the velocity field. In other words, Eulerian eddies can be identified with a single SSH snapshot, while Lagrangian eddies are identified by methods that integrate the velocity into trajectories and thus are defined by material coherence. We use “Eulerian eddies” specifically to distinguish the approach used by Pegliasco et al (2022), which is based on SSH extrema and contours obtained AVISO+ interpolated fields, from the method used by Liu and Abernathey (2023). This distinction is important for readers comparing different eddy identification frameworks and also because we plan to include Lagrangian eddies in future versions of this dataset. We also find the term pedagogically useful, as it provides a concise and internally consistent way to emphasize the differences in the assumptions taken by each method while avoiding the misleading implication of material coherence. We therefore prefer to retain the use of this term.
We appreciate the reviewer’s observation regarding the distinction between daily eddy observations and unique eddies tracked over their full lifetimes. To clarify this point, we have revised the text around page 10 to define the terminology more explicitly. [L27-31]
Regarding the eddy-tracking algorithm, we have now added a short explanation in Section 3, clarifying that eddies are tracked based on spatial overlap and similarity of geometric properties between consecutive days of closed sea-surface height contours around local extrema (Pegliasco et al., 2022). [L164-167]
We also agree that the level of detail available in each profile–eddy match is an important feature of the dataset and needs a more explicit description. To address this, we have added a clarifying paragraph at the beginning of the “Combining datasets” section (Section 3), where we now specify the full set of eddy and profile properties stored in the matched dataset. This includes the eddy center coordinates, effective and speed radii, amplitude, Rossby number, and a persistent eddy identifier that allows users to track the full eddy trajectory over time. The profile metadata (e.g., location, time, depth, instrument type) is also preserved, enabling composite and evolutionary analyses of eddy structures. [L227-L231]
Minor Comments (following the order):
- We deleted the word “Data” from the title.
- Yes, each eddy is assigned a unique identification number (track), which is maintained consistently throughout its lifetime. This allows eddies to be connected across different monthly files. The 120 "chunks" refer to divisions of the full dataset used for efficient parallel processing. These chunks are independent of the monthly separation and are used during the filtering stage, for example, to remove eddies with short lifetimes or low amplitudes. After filtering, the resulting dataset is loaded into memory as a whole, and only then is it split into individual monthly NetCDF files for output.
- We made the suggested changes in the caption of Figure 5.
- We made the suggested changes in the caption of Figures 7-10 and the reference to the panels in the text.
Following a comment from the open discussion, we have now added an example of a composite analysis of a unique eddy in the repository and included an alternative way to access the data through AWS S3 bucket.
https://github.com/iuryt/vortex_profile_matching/blob/main/examples/unique_eddy.ipynb
Citation: https://doi.org/10.5194/essd-2025-40-AC3
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AC3: 'Reply on RC2', Iury Simoes-Sousa, 26 Aug 2025
Data sets
Vortex-Profile Matching Dataset Iury Simoes-Sousa, Cesar Barbedo Rocha, Amit Tandon, and Andre Schmidt http://www.smast.umassd.edu:8081/thredds/catalog/Vortex_profiles/vortex_profiles/catalog.html
Video abstract
Matching World Ocean Database with Eulerian Altimetry-Based Eddies Iury Simoes-Sousa https://youtu.be/9xzhtrzLRdo
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