the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Feb 2026
A global eddy-collocated temperature and salinity profile dataset (v1.0): integrating multiplatform in situ observations with satellite-detected mesoscale eddies
Abstract. Mesoscale eddies are a fundamental component of ocean circulation and play a crucial role in shaping the three-dimensional distribution of ocean temperature and salinity. However, observational constraints have long limited systematic, global-scale quantification of eddy-induced thermohaline variability. Here, we present a global eddy-collocated historical temperature and salinity profile dataset spanning 29 years (1993–2021), constructed by integrating in situ hydrographic profile observations with satellite-derived mesoscale eddy tracking products. The dataset contains 2.35 million quality-controlled temperature-salinity profiles, each collocated with the nearest mesoscale eddy on the sampling day that may have influenced the observed water column. The profiles provide broad global coverage, with most 2°×2° grid boxes containing more than 150 observations, enabling statistically robust analyses from regional to global scales. Validation against well-documented regional eddy signatures shows that the dataset consistently reproduces well-established eddy-induced temperature and salinity anomaly structures across diverse ocean regions. Example applications demonstrate the dataset’s capability to investigate the spatial heterogeneity and vertical extent of eddy-induced thermohaline anomalies, eddy impacts on mixed-layer depth and stratification, eddy contributions to subsurface extreme temperature events, and eddy-driven heat and material transports. This dataset provides a comprehensive observational foundation for advancing quantitative assessments of mesoscale eddy impacts on regional to global ocean physical environment, heat budgets, and climate change.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2026-89', Anonymous Referee #1, 09 Apr 2026
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AC1: 'Reply on RC1', Qingyou He, 08 Jun 2026
In this work by He and coauthors a dataset providing collocated temperature and salinity data with satellite-derived eddy information. The paper would require major changes to improve its clarity and refine the scope. I have three major concerns as described below, as well as a number of minor suggestions.
Response: Thank you very much for your valuable comments and suggestions. We have carefully studied all of your remarks and revised the manuscript accordingly to improve its clarity and better refine the scope of the study. We hope that the revised version now meets your expectations. Our point-by-point responses are provided below.
The novelty of this contribution is not clearly communicated, as the processing of the input data (WOD and AVISO) appears to be relatively basic (essentially interpolation). Beyond easier access to existing resources, the value added by the authors should be made explicit.
Response: Thank you for this insightful comment. To better highlight the novelty and scientific value of this dataset, we expanded the Introduction to include a more comprehensive review of the two previously published eddy-collocated profile datasets by Ioannou et al. (2024) and Simoes-Sousa et al. (2026). We note that, in these two datasets, profiles were only classified according to whether they were located inside or outside eddies. Whereas, our dataset additionally provides the relative distance and azimuthal angle between each profile and its collocated eddy. This information enables the reconstruction of three-dimensional eddy thermohaline structures and facilitates analyses of how eddy impacts vary with distance from the eddy center, as well as the reconstruction of regional mean three-dimensional eddy thermohaline structure (see revised manuscript, Lines 84–94).
In addition, we updated the hydrographic profile dataset in the revised manuscript by replacing the previously used profiles with a systematically re-quality-controlled and bias-corrected dataset (CODC-v1) (Zhang et al., 2024; Tan et al., 2025). This updated data retained as many valid observations as possible while ensuring data quality, increasing the total number of collocated profiles from approximately 2 million to 5.46 million (see revised manuscript, Lines 142–147). We further compared the updated dataset with previous products and demonstrated their strong consistency in representing regional mean eddy thermohaline impacts. The substantial increase in profile number may provide important support for investigating eddy-induced variability at finer spatiotemporal scales, including sub-basin variability and seasonal/interannual changes (see revised manuscript, Figs. 7–10 and Lines 294–352).
Furthermore, building upon the methodological descriptions provided by Ioannou et al. (2024) and Simoes-Sousa et al. (2026), we further discussed the potential applications of this dataset in studying eddy three-dimensional structures, eddy impacts on thermohaline distributions, stratification and mixing, heat and salt transport, extreme temperature and salinity events, and climate-change-related variability, thereby expanding the potential research applications of the dataset. (see revised manuscript, Lines 451–461)
References:
Simoes-Sousa, I. T., C. Rocha, A. Tandon, and A. Schmidt (2026), Integrating Global Ocean Profiles Data and Altimetry-Derived Eddies, Earth System Science Data, doi:10.5194/essd-2025-40.
Ioannou, A., L. Guez, R. Laxenaire, and S. Speich (2024), Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements, Remote Sensing, 16(22), doi:10.3390/rs16224336.
Tan, Z., L. Cheng, V. Gouretski, B. Zhang, Y. Wang, F. Li, Z. Liu, and J. Zhu (2023), A new automatic quality control system for ocean profile observations and impact on ocean warming estimate, Deep Sea Research Part I: Oceanographic Research Papers, 194, doi:10.1016/j.dsr.2022.103961.
Zhang, B., et al. (2024), CODC-v1: a quality-controlled and bias-corrected ocean temperature profile database from 1940-2023, Scientific data, 11(1), 666, doi:10.1038/s41597-024-03494-8.
Discussion on the effect of trends (e.g., salinity and temperature, eddy, other datasets used) is not provided adequately (global warming mentioned once in sect. 2.2), but may be very relevant. Please expand the discussion and provide context also for the possible influence of observational changes through time.
Response: Thank you for the valuable suggestion. We have added a new subsection discussing the potential application of this dataset in the study of the impacts of climate change on eddy-induced thermohaline anomalies. Regarding the influence of temporal changes in observational coverage, we added a note in the revision that the number of profiles in this dataset has remained relatively stable at more than 200,000 profiles per year since 2004, which may support basin-scale trend analyses (see revised manuscript, Lines 545–561).
The level of detail and support of several pieces of text is insufficient; for example, the discussion of temperature extremes is missing a description of the methodology (and several key parameters, such as the baseline used to define such extremes), and the application section is very scarce and limited mostly to citation of works by the paper authors. The authors should clarify that the dataset is not gridded (even though a 2x2 grid is mentioned in various places).
Response: Thank you for the helpful suggestions. Following your recommendation, we added a clearer description of the methodology used to identify extreme temperature anomalies. Specifically, extreme high (low) temperature anomalies were defined as temperature anomalies exceeding the 95th percentile (falling below the 5th percentile) within each 2° × 2° grid box over the entire study period (1993–2021), relative to the local monthly climatology (see revised manuscript, Lines 500–503).
We also expanded the result section by adding comparisons between our dataset and previously published datasets in terms of the spatial patterns of eddy-induced thermohaline anomalies (Section 3.2 and Figs.7-10 in the revision), as well as comparisons of reconstructed vertical eddy structures with earlier studies (see revised manuscript, Lines 375-381, 427–434, and 481-485).
In addition, we revised the subsection titles from “3.1” to “Overview of the eddy-collocated profile dataset” and from “3.2” to “Validation of the eddy-collocated profile dataset” to clarify that the dataset itself is not gridded.
Minor suggestions:
36 why heat budgets?
Response: We have replaced “heat budgets” with “heat/salt transport”. Thank you.
52 why freshwater?
Response: We have replaced “freshwater” with “salt”. Thank you.
62 add reference
Response: Done. Thank you.
- Chelton, D. B., M. G. Schlax, R. M. Samelson, and R. A. de Szoeke (2007), Global observations of large oceanic eddies, Geophys Res Lett, 34(15), doi:10.1029/2007gl030812.
- Dufau, C., M. Orsztynowicz, G. Dibarboure, R. Morrow, and P. Y. Le Traon (2016), Mesoscale resolution capability of altimetry: Present and future, J Geophys Res Oceans, 121(7), 4910-4927, doi:10.1002/2015jc010904.
86 this reads like a straw man argument
Response: We removed this sentence from the revised manuscript. Thank you.
89 collocation may be working at the surface, but how deep this would hold?
Response: Thank you for this insightful comment. Because the subsurface shape of eddies cannot be directly observed, we followed previous studies by assuming that eddies have vertically coherent columnar structures during the profile–eddy collocation process (Ioannou et al., 2024; Simoes-Sousa et al., 2026). The reconstructed three-dimensional thermohaline anomaly structures based on the collocated dataset support this assumption and show that eddy impacts can penetrate several hundred meters and even exceed 1000 m, with substantial regional variability (see Figs.11-12 in the revision or the Figs. below). We should note that, beneath the surface, eddies may exhibit vertically tilted structures, subsurface-intensified cores, or other forms of structural variability (Laxenaire et al., 2019; Zhang et al., 2016; Zhang et al., 2017). Through composite averaging, the present dataset can reconstruct the mean three-dimensional eddy structure within a target region and reveal systematic regional differences among eddies. However, individual eddies may deviate substantially from the regional mean structure, and such composite analyses may not capture the full range of individual eddy variability. We have added a discussion on this limitation to lines 571-577 in the revision.
Fig.R1 Composite mean eddy temperature anomaly structures in representative regions of the global ocean. a-g, West-east sections of mean temperature anomalies across the composite centers of anticyclonic (AE) and cyclonic (CE) eddies in the South China Sea (SCS), the Kuroshio Extension (KE), the Gulf Stream (GS), the tropical southeastern Indian Ocean (TSIO), the southeastern Pacific Ocean (SEP), the Southern Ocean (SO), and the Brazil‐Malvinas Confluence (BMC). h, The statistical regions (boxes) of (a-g).
Fig.R2 The same as Fig.R1, but for eddy salinity anomaly structures.
References:
Simoes-Sousa, I. T., C. Rocha, A. Tandon, and A. Schmidt (2026), Integrating Global Ocean Profiles Data and Altimetry-Derived Eddies, Earth System Science Data, doi:10.5194/essd-2025-40.
Ioannou, A., L. Guez, R. Laxenaire, and S. Speich (2024), Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements, Remote Sensing, 16(22), doi:10.3390/rs16224336.
90 text in brackets is puzzling
Response: We removed the text in brackets in the revised manuscript. Thank you.
103 fix typo in flowchart
Response: Corrected. Thank you.
128 I am confused by the reference on Captain Cook; please clearly list which data sources (Argo, moorings,...) are used in this dataset. Profiles are only possible with Argo, right?
Response: Our original intention was to briefly indicate that the World Ocean Database contains hydrographic observations spanning a long historical period. Actually, the present study only used profiles collected during 1993–2021, when satellite-derived eddy observations are available. To avoid redundancy and confusion, we removed the sentence referring to Captain Cook.
To maximize the number of usable profiles, we included all systematically quality-controlled and bias-corrected temperature and salinity profiles, including observations from Argo, Conductivity-Temperature-Depth (CTD) instruments, expendable bathythermographs (XBT), autonomous pinniped bathythermograph (APB), gliders, and other platforms (Fig.5c in the revision). These details are now clarified in Lines 137–147 of the revised manuscript. Thank you.
134 while eddies are daily, are the WOD data provided with the same resolution? This means that profiles for a period shorted than a day are aggregated?
Response: The World Ocean Database profiles used in this study are discrete profile observations. For each profile, we searched for and collocated the nearest eddy identified on the same sampling day (see revised manuscript, Line 158). Therefore, no temporal averaging was applied, even when multiple profiles were collected within the same day. This situation may occur for ship-based CTD, and high frequency Argo, gliders, and/or APB observations, while the sampling location varies for each profile. Therefore, we consider daily averaging unnecessary.
137 the QC applied by WOD should be outlined
Response: Thank you for the suggestion. In the revised manuscript, we replaced the previously used World Ocean Database profiles with systematically re-quality-controlled and bias-corrected dataset (CODC-v1) from Zhang et al. (2024) and Tan et al. (2025), making the original World Ocean Database quality-control description unnecessary. We therefore revised this section accordingly (see revised manuscript, Lines 142-147) and removed the original sentence.
143 well then the grid is not uniform in general, only by layer. How do you define this layers, anyway?
Response: Thank you for pointing this out. We reduced the vertical grid resolution from 5 m to 10 m below 1,000 m and to 50 m below 1,500 m mainly to decrease storage requirements and computational memory usage. Considering that deep-ocean temperature variations are much weaker and smoother than those near the surface, this adjustment may not substantially affect the statistical characteristics of eddy-induced thermohaline anomalies (see Figs.11-12 in the revised manuscript). We have added a explanation of this to Lines 150-153 in the revision.
149 what is the maximum distance allowed?
Response: Thank you for bringing this to our attention. Considering that the influence of mesoscale eddies typically extends to approximately 1.5 times the eddy radius, profiles located beyond twice the eddy radius are all treated as background profiles unaffected by the eddy. Therefore, no strict maximum search distance is required.
However, to improve computational efficiency during the collocation process, we added a restriction in the revised manuscript such that eddy searching is only performed within a 10° × 10° grid box centered on each profile location (see revised manuscript, Line 157). Thank you for the helpful suggestion.
163 explain in the text how d, D, and R are defined
Response: The variable d denotes the distance between a profile and its collocated eddy center, D represents the distance from the eddy center to the eddy boundary along the same azimuthal direction as the profile, and R is eddy radius. We have added these definitions to Lines 177–176 in the revised manuscript. Thank you.
167 I don't understand this. If profiles are extracted only in the vicinity of eddies, wouldn't this induce a bias in the selection, which would not be random?
Response: We apologize for this incorrect wording. This sentence is revised as: “As most of the profiles were discretely sampled around or within different locations of different eddies, we cannot obtain the three-dimensional structure of a specific eddy”. (see revised manuscript, Lines 178-179)
173 this point is confusing. Being a 30-year period, trends may be relevant. How are these accounted for in your method? And aren't somehow eddy signatures visible in areas where those are more persistent? Also, how good is this product closer to the coasts?
Response: We apologize for the unclear wording. Our original intention was to indicate that monthly climatological means were removed from each profile to suppress seasonal-cycle signals and reduce aliasing caused by spatially heterogeneous historical sampling. We agree with you that ocean warming trend may be relevant. We intentionally retained this signal to facilitate applications of investigating recent trends in eddy-induced thermohaline anomalies and their potential driving mechanisms. Accordingly, we revised the sentence as follows: “To remove seasonal cycle signals and diminish aliasing due to sparse historical sampling, temperature/salinity anomalies were estimated for each profile by subtracting the corresponding climatological monthly mean value (Swart et al., 2018).” (see revised manuscript, Lines 183-184)
Satellite-observed eddy trajectories indicate that most oceanic eddies are continuously propagating features rather than stationary structures. Therefore, removing the local climatological monthly mean helps isolate eddy-induced anomalies from the background ocean state. We also note that eddy-induced anomalies could alternatively be estimated by directly computing mean differences between profile observations inside and outside eddies. We therefore provided the original profile data in the dataset to allow users to apply alternative approaches if desired. (see revised manuscript, Table 1)
As shown in the spatial distributions of profiles and collocated eddies (Figs.5a–5b), the vast majority of observations are located in the open ocean, where mesoscale eddies are more likely to persist. Therefore, we did not apply additional filtering for shallow coastal regions. The successful reconstruction of eddy three-dimensional structures in the northern Bay of Bengal example (Fig. 4) also suggests that this issue does not substantially affect the analysis. Nevertheless, users interested in coastal applications may choose to exclude shallow-water profiles depending on their research objectives.
186 "ambientes"?
Response: We have replaced “ambientes” with “eddy peripheries”. Thank you.
Fig 4a typo
Response: Corrected. Thank you.
Table 1 is missing units
Response: Units for all variables have been added in Table 1. Thank you.
Fig. 5 I don't understand if/how (a) and (b) should differ. Can also "glider" and "others" provide profiles? It would be useful to report data density by depth
Response: Thank you for bringing this to our attention. You are right that the spatial distributions of the profiles and their collocated eddies are identical because each profile is associated with one eddy. Therefore, we replaced panel (b) with the spatial distribution of all altimeter-detected eddy numbers. In addition, we added new panels showing the vertical distribution of data density and the seasonal variation of profile numbers (see Fig.5 in the revised manuscript or below).
The WOD dataset does include temperature and salinity profiles collected by gliders (Mishonov et al., 2024; Zhang et al., 2024). The “others” category mainly includes observations from ocean stations, moored buoys, and Mechanical Bathythermographs (MBTs). Since the numbers of these observations are relatively small, they were grouped together.
References:
Mishonov A.V., T. P. Boyer, O. K. Baranova, C. N. Bouchard, S. Cross, H. E. Garcia, R. A. Locarnini, C. R. Paver, J. R. Reagan, Z. Wang, D. Seidov, A. I. Grodsky, J. G. Beauchamp, (2024): World Ocean Database 2023. C. Bouchard, Technical Ed., NOAA Atlas NESDIS 97, 206 pp., doi.org/10.25923/z885-h264,
Zhang, B., et al. (2024), CODC-v1: a quality-controlled and bias-corrected ocean temperature profile database from 1940-2023, Scientific data, 11(1), 666, doi:10.1038/s41597-024-03494-8.
Fig.R3 Spatial and temporal distributions of eddy-collocated temperature and salinity (T-S) profile data in the global ocean between 1993 and 2021. a, Geographic distribution of the number of T-S profiles within 2°×2° grid boxes. b, The same as a but for the number of satellite-detected eddies. c, Yearly statistics of T-S profiles from different instruments. d, Yearly statistics of T-S profiles within cyclonic eddies (CE, d<R), anticyclonic eddies (AE, d<R), at eddy edges (R<d<2R), and at background fields (BG, d>2R). The purple and cyan lines are the percentages of profiles within CEs and AEs, respectively. e, The same as (c), but for the density of profile observations as a function of depth. f, The same as (d), but for monthly statistics of the profile data.
245 repetition, rephrase
Response: This sentence is revised as “Although the number of profiles exhibits pronounced interannual variability, the fraction of profiles located within mesoscale eddies remains relatively stable, with nearly 10 % occurring within cyclonic eddies and another 10 % within anticyclonic eddies, throughout the study period” (see revised manuscript, Lines 268-270). Thank you.
250 I am not following your reasoning. Wouldn't eddy trap lagrangian sensors?
Response: Thank you for pointing this out. We fully agree that eddies can trap Lagrangian sensors. However, XBT and CTD profiles are mainly collected from moving research vessels, APB observations follow marine animal trajectories, and gliders sample along pre-designed routes. Among the observing systems used here, only Argo floats drift with ocean currents. However, the deployment locations of Argo floats are largely random, with some floats entering eddies and others remaining outside. Additionally, most Argo floats park near 1000 m, where they are generally less influenced by mesoscale eddies, except in a few cases involving exceptionally strong eddies or shallow parking depths.
As shown in Figs.5-6, although the yearly number of profiles varies substantially, the fraction of profiles located within eddies stays at a relatively level of 10%. Additionally, the spatial distribution of the fraction of profiles within eddies is highly consistent with the occurrence probability of mesoscale eddies. Therefore, we conclude that, statistically, these profiles can be regarded as approximately randomly distributed relative to mesoscale eddy fields. We have added a detailed discussion on this to Lines 268-274 in the revised manuscript.
Fig. 6 why is the resolution different between left and right maps? What are Gamma and P?
Response: Thank you for pointing this out. In the left panels of Fig.6, percentages of profiles located within eddies were estimated within 2° × 2° grid boxes. This grid size represents a compromise between maintaining sufficiently high spatial resolution and ensuring an adequate number of profile samples within each grid box for the subsequent estimation of eddy thermohaline impacts.
In contrast, the right panels show the fraction of days during which each location was occupied by mesoscale eddies (within eddy interiors) over the study period. Since this calculation is not limited by sample numbers, we chose to provide a high spatial detail by performing the statistics directly on the original altimetry grid points (0.25° × 0.25°).
Gamma (Γ) denotes the percentage of profile data within eddies and P represents the occurrence probability of mesoscale eddies. These definitions have been added to the revised Figure caption.
204 please explain how interpolation is made
Response: We added a description of the interpolation method in the revised manuscript. Specifically, at each depth level, profile observations were interpolated onto standard 0.1R×0.1R grid boxes using ordinary kriging interpolation. (see revised manuscript, Lines 216-217)
271 this is not clear
Response: This sentence has been revised as: “The resulting profiles are relatively evenly distributed both within eddies and in the surrounding regions” (see revised manuscript, Line 359-360). Thank you.
296 this should be shown with your dataset, as it should be possible
Response: Thank you for your suggestion. To maintain the overall organization and clarity of the figures, we did not include a separate comparison of the vertical stratification structures (e.g., vertical temperature gradients) between the two regions. Instead, we added contours of the vertical temperature structure within eddies (gray contours) to the Figs.11 and 12. These contours show that the Gulf Stream region exhibits denser temperature contours than the Kuroshio Extension region, indicating stronger vertical temperature gradients and thus weaker vertical stratification in the Gulf Stream region. Since this section mainly aims to demonstrate potential applications of the dataset in eddy studies, we removed the speculative discussion regarding the underlying mechanisms in the revised manuscript to avoid overinterpretation.
344 no references in these and close sections, only self-citation?
Response: Thank you for pointing this out. We have added citations to additional relevant previous studies to lines 425, 429, 432, 443, 457, and 459 in the revised manuscript.
352 as in 296, this remains speculative and should be verified
Response: Thank you for your suggestion. We agree that this is an interesting phenomenon whose underlying mechanisms deserve dedicated investigation in future studies. Since the primary focus of this manuscript is to introduce the dataset and demonstrate its potential applications, we did not attempt a detailed mechanistic analysis here. In the revised manuscript, we replaced the mechanism discussion with a state that “This result highlights that eddies with similar surface signatures can exhibit markedly different subsurface structures. The dataset therefore provides important observational constraints on the subsurface characteristics of mesoscale eddies that are not discernible from satellite observations alone”. (see revised manuscript, Lines 434-437)
386 was this defined already?
Response: Thank you for your careful reading. MLD here was calculated for each profile as the depth at which the increase in potential density relative to the surface equals the increase in surface potential density associated with a 0.5°C decrease in sea-surface temperature (de Boyer Montégut et al., 2004). We have added the definition of MLD to Lines 466–469 in the revised manuscript.
408 include references and key details, e.g. the climatology used
Response: Thank you for your suggestion. We added the references regarding marine heatwaves and cold spells (Oliver et al., 2021; Schlegel et al., 2021) to Lines 495–496 of the revised manuscript.
References:
Oliver, E. C. J., J. A. Benthuysen, S. Darmaraki, M. G. Donat, A. J. Hobday, N. J. Holbrook, R. W. Schlegel, and A. Sen Gupta (2021), Marine Heatwaves, Annual review of marine science, 13, 313-342, doi:10.1146/annurev-marine-032720-095144.
Schlegel, R. W., S. Darmaraki, J. A. Benthuysen, K. Filbee-Dexter, and E. C. J. Oliver (2021), Marine cold-spells, Prog Oceanogr, 198, 102684, doi:10.1016/j.pocean.2021.102684.
416 daily or coarser climatology?
Response: Because the profile observations are not temporally continuous, the threshold for identifying extreme temperature anomalies was defined as the 95th percentile of all temperature anomalies within the study period (1993–2021). We added this clarification in Lines 500–503 of the revised manuscript. Thank you.
Fig. 12 is the spatial resolution still 2 deg in these plots?
Response: Yes. Owing to the substantial increase in the number of collocated profiles in the updated dataset, we used a 2° × 2° statistical grid in this analysis. The resulting spatial patterns are highly consistent with our previous results based on 5° × 5° grid boxes, suggesting that the updated dataset enables investigation of eddy impacts on extreme temperature events at finer spatial scales. We added discussion of this point in Lines 518-522 of the revised manuscript.
436 this section seems too sketched and unsupported
Response: Thank you for this helpful comment. We agree that eddy-induced heat and salt transport is a complex topic that cannot be adequately addressed through a brief analysis. Reliable estimates of eddy-induced heat, salt, and water-mass transports require not only detailed thermohaline information but also constraints on eddy velocity structures and associated dynamical fields, which remain challenging to obtain from observations alone. Consequently, substantial uncertainties still exist in observational estimates of eddy transports, and a comprehensive assessment is beyond the scope of the present study.
Our intention in this section was not to provide quantitative estimates of eddy-induced heat or salt transport, but rather to highlight a potential application of the dataset. As this manuscript focuses on the description and evaluation of the dataset, we believe it is useful to discuss how the expanded profile archive may support future observational studies of eddy transport processes. Following your comment, we have revised this section in a more cautious discussion-oriented tone. The revised text now emphasizes the potential of the dataset to provide observational constraints on future transport estimates, rather than implying that such estimates are performed or validated in the present study. (see revised manuscript, Lines 529-544)
472 a single 10GB file is not ideal; why not splitting by basin, for example? The file is also lacking metadata (reference, data sources) and units. Why is "contour" and "char" set to 20?
Response: Thank you for your valuable suggestion. We have split the original large dataset into multiple smaller files by year and added metadata information, including data sources and references, and units. The variables “contour” and “char” remain set to 20 because these values were directly inherited from the original eddy dataset, and we did not modify them.
475 the specific link should be provided
Response: Thank you for pointing this out. We updated the manuscript with the specific link to the eddy dataset: https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product/meta3-2-dt.html. (see revised manuscript, Lines 616-617)
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AC1: 'Reply on RC1', Qingyou He, 08 Jun 2026
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RC2: 'Comment on essd-2026-89', Anonymous Referee #2, 15 Apr 2026
Below are my comments on the eddy-collocated temperature and salinity profile dataset by He et al.
This manuscript presents a global eddy-collocated T–S profile archive that links 2.35 million quality-controlled WOD profiles to satellite-detected mesoscale eddies over the altimetry era. The scale is impressive, with 2.35 million quality-controlled profiles spanning 1993–2021, broad spatial coverage, and a standardized radius-normalized eddy coordinate system. The manuscript is generally well structured, the motivation is clear. Generally, I think this manuscript is worthwhile to publish, but there are several major concerns must be addressed before the publication.
Major Comments
1. Independent dataset cross-validation: One of the major limitations of the manuscript is the complete absence of any reference to, or comparison with, two datasets that are directly analogous in scope and purpose to the one proposed here.
First, Ioannou et al. (2024) presents a global TOEddies atlas explicitly integrated with nearly 3 million collocated Argo float profiles spanning 2000–2023, with the express aim of providing "a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties", language that is nearly identical to the framing of the present manuscript. The accompanying open-access dataset (Laxenaire et al., 2024; SEANOE, https://www.seanoe.org/data/00917/102877/) is publicly available. Second, Simoes-Sousa et al. (2026, Earth System Science Data, https://doi.org/10.5194/essd-18-1089-2026) also presents another global ocean profile and altimetry-derived eddy colocation product published in the same journal. Neither work is cited anywhere in the manuscript.
These omissions are not peripheral. Any reader of an ESSD paper will immediately ask: how does this dataset differ from the TOEddies + Argo product, and from Simoes-Sousa et al. 2026? Which is preferable for which applications? What does the added value of the proposed dataset? The authors should directly and substantively engage with these prior works. At a minimum, this requires (a) citing and describing both datasets in the Introduction, (b) articulating clearly what the proposed product offers that the others do not, and (c) providing a quantitative cross-dataset comparison in at least one or two representative regions. Without this discussion, the novelty claim of the manuscript is substantially undermined.
2. Validation is largely qualitative and insufficient: The current validation strategy relies primarily on reproducing mean eddy structures that are already established in the literature (Figs. 4, 7, and 8) and on spatial percentage maps (Figs. 9–12). While demonstrating consistency with prior results is a useful first step, it does not by itself persuasively establish the accuracy, uncertainty, or false-match rate of the proposed dataset. I would recommend two or three of the following additions:
(1) Can add quantitative metrics beyond visual pattern comparison. In selected representative regions, the authors should directly compare anomaly amplitudes, core depths, radial structure widths, and sign-consistency rates between their product and those of independently published composites. Particular attention should be paid to energetic regions, the Gulf Stream, Kuroshio Extension, ACC, and the Agulhas leakage region, where eddies cluster densely and the nearest-eddy assumption is most susceptible to failure (see Major Comment 6 below).
(2) Can add time-series validation. Showing that the seasonal and interannual variability of eddy thermohaline anomalies recovered from the dataset is consistent with independent observations in selected regions would substantially strengthen confidence in the product.
(3) Carry out cross-product validation against the TOEddies + Argo dataset (Ioannou et al., 2024; Laxenaire et al., 2024) and/or the Simoes-Sousa et al. (2026) product. Differences in anomaly amplitude, radial structure, and regional data density would reveal where the methodological choices (eddy product, colocation rule, QC criteria) lead to materially different results, which is itself valuable scientific information.
3. The Agulhas retroflection and the Cape Basin are one of the hotspots of global eddy activity (Schubert et al., 2021) and have been studied in detail using eddy-profile colocation methods by Laxenaire et al. (2019, 2020). Given that it is precisely this region where the limitations of the chosen eddy product and colocation methodology are most acute (see Major Comment 6 below), It would therefore be valuable for the authors to include more discussion of this region (e.g., Figs 7-8). This region deserves a dedicated attention. For example, can reference or compare with the individual-eddy reconstructions of Laxenaire et al. (2019, 2020)
4. The quality control processes of the original WOD profile: In Sections 2.2 and 2.3, the dataset is derived using WOD quality flags.
First, the ambiguity in the flag specification must be resolved. In Line 134, the author said that ‘we extracted temperature and salinity profiles with quality control flags marked as ‘0’ (accepted) during the period of satellite’. Actually, WOD provides several types of quality control flags (Salinity_WODflag, Salinity_WODprofileflag, Depth_WODflag etc.), and different types of flags have different performance to identify outliers (an example can be found in the Fig. 9a of Tan et al., 2025). The manuscript does not specify which flags were applied or how they were combined. This must be clarified.
Second, the WOD quality-control flags may be too weak to identify outliers. This issue has been discussed in detail by Tan et al. (2023, Figs. 14–15), Tan et al. (2025, Fig. 14), and Good et al. (2023, Fig. 2). These serial studies suggest that, even after quality control based only on WOD flags, the temperature and salinity data may still contain a substantial number of non-negligible outliers, especially in observations in the pre-Argo era. Visual evidence of residual outliers appears to be present in the author’s manuscript: the vertical sections in Figs. 8a, 8e, and 8f display suspicious spikes or 'dirty points' at approximately 700 m, 1000 m, and 500 m depth, respectively, and Fig. 10c shows an anomalously large spike in the orange line near 35°N that is absent from the corresponding blue line at the same latitude. Such outliers can strongly bias mean and standard deviation estimates (as shown in Fig. 8 of Tan et al. (2025) and Supplementary Fig. S1 of Zhang et al. (2024)) and propagate errors through vertical interpolation into the final eddy anomaly composites. Another example is that when I tried to validate the author’s netCDF dataset, if I calculate the salinity standard deviation in each grid box at some selected standard depth levels, I can find that there are many ‘suspicious spikes’ or ‘discontinuous blood-red spots’ in the open seas (see my attachment), and this is very likely due to the QC performance. Moreover, if the authors try to calculate the standard deviation map of the Fig. 7 and Fig. 8, it is likely that the these ‘discontinuous blood-red spots’ occurs.
Anyway, quality control is non-negligible and is one of the largest uncertainty sources in the T(OHC)/S estimate (Boyer et al., 2016), especially to resolve the mesoscale process (Tan et al., 2022 states ‘Although the large-scale pattern is similar on a global-basin scale, its meso-micro scale features are visibly different’; Yuan et al., 2026 states that ‘An investigation indicates that the WOD local climatological range in its QC check, which is constructed by all historical data, mainly represents the historical ocean conditions and thus removes more positive but realistic positive temperature anomalies in the eddy-rich regions (Boundary Currents and Antarctic Circumpolar Currents regions) than CODC’). Therefore, the impact of QC on the final dataset should be taken carefully.
One possible solution is that the author could use the CODC (CAS-Oceanographic Data Center, Global Ocean Science Database) quality-controlled and bias-corrected in situ ocean temperature and salinity observation dataset (http://www.ocean.iap.ac.cn/ftp/cheng/CODCv2_Insitu_T_S_database/) either as a primary data source or as a cross-check. The main data source of the CODC is also the WOD, but it provides the quality control flag that can remove the outliers as much as possible (more than the WOD-QC) with minimizing the possibility of mistakenly flagging good data (more details could be found in Zhang et al., 2024, and Tan et al., 2025).
In addition, the author should remove profiles on the Argo grey list (WOD doesn’t remove them in their quality control scheme), which may contain significant salinity drift. I didn’t find any information about whether the author removed the grey list or not in the manuscript. If the author hasn’t removed it yet, please remove it.
5. Systematic instrument biases: Figure 5c shows that XBT, bottle (OSD), and APB data are included in the dataset, although as a small fraction of the total. Each of these instrument types is known to have systematic instrumental biases in the WOD archive: XBT depth and temperature errors have been documented and corrected in many studies (e.g., Cheng et al., 2014); bottle–CTD temperature inconsistencies have been quantified by Gouretski et al. (2022); and APB temperature biases, which are especially relevant for Southern Ocean coverage, have been characterised by Gouretski et al. 2024. These biases are not negligible when the goal is to quantify mesoscale eddy thermohaline anomalies at the level of tenths of a degree Celsius or hundredths of a practical salinity unit.
The authors should either apply the published bias corrections for each instrument type, or demonstrate quantitatively that including these instruments does not materially affect the eddy anomaly estimates in the relevant regions and time periods. For example, the CODC quality-controlled and bias-corrected in situ ocean temperature and salinity dataset mentioned in my previous point also includes temperature profiles that had been bias-corrected. It would be valuable if the authors could consider using bias-corrected data in the proposed dataset, or at least assess whether excluding XBT, OSD, and APB data would have a disproportionate effect on certain regions or time periods.
6. The choice of eddy detection product and the nearest-eddy colocation assumption: The manuscript uses the META3.2 product for eddy detection and assigns each profile to the nearest eddy on its sampling day. Both choices carry limitations that deserve explicit discussion:
(1) META3.2 versus more sophisticated eddy atlases. Laxenaire et al. (2018) developed the TOEddies algorithm and validated it systematically against an independent dataset of upper-ocean eddies identified from surface drifters. Their results show that TOEddies correctly identifies approximately 10-15% more validated eddies than META, with lower polarity-mismatch rates, particularly for structures in the 25–60 km radius range. Critically, META3.2 does not detect eddy merging and splitting events. Laxenaire et al. (2018) and Ioannou et al. (2024) demonstrate that these events are abundant, concentrated in the most energetic regions, such as the Cape Basin, western boundary currents, and the ACC, and affect approximately 3% of all detected eddies. When a splitting event occurs while a profile is sampled, META will assign the profile to one fragment while the hydrographic anomaly may be centred in another. The manuscript neither acknowledges this source of contamination nor discusses the choice of META over alternatives.(2) Subsurface-intensified eddies. Laxenaire et al. (2019) demonstrate through a Lagrangian reconstruction of a single Agulhas ring that eddies can transition from surface-intensified to subsurface-intensified structures as they propagate, retaining large density and temperature anomalies at depth (200–1200 m) while their sea-surface height signature diminishes. Laxenaire et al. (2020) confirm statistically that the majority of Agulhas rings in the South Atlantic are subsurface-intensified. For these eddies, the eddy centre and radius inferred from altimetry will not accurately reflect the location of the thermohaline anomaly core, meaning that radius-normalised distances assigned to the colocated profiles are systematically in error. This limitation may apply not only in the Agulhas region but also to any subsurface-intensified eddy family.
(3) The nearest-eddy assumption in energetic regions. In regions where eddies cluster densely, including the Gulf Stream, Kuroshio Extension, ACC, and Cape Basin, many profiles will be located near multiple eddies simultaneously, and the nearest eddy in terms of centre-to-centre distance may not be the one whose dynamics dominate the observed hydrography. The manuscript acknowledges this issue briefly (lines 155–161) but does not quantify its magnitude. A useful diagnostic would be to compute, as a function of region, the fraction of profiles for which the nearest eddy is also the eddy within whose boundary (outer contour) the profile falls, information that is directly available from the META3.2 eddy contour data.
Minor Comments1. Dataset temporal coverage: The abstract and methods emphasize ‘1993–2021’, whereas the conclusions state ‘1993–2022’. This must be reconciled throughout.
2. Table 1 should include units for each variable.
3. Section 2.3: How did the author handle the large vertical gap below 200m? Are there any criteria for these intervals below 200m? Maybe the author can also refer to the methods used in Gouretski 2018 (h = 20 + 0.24⋅z, where z is the mean distance between the two adjacent levels in meters).
4. Figure 5a and Figure 5b: add the upper triangle to the colorbar to indicate ‘more than’ 400 profiles.
5. Figure 7a: The grey central band visible in the South China Sea cyclonic eddy panel of Fig. 7a is unexplained. Please add the explanation.
6. Figure 11 and Section 4.2: how did the author define the MLD? Please add the details about the definition. For example, the density threshold, temperature threshold, or hybrid algorithm used (and the reference depth).
7. For Figures 9–12, it would be great to overlay or provide sample count maps/contour lines, because visual interpretation of low-data regions is otherwise difficult.
8. Laxenaire et al., 2020 shows that composite methods systematically underestimate peak heat content anomalies at the eddy centre relative to individual reconstructions, because eddies of different sizes and intensities are pooled in a common normalised coordinate system. The manuscript should acknowledge this limitation and note that the mean anomaly fields it provides do not capture the full range of eddy variability.
9. Some typos should be taken care of. For example:
Line 90: “form the WOD” should be “from the WOD.”
Line 193: “the choose of profiles” should be “the choice of profiles.”
Line 251: “random sampled” should be “randomly sampled.”
Line 453: “to what extend” should be “to what extent.”
Line 471: “Zendo” should be “Zenodo” in the data availability section
References
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Cheng, L., J. Zhu, R. Cowley, T. P. Boyer, and S. Wijffels, 2014: Time, probe type and temperature variable bias corrections on historical expendable bathythermograph observations. J. Atmos. Oceanic Technol., 31, 1793–1825.
Good, S., and Coauthors, 2023: Benchmarking of automatic quality control checks for ocean temperature profiles and recommendations for optimal sets. Front. Mar. Sci., 9, 1075510.
Gouretski, V., 2018: World Ocean Circulation Experiment–Argo global hydrographic climatology. Ocean Sci., 14, 1127–1146.
Gouretski, V., Cheng, L., and Boyer, T., 2022: On the consistency of the bottle and CTD profile data. J. Atmos. Oceanic Technol., 39, 1869–1887.
Gouretski, V., Roquet, F., and Cheng, L., 2024: Measurement biases in ocean temperature profiles from marine mammal dataloggers. J. Atmos. Oceanic Technol., 41, 629–645.
Ioannou, A., Guez, L., Laxenaire, R., and Speich, S., 2024: Global assessment of mesoscale eddies with TOEddies: comparison between multiple datasets and colocation with in situ measurements. Remote Sensing, 16, 4336.
Laxenaire, R., Speich, S., Blanke, B., Chaigneau, A., Pegliasco, C., and Stegner, A., 2018: Anticyclonic eddies connecting the western boundaries of Indian and Atlantic Oceans. J. Geophys. Res. Oceans, 123, 7651–7677.
Laxenaire, R., Speich, S., and Stegner, A., 2019: Evolution of the thermohaline structure of one Agulhas Ring reconstructed from satellite altimetry and Argo floats. J. Geophys. Res. Oceans, 124, 8969–9003.
Laxenaire, R., Speich, S., and Stegner, A., 2020: Agulhas ring heat content and transport in the South Atlantic estimated by combining satellite altimetry and Argo profiling floats data. J. Geophys. Res. Oceans, 125, e2019JC015511.
Laxenaire, R., Guez, L., Chaigneau, A., Isic, M., Ioannou, A., and Speich, S., 2024: TOEddies Global Mesoscale Eddy Atlas Colocated with Argo Float Profiles. SEANOE. https://www.seanoe.org/data/00917/102877/
Pegliasco, C., Delepoulle, A., Mason, E., Morrow, R., Faugère, Y., and Dibarboure, G., 2022: META3.1exp: a new global mesoscale eddy trajectory atlas derived from altimetry. Earth Syst. Sci. Data, 14, 1087–1107.
Schubert, R., and Coauthors, 2021: Evidence of eddy-related deep-ocean current variability in the northeast Atlantic Ocean induced by the Gulf Stream and the Brazilian Current / North Brazil Current systems. Ocean Sci., 17, 1–25.
Simoes-Sousa, I., and Coauthors, 2026: Integrating global ocean profiles and altimetry-derived eddies. Earth Syst. Sci. Data, 18, 1089–1101.
Tan, Z., Zhang, B., Wu, X., Dong, M., and Cheng, L., 2022: Quality control for ocean observations: from present to future. Sci. China Earth Sci., 65, 215–233.
Tan, Z., and Coauthors, 2023: A new automatic quality control system for ocean profile observations and impact on ocean warming estimate. Deep-Sea Res. I, 194, 103961.
Tan, Z., and Coauthors, 2025: CODC-S: a quality-controlled global ocean salinity profiles dataset. Sci. Data, 12, 917.
Zhang, B., and Coauthors, 2024: CODC-v1: a quality-controlled and bias-corrected ocean temperature profile database from 1940–2023. Sci. Data, 11, 666.
Yuan, H., Cheng, L., Pan, Y., Zhang, B., Meyssignac, B., Trenberth, K., Zhu, Y., Song, X., Zheng, H., Bao, S. and Du, J., 2026. Six-fold reduction in ocean heat content estimate uncertainty since 1960. (preprint at https://doi.org/10.21203/rs.3.rs-9248956/v1)-
AC2: 'Reply on RC2', Qingyou He, 08 Jun 2026
Comment from Referee #2
Below are my comments on the eddy-collocated temperature and salinity profile dataset by He et al.
This manuscript presents a global eddy-collocated T–S profile archive that links 2.35 million quality-controlled WOD profiles to satellite-detected mesoscale eddies over the altimetry era. The scale is impressive, with 2.35 million quality-controlled profiles spanning 1993–2021, broad spatial coverage, and a standardized radius-normalized eddy coordinate system. The manuscript is generally well structured, the motivation is clear. Generally, I think this manuscript is worthwhile to publish, but there are several major concerns must be addressed before the publication.
Response: Thank you very much for your positive evaluation of our manuscript. We greatly appreciate your constructive comments and suggestions. We have carefully studied all of your concerns and performed substantial revisions to improve the quality, clarity, and robustness of the manuscript and dataset. In particular, we updated the profile dataset using the CODC quality-controlled and bias-corrected observations, expanded the validation analyses, added comparisons with recently published eddy-collocated datasets, clarified methodological details, and strengthened the discussion of dataset limitations and potential applications. Below, we provide a detailed point-by-point response to all of your comments and suggestions.
Major Comments
- Independent dataset cross-validation: One of the major limitations of the manuscript is the complete absence of any reference to, or comparison with, two datasets that are directly analogous in scope and purpose to the one proposed here.
First, Ioannou et al. (2024) presents a global TOEddies atlas explicitly integrated with nearly 3 million collocated Argo float profiles spanning 2000–2023, with the express aim of providing "a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties", language that is nearly identical to the framing of the present manuscript. The accompanying open-access dataset (Laxenaire et al., 2024; SEANOE, https://www.seanoe.org/data/00917/102877/) is publicly available. Second, Simoes-Sousa et al. (2026, Earth System Science Data, https://doi.org/10.5194/essd-18-1089-2026) also presents another global ocean profile and altimetry-derived eddy colocation product published in the same journal. Neither work is cited anywhere in the manuscript.
These omissions are not peripheral. Any reader of an ESSD paper will immediately ask: how does this dataset differ from the TOEddies + Argo product, and from Simoes-Sousa et al. 2026? Which is preferable for which applications? What does the added value of the proposed dataset? The authors should directly and substantively engage with these prior works. At a minimum, this requires (a) citing and describing both datasets in the Introduction, (b) articulating clearly what the proposed product offers that the others do not, and (c) providing a quantitative cross-dataset comparison in at least one or two representative regions. Without this discussion, the novelty claim of the manuscript is substantially undermined.
Response: Thank you for this important suggestion. We carefully reviewed both of the two studies and datasets, which are indeed highly relevant to the present work. Ioannou et al. (2024) presented a global TOEddies atlas integrated with nearly 3 million collocated Argo float profiles spanning 2000–2023. Subsequently, Simoes-Sousa et al. (2026) expanded this framework by incorporating additional profile observations from other instruments, including CTD, XBT, autonomous pinniped bathythermograph (APB), and gliders, producing an eddy-collocated dataset based on 4.2 million profiles and the altimetry-derived META3.2 DT eddy product. However, in both of the two datasets, profiles were only classified according to whether they were located inside or outside eddies. The absence of information on the distance and azimuth between profiles and eddy centers limits the reconstruction of three-dimensional eddy thermohaline structures and characterization of the radial dependence of eddy impacts. In the present dataset, we fill this important gap by providing the relative distance and azimuthal angle of each profile relative to its collocated eddy, thereby facilitating reconstruction of regional mean three-dimensional eddy structures and analyses of eddy impacts on regional ocean environments.
In this revised version, we updated the profile database by adopting the quality-controlled and bias-corrected CODC temperature and salinity profiles recommended by you. This update increases the number of high-quality collocated profiles to 5.46 million, substantially improving the capability for investigating eddy-induced thermohaline anomalies at finer spatial and temporal scales.
Accordingly, we have added descriptions and citations of both of the two datasets in the Introduction (Lines 84–94 of the revised manuscript), clarified the differences and advantages of the present product (Lines 251–258 of the revised manuscript), and included cross-dataset comparisons between this new dataset, previous dataset, and the Simoes-Sousa et al. (2026) product in terms of the spatial distributions of eddy-induced temperature and salinity anomalies (Figs.7-10 and Lines 294–352 of the revised manuscript). We sincerely appreciate this valuable suggestion.
- Validation is largely qualitative and insufficient: The current validation strategy relies primarily on reproducing mean eddy structures that are already established in the literature (Figs. 4, 7, and 8) and on spatial percentage maps (Figs. 9–12). While demonstrating consistency with prior results is a useful first step, it does not by itself persuasively establish the accuracy, uncertainty, or false-match rate of the proposed dataset. I would recommend two or three of the following additions:
(1) Can add quantitative metrics beyond visual pattern comparison. In selected representative regions, the authors should directly compare anomaly amplitudes, core depths, radial structure widths, and sign-consistency rates between their product and those of independently published composites. Particular attention should be paid to energetic regions, the Gulf Stream, Kuroshio Extension, ACC, and the Agulhas leakage region, where eddies cluster densely and the nearest-eddy assumption is most susceptible to failure (see Major Comment 6 below).
(2) Can add time-series validation. Showing that the seasonal and interannual variability of eddy thermohaline anomalies recovered from the dataset is consistent with independent observations in selected regions would substantially strengthen confidence in the product.
(3) Carry out cross-product validation against the TOEddies + Argo dataset (Ioannou et al., 2024; Laxenaire et al., 2024) and/or the Simoes-Sousa et al. (2026) product. Differences in anomaly amplitude, radial structure, and regional data density would reveal where the methodological choices (eddy product, colocation rule, QC criteria) lead to materially different results, which is itself valuable scientific information.
Response: Thank you for your valuable suggestions. Following your recommendations (as well as Major Comments 4 and 5 below), we replaced the original profiles with the quality-controlled and bias-corrected CODC dataset, increasing the total number of high-quality profiles to 5.46 million. We then compared the revised dataset with both our previous product and the Simoes-Sousa et al. (2026) dataset. Specifically, we estimated mean eddy-induced temperature and salinity anomalies within each 2° × 2° grid box at representative depths of 20 m, 200 m, and 800 m, and compared the results with those derived from He et al. (2024b) and Simoes-Sousa et al. (2026). The horizontal distributions and vertical structures of eddy-induced temperature and salinity anomalies show high consistency among the three products, with spatial correlation coefficients exceeding 0.8 at most of the analyzed depths (p<0.05) (p < 0.05). (see Figs. 7–10 in the revised manuscript or the Figs. below)
Fig.R1 Geographic distribution of mean temperature anomalies within anticyclonic eddies, estimated within each 2°×2° grid box, at the depths of (top to bottom) 20 m, 200 m, and 800 m. The left three panels are eddy-induced temperature anomalies estimated from the profile dataset in this study, He et al. (2024b) (He24), and Simoes-Sousa et al. (2026) (SS26), respectively. The right panel is the zonal mean of the left three panels.
Fig.R2 The same as Fig.R1, but for mean temperature anomalies within cyclonic eddies.
Fig.R3 The same as Fig.R1, but for mean salinity anomalies within anticyclonic eddies.
Fig.R4 The same as Fig.R2, but for mean salinity anomalies within cyclonic eddies.
Notably, temperature anomalies derived from the Simoes-Sousa et al. (2026) dataset exhibit stronger noisy features near the sea surface than those from the other two datasets (Figs. 7a–7c and 8a–8c). A likely explanation is that their dataset excluded eddies with lifetimes shorter than 30 days or mean amplitudes smaller than 2.5 cm. As a result, some profiles that would otherwise be associated with these weak eddies may instead be assigned to neighboring eddies, introducing additional uncertainty into the estimated temperature anomalies. Because these excluded eddies are typically weak and have relatively limited vertical influence, the impact of their exclusion is expected to decrease with depth, consistent with the reduced differences among the datasets in the subsurface ocean.
Because approximately 80% of the profiles used in He et al. (2024b) were derived from Argo observations, the corresponding results can be regarded as largely Argo-based, similar to the TOEddies + Argo dataset (Ioannou et al., 2024; Laxenaire et al., 2024). Thus, we did not specifically provide the results of TOEddies + Argo dataset in the manuscript. In the updated dataset in present study, a substantial number of observations from additional platforms were incorporated. Yet, the resulting temperature anomaly patterns remain highly consistent with the earlier results (Figs.7 and 8). This agreement suggests that the inclusion of observations from multiple observing systems does not materially alter the statistical characteristics of the estimated eddy imprints. Rather, the increased sampling density may enhance the reconstruction of mean three-dimensional eddy structures, particularly at regional scales (Fig.4).
In addition, we expanded the discussion and comparison of eddy-induced temperature anomaly intensities and core depths in representative regions, including the Bay of Bengal (Sarma et al., 2018; Sarma et al., 2020), the South China Sea (He et al., 2018; Sun et al., 2018; Yang et al., 2015), tropical southeastern Indian Ocean (Yang et al., 2015), southeastern Pacific Ocean (Chaigneau et al., 2011), Kuroshio Extension (Dong et al., 2017; Sun et al., 2017), Southern Ocean ( Frenger et al., 2015), and Brazil–Malvinas Confluence (Mason et al., 2017). (see revised manuscript, Lines 365-367 and 375-381)
Due to limitations in observational sampling, previous studies have rarely examined radial structure widths or the seasonal/interannual variability of eddy thermohaline anomalies. Therefore, we did not include additional validation for these aspects here. Nevertheless, the substantially enlarged profiles in this updated produced provides strong support for future analyses in these directions. We have added a note of this to Lines 455-458 in the revised manuscript. We sincerely appreciate your valuable suggestions.
- The Agulhas retroflection and the Cape Basin are one of the hotspots of global eddy activity (Schubert et al., 2021) and have been studied in detail using eddy-profile colocation methods by Laxenaire et al. (2019, 2020). Given that it is precisely this region where the limitations of the chosen eddy product and colocation methodology are most acute (see Major Comment 6 below), It would therefore be valuable for the authors to include more discussion of this region (e.g., Figs 7-8). This region deserves a dedicated attention. For example, can reference or compare with the individual-eddy reconstructions of Laxenaire et al. (2019, 2020).
Response: Thank you for your valuable suggestion. As you noted, the Agulhas retroflection and Cape Basin are among the global hotspots of mesoscale eddy activity. Unfortunately, we were unable to locate the specific reference listed as “Schubert et al. (2021)” in your comments. In the revised manuscript, we present the vertical structures of eddy-induced temperature and salinity anomalies in the Brazil–Malvinas Confluence region and compare them with the results reported by Mason et al. (2017) (see Figs.11g and 12g). We also carefully studied the work by Laxenaire et al. (2019, 2020), which elegantly demonstrated the evolution of Agulhas ring thermohaline structures during their westward propagation. Following your suggestion, we added citations and discussion of these studies in Lines 455–458 of the revised manuscript to further highlight the potential application of the present dataset for investigating temporal evolution of eddy structures.
- The quality control processes of the original WOD profile: In Sections 2.2 and 2.3, the dataset is derived using WOD quality flags.
First, the ambiguity in the flag specification must be resolved. In Line 134, the author said that ‘we extracted temperature and salinity profiles with quality control flags marked as ‘0’ (accepted) during the period of satellite’. Actually, WOD provides several types of quality control flags (Salinity_WODflag, Salinity_WODprofileflag, Depth_WODflag etc.), and different types of flags have different performance to identify outliers (an example can be found in the Fig. 9a of Tan et al., 2025). The manuscript does not specify which flags were applied or how they were combined. This must be clarified.
Second, the WOD quality-control flags may be too weak to identify outliers. This issue has been discussed in detail by Tan et al. (2023, Figs. 14–15), Tan et al. (2025, Fig. 14), and Good et al. (2023, Fig. 2). These serial studies suggest that, even after quality control based only on WOD flags, the temperature and salinity data may still contain a substantial number of non-negligible outliers, especially in observations in the pre-Argo era. Visual evidence of residual outliers appears to be present in the author’s manuscript: the vertical sections in Figs. 8a, 8e, and 8f display suspicious spikes or 'dirty points' at approximately 700 m, 1000 m, and 500 m depth, respectively, and Fig. 10c shows an anomalously large spike in the orange line near 35°N that is absent from the corresponding blue line at the same latitude. Such outliers can strongly bias mean and standard deviation estimates (as shown in Fig. 8 of Tan et al. (2025) and Supplementary Fig. S1 of Zhang et al. (2024)) and propagate errors through vertical interpolation into the final eddy anomaly composites. Another example is that when I tried to validate the author’s netCDF dataset, if I calculate the salinity standard deviation in each grid box at some selected standard depth levels, I can find that there are many ‘suspicious spikes’ or ‘discontinuous blood-red spots’ in the open seas (see my attachment), and this is very likely due to the QC performance. Moreover, if the authors try to calculate the standard deviation map of the Fig. 7 and Fig. 8, it is likely that the these ‘discontinuous blood-red spots’ occurs.
Anyway, quality control is non-negligible and is one of the largest uncertainty sources in the T(OHC)/S estimate (Boyer et al., 2016), especially to resolve the mesoscale process (Tan et al., 2022 states ‘Although the large-scale pattern is similar on a global-basin scale, its meso-micro scale features are visibly different’; Yuan et al., 2026 states that ‘An investigation indicates that the WOD local climatological range in its QC check, which is constructed by all historical data, mainly represents the historical ocean conditions and thus removes more positive but realistic positive temperature anomalies in the eddy-rich regions (Boundary Currents and Antarctic Circumpolar Currents regions) than CODC’). Therefore, the impact of QC on the final dataset should be taken carefully.
One possible solution is that the author could use the CODC (CAS-Oceanographic Data Center, Global Ocean Science Database) quality-controlled and bias-corrected in situ ocean temperature and salinity observation dataset (http://www.ocean.iap.ac.cn/ftp/cheng/CODCv2_Insitu_T_S_database/) either as a primary data source or as a cross-check. The main data source of the CODC is also the WOD, but it provides the quality control flag that can remove the outliers as much as possible (more than the WOD-QC) with minimizing the possibility of mistakenly flagging good data (more details could be found in Zhang et al., 2024, and Tan et al., 2025).
In addition, the author should remove profiles on the Argo grey list (WOD doesn’t remove them in their quality control scheme), which may contain significant salinity drift. I didn’t find any information about whether the author removed the grey list or not in the manuscript. If the author hasn’t removed it yet, please remove it.
Response: Thank you for your careful analysis and valuable suggestions regarding quality control. In the original manuscript, our quality-control strategy consisted of selecting profiles with “WOD_observation_flag = 0” (accepted), and further excluding profiles with course vertical resolution, including profiles that (1) lacked measurements shallower than 20 m or deeper than 200 m, (2) contained fewer than 10 unique samples within the upper 200 m, or (3) exhibited vertical sampling intervals larger than 15 m between 0–100 m or larger than 25 m between 100–200 m. As you pointed out, these procedures may be insufficient to fully identify outliers. Following your recommendation, we replaced the original WOD profiles with the CODC quality-controlled and bias-corrected in situ temperature and salinity dataset in this revised version. We then compared the resulting product with both our previous dataset and the Simoes-Sousa et al. (2026) product, and found high consistency among the three datasets in terms of the horizontal and vertical distributions of eddy-induced thermohaline anomalies. (see Figs. 7–10 and Lines 294-352 in the revised manuscript)
After adopting the CODC dataset, the spikes and “dirty points” previously visible in Figs. 8a, 8e, 8f, and Fig. 10c disappeared (see Figs. 9,10, and 12 in the revised manuscript). At the same time, we also noticed that even this systematically quality-controlled and bias-corrected dataset cannot completely eliminate all problematic profiles during large-sample automated processing. Some isolated spikes remain in tropical and subtropical regions, although they are substantially fewer than in the previous datasets. We note that these residual outliers may have little influence on basin-scale or global-scale analyses of eddy impacts. However, for studies focusing on specific small regions, additional region-specific quality control may still be necessary. We have added discussion of this issue in Lines 338-348 of the revised manuscript.
- Systematic instrument biases: Figure 5c shows that XBT, bottle (OSD), and APB data are included in the dataset, although as a small fraction of the total. Each of these instrument types is known to have systematic instrumental biases in the WOD archive: XBT depth and temperature errors have been documented and corrected in many studies (e.g., Cheng et al., 2014); bottle–CTD temperature inconsistencies have been quantified by Gouretski et al. (2022); and APB temperature biases, which are especially relevant for Southern Ocean coverage, have been characterised by Gouretski et al. 2024. These biases are not negligible when the goal is to quantify mesoscale eddy thermohaline anomalies at the level of tenths of a degree Celsius or hundredths of a practical salinity unit.
The authors should either apply the published bias corrections for each instrument type, or demonstrate quantitatively that including these instruments does not materially affect the eddy anomaly estimates in the relevant regions and time periods. For example, the CODC quality-controlled and bias-corrected in situ ocean temperature and salinity dataset mentioned in my previous point also includes temperature profiles that had been bias-corrected. It would be valuable if the authors could consider using bias-corrected data in the proposed dataset, or at least assess whether excluding XBT, OSD, and APB data would have a disproportionate effect on certain regions or time periods.
Response: Thank you for your valuable suggestion. Following your recommendation, we replaced the original profiles with the CODC quality-controlled and bias-corrected temperature and salinity dataset in this revised version. We sincerely appreciate your valuable suggestion, which substantially improved the quality and reliability of the dataset.
- The choice of eddy detection product and the nearest-eddy colocation assumption: The manuscript uses the META3.2 product for eddy detection and assigns each profile to the nearest eddy on its sampling day. Both choices carry limitations that deserve explicit discussion:
(1) META3.2 versus more sophisticated eddy atlases. Laxenaire et al. (2018) developed the TOEddies algorithm and validated it systematically against an independent dataset of upper-ocean eddies identified from surface drifters. Their results show that TOEddies correctly identifies approximately 10-15% more validated eddies than META, with lower polarity-mismatch rates, particularly for structures in the 25–60 km radius range. Critically, META3.2 does not detect eddy merging and splitting events. Laxenaire et al. (2018) and Ioannou et al. (2024) demonstrate that these events are abundant, concentrated in the most energetic regions, such as the Cape Basin, western boundary currents, and the ACC, and affect approximately 3% of all detected eddies. When a splitting event occurs while a profile is sampled, META will assign the profile to one fragment while the hydrographic anomaly may be centred in another. The manuscript neither acknowledges this source of contamination nor discusses the choice of META over alternatives.
(2) Subsurface-intensified eddies. Laxenaire et al. (2019) demonstrate through a Lagrangian reconstruction of a single Agulhas ring that eddies can transition from surface-intensified to subsurface-intensified structures as they propagate, retaining large density and temperature anomalies at depth (200–1200 m) while their sea-surface height signature diminishes. Laxenaire et al. (2020) confirm statistically that the majority of Agulhas rings in the South Atlantic are subsurface-intensified. For these eddies, the eddy centre and radius inferred from altimetry will not accurately reflect the location of the thermohaline anomaly core, meaning that radius-normalised distances assigned to the colocated profiles are systematically in error. This limitation may apply not only in the Agulhas region but also to any subsurface-intensified eddy family.
(3) The nearest-eddy assumption in energetic regions. In regions where eddies cluster densely, including the Gulf Stream, Kuroshio Extension, ACC, and Cape Basin, many profiles will be located near multiple eddies simultaneously, and the nearest eddy in terms of centre-to-centre distance may not be the one whose dynamics dominate the observed hydrography. The manuscript acknowledges this issue briefly (lines 155–161) but does not quantify its magnitude. A useful diagnostic would be to compute, as a function of region, the fraction of profiles for which the nearest eddy is also the eddy within whose boundary (outer contour) the profile falls, information that is directly available from the META3.2 eddy contour data.
Response: Thank you for your insightful comments. We fully agree that mesoscale eddies exhibit substantial individual variability, and that the present dataset still has several important limitations. Following your suggestions and our own further considerations, we added a dedicated discussion section (Section 5) in the revised manuscript addressing these limitations and providing references for future users.
Specifically, we now clarify that:
- First, mesoscale eddies were identified from satellite altimetry and therefore only eddies with sufficiently strong sea surface height anomaly signatures can be detected. Consequently, eddies during their formation and dissipation stages may be underrepresented. In addition, although the META eddy product used in this study is among the most widely used global eddy datasets, it has known limitations in representing eddy splitting and merging processes, which may introduce uncertainties in profile–eddy associations during such events (Ioannou et al., 2024; Laxenaire et al., 2018).
- Second, satellite observations characterize only the surface expression of mesoscale eddies. Beneath the surface, eddies may exhibit vertically tilted structures, subsurface-intensified cores, or other forms of structural variability (Laxenaire et al., 2019; Zhang et al., 2016; Zhang et al., 2017). Through composite averaging, the present dataset can reconstruct the mean three-dimensional eddy structure within a target region and reveal systematic regional differences among eddies. However, individual eddies may deviate substantially from the regional mean structure, and such composite analyses may not capture the full range of individual eddy variability.
- Third, during the collocation procedure, each profile was assigned to its nearest eddy to avoid multiple associations. Although this approach provides a consistent framework for large-scale statistical analyses, hydrographic conditions may occasionally be influenced by multiple neighboring eddies, particularly in regions of dense eddy activity or strong eddy–eddy interactions. Such effects are expected to occur primarily near eddy boundaries and are therefore likely to have a limited influence on estimates of eddy-core thermohaline anomalies.
- Finally, subsurface and deep-ocean eddies that do not produce detectable sea surface height anomaly signatures cannot be represented in the present dataset. Consequently, the thermohaline structures, transports, and environmental impacts associated with these eddies remain unresolved and require dedicated observational approaches beyond satellite-altimetry-based eddy identification.
We sincerely appreciate your thoughtful comments, which helped us substantially improve the rigor and clarity of the manuscript.
Minor Comments
- Dataset temporal coverage: The abstract and methods emphasize ‘1993–2021’, whereas the conclusions state ‘1993–2022’. This must be reconciled throughout.
Response：Thank you for your careful reading. The temporal coverage of the dataset is 1993–2021. We have carefully checked and corrected this inconsistency throughout the manuscript.
- Table 1 should include units for each variable.
Response：Done, thank you.
- Section 2.3: How did the author handle the large vertical gap below 200m? Are there any criteria for these intervals below 200m? Maybe the author can also refer to the methods used in Gouretski 2018 (h = 20 + 0.24⋅z, where z is the mean distance between the two adjacent levels in meters).
Response：Thank you for your suggestion. Since we replaced the original profiles with the quality-controlled and bias-corrected CODC dataset, this quality-control criterion is no longer applied and thus deleted in the revised manuscript.
- Figure 5a and Figure 5b: add the upper triangle to the colorbar to indicate ‘more than’ 400 profiles.
Response: Corrected. Thank you.
- Figure 7a: The grey central band visible in the South China Sea cyclonic eddy panel of Fig. 7a is unexplained. Please add the explanation.
Response: Thank you for your careful observation. This feature is likely caused by the very limited number of profiles located near eddy centers, which may introduce artifacts during spatial interpolation. After adopting the new CODC data with more than doubled profiles, this band vanished. (see Fig.11a in the revised manuscript)
- Figure 11 and Section 4.2: how did the author define the MLD? Please add the details about the definition. For example, the density threshold, temperature threshold, or hybrid algorithm used (and the reference depth).
Response: In this study, mixed layer depth (MLD) was estimated for each profile using a density-threshold method, defined as the depth at which the increase in potential density relative to the surface equals the increase in surface potential density associated with a 0.5°C decrease in sea-surface temperature (de Boyer Montégut et al., 2004). We added this description in Lines 466-469 of the revised manuscript. Thank you.
- For Figures 9–12, it would be great to overlay or provide sample count maps/contour lines, because visual interpretation of low-data regions is otherwise difficult.
Response: Thank you for your suggestion. We tested adding sample-count contours to these figures, but found that the contours substantially reduced figure readability. Instead, we provided the spatial distribution of profile numbers in Fig. 5a, the vertical distribution of profile density in Fig. 5e, and the seasonal variations of both total profiles and the fraction located within eddies in Fig. 5f, to help users access data coverage. (see Fig. 5 in the revised manuscript or the Fig. below)
Fig.R5 Spatial and temporal distributions of eddy-collocated temperature and salinity (T-S) profile data in the global ocean between 1993 and 2021. a, Geographic distribution of the number of T-S profiles within 2°×2° grid boxes. b, The same as a but for the number of satellite-detected eddies. c, Yearly statistics of T-S profiles from different instruments. d, Yearly statistics of T-S profiles within cyclonic eddies (CE, d<R), anticyclonic eddies (AE, d<R), at eddy edges (R<d<2R), and at background fields (BG, d>2R). The purple and cyan lines are the percentages of profiles within CEs and AEs, respectively. e, The same as (c), but for the density of profile observations as a function of depth. f, The same as (d), but for monthly statistics of the profile data.
- Laxenaire et al., 2020 shows that composite methods systematically underestimate peak heat content anomalies at the eddy centre relative to individual reconstructions, because eddies of different sizes and intensities are pooled in a common normalised coordinate system. The manuscript should acknowledge this limitation and note that the mean anomaly fields it provides do not capture the full range of eddy variability.
Response: Thank you for this important suggestion. We added a discussion of this limitation in the revised manuscript, noting that composite averaging may underestimate peak eddy-core anomalies and cannot capture the full range of eddy variability. (see revised manuscript, Lines 571-577)
- Some typos should be taken care of. For example:
Line 90: “form the WOD” should be “from the WOD.”
Response: Corrected. Thank you. (see revised manuscript, Line 97)
Line 193: “the choose of profiles” should be “the choice of profiles”
Response: Corrected. Thank you. (see revised manuscript, Line 204)
Line 251: “random sampled” should be “randomly sampled.”
Response: Corrected. Thank you. (see revised manuscript, Line 274)
Line 453: “to what extend” should be “to what extent.”
Response: The paragraph is rewritten and these words are deleted in the revision. Thank you.
Line 471: “Zendo” should be “Zenodo” in the data availability section
Response: Corrected. Thank you. (see revised manuscript, Line 613)
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AC2: 'Reply on RC2', Qingyou He, 08 Jun 2026
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RC3: 'Comment on essd-2026-89', Anonymous Referee #3, 16 Apr 2026
This dataset provides in situ temperature and salinity observations in the new coordinates of mesoscale eddies. The concept of the eddy coordinate itself is not significantly novel, but I think this global product is quite useful not only for physical oceanographers but also for biological and chemical oceanographers. I basically agree with accepting this manuscript, but I would like to add minor comments that could help to further improve its usefulness.
The present dataset is directly applicable if the eddies are assumed to move with frozen structures. However, it would not be straightforward if temporal variations of the structure were considered. Even with the same eddy ID, the structure of the eddy would vary temporally, partly due to the vorticity dissipation, especially for eddies that last more than a few years. Therefore, I think it would be useful to include an index like “eddy age” (the period after its generation), which can be extracted from the original eddy database based on satellite altimetry data.
Another index I believe useful is “vertical displacement." Some eddies behave like waves, so the vertical displacement of the thermocline propagates westward rather than holding the water mass. The eddy's movement keeps the temperature and salinity anomaly when the specific water mass moves with it. Meanwhile, when the vertical displacement propagates, the strength of the temperature and salinity anomaly would depend on the background structure. Therefore, I think it would be useful to estimate the vertical displacement using the background structure, in addition to the anomalies.
Including those indexes would significantly increase the advantage of this database.
Citation: https://doi.org/10.5194/essd-2026-89-RC3 -
AC3: 'Reply on RC3', Qingyou He, 08 Jun 2026
Comment from Referee #3
This dataset provides in situ temperature and salinity observations in the new coordinates of mesoscale eddies. The concept of the eddy coordinate itself is not significantly novel, but I think this global product is quite useful not only for physical oceanographers but also for biological and chemical oceanographers. I basically agree with accepting this manuscript, but I would like to add minor comments that could help to further improve its usefulness.
Response: Thank you very much for your positive evaluation and constructive suggestions. We greatly appreciate your recognition that this global dataset with mesoscale eddy-following coordinates is valuable for research across physical, biological and chemical oceanography. We have carefully studied your suggestions and revised the manuscript accordingly to improve the quality of the manuscript and dataset. Specifically, we added a normalized eddy-age variable to the dataset, expanded the validation analyses, and strengthened the discussion of dataset limitations and potential applications. We hope that the revised version meets your expectations. Our point-by-point responses are provided below.
The present dataset is directly applicable if the eddies are assumed to move with frozen structures. However, it would not be straightforward if temporal variations of the structure were considered. Even with the same eddy ID, the structure of the eddy would vary temporally, partly due to the vorticity dissipation, especially for eddies that last more than a few years. Therefore, I think it would be useful to include an index like “eddy age” (the period after its generation), which can be extracted from the original eddy database based on satellite altimetry data.
Response: Thank you for your valuable suggestion. We have added a normalized eddy-age variable to the revised dataset. Specifically, the observation sequence number along each eddy trajectory was normalized to a range between 0 and 1. As you suggested, we expect this information to facilitate future investigations of temporal variations in eddy structure during eddy evolution. Relevant descriptions have been added in Lines 131–133 and Table 1 of the revised manuscript.
Another index I believe useful is “vertical displacement." Some eddies behave like waves, so the vertical displacement of the thermocline propagates westward rather than holding the water mass. The eddy's movement keeps the temperature and salinity anomaly when the specific water mass moves with it. Meanwhile, when the vertical displacement propagates, the strength of the temperature and salinity anomaly would depend on the background structure. Therefore, I think it would be useful to estimate the vertical displacement using the background structure, in addition to the anomalies.
Including those indexes would significantly increase the advantage of this database.
Response: Thank you for this insightful suggestion. We agree that separating water-mass transport signals from vertical displacement signals is important for understanding eddy-induced thermohaline variability. In the present dataset, we provide both the original temperature/salinity profiles and the corresponding anomaly fields relative to the climatological monthly mean state, which may facilitate future estimation of vertical displacement signals based on background stratification structures.
However, robust estimation of vertical displacement requires additional assumptions regarding eddy trapping ability (or nonlinearity), and may vary substantially among different oceanic regions and eddy individuals. Considering that the primary goal of this study is to provide a general-purpose eddy-collocated profile dataset, we did not include a dedicated vertical-displacement index in the current version. Nevertheless, we agree that this is a valuable direction for future development and will keep it in mind. Thank you again for this insightful comment.
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AC3: 'Reply on RC3', Qingyou He, 08 Jun 2026
Data sets
Codes and data for "A global eddy-collocated temperature and salinity profile dataset (v1.0): integrating multiplatform in situ observations with satellite-detected mesoscale eddies" Qingyou He https://doi.org/10.5281/zenodo.18590979
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- 1
In this work by He and coauthors a dataset providing collocated temperature and salinity data with satellite-derived eddy information. The paper would require major changes to improve its clarity and refine the scope. I have three major concerns as described below, as well as a number of minor suggestions.
The novelty of this contribution is not clearly communicated, as the processing of the input data (WOD and AVISO) appears to be relatively basic (essentially interpolation). Beyond easier access to existing resources, the value added by the authors should be made explicit.
Discussion on the effect of trends (e.g., salinity and temperature, eddy, other datasets used) is not provided adequately (global warming mentioned once in sect. 2.2), but may be very relevant. Please expand the discussion and provide context also for the possible influence of observational changes through time.
The level of detail and support of several pieces of text is insufficient; for example, the discussion of temperature extremes is missing a description of the methodology (and several key parameters, such as the baseline used to define such extremes), and the application section is very scarce and limited mostly to citation of works by the paper authors. The authors should clarify that the dataset is not gridded (even though a 2x2 grid is mentioned in various places).
Minor suggestions:
36 why heat budgets?
52 why freshwater?
62 add reference
86 this reads like a straw man argument
89 collocation may be working at the surface, but how deep this would hold?
90 text in brackets is puzzling
103 fix typo in flowchart
128 I am confused by the reference on Captain Cook; please clearly list which data sources (Argo, moorings,...) are used in this dataset. Profiles are only possible with Argo, right?
134 while eddies are daily, are the WOD data provided with the same resolution? This means that profiles for a period shorted than a day are aggregated?
137 the QC applied by WOD should be outlined
143 well then the grid is not uniform in general, only by layer. How do you define this layers, anyway?
149 what is the maximum distance allowed?
163 explain in the text how d, D, and R are defined
167 I don't understand this. If profiles are extracted only in the vicinity of eddies, wouldn't this induce a bias in the selection, which would not be random?
173 this point is confusing. Being a 30-year period, trends may be relevant. How are these accounted for in your method? And aren't somehow eddy signatures visible in areas where those are more persistent? Also, how good is this product closer to the coasts?
186 "ambientes"?
Fig 4a typo
Table 1 is missing units
Fig. 5 I don't understand if/how (a) and (b) should differ. Can also "glider" and "others" provide profiles? It would be useful to report data density by depth
245 repetition, rephrase
250 I am not following your reasoning. Wouldn't eddy trap lagrangian sensors?
Fig. 6 why is the resolution different between left and right maps? What are Gamma and P?
204 please explain how interpolation is made
271 this is not clear
296 this should be shown with your dataset, as it should be possible
344 no references in these and close sections, only self-citation?
352 as in 296, this remains speculative and should be verified
386 was this defined already?
408 include references and key details, e.g. the climatology used
416 daily or coarser climatology?
Fig. 12 is the spatial resolution still 2 deg in these plots?
436 this section seems too sketched and unsupported
472 a single 10GB file is not ideal; why not splitting by basin, for example? The file is also lacking metadata (reference, data sources) and units. Why is "contour" and "char" set to 20?
475 the specific link should be provided