Unveiling the Deep Ocean warming: observed bottom ocean dataset across Mediterranean Sea

Lo Bue, Nadia; Giambenedetti, Beatrice; Embriaco, Davide; Bagiacchi, Paolo; Fratianni, Claudia; Vagni, Riccardo; Marinaro, Giuditta

doi:10.5194/essd-2025-739

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https://doi.org/10.5194/essd-2025-739

Preprints

29 Dec 2025

| 29 Dec 2025

Status: this preprint is currently under review for the journal ESSD.

Unveiling the Deep Ocean warming: observed bottom ocean dataset across Mediterranean Sea

Nadia Lo Bue, Beatrice Giambenedetti, Davide Embriaco, Paolo Bagiacchi, Claudia Fratianni, Riccardo Vagni, and Giuditta Marinaro

Abstract. The deep ocean was long assumed to be in a quasi-stationary state, and therefore excluded from studies on climate variability. The awareness of the unsteady state of the deep ocean is a fairly recent achievement, but despite its pivotal role in the assessment of climate variability, the understanding of abyssal ocean dynamics remains largely unknown, primarily due to the scarcity of observations. This is why any observations below 2000 meters depth, although poor or widely dispersed, constitute valuable knowledge that is mandatory to enhance and make available.

This work presents validated oceanographic time series collected by benthic multidisciplinary observatories across key locations in the Mediterranean Sea region. It includes details on the data processing and quality control methods used to ensure reliability and aims to deliver high-quality data, as well as standardization in the quality control procedures for deep-sea measurements.

The dataset provides a comprehensive description of seafloor observations collected over different time periods during the past decade, contributing to the long-term characterization and understanding of abyssal ocean variability in the region.

Received: 02 Dec 2025 – Discussion started: 29 Dec 2025

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Nadia Lo Bue, Beatrice Giambenedetti, Davide Embriaco, Paolo Bagiacchi, Claudia Fratianni, Riccardo Vagni, and Giuditta Marinaro

Status: final response (author comments only)

RC1:
'Comment on essd-2025-739', Anonymous Referee #1, 06 Mar 2026
This study presents the work carried out by deep-sea benthic observatories located at key sites in the Mediterranean region, including data acquisition, quality assurance and quality checks, and data banking. These autonomous or cabled multidisciplinary systems are equipped with physical sensors, such as current meters and CTDs, as well as biogeochemical sensors, primarily optical turbidity sensors. The methodology involved applying and standardising rigorous quality assurance and quality control protocols for the different types of instruments, as well as filling data gaps. These datasets help remedy the critical shortage of deep-ocean observations below 2,000 metres and improve assessments of high- and low-frequency variability in the thermohaline and hydrodynamic conditions of these sites. The authors present some results, including the detection of deep-water warming trends at all sites, and a notable change in the direction of dominant currents in the Ionian Sea over a decade.
Overall, the manuscript is well presented and illustrated. The datasets are easily accessible via the provided links. However, I have a few comments and suggestions.
In the introduction, the authors rightly point out that long-term observations in the deep regions of the Mediterranean Sea are rare. They should also mention the various types of operational deep observatories that complement their own. Notably, these include the HYDROCHANGES network (Schroeder et al., 2013; https://ciesm.org/our-science/hydrochanges/), which has continuously monitored hydrological and hydrodynamic variability at various locations in the Mediterranean since 2002. This includes deep sites in the Adriatic and northwestern basins, as well as key locations such as the Corsican, Balearic, Sicilian and Gibraltar straits. Further results concerning the long-term evolution of hydrological conditions in the deep basins of the Mediterranean can be found in Chiggiato et al. (2023). It would be interesting to include a perspective on the convergence of measurements and the complementarity of the different datasets in the introduction or conclusion.

Regarding the post-processing of current meter data, I was interested in the results in Figure 3, which compare current measurements obtained by an ADCP and a punctual current meter. According to Table 1, the acoustic punctual current meter (Falmouth 3D-ACM) operates at 2 Hz, while the ADCP (RDI WH 300 kHz) operates at 2.8×10⁻⁴ Hz (1 sample/hour), and the comparison is made using hourly data. The hourly average of 7200 measurements from the punctual current meter is then compared to the hourly measurement from the ADCP. Is the hourly ADCP measurement the result of a single ping, given that the standard deviation for a single ping is at least 2 cm/s? Or is it the result of averaging an ensemble of pings, in which case the standard deviation would be lower? Information on the acquisition modes specific to certain instruments and the standard deviation associated with the measurements for each instrument is important for making comparisons. Additionally, it would be preferable for this figure to plot the 1:1 line rather than the least-square regression line.

Figure 4 illustrates the variation in data quality across different sensors and sites, categorising data as good (flag = 1), interesting/suspicious (flag = 3), or missing/removed (flag = 9). Firstly, the flag labels should be consistent between the figures and the legend. Furthermore, it is surprising that most of the data (between 50% and 90%) for the different sensors is considered interesting/suspicious. Could the authors provide details and explain why this percentage is so high after the data has undergone a quality check? Are these percentages associated with the raw or final processed data?

Figure 6 shows the quality control performed on a time series of current intensity, as measured by an ADCP, as well as the two-level time series. Why does the Hovmöller diagram not start in mid-December 2003, as it does for the time series? This would enable us to observe the filtering of the initial data, which appears very noisy in the upper part of the water column sampled by the ADCP. The depth and altitude relative to the bottom of the measurements should also be indicated systematically in the figure titles and legend for the time series to help guide the reader.

Figure 8 shows time series for which missing data has been filled using a method that combines singular spectral analysis and optimal interpolation. It may be helpful to review the definitions of the K1 (lunisolar diurnal) and M2 (principal lunar semidiurnal) tide components, and the origin of the inertial frequency f. The sentences “As shown in Figure 8, the warming signal in the reconstructed time series confirms the robustness of the spectral estimate” (lines 443–446) are unclear to me. Could you please explain your reasoning more clearly?

Finally, it would be interesting if the authors could provide an example of the simultaneous variation of different variables, such as temperature, salinity, current, and turbidity / oxygen. This could be over a short period to illustrate an event for which high-frequency measurements are useful, or over a longer period to illustrate seasonal or longer-term variability.

Schroeder, K., Millot, C., Bengara, L., Ben Ismail, S., Bensi, M., Borghini, M., Budillon, G., Cardin, V., Coppola, L., Curtil, C., Drago, A., El Moumni, B., Font, J., Fuda, J. L., García-Lafuente, J., Gasparini, G. P., Kontoyiannis, H., Lefevre, D., Puig, P., Raimbault, P., Rougier, G., Salat, J., Sammari, C., Sánchez Garrido, J. C., Sanchez-Roman, A., Sparnocchia, S., Tamburini, C., Taupier-Letage, I., Theocharis, A., Vargas-Yáñez, M., and Vetrano, A.: Long-term monitoring programme of the hydrological variability in the Mediterranean Sea: a first overview of the HYDROCHANGES network, Ocean Sci., 9, 301–324, https://doi.org/10.5194/os-9-301-2013, 2013.
Chiggiato J., V. Artale, X. Durrieu de Madron, K. Schroeder, I. Taupier-Letage, D. Velaoras, M. Vargas-Yáñez (2023) Chapter 9 – Recent changes in the Mediterranean Sea, Editor(s): Katrin Schroeder, Jacopo Chiggiato, Oceanography of the Mediterranean Sea, Elsevier, 2023, Pages 289-334, ISBN 9780128236925, https://doi.org/10.1016/B978-0-12-823692-5.00008-X
Citation: https://doi.org/10.5194/essd-2025-739-RC1
- AC1: 'Reply on RC1', Nadia Lo Bue, 11 Mar 2026
  
  We thank the reviewer for the positive evaluation of our manuscript and for the accurate summary of the study. We are also grateful for the reviewer’s comments and suggestions provided below, which have helped us improve the clarity and quality of the manuscript. Our detailed responses are reported point by point
  In the introduction, the authors rightly point out that long-term observations in the deep regions of the Mediterranean Sea are rare. They should also mention the various types of operational deep observatories that complement their own. Notably, these include the HYDROCHANGES network (Schroeder et al., 2013; https://ciesm.org/our-science/hydrochanges/), which has continuously monitored hydrological and hydrodynamic variability at various locations in the Mediterranean since 2002. This includes deep sites in the Adriatic and northwestern basins, as well as key locations such as the Corsican, Balearic, Sicilian and Gibraltar straits. Further results concerning the long-term evolution of hydrological conditions in the deep basins of the Mediterranean can be found in Chiggiato et al. (2023). It would be interesting to include a perspective on the convergence of measurements and the complementarity of the different datasets in the introduction or conclusion.
  We thank the reviewer for this suggestion. The Introduction has been revised accordingly [lines 58–63 and 67–68] to improve clarity and provide additional context, and relevant references have been added.
  Regarding the post-processing of current meter data, I was interested in the results in Figure 3, which compare current measurements obtained by an ADCP and a punctual current meter. According to Table 1, the acoustic punctual current meter (Falmouth 3D-ACM) operates at 2 Hz, while the ADCP (RDI WH 300 kHz) operates at 2.8×10⁻⁴ Hz (1 sample/hour), and the comparison is made using hourly data. The hourly average of 7200 measurements from the punctual current meter is then compared to the hourly measurement from the ADCP. Is the hourly ADCP measurement the result of a single ping, given that the standard deviation for a single ping is at least 2 cm/s? Or is it the result of averaging an ensemble of pings, in which case the standard deviation would be lower? Information on the acquisition modes specific to certain instruments and the standard deviation associated with the measurements for each instrument is important for making comparisons. Additionally, it would be preferable for this figure to plot the 1:1 line rather than the least-square regression line.
  The Falmouth 3D-ACM current meter recorded velocities at 2 Hz. For comparison with the ADCP record, the time series was smoothed using a 1-hour moving average and sampled at hourly intervals to obtain an hourly estimate of the flow. The RDI Workhorse 300 kHz ADCP was configured with 100 pings per ensemble and a ping interval of 6 s, resulting in ensemble durations of approximately 10 min. Each reported velocity profile therefore represents an internal average of multiple pings, and the hourly ADCP measurements used in this study correspond to these ensemble-averaged profiles. In accordance with reviewer’s suggestion, Figure 3 has been revised to display the 1:1 line as well as the least-squares regression line. The text has been updated accordingly [lines 229-239].
  Figure 4 illustrates the variation in data quality across different sensors and sites, categorising data as good (flag = 1), interesting/suspicious (flag = 3), or missing/removed (flag = 9). Firstly, the flag labels should be consistent between the figures and the legend. Furthermore, it is surprising that most of the data (between 50% and 90%) for the different sensors is considered interesting/suspicious. Could the authors provide details and explain why this percentage is so high after the data has undergone a quality check? Are these percentages associated with the raw or final processed data?
  We thank the reviewer for this comment. The original figure contained an error in the color reference of the quality flags, which were inadvertently inverted. This has been corrected in the revised figure, where the labels are now properly aligned. As a result, most of the data correspond to the good quality flag, while the suspicious and rejected categories represent only a small fraction of the dataset. The figure and caption have been corrected accordingly in the revised manuscript [lines 293-294].
  Figure 6 shows the quality control performed on a time series of current intensity, as measured by an ADCP, as well as the two-level time series. Why does the Hovmöller diagram not start in mid-December 2003, as it does for the time series? This would enable us to observe the filtering of the initial data, which appears very noisy in the upper part of the water column sampled by the ADCP. The depth and altitude relative to the bottom of the measurements should also be indicated systematically in the figure titles and legend for the time series to help guide the reader.
  We originally displayed the Hovmöller diagram over a shorter time window to allow a clearer comparison between the raw and quality-controlled data, focusing on a representative month where the effect of the filtering could be easily visualized. However, following the reviewer’s suggestion, the Hovmöller diagram has now been extended to cover the full time series, aligned with the temporal range shown in the time series panels below. This modification allows the reader to observe the initial noisy period in December 2003 and the effect of the quality control on those data. In addition, the labels have been revised to report the absolute depth of the measurements, improving readability and making the information on sensor position clearer in the figure titles and legends. Caption has been updated accordingly [lines 388-390].
  Figure 8 shows time series for which missing data has been filled using a method that combines singular spectral analysis and optimal interpolation. It may be helpful to review the definitions of the K1 (lunisolar diurnal) and M2 (principal lunar semidiurnal) tide components, and the origin of the inertial frequency f. The sentences “As shown in Figure 8, the warming signal in the reconstructed time series confirms the robustness of the spectral estimate” (lines 443–446) are unclear to me. Could you please explain your reasoning more clearly?
  We agree that the original wording could be misleading. The sentence was intended to indicate that the spectral estimates derived from the reconstructed time series are supported by the 95% confidence limits, within which the main spectral features are contained. We have therefore rephrased this part of the manuscript [lines 454–470] to clarify this point and improve the overall explanation.
  Finally, it would be interesting if the authors could provide an example of the simultaneous variation of different variables, such as temperature, salinity, current, and turbidity / oxygen. This could be over a short period to illustrate an event for which high-frequency measurements are useful, or over a longer period to illustrate seasonal or longer-term variability.
  We appreciate the reviewer’s comment. Figure 7 is intended to summarize the variability observed in the temperature time series at the four sites, together with the hodographs relating measured velocities to water-mass densities, in order to highlight the main dynamical features of the observed variability. The figure was carefully designed to provide a comprehensive overview within a single panel, while avoiding the inclusion of additional plots that would largely repeat the same information and unnecessarily increase the length of the manuscript. We believe that this approach helps maintain clarity and conciseness while still conveying the key dynamical aspects of the dataset.
  
  Citation: https://doi.org/10.5194/essd-2025-739-AC1
RC2:
'Comment on essd-2025-739', Anonymous Referee #2, 21 Apr 2026
This study presents the data acquisition, the post-processing, the quality assurance (QA) and the quality control (QC) of data recorded by deep-sea observatories across the Mediterranean region (Gulf of Cadiz, Tyrrhenian Sea, Ionian Sea and Marmara Sea). The data recorded by these observatories include physical (for instance CTD, ADCP, current meter) and biogeochemical parameters, allowing a robust characterization of deep-sea dynamics. The article presents clearly a standardization of the QC for these four observatories, using international standards (QUARTOD) for the different instruments considered making results found in the article even more trustable. It also provides figures helping the reader to observe the benefit of the methodology used. The high frequency of the datasets is a necessity toward a greater understanding of the deep-sea dynamics, from the long-term analysis to the short scale variability, which is still lacking observations; in this sense, this study and these standardized datasets are precious. The authors show especially a warming of the deep waters in the different sites.
The manuscript format is clear, with successive presentations of data acquisition system, a dataset considered, and methodology applied to finish on a result part which contain a comparison of temperature time series in the different sites as well as a presentation of a gap filling method. The manuscript is of good quality, the datasets presented are valuable and the QC framework is well structured. The comments below are mainly aimed at improving methodological clarity, the robustness of some interpretations, and some curiosity I had. The datasets are well referenced and are easily accessible.
Main comments
In L.242 it is mentioned “cross-correlation techniques” used, particularly between current meters and ADCPs. Could the author explain what are these techniques? Because in the following part of this paragraph the R² coefficient is used, which is a regression analysis coefficient considering no lag, while cross-correlation depends on it. If there was a confusion, I would suggest to just change “cross-correlation techniques” by “regression analysis”. I would also like to point out that the R²=0.28 named as “reasonable agreement” is more a “moderate agreement which requires particular attention”. What is written in L.232, if depending only on R², should be subject to more caution: “This consistency…” could be rephrase to “moderate consistency”. Concerning properly the Fig.3, a sentence in the text explaining that the “ADCP range=11.33” represent the depth of the current meter (I think so at least) is necessary. There is a last element I was curious about: what were the result for v, the other component of the horizontal velocity?

On the figure 4, I was wondering if the result is the final one or just after one step of QC because a large majority of “Interesting/Suspicious Data” is present. I would have also added a horizontal space between bars when moving to another site for readability (bigger horizontal space between bar 5 and bar 6, between bar 11 and bar 12, and between bar 16 and bar 17).

L.320: It is written that the rate of change (ROC) is “equivalent to removing outliers, i.e. data over three standard deviations from the mean”. However, up to what I read on the QUARTOD manual, the ROC is not demonstrated to be equivalent to that. ROC checks the temporal consistency of a time series by comparing changes between successive observations while the mean+3 standard deviations is an outlier detection method relative to the mean. I kindly ask the author to explain from where the equivalence assertion comes from.

L.330: Concerning the sensor tilt test, it is written “this test can be replaced with a rate of change test performed along the vertical (i.e., along depth instead of time)”. Here again, this requires further clarification. The vertical gradients may provide information, but the equivalence of the proposed method with a tilt detection test is not generally established.

L.342: “All tests were conducted within the valid measurement range for the instrument, as specified in Table 4”. Table 4 provides min, max, mean, and standard deviation but the “valid measurement range” is nowhere to be found. I kindly ask the author to explain what they intended by it (manufacturer specifications?, QC thresholds?)

Figure 7: This figure is presenting the time series of potential temperature and hodographs in the four different sites, but, even if I like the idea of presenting a map with the results, is hardly readable. I am unable to read legend of time series figures, hodographs are too small, their ticks label, colorbar values and labels, title are far too small. Concerning hodographs, it is also written “Eastward [Northward] velocity (km)”, and a velocity unity is not km. Hodograph are usually presented with the North as 0° and angles increasing in the clockwise direction. I would suggest the author to refine the figure so a person that print the article can read it. I suggest the author the use of tools like Gimp to merge different figures and save the result in a high quality (300dpi). On this figure caption “Temperature” should be changed to “Potential temperature”, and a more accurate description of the temporal scale at which the “variability […] in the Tyrrhenian Sea” is observed is necessary.

L.398: “in agreement with the global warming trend” should be nuanced as the amount of data is lower than 5 years for 3 of the sites. Such trends could only reflect a regional or decadal variability rather than a long-term climate change. I kindly ask the author to temper this statement or clarify its limitations.

L.404: “providing a coherent and reliable estimate of a decadal scale warming process”, L.410: “not a simple warming signature but a real change of the deep water masses”, L.417: “[The shift in the hodograph] may be attributed to the alternating advection of water masses that the Ionian basin receives from the Adriatic or Aegean Sea, which could sustain the better known decadal reversals (BIOS)”. I kindly ask the author to clarify whether the observed warming is really reflecting a long-term trend or more a variability associated with BIOS phases. In the case where a doubt subsist, reliability of the trend estimate can be debated.

The main concern I have is about the last part of the article and the use of SSA/OI. While using SSA/OI is reasonable in the case of gaps mitigation, some statements may be overstated there. L.430 “SSA is a […] method particularly suitable for time series with long and continuous gaps” while Kondrashov and Ghil (2006) (cited in the article) concluded that “The accuracy and reliability of the method depend on the pattern of missing data, the relative length of the gaps with respect to the total length of the data set” ; the article presented in Fig.8 a time series of 3 year with a gap of more than a year, so the accuracy of SSA in this case is not proved. On L.431 it is also written “does not require a priori assumptions”, but this is not fully true as for instance the number of modes retained is important. Concerning the modes used for this analysis, the author used “the dominant modes (6 mode)” without giving any explanation for this choice. Could the author clarify this point in the text? L.441: “ensuring temporal coherence and dynamical consistency” This is I think may be overstated because SSA/OI will provide a statistical coherence, but it does not guarantee physical/dynamical consistency. L447-L.448: “dominant energy at low frequency, reflecting the slow and persistent variability typical of long-term warming process evident in the reconstructed time series” Here it is not that “evident” because the low frequency is not necessarily equal to the warming trend, it could be due to decadal variability, changes in the circulation, all the more as the time series chosen for applying the SSA/OI method are both less than 3 years short. The “robustness of the spectral estimate” (L.446) may be acceptable but it refers on a time series which is too short to be fully statistically significant. I suggest the author to refine the part concerning SSA/OI, avoiding the overstatement I mentionned above and providing a more balanced discussion with the limitations of this method. It was an interesting analysis which has still its place in this article and which could benefit comparison with models outputs, reference measurements in another article. Following this comment, the conclusion has to be modified in consequence; for instance in “the reconstruction of longer and more continuous records increase the resolution and robustness of spectral estimates…” the “robustness” may not guarantee by reconstruction especially with SSA/OI and in “the application of advanced analytical methods […] minimized spectral leakage and enhances the reliability of frequency domain diagnostics”, the article did not show a leakage reduction quantitatively, neither a quantitative validation for the “reliability”.

Secondary comments
L.188 : “storage efficiency” while on L.130 it is named “data acquisition efficiency” and in Table.1 “sensor efficiency”. It is necessary to use every time the same definition. The definition provided L.213-L.214 could be given earlier in the text (L.130 or L.188).

Table 1: Change “OXYGEN” by “DISSOLVED OXYGEN”. Consider that in Table 4 it is referred as O₂ too.

Figure 3: it is often written in the text “horizontal velocity component (u)” to refer to the meridional velocity component (u). Horizontal velocity component could be misleading as it is constituted by (u,v). I found occurrences in L.223, Fig.3 caption, and L.337. In L.341 it is mentioned more clearly “eastward velocity component”.

L.240: What does the author mean by “stability range”? This could be explained more carefully.

L.241: I would mention after “CTD casts” that they are performed by CTD instrument that have higher performance than the mooring CTDs (more likely it is SBE911 or an equivalent). The sentence is already good but adding this would improve it.

L.244: Would the author have a reference for “even the most sophisticated QC procedures cannot fully compensate for poorly calibrated or improperly functioning sensors”?

L.250: It is written “enabling prompt corrections that can minimize data gaps” but a correction will not minimize a gap as there is no data to correct. In this sentence “data gaps” should be removed.

Table 3 is missing at least one reference.

L.252: “It involves comparing with reference datasets, historical records, or model outputs”. For a comparison, models are ok. For a correction it is trickier. As the sentence before mentions about a “more comprehensive review to improve data accuracy” and after “correction of errors missed” is written, I would suggest the author to be cautious when mentioning models in a part concerning quality control, all the more as the study consider deep-sea measurements.

L.266-L.269: To be sure, “a QC protocol was customized […] Custom thresholds”, means that the QC was following QUARTOD with modification of some coefficient in the tests considering the different sites area? If so, it is, of course, not a problem as QUARTOD is internationally recognized.

L.290: I do not understand what the author is referring to when writing “sensible”? “Sensible” is too vague if not explained clearly.

L.303 to L.306: the sentence “For Teledyne […] only one beam.” is a bit long and difficult to understand. Maybe the author could split it in two sentences and improve its clarity.

L.321 and L.349: “dynamic nature” referring to the oxygen measurements is too vague. It should be explained more carefully (biological activity, physical mixing,…) and supported by a reference.

Figure 5: I really like the figure because it is visual and we clearly see the improvement given by the QC. I have a few suggestions. On the legend of the second subplot, “Raw after step 1” is ambiguous. It would be better to write “Data after step 1”. In the caption, change “Quality control” by “QC” as QC was widely used in the previous pages, “(a) Raw data showing outliers” could be replaced simply by “(a) Raw data”

L.362: “… where the measurements are noisier.” Here clarifying why the measurements are noisier would improve the sentence (surface wave effects, turbulence, or instrument limitations,…)

Table 4: “W” is not defined earlier in the text; the reader should be able to clearly understand it refers to vertical velocity component. Minimum and maximum are written on two lines which Is not visually pleasant.

Figure 4: The author could have written in the text a sentence about the strong velocity difference present (at maximum 15cm/s) between 5.33m range and 20.33m range, which is an interesting aspect. On the caption, “2004” is missing after “January”

L.386 to L.389: “After post-processing a QC validation routine has been performed, potential temperature and density anomaly data derived following TEOS-10 international standards (https://teos-10.org/), exhibit interesting variability at all sea-bottom sites monitored during these years (Figure 7)” could be replaced with “After post-processing, a QC validation routine has been performed, potential temperature (θ) and density anomaly (σ₂₀₀₀) data were derived following TEOS-10 international standards (https://teos-10.org/, [date of last access]). They exhibit interesting variability at all sea-bottom sites monitored during these years (Figure 7)”. Maybe the definition of potential density anomaly given L.412 could be also included here for more clarity. Concerning the definition given L.412, σ² is the symbology used, but it could be interpreted to a variance. The author should consider using σ₂₀₀₀.

L.397: “measured between 2002 and 2013 at all monitored deep sites” can be misleading as all the different sites do not have data for the period 2002-2013, only one have. A more accurate sentence should be written.

L.411: “This is evident looking at the current hodograph” Here “evident” may be overstated as no information about dates are given on the hodograph. The sentence could be changed to “This is evident looking at the current hodograph, also showing potential density anomaly, where velocities are found to be oriented toward …° with a mean potential density anomaly of …kg/m³on the period … while they are oriented toward …° with a mean potential density anomaly of …kg/m³ for the other period…”

L.413 to L.415: “four times bigger than the usual range of inter-annual variability expected at these depths in the Ionian bottom water” Here a reference is missing to justify this assertion.

Other small comments (editorial corrections, rephrasing, missing reference in the list)
L.25: it would be better to replace “high-resolution” by “high temporal resolution”

L.35 an L.37: “The unsteady state of the deep ocean is a quite new achieved knowledge […] that emphasizes the significance of abyssal processes in redistributing heat and energy, thereby influencing surface climate variability”. Here, first, I would replace “quite new” by “recent”.

L.41: “The deep sea is indeed a highly complex and interconnected environment” is vague. I would suggest changing it by something like “The deep sea is indeed governed by physical and biogeochemical processes occurring on a wide range of temporal and spatial scales, resulting in a complex system.”

Following my previous comment I would modify the sentence L.42 to “It is influenced by the continuous vertical exchanges across the water column and at the air-sea interface, as well as by lateral exchange with surrounding ocean basins, guided by morphological dynamics”

L.81: “challenging […] conditions” could be removed, it is already written in L.78 “extreme conditions”

L.187: “Preliminary analysis was always conducted, as the first step…” Here “first step” is just a repetition as “preliminary” was mentioned before, so I would only keep “preliminary”

L.189: “custom-designed software tailored” is a bit heavy in my opinion, it would be better to keep it simple as the “custom-designed software” are not presented here. I would stay with “software tailored”

L.192-193: “This process enhances the accuracy, reliability, and interpretability of the raw data, refining its quality and facilitating better interpretation” Here “refining its quality and facilitating better interpretation” is not necessary as it is just repeating the information which is given in the first part of the sentence.

L.209-L.211: “thorough […] meticulous” Using both is redundant, it would be better to keep only one of them.

L.212: “[…] providing a solid foundation for subsequent analysis and interpretation” I do not find this necessary; it was already clear that we were talking about the first stage of post-processing.

L.215: “Marmara Sea (Table 2)” should be changed to “Marmara Sea (Table 1)”

L.215: “very high” Instead of this, giving a value would be more appreciated “greater than 90%”

L.233: I would replace “height” by “depth”

L.238: The use of “however” in the middle of the first sentence of the paragraph is confusing. Considering the sentence, it can simply be removed.

L.248: Maybe it is only my opinion but “invaluable” is a bit too much. “necessary” would have been enough.

Table 3: Space missing between “Tyrrhenian” and “Sea”

L.289: “timestamps requires” should be changed to “timestamps. It requires”

L.296: “The global range” should not be replaced by “The gross range”?

L.301: “the beams’ percent good” would be better written “the percent good of the beams”

L.314: I would change “This test checks for single value spikes usually due to an electrical signal from the sensor, relative to adjacent data points” to “This test checks for single value spikes relative to adjacent data points, usually due to an electrical signal from the sensor.”

L.322: “outliers’ removal” should be changed to “the removal of outliers”

L.324 and L.326: “since” is used two times in the same sentence. The second since could be replaced by “which”

L.358: “This sensor measures current time series” is not totally correct as ADCP measures acoustic Doppler shifts and then derives current velocity. The sentence could be simply modified to “This sensor measures indirectly current time series”

L.359: “Consequently, visual inspection of the raw dataset is more difficult to interpret, as can be seen in Figure 6(a), noisy data is not immediately evident in the raw data” could be modified to be clearer to “Consequently, visual inspection of the raw dataset is more difficult to interpret and noisy data is not immediately evident in the raw data, as can be seen in Figure 6(a)”

L.384: “EOVs” should be explained “EOVs (Essential Ocean Variables)”

L.410 “one decade is” should be replaced by “one decade, it is”

Ghil et al., 2002 is missing in the references list
Citation: https://doi.org/10.5194/essd-2025-739-RC2

Nadia Lo Bue, Beatrice Giambenedetti, Davide Embriaco, Paolo Bagiacchi, Claudia Fratianni, Riccardo Vagni, and Giuditta Marinaro

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Dec 2025	72	13	3	88
Jan 2026	109	127	9	245
Feb 2026	40	92	5	137
Mar 2026	88	83	20	191
Apr 2026	59	54	8	121

Cumulative views and downloads (calculated since 29 Dec 2025)

Month	HTML	PDF	XML	Total
Dec 2025	72	13	3	88
Jan 2026	109	127	9	245
Feb 2026	40	92	5	137
Mar 2026	88	83	20	191
Apr 2026	59	54	8	121

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Total article views: 775 (including HTML, PDF, and XML) Thereof 775 with geography defined and 0 with unknown origin.

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Latest update: 01 May 2026

Short summary

We present a set of long-term, high-resolution oceanographic datasets collected by benthic observatories across the deep Mediterranean Sea. These datasets provide valuable insight in the study of deep ocean dynamics and its possible impact on climate variability. By applying standardized post-processing and quality control procedures, we ensure the data's reliability and usability for further analysis and model validation, addressing critical gaps in deep-sea monitoring.


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