the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Sep 2024
30 months dataset of glider physico-chemical data off Mayotte Island near the Fani Maoré volcano
Abstract. In May 2018, an unprecedented long and intense seismic-volcanic crisis broke out off the island of Mayotte (Indian Ocean) and was associated with the birth of an underwater volcano (Fani Maoré). Since then, an integrated observation network has been created (REVOSIMA), with the given objective of monitoring and better understanding underwater volcanic phenomena. Recently, an unmanned submarine glider (SeaExplorer glider) has been deployed to supplement the data obtained during a series of oceanographic surveys (MAYOBS) carried out on an annual basis. Operated by ALSEAMAR, the glider performed a continuous monitoring of 30 months of the water column from the sea surface to 1250 meters water depth with the objective to acquire hydrological properties, water currents and dissolved gas concentrations. This monitoring already showed that it is feasible and valuable to measure autonomously, continuously and at a high spatio-temporal scale, physical (temperature, salinity, ocean current) and biogeochemical parameters (O2, CH4, CO2, bubbles/droplets, vertical speeds anomalies related to droplets) over several months from a glider. In particular, innovating sensing capabilities (e.g., MINICO2, ADCP) have shown a great potential in the context of the Mayotte seismic volcano crisis, despite technical challenges (complex algorithms, sensor capabilities, etc.).
- Preprint
(6957 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on essd-2024-377', Anonymous Referee #1, 22 Nov 2024
This data paper present a unique dataset of glider data equipped of both hydrological, biogeochemical sensors to investigate the source, fate and fluxes of magmatic fluid into the ocean close to Mayotte.
This 30 months data-set combined measurements acquired with different sensors and an effort was made to fix discontinuity in the dataset.
The datapaper is well presented and illustrations are clear. The present datapaper is suitable to be published in ESSD but I have several comments to be adressed before.
The strategy of underwater glider profiles to detect fluid dynamics is not well described. It seems that a multiple yos strategy between the bottom and about 100m above the seabed was adopted. Please clarify. How is the impact of this strategy on the estimation of the currents? Please give some uncertainty.
Corrections were applied such as thermal lag on CTD measurements but also on O2 measurements. It would be useful to show some vertical profiles with and without corrections to show the effectiveness of the correction.
Two CTD sensors were used during the 30 months glider deployment, how do they compare or not? is there some periods with concomitant measurements?
Additional comments are included in the additional pdf files I added.
-
RC2: 'Comment on essd-2024-377', Anonymous Referee #2, 04 Feb 2025
In this paper entitled «30 months dataset of glider physico-chemical data off Mayotte Island near the Fani Maoré volcano», Heumann et al. Present a data set from a continuous monitoring of an active volcanic site off Mayotte Island made with autonomous glider.
The data presented by the paper is very unique and relevant to be published in ESSD. However, the paper needs some important revisions in order to better clarify aspects of the data processing and calibration. See my comments below.
Major comments :
- Section 2.2 : There is no mention of data acquisition frequency of the glider. Are the data kept in time series or bin-average in vertical profiles, or something else ? There should be description of the process leading to the time series at 30s found in SEANOE (sub-sampling ? Bin-averaging?). Same issue for section 2.2.3 about ADCP data. There is no mention of the procedure applied to produce the 2-dbar profiles one can find in SEANOE.
- There seems to be no comparison done with cruises that have been led in the area (MAYOBS). Shipborne measurements are always crucial to get accurate glider data. MAYOBS should be introduced and used here. Especially when looking at the TS diagram in Fig 5, in the intermediate and deep waters one can clearly identified group of profiles corresponding to different deployments/instruments… + L363-366: Including these comparisons in the present paper would be real asset, as it is hard to judge in the present form the accuracy of the data...
- The authors mention on several occasions that they followed international standards of Oceangliders. However, I have the feeling that these were not completely followed.
CTD : In particular, RBR unpumped CTD Legato salinity are kept as CTD output which certainly not recommended by any international standards, as they can suffer from important thermal lag issue. These thermal lag issues are known to be less pronounced for pumped CTD (such as GPCTD) for which you did apply a correction. Could you evaluate the thermal issue in your RBR time series, check whether it is problematic or not, and correct it ? Apart from thermal lag issue, salinity data also suffer from accuracy issues when used as raw data. In my opinion, some efforts have to be put into making the salinity time series consistent. Looking at TS diagram in Fig 5 and time series in Fig 6, variability at scale of the missions can be seen (about 15 days). The average TS diagram for each mission compare with each other ? Again, if CTD data are available from MAYOBS cruises, they should be used as reference. Alternatively data from World Ocean Atlas (WOA) climatology could be used.
DO : Electrochemical sensors (like SBE43F) are known to have slow time response which can cause hysteresis between up and downcasts. There is no mention of such features. Did you apply any correction for this ? l109-113 : How are «regimes » defined in this context ? (« Ocean glider » → OceanGliders). In my opinion, it seems essential that the data are compared with reference shipborne DO measurements (Winkler method) in order to qualify their accuracy; or at least compared to WOA climatology. Table A2 : « Gain and offset » → where are the offsets ? What are the group of profiles referring to ? The corrections aims to produce continuous time series of DO but clear jumps can still be seen.
Comments on Figures and Tables :
- please consider improving the readiness of your figures by increasing some of the axis labels that are sometimes too small to be read (eg Fig. 1, 10). Better settings of figure and label size would help to get a uniform rendering throughout the paper.
- Figure 1 : Please add a colorbar or depth contours in (a) ; caption : « red triangles » → « red star » ; which area corresponds to (c)? Please add axis label or box on (a) to show it.
- Fig 2 : «(a) Maps illustrating the sampling… (c) same for profiles down to 1250 m. What is the bin size considered considered to show the profiles density? From the latitude axis, there are about 7 squares for 2’ (ie 2/60*111 = 3.7km) which is equivalent to a square size of 0.5 by 0.5 km, so a surface of 0.25km2. Please add a scale to the map.
- Fig 3 : Temperature is described in the text before salinity. I would swap them. What is preventing density to be shown from Jan 2024 ? You could gain space by removing title (adding an inset with text of an axis to the colorbar) and x-axis label, in order to make your subplots larger using the same figure size. Contour at well chosen values could also help to read the values.
- Fig 4 : It could be useful to have an idea of the variability behind those mean profiles.
- Fig 5 : It seems that a cut-off in salinity has been applied in surface value for S<34.84psu, which is not described in the paper. Also visible in missing surface values in fig 3.
- Fig 6 : Since the data set comes from a large number of deployments in would interesting to show these with different colors (alternate dark and light blue for instance).
- Fig 7 : « isopycnals ». I would be instructive to know where the data have been sampled by showing the glider trajectory in a thin or dotted line.
- Fig 9: Values of dive-average currents could be added to the figure to compare with ADCP data.
- Fig 10 and 11 : Axis labels are too small. - You don’t need to squares for every table cells, that makes the tables not easily readable.
Table 3 : The TSO2 ranges could be regionalised using WOA climatology.
Specific minor comments :
l3 : with the objective
l4 : autonomous ocean glider (ALSEAMAR’s SeaExplorer)
l6 : 30 months from XX to YY
l7 : showed the feasibility and value to continuously and autonomously measure at high spatio-temporal …
l23 : Please spell out the names of institutions before introducing their acronym, or consider adding a list of acronyms at the end.
L40 : 23 active emission sites identified to date
l42 : The Agulhas current flows south of the Mozambique channel as the East Madagascar Current and Mozambique Current converge and form a Western Boundary Current. However, Mayotte is located North-West of Madagascar, ie north of the Mozambique channel. The influence of the Agulhas Current on the circulation is hence not precise enough. Please revise the description of the regional circulation and add relevant references.
L48-52 : Please revise this paragraph, add relevant literature and be more precise about the scales of variability of the ocean you describe (sub-mesoscale, internal waves/tides, etc.).
l51 : what is deep ? Are these numbers from observations or literature ?
L57 : 2022 → 2021. How long is the observation planned to continue ? What is the current status of the monitoring ?
L67 : You should mention the GOOS’s component OceanGliders here.
L80 : I find the notation « mbss » heavy and oceanographers understand that depth are below sea surface without having to repeat it.
L83 : what you refer as « transects » are dives if I understand correctly (as a full dive to 1000m + 10 yos of 100m would take about 8h to complete). Please clarify.
L85 : The sampling strategy is not a simple dive-climb pattern typical for gliders. Please consider adding a figure/schematics illustrating the different sampling strategy.
L89 : what is the typical time between two surfacing for 1250m dives ?
L99 : I guess you are calculating practical salinity, not as absolute salinity ?
L103 : OceanGliders
L105 : Authors mention a method for correcting thermal lag issue with glider’s CTD. GPCTD are typical , while RBR unpumped CTD can be more affected by thermal lag issue.
Section 2.2.2 : Oxygen is also a dissolved gas, please revise your title.
L133 : uplift and downlift → upcast and downcast
Section 2.3 : Is the QF scheme following any existing QF like SEADATANET or WOA ? Since you have a sensor with high detection limit (CH4), you should have a flag describing « below detection level ».
l147 : what is the hydrodynamical model used to compute dive-average currents ? What is the statistics of the comparison with currents from ADCP ? Can you give a range of error for the final data ?
L149 : Can you develop more why data could be kept as profiles ?
L150 : How does the yo-averaging procedure affect the result ?
l151 : Please refer to the typical diving time during the mission and how it compares with the tidal period.
l153 : scatters in the water
l167 : The tests described are the same as the ones applied for Argo floats. It should probably be mentionned.
l169 : using
l199 : potential density(?)
l211 : (viewable → visible) There are also disruptions that can be caused by when changing sensors/glider ? Please justify at what scales you expect to see mesoscale variability and how does it translate in your data.
L215 : »puzzling » : they can be related to thermal lag issue in the seasonal thermocline. How does TS diagram of upcasts compare with downcasts ?
L226 : The tidal oscillations even reach O(100m) in the deep layers.
L227 : I have to disagree with this sentence. Gliders have long proven to be able to sample internal tides (see https://os.copernicus.org/articles/20/945/2024/ and references therein) Your sampling strategy combined ADCP could very well be used to study internal tides in the area. The limitations reside more in the averaging applied between consecutive surfacing to produce vertical profiles.
L233: AOU could be calculated or mean profile of O2 saturation shown in fig 4c along with the O2 profile.
l257 : These numbers would fit better in section 2.1. Also how are profiles defined regarding the sampling strategy ?
L284; down to
l288: It seems that the strong currents align with the continental slope and could be related to barotropic currents.
L297 resulting from
l300: There is no mention of how vertical velocities are calculated. Are they calculated from the ADCP or a flight model? L320 and after does not talk about currents not backscatter index… Consider making another subsection.
L339: “The overall quality of the produced dataset is remarkable” This is not how a data paper conclusion should start. It is indeed truly remarkable to be collecting such a data set. Regarding the data quality, the last sentence of the manuscript let think that it can actually still be improved.
L344: Please provide reference or website and acronym for GEORGE project.
L361 several studies?
Citation: https://doi.org/10.5194/essd-2024-377-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 396 | 93 | 48 | 537 | 14 | 21 |
- HTML: 396
- PDF: 93
- XML: 48
- Total: 537
- BibTeX: 14
- EndNote: 21
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1