the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Jan 2023
Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region
Abstract. The Weddell Gyre is a major feature of the Southern Ocean and an important component of the planetary climate system; it regulates air-sea exchanges, controls the formation of deep and bottom waters, and hosts upwelling of relatively warm subsurface waters. It is characterized by extremely low sea surface temperatures, ubiquitous sea ice formation, and widespread salt stratification that stabilises the water column. Observing the Weddell Gyre is challenging, as it is extremely remote and largely covered with sea ice. At present, it is one of the most poorly-sampled regions of the global ocean, highlighting the need to extract as much value as possible from existing observations. Here, we apply a profile classification model (PCM), which is an unsupervised classification technique, to a Weddell Gyre profile dataset to identify coherent regimes in temperature and salinity. We find that, despite not being given any positional information, the PCM identifies four spatially coherent thermohaline domains that can be described as follows: (1) a circumpolar class, (2) a transition region between the circumpolar waters and the Weddell Gyre, (3) a gyre edge class with northern and southern branches, and (4) a gyre core class. PCM highlights, in an objective and interpretable way, both expected and under-appreciated structures in the Weddell Gyre dataset. For instance, PCM identifies the inflow of Circumpolar Deep Water (CDW) across the eastern boundary, the presence of the Weddell-Scotia Confluence waters, and structured spatial variability in mixing between Winter Water and CDW. PCM offers a useful complement to existing expertise-driven approaches for characterising the physical configuration and variability of the Weddell Gyre and surrounding regions.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Status: closed
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RC1: 'Comment on egusphere-2022-1484', Anonymous Referee #1, 02 Feb 2023
General Comments
This manuscript applies a method of unsupervised machine learning called a profile classification model (PCM) to ocean profile observations in the Weddle Gyre region with the goal to identify and classify areas, or sub-regions, within the Weddle Gyre that share similar temperature and salinity characteristics.
This manuscript clearly highlights how unsupervised classification schemes, such as PCM, can be powerful tools when applied to poorly sampled regions as they are able to identify patterns within highly complex data with no user input. Importantly, the manuscript stresses that PCM is a complementary technique in addition to other types of analysis techniques and confirms previously known thermohaline structures as well as sheds new light on more subtle thermohaline patterns within the Weddle Gyre. The authors use PCM to identify and analyze four categories of ocean profiles within the Weddle Gyre as follows: i) the circumpolar class, ii) a transition class, iii) a gyre edge class, and iv) a gyre core class.
This manuscript is clearly written and highlights a powerful, yet under-utilized technique in oceanography and climate science. I think this work is very interesting and I recommend it is accepted for publication after the following concerns are addressed and several modifications are made.
Specific Comments
-The authors stress one of the benefits of the PCM method is that it identifies “both expected and underappreciated structures” (line 499). However, the results are not clear about which structures are the novel, underappreciated, or previously unknown ones. The manuscript would benefit from a short discussion or clarification on which individual results from the PCM technique are the most critical or important to the research community and which results simply confirm already known patterns.
-The description of the training process for the PC model would benefit from more details. The spatial bias is carefully considering in the training process, however the data contains significant temporal biases as well. Summer months are more heavily observed, as well as a general pattern of increasing observations through time (with spikes in recent years as well as around year 2010). How are the seasonal and annual temporal biases accounted for in the training process? What impact may this have on the results? Additionally, it is not mentioned how large the training data set is, or what the ‘training’ process looks like for the PC model. Are the final PCM conclusions sensitive to how the PC model is trained?
-Section 3.2-3.4 Figure 6/7/8 – Are these figures showing the mean of all individual profiles assigned each class at every spatial grid box? Why do you show the mean for these metrics, yet describe the profile classifications with the median? How sensitive are these metrics or the profile classifications to outliers?
Furthermore, did you look at the seasonal variations in space for the depth of mixed layer depth/minimum/ maximum temperature? You suggest the patterns represent deep winter convection in the shelf waters, yet the data is averaged over time for each grid box, and the observations contain more observations during summer months. Figure 10 is helpful to understand the seasonal variability of the classes – but it would be interesting to also analyze the spatial distribution of the depth metrics by season. For example, are the MLD and min/max temperature depths distinct in wintertime vs summer over the near coast shelf? Do we even have observations in those regions in the wintertime to identify known wintertime signatures with this method?-Line 351-351/Figure 12: Part A: The strongest upwelling does appear to be co-located with the circumpolar and transition classes in most of the domain, however there is some strong upwelling in the far western part of the domain (between 40-60W, south of the SBDY) that do not seem to overlap with the circumpolar or transition class profiles, and seems to overlap more with gyre edge profiles, yet the upwelling here is stronger than the general large-scale gyre class upwelling. Do you have an explanation why this region seems to be unique?
- Line 351-351/Figure 12: Part B) If more observations existed in the near coast downwelling region, would you expect to identify an additional ‘near coastal’ class in this region? Would the exceptionally large seasonal cycle in vertical mixing in the near coast shelf region impact the results?
- Section 4.6: Figure 14 shows several profiles which lie separately from both the transition class and gyre core class (in PC space) – this grouping is comprised primarily of both transition class and gyre core. Is this separation in PC space meaningful? Does this grouping have some traits in common that results in grouping them together in PC space? For example, are they co-located in space or time within the Weddell gyre region? Or do they have certain temperature/salinity traits that can be attributed to specific PC’s in common so that they are clustered and isolated in this PC space, yet are categorized in different classes? Does increasing the number of classes used in the PCM change how these ‘isolated’ profiles are categorized?
- Line 584: Please clarify. What process is applied 20 times for each value of K? It seems that the process of applying PCM can produce in multiple realistic results (a differnent answer for any given iteration). Why were 20 iterations chosen? Are the results sentive to the number of iterations?
Technical corrections
- The grey profiles on the figures (for example: Fig 3; Fig 10) are very difficult to see. Recommend a darker shade of grey.
- Line 89: define ENSO
- line 135 – either define PF or spell it out.
- Figure 2a: add units/label to color bar. Figure 2b: There are only 11 bars plotted in the monthly chart.
- Line 189: define WW
- line 245-255- Figure 5 is never referenced
- line 251- the text specifies the ‘mean’ yet, the metric given is the median.
- line 290: typo – oC?
- Figure 11: make color bar labels+unit text larger
- line 428 – change ‘lighter’ to ‘less dense’
- Figure A1 caption: typo. quantity shown "is"
- The contents of Appendix A are very difficult to interpret since the methods are not presented until Appendix B. Add references to Eqns B1 and B2 for the AIC and BIC, and refer reader to Appendix B.
- Line 567 and Eqns B1, B2: K is never defined
Citation: https://doi.org/10.5194/egusphere-2022-1484-RC1 - AC1: 'Reply on RC1', Dan(i) Jones, 05 Apr 2023
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AC3: 'Reply on RC1: Revised profile distribution plot', Dani Jones, 25 Apr 2023
As suggested by the editor, we are updating some of our figures using discrete colourbars. Please find attached the updated distribution of profile by season. Note the uneven bounds - we make a distinction between grid cells with at least one profile versus grid cells with zero profiles.
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RC2: 'Comment on egusphere-2022-1484', Anonymous Referee #2, 03 Mar 2023
This study applies an unsupervised classification technique, called profile classification model (PCM), to a profile data set in the Weddell Sea region. The authors highlight the importance of this technique as a powerful tool to identify spatially coherent structures in poorly observed regions, such as the Weddell Sea, in addition to other analysis techniques. Without any given spatial information, the PCM identified four spatially coherent classes: 1) circumpolar, 2) transition, 3) gyre edge and 4) gyre core, with sometimes overlapping but distinct properties of temperature and salinity for each class.
The authors emphasize that the PCM is able to reproduce expected structures from previous studies, but is also able to shine light on more subtle thermohaline structures in previously under-appreciated locations in the Weddell Gyre region.
The manuscript provides an interesting approach and a powerful technique to investigate thermohaline structures in poorly observed regions. The method is applicable to other branches of climate science and is thus a valuable contribution to the climate science community.
Before I recommend this study to be accepted for publication, I would like to see an updated version where specific concerns and questions have been addressed and modified.
Specific comments:
The manuscript would benefit from changes to the structure throughout. In some places the manuscript is quite repetitive, whereas in other places there is not enough discussion with previous literature. I think this is related to the large number of subsections (17 in total) in the manuscript that interrupt the flow and make it very difficult to follow at times. I suggest merging some of the subsections to increase readability and avoid repetition:
Introduction: I suggest merging the subsection 1.1 and 1.2 in order to strengthen the storyline for the reader to follow. Simply, stating that this region is poorly observed is not strong enough. Note that many scientists are not familiar with PCM, so I suggest introducing it first, before giving a literature review of which publications have used this method. What specific questions are the authors going to address? What has not been investigated before? What is PCM and why using it? What is the aim of this study?
Data and Methods: This section is quite repetitive. I suggest merging the subsections to avoid repetition. Can you elaborate more on how you use PCA to reduce the dimensionality? Or at least refer to the supplementary material if not discussed in the text?
- There is an entire paragraph on the difficulty of classification as it becomes less interpretable the more complexity is added to the classes? Rather than explaining possible options can you specifically state how you overcame this issue?
- 2: The label font sizes are inconsistent. a) The colorbar is not labelled. Does the colorbar indicate the number of profiles you have for each location? Why 2° latitude-longitude bins when you use 1° bins later on?
- What are the advantages and disadvantages of the PCM overall?
Results and Discussion: It is very difficult to jump between discussion and results due to the large number of subsections. I think the results and discussion would benefit from one another if they would be merged. Merging results and discussion avoids repetition, but also maintains readability and provides the ability for the reader to logically order the results and how this compares to previous studies.
- A contour plot or similar would be really helpful to highlight where the classes overlap.
- L210-229: This paragraph contains a lot of speculation (lots of ‘may’) and would benefit from comparison to previous studies for justification. Are these results expected? Is it comparable to previous studies? What are the novel findings?
- L244-246: This sentence is very unclear to me. Are you indicating that a large I-metric suggests regions of increased mixing or a transformation in profile types or both? Please clarify.
- L247-250: Can you specify what a low I-metric indicates?
Furthermore, the authors emphasize that PCM highlights both expected and underappreciated structures and is thus a helpful tool to develop new research questions any hypothesis. It is unclear, which structures are expected and which are novel or previously underappreciated. Which new findings did this method provide and which additional analysis steps are suggested to investigate the novel findings in more detail?
The authors further discussed seasonal variations within each class specifically with respect to vertical profiles and Theta-S diagrams. Did you also consider seasonal variations in mixed layer depth, signatures of WW (temperature minimum), signatures of CDW (temperature maximum) and how those vary spatially within the classes? How did you deal with the varying number of profiles per season (less profiles in winter months)? How robust are the results?
Minor comments:
- Suggest removing preambles in all sections as they are mostly repetition to the manuscript structure you have already introduced in the introduction.
- Adjust spelling as required by ocean sciences (British or American spelling not both)
- L3, L4, L23: Can you define extremely? Suggest giving values here.
- Suggest adding more information how this method is valuable to the wider community in the abstract.
- L84-96: This is just a list of references who have used PCM before. How is this relevant to this study? Note that at this point you have not introduced the method yet.
- L96-99: Is additional motivation that PCM can be used to redefine boundaries and fronts? You don’t mention it again? It would be really interesting if you could elaborate more on that.
- L189: Define Winter Water
- L208-209: How much does it deepen the further you go away from the gyre core?
- L221-223: Can you elaborate more on why these are indicators of e.g. Weddell-Scotia Confluence waters? Is this new information? Is this comparable to previous studies?
- L247-262: Can you refer to the Figs. And subplots here?
- Equations B1 and B2. Define K.
Figure comments
Overall, I suggest using different colormaps for Figures, showing different variables. Consider changing the colormaps to increase contrast between contours and increase visibility.
- Fig 1: Can you describe the choice of arrow colors on this map? Did you choose the colors based on temperature? If so, why do CDW and WDW have the same color? On a printed version you can barely see the arrows underneath the geographic locations added to the Fig. I suggest moving the all geographic locations labelled in the Fig. outside to achieve a clear view. Further the font size of the x- and y-axis labels needs to be increased.
- 3: In section 3.1 you discuss the differences between each identified class. You specifically mention the median depth of the temperature minimum. I suggest adding the median depth as a horizontal line to each class to visually highlight the differences in each class.
- On Figs. 4, 5, 6, 7, 8 the grey lines are hardly visible. Suggest using a darker shade of grey and thickening the lines. I also suggest using a different colormap for each variable to visually differentiate between the plots. The blue is very dominating, so other structures are very hard to see.
- Figs. 7 and 8. Figures are much too small. Labels are too small.
- Figs. 9. You are using conservative temperature. Shouldn’t it be Θ-S diagrams? Suggest changing this throughout.
- Fig. 11 The background is too dark. I cannot read the isopynal labels (much to small, contrast too low)
- Fig. 12. Suggest labeling the colorbars. In b) suggest adjusting the colormap. It is really hard to see the sea ice freezing line.
Citation: https://doi.org/10.5194/egusphere-2022-1484-RC2 - AC2: 'Reply on RC2', Dan(i) Jones, 05 Apr 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-1484', Anonymous Referee #1, 02 Feb 2023
General Comments
This manuscript applies a method of unsupervised machine learning called a profile classification model (PCM) to ocean profile observations in the Weddle Gyre region with the goal to identify and classify areas, or sub-regions, within the Weddle Gyre that share similar temperature and salinity characteristics.
This manuscript clearly highlights how unsupervised classification schemes, such as PCM, can be powerful tools when applied to poorly sampled regions as they are able to identify patterns within highly complex data with no user input. Importantly, the manuscript stresses that PCM is a complementary technique in addition to other types of analysis techniques and confirms previously known thermohaline structures as well as sheds new light on more subtle thermohaline patterns within the Weddle Gyre. The authors use PCM to identify and analyze four categories of ocean profiles within the Weddle Gyre as follows: i) the circumpolar class, ii) a transition class, iii) a gyre edge class, and iv) a gyre core class.
This manuscript is clearly written and highlights a powerful, yet under-utilized technique in oceanography and climate science. I think this work is very interesting and I recommend it is accepted for publication after the following concerns are addressed and several modifications are made.
Specific Comments
-The authors stress one of the benefits of the PCM method is that it identifies “both expected and underappreciated structures” (line 499). However, the results are not clear about which structures are the novel, underappreciated, or previously unknown ones. The manuscript would benefit from a short discussion or clarification on which individual results from the PCM technique are the most critical or important to the research community and which results simply confirm already known patterns.
-The description of the training process for the PC model would benefit from more details. The spatial bias is carefully considering in the training process, however the data contains significant temporal biases as well. Summer months are more heavily observed, as well as a general pattern of increasing observations through time (with spikes in recent years as well as around year 2010). How are the seasonal and annual temporal biases accounted for in the training process? What impact may this have on the results? Additionally, it is not mentioned how large the training data set is, or what the ‘training’ process looks like for the PC model. Are the final PCM conclusions sensitive to how the PC model is trained?
-Section 3.2-3.4 Figure 6/7/8 – Are these figures showing the mean of all individual profiles assigned each class at every spatial grid box? Why do you show the mean for these metrics, yet describe the profile classifications with the median? How sensitive are these metrics or the profile classifications to outliers?
Furthermore, did you look at the seasonal variations in space for the depth of mixed layer depth/minimum/ maximum temperature? You suggest the patterns represent deep winter convection in the shelf waters, yet the data is averaged over time for each grid box, and the observations contain more observations during summer months. Figure 10 is helpful to understand the seasonal variability of the classes – but it would be interesting to also analyze the spatial distribution of the depth metrics by season. For example, are the MLD and min/max temperature depths distinct in wintertime vs summer over the near coast shelf? Do we even have observations in those regions in the wintertime to identify known wintertime signatures with this method?-Line 351-351/Figure 12: Part A: The strongest upwelling does appear to be co-located with the circumpolar and transition classes in most of the domain, however there is some strong upwelling in the far western part of the domain (between 40-60W, south of the SBDY) that do not seem to overlap with the circumpolar or transition class profiles, and seems to overlap more with gyre edge profiles, yet the upwelling here is stronger than the general large-scale gyre class upwelling. Do you have an explanation why this region seems to be unique?
- Line 351-351/Figure 12: Part B) If more observations existed in the near coast downwelling region, would you expect to identify an additional ‘near coastal’ class in this region? Would the exceptionally large seasonal cycle in vertical mixing in the near coast shelf region impact the results?
- Section 4.6: Figure 14 shows several profiles which lie separately from both the transition class and gyre core class (in PC space) – this grouping is comprised primarily of both transition class and gyre core. Is this separation in PC space meaningful? Does this grouping have some traits in common that results in grouping them together in PC space? For example, are they co-located in space or time within the Weddell gyre region? Or do they have certain temperature/salinity traits that can be attributed to specific PC’s in common so that they are clustered and isolated in this PC space, yet are categorized in different classes? Does increasing the number of classes used in the PCM change how these ‘isolated’ profiles are categorized?
- Line 584: Please clarify. What process is applied 20 times for each value of K? It seems that the process of applying PCM can produce in multiple realistic results (a differnent answer for any given iteration). Why were 20 iterations chosen? Are the results sentive to the number of iterations?
Technical corrections
- The grey profiles on the figures (for example: Fig 3; Fig 10) are very difficult to see. Recommend a darker shade of grey.
- Line 89: define ENSO
- line 135 – either define PF or spell it out.
- Figure 2a: add units/label to color bar. Figure 2b: There are only 11 bars plotted in the monthly chart.
- Line 189: define WW
- line 245-255- Figure 5 is never referenced
- line 251- the text specifies the ‘mean’ yet, the metric given is the median.
- line 290: typo – oC?
- Figure 11: make color bar labels+unit text larger
- line 428 – change ‘lighter’ to ‘less dense’
- Figure A1 caption: typo. quantity shown "is"
- The contents of Appendix A are very difficult to interpret since the methods are not presented until Appendix B. Add references to Eqns B1 and B2 for the AIC and BIC, and refer reader to Appendix B.
- Line 567 and Eqns B1, B2: K is never defined
Citation: https://doi.org/10.5194/egusphere-2022-1484-RC1 - AC1: 'Reply on RC1', Dan(i) Jones, 05 Apr 2023
-
AC3: 'Reply on RC1: Revised profile distribution plot', Dani Jones, 25 Apr 2023
As suggested by the editor, we are updating some of our figures using discrete colourbars. Please find attached the updated distribution of profile by season. Note the uneven bounds - we make a distinction between grid cells with at least one profile versus grid cells with zero profiles.
-
RC2: 'Comment on egusphere-2022-1484', Anonymous Referee #2, 03 Mar 2023
This study applies an unsupervised classification technique, called profile classification model (PCM), to a profile data set in the Weddell Sea region. The authors highlight the importance of this technique as a powerful tool to identify spatially coherent structures in poorly observed regions, such as the Weddell Sea, in addition to other analysis techniques. Without any given spatial information, the PCM identified four spatially coherent classes: 1) circumpolar, 2) transition, 3) gyre edge and 4) gyre core, with sometimes overlapping but distinct properties of temperature and salinity for each class.
The authors emphasize that the PCM is able to reproduce expected structures from previous studies, but is also able to shine light on more subtle thermohaline structures in previously under-appreciated locations in the Weddell Gyre region.
The manuscript provides an interesting approach and a powerful technique to investigate thermohaline structures in poorly observed regions. The method is applicable to other branches of climate science and is thus a valuable contribution to the climate science community.
Before I recommend this study to be accepted for publication, I would like to see an updated version where specific concerns and questions have been addressed and modified.
Specific comments:
The manuscript would benefit from changes to the structure throughout. In some places the manuscript is quite repetitive, whereas in other places there is not enough discussion with previous literature. I think this is related to the large number of subsections (17 in total) in the manuscript that interrupt the flow and make it very difficult to follow at times. I suggest merging some of the subsections to increase readability and avoid repetition:
Introduction: I suggest merging the subsection 1.1 and 1.2 in order to strengthen the storyline for the reader to follow. Simply, stating that this region is poorly observed is not strong enough. Note that many scientists are not familiar with PCM, so I suggest introducing it first, before giving a literature review of which publications have used this method. What specific questions are the authors going to address? What has not been investigated before? What is PCM and why using it? What is the aim of this study?
Data and Methods: This section is quite repetitive. I suggest merging the subsections to avoid repetition. Can you elaborate more on how you use PCA to reduce the dimensionality? Or at least refer to the supplementary material if not discussed in the text?
- There is an entire paragraph on the difficulty of classification as it becomes less interpretable the more complexity is added to the classes? Rather than explaining possible options can you specifically state how you overcame this issue?
- 2: The label font sizes are inconsistent. a) The colorbar is not labelled. Does the colorbar indicate the number of profiles you have for each location? Why 2° latitude-longitude bins when you use 1° bins later on?
- What are the advantages and disadvantages of the PCM overall?
Results and Discussion: It is very difficult to jump between discussion and results due to the large number of subsections. I think the results and discussion would benefit from one another if they would be merged. Merging results and discussion avoids repetition, but also maintains readability and provides the ability for the reader to logically order the results and how this compares to previous studies.
- A contour plot or similar would be really helpful to highlight where the classes overlap.
- L210-229: This paragraph contains a lot of speculation (lots of ‘may’) and would benefit from comparison to previous studies for justification. Are these results expected? Is it comparable to previous studies? What are the novel findings?
- L244-246: This sentence is very unclear to me. Are you indicating that a large I-metric suggests regions of increased mixing or a transformation in profile types or both? Please clarify.
- L247-250: Can you specify what a low I-metric indicates?
Furthermore, the authors emphasize that PCM highlights both expected and underappreciated structures and is thus a helpful tool to develop new research questions any hypothesis. It is unclear, which structures are expected and which are novel or previously underappreciated. Which new findings did this method provide and which additional analysis steps are suggested to investigate the novel findings in more detail?
The authors further discussed seasonal variations within each class specifically with respect to vertical profiles and Theta-S diagrams. Did you also consider seasonal variations in mixed layer depth, signatures of WW (temperature minimum), signatures of CDW (temperature maximum) and how those vary spatially within the classes? How did you deal with the varying number of profiles per season (less profiles in winter months)? How robust are the results?
Minor comments:
- Suggest removing preambles in all sections as they are mostly repetition to the manuscript structure you have already introduced in the introduction.
- Adjust spelling as required by ocean sciences (British or American spelling not both)
- L3, L4, L23: Can you define extremely? Suggest giving values here.
- Suggest adding more information how this method is valuable to the wider community in the abstract.
- L84-96: This is just a list of references who have used PCM before. How is this relevant to this study? Note that at this point you have not introduced the method yet.
- L96-99: Is additional motivation that PCM can be used to redefine boundaries and fronts? You don’t mention it again? It would be really interesting if you could elaborate more on that.
- L189: Define Winter Water
- L208-209: How much does it deepen the further you go away from the gyre core?
- L221-223: Can you elaborate more on why these are indicators of e.g. Weddell-Scotia Confluence waters? Is this new information? Is this comparable to previous studies?
- L247-262: Can you refer to the Figs. And subplots here?
- Equations B1 and B2. Define K.
Figure comments
Overall, I suggest using different colormaps for Figures, showing different variables. Consider changing the colormaps to increase contrast between contours and increase visibility.
- Fig 1: Can you describe the choice of arrow colors on this map? Did you choose the colors based on temperature? If so, why do CDW and WDW have the same color? On a printed version you can barely see the arrows underneath the geographic locations added to the Fig. I suggest moving the all geographic locations labelled in the Fig. outside to achieve a clear view. Further the font size of the x- and y-axis labels needs to be increased.
- 3: In section 3.1 you discuss the differences between each identified class. You specifically mention the median depth of the temperature minimum. I suggest adding the median depth as a horizontal line to each class to visually highlight the differences in each class.
- On Figs. 4, 5, 6, 7, 8 the grey lines are hardly visible. Suggest using a darker shade of grey and thickening the lines. I also suggest using a different colormap for each variable to visually differentiate between the plots. The blue is very dominating, so other structures are very hard to see.
- Figs. 7 and 8. Figures are much too small. Labels are too small.
- Figs. 9. You are using conservative temperature. Shouldn’t it be Θ-S diagrams? Suggest changing this throughout.
- Fig. 11 The background is too dark. I cannot read the isopynal labels (much to small, contrast too low)
- Fig. 12. Suggest labeling the colorbars. In b) suggest adjusting the colormap. It is really hard to see the sea ice freezing line.
Citation: https://doi.org/10.5194/egusphere-2022-1484-RC2 - AC2: 'Reply on RC2', Dan(i) Jones, 05 Apr 2023
Peer review completion
Journal article(s) based on this preprint
Data sets
SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints Dani Jones, Shenjie Zhou https://doi.org/10.5281/zenodo.7468655
South Atlantic Ocean profile dataset: identification of near-Antarctic profiles using unsupervised classification Dani Jones, Shenjie Zhou https://doi.org/10.5281/zenodo.7465132
Model code and software
so-wise/weddell_gyre_clusters: First release Dani Jones https://doi.org/10.5281/zenodo.7465388
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Maike Sonnewald
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Andrew J. S. Meijers
Isabella Rosso
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Michael P. Meredith
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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