Glacial Meltwater in the Southeast Amundsen Sea: A timeseries from 1994&ndash;2020

Hennig, Andrew Nicholas; Mucciarone, David A.; Jacobs, Stanley S.; Mortlock, Richard A.; Dunbar, Robert B.

doi:https://doi.org/10.5194/egusphere-2023-141

Preprints

https://doi.org/10.5194/egusphere-2023-141

Preprints

15 Feb 2023

| 15 Feb 2023

Glacial Meltwater in the Southeast Amundsen Sea: A timeseries from 1994–2020

Andrew Nicholas Hennig, David A. Mucciarone, Stanley S. Jacobs, Richard A. Mortlock, and Robert B. Dunbar

Abstract. Ice sheet mass loss from Antarctica is greatest in the Amundsen Sea sector, where ‘warm’ deep seawater melts and thins the bases of ice shelves hundreds of meters below the sea surface. We use nearly 1000 paired salinity and oxygen isotope analyses of seawater samples collected on seven expeditions from 1994 to 2020 to produce a time series of glacial meltwater inventory on the Southeast Amundsen Sea continental shelf. Water column salinity-ẟ¹⁸O yield freshwater endmember ẟ¹⁸O values from −30.2 ‰ to −28.4 ‰, demonstrating that regional freshwater content is dominated by deep glacial melt. The meltwater fractions display temporal variability in basal melting, with 800 m water column meltwater inventories from 7.7 m to 9.2 m. This result corroborates recent studies suggesting interannual variability in basal melt rates of West Antarctic ice shelves and is consistent with the Amundsen region’s influence on ocean salinity and density downstream in the Ross Sea.

Received: 01 Feb 2023 – Discussion started: 15 Feb 2023

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Journal article(s) based on this preprint

20 Feb 2024

Meteoric water and glacial melt in the southeastern Amundsen Sea: a time series from 1994 to 2020

Andrew N. Hennig, David A. Mucciarone, Stanley S. Jacobs, Richard A. Mortlock, and Robert B. Dunbar

The Cryosphere, 18, 791–818, https://doi.org/10.5194/tc-18-791-2024,https://doi.org/10.5194/tc-18-791-2024, 2024

Short summary

Andrew Nicholas Hennig, David A. Mucciarone, Stanley S. Jacobs, Richard A. Mortlock, and Robert B. Dunbar

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-141', Anonymous Referee #1, 14 Mar 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-141/egusphere-2023-141-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-141-RC1
- AC1: 'Reply on RC1', Andrew Hennig, 09 May 2023
  
  Please see attached PDF for detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-141-AC1
RC2:
'Comment on egusphere-2023-141', Anonymous Referee #2, 19 Apr 2023
Review of “Glacial Meltwater in the Southeast Amundsen Sea: A timeseries from 1994-2020” by Hennig et al. in The Cryosphere
General comments:
The pace of melting of Antarctic ice shelves due to warming along the coastal margin and the associated changes in the grounded ice sheet are a major concern in terms of future sea level rise. Models that are used to project future changes still entail large uncertainties and current estimates of changes largely stem from remote sensing data. Ocean tracer measurements that can be used to quantify the glacial meltwater content and its changes accumulated in the ocean provide an opportunity to better understand the melting of ice shelves and its temporal variability.
The study by Hennig et al. provides novel data collected over more than two decades from the Amundsen Sea sector, which is a region where a large increase in melt has been reported previously, mainly driven by warm water intrusion on the shelf. Using the isotopic composition, they find that the regional freshwater budget is dominated by glacial meltwater and that the meltwater inventory exhibits large decadal fluctuations superimposed on a comparatively small long-term trend. These results support other recent studies based on remote sensing data that have found substantial fluctuations of the ice shelf melt on decadal time scales.
This is a very timely and interesting study that is of importance to the wider Antarctic ice shelf and ice sheet community as well as the oceanographic community. It is overall well written and I think that the methods are mostly robust and support the results. Particularly the authors’ approach to circumvent issues of laboratory offsets in the isotopic measurements, that have been a known issue for a while, is quite elegant and I think leads to meaningful results. However, I also think that the paper would benefit from a more in-depth comparison to previous work and from highlighting the novel aspects of this work more clearly. In addition, I have some concerns regarding the uncertainty discussion, in particular to biases induced by the spatial sampling and I think that caveats should be communicated more clearly. Overall, I think that the manuscript is suitable in principal for publication in The Cryosphere, after addressing some points.
Specific comments:
I think that the motivation for this study and the importance of the results is not communicated sufficiently. Currently there is a strong focus and emphasis on the collection of a timeseries, but very little on why the timeseries is collected and what we can learn from such a timeseries. I think that discussing this in more detail, in particular in relation to the recent literature on the temporal evolution of melt in the Amundsen Sea, is critical to highlight the novelty of the results. A particular example is the following sentence in the introduction (P2L31-33): “[…] some studies have shown a greater interannual variability in the basal melt rates than increase […], and some have even suggested a slowing of basal melt rates […] and grounding line retreat […].” I think that this point has to be extended by rewriting the sentence, adding a time perspective (what happened when / what timescales are we talking about), has to put into perspective of natural climate variability versus anthropogenic forcing, and used as an explicit motivation for the study and how the seawater isotopic composition might help to contribute to this discussion.

Following the point above, I think that the paper would benefit from an extension of the discussion on the temporal variability shown in Figure 4. To me, this is the key result of the paper. However, the discussion on details in variability seen in this Figure and how they relate to other recent findings and what new aspects can be learned from this Figure is very limited. In fact, there is not even a reference to Figure 4 in the main text.

P4-5L80-82: I think that the approach taken here indeed mitigates some of the known issues of salt effects between IRMS and CRDS. However, it is not very clear in these sentences here that the salt effect is indirectly removed by using different CDW reference values for each respective data set. I think that should be written more explicitly at this point. In addition it might be useful to actually point to the differences in CDW d18O in Table 2 where the CRDS measurements (2019/2020) yield a much lower CDW value than the IRMS measurements (2014). Is this difference in line with the values reported for the salt effect in literature?

P7L132-135: I think it is important to discuss the difference in results associated with using a constant and varying mCDW and meteoric endmember at this point. A constant value would yield a GMW estimate that is spatially integrated and the varying endmember yields local fluxes. Likely, this choice will also affect the long-term trends in the GMW estimate (largely through changes in the meteoric endmember), which I think should be discussed as a possible caveat at this point.

I am still a bit concerned about potential artifacts from the changes in the spatial sampling from one year to the other. Fig. 1 and also Fig. A3 clearly show substantial spatial differences in GMW content in the region and I think that the paragraph on p. 9 Lines 184-187 is not sufficiently accounting for the issue. I appreciate that this issue is investigated in Section A4. However, I think that the manuscript would benefit in terms of the credibility of the results, if a more detailed spatial analysis was added to the main text. In the end, the main results in Figure 4 are interannual variations with a magnitude of about 1.5m, which seems to be within the range of spatial variations shown in Figs. 1 and A3.
So, I am wondering if the reported uncertainties in Table A2, last column (“Average GMW inventory (m)), as well as the uncertainties shown in the main text also include the spatial standard deviation of the samples? Is this included in the “environmental” uncertainty within the Montecarlo simulation? I think that it would be transparent and beneficial to simply report the spatial standard deviation of GMW for each box also in Table A2, which would give a measure of the range of spatial variations.

In addition, I have difficulties understanding how the boxes were chosen and why they seem to be not consistent between the years, i.e. sometimes a location falls in one box and sometimes in another. I think it would be helpful to have boxes that are rather fixed in time and represent certain regimes within the region. For example, I found the Boxes in Fig. A3 for 2014 quite logic, since there is an “offshore” box (c), a TGT box (d), a PIIS box (a) and a central box (b). Looking at these boxes over all years and samples would be, i.e. having a figure similar to Figure 4 for each of these regions would be very helpful to understand how the variability might differ spatially and if the variability is a signal that is consistent across the entire domain or just arises from local signals would be very helpful to have. I would suggest to actually have a figure like this with a brief discussion in the main text if possible.

I am a bit concerned about the conclusion (P11 Line 243) that the long-term trend is insignificant without discussing the fact that this only reflects the data presented here but might not reflect the actual trend in the melting. It would be good to discuss some of the caveats of the use of the data set and its limitations. In particular, I think that the data set will not capture the entire amount of meltwater coming from the Amundsen Sea, as the authors’ report that the residence time of the water in the region is only about 1 year. So, it may well be that there is a strong long-term trend in glacial melt in the region, but that the signal largely propagates out of the region and does not accumulate there. Also, the fact that the endmembers vary throughout the years, in particular the glacial melt endmember, could affect the long-term trend. So, I think it is important to discuss such potential limitations here.

Technical corrections:
P2L35: I don’t think that “SE” has been defined yet.

Figure 1: Please do not use “rainbow” colormaps that are not scientific colormaps. For detailed reasons and tools to generate an appropriate colorbar e.g. for Matlab, please see for example this paper by Stauffer et al. (2015; https://doi.org/10.1175/BAMS-D-13-00155.1)

Figure 2: I found it difficult to depict the difference in blue. Since only dark blue is used, it may be good to keep those dark blue sample and exchange the other blue(s) by gray.

P6L107: Probably important to add that also “sea ice formation and melt” will affect the signal at this point.

Equations 1-3: the placement of these equations seems odd as there are somewhere in the text where they are not discussed. Please place them right below a description of and reference to these Equations.

P6L125: I guess it should be not just sea ice melt but also “formation”

P7L149: I think that “extremely unlikely” is a stretch here. Please reformulate to “[…] mean, the potential impact of analytical calibration offsets between laboratories on the calculated GMW fractions are mitigated.

P8L154: “mathematical artifacts” seems odd and would not understand what is meant, do you mean “sampling and analytical uncertainties”?

P8L166: It is unclear at this point where the uncertainty estimate is coming from. Could you please refer to the part of the manuscript where it is calculated and/or briefly mention it here.

Figure 4: Is this trend statistically significant or not. Please report the statistical significance here.

P10L194: I have difficulties understanding the meaning of “a range of the range” (e.g. +- 1.5 – 1.7 g/kg) in uncertainty, if I understand this correctly. Please report a single range, e.g. +-1.7 g/kg.

P10L201: Change to “This minimizes systematic isotopic offsets”

P11L249: I think what the authors are really trying to say here is that the decadal variability of the melt is actually substantially larger than the long term trend (1994 to 2020). The way that this is currently written it is difficult to understand what is actually meant. It seems not surprising that there is interannual variability in the first place, but what is in fact interesting is the magnitude of the variability compared to the trend and the time scale over which this variability occurs. I think that needs clarification.

P11/12L252-253: It would be helpful to note at this point that the tracer approach has the advantage that the ocean integrates the temporal meltwater changes and thus a single measurement actually reflects a longer period of melting.

P13L260-264: Please correct typological and formatting errors.

P17L331: I have difficulties understanding the meaning of “a range of the range” (e.g. +- 0.5 – 0.7 m) in uncertainty, if I understand this correctly. Please report a single range, e.g. +-0.7 m.

P17L333: I think that this should read “95.1%”, right? Otherwise I would not understand this number.

Generally, the numbering of subsections is wrong; always starts with “1.x”

References:
Stauffer, R., Mayr, G. J., Dabernig, M., & Zeileis, A. (2015). Somewhere Over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations, Bulletin of the American Meteorological Society, 96(2), 203-216. https://doi.org/10.1175/BAMS-D-13-00155.1
Citation: https://doi.org/10.5194/egusphere-2023-141-RC2
- AC2: 'Reply on RC2', Andrew Hennig, 09 May 2023
  
  Please see attached PDF for detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-141-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-141', Anonymous Referee #1, 14 Mar 2023

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-141/egusphere-2023-141-RC1-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2023-141-RC1
- AC1: 'Reply on RC1', Andrew Hennig, 09 May 2023
  
  Please see attached PDF for detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-141-AC1
RC2:
'Comment on egusphere-2023-141', Anonymous Referee #2, 19 Apr 2023
Review of “Glacial Meltwater in the Southeast Amundsen Sea: A timeseries from 1994-2020” by Hennig et al. in The Cryosphere
General comments:
The pace of melting of Antarctic ice shelves due to warming along the coastal margin and the associated changes in the grounded ice sheet are a major concern in terms of future sea level rise. Models that are used to project future changes still entail large uncertainties and current estimates of changes largely stem from remote sensing data. Ocean tracer measurements that can be used to quantify the glacial meltwater content and its changes accumulated in the ocean provide an opportunity to better understand the melting of ice shelves and its temporal variability.
The study by Hennig et al. provides novel data collected over more than two decades from the Amundsen Sea sector, which is a region where a large increase in melt has been reported previously, mainly driven by warm water intrusion on the shelf. Using the isotopic composition, they find that the regional freshwater budget is dominated by glacial meltwater and that the meltwater inventory exhibits large decadal fluctuations superimposed on a comparatively small long-term trend. These results support other recent studies based on remote sensing data that have found substantial fluctuations of the ice shelf melt on decadal time scales.
This is a very timely and interesting study that is of importance to the wider Antarctic ice shelf and ice sheet community as well as the oceanographic community. It is overall well written and I think that the methods are mostly robust and support the results. Particularly the authors’ approach to circumvent issues of laboratory offsets in the isotopic measurements, that have been a known issue for a while, is quite elegant and I think leads to meaningful results. However, I also think that the paper would benefit from a more in-depth comparison to previous work and from highlighting the novel aspects of this work more clearly. In addition, I have some concerns regarding the uncertainty discussion, in particular to biases induced by the spatial sampling and I think that caveats should be communicated more clearly. Overall, I think that the manuscript is suitable in principal for publication in The Cryosphere, after addressing some points.
Specific comments:
I think that the motivation for this study and the importance of the results is not communicated sufficiently. Currently there is a strong focus and emphasis on the collection of a timeseries, but very little on why the timeseries is collected and what we can learn from such a timeseries. I think that discussing this in more detail, in particular in relation to the recent literature on the temporal evolution of melt in the Amundsen Sea, is critical to highlight the novelty of the results. A particular example is the following sentence in the introduction (P2L31-33): “[…] some studies have shown a greater interannual variability in the basal melt rates than increase […], and some have even suggested a slowing of basal melt rates […] and grounding line retreat […].” I think that this point has to be extended by rewriting the sentence, adding a time perspective (what happened when / what timescales are we talking about), has to put into perspective of natural climate variability versus anthropogenic forcing, and used as an explicit motivation for the study and how the seawater isotopic composition might help to contribute to this discussion.

Following the point above, I think that the paper would benefit from an extension of the discussion on the temporal variability shown in Figure 4. To me, this is the key result of the paper. However, the discussion on details in variability seen in this Figure and how they relate to other recent findings and what new aspects can be learned from this Figure is very limited. In fact, there is not even a reference to Figure 4 in the main text.

P4-5L80-82: I think that the approach taken here indeed mitigates some of the known issues of salt effects between IRMS and CRDS. However, it is not very clear in these sentences here that the salt effect is indirectly removed by using different CDW reference values for each respective data set. I think that should be written more explicitly at this point. In addition it might be useful to actually point to the differences in CDW d18O in Table 2 where the CRDS measurements (2019/2020) yield a much lower CDW value than the IRMS measurements (2014). Is this difference in line with the values reported for the salt effect in literature?

P7L132-135: I think it is important to discuss the difference in results associated with using a constant and varying mCDW and meteoric endmember at this point. A constant value would yield a GMW estimate that is spatially integrated and the varying endmember yields local fluxes. Likely, this choice will also affect the long-term trends in the GMW estimate (largely through changes in the meteoric endmember), which I think should be discussed as a possible caveat at this point.

I am still a bit concerned about potential artifacts from the changes in the spatial sampling from one year to the other. Fig. 1 and also Fig. A3 clearly show substantial spatial differences in GMW content in the region and I think that the paragraph on p. 9 Lines 184-187 is not sufficiently accounting for the issue. I appreciate that this issue is investigated in Section A4. However, I think that the manuscript would benefit in terms of the credibility of the results, if a more detailed spatial analysis was added to the main text. In the end, the main results in Figure 4 are interannual variations with a magnitude of about 1.5m, which seems to be within the range of spatial variations shown in Figs. 1 and A3.
So, I am wondering if the reported uncertainties in Table A2, last column (“Average GMW inventory (m)), as well as the uncertainties shown in the main text also include the spatial standard deviation of the samples? Is this included in the “environmental” uncertainty within the Montecarlo simulation? I think that it would be transparent and beneficial to simply report the spatial standard deviation of GMW for each box also in Table A2, which would give a measure of the range of spatial variations.

In addition, I have difficulties understanding how the boxes were chosen and why they seem to be not consistent between the years, i.e. sometimes a location falls in one box and sometimes in another. I think it would be helpful to have boxes that are rather fixed in time and represent certain regimes within the region. For example, I found the Boxes in Fig. A3 for 2014 quite logic, since there is an “offshore” box (c), a TGT box (d), a PIIS box (a) and a central box (b). Looking at these boxes over all years and samples would be, i.e. having a figure similar to Figure 4 for each of these regions would be very helpful to understand how the variability might differ spatially and if the variability is a signal that is consistent across the entire domain or just arises from local signals would be very helpful to have. I would suggest to actually have a figure like this with a brief discussion in the main text if possible.

I am a bit concerned about the conclusion (P11 Line 243) that the long-term trend is insignificant without discussing the fact that this only reflects the data presented here but might not reflect the actual trend in the melting. It would be good to discuss some of the caveats of the use of the data set and its limitations. In particular, I think that the data set will not capture the entire amount of meltwater coming from the Amundsen Sea, as the authors’ report that the residence time of the water in the region is only about 1 year. So, it may well be that there is a strong long-term trend in glacial melt in the region, but that the signal largely propagates out of the region and does not accumulate there. Also, the fact that the endmembers vary throughout the years, in particular the glacial melt endmember, could affect the long-term trend. So, I think it is important to discuss such potential limitations here.

Technical corrections:
P2L35: I don’t think that “SE” has been defined yet.

Figure 1: Please do not use “rainbow” colormaps that are not scientific colormaps. For detailed reasons and tools to generate an appropriate colorbar e.g. for Matlab, please see for example this paper by Stauffer et al. (2015; https://doi.org/10.1175/BAMS-D-13-00155.1)

Figure 2: I found it difficult to depict the difference in blue. Since only dark blue is used, it may be good to keep those dark blue sample and exchange the other blue(s) by gray.

P6L107: Probably important to add that also “sea ice formation and melt” will affect the signal at this point.

Equations 1-3: the placement of these equations seems odd as there are somewhere in the text where they are not discussed. Please place them right below a description of and reference to these Equations.

P6L125: I guess it should be not just sea ice melt but also “formation”

P7L149: I think that “extremely unlikely” is a stretch here. Please reformulate to “[…] mean, the potential impact of analytical calibration offsets between laboratories on the calculated GMW fractions are mitigated.

P8L154: “mathematical artifacts” seems odd and would not understand what is meant, do you mean “sampling and analytical uncertainties”?

P8L166: It is unclear at this point where the uncertainty estimate is coming from. Could you please refer to the part of the manuscript where it is calculated and/or briefly mention it here.

Figure 4: Is this trend statistically significant or not. Please report the statistical significance here.

P10L194: I have difficulties understanding the meaning of “a range of the range” (e.g. +- 1.5 – 1.7 g/kg) in uncertainty, if I understand this correctly. Please report a single range, e.g. +-1.7 g/kg.

P10L201: Change to “This minimizes systematic isotopic offsets”

P11L249: I think what the authors are really trying to say here is that the decadal variability of the melt is actually substantially larger than the long term trend (1994 to 2020). The way that this is currently written it is difficult to understand what is actually meant. It seems not surprising that there is interannual variability in the first place, but what is in fact interesting is the magnitude of the variability compared to the trend and the time scale over which this variability occurs. I think that needs clarification.

P11/12L252-253: It would be helpful to note at this point that the tracer approach has the advantage that the ocean integrates the temporal meltwater changes and thus a single measurement actually reflects a longer period of melting.

P13L260-264: Please correct typological and formatting errors.

P17L331: I have difficulties understanding the meaning of “a range of the range” (e.g. +- 0.5 – 0.7 m) in uncertainty, if I understand this correctly. Please report a single range, e.g. +-0.7 m.

P17L333: I think that this should read “95.1%”, right? Otherwise I would not understand this number.

Generally, the numbering of subsections is wrong; always starts with “1.x”

References:
Stauffer, R., Mayr, G. J., Dabernig, M., & Zeileis, A. (2015). Somewhere Over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations, Bulletin of the American Meteorological Society, 96(2), 203-216. https://doi.org/10.1175/BAMS-D-13-00155.1
Citation: https://doi.org/10.5194/egusphere-2023-141-RC2
- AC2: 'Reply on RC2', Andrew Hennig, 09 May 2023
  
  Please see attached PDF for detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-141-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (05 Jun 2023) by Nicolas Jourdain

AR by Andrew Hennig on behalf of the Authors (16 Jun 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 Jul 2023) by Nicolas Jourdain

RR by Anonymous Referee #1 (09 Sep 2023)

RR by Anonymous Referee #2 (27 Sep 2023)

Suggestions for revision or reasons for rejection

Review of “Meteoric water and glacial melt in the Southeast Amundsen Sea: A timeseries from 1994-2020” by Hennig et al. in The Cryosphere

I would like to thank the authors for considering the comments of both reviewers in the previous round. While I think that some aspects of the paper have improved and some concerns could be alleviated, one main concern raised by both reviewers, i.e. the novelty of the results and their implications, could have been given a bit more thought in the revision and the responses. I think that the paper remains rather technical without adding much discussion and understanding regarding the temporal variability of meltwater in the region, but certainly highlighting the usefulness of seawater isotopes and potential solutions to overcome certain issues. Therefore, I still think that these results should be published in principal, but my recommendation to think more about the implications still holds and might be further addressed in a revision (specific comment 1 below). While the revisions helped to clarify and better understand certain aspects of the analysis, they also brought up some new issues, that I think require further consideration in another major revision (specific comments 2-5 below). These issues concern in particular the interpretation of the sea ice melt fraction and the implication for the meteoric fraction in that balance, as well as the spatial analysis.

Specific comments:
1. I still think that putting the results a bit more in context would help. A particular example is the last sentence of the abstract. What does this imply? Could you add a last sentence with the implication of this result?
2. Sea ice melt: I found the added discussion on sea ice melt/formation quite useful, but I also have some issues with the current interpretation:
a. The coastal/shelf regions around the Antarctic are well known as sea ice production sites, where ice is preferentially formed and then exported to the open ocean. Therefore, I would have expected a net sea ice formation signal from the region, as it is reported in the discussed paper by Biddle et al. (2019). I am surprised to see such strong positive signals in the column integrated sea ice melt. I understand that the samples come from the summer season. So a net sea ice melt at the surface can be expected. However, integrated over the water column, the signal should be close to zero or negative, unless sea ice is imported to the region or waters that are formed during sea ice formation in winter are not captured by these measurements. I understand that sea ice melt is not the focus of this study, but the results show that either the meteoric water endmember is too low (leading to an underestimation of sea ice formation), or water masses that are formed in winter during sea ice formation are not captured by these measurements. At least, I would be much more cautious on the interpretation of this result, as it seems counterintuitive.
b. The issue might arise from the way that the meteoric endmember is defined in this study using the water samples below 200m. As these samples have the advantage of not being influenced by sea ice and snow melt or precipitation at the surface (as motivated in the paper), they could well be influenced by sea ice formation as sea ice is formed in winter and the brine mixes deep in the water column. Consequently, this signal could pull the regression line towards saltier values, which could result in an artificial lowering of the meteoric endmember derived from the regression line. I think this issue might require some attention.
c. As a consequence, I do not fully agree with the interpretation in lines 301 – 312. If anything, I would argue that in light of the net water column sea ice melt signal detected in this study, the higher estimate by Biddle et al. (2019), or some intermediate solution, seem more realistic.
d. I don’t quite understand the reasoning (lines 648 – 655) that sea ice melt/formation signals might be an artifact of the potential evaporation from the bottles. Ultimately, one of the strength of using seawater d18O and salinity is to separate meteoric water and sea ice melt/formation signals. So, if there is an issue of evaporation in the bottles is should show up as a meteoric water signal and not a sea ice signal.
3. I have to say that I do not understand the unit of the meteoric water fraction e.g. in Tables 3 and 4. Why is it in g/kg? According to the definition in Eq. 1-3 it should be in %.
4. I do not understand what is shown in Table 4. Why do the results differ from Table 2 and 3 and what numbers should the reader trust? Wouldn’t it make sense to report a single number that includes all the uncertainties (analytical, environmental, spatial)?
5. A) I found the spatial analysis that is being done quite informative. It appears that the spatial variability is larger than most of the temporal variability. This also suggests, that the small positive trend shown in Figure 4 may be an artifact or at least not coming from the Amundsen Sea glaciers. None of the meltwater source regions b-d in Table 5 show a positive trend, i.e. an indication of increased melt from PIIS or TIS, or a large interannual variability. The overall variability in Figure 4, seems to be dominated by the variability in region a. So, what is driving the variability in region a? I found it very difficult to interpret this result and as a consequence the result in Figure 4 and some of the overall findings of the paper. Could it be, that the variability observed in Figure 4 is dominated by variability induced elsewhere? Or could it be due to the variability in the source water mass, i.e. mCDW?
B) I also do not understand how to reconcile the total region average meteoric water column inventory (Table 3 and 4) and the average values for the subregions for a given year (Table 5). In 2020, the total values in table 3 and 4 are 9.2 and 8.8 m, respectively. However, the regional values are all 8.7 m or lower. How is it possible that the overall average is higher than the regions separately?

Technical corrections:
- Lines 212-221: Despite the previous review comment, Figure 4 is still not referenced in this paragraph.

Hide

ED: Publish subject to revisions (further review by editor and referees) (05 Oct 2023) by Nicolas Jourdain

AR by Andrew Hennig on behalf of the Authors (21 Nov 2023) Author's response Author's tracked changes

EF by Sarah Buchmann (22 Nov 2023) Manuscript

ED: Publish subject to minor revisions (review by editor) (09 Dec 2023) by Nicolas Jourdain

Dear Andrew Hennig and co-authors,

Thank you for having replied to the comments from the two reviewers. I think that the manuscript is now easier to read and clearer on the methods and results. There are nonetheless still a few minor points to address before publication as you can see below.

Best regards,

Nicolas Jourdain

----------
Comments:

- L. 60: expand GMW.

- L. 237 and caption of Fig. 4: I don’t get why negative sea ice melt fractions would indicate regions of net sea ice formation. I would argue that it results from sea ice formation, possibly during previous months, but not necessarily that this location experiences net annual sea ice formation. Sea-ice formation and associated convection can indeed produce a signal in the subsurface and be followed by the opposite signal closer to the surface during the sea-ice melting season, whatever the sign of the annual net balance between sea ice melt and sea ice formation.

- Figs 3-4: given that the meteoric water fraction and the sea ice melt fraction are defined in [0-1] with no unit in equations 1-3, you need to clarify the g/kg by adding something like “g/kg, i.e., g of meteoric water per kg of seawater” in the captions.

- The caption and labels of Fig. 4 would be clearer with “sea ice melt fraction” than with “sea ice melt”.

- L. 268-270 “These results are consistent with recent studies showing an increase in basal melt through the 1990s, followed by relative stability and interannual variability from 2000 through 2020, with interannual variability that is larger than the increasing trend in meteoric water content”. Please provide references for this. If you look at the hydrographic estimates of ice shelf melt rates summarised in Fig. 4 of Joughin et al. (2021), basal melt seems significantly higher in ~2009 than in the early 2000s. And 1994 appears as an average year, so what would explain such low meteoric water column inventory for 1994?

Joughin, I., Shapero, D., Dutrieux, P. and Smith, B. (2021). Ocean-induced melt volume directly paces ice loss from Pine Island Glacier. Science advances, 7(43), eabi5738.

- L. 275-276: replace “where icebergs are exported out of the study area” with “where icebergs melt out of the study area”.

- L. 358: “show mixing clear mixing lines”.

- L. 422: “we have estimate” -> we have estimated?

- L. 438-442: Things are a bit less clear than what is suggested in this sentence. Some of the first references do not clearly support stable ice-shelf melt rates. It is not clear to me which part of Paolo et al. (2018) supports this statement. No melt rates were estimated by Dotto et al. (2019). You should probably cite Paolo et al. (2023) that shows some trend. Then, you pick up the modelling study of Flexas et al. (2022), but other models show different variability. Check for example Fig. S4 of Naughten et al. (2022).

Naughten, K. A., Holland, P. R., Dutrieux, P., Kimura, S., Bett, D. T. and Jenkins, A. (2022). Simulated twentieth century ocean warming in the Amundsen Sea, West Antarctica. Geophysical Research Letters, 49, e2021GL094566.

Paolo, F. S., Gardner, A. S., Greene, C. A., Nilsson, J., Schodlok, M. P., Schlegel, N.-J., and Fricker, H. A. (2023). Widespread slowdown in thinning rates of West Antarctic ice shelves, The Cryosphere, 17, 3409–3433.

- L. 490-491, in the conclusion: it may be worth reminding that a large part of the local precipitation is likely exported by sea ice without entering the ocean surface layer.

- L. 706: “at had”

- L. 749-750 “likely the resulted”.

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AR by Andrew Hennig on behalf of the Authors (14 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (08 Jan 2024) by Nicolas Jourdain

AR by Andrew Hennig on behalf of the Authors (09 Jan 2024) Manuscript

Journal article(s) based on this preprint

20 Feb 2024

Meteoric water and glacial melt in the southeastern Amundsen Sea: a time series from 1994 to 2020

Andrew N. Hennig, David A. Mucciarone, Stanley S. Jacobs, Richard A. Mortlock, and Robert B. Dunbar

The Cryosphere, 18, 791–818, https://doi.org/10.5194/tc-18-791-2024,https://doi.org/10.5194/tc-18-791-2024, 2024

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Andrew Nicholas Hennig, David A. Mucciarone, Stanley S. Jacobs, Richard A. Mortlock, and Robert B. Dunbar

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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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Glacial ice has distinct oxygen isotope fingerprints that can facilitate estimation the fraction of its meltwater in coastal Antarctic seas. ẟ¹⁸O and salinity data from seven cruises on the SE Amundsen Sea between 1994 and 2020 reveal a deep freshwater source with ẟ¹⁸O −29.3 ± 0.9 ‰, consistent with melting from the base of the ice shelf. Glacial meltwater content was variable over time and space, but the lowest in 1994 and highest in 2020.


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