the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Feb 2023
Glacial Meltwater in the Southeast Amundsen Sea: A timeseries from 1994–2020
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.
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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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Journal article(s) based on this preprint
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
- AC1: 'Reply on RC1', Andrew Hennig, 09 May 2023
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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
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
- AC1: 'Reply on RC1', Andrew Hennig, 09 May 2023
-
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
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Andrew Nicholas Hennig
David A. Mucciarone
Stanley S. Jacobs
Richard A. Mortlock
Robert B. Dunbar
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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