the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Aug 2023
Amundsen Sea Embayment accumulation variability measured with GNSS-IR
Abstract. In order to improve projections of the future ice-sheet surface mass balance and the interpretation of the isotopic signals of past accumulation preserved in ice cores, it is critical to understand the mechanisms that transport water vapor to the Antarctic continent. Global Navigation Satellite System (GNSS) receivers distributed across Antarctica to monitor ice velocity and solid Earth motion can be used to understand accumulation, ablation, and snow redistribution at the ice-sheet surface. Here, we present a forward model for reflector height change between the GNSS antenna phase center and the snow surface and an inverse framework to determine accumulation rate and near-surface firn densification from the reflector height time series. We use this model to determine accumulation at the sites of three long-term on-ice GNSS receivers located in the Amundsen Sea Embayment (ASE) and at a network of GNSS receivers deployed in 2007–2008, 2008–2009, and 2009–2010 austral summers. From the GNSS-IR accumulation reconstructions, we find that extreme precipitation dominates total precipitation and that extreme event frequency varies seasonally. We use our GNSS-IR accumulation reconstructions together with reanalysis products to characterize the atmospheric conditions that promote extreme snowfall in the ASE. The blocking pressure systems that promote extreme accumulation on Thwaites Glacier are facilitated by tropical teleconnections, specifically convection that promotes Rossby waves trains from the Western Pacific, Indian, and Atlantic Oceans to the Amundsen and Bellingshausen Seas.
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Status: closed (peer review stopped)
- AC1: 'supplemental video and Atmospheric river catalog', Andrew Hoffman, 08 Aug 2023
-
RC1: 'Comment on tc-2023-114', Daniel Emanuelsson, 26 Nov 2023
Review Hoffman et al. 2023
Hoffman et al. present GNSS records from the Thwaites Glacier region. They highlight the benefits of this equipment over AWS. And they make composites of reanalysis parameters at the time and before extreme accumulation events. They show that blocking events with distant origins is important in blocking the westerly flow and channeling humid air masses into WA.
I suggest that the manuscript get accepted but with major revisions.
Major comments
- The notion that Amundsen Bellingshausen Sea blocking events drive marine air mass intrusions which results in major WA precipitation events has already been shown (Emanuelsson et al. 2018).
- You can tell the reader more clearly the mechanism that you suggest is at play. While it might be obvious to many readers of this journal, not everyone will know. The manuscript would become clearer with a hypothesis and the authors specify what part each reanalysis parameter that they use can tell us.
- The type of composite of plots non-anomalies that are presented in Figs. 8b, e and 9b, e are not meaningful. As the blocking events will not show an exact spatial overlap, the composite average will not show the anti-cyclone, reversal of the westerlies, characteristic of blocking that you would appear if you looked at individual blocking events. Better than to show some examples of certain events if you want to show this type of figure. Like you do in Figure 5. You can discuss Figure 5 more, it is only referenced one time in the text. And then maybe remove the 8b,e and 9b,e panels. Increase the size of Figure 5 so that it is easier to see. Dedicate one page for this figure, and show it in landscape layout.
- The wind vectors in the figures are hard to see and it is hard to differentiate them and the stippling if you don’t zoom in. If the stippling shows where the Z500 is significant perhaps you can show where the winds are significant by just displaying significant winds or by showing significant winds in a different color. And don’t use black color for both the winds and the stippling.
- 107. Accumulation has been reconstructed in many studies using deep cores. Not just from firn cores, e.g., (Thomas et al. 2015; Winstrup et al. 2017). Emanuelsson et al. were able to illuminate the importance of blocking for West Antarctic (WA) airmass intrusions using annual dD and accumulation records from the RICE ice core. They investigated these relationships further using high-resolution ERA records of precipitation and accumulation from AWG measurements from AWS (Emanuelsson 2016; Emanuelsson et al. 2018).
- 213. Are these seasonal differences significant? How do they compare to a precipitation record obtained from ERA5 over the overlapping period and using the whole available ERA5 period (40+ years)?
- 3.1 Data: reanalysis
- There is plenty of material about ASL, but no introduction about blocking in the high southern latitudes. Plenty has been written about this and blockings linkage to the tropical Pacific, e.g. (Renwick and Revell 1999; Renwick 2005).
- L 238-256. This paragraph about the ASL is more suited for the introduction than under the header describing the reanalysis data.
- Specify how many percent of the whole data series are gaps. The KHLR time series seems to only be 8 years.
- As there are so few events, 19, it would be reasonable to look at individual events and check their origin. As you do in Figure 5 but including latitudes farther north. Then you can check the origin of the wave trains without the risk that the pressure anomalies get cancelled in certain regions in the composite. That is if there is a high pressure in one region for one event and a low in the same region for another event they will get cancelled in the composite.
- Bear in mind that you are just looking at 10 years. Considering decadal-scale variability, it could be that wave trains during the period could have a certain origin say mainly Atlantic, while if you had a record from the preceding 10 years there could be another main origin. Just due to decadal-scale variability.
- 259 to 263. These sentences are confusing and not precise. Be quantitative. Use statistics to back up your argument, p- and r-values. It is hard to see in the suppl. figure which reanalysis dataset is best. Can there be a benefit of showing the time series of the data, and comparing the measured data with the reanalysis data? And mark the extreme events. Like Figure 4 but with the reanalysis added. For example, by extracting time series of precipitation data from the grid point(s) that are closest to the sites. Something similar to fig. 1.11 in this PhD thesis (Emanuelsson 2016).
- Is it hourly data that you use?
- Is it hourly data that you use?
Minor comments
Turner et al. have also highlighted the importance of large precipitation events (Turner et al. 2019).
Pg.1 L20. For accumulation increase seen in ice cores, you can cite Thomas et al. (Thomas et al. 2015).
- 58. Delete “in order”
- 72. Cite Mayewski here too (Mayewski et al. 2005).
- 74. …. from shallow ice cores and extended… does this sound better?
- 92. .. resolution that reaches further back into the past,…
- 107. The references are missing.
- 115. Thwaits is considered a coastal not an interior site.
- 116. Use the acronym.
- 119. In the Southern Ocean Pacific region.
- 137. … due to challenges in maintaining…. GNSS receiver networks. GNSS networks have traditionally been...
- 140. (Figures 1, 2)
- 212. Present the figures in order of appearance. And close to where they are mentioned in the text.
- 221. Split this up into two sentences and put this part into parenthesis “(with the two long-term Thwaites sites)”?
- 228. I don’t see the benefit in splitting up the method and results sections in this way.
L 237. Change the header to, Reanalysis data.
L 242. Cite Rapheal’s zonal wave number three paper (Raphael 2004).
L 243. … highest variability in atmospheric circulation in the Southern Hemisphere (Connolley 1997; Lachlan-Cope et al. 2001).
- 266. Suggestion: ...S). The northern limit is set this far north to be able to evaluate the possibility of tropical teleconnections...
- 276. Define IVT here at its first mention.
- 292. 55°–75°S, 120°–45°W. Show this region as a box in a figure. Isn’t 45W too far east to be considered the Amundsen Sea?
- 333. So, if there are multiple atmospheric river events for one extreme precipitation event, do you disregard the event? That seems strange. Such an event would still indicate that rivers are important right? Is the last subplot in Fig. 5 with two composite maps from such an event?
- 337. Composite?
- 350. Emanuelsson et al. showed the importance of blocking for major and extreme WA precipitation events. And highlighted that these blocking events occur in an area with an average climatological low-pressure anomaly, the ASL (Emanuelsson et al. 2018).
- 354. EOF2, is this the PSA1, Pacific South American patterns (Kidson 1988; Karoly 1989; Mo and Higgins 1998)?
- 405. Are the seasonality results significant? Considering that the record is only 10 years long and there are only 12 (19) events? If so this would be valid for Thwaits cores but not WA cores in general which can have other seasonal biases or no bias (Küttel et al. 2012). There can also be a temperature bias associated with these extreme events (Sime et al. 2009; Emanuelsson 2016).
L 415-418. Do you have a paper that you can cite for the finding that blocking is not well-represented in models?
L480. Reference the NOASS datasets together with the other datasets in section 3.1. Or thank some more reanalysis providers here.
Figures
Fig. 1. Do the red lines indicate the same as the gray? …., 2016 (light blue contour).
Fig. 5. Can you explain a bit better what we see in the figure, the contour indicates atmospheric rivers.
Some of the text in the figures is very small. I don’t think you need to have both a dot and parenthesis for the subplots in the figures.
Figure 7. Explain the colors again. What is the shading for, std? What is the difference between light and dark orange? One is std and the other is the mean of the std for the two Thwaits sites? Split up 7a into three subplots as the shading from one site can hide the shading for the neighboring sites? Or is it enough to make the plot larger?
Figure 12. The stars for the sites are hard to see here. Increase their size.
Supl. M.
Caption Fig. s1. …” against accumulation determined from reanalysis products for (D) LTHW, (E) UTHW, and (F) KHLR GNSS sites.”
For the accumulation vs. reanalysis accumulation plots. How do you see that the ERA5 record is best? Provide some more statistics, r-values? Add a line in the plots and text boxes with r and p values. Do you compare hourly data from the different datasets? Perhaps you need to average the data over a longer period to make them comparable. Again plotting the measurements together with the reanalysis data would be good as a first step to confirm if they capture the same events and if the timing agrees.
- 446. The supplementary animation looks interesting. Please provide information on what it is showing. Do you compare the GNSS measurements with ERA5 precipitation data over the period when you have good spatial coverage? This period with an expanded network seems unique and something that you could evaluate and discuss more.
References
Connolley WM (1997) Variability in annual mean circulation in southern high latitudes. Clim Dyn 13:745–756. https://doi.org/10.1007/s003820050195
Emanuelsson BD, Bertler NAN, Neff PD, et al (2018) The role of Amundsen–Bellingshausen Sea anticyclonic circulation in forcing marine air intrusions into West Antarctica. Clim Dyn 51:3579–3596. https://doi.org/10.1007/s00382-018-4097-3
Emanuelsson D (2016) High-Resolution Water Stable Isotope Ice-Core Record: Roosevelt Island, Antarctica: a thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy (Geology) / by B. Daniel Emanuelsson. Thesis (Ph.D.)--Victoria University of Wellington, 2016.
Karoly DJ (1989) Southern Hemisphere Circulation Features Associated with El Nino-Southern Ocscillation Events. J. Clim. 2:1239–1252
Kidson JW (1988) Interannual Variations in the Southern Hemisphere Circulation. J. Clim. 1:939–953
Küttel M, Steig EJ, Ding Q, et al (2012) Seasonal climate information preserved in West Antarctic ice core water isotopes: relationships to temperature, large-scale circulation, and sea ice. Clim Dyn 39:1841–1857. https://doi.org/10.1007/s00382-012-1460-7
Lachlan-Cope TA, Connolley WM, Turner J (2001) The role of the non-axisymmetric antarctic orography in forcing the observed pattern of variability of the Antarctic climate. Geophys Res Lett 28:4111–4114. https://doi.org/10.1029/2001GL013465
Mayewski PA, Frezzotti M, Bertler N, et al (2005) The International Trans-Antarctic Scientific Expedition (ITASE): an overview. Ann Glaciol 41:180–185. https://doi.org/DOI: 10.3189/172756405781813159
Mo KC, Higgins RW (1998) The Pacific–South American Modes and Tropical Convection during the Southern Hemisphere Winter. Mon Weather Rev 126:1581–1596. https://doi.org/10.1175/1520-0493(1998)126<1581:TPSAMA>2.0.CO;2
Raphael MN (2004) A zonal wave 3 index for the Southern Hemisphere. Geophys Res Lett 31:1–4. https://doi.org/10.1029/2004GL020365
Renwick JA (2005) Persistent Positive Anomalies in the Southern Hemisphere Circulation. Mon Weather Rev 133:977–988. https://doi.org/10.1175/MWR2900.1
Renwick JA, Revell MJ (1999) Blocking over the South Pacific and Rossby Wave Propagation. Mon Weather Rev 127:2233–2247. https://doi.org/10.1175/1520-0493(1999)127<2233:BOTSPA>2.0.CO;2
Sime LC, Marshall GJ, Mulvaney R, Thomas ER (2009) Interpreting temperature information from ice cores along the Antarctic Peninsula: ERA40 analysis. Geophys Res Lett 36:1–5. https://doi.org/10.1029/2009GL038982
Thomas ER, Hosking JS, Tuckwell RR, et al (2015) Twentieth century increase in snowfall in coastal West Antarctica. Geophys Res Lett 42:9387–9393. https://doi.org/https://doi.org/10.1002/2015GL065750
Turner J, Phillips T, Thamban M, et al (2019) The Dominant Role of Extreme Precipitation Events in Antarctic Snowfall Variability. Geophys Res Lett 46:3502–3511. https://doi.org/https://doi.org/10.1029/2018GL081517
Winstrup M, Vallelonga P, Kjær HA, et al (2019) A 2700-year annual timescale and accumulation history for an ice core from Roosevelt Island, West Antarctica. Clim Past 15:751–779. https://doi.org/10.5194/cp-15-751-2019
Citation: https://doi.org/10.5194/tc-2023-114-RC1 -
RC2: 'Comment on tc-2023-114', ALESSANDRA Borghi, 28 Nov 2023
General comments:
In the manuscript “Amundsen Sea Embayment accumulation variability measured with GNSS-IR” the authors use the GNSS-IR technique to find extreme snowfall precipitations in Amundsen Sea Embayment (Antarctica). The geodetic results are the starting point to investigate the drivers of extreme precipitation in this area.
The manuscript requires major revisions, because the presentation of the results has to be improved to better understand.
Specific comments:
- check the numeration of all the figures: figures 6 and 8 are discussed before figure 5.
- anticipate entering the parameter “B” of Equation 6, because it is introduced only many lines later (after Equation 8)
- Figure 1: the legend of panel B) (time scale) is too small, and the color legend doesn’t seem coherent with the plotted receiver positions. In Panel D) could you explain the meaning of the dashed red lines?
- Figure 2: what about the gray vertical lines?
- Line 189: The authors indicate the variance with the Greek letter sigma, but usually square sigma is used. Also the quantities in equation 5) are called “least squares differences” but do you just mean “squared differences”?
- Figure 3 is not clear. In the figure caption, could you explain the quantities represented in the plot?
- Figure 5: in the xy-plots it’s impossibile to read the labels because they are too small. Furthermore, do the color lines in the plots refer to the LTHW, UTHW and KHLR GNSS stations? In this case a station legend should be repeated also in this figure. Do the vertical red lines represent the height changes observed with GNSS-IR? Why the thickness of the red vertical line is different?
- Line 223: The authors affirm that “during the shorter-duration campaigns we observe 7 extreme precipitation events during the two summers (Figure 6)”, but in Figure 6 these events are not represented.
- Figure S1: the diamond symbol that should represent ERA5 is not plotted in the panels. It seem to be substituted by “X” symbols. Moreover, do you refer to panel (D), (E) and (F) instead of (A), (B) and (C) in the sentence “Accumulation measured with GNSS-IR plotted against accumulation determined from reanalysis products for (A) LTHW, (B) UTHW, and(C) KHLR GNSS sites”, aren’t you?
Technical corrections:
- Line 216: “Following, (Maclennan and Lenaerts, 2021),” —> “Following (Maclennan and Lenaerts, 2021)”, without commas.
- Line 224: With “2-4x” do you mean “2-4 times”?
Citation: https://doi.org/10.5194/tc-2023-114-RC2
Status: closed (peer review stopped)
- AC1: 'supplemental video and Atmospheric river catalog', Andrew Hoffman, 08 Aug 2023
-
RC1: 'Comment on tc-2023-114', Daniel Emanuelsson, 26 Nov 2023
Review Hoffman et al. 2023
Hoffman et al. present GNSS records from the Thwaites Glacier region. They highlight the benefits of this equipment over AWS. And they make composites of reanalysis parameters at the time and before extreme accumulation events. They show that blocking events with distant origins is important in blocking the westerly flow and channeling humid air masses into WA.
I suggest that the manuscript get accepted but with major revisions.
Major comments
- The notion that Amundsen Bellingshausen Sea blocking events drive marine air mass intrusions which results in major WA precipitation events has already been shown (Emanuelsson et al. 2018).
- You can tell the reader more clearly the mechanism that you suggest is at play. While it might be obvious to many readers of this journal, not everyone will know. The manuscript would become clearer with a hypothesis and the authors specify what part each reanalysis parameter that they use can tell us.
- The type of composite of plots non-anomalies that are presented in Figs. 8b, e and 9b, e are not meaningful. As the blocking events will not show an exact spatial overlap, the composite average will not show the anti-cyclone, reversal of the westerlies, characteristic of blocking that you would appear if you looked at individual blocking events. Better than to show some examples of certain events if you want to show this type of figure. Like you do in Figure 5. You can discuss Figure 5 more, it is only referenced one time in the text. And then maybe remove the 8b,e and 9b,e panels. Increase the size of Figure 5 so that it is easier to see. Dedicate one page for this figure, and show it in landscape layout.
- The wind vectors in the figures are hard to see and it is hard to differentiate them and the stippling if you don’t zoom in. If the stippling shows where the Z500 is significant perhaps you can show where the winds are significant by just displaying significant winds or by showing significant winds in a different color. And don’t use black color for both the winds and the stippling.
- 107. Accumulation has been reconstructed in many studies using deep cores. Not just from firn cores, e.g., (Thomas et al. 2015; Winstrup et al. 2017). Emanuelsson et al. were able to illuminate the importance of blocking for West Antarctic (WA) airmass intrusions using annual dD and accumulation records from the RICE ice core. They investigated these relationships further using high-resolution ERA records of precipitation and accumulation from AWG measurements from AWS (Emanuelsson 2016; Emanuelsson et al. 2018).
- 213. Are these seasonal differences significant? How do they compare to a precipitation record obtained from ERA5 over the overlapping period and using the whole available ERA5 period (40+ years)?
- 3.1 Data: reanalysis
- There is plenty of material about ASL, but no introduction about blocking in the high southern latitudes. Plenty has been written about this and blockings linkage to the tropical Pacific, e.g. (Renwick and Revell 1999; Renwick 2005).
- L 238-256. This paragraph about the ASL is more suited for the introduction than under the header describing the reanalysis data.
- Specify how many percent of the whole data series are gaps. The KHLR time series seems to only be 8 years.
- As there are so few events, 19, it would be reasonable to look at individual events and check their origin. As you do in Figure 5 but including latitudes farther north. Then you can check the origin of the wave trains without the risk that the pressure anomalies get cancelled in certain regions in the composite. That is if there is a high pressure in one region for one event and a low in the same region for another event they will get cancelled in the composite.
- Bear in mind that you are just looking at 10 years. Considering decadal-scale variability, it could be that wave trains during the period could have a certain origin say mainly Atlantic, while if you had a record from the preceding 10 years there could be another main origin. Just due to decadal-scale variability.
- 259 to 263. These sentences are confusing and not precise. Be quantitative. Use statistics to back up your argument, p- and r-values. It is hard to see in the suppl. figure which reanalysis dataset is best. Can there be a benefit of showing the time series of the data, and comparing the measured data with the reanalysis data? And mark the extreme events. Like Figure 4 but with the reanalysis added. For example, by extracting time series of precipitation data from the grid point(s) that are closest to the sites. Something similar to fig. 1.11 in this PhD thesis (Emanuelsson 2016).
- Is it hourly data that you use?
- Is it hourly data that you use?
Minor comments
Turner et al. have also highlighted the importance of large precipitation events (Turner et al. 2019).
Pg.1 L20. For accumulation increase seen in ice cores, you can cite Thomas et al. (Thomas et al. 2015).
- 58. Delete “in order”
- 72. Cite Mayewski here too (Mayewski et al. 2005).
- 74. …. from shallow ice cores and extended… does this sound better?
- 92. .. resolution that reaches further back into the past,…
- 107. The references are missing.
- 115. Thwaits is considered a coastal not an interior site.
- 116. Use the acronym.
- 119. In the Southern Ocean Pacific region.
- 137. … due to challenges in maintaining…. GNSS receiver networks. GNSS networks have traditionally been...
- 140. (Figures 1, 2)
- 212. Present the figures in order of appearance. And close to where they are mentioned in the text.
- 221. Split this up into two sentences and put this part into parenthesis “(with the two long-term Thwaites sites)”?
- 228. I don’t see the benefit in splitting up the method and results sections in this way.
L 237. Change the header to, Reanalysis data.
L 242. Cite Rapheal’s zonal wave number three paper (Raphael 2004).
L 243. … highest variability in atmospheric circulation in the Southern Hemisphere (Connolley 1997; Lachlan-Cope et al. 2001).
- 266. Suggestion: ...S). The northern limit is set this far north to be able to evaluate the possibility of tropical teleconnections...
- 276. Define IVT here at its first mention.
- 292. 55°–75°S, 120°–45°W. Show this region as a box in a figure. Isn’t 45W too far east to be considered the Amundsen Sea?
- 333. So, if there are multiple atmospheric river events for one extreme precipitation event, do you disregard the event? That seems strange. Such an event would still indicate that rivers are important right? Is the last subplot in Fig. 5 with two composite maps from such an event?
- 337. Composite?
- 350. Emanuelsson et al. showed the importance of blocking for major and extreme WA precipitation events. And highlighted that these blocking events occur in an area with an average climatological low-pressure anomaly, the ASL (Emanuelsson et al. 2018).
- 354. EOF2, is this the PSA1, Pacific South American patterns (Kidson 1988; Karoly 1989; Mo and Higgins 1998)?
- 405. Are the seasonality results significant? Considering that the record is only 10 years long and there are only 12 (19) events? If so this would be valid for Thwaits cores but not WA cores in general which can have other seasonal biases or no bias (Küttel et al. 2012). There can also be a temperature bias associated with these extreme events (Sime et al. 2009; Emanuelsson 2016).
L 415-418. Do you have a paper that you can cite for the finding that blocking is not well-represented in models?
L480. Reference the NOASS datasets together with the other datasets in section 3.1. Or thank some more reanalysis providers here.
Figures
Fig. 1. Do the red lines indicate the same as the gray? …., 2016 (light blue contour).
Fig. 5. Can you explain a bit better what we see in the figure, the contour indicates atmospheric rivers.
Some of the text in the figures is very small. I don’t think you need to have both a dot and parenthesis for the subplots in the figures.
Figure 7. Explain the colors again. What is the shading for, std? What is the difference between light and dark orange? One is std and the other is the mean of the std for the two Thwaits sites? Split up 7a into three subplots as the shading from one site can hide the shading for the neighboring sites? Or is it enough to make the plot larger?
Figure 12. The stars for the sites are hard to see here. Increase their size.
Supl. M.
Caption Fig. s1. …” against accumulation determined from reanalysis products for (D) LTHW, (E) UTHW, and (F) KHLR GNSS sites.”
For the accumulation vs. reanalysis accumulation plots. How do you see that the ERA5 record is best? Provide some more statistics, r-values? Add a line in the plots and text boxes with r and p values. Do you compare hourly data from the different datasets? Perhaps you need to average the data over a longer period to make them comparable. Again plotting the measurements together with the reanalysis data would be good as a first step to confirm if they capture the same events and if the timing agrees.
- 446. The supplementary animation looks interesting. Please provide information on what it is showing. Do you compare the GNSS measurements with ERA5 precipitation data over the period when you have good spatial coverage? This period with an expanded network seems unique and something that you could evaluate and discuss more.
References
Connolley WM (1997) Variability in annual mean circulation in southern high latitudes. Clim Dyn 13:745–756. https://doi.org/10.1007/s003820050195
Emanuelsson BD, Bertler NAN, Neff PD, et al (2018) The role of Amundsen–Bellingshausen Sea anticyclonic circulation in forcing marine air intrusions into West Antarctica. Clim Dyn 51:3579–3596. https://doi.org/10.1007/s00382-018-4097-3
Emanuelsson D (2016) High-Resolution Water Stable Isotope Ice-Core Record: Roosevelt Island, Antarctica: a thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy (Geology) / by B. Daniel Emanuelsson. Thesis (Ph.D.)--Victoria University of Wellington, 2016.
Karoly DJ (1989) Southern Hemisphere Circulation Features Associated with El Nino-Southern Ocscillation Events. J. Clim. 2:1239–1252
Kidson JW (1988) Interannual Variations in the Southern Hemisphere Circulation. J. Clim. 1:939–953
Küttel M, Steig EJ, Ding Q, et al (2012) Seasonal climate information preserved in West Antarctic ice core water isotopes: relationships to temperature, large-scale circulation, and sea ice. Clim Dyn 39:1841–1857. https://doi.org/10.1007/s00382-012-1460-7
Lachlan-Cope TA, Connolley WM, Turner J (2001) The role of the non-axisymmetric antarctic orography in forcing the observed pattern of variability of the Antarctic climate. Geophys Res Lett 28:4111–4114. https://doi.org/10.1029/2001GL013465
Mayewski PA, Frezzotti M, Bertler N, et al (2005) The International Trans-Antarctic Scientific Expedition (ITASE): an overview. Ann Glaciol 41:180–185. https://doi.org/DOI: 10.3189/172756405781813159
Mo KC, Higgins RW (1998) The Pacific–South American Modes and Tropical Convection during the Southern Hemisphere Winter. Mon Weather Rev 126:1581–1596. https://doi.org/10.1175/1520-0493(1998)126<1581:TPSAMA>2.0.CO;2
Raphael MN (2004) A zonal wave 3 index for the Southern Hemisphere. Geophys Res Lett 31:1–4. https://doi.org/10.1029/2004GL020365
Renwick JA (2005) Persistent Positive Anomalies in the Southern Hemisphere Circulation. Mon Weather Rev 133:977–988. https://doi.org/10.1175/MWR2900.1
Renwick JA, Revell MJ (1999) Blocking over the South Pacific and Rossby Wave Propagation. Mon Weather Rev 127:2233–2247. https://doi.org/10.1175/1520-0493(1999)127<2233:BOTSPA>2.0.CO;2
Sime LC, Marshall GJ, Mulvaney R, Thomas ER (2009) Interpreting temperature information from ice cores along the Antarctic Peninsula: ERA40 analysis. Geophys Res Lett 36:1–5. https://doi.org/10.1029/2009GL038982
Thomas ER, Hosking JS, Tuckwell RR, et al (2015) Twentieth century increase in snowfall in coastal West Antarctica. Geophys Res Lett 42:9387–9393. https://doi.org/https://doi.org/10.1002/2015GL065750
Turner J, Phillips T, Thamban M, et al (2019) The Dominant Role of Extreme Precipitation Events in Antarctic Snowfall Variability. Geophys Res Lett 46:3502–3511. https://doi.org/https://doi.org/10.1029/2018GL081517
Winstrup M, Vallelonga P, Kjær HA, et al (2019) A 2700-year annual timescale and accumulation history for an ice core from Roosevelt Island, West Antarctica. Clim Past 15:751–779. https://doi.org/10.5194/cp-15-751-2019
Citation: https://doi.org/10.5194/tc-2023-114-RC1 -
RC2: 'Comment on tc-2023-114', ALESSANDRA Borghi, 28 Nov 2023
General comments:
In the manuscript “Amundsen Sea Embayment accumulation variability measured with GNSS-IR” the authors use the GNSS-IR technique to find extreme snowfall precipitations in Amundsen Sea Embayment (Antarctica). The geodetic results are the starting point to investigate the drivers of extreme precipitation in this area.
The manuscript requires major revisions, because the presentation of the results has to be improved to better understand.
Specific comments:
- check the numeration of all the figures: figures 6 and 8 are discussed before figure 5.
- anticipate entering the parameter “B” of Equation 6, because it is introduced only many lines later (after Equation 8)
- Figure 1: the legend of panel B) (time scale) is too small, and the color legend doesn’t seem coherent with the plotted receiver positions. In Panel D) could you explain the meaning of the dashed red lines?
- Figure 2: what about the gray vertical lines?
- Line 189: The authors indicate the variance with the Greek letter sigma, but usually square sigma is used. Also the quantities in equation 5) are called “least squares differences” but do you just mean “squared differences”?
- Figure 3 is not clear. In the figure caption, could you explain the quantities represented in the plot?
- Figure 5: in the xy-plots it’s impossibile to read the labels because they are too small. Furthermore, do the color lines in the plots refer to the LTHW, UTHW and KHLR GNSS stations? In this case a station legend should be repeated also in this figure. Do the vertical red lines represent the height changes observed with GNSS-IR? Why the thickness of the red vertical line is different?
- Line 223: The authors affirm that “during the shorter-duration campaigns we observe 7 extreme precipitation events during the two summers (Figure 6)”, but in Figure 6 these events are not represented.
- Figure S1: the diamond symbol that should represent ERA5 is not plotted in the panels. It seem to be substituted by “X” symbols. Moreover, do you refer to panel (D), (E) and (F) instead of (A), (B) and (C) in the sentence “Accumulation measured with GNSS-IR plotted against accumulation determined from reanalysis products for (A) LTHW, (B) UTHW, and(C) KHLR GNSS sites”, aren’t you?
Technical corrections:
- Line 216: “Following, (Maclennan and Lenaerts, 2021),” —> “Following (Maclennan and Lenaerts, 2021)”, without commas.
- Line 224: With “2-4x” do you mean “2-4 times”?
Citation: https://doi.org/10.5194/tc-2023-114-RC2
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