the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Feb 2024
Arctic Sea Surface Determination with Combined CryoSat-2 and ICESat-2 Data
Abstract. Due to the presence of sea ice, determining the sea surface height in the Arctic Ocean remains a significant challenge. State-of-the-art Arctic Mean Sea Surface (MSS) products are primarily derived from radar altimetry missions like CryoSat-2. However, the ICESat-2 laser altimeter can offer valuable sea surface observations up to 88° N latitude, extending the observational reach. This paper analyses the performance of combined CryoSat-2 and ICESat-2 data in determining the Arctic sea surface. Comparisons of overlapping observations from both missions reveal excellent consistency, with an inter-mission bias of less than 1 cm in the Arctic. Different geophysical corrections are considered, and the results suggest that only the ocean tide correction needs to be unified, while other corrections show minimal discrepancies. The MSS derived from combined data boasts both superior spatial coverage and precision compared to individual missions. The impact of summer melt pond is also discussed. The data from June, July and August are seriously contaminated, but only have limited effect on the mean sea surface calculation. Overall, the combined use of CryoSat-2 and ICESat-2 data offers a promising approach to accurately determining the Arctic sea surface, paving the way for improved understanding of sea level change and its implications in this critical region.
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Status: open (until 26 Apr 2024)
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RC1: 'Comment on egusphere-2023-3030', Ole Baltazar Andersen, 28 Feb 2024
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Dear Authors.
Thanks for the submission on Arctic sea surface determination with combined C2 and I2. I have had a look at the manuscript which primarily focuses on the determination of Mean sea surface in the Arctic Ocean. Despite the fact that the manuscript have very interesting elements (particularly the comparison between I2 and C2 in the Cryo2ice period, I have decided to reject the manuscripts because I think that the scope is wrong.
The authors computes what they call mean sea surface but it is not what they are doing. A MSS requires many years of data to be a MSS. Here they are only considering a few years. So what the authors should focus on is the comparison and the temporal changes instead and NOT THE MSS, because the MSS they determine is not correct. They are at most computing an annual mean, but this is also interesting.
The authors also ignores the sea state bias in their investigation which is problematic (chapter 5) It is well known that the sea state bias should be applied in the Arctic Ocean, but in this paper it is not even mentioned and basically ignored (because the authors can not determine it with their data). Indeed but then they are comparing a MSS (without SSB) with a MSS with SSB). This is not scientifically sound and in this way the authors are comparing apples with pears.
Also they they are comparing with MSS models they should adjust for sea level change int he period over which they are doing their comparison. Othervise they are just showing the interannual variability like in figure 7 which is related to the AO oscillation.
The investigation of meltpond is very badly documented. For one thing the figure 11 exhibit the same color for the month of July and December. This means that its not possible for the reader to make sense of this.
I suggest the authors to rewrite the manuscript focusing on the sea level variations and re-.submit to this or another journal after revision.
All the best
Ole B. Andersen
Citation: https://doi.org/10.5194/egusphere-2023-3030-RC1 -
CC2: 'Reply on RC1', Guodong Chen, 26 Apr 2024
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Thank you very much for your suggestions. We are very grateful that you took your precious time to review our manuscript.
Your suggestions are very helpful to us, and we will focus on sea level variations while rewriting the paper.
About sea state bias, since we used ice concentration data to sort out the sea ice covered areas, and it is generally believed that the impact of SSB is small in these areas, so we did not correct it. We will try to solve this problem if the open ocean data is used.
Citation: https://doi.org/10.5194/egusphere-2023-3030-CC2
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CC2: 'Reply on RC1', Guodong Chen, 26 Apr 2024
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RC2: 'Comment on egusphere-2023-3030', Alek Petty, 23 Apr 2024
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The preprint "Arctic Sea Surface Determination with Combined CryoSat-2 and ICESat-2 Data" by Chen et al., investigates the use of radar and laser altimetry data from the CryoSat-2 and ICESat-2 satellites to measure sea surface height across the Arctic Ocean (within the ice pack). Accurate determination of sea surface heights in the Arctic, a region heavily affected by sea ice, is challenging, but also crucial for understanding Arctic Ocean circulation amongst other things.
Overall, the paper extends earlier work from Bagnardi et al., (2021) to highlight the good agreement between the IS-2 and CS-2 products (leveraging cryo2ice granules) and the potential benefits of merging data from both satellites for SSH determination, as well as a discussion of important caveats, e.g. melt pond contamination and the role of differences in geophysical corrections on the results. I appreciated the fact they discussed different lead types from ICESat-2 and included comparisons to tide gauges. Clearly a decent amount of effort when into this study.
However, determining SSH within the sea ice pack from altimeters is hard, and merging data from different sensors towards estimates of SSHA is a complex issue that I feel is not given enough care/sophistication. Overall, my concern is how to interpret this work – is this a merged SSHA product we can reliable use for science, with clear errors and understanding of how ICESat-2 and CryoSat-2’s unique capabilities have produced this data? I’m not so sure.
Main comments
- The ICESat-2 beams are not fully aligned and there are known biases across the beams (Bagnardi et al., 2021). For this reason, the official ICESat-2 monthly gridded SSHA product only uses data from the middle strong beam (see description here: https://nsidc.org/sites/default/files/documents/user-guide/atl21-v003-userguide.pdf). I was surprised there was no discussion of this important issue in the paper!
- The discussion of lead types and how they compare to CS-2 I think is an important thing to include now we have more years of data, so I’m glad the authors included some of this analysis. However there was no discussion of what the different IS-2 lead types represent and why that might lead to better or worse performance which was surprising. How does background rate impact lead finding do we think? High vs low specular leads? Saturation issues? Also more importantly, you frame this analysis as though CS-2 is a truth for validation which is quite dangerous, especially with the focus on mean biases. I think you need to be much smarter here about how to assess lead types and which ones we might want to rely on more for SSH determination. I also think you really need to talk about summer and melt much earlier as the IS-2 product clearly states any surface class could be viewed as a melt pond. Capturing melt ponds from leads in CS-2 is also very challenging (Dawson et al., 2022) and that wasn’t discussed enough.
- I am quite confused by the approach used to generate the monthly SSHA grid. Are you calculating the trend for each month, or just calculate a trend across the entire time-series? In either case, the fact you apply this to small 5 km grids with no spatial interpolation is surprising. You are basically smoothing out significantly the seasonal cycle but applying no spatial smoothing. Why? I guess you are saying for each box we can’t be truly confident of those values I guess because of sampling issues, so we should smooth in time, but then you don’t do the same spatially? I think most studies doing similar things have historically applied quite large spatial smoothing windows for this kind of analysis. You can see the issues when you look at your maps. And to be clear, you do not account for when data is collected in the month, it’s just a simple binning?
- The discussion of CryoSat-2 data I found to be quite simplistic. CryoSat-2 is a radar altimeter so profiles leads in a very different manner to ICESat-2 (specular leads can really dominate the power return) but there wasn’t much appreciation of this in the paper and the averaging of ICESat-2 to a CryoSat-2 footprint doesn’t really reflect the reality of how these sensors work. There was also not much background regarding the CS-2 data processing, how this may differ to other datasets/processing chains, and background on the geophysical corrections relevant to this kind of intercomparison.
- Can you confirm how you process the MSS dataset? Are you removing the ICESat-2 MSS then applying this DTU MSS to both, right? I was suprisided there was no discussion on the fact IS-2 uses its own MSS in the ATL07 processing (A combination of DTU and CS-2)
- Merging the data: Figure 8 and 12 – there’s clearly lots of aliasing in these results, I just don’t know how much we should trust that data. There are more sophisticated methods for interpolation and fusing data from satellite altimeters that have recently been proposed (Gregory et al., 2023) that I think need to be considered if you want merging to be the point of the paper..
- Figure 5 STD analysis - you are comparing STDs after averaging of ICESat-2 data into the same window as CS-2 (~305 along-track) and then assessing STD in lead heights within 5 km sections. That’s not many data points as I’m guessing in a lot of those 5 km sections there may only be a few leads, if any… You describe the results as though lower STD is better but that doesn’t really make sense to me as sampling success seems like it would be a big issue here.. Also you are removing the ‘linear trend’ of the data within each 5 km section, so these results only represent the deviations around a linear trend, which again seems odd when there shouldn’t be a big signal from the geophysical corrections at such short spatial/temporal scales. Finally, I didn’t think the description of the results was very compelling!
- Geophysical correction analysis and Table 5 –Are they doing these comparisons using the monthly binned data fit using linear regression? If so that doesn’t seem ideal. Also the agreement could improves for the wrong reason/chance.
- Validating with coastal data is not great considering the lack of IS-2 data at the coastline and the more serious issues with tide models there, any thoughts?
- One of the big issues of producing a merged product is the ice edge and the filtering of low concentrations. A lot of the on-going work is trying to deal with that, so this study seems a bit limited in scope just focusing on the pack ice, and not really discussing how the changing nature of the ice edge may even be impacting the results.
- Figure 7 and the associated comparisons of mean sea surface form this and another dataset (CNES_CLS 2022 MSS here) I think are just not appropriate. What’s the goal of this? Validation or intercomparison?
Specific points:
Section 3.2 includes a lot of just background on the ICESat-2 products which should be moved earlier.
Figure 1 – I think this is just a plotting thing where the higher latitude region has lots of overlaps but you just show the latest value which makes it look later then the mean?
Figure 2 - You could apply a stats test on the lead height distributions to really see if they are not normal, seems you just based this on visual interpretation? On that topic - what’s going on with the top left panel histogram? Looks like there is a big spike at 0.3 m?
Figure 4 – what period is this? Caption needs more information.
Figure 7 – why show the mean sea surface here?
L220 – What is an accidental error?
L222 - 1-2 cm is acceptable? Why??
L270 – This is a big simplification. They have different coverage and criteria, and you haven’t yet shown that CS-2 is more successful at finding leads.
L271 – what do you mean more leads than the official product?
L279 – you really haven’t shown that! Also, no mention of clouds either, or how they behave seasonally.
L310 – I don’t think it’s right to mention precision here as that’s not what your analysis has shown.
L313 – well this I think is one of the issues I was hoping to see in this paper!
L316 – but you removed the seasonal signal at the grid-cell level I believe?!
L387-389 – that seems quite unbelievable to me, you really need to do more to understand the impact and potential benefits of merging these datasets together.
Figure 11 – this analysis seems extremely limited considering the lack of open ocean data included.
Table 5 – applied to one or both datasets?
References
Bagnardi, M., Kurtz, N. T., Petty, A. A., and Kwok, R.: Sea Surface Height Anomalies of the Arctic Ocean From ICESat-2: A First Examination and Comparisons With CryoSat-2, Geophysical Research Letters, 48, e2021GL093155, https://doi.org/10.1029/2021GL093155, 2021.
Dawson, G., Landy, J., Tsamados, M., Komarov, A. S., Howell, S., Heorton, H., and Krumpen, T.: A 10-year record of Arctic summer sea ice freeboard from CryoSat-2, Remote Sensing of Environment, 268, 112744, https://doi.org/10.1016/j.rse.2021.112744, 2022.
Gregory, W., Lawrence, I. R., and Tsamados, M.: A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations, The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, 2021.
Citation: https://doi.org/10.5194/egusphere-2023-3030-RC2 -
CC1: 'Reply on RC2', Guodong Chen, 26 Apr 2024
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Thank you very much for the comments. We are very pleased that you read our manuscript so carefully. Many of the issues you raised are indeed things we had not considered, and your comments can surely help us to broaden our thinking. We will make more efforts on these issues, such as the difference between IS-2 beams, interpretation of lead types, merging of the two datasets, and so on.
Here are our responses to some of the points you raised.
processing of DTU21 MSS: ATL07 provides height relative to the MSS model, and it also provides mean sea surface height above ellipsoid. We obtained the sea ice/lead height above ellipsoid by these two values, then we removed the DTU21 MSS to calculate ssha. We also applied the height system conversion from WGS-84 to T/P ellipsoid, and from free tide system to mean tide system, so that the CS-2 and IS-2 data can match each other.
Figure 5: within each 5km along track segment, there are at most 17 points considering the spatial resolution of CS-2, and at least 5 points according to our selection principle. The average was 9.3 point per section in Figure 5. Since overlapped IS-2 and CS-2 data are used here, there should be no samling difference. The linear trend mainly represents the dynamic ocean topography (DOT), which is not corrected during the data processing. According to our data (not shown in manuscript), the DOT can vary several centimeters in some areas in Arctic. After removing the linear trend, we think the leads can be assumed to be almost flat surface, then the STD can represents the precisions inside each datasets.
Table 5: The geophisical corrections mentioned in the manuscript were all applied to individual IS-2 or CS-2 measurements, not for the binned data. the corrections were applied to both dataset, replacing their original values.
Figure 2: the spike at 0.3m in top left panel of figure 2 represents all the values larger than 0.3m, not just around 0.3m. There are just too many gross errors in this group.
L222: Considering the precision of both altimeters is no better than 1-2 cm, we think errors of this level is acceptable.
L316: Figure 6 shows the mean differences between monthly averages of the two data, the seasonal signals were not removed.
Figure 11, lack of open ocean data : Yes, it is limited, especially in those seasonal sea ice regions. However, in the perpetual sea ice region where the monthly ssha time series covers the whole times span, figure 11 also showed peaks of annual cycles in June, this is inconsistent with current understanding.
Citation: https://doi.org/10.5194/egusphere-2023-3030-CC1
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