the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Variability of CryoSat-2 derived Sea Ice Thickness introduced by modelled vs. empirical snow thickness, sea ice density and water density
Henriette Skourup
Till A. S. Rasmussen
Abstract. To derive sea ice thickness (SIT) from CryoSat-2 freeboard (FB) estimates, assumptions about snow thickness, snow density, sea ice density and water density need to be made. These parameters are close to impossible to observe alongside FB, so many existing products use climatologies, or empirical values. A resent study proposed to use model parameters for snow thickness, sea ice density and water density instead. In this study, we are evaluating this values against in situ observations and the commonly used climatologies and empirical values. We show that the snow thickness and water density is in better agreement with observations, and that the sea ice density is overall too light. Analyzing the difference in SIT resulting from the model parameter vs. the empirical values, we find that the snow thickness leads to the largest differences with up to 30 cm, closely followed by the sea ice density with 20 cm. For the water density we find an up to 7.5 cm difference, which is small in comparison to the snow thickness and sea ice density, but not negligible, as most studies currently argue. We find that the origin of the assumption that water density is negligible in the FB to SIT conversion originates from a study investing the seasonal Arctic sea ice density variability, not taking into account the spacial variability. For CryoSat-2 based SIT products we recommend to either use a water density climatology, or an uncertainty value of 2.5 kgm-3 instead of the commonly used value of 0 to 0.5 kgm-3.
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Imke Sievers et al.
Status: final response (author comments only)
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RC1: 'Comment on tc-2023-122', Anonymous Referee #1, 12 Sep 2023
The manuscript seeks to address an important topic of contributions of different sources of uncertainty to sea ice thickness observations relevant to past, current and future altimetry missions. There are some potentially useful insights from this analysis. However, the manuscript is difficult to read and suffers from three major issues:
1. The objective(s) are unclear, and not given in the abstract. What is the overall aim? Which values (line 4) are being compared to which, and for what purpose?
2. There are previous studies e.g. by Landy and Mallett which address some of the questions the study seeks to answer, however, these are not referenced - they should be discussed in the introduction with an explanation of how this work builds on them and/or differs to them.
3. The methodology is very difficult to understand, there are number of observational datasets, model assimilation and outputs being discussed but it is not clear how these were selected, what is being compared to what etc. and as above, for what purpose. It seems as though everything is being investigated/varied at once without a clear strategy.
It seems likely there could be some value of this study, though previous studies have already investigated some of this and it will need to be clarified how this is distinct. In particular the findings relating to water density could be of interest to the community. In the current form it is not possible for a reader to gain this understanding from the manuscript.Citation: https://doi.org/10.5194/tc-2023-122-RC1 -
RC2: 'Comment on tc-2023-122', Anonymous Referee #2, 13 Sep 2023
Review of “The Variability of CryoSat-2 derived Sea Ice Thickness introduced by modelled vs. empirical snow thickness, sea ice density and water density” by Sievers et al.
The MS is evaluating the sea ice thickness retrieval uncertainty from the radar freeboard due to the buoyancy effects of snow thickness, sea ice density and ocean water density. The list of literature describing the uncertainties when deriving sea ice thickness from the radar freeboard is long and a narrow selection of those articles are presented in the introduction. The main novelty of the MS is the “overlooked” ocean density variability in previous studies.
The topic is definitely relevant and actual and the future assimilation of radar freeboards in sea ice and ocean models has a large potential. However, the presentation of the results is raw, starting already in the abstract, it is difficult grasp what the MS is presenting and the figures are missing units and consistent ranges of the color scales for comparison. These things can be handled in a revision; however, my main concern is the large ocean water density variability in the model (C6N4), the novelty here. The water column of the sea ice covered parts of the Arctic Ocean have the so called “polar waters” on top of the “Pacific” or “Atlantic” waters underneath in a very stable stratification. Does the ocean model (C6N4) replicate the polar waters and that stratification? And how does the model compare to surface salinity (CTD) measurements?
The surface water density colorbar (I presume in [kg/m3]) in figure 7 (top row) varies between 1018 kg/m3 and 1028 kg/m3. This corresponds to a salinity range between about 23 and 35 psu. The AWI value of 1024 kg/m3 corresponds to approximately 30 psu. 35 psu corresponds approx. to the Atlantic Water salinity. It might be useful to also show that density/salinity of the waters not covered by sea ice. I could be wrong, but I am skeptical about the large range of salinities (densities) for the polar waters and it would be reassuring to make a comparison with in situ CTD measurements (fx. MOSAiC data) to see if C6N4 is capturing the Arctic Ocean halocline.
Selected specific comments (I think that the MS needs a complete rewrite with attention to clarity and even grammar, here are some suggestions):
P1,L1: “assumptions” these are not necessarily assumptions (as you describe later) and please rewrite the abstract so that it is clear what you have done and what you have found.
P1,L2: “close to impossible”: this statement is too pessimistic, and I think that you have to acknowledge the effort put into retrieving or simulating these variables.
P1,L4: “this”, please rewrite, it is difficult to understand what “this” is.
P1,L6: “light” use low or underestimated.
P.1,L15: you mention “laser”, please include a reference to laser.
P1,L18: Cryosat-2 was not designed under the assumption that … please rewrite.
P1,L18: replace “reflected” by “scattering”, also line 20.
P1,L19: Please check the Beaven et al. reference, to see if it can really support that statement.
P2,L25: “error estimate study” -> “sensitivity study”
P2,L26: “contributors” to what?, please rewrite.
P2,L28: “…sea ice density…” add “for FY and MY ice”
P2,L30: “deriving” to “distinguishing”
P3,L56: move ref’s to the end of the sentence.
P3, L59: capital “R” after full-stop.
P3,L70: after “…density…” add “variability”
P3, L78: after “forcing” add “which is applied”
P7,T1: This table is difficult to understand. Why is the “disagreement” metric constrained to the interval 0-2? And what is the unit [m]?
P10,F3: use same scale on colorbar, what is the unit [m?], give meaningful heading to lower left figure, why does the SIT difference plot only have positive values?
P11, L223: please rewrite.
P11,L231: Does the IceBird derived densities include snow? And AWI and C6N4?
P13:F5: What are the trend-lines for? I don’t see a trend except that the density increases at the onset of melt. I think that those different densities would result in a systematic uncertainty, why do you quantify it as a RMSD? Please add the AWI density.
P14,F6: Add units, why is the SIT difference only positive?
P16,F7: Add units to both panels (see also comment above).
P17, L324: There is no scattering at intermediate depth in the snow, you could write something like “the extinction in the snow is affecting the radar track-point” see also P18,L341.
P19,L395: This statement is speculative and you should validate with observations.
P20,L404: water density variation by 10 kg/m3, you should really check with observations.
Citation: https://doi.org/10.5194/tc-2023-122-RC2
Imke Sievers et al.
Imke Sievers et al.
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