the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Feb 2025
Drift-aware sea ice thickness maps from satellite remote sensing
Abstract. The standard approach to derive gridded sea ice thickness (SIT) is to aggregate the original along-track estimates from satellite altimeters over a one-month period. However, this approach neglects processes like sea ice advection, deformation, and thermodynamic growth that occur within the aggregation period. To address these limitations, we propose a drift-aware method that accounts for sea ice motion and SIT changes due to dynamics and thermodynamics in monthly SIT products. We present a method to derive daily drift-aware sea ice thickness (DA-SIT) maps for the Arctic, based on Envisat and CryoSat-2 along-track data. The approach is validated against buoys, airborne SIT surveys and moored upward-looking sonar (ULS) measurements. DA-SIT demonstrates the ability to register sea ice thickness anomalies, which are also observed by daily ULS SIT averages, while being overlooked by the conventional gridded SIT data. Comparative analysis reveals that DA-SIT reduces orbit trackiness patterns and improves consistency in regions with significant ice drift, such as the Transpolar Drift. The drift-awareness enables detailed studies of regional sea ice dynamics and fluxes, while improving co-registration of multi-mission satellite data. However, when considering pan-Arctic estimates of ice volume, we do not expect significant changes in time series and trends compared to existing studies.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-359', Anton Korosov, 16 Feb 2025
The manuscript “Drift-aware sea ice thickness maps from satellite remote sensing” by Ricker et al. describes a new algorithm for aggregation of along-track sea ice thickness measurements using corrections from satellite-derived sea ice drift. The produced sea ice thickness dataset is thoroughly validated and supplied with an uncertainty estimate. The manuscript presents new results that are important for climate and cryosphere research. It is well structured and contains all the necessary algorithm details and dataset description details. Nevertheless, a few open questions need to be addressed in the manuscript. I believe it can be recommended for publication after a major revision.
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RC2: 'Comment on egusphere-2025-359', Harry Heorton, 18 Mar 2025
This paper documents the development of a new data production algorithm that considers the drift of sea ice when gridding satellite altimeter measurements of sea ice thickness. This new processing method is a valuable addition to sea ice thickness observations and a timely and important development.
The manuscript is detailed and covers the many intricacies and details involved in its development well, although some of these details can be tricky to follow in the current from. The figures are particularly well presented and well communicate the processing method and impact of incorporating sea ice drift into sea ice thickness gridding. I recommend the article for publication with only minor textual corrections to aid understanding and to make some technical aspects clearer on a first reading – with a single question on the results.
The one result that I am interested to see that was not included in the current draft, is how the linear model used to deal with the variation in thickness over a lag period relates to the mean thickness presented elsewhere. As equation 1 is a function of n, where n=0 is the centre day of a lag window, does this mean p0 will represent the modelled thickness at this centre day whilst considering thermodynamics? How does this p0 relate to the mean of DA measurement stack or the daily data fields? Which of these variables are recommended and what are the best use cases for each? While the final conclusions well cover the context and improvements of the new algorithm, it be will good to also see some recommendations of when best to use the data created here.
I hope the following minor corrections are helpful and aid in the improvement of the manuscript.
Harry Heorton
L 1. This sentence needs to reference ‘satellite derived thickness’ or similar otherwise the ‘along track’ part doesn’t work.
L 9 – ‘trackiness’ this makes sense for those in the sea ice remote sensing field, but may need expanding for a wider audience
L 16 – summer sea ice extent declined
L 25. This intro needs the reasons for ice drift to be described – winds and currents.
L 30 -32. This sentence is out of place for this paragraph and will be better later (L51 and beyond)
L 34 The beginning of the sentence can be removed. Start with “Within the Transpolar drift …”
L 35 remove ‘processes like’, they have all been listed!
L 40. Some quantification of these anomalies is needed - I’m not sure which anomaly is being referred to here.
L 42 – this sentence repeats the previous paragraph and is not needed unless there something specific about the anomalies that is different to thickness measurements.
L 44 observer – observe
L45 this whole paragraph needs to be reordered. The details about Cryo2Ice can come first as this will make the rest more clear.
2.1.1 first paragraph – this general information is not necessarily needed and it may be better just incorporated into the next paragraph. While IceSat2 is mentioned in the intro, the rationale for only using ESA radar altimeters needs to be included. Some info for wider readers on what is meant by level-2 will help here. The crucial aspect for context in wider studies is the choice of SIT over freeboard is needed. This will need a description of the snow data used in the ESA CCI product as this is relevant to the drift of sea ice (for example in the SM-LG product that also considers it – Liston et al (2020).)
L 128 Last sentence needs rewriting – do you mean that some had drifted further than 200km?
L 155 this sentence is awkward in the flow of the whole paper. A more coherent sentence or possibly the whole paragraph is that – the collection of drift corrected thickness measurements will represent the same ice during changes to thickness. Thus we use a linear model. The logic at the moments is the other way round – in order to represent the thickness change we must use drift and intersections – this is against the flow from the previous section.
L 160. While it is fine to use this resolution this argument neglects important sub-kilometer or floe to floe thickness distributions. Perhaps just state that the method is representative of average ice thickness at this scale length.
Equation 1 While p1 is well discussed, does p0 represent anything physically? Is it related to the mid- or n=0 day thickness? Does this correspond well to the later arithmetic mean?
L 169 to calculate the covariance matrix (p0,p1) does the data uncertainty need to be given to the least squares fitting algorithm?
L170 while it is very helpful to have this illustrated information within this section, the detailed description of the uncertainty may be more helpful in the results section.
L 204 In any case.. this sentence can be removed and the next be reduced to state the incompleteness windows.
L 206 Is this missing value issue/method for stacks also true for cells near an advancing/retreating ice edge? How does this all relate to equation 5? Figure 4 shows some peak values near the ice edge in the Chukchi sea, is this related to missing values in the stack and a minimal thickness measurement from CryoSat2? (due to the sensors vertical resolution)
Equation 5 – the nanmean approach makes sense for missing values – but does this result in a time bias at the beginning and end of a record – and possibly for advancing retreating ice edge? How does this relate to the earlier comment on equation 1 and p0?
L 218 Is the sigma for each along track taken from the CS2 L2 track files? What variable name is this?
L 255 is it just the larger uncertainty in drift or just due to the drift being faster? The next sentence is just a repetition – or is there are more detailed reason why the uncertainty is higher near coastlines?
L 256 - often results…. Is this comment about the paper methods? I’m assuming it is but it will be good to make this certain.
L 258 – this paragraph all has good information on the drift data. Is there a citation that contains more detailed explanations on the uncertainty? There may not be, these aspects are not always well documented.
L 259 – this sentence needs to be added to the previous paragraph. A figure needs to be cited here. A similar pattern of uncertainty can be seen in figure 2 of Heorton et al (2025).
L 263 this paragraph will benefit from an opening sentence on what metric the buoy data produce to validate the data - a measure of the accumulated uncertainty in parcel location over the lag window. Is this correct?
L 267 ‘which require’ the integration of sea ice thickness measurements over at least….
L 287 about - approximately
L 300 – it will be worth repeating here the differences between the AEM total thickness and the thickness of the DA data – I assume this is due to the AEM coming from the snow air interface and representing the thickness of the combined snow and ice thickness?
L 329 is this ‘conventional gridding’ performed by the authors for this study, or data from a prior study? A citation or reference back to the data section is needed here.
L 382 – similar to an earlier point, has C-SIT been created for this study?
Figure 11 – caption needs to say that the SD here is the SD in differences as described in the text.
Heorton, H. et al. 2025. Observationally constrained estimates of the annual Arctic sea-ice volume budget 2010–2022. Annals of Glaciology. 66, (Jan. 2025), e9. DOI:https://doi.org/10.1017/aog.2025.3.
Liston, Glen E., Polona Itkin, Julienne Stroeve, Mark Tschudi, J. Scott Stewart, Stine H. Pedersen, Adele K. Reinking, and Kelly Elder. ‘A Lagrangian Snow-Evolution System for Sea-Ice Applications (SnowModel-LG): Part I—Model Description’. Journal of Geophysical Research: Oceans 125, no. 10 (2020): e2019JC015913. https://doi.org/10.1029/2019JC015913.
Citation: https://doi.org/10.5194/egusphere-2025-359-RC2
Data sets
Drift-aware sea ice thickness maps from satellite remote sensing Robert Ricker, Thomas Lavergne, Stefan Hendricks, Stephan Paul, Emily Down, Mari Anne Killie, and Marion Bocquet https://doi.org/10.5281/zenodo.14733132
Model code and software
Drift-Awareness for Sea Ice Altimetry (DriftAware-SIAlt) Robert Ricker https://doi.org/10.5281/zenodo.14732875
Video supplement
Animated DA-SIT time series from 2019–2020 Robert Ricker https://doi.org/10.5281/zenodo.14736322
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