the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 May 2024
Seasonal evolution and parameterization of Arctic sea ice bulk density: results from the MOSAiC expedition and ICESat-2/ATLAS
Abstract. Satellite retrievals of Arctic sea ice thickness typically assume a constant sea ice bulk density (IBD), overlooking its seasonal variations influenced by ice internal texture and contaminants. This study unveils the initial insights into the seasonal evolution and parameterization of IBD during the Arctic freezing season from October to April. To retrieve IBD, we combined in situ observations obtained from ice mass balance buoys, snow pits, and snow transects during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, as well as laser freeboard data derived from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Assuming hydrostatic equilibrium, local-scale IBDs for the level ice component of the MOSAiC ice floes, predominantly consisting of second-year ice, were obtained at a spatial scale of approximately 50 km. The results indicated a statistically significant seasonal decreasing trend in IBD at a rate of ~16 kg m−3 per month (P < 0.001) from mid-October to mid-January, likely attributable to increased internal porosity as the sea ice aged. This was followed by a relatively stable period from mid-January to mid-April, with an average IBD of ~897 ± 11 kg m−3. Core-based IBDs from eight MOSAiC sites showed a similar seasonal pattern, but with a narrower range of variation and an earlier onset of the relatively stable period, possibly owing to the spatial heterogeneity of the MOSAiC ice floes. Based on regression analyses, we developed updated parameterizations for IBD that are anticipated to be applicable throughout the freezing season, encompassing both first- and second-year ice. In particular, the ice draft-to-thickness ratio emerged as the most efficient parameter for determining IBD (R2 = 0.99, RMSE = 1.62 kg m−3), with potential application to multi-year ice and deformed ice as well. Our updated parameterizations have the potential to optimize basin-scale satellite-derived sea ice thickness, thereby contributing to more accurate monitoring of changes in sea ice volume.
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CC1: 'Comment on egusphere-2024-1240', Arttu Jutila, 15 May 2024
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Community comment on the manuscript by Zhou, Y., Wang, X., Lei, R., von Albedyll, L., Perovich, D. K., Zhang, Y., and Haas, C.: Seasonal evolution and parameterization of Arctic sea ice bulk density: results from the MOSAiC expedition and ICESat-2/ATLAS, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1240, 2024.
Dear Zhou et al.,
Dear handling editor,The competing interests policy of The Cryosphere prohibits me from acting as an official referee for this manuscript due to recent collaborations with some of the coauthors. Therefore, I am posting this comment as a member of the scientific community to discuss some matters related to it.
In the manuscript by Zhou et al., now under peer-review process and public discussion, sea-ice bulk density is derived using the hydrostatic equilibrium equation and values of modal total freeboard from the satellite laser altimeter ICESat-2, mean snow depth and sea-ice thickness from 15 autonomous ice mass-balance buoys (IMB) deployed within the MOSAiC Distributed Network (DN), and mean snow density from snow pit measurements conducted in the MOSAiC Central Observatory (CO) from October 2019 to April 2020.
In 2022, I have authored a paper in The Cryosphere on the same general topic, sea-ice bulk density, using a similar approach but simultaneous airborne multi-sensor measurements from the AWI IceBird program:
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., Krumpen, T., and Haas, C.: Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements, The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, 2022.
This work is referenced many times and data originating from this study are used in the manuscript by Zhou et al.
First, I want to inform that there is a recently published new version of the AWI IceBird airborne sea-ice parameter dataset. In the new version, the quality flag identifying level and deformed ice has been rectified.
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., and Haas, C.: Airborne sea ice parameters during the IceBird Winter 2019 campaign in the Arctic Ocean, Version 2, https://doi.org/10.1594/PANGAEA.966057, 2024.
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., and Haas, C.: Airborne sea ice parameters during the PAMARCMIP2017 campaign in the Arctic Ocean, Version 2, https://doi.org/10.1594/PANGAEA.966009, 2024.
Regarding the study of Zhou et al., I would like to raise general concerns and perhaps some misunderstandings of my paper. The general points are the following:
- Spatial scales. While the presented study broadens the knowledge with the aspect of seasonal evolution of remotely sensed sea-ice bulk density, I am concerned about the different magnitudes of spatial scales utilized in the derivation. More specifically, you use total freeboard from the ICESat-2 satellite laser altimeter orbits extracted within a circle around the CO that has a diameter of 100 km; snow depth and sea-ice thickness derived from 15 autonomous IMBs in the DN within circle around the CO that has a diameter of 70 km (in the beginning of the drift, but how about later after being affected by sea-ice dynamics for months?) while the data are inherently point measurements; and snow density derived from snow pit measurements within the CO that extends over an area with a diameter of only few hundred meters while the data are inherently point measurements. None of these data sources have real spatial overlap with each other. This effectively diminishes the study to use ice-type-averaged values (not far from Alexandrov et al.’s (2010) multi-year ice density derivation with climatological values from literature) as the measurements are not from the same piece of ice – not even remotely.
In addition, using the term “local-scale” with data originating extending 100 km, when local is generally understood as <~1 km, definitely raised my eyebrows throughout the manuscript.
Why was ICESat-2 ATL10 rev5 used when rev6 is available? Actually, why not use the publicly available, more local MOSAiC helicopterborne laser scanner data by Hutter et al. (2023), which you also cite in the manuscript? I think that could be a feasible option to explore and it would back up better the local-scale aspect of this study. At this point, however, I must point out that I was involved in collecting and processing said data, too. - Level ice. You state that the chosen IMBs were deployed on level ice. I agree that this is a correct approach, to consider level ice only. However, the publicly available deployment documents for the buoys T63, T65, T70, and I1 indicate that ridged ice was already in close vicinity during deployment.
How was it ensured that ICESat-2 data was considered over level ice only? How long are the data segments, did they include only level data? While the modal value of the log-normal fitted freeboard is an estimate of the thermodynamically grown sea ice, it does not strictly exclude e.g. thin sea ice that has deformed and gained the same freeboard as thermodynamically grown undeformed sea ice.
How about snow pits, have you considered that pressure ridge sites were sampled on MOSAiC, too? Level ice tends to have thinner snowpack with larger temperature gradients that lead to snow metamorphism affecting the snow density profile.
When comparing your data to the AWI IceBird dataset, did you choose measurements on level ice only quality flag? I would suggest doing so, and in that case also using the updated version of the dataset.
More specific comments:
L62ff: Alexandrov et al. (2010) did not use airborne multi-sensor data. They used ground-based drill-hole measurements achieved through landing airplanes on the sea ice in the 1980s (Soviet Sever expeditions). So far, I am not aware of any other study utilizing airborne multi-sensor measurements to derive sea-ice bulk density than Jutila et al. (2022).
L79ff: While Shi et al. (2023) have more recently argued the point, it was mentioned earlier in Jutila et al. (2022), to which also Shi et al. (2023) refer.
L179ff: Sea-ice freeboard and thickness are found to follow log-normal or exponential distribution, but does total freeboard behave the same? And how about on the 100 km scale?
L232ff: Both your snow depth and sea-ice thickness measurements come from the IMBs. Therefore, are their uncertainties not independent and the assumption thus wrong?
L244ff: Were any other formulations than first and second order polynomials investigated?
Figure 5 & L313ff: Which values are you using for the three J22 data points? To my eyes, they do not match the values from Table 3 in Jutila et al. (2022) that list the average bulk densities on 800 m spatial scale. Or did you perhaps derive those values from the nominal resolution datasets? Did you use all values or only the level ice ones? Furthermore, I recommend using the same marker shape for the same ice type, adding citations also to the main text, and explaining the acronym “J22” (now only on L361).
L480ff: The data consists of several profiles covering a total distance of more than 3000 km (3410 km). Surveyed sea ice was primarily first-year ice (100 % in 2017) and multi-year ice, with only very little second-year ice. While you mention the spatial resolution of the data, I also think it’s important to distinguish between the nominal measurement spacing (5-6 m) and the footprint size (40 m) of the measurement.
L493ff & Figure 10: Jutila et al. (2022) applied inverse-uncertainty weighted mean, not inverse distance. Are all ice types included in this analysis, also level ice and first-year ice, even though you’re targeting to analyze rough and older ice? The AWI IceBird airborne sea-ice parameter datasets can easily distinguish different ice types using the provided quality flags.
L523ff: The “new approach proposed in this study to determine [ice bulk density] at the basin scale using satellite altimetry data” is not new as this capability has been previously demonstrated in Jutila et al. (2022). If you mean using satellite altimetry data in your approach to determine sea-ice bulk density (together with ground-based point measurements), you need to present and discuss the effect of different scales for the reasons brought up earlier. The study also seems to highlight the parameterization applying the ice draft-to-thickness ratio, but there is no current or planned satellite mission that can directly observe sea-ice draft, thickness, nor their ratio.
Citation: https://doi.org/10.5194/egusphere-2024-1240-CC1 -
AC1: 'Reply on CC1', Yi Zhou, 18 May 2024
reply
Dear Dr. Arttu Jutila,
Thank you for your constructive comments. We will address your insights with comprehensive clarifications and revisions throughout our manuscript. We have outlined the original comments in black with our planned responses highlighted in blue. Kindly refer to the attached document.
Best regards,
Yi Zhou and other co-authors.
- Spatial scales. While the presented study broadens the knowledge with the aspect of seasonal evolution of remotely sensed sea-ice bulk density, I am concerned about the different magnitudes of spatial scales utilized in the derivation. More specifically, you use total freeboard from the ICESat-2 satellite laser altimeter orbits extracted within a circle around the CO that has a diameter of 100 km; snow depth and sea-ice thickness derived from 15 autonomous IMBs in the DN within circle around the CO that has a diameter of 70 km (in the beginning of the drift, but how about later after being affected by sea-ice dynamics for months?) while the data are inherently point measurements; and snow density derived from snow pit measurements within the CO that extends over an area with a diameter of only few hundred meters while the data are inherently point measurements. None of these data sources have real spatial overlap with each other. This effectively diminishes the study to use ice-type-averaged values (not far from Alexandrov et al.’s (2010) multi-year ice density derivation with climatological values from literature) as the measurements are not from the same piece of ice – not even remotely.
Data sets
Sea ice bulk density during the MOSAiC expedtion Yi Zhou https://zenodo.org/doi/10.5281/zenodo.11055727
IS2 modal freeboard during the MOSAiC expedtion Yi Zhou https://zenodo.org/doi/10.5281/zenodo.11055727
Snow depth and sea ice thickness data derived from SIMBA buoy measurements Ruibo Lei https://doi.org/10.1594/PANGAEA.938244
Snow depth and sea ice thickness data derived from SIMB buoy measurements Donald K. Perovich https://doi.org/10.18739/A20Z70Z01
Model code and software
IS2 modal freeboard extraction and sea ice bulk density retrieval Yi Zhou https://zenodo.org/doi/10.5281/zenodo.11055727
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