| 14 Jan 2026
Uncertainty of Antarctic sea ice concentration using passive microwave retrievals in the marginal ice zone
Abstract. Antarctic sea ice has experienced an unprecedented decline in the past decade (2016–2025). Changes in sea ice concentration (SIC) and derived sea ice extent have been monitored using microwave radiometers since the late 1970s, providing information about the polar response to global climate change, hence making SIC an invaluable variable for numerical models. However, in the highly dynamic Marginal Ice Zone (MIZ), the region in between the pack ice and the open ocean, physical properties undergo intense variability, which may impact the accuracy of the SIC products retrieved from brightness temperature measurements. For the purpose of this study the MIZ is defined as the area with SIC between 15 % and 80 %. We simulate the variations of brightness temperature due to changes in the physical parameters describing the sea ice, the snow and the ocean with the Snow Microwave Radiative Transfer Model (SMRT) and the Passive and Active Reference Microwave to Infrared Ocean model (PARMIO) for a range of prescribed SIC. We then apply the core of the Bootstrap SIC algorithm on the simulated brightness temperatures and compare the retrieved and prescribed SIC, yielding the SIC error. This allows us to assess the impact of changes on the SIC retrieval by means of numerical radiative transfer simulations. Our work identifies the key parameters leading to high uncertainty in the retrieval: in the snowpack, the liquid water content and snow grain size cause SIC uncertainties of 5–10 % in the summer MIZ. In the cold season, the most influential factor is the presence of thin ice, inducing errors up to 30 %. Ocean roughness caused by the high-wind conditions affects both warm and cold seasons and gives rise to biases up to 15 % on the lower SIC MIZ boundary. However, other snowpack parameters that were expected to modify the SIC results, such as the salinity or temperature, showed a negligible impact in the tested range. We found that the core of the Bootstrap algorithm is largely robust to the variations in the snowpack, with no parameter introducing errors greater than 10 % across the MIZ SIC range. In contrast, ocean surface roughness due to wind speed and the presence of thin ice in the pixel are the variables leading to the greatest uncertainties, suggesting they are the primary targets to achieve more accurate SIC retrievals.
Competing interests: At least one of the (co-)authors, Petra Heil, is a member of the editorial board of The Cryosphere.
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Review of “Uncertainty of Antarctic sea ice concentration using passive microwave retrievals in the marginal ice zone” by Marta Stentella et al.
The MS is describing the simulated sensitivity of the Bootstrap algorithm sea ice concentration estimates to snow-ice-ocean surface parameters simulated with SMRT for sea ice and PARMIO for open water in the MIZ - Antarctica. There are two cases with two first-year sea ice profiles (one in summer conditions and one in winter) and the simulated brightness temperatures are tuned by varying each of the snow-ice-ocean surface parameters (LWC, SGS…) until a match is made with observations. The default surface parameter values and range of variability are picked from the literature without specific reference.
Some things are unclear and needs to be specified, for example, judging from the plots, the sea ice concentration algorithm, which is used, is the frequency mode bootstrap, i.e. the part of the bootstrap algorithm normally used over open water. Is that correct?
The range over which the snow-ice-ocean parameter is varied has a large impact on the magnitude of the SIC variability. The handle on the parameter variability and the initial profile is very loose and therefore the magnitude of the SIC variability is very uncertain. I would suggest either to constrain the snow-ice-ocean parameter variability with models or observations or both.
Different sea ice concentration algorithms have different sensitivities to snow-ice-ocean variability (e.g. Tonboe et al. 2022) and for other commonly used algorithms (e.g. NT, NT2, ASI…), the sensitivity response may be different. I would suggest either to include more algorithms or to make this a case study for the Bootstrap algorithm.
The atmosphere (in addition to windspeed) is somehow included in the simulations, but the SIC variability due to water vapor and cloud liquid water is not quantified. At 36GHz and at low concentrations this is non-negligible. Anyway, a description of what is going on with the atmosphere is needed.
One of the major components of the SIC uncertainty in the MIZ is the resampling uncertainty (Tonboe et al., 2016; Lavergne et al., 2019). You also use a resampled dataset and part of the uncertainty in those data are due to this resampling uncertainty and part due to geophysical noise (that you simulate). It is not clear if the observed TB variability has an influence on the snow-ice-ocean parameter range of variability in T1, but you are only characterizing part of the ‘uncertainty’ with the simulations. I think that you should mention that.
I think that some references are missing from the reference list, some suggestions are given below.
Specific comments:
I suggest to change the title, for example: “Sensitivity of the Bootstrap SIC to surface parameters in the Antarctic MIZ”
P1, L3: delete “global”
P2, L40: Three surface types are assumed, first-year ice, multiyear ice and open water. New-ice and bare ice does not fit the ice-line. Please clarify.
P3, L83: replace “pure ice” with “100% ice”, and in general ‘pure water’ with ‘open water’.
P3, L86: add after “properties”: “or the fraction of new-ice within the resolution cell.”
P4, L88: Is the water tie-point adjusted daily? Please clarify.
P4, L91: why do you have empty sections?
P4, L114: It is unclear if there is a relationship between salinity, temperature and brine volume? I think that there should be.
P5, L118: Please explain why you have a microstructure model with hard spheres and then you use the IBA for computing the scattering. Is that consistent?
P5, L124: why this set-up and why 13PSU and 0.05m? please provide some references or explanation.
P5, L135: What other atmospheric contributions other than wind?
P5, L143: This is unclear… you only include datapoints with SIC >15%? And what do you use it for?
T1: please provide some specific references to these values. I guess that they are static when they don’t have a min and max?
P7, L157: Several of these parameters are closely correlated, e.g. brine volume and temperature, they will never vary independently. Please explain.
F1: P should be half way between ‘O’ and the intersect with the ice line if ‘P’ represents 50%, but it is not. Is it just a sketch? There is some confusion between the ice tie-point ‘A’ and the ice line ‘I’ in the figure caption. I think that you should include both the multiyear and first-year ice (‘A’) tie-points in the plot.
T2: Please explain: ‘Water substrate – True’? Please use sea ice terminology.
P15, L242: delete ‘existing’
Suggested references:
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019.
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016.
Tonboe, R., Nandan, V., Mäkynen, M. P., Pedersen, L. T., Kern, S., Lavergne, T., Øelund, J., Dybkjar, G., Saldo, R., & Huntemann, M. (2022). Simulated Geophysical Noise in Sea Ice Concentration Estimates of Open Water and Snow-covered Sea Ice. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1309-1326. https://doi.org/10.1109/JSTARS.2021.3134021.