Sensitivity of the Bootstrap sea ice concentration algorithm to surface parameters in the Antarctic marginal ice zone using passive microwave retrievals

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 climate change, making SIC an invaluable variable for numerical models. Antarctic sea ice has experienced an unprecedented decline in the past decade (2016–2025). 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 SIC with the prescribed true SIC, yielding the SIC retrieval uncertainty. This allows us to assess the impact of changes on the SIC retrieval by means of numerical radiative transfer simulations. The work identifies the key parameters leading to high uncertainty in the retrieval. In the snowpack, the liquid water fraction, snow grain size, thickness, and snow–ice interface temperature each cause SIC uncertainties within the 5 % range, with some parameters reaching up to 10 % depending on the season. However, the most dominant uncertainty in the cold season comes from the presence of thin ice types like dark nilas and grease, characterised by high salinity or liquid water fraction, which induce uncertainties of up to 70 %. This uncertainty is comparable to that caused by slush, which can be found in the MIZ all year round. Ocean surface impacted by the high-wind conditions affects both warm and cold seasons and gives rise to uncertainties of up to 10 % on the lower SIC MIZ boundary. However, other parameters that were expected to modify the SIC results, such as the temperature and salinity in the snowpack overlying the first-year ice, 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 properties. In contrast, the presence of thin ice types and slush and ocean surface affected by high wind speeds in the grid cell are the variables leading to the greatest uncertainties, suggesting they are the primary targets to achieve more accurate SIC retrievals in the MIZ.