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.