Detection of multi-year ex-fast ice in the Weddell Sea, Antarctica, using ICESat-2 satellite altimeter data

Abstract

Landfast ice, sea ice fastened to coastal land or ice shelves, generally undergoes distinctive thermodynamic growth and less active dynamic deformation due to its prolonged attachment to the land, resulting in a thicker and smoother surface compared to drifting pack ice. In 2019, large landfast ice floes were detached from the Ronne Ice Shelf, and the broken pieces started to drift into the Weddell Sea. This study employs a random forest (RF) machine learning model to detect these ex-fast ice floes using six key variables from the ICESat-2 ATL10 sea ice freeboard product: freeboard, Gaussian width of photon height distribution, standard deviation of freeboard, floe length, modal freeboard, and sea ice concentration. The RF model achieves an overall accuracy of 99 % in detecting ex-fast ice, effectively capturing the drift, freeboard distribution, and size distribution of ex-fast ice floes across the western Weddell Sea in 2019. Among six variables, freeboard, standard deviation of freeboard, and Gaussian width of photon height distribution contribute over 94 % to the model performance. Furthermore, the detection of ex-fast ice improves the quantification of sea ice topographical features derived from ICESat-2, including modal freeboard, ridge fraction, and surface roughness. This study highlights the effectiveness of discriminating heterogeneous ex-fast ice from typical pack ice to enhance sea ice measurements using ICESat-2 satellite altimeter data.