Representation of sea ice regimes in the Western Ross Sea, Antarctica, based on satellite imagery and AMPS wind data

Abstract

Sea ice drift data at high spatial resolution and surface wind model output are used to explore atmosphere-sea ice interactions in the Western Ross Sea including the three main polynyas areas; McMurdo Sound polynya (MSP), Terra Nova Bay polynya (TNBP), and the Ross Sea polynya (RSP). This study quantifies the relationship between the winds and sea ice drift and observes the average and annual anomalies across the region. Sea ice drift velocities are based on high-resolution (150 m) Advanced Synthetic Aperture Radar (ASAR) images from Envisat for winters between 2002 and 2012. Sea ice motion vectors were first correlated with the corresponding Antarctic Mesoscale Prediction System (AMPS) surface wind velocities, and the sensitivity of the spatial correlations and residuals were examined. Four drift parameters were selected (mean drift, the correlation between drift and wind, drift to wind scaling factor, and the directional drift constancy) to perform an unsupervised k-means classification to automatically distinguish six zones of distinctive sea ice characteristics solely based on ice drift and wind information. Results indicate a heterogeneous pattern of sea ice movement at a rate ranging from 0.41 to 2.24% of the wind speed in different areas. We also find that the directional constancy of sea ice drift is closely related to the wind fields. Sea ice drift and wind velocities display the highest correlation in free-drift areas (R = 0.70), followed by deformational drift zones (R = 0.54), and more random drift areas (R = 0.28). The classification illustrates the significance of localized wind-driven sea ice drift in this coastal area resulting in zones of convergence, shear, and free drift. The results also indicate that the most persistent patterns of sea ice motion are near the RSP and TNBP areas, both being driven by strong localized winds. Our findings identify that large-scale sea ice motion is predominantly wind-driven over much of the study area while ocean currents play only a minor role.

The Role of the Pacific‐Antarctic Ridge in Establishing the Northward Extent of Antarctic Sea‐Ice

“…The PAR is a divergent tectonic plate boundary located on the seafloor extending from approximately 56°S to Antarctica that separates the Pacific and Antarctic plates (Figure 2) and represents an important hot spot of EKE (Falco & Zambianchi, 2011). The observed sea‐ice protrusion has been previously linked to atmospheric factors, such as the Amundsen Sea Low (e.g., Fogt and Scambos, 2013, 2014; Fogt and Stammerjohn, 2015; Scambos and Stammerjohn, 2018, 2019; Stammerjohn, 2016; Stammerjohn and Scambos, 2017; Turner et al., 2009, 2016), the Southern Annular Mode (e.g., Marshall, 2003; Son et al., 2010) and the katabatic wind regimes (e.g., Farooq et al., 2023) that can explain part of its seasonal and interannual variability. Conversely, few studies have analyzed if/how local bathymetry exerts a control on this protrusion and what allows its recurrent formation, besides the seasonal and interannual variability driven by oceanic and atmospheric forcing (Nghiem et al., 2016).…”

Unsupervised classification of the Antarctic marginal ice zone

“…The definition utilises an unsupervised machine learning algorithm (k-means clustering) to classify the data into distinct sea ice regions. Unsupervised methods have been used to separate different sea ice types (e.g., Massom et al, 1999, used satellite data), and the k-means algorithm has been shown to be appropriate for climate science applications (Wilks, 2011) including sea ice data retrieved from satellites (Farooq et al, 2023) and models (LIM3 Moreno-Chamarro et al, 2020). We specify the number of clusters to k-means based on a wave heuristic, such that the outer cluster is the regularly wave-affected sea ice region, i.e., the description of the MIZ, without explicitly including waves properties in the clustered dataset.…”