The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h.

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.

Snow on sea ice is a controlling factor for ocean-atmosphere heat flux and thus ice thickness growth, and surface albedo. Active and passive microwave remote sensing is the most promising way to estimate snow depths over large sea ice areas although improved validation is understood as a missing information to support further progress. However, severe limitations in the representative measurement of snow depth over sea ice persist, which exacerbates sea ice mass balance assessments as well as the indirect estimation of consolidated ice thickness from remotely sensed freeboard.We have designed and flown a snow radar in combination with an electromagnetic induction device for sea ice thickness. The goal was the simultaneous measurement of both the consolidated sea ice thickness and the snow depth on top as a tool to derive snow and ice statistics for satellite validation. The snow radar was integrated into an EM-bird and flown about 15 m above the surface by suspending the instrument from a helicopter. The combination of the applied technologies hasn’t been deployed in this configuration before. The helicopter flight speed was around 70 knots, resulting in a snow measurement about every four meters. The EM instrument can detect ice thickness at 0.1m accuracy, whereas the snow radar is designed to measure snow depth at 0.05m accuracy.Our field area was the land-fast sea ice and adjacent ice shelf in McMurdo Sound (Antarctica) in November 2021. During this time we found a relatively shallow but variable snow cover (up to about 0.3m) above sea ice of about 2m thickness. Deeper snow was only measured at the transition from the sea ice to the ice shelf, and on the ice shelf itself, where the maximum radar penetration in snow in ideal conditions is estimated to be around 2-3 meters.We present first results of snow cover statistics in comparison to ground validation and observed snow characteristics, and we compare these results to airphotos and optical satellite imagery. We show that the measurement set-up meets the requirements for level ice and rough fast ice with patchy but dry snow cover. The system still needs to be tested over pack ice with potentially more complex snow morphology.