From the abstract of the referenced paper: Satellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide-ranging meso- and top-predator species: Adelie, Pygoscelis adeliae and emperor, Aptenodytes forsteri penguins, light-mantled albatross, Phoebetria palpebrata, Antarctic fur seals, Arctocephalus gazella, southern elephant seals, Mirounga leonina, and Weddell seals, Leptonychotes weddellii. The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea-ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. The results emphasize the importance of on-shore and sea-ice processes to Antarctic marine ecosystems. Our study provides ocean-basin-scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts. The data files provided here comprise the model predictions of the preferred habitat for each of the six species listed above, as well as the overlap results obtained by combining these six sets of results. See the paper for methods used to generate the model predictions and to combine the individual species results. File names for individual species are of the form results_SPP_TYPE.asc, where SPP is one of "afs" (Antarctic fur seal), "ap" (Adelie penguin), "ep" (emperor penguin), "lma" (light-mantled albatross), "ses" (southern elephant seal), or "ws" (Weddell seal. TYPE is either "mean" (mean estimate of habitat preference) or "iqr" (inter-quartile range of uncertainty in the estimate; see paper for details). Data values for individual species results are percentiles of the study area, so that values of 90% or higher are pixels corresponding to the most important 10% of habitat for that species, values of 80% or greater are the top 20% of habitat, and so on. The overlap results files are named overlay_results_mean.asc and overlay_results_iqr.asc. Values in these files represent the average of the top four individual species results in a given pixel (see paper for details).

This dataset comprises high spatial- and temporal-resolution maps of coastal landfast sea ice (fast ice) distribution in the vicinity of the Cape Darnley Polynya in East Antarctica, in the June-November (winter-spring) periods of 2008 and 2009. The maps were derived from cross-correlation of pairs of spatially-overlapping Envisat Advanced Synthetic Aperture Radar (ASAR) images, using a modified version of the IMCORR algorithm to determine vectors of sea-ice motion (as described in Giles et al., 2011). Fast ice is then distinguished from moving pack ice by the fact that it is stationary. The raw ASAR WSM data (swath width 500 km) were processed using ENVI image processing software to produce geo-referenced images with a 75m pixel size. Use of SAR data ensures coverage uninterrupted by cloud cover or polar darkness. Image pairs were chosen with a time separation between 2 and 21 days. IMCORR processing of the image pairs for mapping fast ice follows Giles et al (2011) – using a reference tile size of 32x32 pixels and a search tile size of 64 x 64 pixels. A land mask was applied to avoid contamination from matches on stationary features over the continental ice sheet. The grid spacing was set to 16 x 16 pixels, so the images were over-sampled by a factor of 2 to provide a more dense set of results. Stationary fast ice vectors were chosen from the IMCORR results using a combination of the cluster search technique and a variation of the z-axis threshold technique as detailed in Giles et al (2011). The cluster search technique was applied to the IMCORR results from each image pair to derive the initial set of valid vectors – this set could contain both stationary fast ice vectors and non-stationary pack ice vectors. Due to registration errors in the image pairs, the stationary vectors will not necessarily be centred around zero, so using a simple window around the zero offset mark to differentiate the fast ice vectors was not possible. To select the stationary vectors, a 2D histogram was constructed from the X-Y vector displacements, and a 2D Gaussian was fitted to this histogram. The fast ice vectors will dominate because of the large image pair time separation and small search tile size, so the Gaussian peak should correspond to the centre of the stationary fast ice vectors. All vectors that are within 5 standard deviations of the Gaussian peak are tagged as valid fast ice vectors. This is a minor modification to the method of Giles et al (2011), who used a simple threshold cut on the z-axis of the 2D histogram to define the fast ice vectors. Data format – one fully annotated (self-describing) netCDF file per image pair containing latitude/longitude coordinates of the stationary fast ice vectors. This technique and dataset complement a lower resolution but longer-term dataset (2000-2014) derived from satellite MODIS visible and thermal infrared data. (AAS_4116_Fraser_fastice_mawson_capedarnley).

Metadata record for data from ASAC Project 1060 See the link below for public details on this project. Taken from the referenced publications: Sea ice exhibits a marked transition in its fluid transport properties at a critical brine volume fraction Pc of about 5 percent, or temperature Tc of about -5 degrees Celsius for salinity of 5 parts per thousand. For temperatures warmer than Tc brine carrying heat and nutrients can move through the ice, whereas for colder temperatures the ice is impermeable. This transition plays a key role in the geophysics, biology, and remote sensing of sea ice. Percolation theory can be used to understand this critical behaviour or transport in sea ice. The similarity of sea ice microstructure to compressed powders is used to theoretically predict Pc of about 5 percent. The snow cover on Antarctic sea ice often depresses the ice below sea level, allowing brine or seawater to infiltrate, or flood the snowpack. This significantly reduces the thermal insulation properties of the snow cover, and increases the ocean/atmosphere heat flux. The subsequent refreezing of this saturated snow or slush layer, to form snow-ice, can account for a significant percentage of the total ice mass in some regions. The extent of saturated snow cannot presently be estimated from satellite remote-sensing data and, because it is often hidden by a layer of dry snow, cannot be estimated from visual observations. Here, we use non-parametric statistics to combine sea-ice and snow thickness data from drillhole measurements with routine visual observations of snow and ice characteristics to estimate the extent of brine-infiltrated snow. During a field experiment in July 1994, while the R.V. Nathaniel B. Palmer was moored to a drifting ice floe in the Weddell Sea, Antarctica, data were collected on the sea-ice and snow characteristics. We report on the evolution of ice which grew in a newly opened lead. As expected with the cold atmospheric conditions, congelation ice initially formed in the lead. Subsequent snow accumulation and large ocean heat fluxes resulted in melt at the base of the ice, and enhanced flooding of the snow on ice surface. This flooded snow subsequently froze, and, five days after the lead opened, all the congelation ice had melted and twenty-six centimetres of snow ice had formed. We use measured sea-ice and snow salinities, thickness and oxygen isotope values of the newly formed lead ice to calculate the salt flux to the ocean. Although there was a salt flux to the ocean as the ice initially grew, we calculate a small net fresh-water input to the upper ocean by the end of the 5 day period. Similar processes of basal melt and surface snow-ice formation also occurred on the surrounding, thicker sea ice. Oceanographic studies in this region of the Weddell Sea have shown that salt rejection by sea-ice formation may enhance the ocean vertical thermohaline circulation and release heat from the deeper ocean to melt the ice cover. This type of deep convection is thought to initiate the Weddell polynya, which was observed only during the 1970s. Our results, which show than an ice cover can form with no salt input to the ocean, provide a mechanism which may help explain the more recent absence of the Weddell polynya.

More than 50 scientists from eight countries conducted the Sea Ice Physics and Ecosystem eXperiment 2012 (SIPEX-2). The 2012 voyage built on information and observations collected in 2007, by re-visiting the study area at about 100-120 degrees East. This was the culmination of years of preparation for the Australian Antarctic Division and, more specifically, the ACE CRC sea-ice group who lead this international, multi-disciplinary, sea ice voyage to East Antarctica. Work began at the sea-ice edge and penetrated the pack ice towards the coastal land-fast ice. The purpose of SIPEX-2 was to investigate relationships between the physical sea-ice environment, marine biogeochemistry and the structure of Southern Ocean ecosystems. While the scientists and crew did not set foot on Antarctic terra firma, a number of multi-day research stations were set up on suitable sea ice floes, and a range of novel and state-of-the-art instruments were used. These included: A Remotely Operated Vehicle (ROV) to observe and film (with an on-board video camera) krill, and to quantify the distribution and amount of sea ice algae associated with ice floes. An Autonomous Underwater Vehicle (AUV) to study the three-dimensional under-ice topography of ice floes. Helicopter-borne instruments to measure snow and ice thickness, floe size and sea ice type. Instruments included a scanning laser altimeter, infrared radiometer, microwave radiometer, camera and GPS. Sea ice accelerometer buoys to measure sea ice wave interaction and its effect on floe-size distribution. Customised pumping systems and light-traps to catch krill from below the ice and on the sea floor. Available at the provided URL in this record, is a link to a file containing the locations of all ice stations from this voyage.