Three Trident Sensors Helix beacons (Unit 1,2,3) were deployed about on ice floes close to latitude 62.8 S and longitude 29.8 E on 4th July 2017 to measure sea ice drift. The region where the instruments were deployed (Antarctic Marginal Ice Zone) consisted of first-year ice on average ~50 cm thick. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle on floes ~5 m in diameter. The temporal resolution is 4 hours. The survival of the sensors depended on staying fixed to the floe and the battery life. Unit 1 provided GPS location from the 5th July 2017 to 1st December 2017, started at 62.84 S and 30.20 E and finished at 61.55 S and 55.99 E. Unit 2 provided GPS location from the 5th July 2017 to 3rd August 2017, started at 62.83 S and 30.20 E and finished at 62.36 S and 31.57 E. Unit 3 provided GPS location from the 5th July 2017 to 15st August December 2017, started at 62.59 S and 29.98 E and finished at 61.16 S and 35.60 E. In the .xlsx submission sheet 1 refers to Unit 1, sheet 2 to Unit 2, and sheet 3 to Unit 3. First column is the Unit Identifier (1,2,3) Second column is the date in the format day/month/year Third column is the UTC time in the format hh:mm:ss Fourth column is the latitude in degrees and decimals, the negative refers to South Fifth column is the longitude in degrees and decimals, the positive refers to East

The ASPeCt - Bio dataset is a compilation of currently available sea ice chlorophyll a (chl-a) data from pack ice (i.e., excluding fast ice) cores collected during 32 cruises to the Southern Ocean sea ice zone from 1983 to 2008 (Table S1). Data come from peer-reviewed publications, cruise reports, data repositories and direct contributions by field-research teams. During all cruises the chl-a concentration (in micrograms per litre) was measured from melted ice core sections, using standard procedures, e.g., by melting the ice at less than 5 degrees C in the dark; filtering samples onto glassfibre filters; and fluorometric analysis according to standard protocols [Holm-Hansen et al., 1965; Evans et al., 1987]. Ice samples were melted either directly or in filtered sea water, which does not yield significant differences in chl-a concentration [Dieckmann et al., 1998]. The dataset consists of 1300 geo-referenced ice cores, consisting of 8247 individual ice core sections, and including 990 vertical profiles with a minimum of three sections. An updated dataset was provided in 2017-12-15, which included a compilation Net CDF file.

Chlorophyll data was used to measure growth rates of sea ice algae in CO2 incubations. Sea ice brine microalgae was collected from sackholes. Replicate samples were incubated in ambient air (~0.04% CO2), 0.1% CO2, 1.0% CO2 and 2.0% CO2 concentrations. AT the end of the incubations the 50 ml samples were filtered through a 25 mm GF/F filter using vacuum filtration. The filters were placed in 15 ml plastic falcon tubes containing 10 ml of methanol, covered in aluminium foil and kept in the dark at 4 degrees C for 12 hours. Chl a concentration was measured using a 10AU Turner fluorometer following the acidification method of Strickland and Parsons (1972). Data in spread sheet shows the extracted chl + phaeophytin, phaeophytin and chlorophyll concentrations (micro grams l-1) for each of the three experiments. Data were collected at SIPEX Ice Stations 1-8 and SIPEX CTD stations 2-5

A 600KHz Teledyne RDI Workhorse Sentinel ADCP was deployed through a 10inch auger hole, flush with the base of the ice, looking downwards. At ice stations 2, 3, and 4 the deployment locations was Ridge site 1, the ridge site closest to the ship. At ice station 7 there were 4 different deployment locations: - Transducer Hole A, by active ridge on 6th October 2012; - Trace Metal / Bio Site; - 100m Core site of ice-physics transect; - Transducer Hole A, re-drilled on 7th October 2012. Length of deployment varies from stations to station and was limited by AUV operations, when our ADCP was switched off. Files contain the data collected in raw format. This format can be read by Teledyne WinSC software. Data files are stored in folders by ice station (see below).

Observations of the sea ice cover at Wilkes base in Autumn-Winter 1963. Includes water temperature, air temperature, wind speed and direction, cloud cover, relative humidity, and general notes. These documents have been archived at the Australian Antarctic Division.

A summary of landfast sea ice coverage and the changes in the distance between the penguin colony at Point Geologie and the nearest span of open water on the Adelie Land coast in East Antarctica. The data were derived from cloud-free NOAA Advanced Very High Resolution Radiometer (AVHRR) data acquired between 1-Jan-1992 and 31-Dec-1999. The areal extent and variability of fast ice along the Adelie Land coast were mapped using time series of NOAA AVHRR visible and thermal infrared (TIR) satellite images collected at Casey Station (66.28 degrees S, 110.53 degrees E). The AVHRR sensor is a 5-channel scanning radiometer with a best ground resolution of 1.1 km at nadir (Cracknell 1997, Kidwell 1997). The period covered began in 1992 due to a lack of sufficient AVHRR scans of the region of interest prior to this date and ended in 1999 (work is underway to extend the analysis forward in time). While cloud cover is a limiting factor for visible-TIR data, enough data passes were acquired to provide sufficient cloud-free images to resolve synoptic-scale formation and break-up events. Of 10,297 AVHRR images processed, 881 were selected for fast ice analysis, these being the best for each clear (cloud-free) day. The aim was to analyse as many cloud-free images as possible to resolve synoptic-scale variability in fast ice distribution. In addition, a smaller set of cloud-free images were obtained from the Arctic and Antarctic Research Center (AARC) at Scripps Institution of Oceanography, comprising 227 Defense Meteorological Satellite Program (DMSP) Operational Linescan Imager (OLS) images (2.7 km resolution) and 94 NOAA AVHRR images at 4 km resolution. The analysis also included 2 images (spatial resolution 140 m) from the US Argon surveillance satellite programme, originally acquired in 1963 and obtained from the USGS EROS Data Center (available at: edcsns17.cr.usgs.gov/EarthExplorer/). Initial image processing was carried out using the Common AVHRR Processing System (CAPS) (Hill 2000). This initially produces 3 brightness temperature (TB) bands (AVHRR channels 3 to 5) to create an Ice Surface Temperature (IST) map (after Key 2002) and to enable cloud clearing (after Key 2002 and Williams et al. 2002). Fast ice area was then calculated from these data through a multi-step process involving user intervention. The first step involved correcting for anomalously warm pixels at the coast due to adiabatic warming by seaward-flowing katabatic winds. This was achieved by interpolating IST values to fast ice at a distance of 15 pixels to the North/South and East/ West. The coastline for ice sheet (land) masking was obtained from Lorenzin (2000). Step 2 involved detecting open water and thin sea ice areas by their thermal signatures. Following this, old ice (as opposed to newly-formed ice) was identified using 2 rules: the difference between the IST and TB (band 4, 10.3 to 11.3 microns) for a given pixel is plus or minus 1 K and the IST is less than 250 K. The final step, i.e. determination of the fast ice area, initially applied a Sobel edge-detection algorithm (Gonzalez and Woods 1992) to identify all pixels adjacent to the coast. A segmentation algorithm then assigned a unique value to each old ice area. Finally, all pixels adjacent to the coast were examined using both the segmented and edge-detected images. If a pixel had a value (i.e. it was segmented old ice), then this segment was assumed to be attached to the coast. This segment's value was noted and every pixel with the same value was classified as fast ice. The area was then the product of the number of fast ice pixels and the resolution of each pixel. A number of factors affect the accuracy of this technique. Poorly navigated images and large sensor scan angles detrimentally impact image segmentation, and every effort was taken to circumvent this. Moreover, sub-pixel scale clouds and leads remain unresolved and, together with water vapour from leads and polynyas, can contaminate the TB. In spite of these potential shortcomings, the algorithm gives reasonable and consistent results. The accuracy of the AVHRR-derived fast ice extent retrievals was tested by comparison with near- contemporary results from higher resolution satellite microwave data, i.e. from the Radarsat-1 ScanSAR (spatial resolution 100 m over a 500 km swath) obtained from the Alaska Satellite Facility. The latter were derived from a 'snapshot' study of East Antarctic fast ice by Giles et al. (2008) using 4 SAR images averaged over the period 2 to 18 November 1997. This gave an areal extent of approximately 24,700 km2. The comparative AVHRR-derived extent was approximately 22,240 km2 (average for 3 to 14 November 1997). This is approximately 10% less than the SAR estimate, although the estimates (images) were not exactly contemporary. Time series of ScanSAR images, in combination with bathymetric data derived from Porter-Smith (2003), were also used to determine the distribution of grounded icebergs. At the 5.3 GHz frequency (? = 5.6 cm) of the ScanSAR, icebergs can be resolved as high backscatter (bright) targets that are, in general, readily distinguishable from sea ice under cold conditions (Willis et al. 1996). In addition, an estimate was made from the AVHRR derived fast ice extent product of the direct-path distance between the colony at Point Geologie and the nearest open water or thin ice. This represented the shortest distance that the penguins would have to travel across consolidated fast ice in order to reach foraging grounds. A caveat is that small leads and breaks in the fast ice remain unresolved in this satellite analysis, but may be used by the penguins. We examine possible relationships between variability in fast ice extent and the extent and characteristics of the surrounding pack ice (including the Mertz Glacier polynya to the immediate east) using both AVHRR data and daily sea ice concentration data from the DMSP Special Sensor Microwave/Imager (SSM/I) for the sector 135 to 145 degrees E. The latter were obtained from the US National Snow and Ice Data Center for the period 1992 to 1999 inclusive (Comiso 1995, 2002). The effect of variable atmospheric forcing on fast ice variability was determined using meteorological data from the French coastal station Dumont d'Urville (66.66 degrees S, 140.02 degrees E, WMO #89642, elevation 43 m above mean sea level), obtained from the SCAR READER project ( www.antarctica.ac.uk/met/READER/). Synoptic- scale circulation patterns were examined using analyses from the Australian Bureau of Meteorology Global Assimilation and Prediction System, or GASP (Seaman et al. 1995).

This dataset contains data relating to an experimental method in which sea-ice samples were measured in an S-band microwave waveguide. This was conducted as a part of the 2012 SIPEX 2 (Sea Ice Physics and Ecosystems EXperiment) marine science voyage. A specially designed waveguide apparatus was connected to an Agilent FieldFox Portable Network Analyzer. Small parallelopipeds (7 cm X 3 cm X 1.9 cm) of sea ice were cut with a hand saw in a specially designed jig which holds an initially cylindrical core. The samples were placed at the end of the waveguide, configured to measure the vertical component of the effective complex permittivity tensor, and microwaves of frequency 2.9 GHz were sent down the tube. The samples were sized precisely to fit snugly in the end of the waveguide in order to minimize spurious reflections. The FieldFox recorded the coefficients of the scattering matrix, from which the complex permittivity can be computed. Sample temperature was taken both before and immediately after insertion into the waveguide. In order to assess the presence of off-vertical components of the electromagnetic field and how they may affect the measurements, a second sample was prepared with an orthogonal orientation, adjacent to the first sample. The same microwave measurements were taken on the second sample, to be later correlated with those from the first sample. The samples were stored in the freezer for later crystallographic analysis, and subsequently melted for salinity measurements. Prior to melting the samples were measured using callipers to determine their dimensions precisely. Samples were measured along each face at their minimum and maximum point for their width in the direction of propagation. In most cases samples were measured in all dimensions for better error analysis. A thin vertical section, approximately 5mm thick, was taken from each microwave sample stored for analysis. These sections were placed between a pair of cross polarized plates and photographed. Photos of the crystallography cores can be found in the crystallography folder, in a sub folder titled microwave. Each photo also contains a tag indicating the core number, site taken, date, as well as a V or an H indicating whether the sample was used for measurement of the vertical (V) or off-vertical (H) response. The scattering parameters recorded by the Field Fox can be found in the Data folder. Each file is named according to the microwave core measurement it represents and whether the measurement was of the vertical (V) or off-vertical (H) response. Each contains a standard S11 scattering parameter, stored as a comma separated value (CSV) file. Raw data can be found in the raw folder, and data that has been processed for ease of Matlab import can be found in the Reformatted_for_matlab folder. This processing involves taking output data that by default has four entries in a single column vector and remapping the data to create a four column matrix, each with a single entry. Recorded values for each microwave sample can be found in the Master_Core_List.xls Excel spreadsheet, within the Microwave worksheet. This worksheet was generated directly from notebook data, and contains the date, core number, depth of interface between the two collected samples, the minimum, maximum, and average thickness along the axis of propagation, The recorded temperatures from before and after measurement, the salinity, and calculated brine volume fraction. Finally, the worksheet contains notes, and a column to indicate whether we believe this data is somehow bad. Measurement information for thicknesses along other axis than that of propagation can be found in notes, but this data may at some stage be incorporated into a separate column. Please see the notes section for reasons why a data point was determined invalid. Typically this was due to the corresponding sample breaking while cutting into the parallelepiped shape. Scans of the original notebooks containing measured salinity values, thicknesses, and temperatures from which the Permeability worksheet were created are provided in the notebooks directory.

Data were collected during deployments of an instrumented Remotely Operated Vehicle on 5 sampling days to determine sea ice physical properties and measure transmitted under-ice radiance spectra (combined with surface irradiance measurements) to estimate the spatial distribution and temporal development of ice algal biomass in land-fast sea ice. The ROV was instrumented with a navigation/positioning system (linked to surface GPS), upward-looking sonar and accurate depth sensor (Valeport 500 (to determine sea-ice draft)), and a upward-looking TriOS Ramses radiance sensor as well as several video-cameras collecting under-ice footage. Parallel measurements included surface irradiance measurements. A readme file in the download explains the folder structure of the dataset.

We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from.

Although the floating sea ice surrounding the Antarctic damps ocean waves, they may still be detected hundreds of kilometres from the ice edge. Over this distance the waves leave an imprint of broken ice, which is susceptible to winds, currents, and lateral melting. The important omission of wave-ice interactions in ice/ocean models is now being addressed, which has prompted campaigns for experimental data. These exciting developments must be matched by innovative modelling techniques to create a true representation of the phenomenon that will enhance forecasting capabilities. This metadata record details laboratory wave basin experiments that were conducted to determine: (i) the wave induced motion of an isolated wooden floe; (ii) the proportion of wave energy transmitted by an array of 40 floes; and (iii) the proportion of wave energy transmitted by an array of 80 floes. Monochromatic incident waves were used, with different wave periods and wave amplitudes. The dataset provides: (i) response amplitude operators for the rigid-body motions of the isolated floe; and (ii) transmission coefficients for the multiple-floe arrays, extracted from raw experimental data using spectral methods. The dataset also contains codes required to produce theoretical predictions for comparison with the experimental data. The models are based on linear potential flow theory. These data models were developed to be applicable to Southern Ocean conditions.