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

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).

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.

Described fully in (https://doi.org/10.21203/rs.3.rs-636839/v1 holder). Data The main CEL method, and a subsidiary Coastal Exposure Index or CEI (both described below), are based on daily sea-ice concentration products for the period 1979 through 2020. These products are derived from the multi-satellite passive-microwave brightness temperature time series using the NASA Team algorithm, mapped at 25 km x 25 km resolution and obtained from the NASA National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC). Both algorithms are designed to be adaptable for different resolution data. Complete coverage of the entire Antarctic coastal and sea-ice zones is obtained on a daily basis, except for 1979-July 1987 (once every two days). Missing single days during this period are interpolated from the adjoining day's sea-ice concentration values. Averages and climatologies are based on the period 1979-2020, unless otherwise stated. The continental land mask used (gsfc_25s.msk) is also obtained from NSIDC, and includes ice shelves (the seaward extremities of which are taken here to be coastline). Coastline grid points are defined from the continental land mask as any ocean grid point that has land/ice sheet adjacent to it. Analysis methods For this study, we developed two new but different algorithms for quantifying and monitoring Antarctic coastal exposure: the Coastal Exposure Index (CEI) and Coastal Exposure Length (CEL) method. The CEI technique is based on the detection of sea ice presence/absence radially out (northwards) from the coastline along each meridian (at one degree longitudinal spacing), following masking of the ice sheet. The CEI is simply defined as the number of longitudes with no sea ice (threshold set to less than 15% following convention) to the north of the continent, and hence runs from zero to 360. This methodology is trivial and code for this is not included. CEL is defined as the length (in kms) of the Antarctic coastal perimeter with no adjacent sea ice anywhere offshore (i.e. total exposure of the coast to the open Southern Ocean with no intervening sea ice), but excluding coastal polynyas. By this method, we use the land mask to determine if each coastal grid point has an immediately-adjacent ocean grid point that is ice-free (i.e. has a sea-ice concentration of less than 15%). If this criterion is met, then a nearest (adjoining) neighbour-testing technique is used to determine whether that ocean grid point is exposed in some way to the wider open ocean or is bound by neighbouring sea ice offshore. If any of the neighbouring grid points are classified as “exposed”, or if the total area of neighbouring ice-free grid points exceeds an arbitrary cut-off of 500,000 km2, then that coastal grid point is classified as “exposed”. Otherwise, the grid point and all sea-ice-free neighbouring grid points are deemed to be bounded by sea ice and are classified as a coastal polynya. The length of individual exposed coastal grid points is estimated by taking the square root of the respective pixel area. The length of coastal exposure, either regionally or net circum-Antarctic, is then simply the sum of the length of exposed coastal grid points. The IDL code used for calculating CEL is included here.

A Langrangian free drift model is developed, including a term for geostrophic currents that reproduces the 13 h period signature in the ice motion observed in the data (CLSC_WIIOS_2017; parent data). The calibrated model is shown to provide accurate predictions of the ice drift for up to 2 days, and the calibrated parameters provide estimates of wind and ocean drag for pancake floes under storm conditions. Model setup is described in "Drift of pancake ice floes in the winter Antarctic marginal ice zone during polar cyclones", Alberello et. al [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JC015418; pre-print https://arxiv.org/pdf/1906.10839.pdf]. The dataset includes model data. Six model outputs are included. (i) "full_t00" includes the full 10 days simulation, with all the forcing switched on (ii) "noge_t00" includes the full 10 days simulation, but the geostrophic current is suppressed (iii) "full_t25_noup" includes the simulation with start at 2.5 days, all the forcing switched on, no update of the drag coefficients (iv) "full_t25_newn" includes the simulation with start at 2.5 days, all the forcing switched on, the drag coefficients are recalibrated (v) "full_t50_noup" includes the simulation with start at 5 days, all the forcing switched on, no update of the drag coefficients (vi) "full_t50_newn" includes the simulation with start at 5 days, all the forcing switched on, the drag coefficients are recalibrated In each file: - rho_a the air density (1.3 kg/m3) - rho_w the water density (1028 kg/m3) - rho_i the ice density (910kg/m3) - C_w the water drag coefficient (calibrated) - C_a the air drag coefficient (calibrated) - turn the turning angle (25 degrees) - Nansen the Nansen number evaluated using C_a and C_w - aalpha a model parameter (proportional to air and ice parameters) - abeta a model parameter (proportional to water and ice parameters) - ag amplitude of the geostrophic current (U_g=0.125m/s) - tg initial phase of the geostrophic current (in radians) - to start time (in matlab format, use "datestr(to)" ), after which model resolution is 60 seconds - wo components of wind in the East and North direction (m/s) - wi components of wind in the East and North direction (m/s) - uo components of modelled ice drift speed in the East and North direction (m/s) - lo longitude and latitude of modelled ice position (degrees) - xo position of modelled ice in the East and North direction (m), given with respect to the initial position (0,0) - wco components in the East and North direction of geostrophic current (m/s)

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.

This data set provides the organochlorine content found in four sea-ice samples collected in the vicinity of Davis station over a three week period in 2014/15. Sea-ice is thought to serve as a reservoir for organochlorine pesticides during the winter. The aim of the study was to investigate the movement of organochlorine pesticides in the seasonal sea-ice during ice melt. A custom made, closed-system, ice melting unit, coupled to an in-situ water filter, was implemented for sampling. Minimal ice-melt or change in organchlorine content was found over the three week period. Changes were attributed to high ventilation of the sea-ice surface caused by high wind speeds found in the Antarctic compared to the Arctic. 4 sea-ice samples were collected in the vicinity of Davis station and contaminant profiles extracted and analysed. Caution should be taken in interpretation of data as the ice/water extraction unit failed during operation.

Metadata record for data from ASAC Project 2500 See the link below for public details on this project. Public Weekly fast-ice and snow thicknesses from an ongoing long-term time-series together with meteorological data will be used to analyse ice-atmosphere interactions. Interannual changes will be related to climate effects. Various sites at each location will be sampled to resolve the influence of oceanic forcing on the fast-ice growth. Project objectives: Landfast sea ice (fast ice) forms on the near-coastal ocean off each of the three Australian Antarctic stations each autumn. At Mawson and Davis stations this ice cover is generally stable, increasing in thickness throughout the winter to reach its maximum thickness in October or November before decaying and eventually breaking out in late spring or summer [Heil and Allison, 2002a]. At Casey, the third Australian station, the fast-ice cover is very unstable and not suitable for the study proposed here. The fast ice at the proposed measuring sites is stationary all through the austral winter. There is no contribution due to mechanical processes (rafting or ridging) on the thickness evolution of the fast ice at the measuring sites [Heil, 2001]. Its growth and decay, and the annual maximum thickness depend primarily on thermodynamic processes [Heil et al., 1996], which are forced by energy and moisture exchanges at the atmosphere-ice interface, the thickness of the snow cover, and the thermal energy supplied to the underside of the ice from the ocean. Starting in the mid 1950s measurements of the fast-ice thickness and snow cover are available for individual years at Mawson and Davis stations. After quality control the combined record for Mawson includes data from 27 seasons; the Davis record includes 20 seasons [Heil and Allison, 2002a]. However, significant gaps exist in these historic records. The scientific value of a continuous record of fast-ice thickness as a climatic indicator has been recognised and as a consequence the fast-ice and snow measurements at Davis and Mawson have been accepted into the State of the Environment (SOE) reporting scheme by the Australian Antarctic Division. Data from ANARE fast-ice measurements have been included in scientific research (e.g., Mellor [1960], Allison [1981], Heil et al. [1996], or Heil and Allison [2002a]). For example, Heil et al. [1996] designed an inverse 1-dimensional thermodynamic sea-ice model and used historic fast-ice data from Mawson together with meteorological observations to derive the seasonal and interannual variability of the oceanic heat flux at the underside of the fast ice. They showed that the interannual variability identified from the fast-ice data was in agreement with changes in the water-mass properties observed upstream of the fast-ice site. Using the historic data together with data from ongoing measurements this project aims to quantify the local-scale interactions between atmosphere and fast ice, to derive the relative impact of oceanic forcing on the fast-ice evolution, to estimate the small-scale spatial variability of the fast-ice growth, and to explore the connection between fast-ice changes and climate change. In particular we aim: - to extend previous analysis from records of fast-ice observations for Mawson and Davis stations; - to exactly determine the growth-melt cycle of East Antarctic fast ice and its modifications due to changing environmental conditions; - to derive the statistical variability of the fast-ice evolution relative to atmospheric and oceanic forcing; - to evaluate the suitability of fast ice as indicator of changes in the Antarctic environment; - to determine the spatial coherence of fast-ice properties. Contribution of this research to achieving the relevant milestones contained in the Strategic Plan: - Contributions to Key Scientific Output 3: This research aims to derive an assessment of the links between fast-ice variability and Southern Hemisphere environmental conditions from in-situ observations. The annual maximum ice thickness, and the date at which this maximum thickness is reached, reflect the integrated conditions of the local atmospheric and oceanic parameters [Heil, in prep.]. The fast-ice measurements together with concurrent meteorological (and oceanic) observations will allow us to study the direct links of variability in the sea-ice thermodynamics to changes in the Southern Hemisphere atmospheric conditions ("weather" in KSO 3.1). This knowledge will aid our understanding of the interannual and long-term variability of the drifting sea ice, as it will allow us to separate thermodynamic effects from dynamic effects [Heil et al., 1998]. Research outcomes from this study will aid the parameterisation of thermodynamic sea-ice processes in coupled climate models, and will provide an outlook towards statistical parameterisation of fast-ice characteristics within numerical models. - Contributions to Key Scientific Output 4: Using historic data and ongoing measurements this project seeks to build an accurate and ongoing record of measurements of fast-ice and snow properties for the monitoring and detection of change in Antarctic and Southern Ocean climate. Changes identified in the fast-ice thickness or in the occurrence of the annual maximum ice thickness are due to changes in either oceanic or atmospheric heat and/or moisture transfer. Using fast-ice measurements from locations around the Antarctic continent in combination with large-scale atmospheric (and oceanic) data the external impact on the sea ice can be extrapolated. Historic climatologies of interannual variability will be updated and extended. These climatologies will be available to expedition operations, scientific research, etc. Assessment basis: * Completion of field work/primary scientific activity: The requirements of data collection for this project are in line with indicator No. 43 "Fast ice thickness at Davis and Mawson" of the State of the Environment (SOE) reporting scheme. Weekly measurements of fast-ice and snow thicknesses are required for the SOE scheme as well as for this project. Additional data on the freeboard of the ice are easily and quickly obtained during the standard measurements [Heil and Allison, 2002b]. It is worthwhile to emphasise the requirement of a long-term commitment for the field measurements in order to obtain meaningful and statistically significant records of fast-ice observations. * Completion of analysis: The evaluation of individual growth-decay seasons will be undertaken once all fast-ice data as well as all required auxiliary data (mainly meteorological measurements) are available to the CI. Where available, opportunistic oceanographic data will be acquired as part of related research projects. Analysis to assess the interaction between fast ice, atmosphere and ocean will be carried out for each growth-decay season. This will include numerical modelling of the thermodynamic processes in fast-ice growth and decay. For years, when measurements of all external forcing fields (oceanic and atmospheric) have been collected, the parameterisations of the thermodynamic model can be evaluated by comparing the model results with the observed fast-ice thickness and growth rates. Following Heil et al. [1996] the thermodynamic model can be reconfigured for use in the inverse mode, using atmospheric and fast-ice data to calculate the oceanic heat flux at the underside of the ice. Long-term records of changes in the oceanic heat flux are not available and this inverse method (driven with data derived from meteorological and fast-ice measurements) will be able to contribute to our understanding of coastal oceanography by using several measuring sites within a small area. Analysis of the interannual variability of the fast ice and its response to changing environmental conditions will be carried out on the long-term data record. The data will be analysed for long-term signals, and will be evaluated for their statistical significance. * Publication of results: Scientific findings will be written up and submitted for publication as they arise. Publications in high-impact international journals are expected about every 2 years.

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.

Two Waves In Ice Observation Systems (Kohout, Alison L., Bill Penrose, Scott Penrose, and Michael J M Williams. 2015. “A Device for Measuring Wave-Induced Motion of Ice Floes in the Antarctic Marginal Ice Zone.” Annals of Glaciology 56 (69): 415–24. doi:10.3189/2015AoG69A600) were deployed about 1.5 km apart on ice floes close to latitude 62.8 S and longitude 29.8 E on 4th July 2017 (NYU1 and NYU2). The region where the instruments were deployed (Antarctic Marginal Ice Zone) consisted of first-year ice on average 40 – 60 cm thick. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle. NYU 1 was deployed on a rectangular ice floe of length 8 m and width 3 m, with a thickness of about 40 – 50 cm. NYU 2 was deployed on a triangular ice floe of length 4 m and thickness 40 cm. The temporal resolution is variability (every 15 minutes to 2 hourly). The survival of the sensors depended on staying fixed to the floe and the battery life. On 12th July, the sampling rate of NYU 2 was reduced from 15 minutes to 2 hourly to extend the battery life. On 13th July, NYU 1 overheated and the battery dropped below the operating voltage. NYU 2 continued to send back data for another six days, but then stopped sending data for an unknown reason on 19th July. Records can support 1. the assessment of metocean conditions in the Southern Oceans; and 2. calibration and validation of wave and global circulation models.