This dataset contains ice motion observations made under the Australian Antarctic Program, Projects 4593 and 4506. Data was obtained using two Spotter wave buoys (Sofar Ocean Technologies), hereafter wave buoys, and two open-source ice motion loggers, hereafter ice buoys. Instruments were deployed on (land)fast ice on the eastern rim of the Amery Ice Shelf, Antarctica (69.2 degr. S, 76.3 degr. E), on 7 December 2019. After the break-up of the ice occurring at the start of January 2020, instrumentation started to drift with the ice. Last transmission recorded was on 10 March 2020. The wave buoys measure their 3-axis motion at 2.5 Hz through GPS and have an accuracy of approximately 2 cm for the recorded significant wave height. The ice buoys measure motion in 9-degrees-of-freedom at 10Hz using a VectorNAV VN-100 IMU, with an accuracy of O(mm) for short waves and O(cm) for long waves. Both instruments also record their geographical location through GPS. Full time series of their motion is processed on board and summaries are send through Iridium. For the ice buoy wave spectra were transmitted roughly every 3 hours. The transmission interval for the wave boys was variable, ranging from every half an hour to every 3 hours. Data transmitted by the wave buoys was either integral wave properties or the complete wave spectrum. In the dataset, WB and IB are abbreviations for wave buoy and ice buoy, respectively. This dataset includes all observations transmitted during the measurement campaign (WB1, WB2, IB1, IB2). E = wave energy spectrum (m2/s); f = wave frequency (Hz); a1, a2, b1, b2 = Fourier coefficients; Hs = significant wave height (m); Tp = peak period (s); Tm01 = mean period (s); Dir_peak/mean = peak and mean wave direction and 'spr' refers to spreading; volt = battery voltage (V). Time is in UTC, and in Matlab’s datenum format (i.e. the number of days since year 0000). The geographical coordinates ‘lat’ and ‘lon’ (latitude and longitude, respectively) are in degrees. Note, as the ice buoys transmit the GPS coordinates and wave data in separate data messages, for the ice buoys ‘time’ refers to the reference time of the wave properties Hs and Tp, whereas ‘GPStime’ refers to the reference time of the geographical coordinates (lat and lon). For the wave buoy, all data is transmitted at the same time.

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.

Raw GPS and ship motion data collected during the Antarctic Circumnavigation Expedition 2016/2017. Waves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. In Antarctic waters, the sea ice cover reflects a large proportion of the wave energy, creating a complicated sea state close to the ice edge. The remaining proportion of the wave energy penetrates deep into the ice-covered ocean and breaks the ice into relatively small floes. Then, the waves herd the floes and cause them to collide and raft. There is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment. By installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Dara were collected during the Antarctic Circmumnavigaion Expedition, which took place from Dec. 2016 to Mar. 2017. The instrumentation operated in any weather and visibility conditions, and at night, monitoring the ocean continuously over the entire Circumnavigation. Records can support 1. the assessment of metocean conditions in the Southern Oceans; and 2. calibration and validation of wave and global circulation models. Data - AAS_4434_ACE_GPS contains basic metereological conditions acquired form the ship’s meteo-station, gepgraphical coordinates (latitude, longitude and altitude) from the ship’s GPS and ship motion data from the ship’s Inertial Measurement Unit (IMU). These data are stored as time series with a sampling frequency of 1Hz.

A numerical model of ocean wave interactions with Antarctic sea ice cover, including: (i) attenuation of wave energy due to the ice cover (based on the empirical model of Meylan, Bennetts, Kohout, 2014, Geophys Res Lett, doi:10.1002/2014GL060809); and (ii) breakup of the ice cover into smaller floes due to strains imposed by wave motion (based on the theory of Williams et al, 2013, Ocean Model., doi:10.1016/j.ocemod.2013.05.010). The model is coded in FORTRAN90 for use as a module in a standalone version of the CICEv4.1 sea ice model (http://oceans11.lanl.gov/trac/CICE). It requires incident wave forcing to be specified at some constant latitude outside the ice cover, which can be user chosen or imported from data files (e.g. data given by Wavewatch III hindcasts, see http://doi.org/10.4225/08/523168703DCC5). Modifications to the existing CICE routines are given to allow integration of the broken floe sizes into its lateral melting scheme, and for incorporation of a floe bonding scheme. Bennetts, O'Farrell and Uotila (submitted) use the model to study the impact of wave-induced ice breakup on model predictions of the concentration and volume of Antarctic sea ice.

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.

Between 07:00 and 08:00 UTC on the 4th July 2017, the South African icebreaker S.A. Agulhas II entered the Antarctic MIZ (62 South and 30 East) during an explosive polar cyclone. A system of two GigE monochrome industrial CMOS cameras with a 2/3 inch sensor was installed on the icebreaker. The cameras provide a field of view of the ocean surface around the port side of the ship. Images were recorded with resolution 2448x2048 pixels and a sampling rate 2 Hz during daylight on the 4th July 2017. The wave acquisition stereo-camera system (WASS; https://www.dais.unive.it/wass/) is used to reconstruct the water surface elevation. Reconstructed surface elevations are given as .nc files (6). The file name is “wass__20170704_hhmm.nc” where hh and mm denote the hour and minute in UTC of the start of each acquisition. X_grid and Y_grid are the grid in x and y direction, resolution 1000mm or 1m. Fps is the acquisition frequency, resolution 2Hz. Time is a dummy variable, time is reconstructed from start time and fps. Z is the surface elevation in space and time, in mm. Missing values are "Nan". Other variables are WASS control variables. Further details on the measurements and use of the data can be found at Alberello et al. “An extreme wave field in the winter Antarctic marginal ice zone during an explosive polar cyclone”.

Reconstructed nonlinear surface from WAMOS (marine radar) data collected during the 3rd leg of Antarctic Circumnavigation Expedition, from the end of January to the end of March 2017. WAMOS data (AAS_4434_ACE_WAMOS) are processed with the Higher Order Spectral Method (HOSM) to provide the nonlinear surface elevation and the corresponding spectrum of waves during ACE. A Montecarlo approach is adopted to reproduce the natural variability of the sea state and gain reliable statistics of the underlying nonlinear surface elevation. Details on the method can be found on Toffoli, Alessandro, et al. "Evolution of weakly nonlinear random directional waves: laboratory experiments and numerical simulations." Journal of Fluid Mechanics 664 (2010): 313-336. File structure: Folder name corresponds to the time stamp of the input spectrum (yyyyMMddhhmmss) from AAS_4434_ACE_WAMOS. Each folder contains: 1. The surface elevation for 250 random realisations at 10 instant in times from initialisation saved every 5 dominant wave periods apart (0,5,10,15,…,50 Tp). The ten digits name is structured as 0000NRRttt where NRR is the number of the random realisation (from 1 to 250) and ttt denotes the time index (from 0 to 10). 2. NEW_SPECTRUM.DAT the 2D spectrum (64x64) as a columnar vector of the initial spectrum read from the AAS_4434_ACE_WAMOS. 3. INPUT_SPECTRUM.DAT the 2D spectrum (256x256) as a columnar vector of the initial spectrum for the HOSM. 4. WAVENUMBERSX.DAT and WAVENUMBERSY.DAT the wavenumber in x and y respectively 5. PP_INFO.DAT contains the peak period (Tp) in seconds 6. RUN_INFO.DAT contains the resolution in x of the WAMOS spectrum (64), the resolution in y of the WAMOS spectrum (64), the delta x for the surface elevation in m, the delta y for the surface elevation in m. Subsequent parameters are flags for the HOSM method. Waves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. In Antarctic waters, the sea ice cover reflects a large proportion of the wave energy, creating a complicated sea state close to the ice edge. The remaining proportion of the wave energy penetrates deep into the ice-covered ocean and breaks the ice into relatively small floes. Then, the waves herd the floes and cause them to collide and raft. There is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment.

Antarctic sea-ice is highly influenced by the dynamic nature of the Southern Ocean. Ocean waves can propagate from tens to hundreds of kilometres into sea-ice, leaving behind a wake of broken ice sheets. As global climate change intensifies, storm intensity will increase in the Southern Ocean. Increased storm intensity will bring stronger winds and bigger waves, which has the potential for waves to travel deeper into the ice pack and increase the likelihood that ice floes break apart. To enhance our understanding of this system, our aim during SIPEXII was to improve on the scarce Antarctic waves-in-ice dataset by collecting a set of wave observations in the MIZ. In order to achieve this, we designed and produced eight custom made wave sensors. The sensors were deployed in the Antarctic marginal ice zone along a transect line perpendicular to the ice edge and spread over approximately 200 km. Every three hours, the sensors simultaneously woke and recorded their location and a burst of wave acceleration data. Each sensor performed on-board data quality control and spectral analysis before returning the wave spectrum via satellite. The sensors were powered via lithium batteries and had enough battery power to last a minimum of 6 weeks. This project involved collaboration between the Australian Antarctic Division (AAD) and the NZ's National Institute for Water and Atmospheric Research (NIWA). The work was funded by a New Zealand Foundation of Research Science and Technology Postdoctoral award to A.L.K.; the Marsden Fund Council, administered by the Royal Society of New Zealand; NIWA, through core funding under the National Climate Centre Climate Systems programme; the Antarctic Climate and Ecosystems Cooperative Research Centre; and Australian Antarctic Science project 4073. Instruments were designed and built by Inprod PTY LTD. Below is a summary of the design and hardware: Accelerometer: Kistler ServoK-Beam accelerometer. Model 8330B3. IMU: Razor IMU (3 axis acceleration, 3 axis magnetometer and 3 axis gyro) ADC: TI ADS1247 Analog-to- Digital converter CPU (main): Arduino Mega 3.3V CPU (maths): BeagleBone from BeagleBoard.org who use Texas Instruments (TI) ARM processors GPS: Skytraq Venus634FLP Temperature readings: SHT15 from SparkFun Transmission: Iridium 9602 Battery: Lithium batteries (enough to survive a minimum of 6 weeks) Inner housing: Explorer 1908OE Outer housing: The case is fitted in a fork lift tyre ( .53 m diameter and .165 m height) with an inner tube to enable floating. Aerial housing: The aerial is housed in a plastic spherical container on top of a .5 m tube attached to the tyre. Feet: 3 screws stick out of the bottom to create friction with the ice. Onboard processing: Every 3 hours, the instruments wake and record wave accelerations for 35 mins. An initial low pass analogue filter is used. We over sample at 64 Hz and decimate down to 2 Hz. Downsampling from 64 Hz to 2 Hz is achieved through a multistage decimation of 8 followed by 4, to achieve a total decimation of 32. Prior to each downsampling stage, a second order lowpass Butterworth filter is applied to remove all components above the nyquist frequency. We first apply the Butterworth filter with a cut off of 1 Hz and sample at 8 Hz and secondly with a cut off of 0.5 Hz and sample at 2 Hz. A high-pass filter was then applied and the acceleration double-integrated to provide displacement. Welch's method, using a 10% cosine window and de-trending on four segments with 50% overlap, was applied to estimate the power spectral density. Sample frequency: 2 Hz Sample duration - raw: 2048 sec Sample duration - fft: 1280 sec No. of discrete bins of fft: 512 No. of segments: 4 Below is a detailed description of each line of the raw output. Header info L1: Longitude (decimal degrees) L2: File name of attachment emailed via Iridium L3: Temperature inside the box (degrees Celsius) L4: Sensor identification number L5: Time wave record starts (24 hr format HHMMSS) L6: Date of wave record (yyyy-mm-dd) L7: Current voltage L8: Elevation (cm) L9: Latitude (decimal degrees) Wave spectrum L10-L64: The power spectral density for wave period bins (secs) centred on [24.38 19.69 18.96 18.28 17.65 17.06 16.51 16.00 15.51 15.05 14.62 14.22 13.83 13.47 3.12 12.80 12.48 12.19 11.90 11.63 11.37 11.13 10.89 10.66 10.44 10.24 10.03 9.84 9.66 9.48 9.30 9.14 8.98 8.82 8.67 8.53 8.39 8.25 8.12 8.00 7.64 7.31 7.01 6.73 6.48 6.24 6.02 5.81 5.50 5.22 4.97 4.74 4.53 4.33 4.16] Spectral moments L65-L70: m-2 - m4 Quality control L71: mean roll (degrees) L72: mean pitch (degrees) L73: mean yaw (degrees) L74: wave direction (degrees) L75: directional spread (degrees) L76: ratio term to evaluate quality of wave direction approximation (should be close to 1) L77: standard deviation of acceleration (m/s2) L78: standard deviation of gyro x axis (radians/s) L79: standard deviation of gyro y axis (radians/s) L80: standard deviation of gyro z axis (radians/s) L81: standard deviation of yaw (radians) L82: Accelerometer quality flag. 0 = good, 1 = accelerometer bad, 2 = accelerometer and imu bad L83: IMU quality flag. 0 = good, 1 = pitch/roll bad, 2 = yaw bad, 3 = both bad L84: mean acceleration removed (m/s2) L85: no. of flat spots in raw acceleration data L86: the maximum number of consecutive flat spots L87: no. of spikes (data point greater than 6 standard deviations of data set) L88: the maximum number of consecutive spikes L89: Quality flag indicating whether the total power in the time domain and frequency domain are equal. 0 = difference less than 0.01, 1 = difference greater than 0.01. Deployment method: The Helicopter Resources team, lead by Leigh Hornsby, and the Aurora crew, lead by Murray Doyle, were a crucial component to the success of the deployment. The first three sensors were deployed via helicopter. The sensor was lowered via a rope onto floe whilst the helicopter hovering about 2 m above floe. Due to weather constraints, the remaining five were deployed via crane. The ship pulled up beside a chosen floe and the sensors were lowered onto it via crane. Once deployed, the ship slowly moved forward until the floe was clear of the turbulence generated by the ship. Both the helicopter and crane deployment methods proved to be successful. See /Waves/Wave Observations/wiios_deployment.pdf for more details on the deployment procedure. Approximate floe dimensions in metres based on the images in /Waves/Ice Observations/Ice_floe/Sensor ID): Sensor ID,Freeboard,Width,Length 1,0.15,28,28 2,0.33,10,12.5 3,0.15,10,15 4,.1,12,12 5,0.15,10,16.5 6,1,10,16.5 7,0.5,11.5,24 8,1,28.5,9 Ice observations: A collection of images and movies of the ice conditions are provided in Waves/Ice Observations. The folders include: Aerial: This folder contains aerial images taken with a gopro hero 2 fixed to the underside of the helicopter. Note that the date stamp on the GoPro is incorrect. Use the following for calibration: 20121022 13:52:00 - GPS - Australian eastern standard (no daylight savings) 20110707 14:00:07 - GoPRO Ice floe: Images of floes the sensors were deployed on. Ship: Images of the ice conditions taken from the ship. /Waves/Wave Observations/raw/sensorID_yyyy-mm-dd_hhmmss.raw Maps and shapefiles.zip - contains an ArcGIS map and shapefiles containing track data. KML.zip - contains KML files (point data) showing point-in-time snapshots of the buoy positions. Raw_NIWA_data.zip - contains the raw data files.

Time series of metocean variables derived form WAMOS (marine radar) data collected during the Antarctic Circumnavigation Expedition (ACE, https://spi-ace-expedition.ch/), from December 2016 to March 2017. Waves in the Southern Ocean are the biggest on the planet. They exert extreme stresses on the coastline of the Sub-Antarctic Islands, which affects coastal morphology and the delicate natural environment that the coastline offers. There is a lack of field data in the Sub-Antarctic and Antarctic Oceans. Thus, wave models are not well calibrated and perform poorly in these regions. Uncertainties relate to the difficulties to model the strong interactions between waves and currents (the Antarctic Circumpolar and tidal currents) and between waves and ice (reflected waves modify the incident field and ice floes affect transmission into the ice-covered ocean). Drawbacks in wave modelling undermine our understanding and ability to protect this delicate ocean and coastal environment. By installing a Wave and Surface Current Monitoring System (WaMoS II, a marine X-Band radar) on the research vessel Akademic Thresnikov and using the meteo-station and GPS on-board, this project has produced a large database of winds, waves and surface currents. Data were collected during the Antarctic Circumnavigation Expedition, which took place from Dec. 2016 to Mar. 2017. The dataset contains timeseries of relevant metocean variables divided in - Sea state and current parameters (PARA, MPAR) - Sea state and current parameters (PEAK, MPEK) - Ship course, position and speed (COURSE) - Wind speed and direction file (WIND) ********************************************************** Sea state and current parameters files (PARA, MPAR) File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: 1) ‘PARA’ : spatial mean of the parameters (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. 2) ‘MPAR’ : temporal average parameters calculated using all data collected during the past dt=20 minutes of the time specified in the file. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters) Time reference: CPU clock. Values of missing parameters are set to -9, -9.0. List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - Hs : Significant wave height (m). - Tp : Peak wave period (s). - Tm2 : Mean wave period (s). - Lp : Peak wave length (m). - MDir : Mean wave direction (deg). - PDir : Peak wave direction (deg). - TpS : First swell system - wave period (s). - PDS : First swell system - peak wave direction (deg). - lpS : First swell system - peak wave length (m). - TpW : Wind sea peak wave period (s). - PDW : Wind sea wave direction (deg). - lpW : Wind sea wave length (m). - Usp : Surface current speed (m/s). - Udir : Surface current direction (deg). - IQ : Quality index, ranging from 0 ('no problems detected') to 999 ('images cannot be analysed'). - NSPEC : Number of averaged spectra. - INDEX : Quality index threshold (OK: IQ<Index). - Hmax : Maximum wave height (m). - Tlim : Limit period to separate Swell/Wind Sea (s). - ELEVL : Error number. - CFG-Date : Date/time of last wamos.cfg change (DD-MM-YYYY HH.MI.SS). ********************************************************** Sea state and current parameters files (PEAK, MPEK): File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: 1) ‘PEAK’ : spatial mean of the parameters (that pass the WaMoS II internal quality control) averaged over WaMoS II analysis areas (up to 9) placed within the radar field of view. 2) ‘MPEK’ : temporal average parameters calculated using all data collected during the past dt=20 minutes of the time specified in the file. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters) Time reference: CPU clock. Values of missing parameters are set to -9, -9.0. List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - Hs : Significant wave height (m). - Tp : Peak wave period (s). - PDir : Peak wave direction (deg). - Lp : Peak wave length (m). - HsW : Wind sea significant wave height (m). - TpW : Wind sea wave period (s). - PDW : Wind sea wave direction deg). - lpW : Wind sea wave length (m). - HSS1 : First swell system significant wave height (m). - Tps1 : First swell system: wave period (s). - PDs1 : First swell peak wave direction (deg). - lps1 : First swell peak wave length (m). - HSS2 : Second swell system significant wave height (m). - Tps2 : Second swell system: wave period (s). - PDs2 : Second swell peak wave direction (deg). - lps2 : Second swell peak wave length (m). - HSS3 : Third swell system significant wave height (m). - Tps3 : Third swell system: wave period (s). - PDs3 : Third swell peak wave direction (deg). - lps3 : Third swell peak wave length (m). - Us : Surface current speed (m/s). - Ud : Surface current direction (deg). - IQ : Quality index. - Tlim : Limit period to separate Swell/Wind Sea (s). - SPR : Mean wave spreading. - CSI : Cross sea index. - GAM : Enhancement factor of the jonswap spectrum. - NORI : from compass or GPS (0 = enable 1 = disable). - ELEVL : Error number - CFG-Date : Date/time of last wamos.cfg change (DD-MM-YYYYHH.MI.SS). ********************************************************** Ship course, position and speed file (COURSE): File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: ‘COURSE’ : Input from NMEA systems. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters) Time reference: CPU clock. Values of missing parameters are set to -9, -9.0. List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - LAT : Latitude (deg). - LONG : Longitude (deg). - GYROC : Ship gyro compass (deg). - GPS : GPS course (deg). - Shipsp : Ship speed (kn). - Depth : Water depth (m). - GPS-Speed : GPS-Speed (kn). - ASDPW : Internal parameter. ********************************************************** Wind speed and direction file (WIND): File Name: -Prefix-_-rigID-_YYYYMM.txt - Prefix: ‘WIND’ : Input from NMEA systems. - YYYY : Year. - MM : Month. - rigID : WaMoS II platform’s ID code (3 letters) Time reference: CPU clock. Values of missing parameters are set to -9, -9.0. List of parameters: - date : Date and TIME of acquisition (YYYYMMDDHHMMSS). - LAT : Latitude (deg). - LONG : Longitude (deg). - WIND SPEED : Wind speed (m/s). - DIR : Wind direction (coming from) (deg). - WIND SPEED10 : Wind speed at 10 meters height (m/s). - TRUE DIR : Wind direction relative to north (deg).

This dataset contains ice motion observations made under the Australian Antarctic Program, projects 4593 and 4506. Data was obtained using two open-source ice motion loggers, hereafter ice buoys. Two ice buoys were deployed on landfast ice just north of the Swain Group, Antarctica (66.2 degr. S, 110.6 degr. E), on 13 October 2020. Instruments were retrieved on 10 November 2020. The ice buoys measure motion in 9-degrees-of-freedom at 10Hz using a VectorNAV VN-100 IMU, with an accuracy of O(mm) for short waves and O(cm) for long waves. Both instruments also record their geographical location through GPS. Full time series of their motion is processed on board and summaries are send through Iridium. Wave spectra and GPS coordinates were transmitted roughly every 4 hours. The dataset comprises the raw data measured by the two ice buoys, we have referred to them as AAD_17 and AAD_18 for administrative reasons. Data output for each buoy is: A = vertical acceleration (mean subtracted) (m/s^2); P = pitch (degrees); R = roll motion (degrees); z = surface elevation (m); t = UTC time (Matlab ‘datenum’ format, i.e., days since year 0000); lat = latitude; lon = longitude. The geographical coordinates ‘lat’ and ‘lon’ are in degrees.