The data comprise images (encapsulated postscript and PNG formats) showing the integrated solar irradiance exposure of sea ice. The exposure value for ice at a given grid point was calculated by computing the motion trajectory of that patch of ice across the autumn/winter season (1-March to 1-November). Daily motion data were obtained from the National Snow and Ice Data Center (http://nsidc.org/data/nsidc-0116.html). The integrated radiation exposure was then calculated using daily estimates of downward solar flux from the NCEP/NCAR re-analyses. The values shown in the images are cumulative photosynthetically active radiation expressed in W-days/m^2. Please contact the data custodian before using these data. This work was done as part of ASAC project 2943 (ASAC_2943). See the link below for public details about the project.

This dataset contains ice motion observations made under the Australian Antarctic Program, Projects 4593 and 4506. Measurements of ice motion where made on (land)fast ice on the eastern rim of the Amery Ice Shelf, Antarctica (69.2 degr. S, 76.3 degr. E) and on landfast ice in Gronfjorden, Svalbard (78.0 degr. N, 14.2 degr. E). Data was obtained using Spotter wave buoys (Sofar Ocean Technologies), hereafter wave buoys, and open-source ice motion loggers, hereafter ice buoys. Instrumentation was deployed on top of the sea ice with the main motivation to measure its vertical motion due to ocean waves. The wave buoys 3-axis measure motion at 2.5 Hz through GPS and have an accuracy of approximately 2 cm for the significant wave height. The ice buoys measure motion in 9-degrees-of-freedom at 10Hz using a VectorNAV VN-100 IMU, accuracy is 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 wave buoy, this occurred at an interval of 30 minutes. For the ice buoy this occurred every 3 hours. In the dataset, WB and IB are abbreviations for wave buoy and ice buoy, respectively. This dataset covers 2-8 January 2020 for the Antarctic campaign (WB1, WB2, IB1, IB2) and 14-28 March for the Arctic campaign (IB3, IB4, IB5) and includes significant wave height, peak period and the geographical coordinates of the instrumentation. ‘Hs’ refers to significant wave height (in meters). ‘Tp’ refers to peak period (in seconds). 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 ‘time_latlon’ refers to the reference time of the geographical coordinates. For the wave buoy, all data is transmitted in one message.

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.

Metadata record for data from ASAC Project 2504 See the link below for public details on this project. In this project a sea-ice model for application in Southern Ocean climate and forecasting studies will be developed to amend identified deficiencies in numerical models (i.e. unaccounted short-term dynamics; or non-suitable ice rheology). In-situ deformation and ice-stress data will be used to derive parameterisations suitable for the Southern Ocean pack. Antarctic sea ice is an important component of the Southern Hemisphere climate. It provides a habitat for algae, plankton and for larger species such as mammals or penguins. It is a transport medium for freshwater and biological matter. On the other hand it acts like a barrier between ocean and atmosphere in regard to the exchange of thermal energy, water vapour and gases. Sea ice affects the polar climate in many ways: E.g., by effectively insulating the ocean from the colder atmosphere the sea ice enables an advection of relatively warm water onto the shallow Antarctic continental shelf. This warmer water is then available to interact with other components of the climate system, such as by basal melting of the continental ice shelves [Jenkins and Holland, 2002]. Also, due to its high albedo, the sea ice has a large-scale effect on the net incoming solar radiation [Ebert et al., 1995] and reduces the absorption of solar energy into the upper ocean. The thermodynamic growth of seaice and the consequent desalination of the ice gives rise to a transport of salt from the ice into the ocean, which increases the water density over the shelf, thereby driving the deep vertical overturning cell in the global ocean circulation. High ice-growth rates (e.g., in regions of polynyas) are generally concentrated in small areas in shallow waters. These regions are often insufficiently resolved or even unresolved in coupled climate models, which are generally configured to run at a spatial resolution of 2 degree longitude by 1 degree latitude or coarser [Zhang and Hunke, 2001]. The specific objectives of this project are to: identify the variabilities in the sea-ice characteristics and the underlying physical processes; identify the time scales, at which the sea ice interacts with the ocean and atmosphere; assess the contribution of sub-daily ice motion and deformation due to tidal forcing and inertial response to changes within the Antarctic ocean-ice-atmosphere system; derive the impact of sub-daily ice dynamics on the sea-ice area, extent and mass on interannual and decadal time scales; determine the scale effect of dynamic processes on the accuracy of modelled sea-ice parameters using a global high-resolution model; identify model uncertainties through comprehensive validation studies. However, logistical problems prevented the project from collecting any data in the field. To overcome the paucity of planned buoy data we used the following data sets to address some of the aspects of the original proposal: 1) Sea-ice buoy data: ISPOL 2004: See AAS #2500 for metadata. 2) Numerical investigations: We have investigated the failure of sea ice using an isotropic model [Hibler, 1979], where ice strength is modelled as a random variable in the model space. In situ weakening was prescribed by a fracture-based Coulombic rheology [Hibler and Schulson, 2000]. We realised this by parameterising weakening with an ice-strength parameter of 1000 and initialising the ice strength across the model grid by random. The simulations were run over a 2000 km by 2000 km region and forced, from rest, with an idealised wind field. We analysed the sensitivity of failure to ice strength and wind stress as well as the intersection angle of the wind stress, and conducted idealised 2D failure experiments.

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.

A mathematical model (Bennetts and Meylan, 2021, doi.org/10.1137/20M13851) has been used to make predictions of ocean wave transfer to Ross Ice Shelf flexure. The transfer is considered along transects of the Ross Ice Shelf and adjoining open ocean, where the ice shelf thickness and seabed profiles along the transects are sampled from the Bedmap2 dataset (Fretwell et al, 2013, doi.org/10.5194/tc-7-375-2013). Our dataset consists of MAT-files, where each file is for a particular transect and holds two structures: 'data_I' as input data and 'data_o' for the model output data. The input data are the profiles from Bedmap2: 'thick' is the shelf thickness, 'draft' is the shelf draught; and 'bed' is the seabed elevation. They are all in vector form with 2001 sample points along the shelf, which was found to give model outputs accurate to 95%. The input data also contains: a 1x2 vector 'L_vec', for which the first entry is the shelf length, and the second entry is the length of the adjoining open ocean, where both values are in metres; and a 1x2 vector 'Int_vec', for which the first entry is the total number of sample points (ocean + shelf) and the second entry is the number of points in the shelf only. The output date are the three matrices where the rows correspond to different wave period and columns are distances along the transect: 'eta_w' is the water displacement (dimensionless); 'eta_s' is the shelf displacement (dimensionless); and 'str' is the flexural shelf strain (1/metres). All three outputs are normalised by the incident amplitude, noting that the model is linear. The output data also contains: a 1x300 vector containing the wave periods 'T', which are log-spaced between 10s and 1000s. The data are divided into two folders: validation/ and transects/. The first group (validation/) are used to validate the model predictions against the observations of Chen et al (Geophysical Research Letters, 2019, doi.org/10.1029/2019GL084123) close to 2 km away from the shelf front, where the results of Chen et al (2019) have been digitised and are contained in 'Chen_paper.mat'. The second group (transects/) can be used to study transfer over a 500km wide region of the Ross Ice Shelf. There are 101 transects with 5 km spacing. We also analysed the shelf displacement and strain over different wave periods at 10 km away from shelf front for all transects to investigate the relations between strain and wave period, these data have stored in 'Transfer_function_x_10km.mat'. Three MATLAB scripts (Fig1.m, Fig2.m, Fig3.m) are included to recreate results from Bennetts et al (submitted). Fig1.m produces plots from observation (Chen et al) and our models. Fig2.m performs strain transfer function analysis for different profiles and Fig3.m generate the strain map and selected region of Ross Ice Shelf for given incident ocean wave. For Fig1.m, it requires “Bedmap2 Toolbox for Matlab” to access the bedmap2 for producing Ross Ice Shelf on the Antarctica map. A link to download this software will be stated in the MATLAB scripts. An updated dataset was provided on 2022-10-25.

This dataset (provided as a series of CF-compatible netcdf file) consists of 432 consecutive maps of Antarctic landfast sea ice, derived from NASA MODIS imagery. There are 24 maps per year, spanning the 18 year period from March 2000 to Feb 2018. The data are provided in a polar stereographic projection with a latitude of true scale at 70 S (i.e., to maintain compatibility with the NSIDC polar stereographic projection).

During the ice stations, sea ice, brine/slush, snow and under-ice water sampling were collected for CO2 concentration measurement as dissolved inorganic carbon (DIC). Ice cores were collected using a Kovacs 9 cm diameter ice corer. The ice core for DIC was cut directly after retrieval with a stainless steel folded saw. The core was cut generally into 10 cm sections (20 cm when ice cores were higher than 200 cm) and put into zip-lock polyethylene bags. Care was taken to use laboratory gloves when collecting the cores. For brine sampling, partial core holes were drilled into the ice (so called sackholes), usually to a depth of 25 cm and 50 cm. At site with flooding, brine collection was not possible, and samples of the surface slush were collected instead. Slush was collected by plastic shovel. Snow samples were also collected. Under-ice water was collected with a Teflon water sampler (GL Science Inc., Japan) 1, 3, 5 m below the bottom of the sea ice. In addition, CTD water sampling was examined at each station. The cores were taken back to the ship, and transferred to the gas tight bag (GL Science Inc., Japan), and then ice was melted at about +4 degrees C in a refrigerator. Melted samples were sub-sampled for each component. The snow samples were treated in the same manner as the sea ice samples for further analysis. The dissolved inorganic carbon (DIC) of seawater was determined by coulometry [Johnson et al. 1985] using a coulometer (CM5012, UIC Inc., Binghamton, NY, USA). DIC measurement was calibrated with reference seawater materials (Batch AG; KANSO Technos Co., Ltd., Osaka, Japan) traceable to the Certified Reference Material distributed by Prof. A. G. Dickson (Scripps Institution of Oceanography, La Jolla, CA, USA). The standard deviation for DIC calculated from 20 subsamples taken from a reference seawater material (DIC = 2084.5 micro mol L-1) was 1.4 micro mol L-1. Data available: excel files containing sampling station name, dates, and DIC concentration.

Metadata record for data expected from ASAC Project 2767 See the link below for public details on this project. A multidisciplinary survey of the processes linking sea ice with biological elements of Antarctic marine ecosystems was conducted in winter 2007. The survey provided large-scale information on sea ice biological and physical parameters in the 100-130 degree East sector off East Antarctica. The distribution of sea ice algae and krill were measured using various methods including ice coring surveys and trawls. These measurements were complemented by shipborne measurements and an intensive sea ice sampling program. Use of an ROV was attempted but did not result in quantitative/geo-referenced data. Under-ice video files are available from the Chief-Investigator. Individual word documents are available from this metadata record for each ice station. These contain information on the ice station number, date and time of record and the parameters/ samples.