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.

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.

These data are contained within a netcdf file of the multi-model mean (MMM) calculated as part of the Realistic ice-shelf/ocean state estimates (RISE) project, with the following variables calculated on a 2 kilometer grid, from the ten contributing models: - longitude degrees east EPSG:4326 - latitude: degrees north EPSG:4326 - easting: meters east EPSG:3031 - northing: meters_north EPSG:3031 - mask: grounded=1,iceshelf=2,conshelf=3,ocean=4 - iceshelf_id: NSIDC iceshelf-id - h: Depth (m) - zice: Ice draft depth (m) - ismr: Average basal iceshelf melt (m/year) - salt_bar: Depth averaged practical salinity (psu) - salt_zice: Average practical salinity (icedraft) (psu) - salt_zice_sa: Average absolute salinity (icedraft) (g/kg) - temp_bar: Average potential temperature - \"theta\" (water column) (degrees C) - temp_tw_zice: Average in-situ temperature (icedraft) (degrees C) - temp_tf_zice: Average in-situ temperature which seawater freezes (icedraft) (degrees C) - tstar_zice: Average thermal driving (degrees C) - u_bar: Average East-west velocity (u) ocean current speed (m/s) - v_bar: Average North-south velocity (v) ocean current speed (m/s) - u_zice: Average East-west velocity (u) ocean current speed (icedraft) (m/s) - v_zice: Average North-south velocity (v) ocean current speed (icedraft) (m/s) - rho_zice: Average in-situ seawater density (icedraft) (kg/m3) - ustar_zice: Average ice-water friction velocity Contextual information taken from the abstract of the referenced paper: Societal adaptation to rising sea levels requires robust projections of the Antarctic Ice Sheet’s retreat, particularly due to ocean-driven basal melting of its fringing ice shelves. Recent advances in ocean models that simulate ice-shelf melting offer an opportunity to reduce uncertainties in ice–ocean interactions. Here, we compare several community-contributed, circum-Antarctic ocean simulations to highlight inter-model differences, evaluate agreement with satellite-derived melt rates, and examine underlying physical processes. All but one simulation use a melting formulation depending on both thermal driving (T ⋆) and friction velocity (u⋆), which together represent the thermal and ocean current forcings at the ice–ocean interface. Simulated melt rates range from 650 to 1277 Gt year−1 (m = 0.45 − 0.91 m year−1), driven by variations in model resolution, parameterisations, and sub-ice shelf circulation. Freeze-to-melt ratios span 0.30 to 30.12 %, indicating large differences in how refreezing is represented. The multi-model mean (MMM) produces an averaged melt rate of 0.60 m year−1 from a net mass loss of 842.99 Gt year−1 (876.03 Gt year−1 melting and 33.05 Gt year−1 refreezing), yielding a freeze-to-melt ratio of 3.92 %. We define a thermo-kinematic melt sensitivity, ζ = m/(T ⋆ u⋆) = 4.82 × 10−5 °C−1 for the MMM, with individual models spanning 2.85 × 10−5 to 19.4 × 10−5 °C−1. Higher melt rates typically occur near grounding zones where both T ⋆ and u⋆ exert roughly equal influence. Because friction velocity is critical for turbulent heat exchange, ice-shelf melting must be characterised by both ocean energetics and thermal forcing. Further work to standardise model setups and evaluation of results against in situ observations and satellite data will be essential for increasing model accuracy, reducing uncertainties, to improve our understanding of ice-shelf–ocean interactions and refine sea-level rise predictions.

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).

Direct Numerical Simulation (DNS) was used to study the effect of sloping the ice-shelves on the dissolution/melt rate at the ice-ocean interface. The simulations were done on the HPC Raijin at NCI, Canberra over March 2015 to June 2017. Numerical experiments were carried out over a range of slope angle (5 degrees – 90 degrees) of the ice-shelves measured from the horizon. Turbulent flow field is simulated over the domain length of 1.8 m, (for slope angle greater than or equal to 50 degrees) and 20 m (for slope angle less than or equal to 20 degrees) respectively; the flow-field is laminar otherwise. A constant ambient temperature 2.3 degrees C and salinity 35 psu is maintained throughout the simulations. The DNS successfully resolved all possible turbulence length scales and relative contributions of diffusive and turbulent heat transfer into the ice wall is measured. Data available: Excel file Profile_salinity_temperature_velocity.xlsx contains along-slope velocity, temperature and salinity as a function of wall normal distance for slope angle 50 degrees, 65 degrees and 90 degrees respectively for the domain length 1.8 m.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice thickness, based on an ensemble of twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in sea ice thickness is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of sea ice thickness change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in sea ice thickness (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

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.

Direct Numerical Simulation (DNS) was used to study the effect of sloping the ice-shelves on the dissolution/melt rate at the ice-ocean interface. The simulations were done on the HPC Raijin at NCI, Canberra over March 2015 to June 2017. Numerical experiments were carried out over a range of slope angle (5 degrees – 90 degrees) of the ice-shelves measured from the horizon. Turbulent flow field is simulated over the domain length of 1.8 m, (for slope angle greater than or equal to 50 degrees) and 20 m (for slope angle less than or equal to 20 degrees) respectively; the flow-field is laminar otherwise. A constant ambient temperature 2.3 degrees C and salinity 35 psu is maintained throughout the simulations. The DNS successfully resolved all possible turbulence length scales and relative contributions of diffusive and turbulent heat transfer into the ice wall is measured. Data available: Excel file Meltrate_vs_slopeangle_lam_turb.xlsx contains both simulated laminar and turbulent dissolution/melt rate as a function of slope angle along with their analytical values based on laminar and turbulent scaling theory respectively.

In this project a simplified computer model was developed to reflect the variation and influences of sea ice on the atmosphere. The model was incorporated into a global general circulation model. The data set resulting from the project consists of simulated sea ice characteristics (concentration etc.) available on a regular global grid. From the abstracts of some of the referenced papers: An observed ocean-drift data set is used as the basis of a wind-driven coupled ocean-sea-ice-atmosphere model including interaction and feedback. The observed characteristics of the Antarctic sea ice are described including the ice thickness, ice concentration and horizontal advection. The atmospheric model computes heat fluxes, sea-ice growth, changes in concentration and advection. Sensitivity studies show reasonable and stable simulations of the observed sea-ice characteristics for the present mean Antarctic winter climate. The response times and feedbacks of the ice-atmosphere system as represented by the model appear to allow scope for the development of some persistence of anomalies. To assess the sensitivity of the southern hemisphere circulation to changes in the fraction of open water in the sea ice we have conducted four experiments with a July 21-wave General Circulation Model (GCM) with this fraction set to 5, 50, 80 and 100%. The mean surface temperatures and the surface atmospheric temperatures over the sea ice increased as the water fraction increased and the largest changes were simulated adjacent to the coast. Significant anomalies in the surface heat fluxes, particularly those of sensible heat, accompanied the decrease in the sea ice concentration. Substantial atmospheric warming was simulated over and in the vicinity of areas in which leads were considered. In all but one experiment there were anomalous easterlies between about 40 and 60S with westerly anomalies further to the south. The surface pressure at high latitudes appears to change in a consistent fashion with the fraction of open water, with the largest changes occurring in the Weddell and near the Ross Seas. Some of the feedbacks which may enhance the responses here, but which are not included in our model are discussed. We present a simple parameterisation of the effect of open leads in a general circulation model of the atmosphere. We consider only the case where the sea ice distribution is prescribed (ie not alternative) and the fraction of open water in the ice is also prescribed and set at the same value at all points in the Southern Hemisphere and a different value in the Northern Hemisphere. We approximate the distribution of sea ice over a model 'grid box' as a part of the box being covered by solid ice of uniform thickness and the complement of the box consisting of open water at a fixed -1.8 degrees C. Because of the nonlinearity in the flux computations, separate calculations are performed over the solid sea ice and over the open leads. The net fluxes conveyed to the atmosphere over the grid box are determined by performing the appropriate area-weighted average over the two surface types. We report on an experiment designed to assess the sensitivity of the modelled climate to the imposition of a 50% concentration in the winter Antarctic sea ice. Significant warming of up to 6 degrees C takes place in the vicinity of and above the Antarctic sea ice and is associated with significant changes in the zonal wind structure. Pressure reductions are simulated over the sea ice, being particularly marked in the Weddell Sea region, and an anomalous east-west aligned ridge is simulated at about 60S. Very large changes in the sensible heat flux (in excess of 200 W per square metre) are simulated near the coast of Antarctica. Increasingly, many aspects of the study of Antarctica and the high southern latitudes are being aided by various types of numerical models. Among these are the General Circulation Models (GCMs), which are powerful tools that can be used to

Balance Ice Velocities for the Antarctic ice sheet. These ice velocities (in m/yr) represent the (hypothetical) distribution of depth-averaged ice velocities that would keep the Antarctic ice sheet in its present shape (i.e. surface topography and thickness), under the influence of a prescribed accumulation distribution. The present fluxes were computed using computer code BalanceV2 (by Warner) (outlined in Budd and Warner 1996, and detailed in Fricker, Warner and Allison 2000), using the surface accumulation dataset of Vaughan et al (1999), the ice sheet surface elevation dataset distributed by BEDMAP (attributed to Liu et al 1999), and the ice sheet thickness compilation distributed by the BEDMAP consortium (Lythe et al 2001).