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.

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).

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.

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.

Public Summary for project 2901 This research will contribute to a large multi-disciplinary study of the physics and biology of the Antarctic sea ice zone in early Spring 2007. The physical characteristics of the sea ice will be directly measured using satellite-tracked drifting buoys, ice core analysis and drilled measurements, with detailed measurements of snow cover thickness and properties. Aircraft-based instrumentation will be used to expand our survey area beyond the ship's track and for remote sampling. The data collected will provide valuable ground-truthing for existing and future satellite missions and improve our understanding of the role of sea ice in the climate system. Project objectives: (i) to quantify the spatial variability in sea ice and snow cover properties over scales of metres to hundreds of kilometres in the region of 110 - 130 degrees E, in order to improve the accuracy of sea ice thickness estimates from satellite altimetry and polarimetric synthetic aperture radar (SAR) data. (ii) To determine the drift characteristics, and internal stress, of sea ice in the region 110 - 130 degrees E. (iii) To investigate the relationships between the physical sea ice environment and the structure of Southern Ocean ecosystems (joint with AAS Proposal 2767). Taken from the abstract of the PhD thesis accompanying the dataset: Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data from an airborne platform is currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels. These are predicted to increase with effects of climate change, and are difficult to measure as snow fall is frequently lost to wind-blown redistribution, sublimation and snow-ice formation. Additionally, accurate regional scale snow thickness data will increase the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. Airborne snow-depth investigation techniques are one method for providing regional estimation of these parameters. The airborne datasets are better suited to validating satellite algorithms, and are themselves easier to validate with in-situ measurement. The development and practicality of measuring snow thickness over sea ice in Antarctica using a helicopter-borne radar forms the subject of this thesis. The radar design, a 2-8 GHz Frequency Modulated Continuous Wave Radar, is a product of collaboration and the expertise at the Centre for Remote Sensing of Ice Sheets, Kansas University. This thesis presents a review of the theoretical basis of the interactions of electromagnetic waves with the snow and sea ice. The dominant general physical parameters pertinent to electromagnetic sensing are presented, and the necessary conditions for unambiguous identification of the air/snow and snow/ice interfaces by the radar are derived. It is found that the roughness's of the snow and ice surfaces are dominant determinants in the effectiveness of layer identification in this radar. Motivated by these results, the minimum sensitivity requirements for the radar are presented. Experiments with the radar mounted on a sled confirm that the radar is capable of unambiguously detecting snow thickness. Helicopter-borne experiments conducted during two voyages into the East Antarctic sea-ice zone show however, that the airborne data are highly affected by sweep frequency non-linearities, making identification of snow thickness difficult. A model for the source of these non-linearities in the radar is developed and verified, motivating the derivation of an error correcting algorithm. Application of the algorithm to the airborne data demonstrates that the radar

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)

These data describe pack ice characteristics in the Antarctic sea ice zone. These data are in the ASPeCt format. National program: Russia Vessel: Akademic Fedorov Dates in ice: 27 Feb 1998 - 26 Mar 1998 Observers: Unknown Translation to ASPeCt data format: Vladimir Smirnov Summary of voyage track: 27-28/2 From ice edge at approx. 67S, 46E to Molodezhnaya (46E) 2-5/3 From Molodezhnaya to Progress (76E) 11-19/3 Oceanographic work in Prydz Bay (approx. 77E) 19-22/3 Prydz Bay to Mirny (93E) 25-26/3 Mirny to ice edge at approx. 64S, 96E The fields in this dataset are: SEA ICE CONCENTRATION SEA ICE FLOE SIZE SEA ICE SNOW COVER SEA ICE THICKNESS SEA ICE TOPOGRAPHY SEA ICE TYPE RECORD DATE TIME LATITUDE LONGITUDE OPEN WATER TRACK SNOW THICKNESS SNOW TYPE SEA TEMPERATURE AIR TEMPERATURE WIND VELOCITY WIND DIRECTION FILM COUNTER FRAME COUNTER FOR FILM VIDEO RECORDER COUNTER VISIBILITY CODE CLOUD WEATHER CODE COMMENTS

These data describe pack ice characteristics in the Antarctic sea ice zone. These data are in the ASPeCt format. National program: Russia Vessel: Akademic Fedorov Dates in ice: 21 Feb 1988 - 01 Mar 1988 Observers: Unknown Translation to ASPeCt data format: Vladimir Smirnov Summary of voyage track: 21/2 Ice edge at approx. 75S, 163W heading east from Leningradskaya 21-23/2 Track along fast ice edge to Russkaya (136W) 27/2-1/3 Russkaya to ice edge at approx. 72S, 124W The fields in this dataset are: SEA ICE CONCENTRATION SEA ICE FLOE SIZE SEA ICE SNOW COVER SEA ICE THICKNESS SEA ICE TOPOGRAPHY SEA ICE TYPE RECORD DATE TIME LATITUDE LONGITUDE OPEN WATER TRACK SNOW THICKNESS SNOW TYPE SEA TEMPERATURE AIR TEMPERATURE WIND VELOCITY WIND DIRECTION FILM COUNTER FRAME COUNTER FOR FILM VIDEO RECORDER COUNTER VISIBILITY CODE CLOUD WEATHER CODE COMMENTS

These data describe pack ice characteristics in the Antarctic sea ice zone. These data are in the ASPeCt format. National program: Russia Vessel: Akademic Fedorov Dates in ice: 28 Apr 1998 - 05 Jun 1998 Observers: Unknown Translation to ASPeCt data format: Vladimir Smirnov Summary of voyage track: 28/4 Ice edge at approx. 63S, 112E 28/4-1/5 From ice edge to Mirny (93E) 2-9/5 At Mirny 10-16/5 Mirny to Progress (76E) 18-22/5 Progress to Molodezhnaya (46E) 28/5-1/6 Molodezhnaya to Novolazarevskaya (12E) 4-5/6 Novolazarevskaya to ice edge at approx. 63S, 10E The fields in this dataset are: SEA ICE CONCENTRATION SEA ICE FLOE SIZE SEA ICE SNOW COVER SEA ICE THICKNESS SEA ICE TOPOGRAPHY SEA ICE TYPE RECORD DATE TIME LATITUDE LONGITUDE OPEN WATER TRACK SNOW THICKNESS SNOW TYPE SEA TEMPERATURE AIR TEMPERATURE WIND VELOCITY WIND DIRECTION FILM COUNTER FRAME COUNTER FOR FILM VIDEO RECORDER COUNTER VISIBILITY CODE CLOUD WEATHER CODE COMMENTS

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.