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).

Balance Ice Fluxes for the Antarctic ice sheet. These ice fluxes (in km^2/yr)represent the (hypothetical) distribution of ice flux that would keep the Antarctic ice sheet in its present shape (i.e. surface topography), 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), and the ice sheet surface elevation dataset distributed by BEDMAP (attributed to Liu et al 1999). This ice flux dataset represents the (hypothetical) distribution of ice flux that would keep the ice sheet topography in its present shape, under the influence of the given accumulation distribution.

Our understanding of how environmental change in the Southern Ocean will affect marine diversity,habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Full details of the methodology are provided in the paper (Xavier et al. (2015)). Briefly, occurrence data were taken from the SCAR Biogeographic Atlas of the Southern Ocean. This compilation was based upon Xavier et al. (1999), with additional data drawn from the Ocean Biogeographic Information System, biodiversity.aq, the Australian Antarctic Data Centre, and the National Institute of Water and Atmospheric Research. The habitat suitability modelling was conducted using the Maxent software package (v3.3.3k, Phillips et al., 2006). Maxent allows for nonlinear model terms by formulating a series of features from the predictor variables. Due to relatively limited sample sizes, we constrained the complexity of most models by considering only linear, quadratic, and product features. A multiplier of 3.0 was used on automatic regularization parameters to discourage overfitting; otherwise, default Maxent settings were used. Predictor variables were chosen from a collection of Southern Ocean layers. These variables were selected as indicators of ecosystem structure and processes including water mass properties, sea ice dynamics, and productivity. A 10-fold cross-validation procedure was used to assess model performance (using the area under the receiver-operating curve) and variable permutation importance, with values averaged over the 10 fitted models. The final predicted distribution for each species was based on a single model fitted using all data: these are the predictions included in this data set. The individual habitat suitability models were overlaid to generate a 'hotspot' index of species richness. The predicted habitat suitability for each species was converted to a binary presence/absence layer by applying a threshold, such that habitat suitability values above the threshold were converted to presences. The threshold used for each species was the average of the thresholds (for each of the 10 training models) chosen to maximize the test area under the receiver-operating curve. The binary layers were then summed to give the number of species estimated to be present in each pixel in the study region.

This parameter set was developed to provide a plausible implementation for the ecological model described in Bates, M., S Bengtson Nash, D.W. Hawker, J. Norbury, J.S. Stark and R. A. Cropp. 2015. Construction of a trophically complex near-shore Antarctic food web model using the Conservative Normal framework with structural coexistence. Journal of Marine Systems. 145: 1-14. The ecosystem model used in this paper was designed to have the property of structural coexistence. This means that the functional forms used to describe population interactions in the equations were chosen to ensure that the boundary eigenvalues of every population were all always positive, ensuring that no population in the model can ever become extinct. This property is appropriate for models such as this that are implemented to model typical seasonal variations rather than changes over time. The actual parameter values were determined by searching a parameter space for parameter sets that resulted in a plausible distribution of biomass among the trophic levels. The search was implemented using the Boundary Eigenvalue Nudging - Genetic Algorithm (BENGA) method and was constrained by measured values where these were available. This parameter set is provided as an indicative set that is appropriate for studying the partitioning of Persistent Organic Pollutants in coastal Antarctic ecosystems. It should not be used for predictive population modelling without independent calibration and validation.

This line shapefile represents the following features of the Antarctic Circumpolar Current: Subtropical Front (STF); Subantarctic Front (SAF); Southern Antarctic Circumpolar Current Front (sACCf); Polar Front (PF); Southern Boundary of the Antarctic Circumpolar Current as described in Alejandro H. Orsi, Thomas Whitworth III, and Worth D. Nowlin Jr (1995) On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research 42 (5), 641-673. The shapefile was created from data provided by lead author Alejandro Orsi to the Australian Antarctic Data Centre in August 2001. The data in the files from Alejandro Orsi was also combined in a csv file. The data available for download includes the original data, the shapefile and the csv file.

This dataset contains numerical simulation results of the wave fields in the Davis Sea from end of December 2019 to start of February 2020. Hindcasts were obtained through the third-generation spectral wave model WAVEWATCH-III (hereafter WW3). A high resolution Davis Sea regional grid (resolution 0.1 degree, 60-80E longitude, 70-60S latitude) was nested into global grid domain (resolution 0.5 degree, 80S-80N latitude). The global model is forced with 0.5 degree sea ice concentration and 10m-wind fields from ECMWF's ERA5 reanalysis. The Davis sea model is forced with 0.1 degree 10m-wind fields from ECMWF's archived forecasts, and high-resolution (3.125km) AMSR2 satellite data for sea ice concentration (Beitsch et al., 2013 updated). Ice-induced wave attenuation is parameterized following Sutherland et al. (2019, doi:10.1016/j.apor.2019.03.023) whilst the break-up of sea ice is parameterized as 'broken' or 'unbroken' based on the break-up parameter of Voermans et al.(2020, doi:10.5194/tc-14-4265-2020). The numerical simulations have been calibrated using the buoy-observations of Voermans (2022, dataset, doi:10.26179/cdmx-n995). Sensitivity of the simulations to sea ice properties was tested and all results are provided in the dataset. The data tree: * global: model outputs for the global domain - ncfield: gridded wave and ice data for this domain in netCDF-4 format - nests: binary data used by WW3 for boundary conditions for the Davis Sea grid - restarts: binary data used by WW3 for restarting this domain * davis_sea: model outputs for the Davis Sea domain - ncfield: gridded wave and ice data for this domain in netCDF-4 format - ncpoint: spectral wave data for a few points in the Davis Sea in netCDF-4 format - nctrack: spectral wave data following the wave buoys of Voermans et al (2022) in the Davis Sea in netCDF-4 format - restarts: binary data used by WW3 for restarting this domain - IHOT: binary text field of broken and unbroken ice for restarting this domain File naming convention (by example): ww3.20200101_20200103_M3D_IHOT_H0P0325_A0P01_YY9P0_SS0P1_HH0P55.nc * 20200101_20200103 identifies the datespan of the simulation in YYYYMMDD format * A0P01 refers to the attenuation coefficient of the model (where P stands for 'point'), in this case, A=0.01 * YY is the Young's Modulus timed 10^9, here, Y-9.0e9 Pa * SS is the ice strength 'sigma' times 10^6, here sigma=0.1e6 * HH is the ice thickness, here h=0.55 m * H0P0325 is proportional to the epsilon calibration coefficient (H=0.5*ice_thickness*epsilon). * M3D refers to the 3rd instantiation of the model * IHOT refers to hot start using the ice breakup field from the previous week. ww3.*_M3D_IHOT_H0P065_A0P05_YY6P0_SS0P55.nc is considered the baseline file (note, this simulation only covers the first two weeks of the study period). Reference: Beitsch, A., Kaleschke, L. and Kern, S. (2013). "AMSR2 ASI 3.125 km Sea Ice Concentration Data, V0.1", Institute of Oceanography, University of Hamburg, Germany, digital media

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.

This integrated stock assessment for the Patagonian toothfish (Dissostichus eleginoides) fishery at the Heard Island and the McDonald Islands in CCAMLR Division 58.5.2, with data until end of July 2015, is based on the best available estimates of model parameters, the use of abundance estimates from a random stratified trawl survey (RSTS), longline tag-release data from 2012-2014 and longline tag-recapture data from 2013-2015, and auxiliary commercial composition data to aid with the estimation of year class strength and selectivity functions of the trawl, longline and trap sub-fisheries.All model runs were conducted with CASAL version 2.30-2012-03-21 (Bull et al. 2012). The assessment model leads to an MCMC estimate of the virgin spawning stock biomass B0 = 87 077 tonnes (95% CI: 78 500-97 547 tonnes). Estimated SSB status in 2015 was 0.64 (95% CI: 0.59-0.69). Using this model, a catch limit of 3405 tonnes satisfies the CCAMLR decision rules. Similarly to the 2014 assessment, the projected stock remains above the target level for the entire projection period.

A meta-analysis was undertaken to examine the vulnerability of Antarctic marine biota occupying waters south of 60 degrees S to ocean acidification. Comprehensive database searches were conducted to compile all English language, peer-reviewed journals articles and literature reviews that investigated the effect of altered seawater carbonate chemistry on Southern Ocean and/or Antarctic marine organisms. A document detailing the methods used to collect these data is included in the download file.

An R data file containing a hierarchical switching state-space model of pygmy blue whale Argos-collected telemetry data using the bsam package (see Jonsen (2016). Joint estimation over multiple individuals improves behavioural state inference from animal movement data. Scientific Reports 6: 20625.) in R. The model estimated location states for each individual at regular 3-h time intervals, accounting for measurement error in the irregularly observed Argos surface locations; and estimated the behavioural state associated with each location. Satellite tags were deployed on pygmy blue whales located in the Bonney Upwelling region, SA, between 7 January and 16 March 2015. File can be opened in R (A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ ) using the code: readRDS('bw_3h_ssm.RDS')