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.

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).

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 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 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 dataset contains environmental layers used to model the predicted distribution of demersal fish bioregions for the paper: Hill et al. (2020) Determining Marine Bioregions: A comparison of quantitative approaches, Methods in Ecology and Evolution. It contains climatological variables from satellite and modelled data that represent sea floor and sea surface conditions likely to affect the distribution of demersal fish including: depth, slope, seafloor temperatures, seafloor current, seafloor nitrate, sea surface temperature, chlorophyll-a standard deviation and sea surface height standard deviation. Layers are presented at 0.1 degree resolution. "prediction_space" is a Rda file for R that consists of two objects: env_raster: a raster stack of the environmental layers pred_sp: a data.frame version of the env_raster where some variables have been transformed for statistical analysis and bioregion prediction. "Env_data_sources.xlsx" contains a description of each environmental variable and it's source.

This model was produced as part of Australian Antarctic Science project 4037 - Experimental krill biology: Response of krill to environmental change - The experimental krill research project is designed to focus on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the project will concentrate on studies into impacts of climate change on key aspects of krill biology and ecology. This metadata record is to reference the paper that describes the model. There is no archived data output from this data product. Taken from the abstract of the referenced paper: Estimates of productivity of Antarctic krill, Euphausia superba, are dependent on accurate models of growth and reproduction. Incorrect growth models, specifically those giving unrealistically high production, could lead to over-exploitation of the krill population if those models are used in setting catch limits. Here we review available approaches to modelling productivity and note that existing models do not account for the interactions between growth and reproduction and variable environmental conditions. We develop a new energetics moult-cycle (EMC) model which combines energetics and the constraints on growth of the moult-cycle. This model flexibly accounts for regional, inter- and intra-annual variation in temperature, food supply, and day length. The EMC model provides results consistent with the general expectations for krill growth in length and mass, including having thin krill, as well as providing insights into the effects that increasing temperature may have on growth and reproduction. We recommend that this new model be incorporated into assessments of catch limits for Antarctic krill.

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.

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

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.