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  • 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).

  • 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.

  • 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.

  • 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 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

  • 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 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.

  • The AA4528 corridor dataset contains the Matlab scripts for the corridor algorithm, ice shelf locations and file extensions. The corridor algorithm is designed to calculate the parts of the ocean which can directly propagate swell into an exposed ice shelf. The algorithm achieves this as an expansion of the coastal exposure algorithm (Reid and Massom, 2021), with the details of the inner working of the algorithm work presented in the paper attached with this dataset. Corridors can be used to calculate the frequency of swell reaching an ice shelf per year and can be combined with hindcasts to extract relevant wave data to an ice shelf for modelling or data analysis purposes. The corridor algorithm requires sea ice concentration data, which was provided by the NSIDC Sea ice concentrations from the Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1 (https://nsidc.org/data/nsidc-0051). Ice shelf coordinates were extracted from the gfsc_25s.msk that come with the sea ice data, with the aid of Antarctic Mapping Toolbox (Greene et al., 2017), and were attached separately to make editing more consistent. As this is designed to use daily sea ice data from the 1st of January 1979 onwards, I’ve also attached the sea ice files for the off-days when the sea-ice data was taken every 2nd day. Th file extensions script was also included to be able to switch through off-day files and changes that occur with the NSIDC file format. The ocean hindcast that the corridor algorithm was built around is the CAWCR Wave Hindcast – Aggregated Collection (https://data.csiro.au/collections/collection/CI39819v005). The corridor algorithm uses daily data to make it consistent with the sea ice data and calculated the maximum significant wave height for each cell present in the hindcast. Data that was extracted from it was the maximum daily significant wave height recorded in the corridor and the direction of that cell. Data was taken from 01/09/1979 to 31/08/2019 giving 40 years of data which accounts for seasonality of corridors. The excel spreadsheet attached contains relevant corridor data for each ice shelf with an area greater than 500 km^2. Area was determined by either the supplementary files from Rignot et. al., 2013, or ice shelf areas from the Antarctic mapping toolbox (Greene et al., 2017). Angle1 and Angle2 were the ones used in the direction filter, and there should be a comment in the filter with how it handles if Angle 1 is greater than Angle 2 or vice versa. Ac is the corridor area, PA is potential corridor area (i.e. the absolute max it could be with the settings we used, Ac_max is the maximum corridor area, D_cor is the days that corridors were present, Hs is significant wave height and LW (large waves) is counting days per year when significant wave heights greater than or equal to 6 m (Morim et al., 2021). Refs: Greene, C. A., Gwyther, D. E. and Blankenship, D. D. (2017) ‘Antarctic Mapping Tools for MATLAB’, Computers and Geosciences, 104, pp. 151–157. doi: 10.1016/j.cageo.2016.08.003. Morim, J. et al. (2021) ‘Global-scale changes to extreme ocean wave events due to anthropogenic warming’, Environmental Research Letters, 16(7), p. 074056. doi: 10.1088/1748-9326/ac1013. Reid, P. and Massom, R. (2021) ‘Change and Variability in Antarctic Coastal Exposure , 1979-2020’. In pre-print (https://assets.researchsquare.com/files/rs-636839/v1/02002d0b-2c6c-402b-8e14-7f77075d8f90.pdf?c=1631885736) Rignot, E. et al. (2013) ‘Ice-shelf melting around antarctica’, Science, 341(6143), pp. 266–270. doi: 10.1126/science.1235798.