These data represent the results of the first study to use Earth System Model (ESM) outputs of SST and chlorophyll-a to simulate circumpolar krill growth potential for the recent past (1960-1989) and future climate change scenarios (2070-2099). Growth potential is obtained using an empirically-derived krill growth model (Atkinson et al. 2006, Limnol. Oceanogr.), where growth is modeled as a function of SST and chlorophyll-a. It serves as an approximation of habitat quality, as areas that support high growth rates are assumed to be good habitat (see Murphy et al., 2017, Sci Rep). To increase confidence in the future projections, ESMs were selected and weighted for each season based on their skill at reproducing observation-based krill growth potential for the recent past. First, eleven ESMs which provided SST and chlorophyll-a outputs were obtained from the Coupled Model Inter-comparison Project 5 archive. These included: CanESM2, CMCC-CESM, CNRM-CM5, GFL-ESM2G, GFDL-ESM2M, GISS-E2-H-CC, HadGEM2-CC, IPSL-CM5A-LR, MPI-ESM-MR, MRI-ESM1 and NorESM1-ME. For each ESM, seasonal surface averages of SST and chlorophyll-a were used to calculate growth potential for the historical scenario (1960-1989), which was then bilinearly interpolated on to the same 1°x1° grid. Satellite observation-based datasets for SST and chlorophyll-a were used to calculate observation-based growth potential for the recent past (1997-2010). These comprised seasonal surface averages of SST (from the OISST v2 daily dataset, 1/4⁰ horizontal resolution) and chlorophyll-a (the mean of the SeaWiFS and Johnson et al. (2013) corrected estimate of SeaWiFS daily datasets, 1/12⁰ horizontal resolution). Observation-based growth potential was then bilinearly interpolated onto the same grid as the ESMs. ESM skill for each season was subsequently assessed against observation-based growth potential using a Taylor Diagram. The ESMs were selected and weighted according to their performance to produce a weighted subset (see "ESM_weighting_method.pdf" file). Of the netcdfs provided, "hist_mean_ensemble.nc" represents the unweighted mean of seasonal growth potential, calculated from the initial ensemble of eleven ESMs for the historical scenario. The "hist_mean_subset.nc" file represents the analogous output of the weighted subset. Future projections of seasonal growth potential for Representative Concentration Pathways (RCPs) 4.5 and 8.5 were obtained using the weighted subset for the period of 2070-2099. These projected seasonal surface averages are provided in the "rcp45_mean_subset.nc" and "rcp85_mean_subset.nc" files. RCPs represent standard climate change scenarios developed by the Intergovernmental Panel on Climate Change, with 4.5 reflecting some mitigation of carbon emissions, and 8.5 being the "business as usual" scenario. Analogous netcdfs for the weighted subset outputs of chlorophyll-a (chl) and SST (tos) for the historical and RCP scenarios are also provided in the "chl_tos_netcdfs.zip" file so that the driving environmental variables underlying growth potential can be examined.

This is a digital version of the grid reference map used to plot all sightings of Weddell seals in the Vestfold Hills. The point of origin is the same as the original map and each grid cell is numbered with the same numbering scheme. This can be used to plot any data using the same numbering scheme by joining (ArcInfo) or linking (ArcView) records to this coverage's polygon attribute table (pat) through the item GRIDREF. The original map was a 1:100 000 map of the Vestfolds, provided by Harry Burton, with a grid drawn over it. The grid references were given as either six or four figure values on which field scientists are to plot their data. This map has the following Antarctic Division drawing reference number: M/75/05A Some research with John Cox revealed that this grid was drawn up over a map digitised from another map with the following specifications: Scale 1: 100 000 Date: 1958 (reprinted 1972) Projection: Polyconic Published by: Division of National Mapping, Canberra Reference number: NMP/58/084 Data are referenced to a 'grid' of 1 minute spacing in x axis and 30 second spacing in y axis. The point of origin is apparently 68 20 S 77 48 E. There are 45 rows and 47 columns. The 'grid reference' is in fact in geographic coordinates (but using arbitrary units) so the projection of the original map became irrelevant. The procedure adopted to create a new digital grid was as follows: (Carried out in Arc/Info) 1. Generate a coverage using the original 'grid references'. 2. Tics were also generated using the corners of the 'grid reference' system. 3. A new coverage was created with tics at the same locations but given the true latitude/longitude vales. 4. The original coverage was then transformed to the new coverage based on the new tic values. 5. The new coverage was then projected from geographic coordinates to UTM metres. The data locations were then viewed in Arc/Info using a coverage of the coastline supplied by the Mapping Officer, Antarctic Division. This had previously been determined to be in the UTM projection. An offset was clearly visible between the data locations and the coastline. In order to determine whether the offset was more or less uniform, ten locations were plotted from the original data onto the original map using the 'grid'. Finally a manual corrected was made by moving all the data locations by a uniform distance of 508 metres north and 68 metres west. Information from John van den Hoff, February 2019: The grid cells were originally labelled from 1 to 47 along the x axis and 1 to 45 along the y axis. The four digit values in the GRIDREF field of the attribute table are the x value followed by the y value. To avoid confusion between x and y values, the grid was later revised so that the y values were prefixed with a ‘1’ so for example 01 became 101. The GRIDREF_X and GRIDREF_Y fields have the x and y values of the revised grid. This needs to be kept in mind when data is sourced from field books. The map shows the revised grid.

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 understand the maintenance of present atmospheric climate and determine its sensitivity to proposed changes. The changes in the ability of GCMSs used over the last two decades to simulate aspects of atmospheric climate at high southern latitudes are traced and it is concluded there has been a steady improvement in model products. The task of assessing model climates in high southern latitudes is made difficult by the uncertainties in the data used for the climatological statistics. It is suggested that the quality of the climates produced by most modern GCMs in many aspects cannot be said to be poor, especially considering the uncertainties in 'observed' climate. There is obviously need for improvements in both modelling and observations. Finally, some topics are highlighted in which the formulation of models could be improved, with special reference to better treatment of physical processes at high southern latitudes.

This dataset is a spreadsheet with planimetric areas of the seabed within the Heard Island and McDonald Islands Marine Reserve and adjacent Conservation Zone. The areas are provided for one hundred metre depth ranges and are given in square kilometres. The areas were calculated for the Wildlife Conservation and Fisheries research group at the Australian Antarctic Division. Depth data was sourced from a bathymetric grid of the Kerguelen Plateau by R.J.Beaman of James Cook University, Australia and P.E.O'Brien of Geoscience Australia and published by Geoscience Australia. See a Related URL below for a link to the metadata record describing the bathymetric grid. The Marine Reserve and Conservation Zone boundaries were sourced from the Australian Government's Australian Marine Parks Division. See the provided URL for a link to the department's website.

CAWCR Hindcast* and ECMWF ERA-5** model predictions of wave spectral properties (wave height and period) and corresponding observed data from ACE. Observations are mapped to model grids. Quality control is applied, i.e. cells with a number of points less than 5 and/or with high data variation (Standard Deviation/Mean greater than 0.2) are eliminated. Files are named as follows: WaMoS_vs_CAWCR_Hs.mat WaMoS_vs_CAWCR_Tm.mat WaMoS_vs_ERA5_Hs.mat WaMoS_vs_ERA5_Tp.mat In each file, columns show Latitude (deg.), Longitude (deg.), Time (number of days from January 0, 0000), Model Parameters (Hs, Tp or Tm) and Observed Parameters (Hs, Tp or Tm), respectively. Hs denotes significant wave height in meters, Tp is peak wave period in seconds and Tm is mean wave period based on the first moment of wave spectrum in seconds. The MATLAB file, WaMoSvsModel_FigurePlot.m, can be used to visualise the results. The files dscatter.m and polyfix.m are functions used in the MATLAB script. A sample figure (SampleFigure.png) is also included for users’ reference. * Durrant, T., Greenslade, D., Hemer, M. and Trenham, C., 2014. A global wave hindcast focussed on the Central and South Pacific (Vol. 40, No. 9, pp. 1917-1941). ** Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Dec. 12, 2018.

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.

Access database containing biological and environmental data collected by the Australian Antarctic Division, Human Impacts Benthic Biodiversity group.

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.

Krill Ecology - Technical Reports and Systems Guides A series of documents detailing work completed and methods used at the Krill Aquarium located at the Australian Antarctic Division. Technical Report # Title and Author Technical Report 1. 26th January 1994. DAPI Epiflourescence Technique. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 2. 5th March 1995. Bag Culture - Cell Growth Count Protocol. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 3. 12th January 1996. Chemical 'Spiking' of Krill Aquarium Bio-filter T12. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 4. 24th June 1996. Cold Temperature Algal Bag Culture Methodology. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 5. 16th April 1997. Algal Bag Culture - Harvesting Method. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 6. 26th October 1999. Aquarium System Bulk Seawater Collection and Storage. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 7. 11th October 1999. Sodium Hypochlorite Treatment of Algal Bag Culture Filtration Unit. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 8. 18th October 1999. Feeding Krill - Algal Strains, Feeding Rate and Nutritional Values. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 9. 22nd November 1999. Krill Biology Section - Parental Algal Culture Maintenance. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 10. 10th April 2000. Krill Group Databases and Maintaining Daily Data Records. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 11. 11th May 2000. Making Up and Use of Iodine Solution as an Indicator of the Presence of Chlorine in Freshwater. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 12. 1st June 2000. Testing for Harmful Ammonia (NH3) in Aquarium Sea Water. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 13. 12th June 2000. Digitron Digilog 2088T Digital Temperature Logger/Gauge - Operating Instructions and Down-Loading Logged Data Guide. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 14. 27th June 2000. Krill Biology - Marine Science Support Shed Gear Storage. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 15. 15th October 2000. Making up of fe Growth Media Stock Solutions for Parental and Algal Bag Culture Production. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 16. 15th January 2001. Algal Bag Culture - Growth Rate Analysis. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 17. 19th July 2004. Protective Epoxy Coating of Onga Seawater Collection Fire Pump. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 18. 27th October 2004. New Krill Aquarium - Bulk Seawater Collection and Storage Logistics. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 19. 11th March 2005. New Krill Aquarium - Algal Bag Culture Filtration System. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 20. 6th April 2005. New Culture Cabinet Bag to Bag Inoculation Procedure. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 21. 17th June 2005. Agar Bacterial Plate Testing for Krill Algal Culture Stocks. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 22. 29th July 2004. New Algal Culture Cabinet - Bag Culture Setup Methodology. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 23. 24th May 2005. Protocol for Sterilization of Bag Culture Air Supply System. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 24. 30th May 2005. 200 litre tank Algal Batch Culture Setup. Author: P. M. Cramp. Australian Antarctic Division. Technical Report 25. 22nd June 2005. Making Up and Shaping Plastic Bags for Algal Culture. Author: P. M. Cramp. Australian Antarctic Division. Techincal Report 26. 19th December 2005. New Krill Aquarium - Algal Strains, Feeding Rates and Nutritional Values. Author: P. M. Cramp. Australian Antarctic Division.

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.