These layers are polar climatological and other summary environmental layers that may be useful for purposes such as general modelling, regionalisation, and exploratory analyses. All of the layers in this collection are provided on a consistent 0.1-degree grid, which covers -180 to 180E, 80S to 30S (Antarctic) and 45N to 90N (Arctic). As far as practicable, each layer is provided for both the Arctic and Antarctic regions. Where possible, these have been derived from the same source data; otherwise, source data have been chosen to be as compatible as possible between the two regions. Some layers are provided for only one of the two regions. Each data layer is provided in netCDF and ArcInfo ASCII grid format. A png preview map of each is also provided. Processing details for each layer: Bathymetry File: bathymetry Measured and estimated seafloor topography from satellite altimetry and ship depth soundings. Antarctic: Source data: Smith and Sandwell V13.1 (Sep 4, 2010) Processing steps: Depth data subsampled from original 1-minute resolution to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation. Reference: Smith, W. H. F., and D. T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277:1957-1962. http://topex.ucsd.edu/WWW_html/mar_topo.html Arctic: Source data: ETOPO1 Processing steps: Depth data subsampled to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation on polar stereographic projection. Reference: Amante, C. and B. W. Eakins, ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp, March 2009. http://www.ngdc.noaa.gov/mgg/global/global.html Bathymetry slope File: bathymetry_slope Slope of sea floor, derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Slope calculated on 0.1-degree gridded depth data (above). Calculated using the equation given by Burrough, P. A. and McDonell, R.A. (1998) Principles of Geographical Information Systems (Oxford University Press, New York), p. 190 (see http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=How%20Slope%20works) CAISOM model-derived variables Variables derived from the CAISOM ocean model. This model has been developed by Ben Galton-Fenzi (AAD and ACE-CRC), and is based on the Regional Ocean Modelling System (ROMS). It has circum-Antarctic coverage out to 50S, with a spatial resolution of approximately 5km. The values here are averaged over 12 snapshots from the model, each separated by 2 months. These parameters should be treated as experimental. Reference: Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214 Floor current speed File: caisom_floor_current_speed Current speed near the sea floor. Floor temperature File: caisom_floor_temperature Potential temperature near the sea floor. Floor vertical velocity File: caisom_floor_vertical_velocity Vertical water velocity near the sea floor. Surface current speed File: caisom_surface_current_speed Near-surface current speed (at approximately 2.5m depth) Chlorophyll summer File: chl_summer_climatology Source data: Near-surface chl-a summer climatology from MODIS Aqua Antarctic: Climatology spans the 2002/03 to 2009/10 austral summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Arctic: Climatology spans the 2002 to 2009 boreal summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Reference: Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. https://oceancolor.gsfc.nasa.gov/ Distance to Antarctica File:

This thesis investigates the improvement of forecasting water temperature in a coastal embayment through the assimilation of satellite sea surface temperature (SST). Port Phillip Bay (PPB) in southeastern Australia was used as a case study, where temperature forecasts could be compared against in situ temperature measurements. Over the long term satellite derived SST observations were found to have negligible bias, however a strong diurnal bias was apparent. The model of PPB replicated the main features of PPB well, although the temperature prediction was warm biased. The actual assimilation of SST data was contrasted against a climatology forecast of PPB temperature. The assimilation of SST, without any specific accounting for the diurnal bias improved the forecast, although errors due to observational bias were noted. Attempts to remove this bias using diurnal correction algorithms failed, owing to a larger than expected cool skin. Conditional merging, which combines spatial and in situ observations, was applied to the SST observations and improved forecast accuracy by reducing the observation bias. This work demonstrates that forecasting models can be improved through the assimilation of satellite derived observations. An examination of the assimilation innovations indicated where the forecast accuracy could be further improved.

This software contains the v1.0.0 release of Nilas: the south ocean mapping platform (https://nilas.org). This mapping tool (beta) has been developed by the Australian Antarctic Division for the Antarctic sea-ice zone to support their research and operational activities. Nilas displays multiple layers of physical and biogeochemical variables. These variables are primarily derived from remotely sensed products and updated as source data become available. The source code is well documented with both readme files and inline comments. This application is written primarily in javascript and was developed using Node.js, vite and a small amount of vue. The Nilas platform was based on the Leaflet open source library. It can be configured to display other Antarctic related geospatial products including raster and vector data. See the related record, "AAS_4506_NILAS_DATA" for data from this project.

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 dataset is a document describing the Scyphomedusae of the Southern Ocean. It lists all the known species and with illustrated diagrams provides a guide to their taxonomic identification. Distribution maps are given for each species. The document is available for download as a pdf from the provided URL.

This dataset contains multiple variables with spatio-temporal information relating to sea-ice and the southern ocean. This collection of data is utilised by the nilas.org platform for dynamically visualising these variables in the web browser. Together they provide a valuable resource for understanding the interactions between physical, climate and biogeochemical parameters. These include variables to understand sea-ice in three dimensions, chlorophyll and sea surface temperature. The time range of these data covers from 1980 until the present and the spatial coverage is Antarctic circumpolar. Name: Daily Sea Ice Concentration Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September. Product: ARTIST (ASI 5) (Spreen et al. 2008) Source: Universität Bremen Resolution: 6.125 km nominal Timeframe: 2012 to present Notes: Concentrations of less than 15% have been removed. Name: Monthly Sea Ice Concentration Desc: Sea ice concentration is a measure of the amount of size ice over an area. It is calculated from satellite observations of sea ice for all areas adjacent the Antarctic coastline. The minimum area of sea ice naturally occurs in February and the maximum in September. Product: Sea Ice Index (Windnagel et al. 2017) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25 km nominal Timeframe: 1980 to present Notes: Concentrations of less than 15% have been removed. Name: Monthly Anomalies in Sea Ice Concentration Desc: Anomalies in sea ice concentration show the monthly variation from the long term mean. Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to present Notes: Anomalies are calculated as the difference between the sea ice concentration and the 1981-2010 mean sea ice concentration for that month. Anomalies less than 7.5% are not shown. Name: Long term monthly mean sea ice extent Desc: Sea ice extent is calculated as contour lines at 15% and 80% sea ice concentration. Product: Sea Ice Index (Windnagel et al. 2017) Source: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Resolution: - Timeframe: 1980 to present Notes: Contours with less than 15 vertices are discarded. Name: Long Term Monthly Mean Sea Ice Extent Desc: Mean monthly sea ice extent over the 1981-2010 time interval. This is calculated as contour lines at 15% and 80% long term mean (1981-2010) sea ice concentration. Product: Sea Ice Index (Windnagel et al. 2017) Source: NSIDC (National Snow and Ice Data Center) Resolution: - Timeframe: Long term monthly mean (1981-2010) Notes: Contours with less than 15 vertices are discarded. Name: Gridded Freeboard (ATL20) IceSat2 Desc: Sea ice freeboard is the distance between the waterline and the surface height of sea ice in open leads. This dataset contains monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10,V3). Product: ATL20 (Petty et al. 2020) Source: NSIDC Resolution: 25 km nominal Timeframe: Oct 2018 to July 2022 Notes: Data greater than 1 metre is shown as 1 metre height. Name: Annual Sea Ice Duration Desc: Sea ice duration (contour lines) is the number of days sea ice concentrations above 15% occur between consecutive sea ice minima (assumed to occur on Feb 16 each year). Product: Climate Data Record and Near Real-Time Sea Ice Concentration (Windnagel et al. 2021) Source: NSIDC (National Snow and Ice Data Center) Resolution: 25km nominal Timeframe: 1980 to 2021 Notes: Name: Sea Ice Duration Anomalies Desc: Anomalies in sea ice duration show difference in duration of sea ice from the long term mean, where sea ice duration is the

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 dataset is a document describing the Pelagic Tunicates of the Southern Ocean. It lists all the known Southern Ocean species and with illustrated diagrams provides a guide to their taxonomic identification. The document is available for download as a pdf from the provided URL.

This dataset is intended for general use in spatial planning and management to identify areas where benthic marine assemblages are likely to differ from each other in the Southern Ocean. We achieve this by using a hierarchical spatial classification of ecoregions, bathomes and environmental types. Ecoregions are defined according to available data on biogeographic patterns and environmental drivers on dispersal. Bathomes are identified according to depth strata defined by species distributions. Environmental types are uniquely classified according to the geomorphic features found within the bathomes in each ecoregion. This circum-Antarctic map of environmental types can be used to support spatial management aimed at conserving benthic biodiversity across the entire Southern Ocean. The study area spans the region managed by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). The northern boundary of this region is a line approximating the location of the Polar Front. The southern boundary was defined as the northern edge of the permanent ice shelf of the Antarctic continent. The shapefile can be used to identify three levels of the hierarchical classification (see Fig. 1 of Douglass et al., 2014): 1) Level 1: Ecoregions 2) Level 2b: Geomorphic features nested in each ecoregion 3) Level 3: Environmental Types The dataset cannot be used to analyse a level 2a nesting since for some geomorphic features (e.g. seamounts and canyons) the nested bathomes were combined when generating environmental types. If a level 2a nesting is required please contact douglass.lucinda@gmail.com The shapefile contains ten fields: EcoID- Abbreviated Level 1 benthic ecoregion names Ecoregion- Level 1 benthic ecoregion names Geomorph2- Geomorphic features BathID- Bathome identification number which can be used to sort the depth classes Bathome2 - Bathome EcoGeo- Level 2b nesting of geomorphic features in each ecoregion EnvTyp- Level 3 environmental types GeoClsID- Geomorphic class identification number GeoCls- Geomorphic classes Sqkm- Area in square kilometers