From the abstract of some of the referenced papers: An expert system is being developed which will apply knowledge-based techniques to the automated interpretation of remotely sensed sea-ice images taken over East Antarctica by the NOAA series of meteorological satellites. It is capable of accepting satellite images, deriving characteristic features from them and then performing knowledge-based reasoning to identify regions of cloud, land, open water and various categories of sea-ice. XXXXXXXXXXXXX This paper describes the system design of SPARTEX, a system developed to use information from remote sensing and geographic information systems linked to expert systems. It aims to automate the process of classifying information about the actual or potential use of part of the earth's surface. See the link below for public details on this project.

Metadata record for data expected from ASAC Project 2767 See the link below for public details on this project. A multidisciplinary survey of the processes linking sea ice with biological elements of Antarctic marine ecosystems was conducted in winter 2007. The survey provided large-scale information on sea ice biological and physical parameters in the 100-130 degree East sector off East Antarctica. The distribution of sea ice algae and krill were measured using various methods including ice coring surveys and trawls. These measurements were complemented by shipborne measurements and an intensive sea ice sampling program. Use of an ROV was attempted but did not result in quantitative/geo-referenced data. Under-ice video files are available from the Chief-Investigator. Individual word documents are available from this metadata record for each ice station. These contain information on the ice station number, date and time of record and the parameters/ samples.

Four ice monitoring stations were set up at Davis in 1993 (a fifth station was added after the first two months), with regular recordings of ice depth, snow cover and ablation made for each station by taking a sea ice core. Observations of the general condition of the drilled cores were also recorded. Observations were made at irregular intervals (roughly every 1-2 weeks). The observations for each individual day are listed, along with a summary table. These records are stored as handwritten files, and are archived at the Australian Antarctic Division.

The development of an operational sea ice mapping system. This metadata record refers to the development and testing of an prototype system, ICEMAPPER, to interpret NOAA AVHRR imagery on a semi-automatic basis, off the Southern Ocean near to the Antarctic coast. From the abstract of one of the referenced papers: This paper reports work towards the development of a semi-automated technique for creating sea-ice and cloud maps from Advanced Very High Resolution Radiometer (AVHRR) images of the Southern Ocean near to the Antarctic coast. The technique is implemented as a computer-based system which applies a number of classification rules to the five bands of an AVHRR image and classifies each pixel in the image as representing open water, low cloud, high cloud or one of several different sea ice concentration categories. The map produced by the system is then displayed and an experienced sea ice forecaster evaluates the result. If it is deemed satisfactory the map is saved on disk. If not, the expert can alter various parameters within the classification rules to produce a satisfactory map. Experience so far has shown that judicious, but reasonably minor, changes to the rule parameters can produce a satisfactory sea-ice map relatively quickly in most cases. The system is also capable of effectively distinguishing cloudy from clear pixels but it does not accurately distinguish high cloud from low cloud in some of the images. Current work is being undertaken to improve the cloud classification rules.

Metadata record for data from ASAC Project 2695 See the link below for public details on this project. Variations in the winter extent of sea ice are thought to have profound effects on biological productivity, such as algal growth and the reproduction of Antarctic krill, with subsequent flow-on effects through the food web. This pilot study aims to measure the winter foraging patterns of Weddell seals (Leptonychotes weddellii) as a first step in investigating their role in the winter sea-ice zone. Our specific objectives are to: 1. Obtain satellite tracks from a sample of adult female Weddell seals 2. Collect diving behaviour (dive depth, duration and frequency) from a sub-set of these seals 3. Collect high precision water temperature data from a subset of these seals These data will enable us to assess the feasibility of including Weddell seals as a candidate species in a long-term study of winter sea-ice and predator performance. This project has now been merged into project number 2794 (ASAC_2794). It also contains data collected as part of project 1171 (ASAC_1171). The download file contains 13 Access Databases containing data from this project. An excel spreadsheet summarising the databases is also included. The data have also been loaded into the Australian Antarctic Data Centre's ARGOS tracking database. The database can be accessed at the provided URL.

Metadata record for data from ASAC Project 2702 See the link below for public details on this project. Sea-ice algae are the basis of the Antarctic food web and are essential for healthy functioning of the Antarctic ecosystem. These algae exploit a unique niche within this extreme environment. Using advanced photosynthetic analysis we will examine the mechanisms which influence the productivity of sea-ice algae. The objective of this project is to understand the processes of light acclimation and photo-protection employed by sea-ice algae under extremely low temperature conditions. Several new hypotheses have been proposed in a recent review of low temperature acclimation of higher plants (Oquist and Huner, 2003). To further understand the remarkable tolerance of sea-ice algae to photoinhibition, we propose to test several of these hypotheses. Sea-ice algae fix inorganic carbon that forms the basis of the Southern Ocean food web. Sea ice covers up to 20 million km2 of the Southern Ocean each year. Global climate change will decrease the sea-ice thickness and distribution (IPCC, 2001); however subtle changes in temperature and light penetration will also have profound negative impacts on the photosynthetic efficiency of the sea-ice microalgae before any macroscale changes take place. Sea-ice algae are essentially the only food source for invertebrates and fish for up to nine months of the year. During winter and spring, krill (Euphausia sp.) have been observed feeding directly on sea-ice algae. Further, changes in sea-ice productivity will have a cascade effect further up the food web. Therefore, understanding how physical driving forces (temperature and light) affect sea-ice algae productivity will be critical to our ability to predict the effects of climate change and sustainably manage this unique and vulnerable ecosystem. Our primary objective is: To understand the processes of light acclimation and photo-protection employed by sea-ice algae under extremely low temperature conditions, with an aim to better understanding the potential implications of global climate change on the Antarctic sea-ice ecosystem.

These data give the maximum extent of sea ice in the southern hemisphere by day and by winter season and the mean maximum extent by month. Data cover the 1979/1980 to 2007/2008 seasons. The data are available in csv files and, in the case of the mean monthly data, as point and line shapefiles.

Water depth measurements were taken in Long Fjord during early winter in 2007. The measurements were collected by Graham Cook, station leader at Davis Station in the Australian Antarctic Territory. The measurements were made by dropping a weighted line off the back of a quad bike, after drilling a hole through the sea ice. Measurements were made approximately every 100 metres. The download file contains a csv spreadsheet which lists each waypoint, plus the corresponding water depth and any comments. The text file contains the waypoint information collected by the Garmin GPS unit. Data in the text file are comma separated and are interpreted as follows: WP,D,001 (waypoint) , -68.51341000, 78.06903000,(Latitude and Longitude) 05/25/2007, 10:25:35, (Date and time Downloaded to Computer) 24-MAY-07 11:40:42 (Date and time of reading). Time is in local time. Vegetation was found on the weight that we used when we first started at the seaward end of the Fjord and then again in shallow water between Brookes Hut and a small island 800 or 900 metres out from Brookes. The weight is quite smooth and does not pick up a lot. The reference given below provides some further information about previously collected bathymetry data in Long Fjord. Furthermore, also see the metadata records: 'Bathymetric data of Long and Tryne Fjords at Vestfold Hills, Antarctica, collected in December 1999 [VH_bathy_99]' 'Interpolated bathymetry of Long and Tryne Fjords, Vestfold Hills, Antarctica [long_tryne_bathy]' The fields in this dataset are: Waypoint Latitude Longitude Water Depth Date Time

In September 2006, twenty-three scientists from six countries attended an Experts Workshop on Bioregionalisation of the Southern Ocean held in Hobart, Australia. The workshop was hosted by the Antarctic Climate and Ecosystems Cooperative Research Centre, and WWF-Australia, and sponsored by Antarctic expedition cruise operator, Peregrine Adventures. The workshop was designed to assist with the development of methods that might be used to partition the Southern Ocean for the purposes of large-scale ecological modelling, ecosystem-based management, and consideration of marine protected areas. The aim of the workshop was to bring together scientific experts in their independent capacity to develop a 'proof of concept' for a broad-scale bioregionalisation of the Southern Ocean, using physical environmental data and satellite-measured chlorophyll concentration as the primary inputs. Issues examined during the workshop included the choice of data and extraction of relevant parameters to best capture ecological properties, the use of data appropriate for end-user applications, and the relative utility of taking a hierarchical, non-hierarchical, or mixed approach to regionalisation. The final method involved the use of a clustering procedure to classify individual sites into groups that are similar to one another within a group, and reasonably dissimilar from one group to the next, according to a selected set of parameters (e.g. depth, ice coverage, temperature). The workshop established a proof of concept for bioregionalisation of the Southern Ocean, demonstrating that this analysis can delineate bioregions that agree with expert opinion at the broad scale. Continuation of this work will be an important contribution to the achievement of a range of scientific, management and conservation objectives, including large-scale ecological modelling, ecosystem-based management and the development of an ecologically representative system of marine protected areas. This metadata record provides links to the report from that workshop, the appendices to that report, and the ArcGIS files and Matlab code used during the workshop. The report is in PDF format. The Appendices to the report are in PDF format and contain: Appendix 1: Approaches to bioregionalisation - examples presented during the workshop Antarctic Environmental Domains Analysis CCAMLR Small-Scale Management Units for the fishery Antarctic krill in the SW Atlantic Australian National Bioregionalisation: Pelagic Regionalisation Selecting Marine Protected Areas in New Zealand's EEZ Appendix 2: Technical information on approach to bioregionalisation Appendix 3: Descriptions of datasets used in the analysis Appendix 4: Results of secondary regionalisation using ice and chlorophyll data Appendix 5: Biological datasets of potential use in further bioregionalisation work Appendix 6: Details of datasets, Matlab code and ArcGIS shapefiles included on the CD The ArcGIS archive is in zip format and contains the shapefiles and other ArcGIS resources used to produce the figures in the report. The Matlab archive is in zip format and contains the Matlab code and gridded data sets used during the workshop. See the readme.txt file in this archive for more information. Description of datasets Sea surface temperature (SST) Mean annual sea surface temperatures were obtained from the NOAA Pathfinder satellite annual climatology (Casey and Cornillon 1999). This climatology was calculated over the period 1985-1997 on a global 9km grid. Monthly values were averaged to obtain an annual climatology. Casey, K.S. and P. Cornillon (1999) A comparison of satellite and in situ based sea surface temperature climatologies, J. Climate, vol. 12, no. 6, pp. 1848-1863. Bathymetry Depth data were obtained from the GEBCO digital atlas (IOC, IHO and BODC, 2003). These data give water depth in metres and are provided on a 1-minute global grid. Centenary Edition of the GEBCO Digital Atlas, published on CD-ROM on behalf of the Intergovernmental Oceanographic Commission and the International Hydrographic Organization as part of the General Bathymetric Chart of the Oceans, British Oceanographic Data Centre, Liverpool, U.K. See http://www.gebco.net and https://www.bodc.ac.uk/projects/data_management/international/gebco/ A metadata record can be obtained from: http://data.aad.gov.au/aadc/metadata/metadata_redirect.cfm?md=AMD/AU/gebco_bathy_polygons Nutrient concentrations Silicate and nitrate concentrations were obtained from the WOCE global hydrographic climatology (Gouretski and Koltermann, 2004). This climatology provides oceanographic data on a 0.5 degree regular grid on a set of 45 standard levels covering the depth range from the sea surface to 6000m. The silicate and nitrate concentrations were calculated from seawater samples collected using bottles from stationary ships. The nutrient concentrations at the 200m depth level were used here; concentrations are expressed in micro mol/kg. https://odv.awi.de/data/ocean/woce-global-hydrographic-climatology/ Gouretski, V.V., and K.P. Koltermann, 2004: WOCE Global Hydrographic Climatology. Technical Report, 35, Berichte des Bundesamtes fur Seeschifffahrt und Hydrographie. Insolation (PAR) The mean summer climatology of the photosynthetically-active radiation (PAR) at the ocean surface was obtained from satellite estimates (Frouin et al.). These PAR estimates are obtained from visible wavelengths and so are not available over cloud- or ice-covered water, or in low-light conditions including the austral winter. Hence in the sea ice zone, this climatology represents the average PAR calculated over the period for which the water was not ice-covered. https://oceancolor.gsfc.nasa.gov/cgi/l3 Robert Frouin, Bryan Franz, and Menghua Wang. Algorithm to estimate PAR from SeaWiFS data Version 1.2 - Documentation. Chlorophyll-a Mean summer surface chlorophyll-a concentrations were calculated from the SeaWiFS summer means. We used the mean of the 1998-2004 summer values. Chlorophyll concentrations are expressed in mg/m^3. https://oceancolor.gsfc.nasa.gov/cgi/l3 Sea ice We calculated the mean fraction (0-1) of the year for which the ocean was covered by at least 15% sea ice. These calculations were based on satellite-derived estimates of sea ice concentration spanning 1979-2003. http://nsidc.org/data/nsidc-0079.html Comiso, J. (1999, updated 2005). Bootstrap sea ice concentrations for NIMBUS-7 SMMR and DMSP SSM/I. Boulder, CO, USA: National Snow and Ice Data Center. Digital media. Southern Ocean Fronts These are the front positions as published by Orsi et al. (1995). Orsi A, Whitworth T, III, Nowlin WD, Jr (1995) On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research 42:641-673 Use of these data are governed by the following conditions: 1. The data are provided for non-commercial use only. 2. Any publication derived using the datasets should acknowledge the Australian Antarctic Data Centre as having provided the data and the original source (see the relevant metadata record listed in the description below for the proper citation).

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: distance_antarctica Distance to nearest part of Antarctic continent (Antarctic only) Source data: A modified version of ESRI's world map shapefile Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. ---- Distance to nearest seabird breeding colony (Antarctic only) File: distance_colony Antarctic source data: Inventory of Antarctic seabird breeding sites, collated by Eric Woehler. http://data.aad.gov.au/aadc/biodiversity/display_collection.cfm?collection_id=61. Processing steps: The closest distance of each grid point to the colonies was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. ---- Distance to maximum winter sea ice extent File: distance_max_ice_edge Source data: SMMR-SSM/I passive microwave estimates of daily sea ice concentration from the National Snow and Ice Data Center (NSIDC). Processing steps: Antarctic: Mean maximum winter sea ice extent was derived from daily estimates of sea ice concentration as described at https://data.aad.gov.au/metadata/records/sea_ice_extent_winter. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Arctic: The median March winter sea ice extent was obtained from the NSIDC at http://nsidc.org/data/g02135.html. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Reference: Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated 2008. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I passive microwave data. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. tp://nsidc.org/data/nsidc-0051.html ---- Distance to shelf break File: distance_shelf Distance to nearest area of sea floor of depth 500m or less. Derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points in less than 500m of water (i.e. over the shelf) were assigned negative distances. See also distance to upper slope ---- Distance to subantarctic islands (Antarctic only) File: distance_subantarctic_islands Distance to nearest land mass north of 65S (includes land masses of e.g. South America, Africa, Australia, and New Zealand). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. ---- Distance to canyon File: distance_to_canyon Distance to the axis of the nearest canyon (Antarctic only) Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances to nearest canyon axis calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. NOTE: source data extend only as far north as 45S. Do not rely on this layer near or north of 45S. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10 ---- Distance to polynya File: distance_to_polynya Distance to the nearest polynya area (Antarctic only) Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: The seaice_gt_85 layer (see below) was used. Pixels which were (on average) covered by sea ice for less than 35% of the year were identified. The distance from each grid point on the 0.1-degree grid to the nearest such polynya pixel was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. (NB the threshold of 35% was chosen to give a good empirical match to the polynya locations identified by Arrigo and van Dijken (2003), although the results were not particularly sensitive to the choice of threshold. Reference: Arrigo KR, van Dijken GL (2003) Phytoplankton dynamics within 37 Antarctic coastal polynya systems. Journal of Geophysical Research, 108, 3271. http://dx.doi.org/10.1029/2002JC001739 ---- Distance to upper slope (Antarctic only) File: distance_upper_slope Distance to the "upper slope" geomorphic feature from the Geoscience Australia geomorphology data set. This is probably a better indication of the distance to the Antarctic continental shelf break than the "distance to shelf break" data (above). Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points inside of an "upper slope" polygon were assigned negative distances. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10 ---- Fast ice File: fast_ice The average proportion of the year for which landfast sea ice is present in a location Source data: 20-day composite records of East Antarctic landfast sea-ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: The average proportion of the year for which each pixel was covered by landfast sea ice was calculated as an average across 2001--2008. Data were regridded to the 0.1-degree grid using bilinear interpolation. Distance to fast ice File: distance_to_fast_ice Distance to the nearest location where fast ice is typically present. Source data: 20-day composite records of East Antarctic landfast sea ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: Pixels in the landfast sea ice data that were associated with fast ice presence for more than half of the year (on average) were identified. The distance from each pixel in the 0.1-degree grid to the nearest of these fast ice pixels was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Reference: Fraser AD, Massom RA, Michael KJ, Galton-Fenzi BK and Lieser JL (2012) East Antarctic landfast sea ice distribution and variability, 2000-08. Journal of Climate 25:1137-1156. See also: http://data.aad.gov.au/aadc/metadata/metadata_redirect.cfm?md=AMD/AU/modis_20day_fast_ice ---- Seafloor temperature File: floor_temperature Source data: Original data derived from World Ocean Atlas 2005 data and provided on a 1-degree grid. Processing steps: Isolated missing pixels (i.e. single pixels of missing data with no surrounding missing pixels) were filled using bilinear interpolation. Data provided in two versions: one regridded from 1-degree grid using nearest neighbour interpolation (floor_temperature) and the other using bilinear interpolation (floor_temperature_interpolated). Reference: Clarke, A. et al. (2009) Spatial variation in seabed temperatures in the Southern Ocean: Implications for benthic ecology and biogeography. Journal of Geophysical Research 114:G03003. doi:10.1029/2008JG000886 ---- Geomorphology File: geomorphology Geomorphic feature classification Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10 Geomorphic feature class names and their corresponding values in the gridded files: 1: Abyssal_Plain 2: Bank_Wave_Affected 3: Coastal_Terrane 4: Contourite_Feature 5: Cross_Shelf_Valley 6: Fracture_Zone 7: Iceshelf_Cavity 8: Island_Arc 9: Island_Coastal_Terrane 10: Lower_Slope 11: Margin_Ridges 12: Marginal_Plateau 13: Mid_Ocean_Ridge_Valley 14: Plateau 15: Plateau_Slope 16: Ridge 17: Rough_Seafloor 18: Seamount 19: Seamount_Ridges 20: Shelf_Bank 21: Shelf_Deep 22: Structural_Slope 23: Trench 24: Trough 25: Trough_Mouth_Fan 26: Upper_Slope 27: Volcano ---- Light budget File: light_budget Annual light budget (cumulative solar radiation) reaching the water surface. Processing steps: As per Clark et al. (in press). Daily incident solar radiation was modelled assuming a cloud-free sky (Suri and Hofierka 2004). Sea ice data (AMSR-E sea ice concentration) were used as a mask: if sea ice was present on a given day then the solar radiation reaching the ocean surface was assumed to be zero. The annual light budget for a given pixel was therefore calculated as the sum of daily solar radiation values on all days when sea ice was not present. The values here are the mean annual light budget over the 2002/03 to 2010/11 austral summer seasons (1-Jul to 30-Jun). Calculations were made on the AMSR-E 6.25km polar stereographic grid, and then interpolated to the 0.1-degree rectangular grid using triangle-based linear interpolation. References: Clark GF, Stark JS, Johnston EL, Runcie JW, Goldsworthy PM, Raymond B, Riddle MJ (in press) Light-driven tipping points in polar ecosystems. Global Change Biology. http://dx.doi.org/10.1111/gcb.12337 Suri M, J Hofierka (2004) A new GIS-based solar radiation model and its application to photovoltaic assessments. Transactions in GIS, 8, 175-190 ---- Mixed layer depth File: mixed_layer_depth_summer_climatology and mixed_layer_depth_summer_climatology_interpolated Summer mixed layer depth climatology from ARGOS data Processing steps: Data provided in two versions: one regridded from 2-degree grid using nearest neighbour interpolation (mixed_layer_depth_summer_climatology) and the other using bilinear interpolation (mixed_layer_depth_summer_climatology_interpolated). Reference: de Boyer Montegut, C., G. Madec, A. S. Fischer, A. Lazar, and D. Iudicone (2004), Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology, J. Geophys. Res., 109, C12003, doi:10.1029/2004JC002378. http://www.ifremer.fr/cerweb/deboyer/mld/home.php ---- Sea ice cover File: seaice_gt85 Proportion of time the ocean is covered by sea ice of concentration 85% or higher. Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: Concentration data from 1-Jan-2003 to 31-Dec-2010 used. The fraction of time each pixel was covered by sea ice of at least 85% concentration was calculated for each pixel in the original (polar stereographic) grid. Data then regridded to 0.1-degree grid using triangle-based linear interpolation. Reference: Spreen, G., L. Kaleschke, and G. Heygster (2008), Sea ice remote sensing using AMSR-E 89 GHz channels, J. Geophys. Res., doi:10.1029/2005JC003384 https://seaice.uni-bremen.de/sea-ice-concentration/ ---- Sea ice summer variability File: seaice_summer_variability Variability of sea ice cover during summer months Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: Daily estimates of sea ice concentration across December, January, and February of a given austral summer season were collated. For each pixel, the standard deviation of these values was calculated. The values given here are averaged over the 2002/03 to 2009/10 austral summer seasons. Reference: Spreen, G., L. Kaleschke, and G. Heygster (2008), Sea ice remote sensing using AMSR-E 89 GHz channels, J. Geophys. Res., doi:10.1029/2005JC003384 https://seaice.uni-bremen.de/sea-ice-concentration/ ---- Sea surface height variables NOTE: The sea surface height-related data are derivative works of level-4 gridded altimetry data (data courtesy of Ssalto/Duacs, Aviso, and CNES; http://www.aviso.oceanobs.com/duacs/). These derivative works are available for scientific purposes ONLY. Sea surface height File: ssh Source data: CNES-CLS09 Mean Dynamic Topography v1.1 (Rio et al., 2009) Processing steps: Regridded to 0.1-degree grid using bilinear interpolation. SSH spatial gradient File: ssh_spatial_gradient The spatial gradient (in mm/km) of the mean dynamic topography. Source data: CNES-CLS09 Mean Dynamic Topography v1.1 (Rio et al., 2009) Processing steps: Gradient calculated on the native 0.25-degree grid and interpolated to 0.1-degree grid using bilinear interpolation. SSH variability File: ssha_variability The variability of sea surface height over time Source data: SSHA data from http://www.aviso.oceanobs.com/en/data/products/sea-surface-height-products/global/index.html Processing steps: Weekly SSHA data covering the period 14-Oct-1992 to 14-Oct-2007 were used. For each pixel in the native 1/3-degree Mercator grid, the standard deviation of SSHA values over that period was calculated. Data were then interpolated to 0.1-degree grid using bilinear interpolation. Reference: Rio, M-H, P. Schaeffer, G. Moreaux, J-M Lemoine, E. Bronner (2009) : A new Mean Dynamic Topography computed over the global ocean from GRACE data, altimetry and in-situ measurements . Poster communication at OceanObs09 symposium, 21-25 September 2009, Venice ---- SST summer File: sst_summer_climatology Source data: Sea surface temperature 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 on polar stereographic grid. 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/ ---- SST spatial gradient File: sst_spatial_gradient Source data: Sea surface temperature summer climatology from MODIS Aqua. Antarctic: Climatology spans the 2002/03 to 2009/10 austral summer seasons. Spatial gradient of the SST (degrees C per km) calculated on the original 9km resolution data, following the equation given in http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=How%20Slope%20works. Gradient values were then 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/ ---- Surface wind File: surface_wind_annual Source data: Average 10m wind (2000-2010) from Monthly NCEP/DOE Reanalysis 2 Processing steps: Monthly mean 10m wind speed (from u- and v-wind components) from Jan-2000 to Dec-2010 was averaged. Data interpolated from original 2.5-degree grid to 0.1-degree grid using bilinear interpolation. Reference: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html ---- Salinity 0m winter File: salinity_0_winter_climatology and salinity_0_interpolated_winter_climatology Salinity winter climatology at 0m depth. Source data: World Ocean Atlas 2009 (National Oceanographic Data Center, Silver Springs, MD, U.S.A.) http://www.nodc.noaa.gov/OC5/WOA09/pr_woa09.html Processing steps: Data regridded to 0.1-degree grid using nearest-neighbour interpolation (salinity_0_winter_climatology) and bilinear interpolation (salinity_0_interpolated_winter_climatology). Reference: Antonov, J. I., D. Seidov, T. P. Boyer, R. A. Locarnini, A. V. Mishonov, and H. E. Garcia, 2010. World Ocean Atlas 2009, Volume 2: Salinity. S. Levitus, Ed. NOAA Atlas NESDIS 69, U.S. Government Printing Office, Washington, D.C., 184 pp. ---- Salinity 0m summer See above (WOA) ---- Salinity 50m winter See above (WOA) ---- Salinity 50m summer See above (WOA) ---- Salinity 200m winter See above (WOA) ---- Salinity 200m summer See above (WOA) ---- Salinity 500m winter See above (WOA) ---- Salinity 500m summer See above (WOA) ---- NOX and Silicate 0m winter See above (WOA) File: nox_0_winter_climatology, nox_0_interpolated_winter_climatology; and si_0_winter_climatology, si_0_interpolated_winter_climatology Reference: Garcia, H. E., R. A. Locarnini, T. P. Boyer, and J. I. Antonov, 2010. World Ocean Atlas 2009, Volume 4: Nutrients (phosphate, nitrate, silicate). S. Levitus, Ed. NOAA Atlas NESDIS 71, U.S. Government Printing Office, Washington, D.C., 398 pp. ---- NOX and Silicate 0m summer See above (WOA) ---- NOX and Silicate 50m summer See above (WOA) ---- NOX and Silicate 50m winter See above (WOA) ---- NOX and Silicate 200m summer See above (WOA) ---- NOX and Silicate 200m winter See above (WOA) ---- Oxygen 0m winter See above (WOA) File: oxygen_0_winter_climatology and oxygen_0_interpolated_winter_climatology Reference: Garcia, H. E., R. A. Locarnini, T. P. Boyer, and J. I. Antonov, 2010. World Ocean Atlas 2009, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation. S. Levitus, Ed. NOAA Atlas NESDIS 70, U.S. Government Printing Office, Washington, D.C., 344 pp. ---- Oxygen 0m summer See above (WOA) ---- Oxygen 50m winter See above (WOA) ---- Oxygen 50m summer See above (WOA) ---- Oxygen 200m winter See above (WOA) ---- Oxygen 200m summer See above (WOA) ---- Temperature 0m winter See above (WOA) File: t_0_winter_climatology and t_0_interpolated_winter_climatology Reference: Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, and H. E. Garcia, 2010. World Ocean Atlas 2009, Volume 1: Temperature. S. Levitus, Ed. NOAA Atlas NESDIS 68, U.S. Government Printing Office, Washington, D.C., 184 pp. ---- Temperature 0m summer See above (WOA) ---- Temperature 50m winter See above (WOA) ---- Temperature 50m summer See above (WOA) ---- Temperature 200m winter See above (WOA) ---- Temperature 200m summer See above (WOA) ---- Temperature 500m summer See above (WOA) ---- Temperature 500m winter See above (WOA) ---- Vertical velocity File: vertical_velocity_250 and vertical_velocity_500 Upward sea water velocity at 250m and 500m depth (Antarctic only) Source data: CSIRO Mk3.5d climate model Processing steps: Mean values calculated from the 20C3M model run 1, averaged over 1980--2000. Values then interpolated from original grid (approximate resolution 0.9 degrees latitude by 1.9 degrees longitude) to 0.1-degree grid using bilinear interpolation. Reference: Gordon et al. (2010) The CSIRO Mk3.5 Climate Model. CAWCR Technical Report 21. http://www.cawcr.gov.au/technical-reports/CTR_021.pdf