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

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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)

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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)

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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/

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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.

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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.

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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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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/

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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/

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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

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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/

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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/

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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

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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.

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Salinity 0m summer

See above (WOA)

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Salinity 50m winter

See above (WOA)

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Salinity 50m summer

See above (WOA)

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Salinity 200m winter

See above (WOA)

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Salinity 200m summer

See above (WOA)

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Salinity 500m winter

See above (WOA)

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Salinity 500m summer

See above (WOA)

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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.

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NOX and Silicate 0m summer

See above (WOA)

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NOX and Silicate 50m summer

See above (WOA)

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NOX and Silicate 50m winter

See above (WOA)

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NOX and Silicate 200m summer

See above (WOA)

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NOX and Silicate 200m winter

See above (WOA)

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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.

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Oxygen 0m summer

See above (WOA)

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Oxygen 50m winter

See above (WOA)

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Oxygen 50m summer

See above (WOA)

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Oxygen 200m winter

See above (WOA)

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Oxygen 200m summer

See above (WOA)

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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.

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Temperature 0m summer

See above (WOA)

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Temperature 50m winter

See above (WOA)

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Temperature 50m summer

See above (WOA)

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Temperature 200m winter

See above (WOA)

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Temperature 200m summer

See above (WOA)

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Temperature 500m summer

See above (WOA)

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Temperature 500m winter

See above (WOA)

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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