The GEBCO_2021 Grid provides global coverage of elevation data in meters on a 15 arc-second grid of 43200 rows x 86400 columns, giving 3,732,480,000 data points. The GEBCO 2021 grid is reformatted as a Cloud Optimised GeoTIFF suitable for online requests and republished for use by science software. Original GEBCO grid was obtained from https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2021/

GEBCO’s (General Bathymetric Chart of the Oceans) gridded bathymetric data set, the GEBCO_2019 Grid, is a global terrain model for ocean and land, providing elevation data, in meters, on a 15 arc-second interval grid. The GEBCO 2019 grid is reformatted as a Cloud Optimised GeoTIFF suitable for online requests and republished for use primarily by software development. Original GEBCO grid was obtained from https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2019/gebco_2019_info.html

Metadata record for data expected ASAC Project 1207 See the link below for public details on this project. ---- Public Summary from Project ---- Project title: 'Effects of variability in ocean surface forcing on the properties of SAMW and AAIW in the South Indian Ocean' This project will study the formation and subduction processes and the properties of Antarctic Intermediate Water and Sub-Antarctic Mode Water as simulated by an Ocean General Circulation model, with particular reference to the South Indian Ocean. The study will attempt to determine how its formation and properties are affected by interannual variations in SST and wind forcing and by differing prescriptions of mixing and convection processes occurring in mid-to high latitude oceanic frontal regions of the Southern Ocean. The investigation of the ocean response in the Indian Ocean will profit from the use of a model employing general orthogonal coordinates and efficient variable resolution grids which are global but concentrated in the Indian sector. From the abstracts of the referenced papers: This article considers how some of the measures used to overcome numerical problems near the North Pole affect the ocean solution and computational time step limits. The distortion of the flow and tracer contours produced by a polar island is obviated by implementing a prognostic calculation for a composite polar grid cell, as has been done at NCAR. The severe limitation on time steps caused by small zonal grid spacing near the pole is usually overcome by Fourier filtering, sometimes supplemented by the downward tapering of mixing coefficients as the pole is approached; however, filtering can be expensive, and both measures adversely affect the solution. Fourier filtering produces noise, which manifests itself in such effects as spurious static instabilities and vertical motions; this noise can be due to the separate and different filtering of internal and external momentum modes and tracers, differences in the truncation at different latitudes, and differences in the lengths of filtering rows, horizontally and vertically. Tapering has the effect of concentrating tracer gradients and velocities near the pole, resulting in some deformation of fields. In equilibrium ocean models, these effects are static and localised in the polar region, but with time-varying forcings or coupling to atmosphere and sea ice it is possible that they may seriously affect the global solution. The marginal stability curve in momentum and tracer time-step space should have asymptotes defined by diffusive, viscous, and internal gravity wave stability criteria; at large tracer time steps, tracer advection stability may become limiting. Tests with various time-step combinations and a flat-bottomed Arctic Ocean have confirmed the applicability of these limits and the predicted effects of filtering and tapering on them. They have also shown that the need for tapering is obviated by substituting a truncation which maintains a constant time step limit rather than a constant minimum wave number over the filtering range. Continuous and finite difference forms of the governing equations are derived for a version of the Bryan-Cox-Semtner ocean general circulation model which has been recast in orthogonal, transversely curvilinear coordinates. The coding closely follows the style of the Geophysical Fluid Dynamics Laboratory modular ocean model No. 1. Curvilinear forms are given for the tracer, internal momentum, and stream function calculations, with the options of horizontal and isopycnal diffusion, eddy-induced transport, nonlinear viscosity, and semiimplicit treatment of the Coriolis force. The model is designed to operate on a rectangular three-dimensional array of points and can accomodate reentrant boundary conditions at both 'northern' and 'east-west' boundaries. Horizontal grid locations are taken as input and need to be supplied by a separate grid generation program. The advantages of using a better behaved and more economical grid in the north polar region are investigated by comparing simulations performed on two curvilinear grids with one performed on a latitude-longitude grid and by comparing filtered and unfiltered latitude-longitude simulations. Resolution of horizontally separated currents in Fram Strait emerges as a key challenge for representing exchanges with the Arctic in global models. It is shown that a global curvilinear grid with variable resolution is an efficient way of providing a high density of grid points in a particular region. In equilibrium experiments using asynchronous time steps, this type of grid has been found to allow a better representation of smaller-scale features in the high-resolution region while maintaining contact with the rest of the World Ocean, provided that lateral mixing coefficients be scaled with grid size so as to maintain marginal numerical stability. In this study, the region of interest is the southern Indian Ocean and, in particular, that of the South Indian Ocean Current. In all experiments, decreased viscosities and diffusivities were found to control tracer gradients on isopycnals but not isopycnal slopes, while thickness diffusivities controlled isopycnal slopes but only to a small degree tracer gradients. Changes to mixing coefficients in the coarse part of the grid had hardly any influence on the frontal properties examined, although they did affect currents in the Indian Ocean to some extent via their control on size of the Antarctic Circumpolar Current and the Pacific-Indian Throughflow.

Metadata record for data from ASAC Project 2584 See the link below for public details on this project. The Southern Ocean plays a significant role in the biogeochemical cycling of sulphur due to high spring-summer fluxes of dimethylsulfide (DMS), particularly south of 60 degrees S. Recent DMS flux perturbation simulations have recently highlighted the key role of the SO between 50-70 degrees S in the DMS-climate feedback hypothesis [Gabric et al., 2003; Gabric et al., 2004]. This project examines the interactions and feedback between marine polar plankton and global climate through the use of biogeochemical and global climate models, and explores the sensitivity of climate to the current and future biogenic production of dimethylsulphide at polar latitudes. This was a modelling project, and as such did not collect any data of its own. Taken from the abstracts of the referenced papers: The global climate is intimately connected to changes in the polar oceans. The variability of sea ice coverage affects deep-water formations and large-scale thermohaline circulation patterns. The polar radiative budget is sensitive to sea-ice loss and consequent surface albedo changes. Aerosols and polar cloud microphysics are crucial players in the radioactive energy balance of the Arctic Ocean. The main biogenic source of sulfate aerosols to the atmosphere above remote seas is dimethylsulfide (DMS). Recent research suggests the flux of DMS to the Arctic atmosphere may change markedly under global warming. This paper describes climate data and DMS production (based on the five years from 1998 to 2002) in the region of the Barents Sea (30-35 degrees E and 70-80 degrees N). A DMS model is introduced together with an updated calibration method. A genetic algorithm is used to calibrate the chlorophyll-a (CHL) measurements (based on satellite SeaWiFS data) and DMS content (determined from cruise data collected in the Arctic). Significant interannual variation of the CHL amount leads to significant interannual variability in the observed and modelled production of DMS in the study region. Strong DMS production in 1998 could have been caused by a large amount of ice algae being released in the southern region. Forcings from a general circulation model (CSIRO Mk3) were applied to the calibrated DMS model to predict the zonal mean sea-to-air flux of DMS for contemporary and enhanced greenhouse conditions at 70-80 degrees N. It was found that significantly decreasing ice coverage, increasing sea surface temperature and decreasing mixed-layer depth could lead to annual DMS flux increases of more than 100% by the time of equivalent CO2 tripling (the year 2080). This significant perturbation in the aerosol climate could have a large impact on the regional Arctic heat budget and consequences for global warming. ############### The response of oceanic phytoplankton to climate forcing in the Arctic Ocean has attracted increasing attention due to its special geographical position and potential susceptibility to global warming. Here, we examine the relationship between satellite derived (sea-viewing wide field-of-view sensor, SeaWiFS) surface chlorophyll-a (CHL) distribution and climatic conditions in the Barents Sea (30-35 degrees E, 70-80 degrees N) for the period 1998-2002. We separately examined the regions north and south of the Polar Front (~76 degrees N). Although field data are rather limited, the satellite CHL distribution was generally consistent with cruise observations. The temporal and spatial distribution of CHL was strongly influenced by the light regime, mixed layer depth, wind speed and ice cover. Maximum CHL values were found in the marginal sea-ice zone (72-73 degrees N) and not in the ice-free region further south (70-71 degrees N). This indicates that melt-water is an important contributor to higher CHL production. The vernal phytoplankton bloom generally started in late March, reaching its peak in late April. A second, smaller CHL peak occurred regularly in late summer (September). Of the 5 years, 2002 had the highest CHL production in the southern region, likely due to earlier ice melting and stronger solar irradiance in spring and summer. ############### Arctic ecosystems and global climate are closely related. This paper studies the distributions and the coupling relationship between Chlorophyll a (Chl a) and aerosol optical thickness (AOD) in Greenland Sea (10 degrees W - 10 degrees E, 70 degrees N - 85 degrees N) during 2003-2009 using satellite ocean colour data from MODIS Aqua. The regression analysis of EViews shows that Chl a and AOD are correlated with a time lag. Based on the lag of Chl a and AOD, co-integration inquiry finds that there is co-integration between them, which means that they will have a long-term equilibrium relationship. In general, Chl a starts from March, and gradually increases to a peak in July. The peak of AOD is usually in May, 11 weeks before Chl a. After shifting the time lag, the correlation between Chl a and AOD is 0.98 in the spring in 80 degrees N - 85 degrees N. Apart from the year of 2005, when Chl a and AOD had no time lag, the other years' intervals increased about 6 weeks within the 7 years. The peaks of AOD shifted one and a half months ahead, while Chl a also shifted about two months ahead. In northern part (75 degrees N - 85 degrees N), Chl a and AOD were much higher in the summer and autumn of 2009 than those in other years. The reason could be the much larger ice melting and higher AOD. The results indicate that the global warming has significant impact on the ecosystem in the Arctic Ocean.

From the parent record held in the GCMD: The data sets in the CDC archive called "Reynolds SST' and "Reconstructed Reynolds SST" were discontinued on 1 April 2003. A new OI SST data set is available as described here, which includes a new analysis for the historical data and updates into the future. NCEP will not provide new data for the "Reynolds SST" after December 2002 and CDC will remove the "Reynolds SST" data set on 1 April 2003. TO SEE THE NEW DATASET, PLEASE SEARCH THE GLOBAL CHANGE MASTER DIRECTORY FOR MORE INFORMATION. REFER TO THE METADATA RECORD (LINKED BELOW): REYNOLDS_SST ############# This metadata record is a modified child record of an original parent record registered at the Global Change Master Directory. (The Entry ID of the parent record is REYNOLDS_SST, and can be found on the GCMD website - see the provided URL). The data described here are a subset of the original dataset. This metadata record has been created for the express use of Australian Government Antarctic Division employees. Reproduced from: http://www.emc.ncep.noaa.gov/research/cmb/sst_analysis/ Analysis Description and Recent Reanalysis The optimum interpolation (OI) sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SSTs plus SSTs simulated by sea ice cover. Before the analysis is computed, the satellite data are adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). A description of the OI analysis can be found in Reynolds and Smith (1994). The bias correction improves the large scale accuracy of the OI. In November 2001, the OI fields were recomputed for late 1981 onward. The new version will be referred to as OI.v2. The most significant change for the OI.v2 is the improved simulation of SST obs from sea ice data following a technique developed at the UK Met Office. This change has reduced biases in the OI SST at higher latitudes. Also, the update and extension of COADS has provided us with improved ship data coverage through 1997, reducing the residual satellite biases in otherwise data sparse regions. The data are available in the following formats: Net CDF Flat binary files Text

Sea ice covers up to 20 million km2 of the Southern Ocean. When present it supports a vigorous ecosystem that provides energy and food for all other marine organisms. Using the latest micro sensor technology, we are examining the factors that effect the productivity of this vital link in the Antarctic marine food web. New data were added to this metadata record in January 2011. These data included FRRF data collected on the CEAMARC, CASO, SIPEX and SAZ-SENSE voyages. A word document in the download file provides details about these datasets, plus those collected on Voyage 1 2009-2010, and voyage 2 2008-2009. The download file also contains a folder labelled "Older data". This data is described below: An explanation of the excel spreadsheet in the download file is as follows: Worksheet 1 is the chlorophyll data Worksheet 3 is the location data CHLOROPHYLL DATA Column A is sample name, the first letter refers to the location data in worksheet 3, the second to the ice flow number and the third to the replicate number Section refers to depth in ice core, measured from the bottom Ignore C Column D is the total volume of melted ice Column E is the volume of D that was filtered Column G is the Fluorometer reading before the addition dilute HCl Column H is the fluorometer reading after the addition of acid Column I is the calculation of chlorophyl concentration in the sample Column K is areal chlorophyll estimate Column L is the mean for the core Column N is the mean for the site Column O is the standard deviation LOCATION DATA Lat, longs and times of each sampling. The first set (B-G) refers to the time sampling started, the second (H-M) to when it finished Project objectives: - Determine the net photosynthesis and primary productivity of the phytoplankton and major sea ice algal communities of the Eastern Antarctic Sea Ice Zone (SIZ). Estimate seasonal and annual algal production and inter annual variability - Obtain data on biomass distribution and variability to establish regional relationships between ice thickness, snow cover, and biomass - Determine the effects of a) Light b) Nutrients (principally nitrate and iron) c) Temperature on photosynthesis and primary production - Determine whether the biomass and productivity of the phytoplankton and sea ice algae in winter and spring limits the biomass or growth of krill - Estimate the effects of climate change on Sea ice Zone primary production Taken from the 2008-2009 Progress Report: Progress against objectives: This project used V2, a spring voyage, to collect underway data to determine surface biomass and primary production. Biomass samples (chlorophyll a) were taken every 3 hours. Productivity estimates by PAM were also made every 3 hours. Productivity measurements by FRRF were made every 1 minute. Nutrient samples were taken at the same time as the biomass samples. Analysis of the biomass samples is complete. Preliminary analysis of the productivity data has commenced. This data is being used for a Masters project (Rob Johnson, IASOS). An iron addition experiment accompanied this monitoring. Iron was added to samples taken every 3 hours and the change in photosynthesis (maximum quantum yield) measured with a PAM. The rate of recovery from iron stress was the principal focus. Most of this data has been submitted as metadata. Using The PAM and FRRF simultaneously also enabled a comparison to be made between these different ways of measuring photosynthesis. Progress was also made on the analysis of FRRF productivity and biomass data collected over several years on the L'Astrolabe transect. Analysis involves quantitative manipulation of FRRF data and correlation with chlorophyll, nutrients, temperature and other biological parameters. A publication arising from this work will be submitted this year. Taken from the 2009-2010 Progress Report: Progress against objectives: We participated in V1 of the Aurora Australis, spring 2009. The objective of this project was to measure surface primary production off East Antarctica. Photosynthetic parameters of phytoplankton under actinic light (L) as well as in darkness (D) were measured using a fast repetition rate fluorometer (FRRF). The parameters included the maximum photochemical efficiency (Fv/FmL,D), the functional absorption cross section of photosystem II (sPSII,L,D) and a turnover time of electron transfer (tL,D). Chlorophyll a concentration was measured by using Turner fluorometer. The photosynthetic parameters, irradiance and chlorophyll a concentration will then be used to estimate primary production of phytoplankton. This field program particularly focussed on the first of the listed objectives, ie 'Determine the net photosynthesis and primary productivity of the phytoplankton and major sea ice algal communities of the Eastern Antarctic Sea Ice Zone (SIZ). Estimate seasonal and annual algal production and inter annual variability'. We have been collecting FRRF-based primary production data from each season and the 2009 data provides the late spring data to supplement data from autumn, winter and summer, collected in previous seasons. We have now built up a comprehensive assessment of season variability which will enable a reliable estimate of annual primary production. These analyses will also provide a detailed snap shot of primary production with which to compare future changes. Preliminary analysis shows clear patterns of variation in Fv/Fm, a parameter that is particularly sensitive to low iron concentration. This data is shown on an accompanying diagram. Productivity analysis is still underway. Much of the work for this project forms part of the PhD project of Cheah Wee.Wee is expected to finish his PhD by December 2010 and it is anticipated that all data analysis for the project will have been completed and the finished manuscripts submitted for publication. He has already had one manuscript form this project accepted (Cheah et al, 2010).

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

These data describe pack ice characteristics in the Antarctic sea ice zone. These data are in the ASPeCt format. National program: Russia Vessel: Akademic Fedorov Dates in ice: 28 Apr 1998 - 05 Jun 1998 Observers: Unknown Translation to ASPeCt data format: Vladimir Smirnov Summary of voyage track: 28/4 Ice edge at approx. 63S, 112E 28/4-1/5 From ice edge to Mirny (93E) 2-9/5 At Mirny 10-16/5 Mirny to Progress (76E) 18-22/5 Progress to Molodezhnaya (46E) 28/5-1/6 Molodezhnaya to Novolazarevskaya (12E) 4-5/6 Novolazarevskaya to ice edge at approx. 63S, 10E The fields in this dataset are: SEA ICE CONCENTRATION SEA ICE FLOE SIZE SEA ICE SNOW COVER SEA ICE THICKNESS SEA ICE TOPOGRAPHY SEA ICE TYPE RECORD DATE TIME LATITUDE LONGITUDE OPEN WATER TRACK SNOW THICKNESS SNOW TYPE SEA TEMPERATURE AIR TEMPERATURE WIND VELOCITY WIND DIRECTION FILM COUNTER FRAME COUNTER FOR FILM VIDEO RECORDER COUNTER VISIBILITY CODE CLOUD WEATHER CODE COMMENTS

These data describe pack ice characteristics in the Antarctic sea ice zone. These data are in the ASPeCt format. National program: Russia Vessel: Mikhail Somov Dates in ice: 28 Feb 1986 - 16 Mar 1986 Observers: Unknown Translation to ASPeCt data format: Vladimir Smirnov Summary of voyage track: 28/2 Ice edge at approx. 69S, 139W 28/2-6/3 In company with vessel 'Kapitan Bondanernko' to approx. 72S, 140W 7-8/3 Independent navigation to Russkaya 14-16/3 Russkaya to ice edge at approx. 68S, 151W The fields in this dataset are: SEA ICE CONCENTRATION SEA ICE FLOE SIZE SEA ICE SNOW COVER SEA ICE THICKNESS SEA ICE TOPOGRAPHY SEA ICE TYPE RECORD DATE TIME LATITUDE LONGITUDE OPEN WATER TRACK SNOW THICKNESS SNOW TYPE SEA TEMPERATURE AIR TEMPERATURE WIND VELOCITY WIND DIRECTION FILM COUNTER FRAME COUNTER FOR FILM VIDEO RECORDER COUNTER VISIBILITY CODE CLOUD WEATHER CODE COMMENTS

These data describe pack ice characteristics in the Antarctic sea ice zone. These data are in the ASPeCt format. National program: Russia Vessel: Akademic Fedorov Dates in ice: 30 Jan 1995 - 15 Mar 1995 Observers: Unknown Translation to ASPeCt data format: Vladimir Smirnov Summary of voyage track: 30/1 Ice edge at approx. 69S, 12E then into Novolazarevskaya (12E) 2-9/2 Novolazarevskaya to Molodezhnaya (46E) to Druzhnaya (76E) 12-16/2 Druzhnaya to Mirny (93E) 22-25/2 Mirny to Druzhnaya 27/2-3/3 Druzhnaya to Molodezhnaya 3-10/3 Manouvering along coast near Molodezhnaya 10-12/3 Molodezhnaya to Novolazarevskaya 15-16/3 Novolazarevskaya to ice edge at approx. 69S, 13E The fields in this dataset are: SEA ICE CONCENTRATION SEA ICE FLOE SIZE SEA ICE SNOW COVER SEA ICE THICKNESS SEA ICE TOPOGRAPHY SEA ICE TYPE RECORD DATE TIME LATITUDE LONGITUDE OPEN WATER TRACK SNOW THICKNESS SNOW TYPE SEA TEMPERATURE AIR TEMPERATURE WIND VELOCITY WIND DIRECTION FILM COUNTER FRAME COUNTER FOR FILM VIDEO RECORDER COUNTER VISIBILITY CODE CLOUD WEATHER CODE COMMENTS