A free GIS for Antarctica
Quantarctica is a collection of Antarctic geographical datasets for research, education, operations, and management in Antarctica, and let you explore, import, visualize, and share Antarctic data. It includes community-contributed, peer-reviewed data from ten different scientific themes and a professionally-designed basemap.
Geographical Information System (GIS) software is designed to capture, store, analyze and view all types of geographical data.
QGIS is a great free, open source alternative to commercial GIS software. It also works without Internet access, so you can bring your GIS software to remote areas during field campaigns.
The Quantarctica data package comprises Antarctic geographic data from data centres worldwide – all wrapped in a project file that works on QGIS. The name “Quantarctica” originated from the old version of QGIS, “Quantum GIS” + Antarctica = Quantarctica.
Contribute to Quantarctica
You are the one to take Quantarctica further – it’s a community effort!
Please send us your suggestions and short justifications for which datasets already available at data centres should be included in a future version of Quantarctica.
Although we welcome all contributions, we’re currently most interested in data from:
- oceanography
- atmospheric sciences
- geology
- biology
We also welcome contributions of your research, such as field data, remote-sensing data, and model outputs. (We can assist with importing your data into Quantarctica – just contact us.)
The Quantarctica package
Quantarctica includes a wide variety of basemap and scientific data. In most cases, the datasets are provided at full resolution and are ready to analyze. You can also mix and match different datasets to create your own custom Quantarctica environment, import them in other software, or share them freely with colleagues.
We provide all metadata and citation information for every dataset in the data folder and in the QGIS project to make using, re-using, and citing data easy!
Your own data can easily be included. Therefore, Quantarctica provides a nice platform for your own GIS package.
So far, we’ve used Quantarctica to:
- Examine geographical data at a range of scales (continental to local)
- Prepare maps for publications and proposals
- View project data together with continental datasets
- Plot GPS-provided current positions with satellite images and other scientific data during field campaigns (real-time GPS positioning using a QGIS plugin)
It could also be a useful tool for teaching about Antarctica in classrooms.
Project Team
Kenny Matsuoka
Principal Investigator
Kenny is a senior researcher at NPI, and has spent over ten field seasons in Antarctica, most recently as part of the MADICE Indo-Norwegian collaboration in Dronning Maud Land. The idea that would eventually become Quantarctica was rooted in a painful experience in his first season as a PI, when his commercial GIS software expired during a traverse in the middle of the Antarctic ice sheet.
Anders Skoglund
Cartographer
Anders is a cartographer and topographer at the NPI’s mapping section, and has produced numerous award-winning maps of the Arctic and Antarctic. He co-founded Quantarctica with Kenny in 2012 and is responsible for compiling and building the basemap layers, layer styling, labeling, and technical optimizations that make Quantarctica look great in any figure or map
George Roth
Project Coordinator
George coordinated the development of Quantarctica 3 from 2016-2018, managing the Editorial Board, discovering and importing new datasets, creating promotional materials, and teaching user workshops.
Stein Tronstad
Head, Environmental Data section, NPI
SCAR SCADM Representative for Norway
Stein directs the Norwegian Polar Data Centre, and helps improve the quality and visibility of Quantarctica. Additionally, as the Norwegian representative to the SCAR SCADM committee, he promotes Quantarctica to the largest international group of Antarctic researchers and coordinators.
Yngve Melvær
Head, Mapping section, NPI
SCAR SCAGI Representative for Norway
Yngve directs NPI’s resources, such as the mapping section, to improve the quality and visibility of Quantarctica. Additionally, as the Norwegian representative to the SCAR SCAGI committee, he promotes Quantarctica to the largest international group of Antarctic researchers and coordinators.
Editorial board
In 2017, the Quantarctica Project Team assembled an Editorial Board of international experts to review and recommend peer-reviewed scientific datasets for all of Quantarctica 3’s scientific themes.
History
Quantarctica was originally developed for in-house use at the Norwegian Polar Institute. Development on a public version started in 2012, and the first version was released in 2013. In 2014, Quantarctica 2 was released, and was recognized as a SCAR product. 2018 saw the release of version 3, which dramatically expanded the breadth and depth of the scientific data package and our outreach efforts.
In 2021, a minor updated version 3.2 was released, which was adapted for the use with QGIS version 3.16.
License and attribution
License
The Quantarctica package is published under a Creative Commons Attribution 4.0 International License.
In published works produced using Quantarctica, you are required to cite each dataset that was used in the work. Quantarctica provides all dataset citation information in each layer’s Metadata tab in QGIS, in a text file in the data folder, and in the online Quantarctica Data Catalog.
Individual datasets may have different licenses, terms of use, and attribution requirements. Please consult the each layer’s Metadata tab in the QGIS layer properties or the online Quantarctica Data Catalog for the licenses, terms, and disclaimers for each dataset.
All geographic names in Quantarctica are provided by the original datasets.
Contact the original data authors for further clarification if necessary.
Attribution
You are required to acknowledge or cite Quantarctica and the Norwegian Polar Institute in your work.
Contact
Please email quantarctica@npolar.no if you have any questions or concerns about these policies.
Download
See the Quantarctica user manual (PDF 1 MB) for installation details and how to optimize QGIS for use with Quantarctica. Updates from version 3.1 are available. See Download Options.
Quick Start
- Download and extract Quantarctica3.zip from a mirror below
- Install QGIS 3.16 from the Software folder or from QGIS.org
- Open Quantarctica32.qgs in QGIS
Download sites
Choose the mirror closest to you for the fastest and most reliable download.
| Country | Hosting institution | Download |
|---|---|---|
| Norway | Norwegian Polar Institute, Tromsø (primary server) | http |
| Australia | Tasmanian Partnership for Advanced Computing at the University of Tasmania, Hobart | ftp http |
| India | National Centre for Antarctic and Ocean Research, Goa | ftp http |
| Japan | Arctic and Antarctic Data archive System at the National Institute of Polar Research, Tokyo | http |
| United States | Polar Geospatial Center, Minneapolis/St. Paul | ftp http |
Thanks to all of our mirror hosts! Is your university or research institution interested in hosting Quantarctica for your part of the world? Send us an email!
Please inform us at quantarctica@npolar.no if you have a download problem.
Download options
Quantarctica3.zip (6 GB, latest version)
- Strongly recommended for most users.
- Includes all basemap, terrain, satellite imagery, and scientific data.
- Project files and documentation
- QGIS 3.16 installers for Windows and Mac
Quantarctica3 (unzipped folder, 8.6 GB, latest version)
- Uncompressed folder exactly as it appears on your hard drive
- Download individual themes, datasets, and files
- Please use an FTP client (we recommend the cross-platform FileZilla) and ensure that you connect to an FTP mirror
Quantarctica31-32updates.zip (2 MB)
- Recommended for existing version 3.1 users.
- Follow these steps carefully.
-
- Unzip the Quantarctica updates zip file in the root folder of Quantarctica.
- When unzipping, replace all files if prompted.
- If the process has created a new subfolder “Quantarctica31-32updates” and unzipped all files and folders in this subfolder, cut and paste its contents one level up in the Quantarctica3 root folder instead.
- Note: The updates package does not contain QGIS software packages and QGIS user guides, these can be downloaded from the QGIS website. See Changelog section below for update content.
-
QGIS for Linux and Android
For Linux/BSD users, please find the instructions to download and install the latest version of QGIS for your platform from the QGIS website. The Quantarctica project file and datasets work with QGIS on these operating systems.
If you’re experiencing any problems or have any tips for these platforms, please share your experiences with us.
Changelog
2021-01-15 New minor release: Quantarctica 3.2
Quantarctica 3.2 includes:
- Quantarctica project file (.qgz) for the latest QGIS version (3.16).
- Updated Quantarctica Get Started guide for Quantarctica in QGIS 3.16.
- Updated guide for making Quantarctica-friendly datasets for QGIS 3.16.
- QGIS specific associated files for every dataset: Style files (.qml) and metadata files (.qmd and .txt)
- The latest QGIS software installers and user guide for Windows and Mac.
2018-06-10 New minor release: Quantarctica 3.1
Quantarctica 3.1 includes:
- New surface mass balance data SAMBA under glaciology theme
- Updated dataset for GeoMap source bibliography under geology theme
- Updated dataset for MEaSUREs flow speed, flow speed errors, and flow speed vectors under glaciology theme
- Minor cartographical improvements in detailed basemap
2018-02-06 New major release: Quantarctica 3.0
Quantarctica 3.0 is optimized for use with QGIS 2.18. This version is Quantarctica’s biggest release yet, adding:
- Eight new themes with over 50 new datasets in over 100 new layers: Atmospheric Science, Biology, Environmental Management, Geology, Ice Cores, Oceanography, Sea Ice, and Social Science
- New additions and updates to the pre-existing Geophysics and Glaciology categories
- The Quantarctica Data Catalog, where you can view preview images, metadata, and citation information for every dataset
- Northward expansion of Quantarctica’s boundary to 40°S
- Improved basemap, terrain, and satellite imagery layers
- Updates, resolution improvements, and stability enhancements to datasets from v1-v2
- New features in QGIS and significant enhancements to project stability, speed, and usability
2014-08-01 New major release: Quantarctica 2.0
Quantarctica 2.0 is optimized for use with QGIS 2.4. This version includes a number of bugfixes, as well as new additions:
- drainage basin boundaries
- blue ice distribution
- new bathymetry data
2013-08-16 Fixed: Satellite imagery index files, script issue
The Actions script for adding satellite image tiles (Landsat CIRREF, Landsat MOS and RADARSAT) contain a redundant line, which may cause errors when trying to load tiles by using the Run Feature Action button and “Add full resolution image from local disk”. To avoid this, follow these steps for all of the three index files inside Quantum GIS:
- Double-click the index layer in the Layers list, so that the Layer Properties window appears.
- In the Actions tab, click once at the “Add full resolution image from local disk” entry in the Action list box, so that the Action properties box below displays the script.
- In the Action box inside Action properties, remove this part of the script: “from os import startfile;”.
- Click the Update selected action button below, then the Save as default button, and OK.
- Save the project.
2013-08-16 Fixed: Satellite imagery index files, script issue
Saving your QGIS project file in another folder than the root of the Quantarctica folder, will make the Actions script for adding satellite image tiles (Landsat CIRREF, Landsat MOS and RADARSAT) not work. Follow these steps for all of the three index files inside Quantum GIS (Note that this will change the reference:
- Double-click the index layer in the Layers list, so that the Layer Properties window appears.
- In the Actions tab, click once at the “Add full resolution image from local disk” entry in the Action list box, so that the Action properties box below displays the script.
- In the Action box inside Action properties, remove this part of the script: “from os import startfile;proj = QgsProject.instance();UriFile = str(proj.fileName());Path = str(os.path.dirname(UriFile));”.
- In the “qgis.utils.iface.addRasterLayer…” part of the script, remove “Path+”
- Insert the location of the Quantarctica Basemap folder on your disk, just before the “/Basemap/Imagery…” part, e.g. “C:/Quantarctica/Basemap/Imagery…”.
- Click the Update selected action button below, (then the Save as default button) and OK.
- Save the project.
Data catalog
Basemaps and miscellaneous data
The Quantarctica basemap is the foundation for any new map project. Whether you’re planning a traverse or starting the all-important Figure 1 for your next paper, the Basemap looks great and runs smoothly thanks to the team of cartographers at the Norwegian Polar Institute.
Simple Basemap Norwegian Polar Institute, 2018
NPI/Quantarctica
A basemap for fast rendering and suitable for initial views and when focusing the most on thematic data viewed on top. The dataset is a merged version of several low resolution datasets in the Antarctic Digital Database (ADD).
Detailed Basemap Norwegian Polar Institute, 2018
NPI/Quantarctica
Antarctic topography and bathymetry, compiled of data from various sources, mainly the Antarctic Digital Database (ADD), RAMP, IBCSO and ETOPO1. It consists of vector themes such as coastlines, rock outcrops, moraines and elevation contours, terrain models (depths) and hillshades. It provides clear but modest cartography, and the level of detail increases when zooming in. Cartographic techniques are used to ensure high rendering performance. See individual layer metadata or contact individual data generators for more information.
COMNAP listed facilities COMNAP, 2017
COMNAP
The Council of Managers of National Antarctic Programs (COMNAP) maintains a curated list of Antarctic facilities (stations, camps, etc.). The COMNAP Antarctic Facilities is a comprehensive list of the 115 Antarctic facilities with a status of ‘Open’ or ‘Temporarily Closed’. See ‘Information’ tab of Excel spreadsheet (`.xls`) in the `dist` directory of the GitHub page for a reference of fields / attributes included in the dataset. The information included in the datasets was provided by each National Antarctic Program to COMNAP and is updated frequently. COMNAP will release scheduled updates of this dataset. If you would like to be notified of changes, you can watch the GitHub repository.
Reference for the use of the material is ‘COMNAP 2017.’
SCAR Composite Gazetteer of Antarctica (CGA) SCAR, 1992/2017
SCAR
The Scientific Committee on Antarctic Research (SCAR) initiates, promotes and co-ordinates a wide range of scientific research programmes in Antarctica, many of which involve significant international collaboration. The SCAR Standing Committee on Antarctic Geographic Information (SC-AGI) co-ordinates the provision of a geographic reference for scientific activities in Antarctica and the dissemination of Antarctic geographic information. The SCAR Composite Gazetteer of Antarctica is an activity conducted in this framework. In 1992 the SCAR Working Group on Geodesy and Geographic Information (WG-GGI), during the XXII SCAR meeting in Bariloche, recognized the need for a composite gazetteer of Antarctica to bring some order to the complex toponymy of Antarctica. The goal of the work programme which evolved from that discussion was to provide the scientific community with two products: 1. compilation of all existing geographic names of Antarctica and 2. a set of guidelines to be followed when proposing new names and when selecting one name from a list of synonyms for a given feature. Because Antarctica does not fall under the sovereignty of any one nation this particular continent is similar, in many respects, to the oceans. In general every country has a recognized body which approves the names of geographic features in the country and also has the power to enforce their use. For Antarctica, however, there is no such single naming authority. Individual countries are responsible for their national policy on, and authorisation and use of, Antarctic names.
SCAR Secretariat (1992, updated 2014 and 2017). Composite Gazetteer of Antarctica, Scientific Committee on Antarctic Research.
IHO-IOC GEBCO Undersea feature names GEBCO, 2017
GEBCO
Point, line, and polygon undersea features for the southern ocean to 40°S. Features are frequently corrected and updated by GEBCO.
Please include the following citation when data from the gazetteer are used or reproduced in reports, presentations and other products: IHO-IOC GEBCO Gazetteer of Undersea Feature Names, www.gebco.net
Additional/Miscellaneous layers NPI, 2018
NPI/Quantarctica
The Quantarctica Project Team has developed and included numerous additional basemap layers, including: Overview place names, Subantarctic stations, Map frame, UTM zones, South Pole, Antarctic Circle, Latitude and Longitude lines, and DEM mosaics (see Terrain Rasters).
http://quantarctica.npolar.no/
Atmosphere
Quantarctica is a unique visualization and analysis environment for atmospheric data, allowing users to show observation and model data with fully customizable color ramps, vector arrows, and more features provided by QGIS. The QGIS Plugin Repository contains user-created tools for managing NetCDF files, contouring point data, and even creating animations from time-stamped layers.
Wind scour zones Nature Geoscience, 2013
Das et al.
Identification of wind-scour zones based on a combination of airborne radar observations, lidar-derived surface roughness and an empirical model.
Das, I., R. E. Bell, T. A. Scambos, M. Wolovick, T. T. Creyts, M. Studinger, N. Frearson, J. P. Nicolas, J. T. M. Lenaerts and M . R. van den Broeke, 2013: Influence of persistent wind scour on the surface mass balance of Antarctica, Nature Geoscience 6, 367-371, doi:10.1038/ngeo1766.
RACMO Average 2m temperature (35km) The Cryosphere, 2014
Van Wessem et al.
RACMO2.3p1 modelled 2 m (near-surface) temperature. Original resolution: ~27 km. Lateral forcing: ERA-Interim. Averaging period: 1979-2011. Unit: C.
Van Wessem, J. M., C. H. Reijmer, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke and E. van Meijgaard, 2014: Updated cloud physics in a regional atmospheric climate model improves the modelled surface energy balance of Antarctica, The Cryosphere 8, 125-135, doi:10.5194/tc-8-125-2014.
RACMO Total sublimation (35km) Journal of Glaciology, 2014
Van Wessem et al.
RACMO2.3p1 modelled total (surface and drifting snow) sublimation. Original resolution: ~27 km. Lateral forcing: ERA-Interim. Averaging period: 1979-2011. Unit: kg m-2 yr-1.
Van Wessem, J. M., C. H. Reijmer, M. Morlighem, J. Mouginot, E. Rignot, B. Medley, I. Joughin, B. Wouters, M. A. DePoorter, J. L. Bamber, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke and E. van Meijgaard, 2014: Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model, Journal of Glaciology 60, 761-770, doi:10.3189/JoG14J051
RACMO Surface mass balance (35km) Journal of Glaciology, 2014
Van Wessem et al.
RACMO2.3p1 modelled surface mass balance. Original resolution: ~27 km. Lateral forcing: ERA-Interim. Averaging period: 1979-2011. Unit: kg m-2 yr-1.
Van Wessem, J. M., C. H. Reijmer, M. Morlighem, J. Mouginot, E. Rignot, B. Medley, I. Joughin, B. Wouters, M. A. DePoorter, J. L. Bamber, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke and E. van Meijgaard, 2014: Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model, Journal of Glaciology 60, 761-770, doi:10.3189/JoG14J051
RACMO Total precipitation rate (35km) Journal of Glaciology, 2014
Van Wessem et al.
RACMO2.3p1 modelled total (rain and snow) annual precipitation. Original resolution: ~27 km. Lateral forcing: ERA-Interim. Averaging period: 1979-2011. Unit: kg m-2 yr-1.
Van Wessem, J. M., C. H. Reijmer, M. Morlighem, J. Mouginot, E. Rignot, B. Medley, I. Joughin, B. Wouters, M. A. DePoorter, J. L. Bamber, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke and E. van Meijgaard, 2014: Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model, Journal of Glaciology 60, 761-770, doi:10.3189/JoG14J051
RACMO Average absolute 10m wind speed (35km) The Cryosphere, 2014
Van Wessem et al.
RACMO2.3p1 modelled absolute 10 m (near-surface) wind speed. Original resolution: ~27 km. Lateral forcing: ERA-Interim. Averaging period: 1979-2011. Unit: m s-1.
Van Wessem, J. M., C. H. Reijmer, J. T. M. Lenaerts, W. J. van de Berg, M. R. van den Broeke and E. van Meijgaard, 2014: Updated cloud physics in a regional atmospheric climate model improves the modelled surface energy balance of Antarctica, The Cryosphere 8, 125-135, doi:10.5194/tc-8-125-2014.
Biology
Quantarctica contains satellite and ship-board measurements of biology throughout Antarctica’s coast and the entire Southern Ocean. Antarctic biologists can quickly and easily import their own field measurements and sample locations and examine their relation to sea ice, ocean nutrients, and more.
MEOP CTD-equipped seal tracks GRL, 2013
Roquet et al.
Traces of available temperature/salinity data collected by sensors attached to marine mammals. Almost all in situ oceanic data in the Southern Ocean were collected in austral summer. Few exceptions are these data collected by marine mammals carrying CTD (conductivity-temperature-depth profiler). We only show the geographic location of data. Data can be downloaded from the MEOP website.
Roquet F., Wunsch C., Forget G., Heimbach P., Guinet C., Reverdin G., Charrassin J.-B., Bailleul F., Costa D. P., Huckstadt L. A., Goetz K. T., Kovacs K. M., Lydersen C., Biuw M., Nøst O. A., Bornemann H., Ploetz, J., Bester M. N., Mcintyre T., Muelbert M. C., Hindell M. A., McMahon C. R., Williams G., Harcourt R., Field I. C., Chafik L., Nicholls K. W., Boehme L., and Fedak M. A., 2013. Estimates of the Southern Ocean General Circulation Improved by Animal-Borne Instruments. Geoph. Res. Letts., 40:1-5. doi: 10.1002/2013GL058304.
Emperor penguin colonies PLoS ONE, 2012
Fretwell et al.
Emperor Penguin colonies and population estimates using very high resolution satellite imagery acquired in the 2009 breeding season.
Fretwell PT, LaRue MA, Morin P, Kooyman GL, Wienecke B, Ratcliffe N, et al. (2012) An Emperor Penguin Population Estimate: The First Global, Synoptic Survey of a Species from Space. PLoS ONE 7(4): e33751. https://doi.org/10.1371/journal.pone.0033751
KRILLBASE Earth Syst. Sci. Data, 2017
Atkinson et al.
KRILLBASE is a data rescue and compilation project to improve the availability of information on two key Southern Ocean zooplankton: Antarctic krill and salps. We provide a circumpolar database that combines 15?194 scientific net hauls (1926 to 2016) from 10 countries. These data provide a resource for analysing the distribution and abundance of krill and salps throughout the Southern Ocean to support ecological and biogeochemical research as well as fisheries management and conservation.
Atkinson, A., Hill, S. L., Pakhomov, E. A., Siegel, V., Anadon, R., Chiba, S., Daly, K. L., Downie, R., Fielding, S., Fretwell, P., Gerrish, L., Hosie, G. W., Jessopp, M. J., Kawaguchi, S., Krafft, B. A., Loeb, V., Nishikawa, J., Peat, H. J., Reiss, C. S., Ross, R. M., Quetin, L. B., Schmidt, K., Steinberg, D. K., Subramaniam, R. C., Tarling, G. A., and Ward, P.: KRILLBASE: a circumpolar database of Antarctic krill and salp numerical densities, 1926–2016, Earth Syst. Sci. Data, 9, 193-210, https://doi.org/10.5194/essd-9-193-2017, 2017.
Important Bird Areas (IBAs) BirdLife International, 2016
Harris et al.
There is a large amount of information on birds in Antarctica but this has never previously been assembled and analysed to determine exactly where the most significant breeding sites for the avifauna as a whole are. Such information is essential in order to inform the conservation actions needed to protect them against the range of threats identified in Antarctica. These include direct disturbance by visitors, disturbance by aircraft or vehicles, exposure to pollutants, ingestion of or fouling by marine debris, competition for prey from fisheries, accidental by-catch on fishing lines or in nets, introduction of disease from other parts of the world and climate change. Recent analyses have identified 204 Important Bird Areas (IBAs) in Antarctica, for all of which detailed site accounts have been compiled. Sites were identified using internationally agreed criteria that have been applied in 200 countries over the past 35 years. The compiled list of IBAs provides a baseline against which change can be measured and conservation actions considered.
Colin Harris, Lincoln Fishpool, Ben Lascelles, Katherina Lorenz, 2016. Important Bird Areas in Antarctica. Antarctic Environments Portal. https://www.environments.aq/information-summaries/important-bird-areas-in-antarctica/
chl-a summer climatology (Johnson, 9km) AAD, 2017
Raymond, B.
This dataset is a climatological summer chlorophyll-a layer for the Southern Ocean south of 40S, following the OC3M algorithm of Johnson et al. (2013). The climatology was calculated from level-3 binned MODISA RRS products spanning the 2002/03 to 2015/16 austral summer seasons (summer taken as day 355 to day 80).
Johnson, R., Sumner, M., Raymond, B. (2017, updated 2017) Southern Ocean summer chlorophyll-a climatology Australian Antarctic Data Centre – doi:10.4225/15/5906b48f70bf9. Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. http://oceancolor.gsfc.nasa.gov/ http://onlinelibrary.wiley.com/doi/10.1002/jgrc.20270/abstract https://data.aad.gov.au/metadata/records/AAS_4343_so_chlorophyll
chl-a summer climatology (NASA, 9km) AAD, 2017
Raymond, B.
This dataset is a climatological summer chlorophyll-a layer for the Southern Ocean south of 40S, following the standard NASA OC3M algorithm. The climatology was calculated from level-3 binned MODISA RRS products spanning the 2002/03 to 2015/16 austral summer seasons (summer taken as day 355 to day 80). The climatology calculated is also included.
Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. http://oceancolor.gsfc.nasa.gov/ http://onlinelibrary.wiley.com/doi/10.1002/jgrc.20270/abstract . Raymond, B. (2012, updated 2014) Polar Environmental Data Layers Australian Antarctic Data Centre – CAASM Metadata (https://data.aad.gov.au/metadata/records/Polar_Environmental_Data)
Pelagic regionalisation AAD, 2014
Raymond, B.
This layer is a circumpolar, pelagic regionalisation of the Southern Ocean south of 40°S, based on sea surface temperature, depth, and sea ice information. The results show a series of latitudinal bands in open ocean areas, consistent with the oceanic fronts. Around islands and continents, the spatial scale of the patterns is finer, and is driven by variations in depth and sea ice.
Raymond B (2014) Pelagic Regionalisation. In: de Broyer C, Koubbi P, Griffiths H, Raymond B et al. (eds) The Biogeographic Atlas of the Southern Ocean. Scientific Committee on Antarctic Research, Cambridge UK, pp. 418-421
Benthic regionalisation PLoS ONE, ADD, 2017
Douglass et al.
This layer is a circumpolar, hierarchical classification of benthic ecoregions, bathomes and environmental types. Ecoregions are defined according to available data on biogeographic patterns and environmental drivers on dispersal. Bathomes are identified according to depth strata defined by species distributions. Environmental types are uniquely classified according to the geomorphic features found within the bathomes in each ecoregion.
Douglass LL, Turner J, Grantham HS, Kaiser S, Constable A, Nicoll R, Raymond B, Post A, Brandt A, Beaver D (2014) A hierarchical classification of benthic biodiversity and assessment of protected areas in the Southern Ocean. PLoS ONE. doi:10.1371/journal.pone.0100551
Environmental management
Quantarctica includes the latest versions of the CCAMLR and ASPA/ASMA databases, letting Antarctic managers get a birds-eye view and helping on-the-ground researchers apply for permits and ensure their field work complies with regulations.
Antarctic Specially Protected Areas (ASPAs) AAD, 2016
Terauds, A.
An Antarctic Specially Protected Area (ASPA) is an area of Antarctica designated by the Committee for Environmental Protection adopted at the Antarctic Treaty Consultative Meeting, which protects outstanding environmental, scientific, historic, aesthetic or wilderness values, and any combination of those values, or ongoing or planned scientific research. Two shapefiles are contained in this update on the location and extent of Antarctic Specially Protected Areas (ASPAs). The first is a point file (centroids of ASPA locations) and the second is a polygon file showing the spatial extent and boundary of each ASPA. This update builds on the point and polygon files originally provided by Environmental Research and Assessment (2011). The update includes the removal of ASPAs that have been de-designated and new ASPAs that have been designated since 2011. New ASPA boundaries were created from coordinates provided in the management plans.
Terauds, A. (2016, updated 2016) An update to the Antarctic Specially Protected Areas (ASPAs), March 2016, Australian Antarctic Data Centre. http://dx.doi.org/10.4225/15/572995579cd36
Antarctic Specially Managed Areas (ASMAs) ATS, 2016
ATS/ERA
An Antarctic Specially Managed Area (ASMA) is an area of Antarctica designated by the Committee for Environmental Protection adopted at the Antarctic Treaty Consultative Meeting. An ASMA is an area where activities are being conducted or may be conducted in the future, to assist in the planning and co-ordination of activities, avoid possible conflicts, improve co-operation between Parties or minimize environmental impacts.
See ‘ERA APAD License Terms and Conditions.pdf’ in the data folder for additional terms and conditions.
Antarctic Conservation Biogeographic Regions (ACBRs) AAD, 2016
Terauds, A. and Lee, J.R.
The Antarctic Conservation Biogeographic Regions (ACBRs), originally proposed in 2012, are now established as an important tool in Antarctic science, conservation, management and policy. Here, we provide a revised version of the ACBRs, reflecting updates in underlying spatial layers, together with the results of new analyses justifying the inclusion of a 16th bioregion. This updated version now covers all ice-free areas of Antarctica and is publicly available through the Australian Antarctic Data Centre. In light of the interest in the ACBRs across a variety of research fields, we also provide a new set of summary statistics for the updated spatial layer, including landscape metrics, climate data, protected area coverage and an overview of human activity. The updated ACBRs represent a contemporary, practical and evidence-based foundation for understanding, conserving and managing Antarctic biodiversity at a continental scale.
Terauds, A. and Lee, J. R. (2016), Antarctic biogeography revisited: updating the Antarctic Conservation Biogeographic Regions. Diversity Distrib., 22: 836–840. doi:10.1111/ddi.12453
CCAMLR CCAMLR, 2017
Included layers: Research Blocks, Marine Protected Areas (MPAs), SSMUs, SSRUs, and Statistical Areas. CCAMLR layers are used globally for the purpose of reporting fishery statistics. CCAMLR’s Convention Area in the Southern Ocean is divided, for statistical purposes, into Area 48 (Atlantic Antarctic) between 70oW and 30oE, Area 58 (Indian Ocean Antarctic) between 30o and 150oE, and Area 88 (Pacific Antarctic) between 150oE and 70oW. These areas, which are further subdivided into subareas and divisions, are managed by CCAMLR. A global register of statistical areas, subareas and divisions is maintained by FAO http://www.fao.org/fishery/area/search/en.
CCAMLR Secretariat (2013)
Geology
Quantarctica provides a diverse package of datasets for exploring Antarctica’s geology, from large scale maps of terrestrial and marine geology to individual rock sample locations.
ADD Rock outcrop (Landsat8) The Cryosphere, 2016
Burton-Johnson et al.
An automated methodology for differentiating rock from snow, clouds and sea in Antarctica from “Landsat 8 imagery: a new rock outcrop map and area estimation for the entire Antarctic continent”
Burton-Johnson, A., Black, M., Fretwell, P. T., and Kaluza-Gilbert, J.: An automated methodology for differentiating rock from snow, clouds and sea in Antarctica from Landsat 8 imagery: a new rock outcrop map and area estimation for the entire Antarctic continent, The Cryosphere, 10, 1665-1677, https://doi.org/10.5194/tc-10-1665-2016, 2016.
OSU BPCRC Polar Rock Repository BPCRC, 2017
Grunow, A.
Geographic footprint of all available samples at the Polar Rock Repository. The PRR houses rock samples from Antarctica, the Arctic, southern South America and South Africa. The polar rock collection and database includes field notes, photos, maps, cores, powder and mineral residues, thin sections, as well as microfossil mounts, microslides and residues. Rock samples may be borrowed for research by university scientists from anywhere in the world. Samples may also be borrowed for educational or museum use in the United States. Visitors are welcome at the PRR by appointment.
Curator: Anne Grunow, curator@bpcrc.osu.edu
IBCSO Multibeam footprint (2016) IBCSO, 2016
ATS/ERA
The IBCSO group also tries to improve multibeam data acquisition in the future by providing information about the current multibeam coverage in the Southern Ocean. The IBCSO SID can be used to determine where multibeam data has already been surveyed. For an easier access to this information we also provide a GIS ready shapefile showing the outline of multibeam surveys as of IBCSO V1.0
Arndt, J.E., H. W. Schenke, M. Jakobsson, F. Nitsche, G. Buys, B. Goleby, M. Rebesco, F. Bohoyo, J.K. Hong, J. Black, R. Greku, G. Udintsev, F. Barrios, W. Reynoso-Peralta, T. Morishita, R. Wigley, “The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0 – A new bathymetric compilation covering circum-Antarctic waters”, 2013, Geophysical Research Letters, Vol. 40, p. 3111-3117, doi: 10.1002/grl.50413
GeoMAP source biblography (v20180228) SCAR, 2016
SCAR, GeoMAP Action Group
The SCAR GeoMAP Action Group has been building a detailed digital geological dataset of Antarctica. An international collaboration is capturing existing geological map data, refining its spatial reliability, improving representation of glacial sequences and geomorphology. The initiative is aimed principally towards providing a continent-wide perspective for cross-discipline use, building a dataset to describe ‘known geology’ of rock exposures rather than ‘interpreted’ sub-ice features. Improved glacial deposit mapping is an important focus due to their potential to contain records of ice fluctuations of relevance to climate change. The ATA_GeoMAP_sources_poly.shp shapefile is a polygon dataset representing the outline of geological maps that have been georeferenced for compilation and classification of the geological database. It represents the key maps that have been useful to the GeoMAP team that shows most, but not necessarily all, available published and unpublished geological maps of Antarctica (including some thesis maps, field sheets and other unpublished material). GeoMAP welcome information on any significant omissions (s.cox@gns.cri.nz).
Footprint provided by the data authors. Citations for individual maps provided in the data table. See https://www.scar.org/science/geomap/about/ For further information contact: s.cox@gns.cri.nz or b.smith.lyttle@gns.cri.nz
USGS Earthquakes (M>2.5, 1900-2017) USGS, 2017
United States Geological Survey
All earthquakes >M2.5 extracted from the USGS Earthquake Catalog south of 40S
USGS (2017). See https://earthquake.usgs.gov/earthquakes/search/ and https://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php for more information.
Tectonic plates and plate boundaries GGG/Github, 2017
Bird, P.
Global tectonic plates compiled from numerous sources
Originally published as Bird, P. (2003), An updated digital model of plate boundaries, Geochem. Geophys. Geosyst., 4, 1027, doi:10.1029/2001GC000252, 3. http://onlinelibrary.wiley.com/doi/10.1029/2001GC000252/abstract . Download updated files at https://github.com/fraxen/tectonicplates
Schematic Geological Map of Antarctica BMR, 1991
Australia Bureau of Mineral Resources
Compiled 1985-86 by R. J. Tingey, BMR Cartography by R. Swoboda. J. Gallagher, BMR Printed by Mercury-Walch, Hobart, Australia. Base map compiled by the Bureau of Mineral Resources from 1:10 000 000 scale publication supplied by the Australian Surveying and Land Information Group, Department of Administrative Services and amendments received from the British Antarctic Survey. Published by the Bureau of Mineral Resources, A Geology and Geophysics, Department of Primary Industries and Energy. Issued under the authority of the Minister for Primary Industries and Energy. © Commonwealth of Australia 1991
1:10 million scale Continent-wide surface schematic geological units and ages, compiled in 1985-1986. Australian Bureau of Mineral Resources. Schematic Geological Map of Antarctica. First Edition. 1:10 000 000. Hobart, Australia: Mercury-Walch, 1991.
Geomorphic Features AAD, 2017
Post et al.
Geomorphic features delineate distinct sedimentary and oceanographic environments that can be related to major habitat characteristics. Such characteristics include sea floor type (hard versus soft substrate), ice keel scouring, sediment deposition or erosion and current regimes.
Post AL, Meijers AJS, Fraser AD, Meiners KM, Ayers J, Bindoff NL, Griffiths HJ, Van de Putte AP, O’Brien PE, Swadling KM, Raymond B (2014) Environmental Setting. In: de Broyer C, Koubbi P, Griffiths H, Raymond B et al. (eds) The Biogeographic Atlas of the Southern Ocean. Scientific Committee on Antarctic Research, Cambridge UK, pp. 46-64. https://demo.ands.org.au/geomorphic-features-antarctic-ocean-2012/1161923
Geophysics
If you want to know what’s happening under the ice, Quantarctica is a great way to start building your geophysical toolbox. Quantarctica v3 includes a new geothermal heat flux layer, updates to ADMAP, and further expansion of the available satellite and aerial geophysics data.
AntGG Gravity Anomaly grid (10km) GRL, 2016
Scheinert et al.
Antarctic-wide gravity data compilation derived from 13 million data points covering an area of 10 million km^2, which corresponds to 73% coverage of the continent. Resulting free-air anomaly and Bouguer anomaly grids are given at 10 km resolution. Compilation originates from collaboration within International Association of Geodesy (IAG) Subcommission 2.3f “Gravity and Geoid in Antarctica” (AntGG).
Scheinert, M., et al. (2016), New Antarctic gravity anomaly grid for enhanced geodetic and geophysical studies in Antarctica, Geophys. Res. Lett., 43, 600–610, doi:10.1002/2015GL067439.
EIGEN-6C4 Gravity Model (5km) ICGEM, 2014
Förste et al.
Synthesis of gravity disturbances and height anomalies from EIGEN-6C4, one of the latest global, high-resolution, combined earth gravity field models. The gravity disturbances are a good approximation of gravity anomalies. The height anomaly is a good approximation of the geoid height (both quantities are the same at the ocean). EIGEN-6C4 provides a maximum resolution of about 10 km (Nmax = 2190), and is calculated from satellite data (GRACE and GOCE satellite gravity missions), satellite altimetry over the ocean, and global terrestrial data. However, over Antarctica terrestrial data could not be included, therefore, you might see considerable differences to the “AntGG Gravity Anomaly Grid”.
Förste, Christoph; Bruinsma, Sean.L.; Abrikosov, Oleg; Lemoine, Jean-Michel; Marty, Jean Charles; Flechtner, Frank; Balmino, G.; Barthelmes, F.; Biancale, R. (2014): EIGEN-6C4 The latest combined global gravity field model including GOCE data up to degree and order 2190 of GFZ Potsdam and GRGS Toulouse. GFZ Data Services.
World Magnetic Model NOAA NGDC, 2016
Chulliat et al.
Magnetic Declination degree lines and magnetic south pole locations
Chulliat, A., S. Macmillan, P. Alken, C. Beggan, M. Nair, B. Hamilton, A. Woods, V. Ridley, S. Maus and A. Thomson, 2014. The US/UK World Magnetic Model for 2015-2020, NOAA National Geophysical Data Center, Boulder, CO, doi: 10.7289/V5TH8JNW [access date]. https://www.ngdc.noaa.gov/geomag/WMM/DoDWMM.shtml
ADMAP Magnetic Anomaly (5km) SCAR, 2001
Golynsky et al.
Antarctic Digital Magnetic Anomaly Map (ADMAP) compiled using Orsted and CHAMP satellite total intensity anomaly data. Data gaps are augmented for the wavelengths larger than 400 km. Map unit is nT.
Golynsky, A., M. Chiappini, D. Damaske, F. Ferraccioli, J. Ferris, C. Finn, M. Ghidella, T. Isihara, A. Johnson, H.R. Kim, L. Kovacs, J. LaBrecque, V. Masolov, Y. Nogi, M. Purucker, P. Taylor, and M. Torta, 2001, ADMAP – Magnetic Anomaly Map of the Antarctic, 1:10 000 000 scale map, in Morris, P., and R. von Frese, eds., BAS (Misc.) 10, Cambridge, British Antarctic Survey.
Geothermal Heat Flux (5km) JGR, 2015
An et al.
Geothermal heat flux (mW/m^2) inferred from seismic velocities
An, M., Wiens, D. A., Zhao, Y., Feng, M., Nyblade, A., Kanao, M., … & Lévêque, J. J. (2015). Temperature, lithosphere-asthenosphere boundary, and heat flux beneath the Antarctic Plate inferred from seismic velocities. Journal of Geophysical Research: Solid Earth, 120(12), 8720-8742. http://onlinelibrary.wiley.com/doi/10.1002/2015JB011917/full
Glaciology
The very first version of Quantarctica was designed for glaciologists who needed an all-in-one mapping environment that they could use on the ice. Modeled ice flow, thickness, mass balance, and snow accumulation coexist with comprehensive databases of subglacial hydrology and grounding lines to give glaciologists a solid jumping-off point for planning, fieldwork, visualization, and modelling.
Surface snow isotopes The Cryosphere, 2016
Touzeau et al.
Isotopic composition from varied snow samples from East Antarctica. The database provides temperature and water isotopes d18O, d-ex, 17O-ex in samples from surface snow, snow pits and precipitation
Touzeau, A., Landais, A., Stenni, B., Uemura, R., Fukui, K., Fujita, S., Guilbaud, S., Ekaykin, A., Casado, M., Barkan, E., Luz, B., Magand, O., Teste, G., Le Meur, E., Baroni, M., Savarino, J., Bourgeois, I., and Risi, C.: Acquisition of isotopic composition for surface snow in East Antarctica and the links to climatic parameters, The Cryosphere, 10, 837-852, https://doi.org/10.5194/tc-10-837-2016, 2016.
SAMBA Surface mass balance measurements The Cryosphere, 2013
Favier et al.
Updated and quality controlled direct measurements of surface mass balance (SMB). The database includes formatted metadata, such as measurement technique, elevation, time covered, etc. Layer points were imported from the “A-rated, 20th century” sheet. See the full data spreadsheet included in the data folder for more information and data options.
Favier, V., Agosta, C., Parouty, S., Durand, G., Delaygue, G., Gallée, H., Drouet, A.-S., Trouvilliez, A., and Krinner, G.: An updated and quality controlled surface mass balance dataset for Antarctica, The Cryosphere, 7, 583-597, https://doi.org/10.5194/tc-7-583-2013, 2013.
GSFC Drainage systems GSFC, 2012
Zwally et al.
This dataset provides boundaries of Antarctic drainage systems for the ice sheet and ice shelves. Characteristics of each system (Table 3 in the reference) are also included in the attribute table. The original dataset shows polygons of full (grounded + floating portions) drainage systems and their grounded portions only. The dataset provided in Quantarctica is recalculated so that grounded and floating portions of each drainage system are shown.
Zwally, H. Jay, Mario B. Giovinetto, Matthew A. Beckley, and Jack L. Saba, 2012, Antarctic and Greenland Drainage Systems, GSFC Cryospheric Sciences Laboratory, at http://icesat4.gsfc.nasa.gov/cryo_data/ant_grn_drainage_systems.php
ASAID Grounding/hydrostatic lines NSIDC, 2011
Bindschadler, Choi, and ASAID Collaborators
High-resolution image-derived grounding line position for the Antarctic Ice Sheet. The data are derived using customized software to combine data from Landsat-7 imagery and Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry, which were primarily collected between 1999 to 2003. The data were developed as part of the Antarctic Surface Accumulation and Ice Discharge (ASAID) project.
Bindschadler, R., H. Choi, and ASAID Collaborators. 2011. High-resolution Image-derived Grounding and Hydrostatic Lines for the Antarctic Ice Sheet. Boulder, Colorado, USA: National Snow and Ice Data Center. Digital media.
RAISED Paleo ice extents QSR, 2014
Bentley et al.
New synthesis of geological and glaciological datasets related to the Antarctic Ice Sheet deglacial history since the Last Glacial Maximum. Series of timeslice maps are provided for 20 ka, 15 ka, 10 ka and 5 ka, including grounding line position and ice sheet thickness changes, along with a clear assessment of levels of confidence
Bentley, M. J., Cofaigh, C. Ó., Anderson, J. B., Conway, H., Davies, B., Graham, A. G., … & Mackintosh, A. (2014). A community-based geological reconstruction of Antarctic Ice Sheet deglaciation since the Last Glacial Maximum. Quaternary Science Reviews, 100, 1-9. http://www.sciencedirect.com/science/article/pii/S0277379114002546?via%3Dihub
Blue ice areas Annals of Glaciology, 2014
Hui et al.
This dataset provides blue ice areas detected using Landsat ETM+ and MODIS satellite data. Areas and perimetres of each blue-ice polygon are included in the attribute, which was calculated by the Quantarctica project.
Hui, F.M., T.Y. Ci, X. Cheng, T.A. Scambos, Y. Liu, Y.M. Zhang, Z.H. Chi, H.B. Huang, X.W. Wang, F. Wang, C. Zhao and Z.Y. Jin 2014. Mapping blue ice areas in Antarctica using ETM+ and MODIS data. Annals of Glaciology, 55(66): 129-137.
Ice rises inventory NPI, 2015
Moholdt, G., & Matsuoka, K.
Inventory of Antarctic ice rises and rumples. Outlines and statistics for over 700 ice rises and rumples, derived from visual interpretation of MODIS and Landsat imagery, MEaSUREs ice velocity and BEDMAP2.
Moholdt, G., & Matsuoka, K. (2015). Inventory of Antarctic ice rises and rumples (version 5) [Data set]. Norwegian Polar Institute.
Subglacial lakes, Wright & Siegert Antarctic Science, 2012
Wright, A., and Sigert, M.
This data set is a new inventory of locations, dimensions and data sources for 379 subglacial lakes. A major advance is the rise in the total number of lakes from the 145 known at the time of the last inventory in 2005.
Wright, A., & Siegert, M. (2012). A fourth inventory of Antarctic subglacial lakes. Antarctic Science, 24(6), 659-664. doi:10.1017/S095410201200048X
Subglacial lakes, Blankenship NSIDC, 2009
Blankenship et al.
Subglacial lake classification collection based on radar reflection properties. The Subglacial lakes are separated into four categories specified by radar reflection properties. Additional information includes: latitude, longitude, length (in kilometers), hydro-potential (in meters), bed elevation (in meters above WGS84), and ice thickness (in meters). Source data used to compile this data set were collected between 1998 and 2001.
Blankenship, David D., Sasha P. Carter, John W. Holt, David L. Morse, Matthew E. Peters, and Duncan A. Young. 2009. Antarctic Subglacial Lake Classification Inventory. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.
Subglacial lakes, Smith Journal of Glaciology, 2009
Smith et al.
Outlines of subglacial lakes identified with ICESat altimetry.
Smith, B. E., H. A. Fricker, I. R. Joughin, and S. Tulaczyk (2009), An inventory of active subglacial lakes in Antarctica detected by ICESat (2003-2008), J. Glaciol., 55(192), 573-595
Recovery Subglacial Lakes Nature, 2007
Bell et al.
Outlines of Recovery Lakes A through D in East Antarctica.
Bell, R., M. Studinger, C.A. Shuman, M.A. Fahnestock and I. Joughin (2007), Large subglacial lakes in East Antarctica at the onset of fast-flowing ice streams, Nature, 445, 904-907.
Vostok Subglacial Lake EPSL, 2003
Studinger et al.
Outlines of subglacial lake Vostok in East Antarctica.
Studinger, M., R.E. Bell, G.D. Karner, A.A. Tikku, J.W. Holt, D.L. Morse, T.G. Richter, S.D. Kempf, M.E. Peters, D.D. Blankenship, R.E. Sweeney, V.L. Rystrom (2003), Ice cover, landscape setting, and geological framework of Lake Vostok, East Antarctica. Earth Planet. Sci. Lett., 205, 195-210.
Subglacial water flux (modelled, 1km) Nature Geoscience, 2013
Le Brocq et al.
Modelled subglacial water flux beneath the grounded ice sheet. Uses BEDMAP2 ice sheet configuration to route subglacial meltwater derived from numerical ice sheet model output (from Frank Pattyn).
Le Brocq, A. M., Ross, N., Griggs, J. A., Bingham, R. G., Corr, H. F., Ferraccioli, F., … & Siegert, M. J. (2013). Evidence from ice shelves for channelized meltwater flow beneath the Antarctic Ice Sheet. Nature Geoscience, 6(11), 945-948. https://www.nature.com/articles/ngeo1977
ALBMAP Masks (5km) ESSD, 2010
Le Brocq et al.
These grids summarise processing which has been carried out on the various ALBMAP datasets described below.
Le Brocq, A. M., Payne, A. J., and Vieli, A.: An improved Antarctic dataset for high resolution numerical ice sheet models (ALBMAP v1), Earth Syst. Sci. Data, 2, 247-260, doi:10.5194/essd-2-247-2010, 2010.
ALBMAP Snow accumulation, Arthern (5km) ESSD, 2010
Arthern et al.
This accumulation dataset was derived from interpolation of in situ point measurements, i.e. snow pits, ice cores and stake measurements. Passive microwave satellite data (firn emissivity) were used as a “forcing field” to control the interpolation. The original data were supplied at a resolution of 25 km. The data were, here, interpolated onto the 5 km grid using spline interpolation. The unit of the data is meters in ice equivalent per year.
Arthern, R. J., Winebrenner, D. P., and Vaughan, D. G.: Antarctic snow accumulation mapped using polarization of 4.3-cm wavelength microwave emission, J. Geophys. Res.-Atmos., 111, D06107, doi:10.1029/2004JD005667, 2006.
ALBMAP Snow accumulation, Van de Berg (5km) ESSD, 2010
Van de Berg et al.
This accumulation dataset is an output from the RACMO regional model. The data were provided as lat-lon point measurements, these were reprojected onto the polar stereographic grid and interpolated onto the 5 km grid using spline interpolation. The unit of the data is meters in ice equivalent per year.
Van de Berg, W. J., van den Broeke, M. R., and van Meijgaard, E.: Reassessment of the Antarctic surface mass balance using calibrated output of a regional atmospheric climate model, J. Geophys. Res., 111, D11104, doi:10.1029/2005JD006495, 2006.
ALBMAP Surface air temperature (5km) ESSD, 2010
Comiso, J.C.
The surface temperature estimates in °C are derived from AVHRR infrared data. Annual mean temperatures from 1982–2004 were averaged to provide the surface air temperature field.
Comiso, J. C.: Variability and trends in Antarctic surface temperatures from in situ and satellite infrared measurements, J. Climate, 13(10), 1674–1696, 2000.
ALBMAP Firn thickness (clipped, 5km) ESSD, 2010
Van den Broeke et al.
The spatial estimate of the firn correction for Antarctica has been produced using a regional climate model (RACMO), clipped to the current Antarctic ice extent. The firn correction is defined as the difference between the actual depth of the firn layer and the depth that the firn would be if it was all at the density of meteoric ice.
Van den Broeke, M. R., van de Berg, W. J., and van Meijgaard, E.: Firn depth correction along the Antarctic grounding line, Antarct. Sci., 20(5), 513–517, doi:10.1017/S095410200800148X, 2008.
ALBMAP Geothermal flux, Fox Maule (5km) ESSD, 2010
Fox Maule et al.
This geothermal flux dataset was derived from satellite magnetic data and a thermal model. The point data provided were interpolated on to the 5 km grid using spline interpolation. The data unit is mWm-2.
Fox Maule, C., Purucker, M., Olsen, N., and Mosegaard, K.: Heat flux anomalies in Antarctica revealed by satellite magnetic data, Science, 309, 464–467, 2005.
ALBMAP Geothermal flux, Shapiro & Ritzwoller (5km) ESSD, 2010
Shapiro, N. M. and Ritzwoller, M. H.
This dataset uses a global seismic model of the crust and upper mantle to extrapolate existing heat-flux measurements to areas where there are few data, using a “structural similarity function”. The original data are gridded on a geographic (lat-lon) grid, so when they are projected to a polar stereographic projection, this creates problems in gridding straight to 5 km resolution, due to the directionality of the points used in the interpolation procedure. The gridding introduces elongated features which are not present in the original data. The effective resolution of the data (in latitude anyway) is 100 km. Therefore the data were first gridded on to a 100 km grid using spline interpolation. The 100 km grid points were then reinterpolated, again using spline interpolation, onto the 5 km grid. This reduces the elongated features whilst retaining most of the detail in the original dataset. The data unit is mWm-2.
Shapiro, N. M. and Ritzwoller, M. H.: Inferring surface heat flux distributions guided by a global seismic model: particular application to Antarctica, Earth Planet. Sc. Lett., 223, 213–224, 2004.
ALBMAP Upper ice surface elevation (5km) ESSD, 2010
Bamber et al., Liu et al.
This DEM is largely derived from the DEM of Bamber et al. (2009) (JLB/JAG DEM), in combination with the RAMP DEM (Antarctic Peninsula, Liu et al., 1999).
Bamber, J. L., Gomez-Dans, J. L., and Griggs, J. A.: A new 1 km digital elevation model of the Antarctic derived from combined satellite radar and laser data – Part 1: Data and methods, The Cryosphere, 3, 101–111, doi:10.5194/tc-3-101-2009, 2009.
Liu, H., Jezek, K., and Li, B.: Development of an Antarctic digital elevation model by integrating cartographic and remotely sensed data: A geographic information system based approach, J. Geophys. Res., 104(B10), 23199–23213, 1999.
ALBMAP Lower ice surface elevation 2 (5km) ESSD, 2010
Griggs et al., Lythe et al., Vaughan et al., Holt et al., Le Brocq et al.
Lower ice surface elevation in m asl from BEDMAP (Version 1) plus recalculated ice shelf thickness derived from new surface DEM data and new data from the Recovery basin.
Griggs, J. A. and Bamber, J. L.: Ice shelf thickness over Larsen C, Antarctica, derived from satellite altimetry, Geophys. Res. Lett., 36, L19501, doi:10.1029/2009GL039527, 2009b.
Lythe, M. B., Vaughan, D. G.. and the BEDMAP Consortium: BEDMAP: A new ice thickness and subglacial topographic model of Antarctica, J. Geophys. Res.-Sol. Ea., 106(B6), 11335– 11351, 2001.
Vaughan, D. G., Corr, H. F. J., Ferraccioli, F., Frearson, N., O’Hare, A., Mach, D., Holt, J., Blankenship, D., Morse, D., and Young, D. A.: New boundary conditions for theWest Antarctic ice sheet: subglacial topography beneath Pine Island Glacier, Geophys. Res. Lett., 33, L09501, doi:10.1029/2005GL025588, 2006.
Holt, J. W., Blankenship, D. D., Morse, D. L., Young, D. A., Peters, M. E., Kempf, S. D., Richter, T. G., Vaughan, D. G., and Corr, H. F. J.: New boundary conditions for the West Antarctic Ice Sheet: Subglacial topography of the Thwaites and Smith glacier catchments, Geophys. Res. Lett., 33, L09502, doi:10.1029/2005GL025561, 2006
Le Brocq, A. M., Hubbard, A., Bentley, M. J., and Bamber, J. L.: Subglacial topography inferred from ice surface terrain analysis reveals a large un-surveyed basin below sea level in East Antarctica, Geophys. Res. Lett., 34, L16503, doi:10.1029/2008GL034728, 2008.
ALBMAP Lower ice surface elevation (5km) ESSD, 2010
Griggs et al., Lythe et al., Vaughan et al., Holt et al.
Lower ice surface elevation in m asl from BEDMAP (Version 1) plus recalculated ice shelf thickness derived from new surface DEM data.
Griggs, J. A. and Bamber, J. L.: Ice shelf thickness over Larsen C, Antarctica, derived from satellite altimetry, Geophys. Res. Lett., 36, L19501, doi:10.1029/2009GL039527, 2009b.
Lythe, M. B., Vaughan, D. G.. and the BEDMAP Consortium: BEDMAP: A new ice thickness and subglacial topographic model of Antarctica, J. Geophys. Res.-Sol. Ea., 106(B6), 11335– 11351, 2001.
Vaughan, D. G., Corr, H. F. J., Ferraccioli, F., Frearson, N., O’Hare, A., Mach, D., Holt, J., Blankenship, D., Morse, D., and Young, D. A.: New boundary conditions for theWest Antarctic ice sheet: subglacial topography beneath Pine Island Glacier, Geophys. Res. Lett., 33, L09501, doi:10.1029/2005GL025588, 2006.
Holt, J. W., Blankenship, D. D., Morse, D. L., Young, D. A., Peters, M. E., Kempf, S. D., Richter, T. G., Vaughan, D. G., and Corr, H. F. J.: New boundary conditions for the West Antarctic Ice Sheet: Subglacial topography of the Thwaites and Smith glacier catchments, Geophys. Res. Lett., 33, L09502, doi:10.1029/2005GL025561, 2006.
ALBMAP Bed/bathymetry elevation 2 (5km) ESSD, 2010
Lythe et al. and BEDMAP, Nitsche et al., Le Brocq et al.
Bed elevation under grounded ice was derived by subtracting the ice thickness from the combined ice surface dataset. Bathymetry beneath ice shelves were reinterpolated from BEDMAP (version 1), and new data plus Recovery basin modification are included.
Lythe, M. B., Vaughan, D. G.. and the BEDMAP Consortium: BEDMAP: A new ice thickness and subglacial topographic model of Antarctica, J. Geophys. Res.-Sol. Ea., 106(B6), 11335–11351, 2001.
Nitsche, F.O., Jacobs, S., Larter, R. D., and Gohl, K.: Bathymetry of the Amundsen Sea continental shelf: Implications for geology, oceanography, and glaciology, Geochem. Geophy. Geosy., 8, Q10009, doi:10.1029/2007GC001694, 2007.
Le Brocq, A. M., Hubbard, A., Bentley, M. J., and Bamber, J. L.: Subglacial topography inferred from ice surface terrain analysis reveals a large un-surveyed basin below sea level in East Antarctica, Geophys. Res. Lett., 34, L16503, doi:10.1029/2008GL034728, 2008.
ALBMAP Bed/bathymetry elevation (5km) ESSD, 2010
Lythe et al. and BEDMAP, Nitsche et al.
Bed elevation under grounded ice was derived by subtracting the ice thickness from the combined ice surface dataset. Bathymetry beneath ice shelves were reinterpolated from BEDMAP (version 1) and new data is included.
Lythe, M. B., Vaughan, D. G.. and the BEDMAP Consortium: BEDMAP: A new ice thickness and subglacial topographic model of Antarctica, J. Geophys. Res.-Sol. Ea., 106(B6), 11335–11351, 2001.
Nitsche, F.O., Jacobs, S., Larter, R. D., and Gohl, K.: Bathymetry of the Amundsen Sea continental shelf: Implications for geology, oceanography, and glaciology, Geochem. Geophy. Geosy., 8, Q10009, doi:10.1029/2007GC001694, 2007.
Firn density (33km) The Cryosphere, 2011
Ligtenberg et al.
Modelled (IMAU Firn Density Model) near-surface (0-1 m depth) firn density and depths for firn densities of 830 and 550 kg/m^3. Forced at top by RACMO2.3p1 mass fluxes and skin temperature. Averaging period:1979-2011.
Ligtenberg, S. R. M., M. M. Helsen and M. R. van den Broeke, 2011: An improved semi-empirical model for the densification of Antarctic firn, The Cryosphere 5, 809-819, doi: doi:10.5194/tc-5-809-2011.
SUMER Ice shelf buttressing (modelled, 1km) Nature Climate Change, 2016
Fürst et al.
This data set consists of high-resolution data about ice-shelf buttressing for the whole of Antarctica. Buttressing is inferred from known ice geometry and ice motion with the Elmer/Ice ice flow model. Input sources are Bedmap2, MEaSUREs surface ice velocities, and the MEaSUREs grounding-line positions. This data set is part of the French National Research Agency’s project on Survey and Modelling of East Antarctica (SUMER)
Fürst, J. J., Durand, G., Gillet-Chaulet, F., Tavard, L., Rankl, M., Braun, M., & Gagliardini, O. (2016). The safety band of Antarctic ice shelves. Nature Climate Change, 6(5), 479-482. http://dx.doi.org/10.5067/FWHORAYVZCE7
Surface melt rate (1999-2009, QuikSCAT, observed, 4.5km) JGR, 2015
Trusel et al.
Surface meltwater production using an empirical relationship between radar backscatter from the QuikSCAT satellite and melt calculated from in situ energy balance observations. Resolution: ~5 km. Averaging period: 1999–2009. Unit: mm weq yr-1 (= kg m-2 yr-1)
Trusel, L. D., K. E. Frey, S. B. Das, P. Kuipers Munneke, and M. R. van den Broeke, 2013: Satellite-based estimates of Antarctic surface meltwater fluxes, Geophysical Research Letters 40, 6148–6153, doi:10.1002/2013GL058138. http://onlinelibrary.wiley.com/doi/10.1002/2013GL058138/abstract
MEaSUREs Antarctic boundaries (v2) NSIDC, 2017
Mouginot et al.
Maps of Antarctic ice shelves, the Antarctic coastline and Antarctic basins. The maps are assembled from 2008-2009 ice-front data from ALOS PALSAR and ENVISAT ASAR data acquired during International Polar Year, 2007-2009 (IPY), the InSAR-based grounding line data (MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry), augmented with other grounding line sources, the Antarctic ice velocity map (MEaSUREs InSAR-Based Antarctica Ice Velocity Map), and the Bedmap-2 DEM. This data set is part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.
Mouginot, J., B. Scheuchl, and E. Rignot. 2017. MEaSUREs Antarctic Boundaries for IPY 2007-2009 from Satellite Radar, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/AXE4121732AD. [Date Accessed]. https://nsidc.org/data/nsidc-0709/versions/2
MEaSUREs Antarctic velocity map (v2) NSIDC, 2017
Rignot et al.
MEaSUREs InSAR-based Antarctica Ice Velocity Map is assembled from multiple satellite interferometric synthetic-aperture radar systems. Data was largely acquired during the International Polar Year 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 was used as needed to maximize coverage. This dataset is part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program.
Rignot, E., J. Mouginot, and B. Scheuchl. 2017. MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/D7GK8F5J8M8R.
MEaSUREs Antarctica Ice Velocity Map (velocity vectors) NSIDC, 2017
Rignot et al.
Velocity vectors are shown at 20 km intervals, which are resampled from the original data.
Rignot, E., J. Mouginot, and B. Scheuchl. 2017. MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/D7GK8F5J8M8R.
Ice cores
Quantarctica includes a comprehensive ice core database with modern and historical ice core locations, links to core data, and basic information about each ice core’s depth, year, and managing program.
Ice cores database ITASE, 2017
ITASE, CCI, and the WAIS Divide Team
Ice Core locations and metadata compiled from multiple sources. See data table for links to data and citation information for individual ice cores.
ITASE IceReader: http://www.icereader.org/icereader/listData.jsp
Climate Change Institute Antarctic Ice Core Data: http://cci.icecoredata.org/Antarctica.html
WAIS Divide Project summary paper: https://www.nature.com/articles/nature12376
Oceanography
By including a complete picture of average temperature, salinity, nutrients, currents, and more, Quantarctica ensures that your view of the Southern Ocean is just as complete as what’s happening on land.
Southern Ocean fronts Deep Sea Research, 1995
Orsi et al.
The subsurface Southern Ocean is characterized by relatively uniform zones separated by zonal fronts. Climatological position of four (three?) of the major fronts were estimated from observed temperature and salinity data.
Alejandro H. Orsi, Thomas Whitworth III, and Worth D. Nowlin Jr (1995), On the meridional extent and fronts of the Antarctic Circumpolar Current., Deep-Sea Research, 42, 5, 641-673
SOSE Mean surface current speed (16km) JPO, 2010
Mazloff et al.
Surface current velocity estimated by data-constrained numerical model. Ocean currents are difficult quantity to measure with variability of wide spatial and temporal scales. Here we provide one of the best estimates of current velocity at the surface. This is output from a state estimate numerical model, SOSE, where ocean state satisfying the laws of physics (e.g. conservation of mass and momentum) were estimated by minimizing differences from available observed data (satellite, ship-based, etc.).
M. Mazloff, P. Heimbach, and C. Wunsch, 2010: “An Eddy-Permitting Southern Ocean State Estimate.” J. Phys. Oceanogr., 40, 880–899. doi: 10.1175/2009JPO4236.1
World Ocean Atlas (WOA) 2013 Temperature NOAA, 2013
Locarnini et al.
Compilation of observed temperature of sea water. All available ocean observation data were quality controlled, correction applied where necessary, and interpolated in the vertical direction to standard depths. In the horizontal direction, “objective mapping” was used to achieve statistically best interpolation to the grid. No adjustment for pressure effects (i.e. “potential temperature”) is applied. Note that most of data were collected in austral summer and seasonally biased. Layers provided for austral summer and winter climatology at 0, 50, 200, and 500 m depths.
Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, H. E. Garcia, O. K. Baranova, M. M. Zweng, C. R. Paver, J. R. Reagan, D. R. Johnson, M. Hamilton, and D. Seidov, 2013. World Ocean Atlas 2013, Volume 1: Temperature. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 73, 40 pp.
World Ocean Atlas (WOA) 2013 Salinity NOAA, 2013
Zweng et al.
Compilation of observed salinity of sea water. All available ocean observation data were quality controlled, correction applied where necessary, and interpolated in the vertical direction to standard depths. In the horizontal direction, “objective mapping” was used to achieve statistically best interpolation to the grid. Salinity is converted to Practical Salinity Scale (1978) hence unitless. Note that most of data were collected in austral summer and seasonally biased. Layers provided for austral summer and winter climatology at 0, 50, 200, and 500 m depths.
Zweng, M.M, J.R. Reagan, J.I. Antonov, R.A. Locarnini, A.V. Mishonov, T.P. Boyer, H.E. Garcia, O.K. Baranova, D.R. Johnson, D.Seidov, M.M. Biddle, 2013. World Ocean Atlas 2013, Volume 2: Salinity. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 74, 39 pp.
World Ocean Atlas (WOA) 2013 Oxygen NOAA, 2013
Garcia et al.
Compilation of observed oxygen concentration of sea water. All available ocean observation data were quality controlled, correction applied where necessary, and interpolated in the vertical direction to standard depths. In the horizontal direction, “objective mapping” was used to achieve statistically best interpolation to the grid. No adjustment for pressure effects (i.e. “potential temperature”) is applied. Note that most of data were collected in austral summer and seasonally biased. Layers provided for austral summer and winter climatology at 0, 50, 200, and 500 m depths.
Garcia, H. E., R. A. Locarnini, T. P. Boyer, J. I. Antonov, O.K. Baranova, M.M. Zweng, J.R. Reagan, D.R. Johnson, 2014. World Ocean Atlas 2013, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation. S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 75, 27 pp.
World Ocean Atlas (WOA) 2013 Phosphate, Nitrate, and Silicate NOAA, 2013
Garcia et al.
Compilation of observed silica concentration of sea water. All available ocean observation data were quality controlled, correction applied where necessary, and interpolated in the vertical direction to standard depths. In the horizontal direction, “objective mapping” was used to achieve statistically best interpolation to the grid. No adjustment for pressure effects (i.e. “potential temperature”) is applied. Note that most of data were collected in austral summer and seasonally biased. Layers provided for austral summer and winter climatology at 0, 50, 200, and 500 m depths.
Garcia, H. E., R. A. Locarnini, T. P. Boyer, J. I. Antonov, O.K. Baranova, M.M. Zweng, J.R. Reagan, D.R. Johnson, 2014. World Ocean Atlas 2013, Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate, silicate). S. Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 76, 25 pp.
Sea ice
In addition to including ten years of measured sea ice extents and concentrations, Quantarctica makes it easy to import outside satellite imagery and satellite-derived sea ice data for cruise logistics, sampling, and model visualization.
Median sea ice extent 1981-2010 NSDIC, 2017
Fetterer et al..
Monthly median sea ice extents for the period 1981-2010.
Fetterer, F., K. Knowles, W. Meier, M. Savoie, and A. K. Windnagel. 2016, updated daily. Sea Ice Index, Version 2. [Indicate subset used]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: http://dx.doi.org/10.7265/N5736NV7. [Date Accessed].
NSIDC February and October Sea Ice Concentration (25km) NSDIC, 2017
Fetterer et al..
February (min) and October (max) satellite-observed sea ice concentrations from 2007-2017.
Fetterer, F., K. Knowles, W. Meier, M. Savoie, and A. K. Windnagel. 2016, updated daily. Sea Ice Index, Version 2. [Indicate subset used]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: http://dx.doi.org/10.7265/N5736NV7. [Date Accessed]. http://nsidc.org/data/g02135
Proportion of year ice covered (6.25km) AAD, 2017
Spreen et al.
Proportion of time the ocean is covered by sea ice of concentration 85% or higher. Calculated from AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution, using concentration data from 1-Jul-2002 to 30-Jun-2011. 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.
Spreen G, Kaleschke L, Heygster G (2008), Sea ice remote sensing using AMSR-E 89 GHz channels, J. Geophys. Res., doi:10.1029/2005JC003384 http://www.ifm.zmaw.de/en/research/remote-sensing-assimilation/sea-ice/amsr-e-sea-ice-concentration/
Social science
Quantarctica v3 has expanded data coverage beyond the physical sciences into the social sciences. Explore old stations, monuments, and other historical sites, and see how your own route or site compares with some of the most famous expeditions in history.
Non-native species incursions Biodiversity and Conservation, 2015
Hughes et al.
Non-native species removal and eradication attempts within the Antarctic continent and off-shore islands
Hughes, K. A., Pertierra, L. R., Molina-Montenegro, M. A., & Convey, P. (2015). Biological invasions in terrestrial Antarctica: what is the current status and can we respond?. Biodiversity and Conservation, 24(5), 1031-1055. https://doi.org/10.1007/s10531-015-0896-6
ADD Historic sites and monuments SCAR, 2016
SCAR ADD
Downloaded June 2016 from http://www.add.scar.org/
Originally Downloaded Feb 2015 from Antarctic Treaty Secretariat: http://www.ats.aq/documents/atcm36/ww/atcm36_ww004_e.pdf
Historic stations Polar Record, 2019
Headland, R.K.
The earliest winter scientific station established in the Antarctic was in 1883 as part of the International Polar Year (IPY) of 1883. Subsequently, to the IPY of 2007-2009, scientific stations have been deployed on 139 sites (103 on the Antarctic continent, 36 on peri-Antarctic islands), by 24 countries for a cumulative total of 2666 winters to that of 2008.
Headland, R. K. (2009). Antarctic winter scientific stations to the International Polar Year, 2007–2009. Polar Record, 45(1), 9-24.
Five historic expedition routes (digitized) AGS/PGC, 1975/2017
Dater, H.M.
A selection of routes and tracks from some of the most significant early Antarctic expeditions, from the first circumnavigation of Antarctica by Fabien Gottlieb von Bellingshausen, to the first Trans-Antarctic flight by Lincoln Ellsworth in 1935.
Dater, Henry M. “History of Antarctic Exploration and Scientific Investigation.” In Antarctic Map Folio Series, edited by Vivian C. Bushnell: American Geographical Society, 1975.
Terrain models
Quantarctica’s topography data has you covered from the bottom of the ocean to the bottom of the ice, from the deepest submarine canyons to the highest ice domes. When you’re asking how high or how deep, Quantarctica has the answer, thanks to the definitive package of DEMs, contours, bathymetry, and bed topography products with user-customizable contour interval and hillshade settings.
BEDMAP2 (1km) BAS/The Cryosphere, 2013
Fretwell et al.
Bed and surface elevation, ice thickness, uncertainty and data masks
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375-393, doi:10.5194/tc-7-375-2013, 2013.
RAMP2 (200m) NSIDC, 2012
Liu et al.
Radarsat Antarctic Mapping Project Digital Elevation Model Version 2 provided for both WGS84 and OSU91a elevation datum, contours, and hillshades.
Liu, H., K. C. Jezek, B. Li, and Z. Zhao. 2015. Radarsat Antarctic Mapping Project Digital Elevation Model, Version 2. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/8JKNEW6BFRVD. [Date Accessed].
CryoSat-2 Elevation model (1km) The Cryosphere, 2014
Helm et al.
CryoSat-2-derived elevation model of Antarctica, with 1000m pixel resolution and uncertainty of 3 m ± 15 m. This DEM will tend to have fewer errors in the inland ice areas and more near the coast or other areas with sharp relief.
Helm, V., Humbert, A., and Miller, H.: Elevation and elevation change of Greenland and Antarctica derived from CryoSat-2, The Cryosphere, 8, 1539-1559, https://doi.org/10.5194/tc-8-1539-2014, 2014.
IBCSO (500m) IBCSO/GRL, 2013
Dater, H.M.
International Bathymetric Chart of the Southern Ocean digital elevation model. Coverage: 90-60°S. Includes bed and surface rasters, hillshades, and contours.
Arndt, J. E., et al. (2013), The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0—A new bathymetric compilation covering circum-Antarctic waters, Geophys. Res. Lett., 40, 3111–3117, doi:10.1002/grl.50413.
ETOPO1 (2km / 1 arc-minute) NOAA NGDC, 2009
Amante, C. and B.W. Eakins
Low resolution global elevation model. Coverage: 90-40°S
Amante, C. and B.W. Eakins, 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. doi:10.7289/V5C8276M [access date].
Satellite imagery
Quantarctica includes low- and medium-resolution satellite mosaics from Landsat, MODIS, and RADARSAT, now with additional coverage of the Subantarctic islands. Need something a little more high-resolution or more recent? Go ahead and import your own satellite or aerial imagery with no extra work required – QGIS is built on the open-source GDAL library, which can read over 150 different image formats.
LIMA Landsat Image Mosaic of Antarctica (15/240m) USGS / Rem. Sens. Environ., 2008
Bindschadler et al.
Low-resolution center-filled Landsat Image Mosaic Of Antarctica (LIMA) and mosaicked 15m CIRREF tiles
R. Bindschadler, P. Vornberger, A. Fleming, A. Fox, J. Mullins, D. Binnie, S.J. Paulsen, B. Granneman, D. Gorodetzky. The Landsat image mosaic of Antarctica, Rem. Sens. Environ., 112 (2008), pp. 4214-4226
Subantarctic Landsat (15m) USGS/NASA, 2017
Pansharpened, clipped 15m Landsat-8 image mosaics of various Antarctic and Subantarctic islands not included in LIMA. Images were selected for best illumination and low cloud cover from the Landsat-8 record (2013-2017) Imagery has been color stretched, converted to 8-bit pixel values and converted to lossy .JP2 format, and is not suitable for remote sensing analysis.
Landsat-8 imagery courtesy of the U.S. Geological Survey
MODIS mosaic (125m) NSIDC, 1999/2013
Haran et al., Scambos et al.
MODIS Mosaic of Antarctica (MOA) image map
Haran, T., J. Bohlander, T. Scambos, T. Painter, and M. Fahnestock. 2005, updated 2013. MODIS Mosaic of Antarctica 2003-2004 (MOA2004) Image Map, Version 1. [Indicate subset used]. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. doi: http://dx.doi.org/10.7265/N5ZK5DM5. [Date Accessed].
Scambos, T., T. Haran, M. Fahnestock, T. Painter, and J. Bohlander. 2007. MODIS-based Mosaic of Antarctica (MOA) data sets: continent-wide surface morphology and snow grain size, Remote Sensing of Environment. 111. 242-257. http://dx.doi.org/10.1016/j.rse.2006.12.020 Rem. Sens. Environ., 111 (2007), pp. 242-257
RAMP RADARSAT mosaic (100m) NSIDC, 1999/2013
Jezek et al.
RADARSAT-1 Antarctic Mapping Project (RAMP) SAR imagery
Jezek, K. C. 1999. Glaciological properties of the Antarctic ice sheet from RADARSAT-1 synthetic aperture radar imagery. Annals of Glaciology 29:286-290. Jezek, K.C. in press. RADARSAT-1 Antarctic mapping project: change detection and surface velocity campaign. Annals of Glaciology 34. ## Jezek, K. C., J. C. Curlander, F. Carsey, C. Wales, and R. G. Barry. 2013. RAMP AMM-1 SAR Image Mosaic of Antarctica. [Indicate subset used]. Boulder, Colorado USA. http://dx.doi.org/10.5067/8af4zrpuls4h NSIDC: National Snow and Ice Data Center. [Date Accessed].
- Consider making your data as Quantarctica-friendly dataset and share it with the community. See requirements and recommended guidelines (PDF 1MB).
- Some datasets were published following an older guideline for Quantarctica-friendly datasets. However, all files are mostly compatible with Quantarctica 3.2 working on QGIS3.
Weddell Sea 1 km ice thickness and bed topography
Jeofry et al., 2018 [Publication link] [Data link]
We present a new ice thickness digital elevation model (DEM) of the Weddell Sea sector, West Antarctica. The DEM consists a total area of ~125,000 km2 covering the Institute, Möller and Foundation ice streams and the Bungenstock Ice Rise with a 1 km spatial resolution. Ice thickness DEM was formed from the available radio-echo sounding (RES) data using the ‘Topo to Raster’ function in ArcGIS. The RES data used in this study were compiled from four main sources which are (1) Scott Polar Research Institute (SPRI) survey collected during several campaigns in the 1970s (Drewry, 1983), (2) British Antarctic Survey (BAS) airborne radar survey conducted during the austal summer 2006/07 (GRADES/IMAGE) (Ashmore et al., 2014), (3) BAS airborne survey accomplished during the Institute and Möller Antarctic Funding Initiative (IMAFI) in 2010/2011 (Ross et al., 2012) and (4) Center for the Remote Sensing of Ice Sheet (CReSIS) data during the NASA Operation IceBridge (OIB) programme in 2012, 2014 and 2016 (Gogineni, 2012). The ice thickness picks were gridded at a uniform 1-km spacing using the Nearest Neighbour interpolation within the Topo to Raster.
Publication citation: Jeofry H., Ross N., Corr H.F.J., Li J., Morlighem M., Gogineni P., and Siegert M.J. (2018), A new bed elevation model for the Weddell Sea sector of the West Antarctic Ice Sheet, Earth Syst. Sci. Data, doi.org/10.5194/essd-10-711-2018.
Data citation: Jeofry H., Ross N., Corr H.F.J., Li J., Gogineni P., and Siegert M.J. (2017), 1-km bed topography digital elevation model (DEM) of the Weddell Sea sector, West Antarctica, Zenodo, doi.org/10.5281/zenodo.1035488.
Author contact: Hafeez Jeofry Email: h.jeofry15@imperial.ac.uk / hafeez.jeofry@umt.edu.my
HCA-GIS Data – Hydrography and Charting
International Hydrograhpic Organization, Hydrographic Commission on Antarctica, 2020 [HCA Website] [Data link] [Guidebook]
The International Hydrographic Organization (IHO) is an international organization which has a principal aim to ensure that all the world’s seas, oceans and navigable waters are surveyed and charted. It has also encouraged the establishment of Regional Hydrographic Commissions (RHCs) to coordinate hydrographic activity and cooperation at the regional level. The Hydrographic Commission of Antarctica (HCA) is one of the RHCs which consists of Hydrographic Offices from 24 countries.
The HCA has operated a Web GIS Service called HCA-GIS to visualize various hydrographic data and metadata around the region. In order to achieve higher profile beyond the hydrographic community, the HCA-GIS dataset is now provided as Quantarictica Friendly Dataset. It contains “Major shipping routes with survey priority”, “Survey areas”, “Electronic navigational charts coverage”, “Tide records” and “Seabed Feature Names in the GEBCO”. For further explanation of data and how to visualize the data on Quantarctica environment, please refer to the guidebook.
Publication citation: International Hydrographic Organization, Hydrographic Commission on Antarctica (2020) HCA-GIS Data; Hydrography and Charting [online] Available at: “https://data-iho.opendata.arcgis.com/”
Data: https://data-iho.opendata.arcgis.com
Contact: International Hydrographic Organization (info@iho.int)
Near-surface permafrost temperatures (1km, 2000-2017)
Obu et al., 2020 [Publication link] [Data link]
Near-surface permafrost temperatures are calculated for ~30 000 km2 of ice-free areas (Burton-Johnson et al., 2016) of the Antarctic including Antarctic and sub-Antarctic islands. The temperatures represent an average for the period 2000-2017 at 1km2 spatial resolution. The dataset is a product of a TTOP (temperature at the top of the permafrost) equilibrium model that was driven by remotely sensed land surface temperatures (MODIS LST). Gap-filling was performed by downscaled ERA-Interim and ERA-5 climate reanalysis data, which were also used to estimate snow cover. The results were validated against in situ-measured ground temperatures from 40 permafrost boreholes, and the resulting root-mean-square error was 1.9 °C.
Publication citation: Obu, J., Westermann, S., Vieira, G., Abramov, A., Balks, M. R., Bartsch, A., Hrbáček, F., Kääb, A., & Ramos, M. (2020). Pan-Antarctic map of near-surface permafrost temperatures at 1 km2 scale. The Cryosphere, 14(2), 497–519. https://doi.org/10.5194/tc-14-497-2020
Data citation: Obu, J., Westermann, S., Kääb, A., & Bartsch, A. (2019). Ground Temperature Map, 2000-2017, Antarctic [Data set]. In University of Oslo. https://doi.org/10.1594/PANGAEA.902576
Contact: Jaroslav Obu (jaroslav.obu@geo.uio.no)
FAQ
The Quantarctica manual (PDF 1 MB) is a step-by-step guide to installing and optimizing QGIS 3.16 and Quantarctica 3.
- What software do I need?
- I have an older version of QGIS (<3.16). Can it still run Quantarctica?
- How much free space do I need for Quantarctica on my computer?
- What’s included in the Quantarctica package?
- How do I acknowledge/cite Quantarctica?
- Can I still use my old version of Quantarctica?
- What is the best way to distributed my published data to other Quantarctica users?
- How can I move data layers I added to my own version of Quantarctica to a Quantarctica 3.2 project?
- Do I need an Internet connection to use Quantarctica?
- My downloads fail / I need an FTP client
- I’m experiencing problems. What do I do?
What software do I need?
You need QGIS 3.16, Quantarctica’s project file (Quantarctica32.qgz) and datasets. You get all this on our downloads page.
NOTE: If you’re not using Windows or Mac, please download QGIS 3.16 for your platform from QGIS’s downloads page.
I have an older version of QGIS (<3.16). Can it still run Quantarctica?
We strongly recommend updating to QGIS 3.16. Though it may run on an older version, we can’t promise that all of Quantarctica 3’s features or datasets will display or run correctly.
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How much free space do I need for Quantarctica on my computer?
The uncompressed package is 8.6 GB. The download size is about 6 GB (zip file).
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What’s included in the Quantarctica package?
The Quantarctica 3 package includes:
- Simple and detailed basemaps
- Satellite image mosaics and terrain rasters
- Datasets for 10 different scientific themes
See a full list of included datasets, citation information, and preview images in our Data Catalog.
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How do I acknowledge/cite Quantarctica?
We request that you acknowledge or cite Quantarctica and the Norwegian Polar Institute when publishing a Quantarctica-made image or map publicly in a journal or online.
Citation: Matsuoka, K., Skoglund, A., & Roth, G. (2018). Quantarctica [Data set]. Norwegian Polar Institute. https://doi.org/10.21334/npolar.2018.8516e961
Acknowledgement (example): We acknowledge the Norwegian Polar Institute’s Quantarctica package.
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Can I still use my old version of Quantarctica?
Yes. However, the older versions are “as is”. There will be no further bug fixes, support, etc. Users are strongly encouraged to use the latest version.
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What is the best way to distributed my published data to other Quantarctica users?
Though QGIS is very good at importing many different types of data, there are certain steps you can take to make your data “Quantarctica-friendly,” or easily imported by other Quantarctica users. We have published a guide about Making Quantarctica-Friendly Datasets (PDF, 1 MB).
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How can I move data layers I added to my own version of Quantarctica to a Quantarctica 3.2 project?
Save your project file containing only the data layers you want to include in Quantarctica 3.2, and open the project file in a new QGIS 3.16 session. In the data layer panel, mark all the entries, right-click on any of them, and select Copy Layer. Inside a QGIS instance of Quantarctica 3.2, right-click somewhere in the blank part of the data layer panel, and select Paste Layer/Group. Note that migrating layers from QGIS 2 to QGIS 3 might not preserve all layer settings.
Do I need an Internet connection to use Quantarctica?
No. An Internet connection is required only for downloading the dataset. Once you’ve downloaded Quantarctica, you can take it with you anywhere!
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My downloads fail / I need an FTP client
We recommend that you use a download mirror close to you. If a particular mirror is down or slow, try a different one.
We provide three different download options for Quantarctica, and recommend using an FTP client. If you don’t already have an FTP client installed, we recommend FileZilla – it’s free.
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I’m experiencing problems. What do I do?
If you have problems related to QGIS, please visit QGIS website. To resolve Quantarctica-specific problems or file a Quantarctica-specific bug report, please contact us.
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