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Data Set ID:

IceBridge BedMachine Greenland, Version 2

This data set contains bed topography beneath the Greenland Ice Sheet based on mass conservation derived from airborne radar tracks and satellite radar. The data set also includes surface and ice thickness measurements.

Version Summary:

Version 2 of the IceBridge BedMachine Greenland data includes improved processing of some basins and adds some Operation IceBridge 2014 data. Heights are now provided with respect to mean sea level, instead of the WGS84 ellipsoid.The geoid is included in an additional field in the data.

Geographic Coverage

Spatial Coverage:
  • N: 90, S: 60, E: 10, W: -80

Spatial Resolution:
  • 150 x 150
  • 400 x 400
Temporal Coverage:
  • 1 January 2007 to 31 December 2007
Temporal Resolution: Not specified
  • Topography > Terrain Elevation > Bedrock
  • Snow/Ice > Ice Depth/Thickness > Ice Thickness
  • Glaciers/Ice Sheets > Glacier Elevation/Ice Sheet Elevation > Surface Elevation
Platform(s) DC-8, P-3B
Sensor(s): MCoRDS
Data Format(s):
  • NetCDF
Version: V2
Data Contributor(s): Mathieu Morlighem, Eric Rignot, Jeremie Mouginot, Helene Seroussi, Eric Larour

Data Citation

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Morlighem, M., E. Rignot, J. Mouginot, H. Seroussi, and E. Larour. 2015. IceBridge BedMachine Greenland, 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/AD7B0HQNSJ29. [Date Accessed].

Literature Citation

As a condition of using these data, we request that you acknowledge the author(s) of this data set by referencing the following peer-reviewed publication.

  • Morlighem, M., E. Rignot, J. Mouginot, H. Seroussi, and E. Larour. 2015. Deeply Incised Submarine Glacial Valleys Beneath the Greenland Ice Sheet, Nature Geoscience. 7. 418-422. http://dx.doi.org/10.1038/ngeo2167

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Detailed Data Description


The data are in netCDF 1.6 file format.

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File and Directory Structure

Data file, MCdataset-2015-04-27.nc, is on the HTTPS site, https://daacdata.apps.nsidc.org/DATASETS/IDBMG4_BedMachineGr/.

In the file name, "MC" refers to Mass Conservation, "nc" indicates netCDF file format, and 2015-04-27 indicates date of data file creation.

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File Size

The data file is approximately 2 GB

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Spatial Coverage

Spatial coverage for the data set currently includes Greenland and the Arctic.

Greenland / Arctic:
Southernmost Latitude: 60° N
Northernmost Latitude: 90° N
Westernmost Longitude: 80° W
Easternmost Longitude: 10° E

Spatial Resolution

The output product is generated at 150 m resolution. The true resolution of the ice thickness data is 400 m.

Projection and Grid Description

Polar Stereographic North (70° N, 45° W), corresponding to EPSG 3413.

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Temporal Coverage

Ice thickness data were collected between 1993 and 2014. The nominal date of this data set is 2007.

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Parameter or Variable

Parameter Description

The BedMachine data file contains parameters as described in Table 1.

Table 1. File Parameters and Units
Parameter Name Description Units
bed Bedrock altitude Meters
errbed Bed topography/ice thickness error Meters
geoid Geoid height above WGS84 Ellipsoid Meters
surface Ice surface elevation Meters
thickness Ice thickness Meters
mask Ice/ocean/land mask none
Source Mass Conservation/kriging none

Sample Data Record

Figure 1 illustrates Greenland bedrock altitude and ice thickness.

Figure 1. Greenland Bedrock Altitude and Ice Thickness

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Software and Tools

Software and Tools

See the NetCDF Resources at NSIDC page for tools to work with netCDF files.

The netCDF data file is compatible with HDF5 libraries, and can be read by HDF readers such as HDFView. If the netCDF file reader you are using does not read the data, seehttp://www.unidata.ucar.edu/software/netcdf/ and http://nsidc.org/data/netcdf/tools.html for information on updating the reader.

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Quality Assessment

An error estimate of the bed elevation and ice thickness is provided in the data set, illustrated in Figure 2.

Figure 2. Error Estimate of Greenland Bed Elevation and Ice Thickness

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Data Acquisition and Processing

Data Acquisition Methods

Source data used in deriving this product include:

  • Operation IceBridge radar-derived thickness data, posted at 15 m, with a vertical precision of 30 m, collected by the MCoRDS radar (https://nsidc.org/data/irmcr2/versions/1/documentation).
  • Ice thickness data from the Doppler focused radar of the Technical University of Denmark (DTU) for the region of 79 North (H. Thomsen, et al. 1997E. L. Christensen, et al. 2000) and Russell (Lindbäck et al. 2014).
  • Ice velocity measurements derived from satellite radar data collected during 2008-2009, posted at 150 m, with errors of 10 m yr-1 in speed and 1.5° in flow direction (Rignot et al. 2012):
    • Japanese Advanced Land Observing System (ALOS) PALSAR
    • Canadian RADARSAT-1 SAR
    • German TerraSAR-X
    • European Envisat Advanced SAR (ASAR)

Ancillary products used include:

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Derivation Techniques and Algorithms

Sparse, airborne, radar sounding-derived ice thickness data are combined with comprehensive, high-resolution, ice motion derived from satellite interferometric synthetic-aperture radar to calculate ice thickness based on Mass Conservation (MC). The MC method solves the mass conservation equation to derive ice thickness, while at the same time minimizing departure from the original radar-derived ice thickness data. The algorithm conserves mass fluxes while minimizing the departure from the original radar-derived ice thickness data. Ice surface motion provides a physical basis for extrapolating sparse ice thickness data to larger areas with few or no data. The method works best in areas of fast flow, where errors in flow direction are small and the glaciers slide on the bed. In the interior regions, where errors in flow direction are larger, kriging is used to interpolate ice thickness (Morlighem, et al 2014).

The algorithm neglects ice motion by internal shear, which is an excellent approximation for fast-flowing glaciers (>100 m yr-1) (Morlighem, et al 2014).

The bed topography is derived by subtracting the ice thickness from the Greenland Mapping Project (GIMP) Digital Elevation Model (http://bpcrc.osu.edu/gdg/data/gimpdem).

Version History

On May 19, 2015, the IceBridge BedMachine Greenland data were replaced by Version 2. Version 2 includes improved processing of some basins and adds some Operation IceBridge 2014 data. Heights are now provided with respect to mean sea level, instead of the WGS84 ellipsoid. The geoid is included in an additional field in the data.

Error Sources

Sources of error include error in ice velocity direction and magnitude, error in surface mass balance and ice thinning rates.

In a trial setting with unusually dense radar sounding coverage, we report errors in the MC-inferred thickness of 36 m, only slightly higher than that of the original data. In areas less well constrained by radar-derived thickness data, or constrained by only one track of data, for example, in south Greenland, errors may exceed 50 m (Morlighem, et al 2013).

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Sensor or Instrument Description

CReSIS Radar

The Center for Remote Sensing of Ice Sheets (CReSIS) Multichannel Coherent Radar Depth Sounder (MCoRDS) operates over a 180 to 210 MHz frequency range with multiple receivers developed for airborne sounding and imaging of ice sheets. See IceBridge MCoRDS L2 Ice Thickness for further information on the MCoRDS radar and the Level-2 data.

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References and Related Publications

Contacts and Acknowledgments

Mathieu Morlighem, Eric Rignot, Jeremie Mouginot
Department of Earth System Science
University of California, Irvine
Irvine CA, 92617, USA

Helene Seroussi, Eric Larour
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California 91109-8099, USA


This work was performed at the University of California Irvine and the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, Cryospheric Sciences Program grant NNX12AB86G.

Document Information


19 May 2015

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