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.
IceBridge BedMachine Greenland, Version 2
There is a more recent version of these data.
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.
Overview
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Citing These Data
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: https://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. https://doi.org/10.1038/ngeo2167
Documentation
Detailed Data Description
The data are in netCDF 1.6 file format.
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.
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.
Geolocation
The following table provides details about the coordinate system for this data set.
Geographic coordinate system | WGS 84 |
---|---|
Projected coordinate system | WGS 84 / NSIDC Sea Ice Polar Stereographic North |
Longitude of true origin | -45° E |
Latitude of true origin | 70° N |
Scale factor at longitude of true origin | 1 |
Datum | WGS 84 |
Ellipsoid/spheroid | WGS 84 |
Units | meters |
False easting | 0 |
False northing | 0 |
EPSG code | 3413 |
PROJ4 string | +proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs |
Reference | https://epsg.io/3413 |
Ice thickness data were collected between 1993 and 2014. The nominal date of this data set is 2007.
Parameter Description
The BedMachine data file contains parameters as described in Table 2.
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
Data Access 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.
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
Data Acquisition and Processing
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 (Thomsen et al., 1997; 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:
- Surface Mass Balance (SMB) averaged for the years 1961 to 1990 at 11 km posting with a precision between 7 percent and and 20 percent in the ablation zone (Ettema et al., 2009; data set available on request to the authors).
- Ice thickening rates combining satellite and airborne altimetry for the years 2003 to 2008, at a 0.1 degree posting, with a precision of 20 cm yr-1. (Schenk & Csatho, 2012).
- Surface elevation from the Greenland Mapping Project (GIMP) Digital Elevation Model (Howat et al., 2014; http://bpcrc.osu.edu/gdg/data/gimpdem).
- Ice and Ocean mask from the Greenland Mapping Project (GIMP) Digital Elevation Model (Howat et al., 2014; http://bpcrc.osu.edu/gdg/data/icemask).
- The International Bathymetric Chart of the Arctic Ocean (IBCAO) Version 3.0 (Jakobsson et al., 2012; http://www.ibcao.org/).
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).
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.
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.