f657816c-dc0c-41ca-945d-a08620494d5b eng utf8 dataset dataset NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom polardatacentre@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact 2016-10-11 ISO 19115 Geographic Information - Metadata ISO 19115:2003(E) European Petroleum Survey Group (EPSG) Geodetic Parameter Registry 2008-11-12 publication European Petroleum Survey Group EPSGadministrator@iogp.org https://www.epsg-registry.org/ EPSG Geodetic Parameter Dataset The EPSG Geodetic Parameter Dataset is a structured dataset of Coordinate Reference Systems and Coordinate Transformations, accessible through this online registry. information publisher urn:ogc:def:crs:EPSG::3031 6.18.3 Spatiotemporal mass balance trends for the Antarctic Ice Sheet, 2003-2013 2016-10-11 creation 2016-10-11 revision 2016-10-11 publication 2016-10-11 released None https://doi.org/10.5285/f657816c-dc0c-41ca-945d-a08620494d5b doi GB/NERC/BAS/PDC/00916 https://data.bas.ac.uk/ NE/I027401/1 award Please cite this item as: Espanol, A., & Bamber, J. (2017). Spatiotemporal mass balance trends for the Antarctic Ice Sheet, 2003-2013 (Version None) [Data set]. Polar Data Centre; British Antarctic Survey, Natural Environment Research Council; Cambridge, CB3 0ET, UK.. https://doi.org/10.5285/f657816c-dc0c-41ca-945d-a08620494d5b The Antarctic mass trends have been collated from a combination of different remote sensing datasets. These are trends of yearly elevation changes over Antarctica for the period 2003-2013 due to the different geophysical processes driving changes in Antarctica: ice dynamics, surface mass balance and glacio-isostatic adjustment (GIA). Net trends can be easily calculated by adding together surface and ice dynamics trends. 20 km gridded datasets have been produced for each process, per year (except the GIA solution which is time-invariant). To convert elevation to mass trends, we also provide the density fields for surface (SMB) and GIA processes used in Martin-Espanol et al (2016). These can be directly multiplied by the dh/dt. To convert dh/dt from ice dynamics, simply multiply by the density of ice. Mass smb = dh/dt smb * d surf Mass ice = dh/dt ice * d ice (not provided) Mass gia = dh/dt gia * d rock NERC grant: NE/I027401/1 No credit. completed Martin-Espanol, Alba University of Bristol Researcher author Bamber, Jonathan University of Bristol Researcher author NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact asNeeded completed EARTH SCIENCE > Cryosphere > Glaciers/Ice Sheets > Glacier Elevation/Ice Sheet Elevation EARTH SCIENCE > Cryosphere > Glaciers/Ice Sheets > Glacier Mass Balance/Ice Sheet Mass Balance EARTH SCIENCE > Hydrosphere > Glaciers/Ice Sheets > Glacier Elevation/Ice Sheet Elevation EARTH SCIENCE > Hydrosphere > Glaciers/Ice Sheets > Glacier Mass Balance/Ice Sheet Mass Balance theme Global Change Master Directory (GCMD) Science Keywords 2026-01-27 publication 23 Global Change Data Center, Science and Exploration Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA) Greenbelt MD United States of America https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords Global Change Master Directory (GCMD) Keywords The information provided on this page seeks to define how the GCMD Keywords are structured, used and accessed. It also provides information on how users can participate in the further development of the keywords. information publisher Antarctic Ice Sheet Elevation Mass balance Remote sensing theme Antarctica place Elevation Hydrography theme GEMET - INSPIRE themes, version 1.0 2021-01-06 revision otherRestrictions no limitations to public access otherRestrictions no limitations license Open Government Licence v3.0 otherRestrictions This data is governed by the NERC Data Policy: https://www.ukri.org/who-we-are/nerc/our-policies-and-standards/nerc-data-policy/ otherRestrictions This data is governed by the NERC data policy and supplied under Open Government Licence v.3 otherRestrictions No restrictions apply. url url crossReference url url crossReference url url crossReference textTable eng utf8 climatologyMeteorologyAtmosphere geoscientificInformation -180.0 180.0 -90.0 -52.3 2003-01-01 2013-12-31 It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk. NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information distributor bytes 1 https://ramadda.data.bas.ac.uk/repository/entry/show/?entryid=f657816c-dc0c-41ca-945d-a08620494d5b WWW:LINK-1.0-http--link Get Data Download data download dataset Methodology: The data used a Bayesian Hierarchical Model driven by different remote sensing datasets: ICESat, Envisat, Cryosat-2, GRACE and a network of elastic-corrected GPS stations. The methodology is described in detail in the following papers: Zammit-Mangion et al (2014), Zammit-Mangion et al. (2015a), Zammit-Mangion et al. (2015b) and Martin-Espanol et al (2016). Data collection: Information about the processing of the different datasets can be found partially in Schoen et al. (2015) (not all data sets are used) and completely in Martin-Espanol et al (2016). Details on the architecture and characteristics of the Bayesian framework can be found in Zammit-Mangion et al (2014) and Zammit-Mangion et al. (2015). Data quality: Uncertainty maps are provided for each of the variables, which are estimated within the Bayesian inversion method taking into account the full error covariance matrices for each data set. Further details are described in the publications related to the project (see references). asNeeded completed