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A data-driven approach for assessing ice-sheet mass balance in space and time

Journal Article


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Abstract


  • Combinations of various numerical models and datasets with diverse observation characteristics have been used to assess the mass evolution of ice sheets. As a consequence, a wide range of estimates have been produced using markedly different methodologies, data, approximation methods and model assumptions. Current attempts to reconcile these estimates using simple combination methods are unsatisfactory, as common sources of errors across different methodologies may not be accurately quantified (e.g. systematic biases in models). Here we provide a general approach which deals with this issue by considering all data sources simultaneously, and, crucially, by reducing the dependence on numerical models. The methodology is based on exploiting the different space–time characteristics of the relevant ice-sheet processes, and using statistical smoothing methods to establish the causes of the observed change. In omitting direct dependence on numerical models, the methodology provides a novel means for assessing glacio-isostatic adjustment and climate models alike, using remote-sensing datasets. This is particularly advantageous in Antarctica, where in situ measurements are difficult to obtain. We illustrate the methodology by using it to infer Antarctica's mass trend from 2003 to 2009 and produce surface mass-balance anomaly estimates to validate the RACMO2.1 regional climate model.

Authors


  •   Zammit-Mangion, Andrew
  •   Bamber, Jonathan (external author)
  •   Schoen, Nana (external author)
  •   Rougier, Jonathon (external author)

Publication Date


  • 2015

Citation


  • Zammit-Mangion, A., Bamber, J. L., Schoen, N. W. & Rougier, J. C. (2015). A data-driven approach for assessing ice-sheet mass balance in space and time. Annals of Glaciology, 56 (70), 175-183.

Scopus Eid


  • 2-s2.0-84982129706

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6312&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5284

Has Global Citation Frequency


Number Of Pages


  • 8

Start Page


  • 175

End Page


  • 183

Volume


  • 56

Issue


  • 70

Place Of Publication


  • United Kingdom

Abstract


  • Combinations of various numerical models and datasets with diverse observation characteristics have been used to assess the mass evolution of ice sheets. As a consequence, a wide range of estimates have been produced using markedly different methodologies, data, approximation methods and model assumptions. Current attempts to reconcile these estimates using simple combination methods are unsatisfactory, as common sources of errors across different methodologies may not be accurately quantified (e.g. systematic biases in models). Here we provide a general approach which deals with this issue by considering all data sources simultaneously, and, crucially, by reducing the dependence on numerical models. The methodology is based on exploiting the different space–time characteristics of the relevant ice-sheet processes, and using statistical smoothing methods to establish the causes of the observed change. In omitting direct dependence on numerical models, the methodology provides a novel means for assessing glacio-isostatic adjustment and climate models alike, using remote-sensing datasets. This is particularly advantageous in Antarctica, where in situ measurements are difficult to obtain. We illustrate the methodology by using it to infer Antarctica's mass trend from 2003 to 2009 and produce surface mass-balance anomaly estimates to validate the RACMO2.1 regional climate model.

Authors


  •   Zammit-Mangion, Andrew
  •   Bamber, Jonathan (external author)
  •   Schoen, Nana (external author)
  •   Rougier, Jonathon (external author)

Publication Date


  • 2015

Citation


  • Zammit-Mangion, A., Bamber, J. L., Schoen, N. W. & Rougier, J. C. (2015). A data-driven approach for assessing ice-sheet mass balance in space and time. Annals of Glaciology, 56 (70), 175-183.

Scopus Eid


  • 2-s2.0-84982129706

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6312&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5284

Has Global Citation Frequency


Number Of Pages


  • 8

Start Page


  • 175

End Page


  • 183

Volume


  • 56

Issue


  • 70

Place Of Publication


  • United Kingdom