Empirical estimation of present-day Antarctic glacial isostatic adjustment and ice mass change
Publication date
2014
Authors
Gunter, B.C.
Didova, O.
Riva, R.E.M.
Ligtenberg, S.R.M.
Lenaerts, J.T.M.
King, M.A.
Broeke, M.R. van den
Urban, T.
Editors
Advisors
Supervisors
Document Type
Article
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(c) UU Universiteit Utrecht, 2014
Abstract
This study explores an approach that simultaneously
estimates Antarctic mass balance and glacial isostatic
adjustment (GIA) through the combination of satellite gravity
and altimetry data sets. The results improve upon previous
efforts by incorporating a firn densification model to
account for firn compaction and surface processes as well as
reprocessed data sets over a slightly longer period of time.
A range of different Gravity Recovery and Climate Experiment
(GRACE) gravity models were evaluated and a new
Ice, Cloud, and Land Elevation Satellite (ICESat) surface
height trend map computed using an overlapping footprint
approach. When the GIA models created from the combination
approach were compared to in situ GPS ground
station displacements, the vertical rates estimated showed
consistently better agreement than recent conventional GIA
models. The new empirically derived GIA rates suggest the
presence of strong uplift in the Amundsen Sea sector in
West Antarctica (WA) and the Philippi/Denman sectors, as
well as subsidence in large parts of East Antarctica (EA).
The total GIA-related mass change estimates for the entire
Antarctic ice sheet ranged from 53 to 103 Gt yr−1, depending
on the GRACE solution used, with an estimated uncertainty
of ±40 Gt yr−1. Over the time frame February 2003–
October 2009, the corresponding ice mass change showed
an average value of −100±44 Gt yr−1 (EA: 5±38, WA:
−105±22), consistent with other recent estimates in the literature,
with regional mass loss mostly concentrated in WA. The refined approach presented in this study shows the contribution
that such data combinations can make towards improving
estimates of present-day GIA and ice mass change,
particularly with respect to determining more reliable uncertainties.