Sensitivity of inverse glacial isostatic adjustment estimates over Antarctica

Abstract

Glacial isostatic adjustment (GIA) is a major source of uncertainty for ice and ocean mass balance estimates derived from satellite gravimetry. In Antarctica the gravimetric effect of cryospheric mass change and GIA are of the same order of magnitude. Inverse estimates from geodetic observations hold some promise for mass signal separation. Here, we investigate the combination of satellite gravimetry and altimetry and demonstrate that the choice of input data sets and processing methods will influence the resultant GIA inverse estimate. This includes the combination that spans the full GRACE record (April 2002-August 2016). Additionally, we show the variations that arise from combining the actual time series of the differing data sets. Using the inferred trends, we assess the spread of GIA solutions owing to (1) the choice of different degree-1 and C20 products, (2) viable candidate surface-elevation-change products derived from different altimetry missions corresponding to different time intervals, and (3) the uncertainties associated with firn process models. Decomposing the total-mass signal into the ice mass and the GIA components is strongly dependent on properly correcting for an apparent bias in regions of small signal. Here our ab initio solutions force the mean GIA and GRACE trend over the low precipitation zone of East Antarctica to be zero. Without applying this bias correction, the overall spread of total-mass change and GIA-related mass change using differing degree-1 and C20 products is 68 and 72 Gt a-1, respectively, for the same time period (March 2003-October 2009). The bias correction method collapses this spread to 6 and 5 Gt a-1, respectively. We characterize the firn process model uncertainty empirically by analysing differences between two alternative surface mass balance products. The differences propagate to a 10 Gt a-1 spread in debiased GIA-related mass change estimates. The choice of the altimetry product poses the largest uncertainty on debiased mass change estimates. The spread of debiased GIA-related mass change amounts to 15 Gt a-1 for the period from March 2003 to October 2009. We found a spread of 49 Gt a-1 comparing results for the periods April 2002-August 2016 and July 2010-August 2016. Our findings point out limitations associated with data quality, data processing, and correction for apparent biases.