Sensitivity of inverse glacial isostatic adjustment estimates over Antarctica

Abstract

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.

Surface Mass Balance Models Vs. Stake Observations: A Comparison in the Lake Vostok Region, Central East Antarctica

“…They are essential for estimates of ice sheet mass balance by the component (or input-output) method (Shepherd et al, 2018;Rignot et al, 2019). They are also used for the attribution of mass changes determined by GRACE and GRACE-FO (e.g., Groh et al, 2020;Velicogna et al, 2020) and ice-surface elevation changes determined by satellite altimetry (e.g., Schröder et al, 2019;Willen et al, 2020) to either dynamically induced changes or SMB-related changes. Over the Antarctic Ice Sheet (AIS) the SMB is dominated by snow precipitation, an order of magnitude larger than the other mass fluxes, while mass losses are dominated by sublimation of surface snow and drifting snow (Agosta et al, 2019).…”

Reconstructing GRACE-type time-variable gravity from the Swarm satellites

“…For nearly 15 years the Gravity Recovery and Climate Experiment (GRACE) mission allowed us to construct monthly maps of changes in the Earth's gravitational field (and thus mass distribution) with unprecedented accuracy. This data has been used to study glacier and ice sheet mass imbalance 1,2 , hydrological change 3,4 (including floods and droughts [5][6][7] ) ocean mass change 8,9 , the solid Earth's response to post-glacial unloading [10][11][12] , and the mechanisms of large earthquakes 13,14 . The GRACE mission surpassed its planned duration three times over, with its mass change times series being brought to a close in June 2017, and the satellites being decommissioned later that same year.…”

Separating Long‐Term and Short‐Term Mass Changes of Antarctic Ice Drainage Basins: A Coupled State Space Analysis of Satellite Observations and Model Products

“…We have tried to constrain the errors of the model products by means of an error assessment analogous to Willen et al. (2020). Doing so, we made use of the SMB product of another regional climate model, namely MAR (Agosta et al., 2019).…”

“…So far we have not identified a suitable parameterization for either errors in the model products, or errors in the satellite data. We have tried to constrain the errors of the model products by means of an error assessment analogous to Willen et al (2020). Doing so, we made use of the SMB product of another regional climate model, namely MAR (Agosta et al, 2019).…”