Feasibility of a global inversion for spatially resolved glacial isostatic adjustment and ice sheet mass changes proven in simulation experiments

Estimating mass changes of ice sheets or of the global ocean from satellite gravimetry strongly depends on the correction for the glacial isostatic adjustment (GIA) signal. However, geophysical GIA models are different and incompatible with observations, particularly in Antarctica. Regional inversions have resolved GIA over Antarctica without ensuring global consistency, while global inversions have been mostly constrained by a priori GIA patterns. For the first time, we set up a global inversion to simultaneously estimate ice sheet mass changes and GIA, where Antarctic GIA is spatially resolved using a set of global GIA patterns. The patterns are related to deglaciation impulses localized along a grid over Antarctica. GIA associated with four regions outside Antarctica is parametrized by global GIA patterns induced by deglaciation histories. The observations we consider here are satellite gravimetry, satellite altimetry over Antarctica and Greenland, as well as modelled firn thickness changes. Firn thickness changes are also parametrized to account for systematic errors in their modelling. Results from simulation experiments using realistic signals and error covariances support the feasibility of the approach. For example, the spatial RMS error of the estimated Antarctic GIA effect, assuming a 10-year observation period, is 31% and 51%, of the RMS of two alternative global GIA models. The integrated Antarctic GIA error is 8% and 5%, respectively, of the integrated GIA signal of the two models. For these results realistic error covariances incorporated in the parameter estimation process are essential. If error correlations are neglected, the Antarctic GIA RMS error is more than twice as large.Highlights We present a globally consistent inversion approach to co-estimate glacial isostatic adjustment effects together with changes of the ice mass and firn air content in Greenland and Antarctica. The inversion method utilizes data sets from satellite gravimetry, satellite altimetry, regional climate modelling, and firn modelling together with the full error-covariance information of all input data. The simulation experiments show that the proposed GIA parametrization in Antarctica can resolve GIA effects unpredicted by geophysical modelling, despite realistic input-data limitations

Mass and volume time series of Antarctic drainage systems derived by a coupled state space analysis of satellite observations and model products

We investigated time series of 17 Antarctic drainage basins from April 2002 until August 2016 using data from the satellite gravimetry mission GRACE, a multi-mission altimetry product, and products from regional climate and firn modeling. The model products are cumulated surface mass balance anomalies (cSMBA) derived from RACMO2 outputs and firn thickness change predicted by the firn densification model (FDM) IMAU-FDM. We simultaneously evaluated these data sets in a state-space model framework to separate time-variable contributions from ice-dynamics and climatological forcing to mass and volume changes of the drainage systems. We parametrize long-term changes by a trend with a time-variable rate. Further we separate residual cyclic, first-order auto regressive (AR(1)), and irregular short-term variations. For each drainage basin we provide a file that includes mass and volume time series of the input data sets and the estimated signals along with their uncertainty (single standard deviation). The basin numbers refer to drainage systems defined by Zwally et al. (2012)

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

The surface mass balance (SMB) is very low over the vast East Antarctic Plateau, for example in the Vostok region, where the mean SMB is on the order of 20–35 kg m-2 a-1. The observation and modeling of spatio-temporal SMB variations are equally challenging in this environment. Stake measurements carried out in the Vostok region provide SMB observations over half a century (1970–2019). This unique data set is compared with SMB estimations of the regional climate models RACMO2.3p2 (RACMO) and MAR3.11 (MAR). We focus on the SMB variations over time scales from months to decades. The comparison requires a rigorous assessment of the uncertainty in the stake observations and the spatial scale dependence of the temporal SMB variations. Our results show that RACMO estimates of annual and multi-year SMB agree well with the observations. The regression slope between modelled and observed temporal variations is close to 1.0 for this model. SMB simulations by MAR are affected by a positive bias which amounts to 6 kg m-2 a-1 at Vostok station and 2 kg m-2 a-1 along two stake profiles between Lake Vostok and Ridge B. None of the models is capable to reproduce the seasonal distributions of SMB and precipitation. Model SMB estimates are used in assessing the ice-mass balance and sea-level contribution of the Antarctic Ice Sheet by the input-output method. Our results provide insights into the uncertainty contribution of the SMB models to such assessments.Fil: Richter, Andreas Jorg. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Astrometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Technische Universität Dresden; AlemaniaFil: Ekaykin, Alexey A.. Arctic And Antarctic Research Institute; Rusia. Saint Petersburg State University; RusiaFil: Willen, Matthias O.. Technische Universität Dresden; AlemaniaFil: Lipenkov, Vladimir Ya.. Arctic And Antarctic Research Institute; RusiaFil: Groh, Andreas. Technische Universität Dresden; AlemaniaFil: Popov, Sergey V.. Polar Marine Geosurvey Expedition; Rusia. Saint Petersburg State University; RusiaFil: Scheinert, Mirko. Technische Universität Dresden; AlemaniaFil: Horwath, Martin. Technische Universität Dresden; AlemaniaFil: Dietrich, Reinhard. Technische Universität Dresden; Alemani

Sensitivity of inverse glacial isostatic adjustment estimates over Antarctica

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