G002-0007
Complex Patterns of Antarctic Ice Sheet Mass Change Resolved by Time-dependent Rate Modeling of GRACE and GRACE Follow-On Observations

Monday, 7 December 2020
Poster
Lei Wang, Ohio State University Main Campus, Columbus, OH, United States, James L Davis, Lamont-Doherty Earth Obervator, Lamont-Doherty Earth Observatory, Palisades, NY, United States and Ian M Howat, Ohio State University, Byrd Polar & Climate Research Center, Columbus, OH, United States
Abstract:
The Antarctic ice sheet (AIS) is a sensitive indicator of changes in climate, a major contributor to current global sea-level change and the largest potential source of future sea-level change. Variability in AIS mass balance at a wide range of spatial and temporal scales obscures the detection of long-term trends, increasing the uncertainty of projections. Nearly two-decades of satellite-based gravimetry observations enable resolution of spatiotemporal variability in AIS mass balance over a climatologically significant period at unprecedented detail. We use a novel stochastic approach to resolve the timescales and amplitudes of variability in mass change rate in monthly measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite missions between 2003 and 2020. We find a higher degree of spatiotemporal variability than expected, with the secular trend in loss substantially effected by shorter term variations in accumulation rate. Whereas loss from the West Antarctic Ice Sheet (WAIS) is characterized by a multi-decadal trend, variations in the mass balance of the East Antarctic Ice Sheet (EAIS) are dominated by substantial, short-term changes in accumulation that impact AIS mass balance as whole. We find that 50% of the EAIS mass gain over the past two decades was due to two extreme snowfall events in 2009 and 2011 that temporarily brought the entire AIS close to equilibrium. This and similarly complex variations over the ice sheet highlight the role of the EAIS in determined AIS mass balance and the need to include stochastic variability in estimates of rates of loss.