Predicting Antarctic Net Snow Accumulation at the Kilometer Scale and Its Impact on Observed Height Changes

Sub-grid-scale processes occurring at or near the surface of an ice sheet have a potentially large impact on local and integrated net accumulation of snow via redistribution and sublimation. Given observational complexity, they are either ignored or parameterized over large-length scales. Here, we train random forest (RF) models to predict variability in net accumulation over the Antarctic Ice Sheet using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the RF models. We find that local net accumulation deviates by as much as 172% of the atmospheric model mean. The correlation in space between the predicted net accumulation variability and satellite-derived surface-height change indicates that surface processes operate differently through time, driven largely by the seasonal anomalies in snow accumulation.

Variability of upper firn processes in West Antarctica observed with GPS reflectometry, 2007-2017

Land ice loss from Antarctica is a significant and accelerating contribution to global sea-level rise; however, Antarctic mass-balance estimates are complicated by insufficient knowledge of surface mass-balance processes such as snow accumulation. These variables are challenging to observe on a continental scale and in situ data are sparse, so we largely rely on estimates from atmospheric models. Here, we employ a novel method, GPS interferometric reflectometry (GPS-IR), to measure upper (<2 m) firn-column thickness changes across a 23-station GPS array in West Antarctica. We compare the results with antenna heights measured in situ to establish the method's daily uncertainty (0.06 m) and with output from two atmospheric reanalysis products to categorize spatial and temporal variability of near-surface processes. GPS-IR is an effective method for monitoring surface mass-balance processes that can be applied to both historic GPS datasets and future experiments to provide critical in situ observations of processes driving surface-height evolution.

Brief Communication: Upper air relaxation in RACMO2 significantly improves modelled interannual surface mass balance variability in Antarctica

The regional climate model (RCM) RACMO2 has been a powerful tool for improving surface mass balance (SMB) estimates from GCMs or reanalyses. However, new yearly SMB observations for West Antarctica show that the modelled interannual variability in SMB is poorly simulated by RACMO2, in contrast to ERA-Interim, which resolves this variability well. In an attempt to remedy RACMO2 performance, we included additional upper air relaxation (UAR) in RACMO2. With UAR, the correlation to observations is similar for RACMO2 and ERA-Interim. The spatial SMB patterns and ice sheet integrated SMB modelled using UAR remain very similar to the estimates of RACMO2 without UAR. We only observe an upstream smoothing of precipitation in regions with very steep topography like the Antarctic Peninsula. We conclude that UAR is a useful improvement for RCM simulations, although results in regions with steep topography should be treated with care.