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Abstract
AbstractWe present continuous estimates of snow and firn density, layer depth and accumulation from a multi-channel, multi-offset, ground-penetrating radar traverse. Our method uses the electromagnetic velocity, estimated from waveform travel-times measured at common-midpoints between sources and receivers. Previously, common-midpoint radar experiments on ice sheets have been limited to point observations. We completed radar velocity analysis in the upper ~2 m to estimate the surface and average snow density of the Greenland Ice Sheet. We parameterized the Herron and Langway (1980) firn density and age model using the radar-derived snow density, radar-derived surface mass balance (2015–2017) and reanalysis-derived temperature data. We applied structure-oriented filtering to the radar image along constant age horizons and increased the depth at which horizons could be reliably interpreted. We reconstructed the historical instantaneous surface mass balance, which we averaged into annual and multidecadal products along a 78 km traverse for the period 1984–2017. We found good agreement between our physically constrained parameterization and a firn core collected from the dry snow accumulation zone, and gained insights into the spatial correlation of surface snow density.
Highlights
The Greenland Ice Sheet (GrIS) expresses high variability in ice loss, and sea level rise, due to the regional scale variability in the processes governing mass balance (Lenaerts and others, 2019)
We present the analysis of multi-channel, multi-offset, radar (MxRadar) imagery along a 78 km traverse in the GrIS dry snow accumulation zone to demonstrate the capability of this method, which has the advantage of ascertaining snow and firn density, and depth, and thereby Surface mass balance (SMB), independently
We find that extrapolating the GreenTrACS Core 15 (GTC15) densities along GTC15 Spur West introduces an insignificant bias to the SMB of −0.004 m w.e. a−1 and rms error of 0.005 m w.e. a−1
Summary
The Greenland Ice Sheet (GrIS) expresses high variability in ice loss, and sea level rise, due to the regional scale variability in the processes governing mass balance (Lenaerts and others, 2019). Surface mass balance (SMB) contributes just over half ( 52%) of GrIS mass loss, but ice-sheet wide SMB simulated from regional climate models maintains 25% uncertainty (Shepherd and others, 2020). Efforts to improve SMB simulation (e.g. Fettweis and others, 2017) are limited by the scarcity of observations, which are required to evaluate the model performance (e.g Noël and others, 2016). SMB measurements are made at the point scale during infrequent field efforts, through the laborious process of excavating snow pits or drilling firn cores. The sparseness of snow pit observations on the GrIS limits the testable correlation lengths and tends to debilitate spatial correlation analysis. In space-borne altimetry retrievals of GrIS mass balance, the uncertainty in modeled corrections for snow densification required to convert a measured change in ice-sheet volume to a change in mass causes 16% uncertainty (Shepherd and others, 2020)
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