Self-affine subglacial roughness: consequences for radar scattering and basal water discrimination in northern Greenland

Abstract

Abstract. Subglacial roughness can be determined at a variety of length scales from radio-echo sounding (RES) data either via statistical analysis of topography or inferred from basal radar scattering. Past studies have demonstrated that subglacial terrain exhibits self-affine (power law) roughness scaling behaviour, but existing radar scattering models do not take this into account. Here, using RES data from northern Greenland, we introduce a self-affine statistical framework that enables a consistent integration of topographicscale roughness with the electromagnetic theory of radar scattering. We demonstrate that the degree of radar scattering, quantified using the waveform abruptness (pulse peakiness), is topographically controlled by the Hurst (roughness power law) exponent. Notably, specular bed reflections are associated with a lower Hurst exponent, with diffuse scattering associated with a higher Hurst exponent. Abrupt waveforms (specular reflections) have previously been used as a RES diagnostic for basal water, and to test this assumption we compare our radar scattering map with a recent prediction for the basal thermal state. We demonstrate that the majority of thawed regions (above pressure melting point) exhibit a diffuse scattering signature, which is in contradiction to the prior approach. Self-affine statistics provide a generalised model for subglacial terrain and can improve our understanding of the relationship between basal properties and ice-sheet dynamics.

Geothermal Heat Flux Reveals the Iceland Hotspot Track Underneath Greenland

“…The presence of a thawed bed in the canyon region is supported by the ice‐penetrating radar studies of Oswald and Gogineni (; bed‐echo data) and Bell et al (; radio‐stratigraphy)—refer to Rogozhina et al () for a data compilation map. However, Greenland water predictions using bed‐echo data can be ambiguous (Jordan et al, ), and assessing the influence of the elevated heat flux on the thermal state will likely require the development of refined radar analysis techniques. Additionally, inferences from thermomechanical models, ice velocity and surface properties, suggest that some of this region is below pressure melting point (MacGregor et al, ), and thus water may not route down the present‐day canyon.…”

Antarctic Topographic Realizations and Geostatistical Modeling Used to Map Subglacial Lakes

“…In some cases, the radar record is ambiguous (Matsuoka, ) and requires more refined processing techniques for interpreting the state of the basal interface (Schroeder et al, ). Less distinct lakes and basal water features have been identified with radar by their specularity (Carter et al, ; Schroeder et al, ; Young et al, ), scattering properties (Jordan et al, ), and reflectivity after correcting for radar attenuation (Chu et al, ). Since their initial discovery, RES surveys have led to the discovery of hundreds of lakes beneath the Antarctic ice sheet (e.g., Siegfried & Fricker, ; Wright & Siegert, ), shown in Figure b.…”

A constraint upon the basal water distribution and basal thermal state of the Greenland Ice Sheet from radar bed-echoes

“…2.1 partially mitigates for roughness-induced scattering loss and the along-track power variability associated with this. Additionally, we later demonstrate that the water detection method/semi-empirical threshold is well tuned to discriminate water in both rough and smooth regions of the ice sheet (Rippin, 2013;Jordan et al, 2017). Finally, Table 1 also 310 indicates that, in exceptional circumstances, ∆[R] > 12 dB could be generated in frozen/dry regions that partially contain sandstone or till that is close to matching the permittivity of ice.…”

“…Notably, there are localised patches of higher σ [R] present in both marginal and interior regions (which are later attributed to the presence of basal water). The ice-sheet scale trends in σ [Pg] and σ [L] = 2 < N > σ h can be related to spatial variation in < N > (MacGregor et al, 2015b;Jordan et al, 2016) and bed roughness (Rippin, 2013;Jordan et al, 2017) (which correlates with σ h ) .…”