Validating Satellite Radar Altimetry-Derived Greenland Near-Surface Density and Surface Roughness using in situ and Airborne Datasets

Recent research has demonstrated how Greenland Ice Sheet (GrIS) near-surface density and wavelength-scale surface roughness can be estimated from satellite radar altimetry surface echo powers by way of the Radar Statistical Reconnaissance (RSR) technique, the adoption of a radar backscattering model (i.e., the small perturbation model, SPM) and calibration using in situ density profiles. Patterns in the estimated density results 1) highlight an inter-annual variability that covaries with known climatic drivers (e.g., extremely warm 2012 Greenland summer temperatures) and 2) suggest that density estimates derived from different frequency radar echoes (Ku-band from ESA CryoSat-2 and Ka-band from CNES/ISRO SARAL) correspond to different depths in the near-surface (with Ku-band densities being deeper than those from Ka). When expressed as fractions of their respective signal wavelengths, the CryoSat-2 and SARAL surface roughness estimates 1) exhibit good agreement, 2) recover anticipated surface roughness conditions (i.e., a smooth GrIS interior and rougher margin) and 3) demonstrate minimal temporal variability.

Here we present on-going work directed at validating both the use of the small perturbation model as well as the implied density-depth sensitivity in these new remote sensing observations. First, the suitability of the SPM is evaluated using the in situ density cores as well as airborne radar/laser altimetry measurements acquired along the EGIG line during the 2017 and 2019 ESA CryoVEx campaigns. Second, to quantify the depth range to which the satellite radar altimetry Ku- and Ka-band density estimates apply, we compare them against more than 400 contemporaneous (2011-2019) measured in situ density cores across the GrIS. These validation exercises are crucial to understanding the nature of these new satellite-based observations of the near-surface properties of the GrIS that, in turn, will facilitate more accurate estimations of the current and future GrIS mass balance.