Relating regional and point measurements of accumulation in southwest Greenland

Abstract

Abstract. In recent decades, the Greenland ice sheet (GrIS) has frequently experienced record melt events, which have significantly affected surface mass balance (SMB) and estimates thereof. SMB data are derived from remote sensing, regional climate models (RCMs), firn cores and automatic weather stations (AWSs). While remote sensing and RCMs cover regional scales with extents ranging from 1 to 10 km, AWS data and firn cores are point observations. To link regional scales with point measurements, we investigate the spatial variability of snow accumulation (bs) within areas of approximately 1–4 km2 and its temporal changes within 2 years of measurements. At three different sites on the southwestern GrIS (Swiss Camp, KAN-U, DYE-2), we performed extensive ground-penetrating radar (GPR) transects and recorded multiple snow pits. If the density is known and the snowpack dry, radar-measured two-way travel time can be converted to snow depth and bs. We spatially filtered GPR transect data to remove small-scale noise related to surface characteristics. The combined uncertainty of bs from density variations and spatial filtering of radar transects is at 7 %–8 % per regional scale of 1–4 km2. Snow accumulation from a randomly selected snow pit is very likely representative of the regional scale of 1–4 km2 (with probability p=0.8 for a value within 10 % of the regional mean for KAN-U, and p>0.95 for Swiss Camp and DYE-2). However, to achieve such high representativeness of snow pits, it is required to determine the average snow depth within the vicinity of the pits. At DYE-2, the spatial pattern of snow accumulation was very similar for 2 consecutive years. Using target reflectors placed at respective end-of-summer-melt horizons, we additionally investigated the occurrences of lateral redistribution within one melt season. We found no evidence of lateral flow of meltwater in the current climate at DYE-2. Such studies of spatial representativeness and temporal changes in accumulation are necessary to assess uncertainties of the linkages of point measurements and regional-scale data, which are used for validation and calibration of remote-sensing data and RCM outputs.

Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G

“…A list of all firn cores used and corresponding references can be found in the Supplement. Firn temperatures are obtained from Vandecrux (2020) (https://doi.org/10.18739/A2833N00P), Polashenski et al (2014) and Harper et al (2012). The upGPR data of the liquid water measurements at Dye-2 are available from Heilig et al (2018).…”

Improved representation of the contemporary Greenland ice sheet firn layer by IMAU-FDM v1.2G

“…Here we used data from a recent study that used upward looking ground penetrating radar (upGPR) at Dye-2 in the higher percolation zone of the southwestern GrIS (2120 ma.s.l., see Fig. 1, Heilig et al (2020)). gradients at Dye-2.…”

Combined GNSS reflectometry/refractometry for automated and continuous in situ surface mass balance estimation on an Antarctic ice shelf

“…These remote sensing techniques cannot provide direct estimates of SMB, snow or firn density, and SWE and thus necessitate accurate and reliable in situ data for calibration and validation. Time series of surface density, averaged over local areas, are spatially representative on the polar plateau and of utmost importance to link remote sensing derived elevation or volume changes to mass balance estimations (e.g., Veldhuijsen et al, 2023;Heilig et al, 2020;Weinhart et al, 2020). Continuous time series support increased understanding of temporal changes in accumulation and melt, allowing improvements of RCM as well as polar snow modelling (e.g., Heilig et al, 2018).…”

“…Snow pillows or scales (Johnson et al, 2015;Beaumont, 1965), cosmic ray sensors (Gugerli et al, 2019;Schattan et al, 2017) and acoustic sounding (Kinar and Pomeroy, 2015) provide SWE. Upward-looking radar systems yield snow depth, snow density, wetness, and SWE (Heilig et al, 2020;Schmid et al, 2015). These existing observation techniques have either low spatial resolution being insensitive to snowpack variability, or sensitive optical and moving parts limit their long-term polar application (Gutmann et al, 2012).…”