Regional climate-model performance in Greenland firn derived from in situ observations

Recent record-warm summers in Greenland (Khan et al. 2015) have started affecting the higher regions of the ice sheet (i.e. the accumulation area), where increased melt has altered the properties of firn (i.e. multi-year snow). At high altitudes, meltwater percolates in the porous snow and firn, where it refreezes. The result is mass conservation, as the refrozen meltwater is essentially stored (Harper et al. 2012). However, in some regions increased meltwater refreezing in shallow firn has created thick ice layers. These ice layers act as a lid, and can inhibit meltwater percolation to greater depths, causing it to run off instead (Machguth et al. 2016). Meltwater at the surface also results in more absorbed sunlight, and hence increased melt in the accumulation area (Charalampidis et al. 2015). These relatively poorly understood processes are important for ice-sheet mass-budget projections

Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic

The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond