Investigating the spatial representativeness of East Antarctic ice cores: a comparison of ice core and radar-derived surface mass balance over coastal ice rises and Dome Fuji

Abstract

Surface mass balance (SMB) of the Antarctic Ice Sheet must be better understood to document the current Antarctic contribution to sea-level rise. In situ point data using snow stakes and ice cores are often used to evaluate the state of the ice sheet's mass balance, as well as to assess SMB derived from regional climate models, which are then used to produce future climate projections. However, spatial representativeness of individual point data remains largely unknown, particularly in the coastal regions of Antarctica with highly variable terrain. Here, we compare ice core data collected at the summit of eight ice rises along the coast of Dronning Maud Land, as well as at the Dome Fuji site, and shallow ice-penetrating radar data over these regions. Shallow radar data have the advantage of being spatially extensive, with a temporal resolution that varies between a yearly and multi-year resolution, from which we can derive a SMB record over the entire radar survey. This comparison therefore allows us to evaluate the spatial variability of SMB and the spatial representativeness of ice-core-derived SMB. We found that ice core mean SMB is very local, and the difference with radar-derived SMB increases in a logarithmic fashion as the surface covered by the radar data increases, with a plateau ∼ 1–2 km away from the ice crest for most ice rises, where there are strong wind–topography interactions, and ∼ 10 km where the ice shelves begin. The relative uncertainty in measuring SMB also increases rapidly as we move away from the ice core sites. Five of our ice rise sites show a strong spatial representativeness in terms of temporal variability, while the other three sites show that it is limited to a surface area between 20–120 km2. The Dome Fuji site, on the other hand, shows a small difference between pointwise and area mean SMB, as well as a strong spatial representativeness in terms of temporal variability. We found no simple parameterization that could represent the spatial variability observed at all the sites. However, these data clearly indicate that local spatial SMB variability must be considered when assessing mass balance, as well as comparing modeled SMB values to point field data, and therefore must be included in the estimate of the uncertainty of the observations.