In situ sampled snow particle sizes of the East Antarctic ice sheet and their relation to physical and remotely sensed snow surface parameters
2013 (English)In: Annals of Glaciology, ISSN 0260-3055, E-ISSN 1727-5644, Vol. 54, no 62, 166-174 p.Article in journal (Refereed) Published
Knowledge of snow properties across Antarctica is important in estimating how climate could potentially influence the mass balance of the Antarctic ice sheet. However, measuring these variables has proven to be challenging because appropriate techniques have not yet been developed and extensive datasets of field estimates are lacking. The goal of this study was to estimate the relationship between field-observed snow particle-size parameters from across the East Antarctic ice sheet and a suite of spatial datasets (i.e. topography, remote-sensing data) using a principal component analysis (PCA). Five snow particle-size parameters were correlated to spatial datasets of the following five groups: (1) relief properties such as elevation and slope; (2) remote-sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and synthetic aperture radar (SAR) sensors; (3) spatially interpolated data (i.e. 10 m maps of temperature and approximate snow accumulation in kg m(-2) a(-1)); (4) field-retrieved data on surface roughness; and (5) in situ elevation and distance from the coast. The results show that the relief parameter slope correlated best with the snow particle length and area (r=0.76, r=0.80). Further, the PCA indicated that the different remote-sensing parameters correlated differently with the size parameters and that the most common parameter in visual analysis, particle length (grain diameter), is not always the optimal parameter to characterize the snow particle size as, for example, area correlates better to slope and aspect than length.
Place, publisher, year, edition, pages
2013. Vol. 54, no 62, 166-174 p.
IdentifiersURN: urn:nbn:se:su:diva-93081DOI: 10.3189/2013AoG62A193ISI: 000322047100006OAI: oai:DiVA.org:su-93081DiVA: diva2:644692
FunderSwedish Research Council