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TitlePerennial snow and ice variations (2000-2008) in the Arctic circumpolar land area from satellite observations
AuthorFontana, F M A; Trishchenko, A PORCID logo; Luo, Y; Khlopenkov, K V; Nussbaumer, S U; Wunderle, S
SourceJournal of Geophysical Research, Earth Surface vol. 115, no. 4, F04020, 2010., Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20181062
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; Nature and Environment; remote sensing
ProgramCanada Centre for Remote Sensing Divsion
Released2010 11 11
AbstractPerennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree-days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = -0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS-derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC-2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment.

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