Title | Perennial snow and ice variations (2000-2008) in the Arctic circumpolar land area from satellite observations |
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Author | Fontana, F M A; Trishchenko, A P ; Luo, Y; Khlopenkov, K V; Nussbaumer, S U; Wunderle, S |
Source | Journal of Geophysical Research, Earth Surface vol. 115, no. 4, F04020, 2010., https://doi.org/10.1029/2010JF001664 Open Access |
Year | 2010 |
Alt Series | Natural Resources Canada, Contribution Series 20181062 |
Publisher | Wiley-Blackwell |
Document | serial |
Lang. | English |
Media | paper; on-line; digital |
File format | pdf |
Subjects | geophysics; Nature and Environment; remote sensing |
Program | Canada Centre for Remote Sensing Divsion |
Released | 2010 11 11 |
Abstract | Perennial 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. |
GEOSCAN ID | 311416 |
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