Articles | Volume 10, issue 6
https://doi.org/10.5194/tc-10-2589-2016
https://doi.org/10.5194/tc-10-2589-2016
Research article
 | 
03 Nov 2016
Research article |  | 03 Nov 2016

Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

Libo Wang, Peter Toose, Ross Brown, and Chris Derksen

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Cited articles

Abdalati, W., Steffen, K., Otto, C., and Jezek, K. C.: Comparison of brightness temperatures from SSM/I instruments on the DMSP F8 and F11 satellites for Antarctica and the Greenland ice sheet, Int. J. Remote Sens., 16, 1223–1229, https://doi.org/10.1080/01431169508954473, 1995.
AMAP: Snow, Water, Ice and Permafrost in the Arctic (SWIPA): Climate Change and the Cryosphere, Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, xii + 538 pp., 2011.
Armstrong, R. L., Knowles, K. W., Brodzik, M. J., and Hardman, M. A.: DMSP SSM/I Pathfinder Daily EASE-Grid Brightness Temperatures, Version 2, (1988–2013), Boulder, Colorado, USA, National Snow and Ice Data Center, 10.5067/3EX2U1DV3434, 1994.
Bartsch, A.: Ten Years of SeaWinds on QuikSCAT for Snow Applications, Remote Sens., 2, 1142–1156, https://doi.org/10.3390/rs2041142, 2010.
Bartsch, A., Kumpula, T., Forbes, B. C., and Stammler, F.: Detection of snow surface thawing and refreezing in the Eurasian Arctic with QuikSCAT: implications for reindeer herding, Ecol. Appl., 20, 2346–2358, 2010.
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Short summary
The conventional wisdom is that Arctic warming will result in an increase in the frequency of winter melt events. However, results in this study show little evidence of trends in winter melt frequency over 1988–2013 period. The frequency of winter melt events is strongly influenced by the selection of the start and end dates of winter period, and a fixed-window method for analyzing winter melt events is observed to generate false increasing trends from a shift in the timing of snow cover season.