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Attribution of snowmelt onset in Northern Canada
Author(s) -
Mioduszewski J. R.,
Rennermalm A. K.,
Robinson D. A.,
Mote T. L.
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2013jd021024
Subject(s) - snowmelt , climatology , environmental science , arctic , context (archaeology) , energy balance , advection , radiative forcing , climate model , latitude , tundra , snow , atmospheric sciences , climate change , geology , meteorology , geography , paleontology , ecology , oceanography , physics , geodesy , biology , thermodynamics
In the region of Earth most sensitive to climate change, spring snowmelt serves as a measurable indicator of climate change and plays a strong role in the feedbacks that amplify Arctic warming. We characterize the melt season and attribute melt onset in a region of northern Canada during the spring snowmelt season from 2003 to 2011. Melt onset dates are obtained from Advanced Microwave Scanning Radiometer for the Earth Observing System retrievals. Energy balance and meteorological fields are obtained from NASA's Modern Era Retrospective Analysis for Research and Applications product. Analysis of three distinct subregions demonstrates that typical values of energy balance terms vary across the region and have different roles in melt attribution. Melt is controlled more by advective energy farther southwest where melt onset begins sooner, compared to higher levels of radiative energy over the tundra. This study demonstrates that a relatively small region can exhibit large differences in controls on spring snowmelt both within the region and interannually, and these differences can be understood in the context of factors ranging from the large‐scale synoptic pattern to land cover and the local energy balance. Being able to attribute melt onset to those drivers that are changing as the high latitudes warm as opposed to those that do not (i.e., insolation) allows better long‐term prediction of melt season dynamics and the climatological processes influenced by snow cover and its feedbacks.