Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

Abstract

High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer-EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change.

Drivers and Environmental Responses to the Changing Annual Snow Cycle of Northern Alaska

“…nearshore terrestrial snowpack disappears and well before the sea ice itself melts. The earlier melt and green-up trends in the Arctic are primarily driven by rising air temperatures (Zhu et al 2016), which are in turn are associated with regional circulation patterns that favor advection of warm air and moisture from the North Pacific during late winter or early spring (Stone et al 2005;Semmens et al 2013;Mioduszewski et al 2015;Mortin et al 2016; this study).…”

Recent changes in spring snowmelt timing in the Yukon River basin detected by passive microwave satellite data

“…A similar threshold based passive microwave melt detection approach was previously applied successfully over a wide spatial domain in the pan-Arctic study by Tedesco et al (2009). In addition, the passive microwave derived melt timing signal (onset and melt refreeze) was corroborated by auxiliary datasets, including ground station data (Global Historical Climate Network), model results from SnowModel (Liston and Hiemstra, 2011), QuikSCAT backscatter change (Bartsch et al, 2010), and North American Regional Reanalysis (NARR) data (Semmens et al, 2013).…”

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

“…MMOD was detected on DOY64 of 2014; however, there was still snow on the ground until DOY108, typical of high-latitude snow cover where melt onset is followed by the spring thaw, which is a sustained period with high diurnal air temperature variation where the snowpack is melting during the day and refreezing at night. At the end of this melt-refreeze period, the snowpack may be actively melting both day and night until snow disappearance, which can take several weeks (Semmens et al, 2013). During winter 2013-2014, 20 melt days in total were detected at Pudasjarvi, all corresponding to days with T max ≥ 0 • C. However, not all days with T max ≥ 0 • C are detected by PMW as melting, for example DOY351-352, for reasons which will be explained further in the validation section.…”

“…These events are linked to intrusion of warm air from southerly or southwesterly flow; may be associated with fog (Semmens et al, 2013), rain and/or freezing rain; and typically last for several days. Previous studies (Cohen et al, 2015;Rennert et al, 2009) have shown that the synoptic conditions associated with these events are closely related to larger modes of atmospheric circulation.…”

“…Satellite active and passive microwave measurements have been widely used for snowmelt detection over various components of the Arctic cryosphere during the spring melt period (e.g., Kim et al, 2011;Markus et al, 2009;Tedesco, 2007;Wang et al, 2011). Only a few satellite studies have focused on winter melt or ROS detection, and are mainly for specific regions or limited time periods (Bartsch, 2010;Bartsch et al, 2010;Dolant et al, 2016;Grenfell and Putkonen, 2008;Semmens et al, 2013;Wilson et al, 2013). Here we develop an algorithm to detect winter melt from satellite passive microwave (PMW) data over panArctic snow-covered land areas north of 50 • N for the period 1988-2013.…”