Observations of late winter Canadian tundra snow cover properties

Abstract

Abstract:Tundra snow cover is important to monitor as it influences local, regional, and global-scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi-year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter-annual consistencies were identified. A regional average density of 0.293 g/cm 3 was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter-annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter-annual snow stratigraphy from different terrain categories is also presented.

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

“…The same geostatistical analysis method is therefore performed for snow surface and residual snow depth. We used the geoR package in statistical software R (Ribeiro Jr. and Diggle, 2001; https: //www.r-project.org/).…”

“…Snow depth characterization in Arctic tundra environments has traditionally been performed using snow depth probes (Benson and Sturm, 1993;Hirashima et al, 2004;Derksen et al, 2009;Rees et al, 2014;Dvornikov et al, 2015) or modeled using terrain and vegetation information (Sturm and Wagner, 2010;Liston and Sturm, 1998;Pomeroy et al, 1997). Recently, there have been several new techniques for estimating snow depth in high resolution and in a noninvasive and spatially extensive manner.…”

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

“…Ye et al (1998) and Kitaev et al (2005) showed that higher air temperatures caused an increase in snowfall in winter from 1936 through 1995, and thus greater snow depth was observed in northern Eurasia. Snow depth distribution and variation are controlled by terrain (i.e., elevation, slope, aspect, and roughness) and vegetation Grünewald et al, 2014;Revuelto et al, 2014;Rees et al, 2014;Dickerson-Lange et al, 2015). Snow depth is closely related to synoptic-scale atmospheric circulation indices such as the North Atlantic OscillationArctic Oscillation (NAO/AO).…”

DIY meteorology: Use of citizen science to monitor snow dynamics in a data-sparse city

“…The costs of snow and ice management alone for the City of Saskatoon reached over $13 million CAD in 2014, but due to unpredictable weather patterns, additional contingency planning was needed as the budgeted finances soon were spent. Amid changing snowfall regimes, there is, thus, a need for better monitoring and provision of scientific support to urban managers for snow services (Brown and Mote 2009;Rees et al 2014). …”