Uncertainty in snow mass retrievals from satellite passive microwave data in lake‐rich high‐latitude environments

Abstract

The distribution and magnitude of winter-season water equivalent stored in the snowpack across the Canadian tundra are presently unknown. Quantifying this storage, as well as the degree of interannual variability, is essential, because the tundra snowpack is a persistent landscape feature and a key parameter in water, energy, and biogeochemical cycling. Tundra snow cover represents the solid storage term in the water balance, affects the energy balance through changes in surface radiative properties, and controls the ground thermal regime and vegetation dynamics. Emphasis must be placed on understanding climate change impacts on terrestrial Arctic and sub-Arctic snow cover because of observed and simulated warming conditions combined with coincident increases in winter precipitation (Watson and Core Writing Team, 2001; ACIA, 2004). However, regional simulations of snow cover and water balances by global and regional climate models are hampered by the lack of appropriate snow cover data for comparison and validation.The determination of past and present snow cover conditions relies heavily on in situ datasets that are limited by recent reductions in manned meteorological stations, differences in instrumentation, and biases in measurement techniques (Yang and Woo, 1999). Furthermore, in situ sampling provides a measure of snow depth, snow water equivalent (SWE), or snowfall at discrete point locations which are often not representative of regional snow cover conditions required for large scale monitoring and modelling. For instance, conventional snow cover observations across the Canadian tundra are extremely sparse and biased to the Arctic coast.The non-uniform snow distribution found in high-latitude environments is primarily a function of complex terrain and landscape features which control wind redistribution. Despite a high degree of heterogeneity, snow distribution patterns can be modelled based on terrain characteristics and meteorological conditions. Slope length, angle, aspect and ground cover, along with wind direction, magnitude and fetch, can all be used to model snow distribution patterns successfully (e.g. Essery and Pomeroy, 2004). This approach can provide a perspective on regional snow cover, but it is still limited by the need for forcing data to generate model simulations and requires independent datasets for model evaluation.Remote sensing techniques have long been employed to monitor terrestrial snow cover. Visible wavelength imagery has been

A review of global satellite-derived snow products

“…This result is in agreement with recent studies (Brown et al, 2007(Brown et al, , 2010Frei and Lee, 2010) which find that over the last decade or so the timing of spring ablation over North America is later, by up to several weeks in the central Canadian Arctic, according to IMS in comparison to other observations. The reasons for these discrepancies, which are found during the entire spring ablation season (April, May, and June; May and June not shown here) over the boreal forest as well as the tundra, are not understood, but may be related to geographic factors such as the forest type and/or the presence of numerous lakes in this (Derksen et al, 2005a;Rees et al, 2006). Investigations into the cause of this problem continue.…”

“…Sources of error include: surface heterogeneity within a passive microwave footprint; temporal and spatial variability in grain size and snow density; obscuration of snow by forests; masking of the passive microwave signal by liquid water in the snow pack; and effects of atmospheric attenuation. The persistent underestimation by AMSR-E with respect to CMC over some regions can be partially explained by considering that snow depth over many of those areas is above the 'saturation' depth to which the passive microwave algorithm is sensitive Markus et al, 2006;Matzler, 1994;Schanda et al, 1983); the presence of a high fraction of lakes over the north east of North America is also believed to be a source of error (Derksen et al, 2005a;Rees et al, 2006).…”

“…Of particular relevance to the monitoring of high latitude snow is the presence of numerous frozen lakes in Arctic regions, which may contribute to the overestimation of snow covered area from visible and infrared based imagery during periods when lakes remain frozen after the snow has melted on adjacent land surfaces (Derksen et al, 2005a;Frei and Lee, 2010), at least when high resolution land surface data sets are not used to filter out the signal from lake surfaces. Passive microwave based estimates of SWE may be underestimated due to the presence of lakes (Derksen et al, 2005a;Rees et al, 2006). On the other hand, surface heterogeneity may assist in the interpretation of snow-covered versus snow-free ground, and of snow-covered versus cloud-covered scenes, when trained analysts are mapping snow extent using visible imagery.…”

Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia

“…-Water bodies strongly affect microwave emission of the ground, which is known to lead to underestimation of SWE in passive microwave-based retrievals (Rees et al, 2006;Lemmetyinen et al, 2011). For the abovementioned N Canada data set, water bodies might explain the significant bias of 36 mm , but the average values (120 mm) are also sufficiently high that saturation effects (Luojus et al, 2010) are likely to contribute to the bias.…”

Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

“…As a result, findings from the linear regression approach indicate weaker associations at tundra sites (R 2 = 0.04-0.20) (Table A1) than detected by the ACE transformations (R 2 = 0.20-0.38) ( Table B1). The associations between growing-season air temperature and SWE are weaker over forested regions, likely due to seasonal discrepancies in the mean growing season and mean snow season air temperatures (Overland et al, 1997;Rigor et al, 2000;Adams et al, 2000;Serreze and Barry, 2005). The following analysis therefore focuses mainly on the associations observed north of the treeline, although all plots can be found in Figs.…”