Physical properties of Arctic versus subarctic snow: Implications for high latitude passive microwave snow water equivalent retrievals

Abstract

Two unique observational data sets are used to evaluate the ability of multi-layer snow emission models to simulate passive microwave brightness temperatures (T B ) in high latitude, observation sparse, snow-covered environments. Data were utilized from a coordinated series of 18 sites measured across the subarctic Northwest Territories and Nunavut, Canada in April 2007 during a 1000 km segment of a 4200 km snowmobile traverse from Fairbanks, Alaska to Baker Lake, Nunavut (~64°N). In April 2011, a network of 22 high Arctic sites was sampled across a 60 × 60 km study area on the Fosheim Peninsula, Ellesmere Island (~80°N). In comparison to sites across the subarctic, high Arctic snow was more spatially variable, thinner (site averages between 15 and 25 cm versus 30 to 40 cm), colder (À25°C versus À10°C), composed of fewer layers, had a proportionally higher fraction of wind slabs (storing 57% of the snow water equivalent (SWE) versus 15%), with these slabs comparatively denser (often exceeding 450 g/cm 3 , compared to 350 g/cm 3 in the subarctic). The physical snow measurements were used as inputs to snow emission model simulations.The radiometric difference between simulations of "typical" arctic and subarctic snow reached 30 K at 37 GHz. Sensitivity analysis showed that this T B difference could be partitioned between the effects of physical temperature (~5 K between À25°C and À10°C), wind slab density (~5 K between 0.40and 0.35 g/cm 3 ), and vertical depth hoar fraction (~20 K between 70% and 30% vertical fraction of total snow depth). Model simulations at the satellite scale (625 km 2 ) were produced using the observational spread for snow depth and snow stratigraphy. The range of T B from simulations with varied stratigraphy extended unrealistically far below the magnitude of satellite measured T B , illustrating that the snow depth first guess is very important for SWE retrieval schemes that are based on forward emission model simulations.

Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

“…Derksen et al, 2014;Essery and Pomeroy, 2004) than forested areas, whereas on the other hand, complicated canopy structure and small-scale ground topography affects the snow accumulation on the ground and increases the small-scale variability (e.g. Dobre et al, 2012;Storck et al, 2002); differences in snow depth and SWE variability (inter-quartile range in box plots in Figs.…”

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

“…These winter melt-refreeze events modify the physical properties of snow (albedo, density, grain size, thermal conductivity), generate winter runoff (Bulygina et al, 2010; and can result in potentially significant impacts on the surface energy budget, hydrology and soil thermal regime (Boon et al, 2003;Hay and McCabe, 2010;Rennert et al, 2009). The refreezing of melt water can also create ice layers that adversely impact the ability of ungulate travel and foraging (Hansen et al, 2011;Grenfell and Putkonen, 2008), and exert uncertainties in snow mass retrieval from passive microwave satellite data (Derksen et al, 2014;Rees et al, 2010). Winter warming and melt events may also damage shrub species and tree roots, affecting plant phenology and reproduction in the Arctic (AMAP, 2011;Bokhorst et al, 2009).…”

Microstructure representation of snow in coupled snowpack and microwave emission models

“…In an attempt to put these results into context, there are a number of studies that have quantified brightness temperature simulation errors for these models. These fall into different categories, depending on sensor characteristics, the source of the evaluation data (ground-based, airborne, satellite) and presence of ice lenses , the treatment of the snow microstructure (Picard et al, 2014), snow type, observation angle, and the specific electromagnetic model , and the underlying substrate (Lemmetyinen et al, 2009;Derksen et al, 2014). Examples of unscaled field observations of microstructure compared with ground-based observations include the HUT simulations of , who found an RMSE of 10-34 K, and Rutter et al (2014), who found a bias of 34-68 K that was reduced to < 0.6 K upon application of grain scale factors of 2.6-5.3.…”