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Identifying and understanding why traits make species vulnerable to changing climatic conditions remain central problems in evolutionary and applied ecology. We used spring snow cover duration as a proxy for phenological timing of wetland ecosystems, and examined how snow cover duration during spring and during the entire snow season affected population dynamics of duck species breeding in the western boreal forest of North America, 1973America, -2007 We predicted that population level responses would differ among duck species, such that late-nesting species with reduced flexibility in their timing of breeding, i.e. scaup (Aythya spp.) and scoter (Melanitta spp.), would be more strongly affected by changing snow cover conditions relative to species better able to adjust timing of breeding to seasonal phenology, i.e. mallard (Anas platyrhynchos) and American wigeon (Anas americana). Population growth rates of scaup and scoter were positively linked to spring snow cover duration; after accounting for effects of density dependence, larger populations resulted after springs with long snow cover duration than after springs with short snow cover duration. In contrast, population growth rates of mallard and wigeon were either negatively or only weakly associated with snow cover duration. Duck population models were then incorporated with snow cover duration derived from climate model simulations under the A2 emission scenario, and these predictions suggested that latenesting duck species will experience the most severe population declines. Results are consistent with a hypothesis that the gradual climatic warming observed in the western boreal forest of North America has contributed to and may continue to exacerbate population declines of scaup and scoter.

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.

Abstract. Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) measurements. In total, 14 sites were evaluated within the Canadian Arctic Archipelago and Arctic Ocean on both first-year (FYI) and multi-year (MYI) sea ice. Sites contained multiple snow pits with coincident SMP profiles as well as unidirectional SMP transects. An existing SMP density model was recalibrated using manual density cutter measurements (n=186) to identify best-fit parameters for the observed conditions. Cross-validation of the revised SMP model showed errors comparable to the expected baseline for manual density measurements (RMSE = 34 kg m−3 or 10.9 %) and strong retrieval skill (R2=0.78). The density model was then applied to SMP transect measurements to characterize variations at spatial scales of up to 100 m. A supervised classification trained on snow pit stratigraphy allowed separation of the SMP density estimates by layer type. The resulting dataset contains 58 882 layer-classified estimates of snow density on sea ice representing 147 m of vertical variation and equivalent to more than 600 individual snow pits. An average bulk density of 310 kg m−3 was estimated with clear separation between FYI and MYI environments. Lower densities on MYI (277 kg m−3) corresponded with increased depth hoar composition (49.2 %), in strong contrast to composition of the thin FYI snowpack (19.8 %). Spatial auto-correlation analysis showed layered composition on FYI snowpack to persist over long distances while composition on MYI rapidly decorrelated at distances less than 16 m. Application of the SMP profiles to determine propagation bias in radar altimetry showed the potential errors of 0.5 cm when climatology is used over known snow density.

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