Abstract: Snowpacks contain many carbonaceous species that can potentially impact on snow albedo and arctic atmospheric chemistry. During the OASIS field campaign, in March and April 2009, Elemental Carbon (EC), Water insoluble Organic Carbon (WinOC) and Dissolved Organic Carbon (DOC) were investigated in various types of snow: precipitating snows, remobilized snows, wind slabs and depth hoars. EC was found to represent less than 5% of the Total Carbon Content (TCC = EC + WinOC + DOC), whereas WinOC was found to represent an unusual 28 to 42% of TCC. Snow type was used to infer physical processes influencing the evolution of different fractions of DOC. DOC is highest in soil influenced indurated depth hoar layers due to specific wind related formation mechanisms in the early season. Apart from this specific snow type, DOC is found to decrease from precipitating snow to remobilized snow to regular depth hoar. This decrease is interpreted as due to cleaving photochemistry and physical equilibration of the most volatile fraction of DOC. Depending on the relative proportions of diamond dust and fresh snow in the deposition of the seasonal snowpack, we estimate that 31 to 76% of DOC deposited to the snowpack is reemitted back to the boundary layer. Under the assumption that this reemission is purely photochemical, we estimate an average flux of VOC out of the snowpack of 20 to 170 μgC m-2 h-1. Humic like substances (HULIS), short chain diacids and aldehydes are quantified, and showed to represent altogether a modest (<20%) proportion of DOC, and less than 10% of DOC + WinOC. HULIS optical properties are measured and could be consistent with aged biomass burning or a possible marine source.

Abstract: We demonstrate the effect of an ecosystem differentiated insulation by snow on the soil thermal regime and on the terrestrial soil carbon distribution in the pan-Arctic area. This is done by means of a sensitivity study performed with the land surface model ORCHIDEE, which furthermore provides a first quantification of this effect. Based on field campaigns reporting higher thermal conductivities and densities for the tundra snowpack than for taiga snow, two distributions of near-equilibrium soil carbon stocks are computed, one relying on uniform snow thermal properties and the other using ecosystem-differentiated snow thermal properties. Those modeled distributions strongly depend on soil temperature through decomposition processes. Considering higher insulation by snow in taiga areas induces warmer soil temperatures by up to 12 K in winter at 50 cm depth. This warmer soil signal persists over summer with a temperature difference of up to 4 K at 50 cm depth, especially in areas exhibiting a thick, enduring snow cover. These thermal changes have implications on the modeled soil carbon stocks, which are reduced by 8% in the pan-Arctic continental area when the vegetation-induced variations of snow thermal properties are accounted for. This is the result of diverse and spatially heterogeneous ecosystem processes: where higher soil temperatures lift nitrogen limitation on plant productivity, tree plant functional types thrive whereas light limitation and enhanced water stress are the new constrains on lower vegetation, resulting in a reduced net productivity at the pan-Arctic scale. Concomitantly, higher soil temperatures yield increased respiration rates (+22% over the study area) and result in reduced permafrost extent and deeper active layers which expose greater volumes of soil to microbial decomposition. The three effects combine to produce lower soil carbon stocks in the pan-Arctic terrestrial area. Our study highlights the role of snow in combination with vegetation in shaping the distribution of soil carbon and permafrost at high latitudes.

Abstract: The effective thermal conductivity of snow, keff, is a crucial climatic and environmental variable. Here, we test the intuition that keff is linked to microstructural and mechanical properties by attempting to relate keff to density ρsnow, and to shear strength σ measured with a handheld shear vane. We performed 106 combined measurements of keff, ρsnow and σ in the Alps, Svalbard, Arctic Alaska, and near the North Pole, covering essentially all snow types. We find a good correlation between keff and ρsnow which is not significantly different from that of Sturm et al. (1997). The correlation between keff and a combination of σ and ρsnow is stronger than with density alone. We propose an equation linking keff, (W m-1 K-1) ρsnow (kg m-3) and σ (Pa): keff = 7.114 10-5 ρsnow σ0.333 + 2.367 10-2. This equation places constraints on the calculation of keff, ρsnow and σ in avalanche warning models where σ is a key variable. For our samples, we calculate σ from measured values of keff and ρsnow using our equation and compare the value to that predicted by the French MEPRA avalanche warning model, which uses density and grain type as input data. MEPRA and the prediction of σ based on keff and ρsnow agree within 8%. MEPRA agrees with observations within 11%. Calculating σ from density only yields values 55% lower than measured, showing the interest of using additional data to predict σ.