Abstract: The energy budget of Arctic sea ice is strongly affected by the snow cover. Intensive sampling of snow properties was conducted near Qikiqtarjuaq in Baffin Bay on typical landfast sea ice during two melt seasons in 2015 and 2016. The sampling included stratigraphy, vertical profiles of snow specific surface area (SSA), density and irradiance, and spectral albedo (300–1100 nm). Both years featured four main phases: (I) dry snow cover, (II) surface melting, (III) ripe snowpack, and (IV) melt pond formation. Each phase was characterized by distinctive physical and optical properties. A high SSA value of 49.3 m2 kg−1 was measured during phase I on surface wind slabs together with a corresponding broadband albedo (300–3000 nm) of 0.87. Phase II was marked by alternating episodes of surface melting, which dramatically decreased the SSA below 3 m2 kg−1, and episodes of snowfall re-establishing pre-melt conditions. Albedo was highly time-variable, with minimum broadband values around 0.70. In phase III, continued melting led to a fully ripe snowpack composed of clustered rounded grains. Albedo began to decrease in the visible as snow thickness decreased but remained steady at longer wavelengths. Moreover, significant spatial variability appeared for the first time following snow depth heterogeneity. Spectral albedo was simulated by radiative transfer using measured SSA and density vertical profiles and estimated impurity contents based on limited measurements. Simulations were most of the time within 1 % of measurements in the visible and within 2 % in the infrared. Simulations allowed the calculations of albedo and of the spectral flux at the snow–ice interface. These showed that photosynthetically active radiation fluxes at the bottom of the snowpack durably exceeded 5 W m−2 (∼9.2 µmol m−2 s−1) only when the snowpack thickness started to decrease at the end of phase II.

Abstract: Surface melting on the Antarctic Ice Sheet has been monitored by satellite microwave radiometry for over 40 years. Despite this long perspective, our understanding of the microwave emission from wet snow is still limited, preventing the full exploitation of these observations to study supraglacial hydrology. Using the Snow Microwave Radiative Transfer (SMRT) model, this study investigates the sensitivity of microwave brightness temperature to snow liquid water content at frequencies from 1.4 to 37 GHz. We first determine the snowpack properties for eight selected coastal sites by retrieving profiles of density, grain size and ice layers from microwave observations when the snowpack is dry during wintertime. Second, a series of brightness temperature simulations is run with added water. The results show that (i) a small quantity of liquid water (≈0.5 kg m−2) can be detected, but the actual quantity cannot be retrieved out of the full range of possible water quantities; (ii) the detection of a buried wet layer is possible up to a maximum depth of 1 to 6 m depending on the frequency (6–37 GHz) and on the snow properties (grain size, density) at each site; (iii) surface ponds and water-saturated areas may prevent melt detection, but the current coverage of these waterbodies in the large satellite field of view is presently too small in Antarctica to have noticeable effects; and (iv) at 1.4 GHz, while the simulations are less reliable, we found a weaker sensitivity to liquid water and the maximal depth of detection is relatively shallow (<10 m) compared to the typical radiation penetration depth in dry firn (≈1000 m) at this low frequency. These numerical results pave the way for the development of improved multi-frequency algorithms to detect melt intensity and the depth of liquid water below the surface in the Antarctic snowpack.

Abstract: In a context of global warming and sea level rise acceleration, it is key to estimate the evolution of the atmospheric hydrological cycle and temperature in polar regions, which directly influence the surface mass balance of the Arctic and Antarctic ice sheets. Direct observations are available from satellite data for the last 40 years and a few weather data since the 1950s in Antarctica. One of the best ways to access longer records is to use climate proxies in firn or ice cores. The water isotopic composition in these cores is widely used to reconstruct past temperature variations. We need to progress in our understanding of the influence of the atmospheric hydrological cycle on the water isotopic composition of ice cores. First, we present a 2-year-long time series of vapor and precipitation isotopic composition measurement at Dumont d’Urville Station, in Adélie Land. We characterize diurnal variations of meteorological parameters (temperature, atmospheric water mixing ratio (hereafter humidity) and δ18O) for the different seasons and determine the evolution of key relationships (δ18O versus temperature or humidity) throughout the year: we find that the temperature vs. δ18O relationship is dependent on synoptic events dynamics in winter contrary to summer. Then, this data set is used to evaluate the atmospheric general circulation model ECHAM6-wiso (model version with embedded water stable isotopes) in a coastal region of Adélie Land where local conditions are controlled by strong katabatic winds which directly impact the isotopic signal. We show that a combination of continental (79 %) and oceanic (21 %) grid cells leads model outputs (temperature, humidity and δ18O) to nicely fit the observations, at different timescales (i.e., seasonal to synoptic). Therefore we demonstrate the added value of long-term water vapor isotopic composition records for model evaluation. Then, as a clear link is found between the isotopic composition of water vapor and precipitation, we assess how isotopic models can help interpret short firn cores. In fact, a virtual firn core built from ECHAM-wiso outputs explains much more of the variability observed in S1C1 isotopic record than a virtual firn core built from temperature only. Yet, deposition and post-deposition effects strongly affect the firn isotopic signal and probably account for most of the remaining misfits between archived firn signal and virtual firn core based on atmospheric modeling.

Abstract: The Arctic is warming nearly four times as rapidly as other regions of the planet, challenging the capacity of organisms to cope with shifting resources and maintain thermal balance. Tracking responses of free-living animals in dynamic environments can be challenging, but is increasingly enabled by advanced biologging approaches. We used data gathered from miniaturized bird-borne devices to demonstrate increases in energy expenditure with declining sea ice conditions and warming sea surface temperatures in a dove-sized seabird, the little auk (also named dovekie; Alle alle). This keystone species feeds on sea ice-associated copepods and inhabits large breeding colonies in the High Arctic.