ALBERT

Description: This study aims to better understand and quantify the uncertainties in microwave snow emission models using the Dense Media Radiative Theory Multi-Layer model (DMRT-ML) with in situ measurements of snow properties. We use surface-based radiometric measurements at 10.67, 19 and 37 GHz in boreal forest and subarctic environments and a new in situ data set of measurements of snow properties (profiles of density, snow grain size and temperature, soil characterization and ice lens detection) acquired in the James Bay and Umiujaq regions of Northern Québec, Canada. A snow excavation experiment – where snow was removed from the ground to measure the microwave emission of bare frozen ground – shows that small-scale spatial variability (less than 1 km) in the emission of frozen soil is small. Hence, in our case of boreal organic soil, variability in the emission of frozen soil has a small effect on snow-covered brightness temperature (TB). Grain size and density measurement errors can explain the errors at 37 GHz, while the sensitivity of TB at 19 GHz to snow increases during the winter because of the snow grain growth that leads to scattering. Furthermore, the inclusion of observed ice lenses in DMRT-ML leads to significant improvements in the simulations at horizontal polarization (H-pol) for the three frequencies (up to 20 K of root mean square error). However, representation of the spatial variability of TB remains poor at 10.67 and 19 GHz at H-pol given the spatial variability of ice lens characteristics and the difficulty in simulating snowpack stratigraphy related to the snow crust. The results also show that, in our study with the given forest characteristics, forest emission reflected by the snow-covered surface can increase the TB up to 40 K. The forest contribution varies with vegetation characteristics and a relationship between the downwelling contribution of vegetation and the proportion of pixels occupied by vegetation (trees) in fisheye pictures was found. We perform a comprehensive analysis of the components that contribute to the snow-covered microwave signal, which will help to develop DMRT-ML and to improve the required field measurements. The analysis shows that a better consideration of ice lenses and snow crusts is essential to improve TB simulations in boreal forest and subarctic environments.

Description: The oldest ice core records are obtained from the East Antarctic plateau. Water stable isotopes records are key for reconstructions of past climatic conditions both over the ice sheet and at the evaporation source. The accuracy of such climate reconstructions crucially depends on the knowledge of all the processes affecting the water vapour, precipitation and snow isotopic composition. Atmospheric fractionation processes are well understood and can be integrated in Rayleigh distillation and complex isotope enabled climate models. However, a comprehensive quantitative understanding of processes potentially altering the snow isotopic composition after the deposition is still missing, especially for exchanges between vapour and snow. In low accumulation sites such as found on the East Antarctic Plateau, these poorly constrained processes are especially likely to play a significant role. This limits the interpretation of isotopic composition from ice core records, specifically at short time scales. Here, we combine observations of isotopic composition in the vapour, the precipitation, the surface snow and the buried snow from various sites of the East Antarctic Plateau. At the seasonal scale, we highlight a significant impact of metamorphism on surface snow isotopic signal compared to the initial precipitation isotopic signal. In particular, in summer, exchanges of water molecules between vapour and snow are driven by the sublimation/condensation cycles at the diurnal scale. Using highly resolved isotopic composition profiles from pits in five East Antarctic sites, we identify a common 20 cm cycle which cannot be attributed to the seasonal variability of precipitation. Altogether, the smaller range of isotopic compositions observed in the buried and in the surface snow compared to the precipitation, and also the reduced slope between surface snow isotopic composition and temperature compared to precipitation, constitute evidences of post-deposition processes affecting the variability of the isotopic composition in the snow pack. To reproduce these processes in snow-models is crucial to understand the link between snow isotopic composition and climatic conditions and to improve the interpretation of isotopic composition as a paleoclimate proxy.

Description: Melt occurrence in Antarctica is derived from L-band observations from the Soil Moisture and Ocean Salinity (SMOS) satellite between the austral summer 2010/11 and 2017/18. The detection algorithm is adapted from a threshold method previously developed for 19 GHz passive microwave measurements from Special Sensor Microwave Imagers (SSM/I, SSMIS). The comparison of daily melt occurrence retrieved from 1.4 GHz and 19 GHz observations shows an overall close agreement, but a lag of few days is usually observed by SMOS at the beginning of the melt season. To understand the difference, we performed a theoretical analysis using a microwave emission radiative transfer model that shows that the sensitivity of 1.4 GHz signal to liquid water is significantly weaker than at 19 GHz if the water is only present in the uppermost tens of centimeters of the snowpack. Conversely, 1.4 GHz measurements are sensitive to water when spread over at least 1 m and when present at depth, up to hundreds of meters. This is explained by the large penetration depth in dry snow and by the long wavelength (21 cm). We conclude that SMOS and higher frequency radiometers provide interesting complementary information on melt occurrence and on the location of the water in the snowpack.

Description: The Green Edge initiative was developed to investigate the processes controlling the primary productivity and the fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797N, 63.7895W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea ice cover from the surface to the bottom at 360 m depth to better understand the factors driving the PSB. Key variables such as temperature, salinity, radiance, irradiance, nutrient concentrations, chlorophyll-a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, carbon stocks and fluxes were routinely measured at the ice camp. Here, we present the results of a joint effort to tidy and standardize the collected data sets that will facilitate their reuse in other Arctic studies. The dataset is available at http://www.seanoe.org/data/00487/59892/ (Massicotte et al., 2019a).

Description: Orbital tuning is central for ice core chronologies beyond annual layer counting, available back to 60 ka (i.e. thousands of years before 1950) for Greenland ice cores. While several complementary orbital tuning tools have recently been developed using δ18Oatm, δO2⁄N2 and air content with different orbital targets, quantifying their uncertainties remains a challenge. Indeed, the exact processes linking variations of these parameters, measured in the air trapped in ice, to their orbital targets are not yet fully understood. Here, we provide new series of δO2∕N2 and δ18Oatm data encompassing Marine Isotopic Stage (MIS) 5 (between 100 and 160 ka) and the oldest part (340–800 ka) of the East Antarctic EPICA Dome C (EDC) ice core. For the first time, the measurements over MIS 5 allow an inter-comparison of δO2∕N2 and δ18Oatm records from three East Antarctic ice core sites (EDC, Vostok and Dome F). This comparison highlights some site-specific δO2∕N2 variations. Such an observation, the evidence of a 100 ka periodicity in the δO2∕N2 signal and the difficulty to identify extrema and mid-slopes in δO2∕N2 increase the uncertainty associated with the use of δO2∕N2 as an orbital tuning tool, now calculated to be 3–4 ka. When combining records of δ18Oatm and δO2∕N2 from Vostok and EDC, we find a loss of orbital signature for these two parameters during periods of minimum eccentricity (∼ 400 ka, ∼ 720–800 ka). Our data set reveals a time-varying offset between δO2∕N2 and δ18Oatm records over the last 800 ka that we interpret as variations in the lagged response of δ18Oatm to precession. The largest offsets are identified during Terminations II, MIS 8 and MIS 16, corresponding to periods of destabilization of the Northern polar ice sheets. We therefore suggest that the occurrence of Heinrich–like events influences the response of δ18Oatm to precession.

Description: Light-absorbing impurities decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snow melt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (hereafter referred as Crocus) to account for impurities deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities mass evolution until snow melts out, accounting for scavenging by melt water. Taking benefits of the recent inclusion of the spectral radiative transfer model TARTES in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at Col de Porte experimental site (French Alps) during the 2013–2014 snow season, against in-situ standard snow measurements and spectral albedo measurements. In-situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates reasonably snowpack evolution in term of snow depth, snow water equivalent and near-surface specific surface area. Indeed, the model performances are not deteriorated compared to our reference Crocus version, while explicitly representing the impact of light-absorbing impurities. We show that the deposition fluxes from ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances from 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo, accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snow melt and avalanche prediction.

Description: The oldest ice core records are obtained from the East Antarctic plateau. Water isotopes records are key to reconstructing past climatic conditions over the ice sheet and at the evaporation source. The accuracy of climate reconstructions depends on knowledge of all the processes affecting water vapour, precipitation and snow isotopic compositions. Fractionation processes are well understood and can be integrated in Rayleigh distillation and isotope enabled climate models. However, a quantitative understanding of processes potentially altering the snow isotopic composition after the deposition is still missing. In low accumulation sites, such as those found in Antarctica, these poorly constrained processes are likely to play a significant role and limit the interpretation of isotopic composition. Here, we combine observations of isotopic composition in the vapour, the precipitation, the surface snow and the buried snow from Dome C, a deep ice core site on the East Antarctic Plateau. At the seasonal scale, we suggest a significant impact of metamorphism on surface snow isotopic signal compared to the initial precipitation signal. Particularly, in summer, exchanges of water molecules between vapour and snow are driven by the sublimation/condensation cycles at the diurnal scale. Using highly resolved isotopic composition profiles from pits in five Antarctic sites, we identify common patterns, despite different accumulation rates, which cannot be attributed to the seasonal variability of precipitation. Altogether, the difference in the signals observed in the precipitation, surface snow and buried snow isotopic composition constitute evidences of post-deposition processes affecting ice core records in low accumulation areas.

Description: Spaceborne radar altimeter is a valuable tool for observing the Antarctica Ice Sheet. The radar wave penetration into the snow provides information both on the surface and the subsurface of the snowpack due to its dependence on the snow properties. However this penetration also induces a negative bias on the estimated surface elevation. Empirical corrections of this space and time-varying bias are usually based on the backscattering coefficient variability. We investigate the spatial and seasonal variations of the backscattering coefficient at the S (3.2 GHz), Ku (13.6 GHz) and Ka (37 GHz) bands. We identified two clearly marked zones over the continent, one with the maximum of Ku band backscattering coefficient in the winter and another with the maximum in the summer. To explain this, we performed a sensitivity study of the backscattering coefficient at the S, Ku and Ka bands to surface snow density, snow temperature and snow grain size using an electromagnetic model. The results show that the seasonal cycle of the backscattering coefficient at the Ka band, is dominated by the volume echo and is mainly explained by snow temperature. In contrast, the cycle is dominated by the surface echo at the S band. At Ku band, which intermediate in terms of wavelength between S and Ka bands, the seasonal cycle is in the first zone dominated by the volume echo and by the surface echo in the second one. Such seasonal and spatial variations of the backscattering coefficient at different radar frequencies should be taken into account the for more precise estimation of the surface elevation changes.

Description: Snow spectral albedo in the visible/near-infrared range has been continuously measured during a winter season at Col de Porte alpine site (French Alps; 45.30° N, 5.77° E; 1325 m a.s.l.). The evolution of such alpine snowpack is complex due to intensive precipitation, rapid melt events and Saharan dust deposition outbreaks. This study highlights that the resulting intricate variations of spectral albedo can be successfully explained by variations of the following snow surface variables: specific surface area (SSA) of snow, effective light-absorbing impurities content, presence of liquid water and slope. The methodology developed in this study disentangles the effect of these variables on snow spectral albedo. The presence of liquid water at the snow surface results in a spectral shift of the albedo from which melt events can be identified with an occurrence of false detection rate lower than 3.5 %. Snow SSA mostly impacts spectral albedo in the near-infrared range. Impurity deposition mostly impacts the albedo in the visible range but this impact is very dependent on snow SSA and surface slope. Our work thus demonstrates that the SSA estimation from spectral albedo is affected by large uncertainties for a tilted snow surface and medium to high impurity contents and that the estimation of impurity content is also affected by large uncertainties, especially for low values below 50 ng g−1 black carbon equivalent. The proposed methodology opens routes for retrieval of SSA, impurity content, melt events and surface slope from spectral albedo. However, an exhaustive accuracy assessment of the snow black properties retrieval would require more independent in situ measurements and is beyond the scope of the present study. This time series of snow spectral albedo nevertheless already provides a new insight into our understanding of the evolution of snow surface properties.