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Arctic snow cover dynamics exhibit modification in terms of extent and duration due to recent changes in climate, i.e. increasing temperatures and changing precipitation patterns, i.e. winter rain-on-snow events (WROS). Remote sensing methods based on active radar images (SAR) have demonstrated a significant advantage for snow monitoring, (i) capturing physical and dielectric properties, (ii) overcoming the weakness of optical images limited by cloud cover and polar night.The aim of this study is dedicated to the analysis of the spatial and temporal variability of snow cover in the Ny-Ålesund area on the BrØgger peninsula, Svalbard (N 78°55’ / E 11° 55’). In-situ snow measurements from two automated weather stations (Ny-Ålesund, and Bayelva), regular snowpits around the village and in spring on the Austre Loéven, were compared with the spaceborne dataset.The RADARSAT Constellation Mission (RCM) is comprised of three satellites into closely coordinated orbits operating in C-band (5.4 GHz, 5.5 cm). The high temporal (4-day repeat cycle) and spatial resolution of the sensors in Quad-Pol mode (9-m) or Compact-Pol mode (5-m) provide a helpful performance for detecting the spatial variability of snow properties. RCM data are also compared to images of the TerraSAR-X satellite (DLR, Germany) operated in X-band (9.6 GHz, 3.1 cm) at 5-m spatial resolution. Both RCM QP mode and TSX data were acquired with medium incidence angles (33° to 39°) providing better snow penetration for volume backscattering. The RCM CP data were only available under low (23°) and high (53°) beam angles.The following two snow properties were analyzed:WROS detection: the focus was set on the 16-17 March 2022 event (+ 5.5 °C, 62 mm). RCM data at cross-polarization VH or HV can clearly detect the impact of rain on snow, indicating an intensity drop of -10 dB, even on the glacier at high elevation.Snow depth retrieval: the study covers spring 2021 (March-June) and the complete winter season 2021-2022 (November-June).- Concerning QP mode, better correlation between snow depth and SAR backscattering is observed by the cross-pol VH component, retrieving more volume backscattering information than co-pol configuration or total backscattering power (Span). We observe also that descending orbit images (06 :30 AM) provide a better correlation with snow depth than ascending orbit (15 :30 PM) data.- Concerning CP mode and Span (RH+RV), the low incidence images (23°) do not match the snow depth observations due to main surface backscattering, contrariwise the high incidence images correlate better with in-situ observations. The analyses of the Stokes vector elements showed a satisfying correlation for the g3 element and the Relative Phase polarimetric decomposition.Finally, a comparison of Span temporal values between RCM at C-band and TSX at X-band indicates similar time profiles, but clearly lower values of -5 to -10 dB at the C-band.

Black carbon (BC) is a significant forcing agent in the Arctic, but substantial uncertainty remains to quantify its climate effects due to the complexity of the different mechanisms involved, in particular related to processes in the snowpack after deposition. In this study, we provide detailed and unique information on the evolution and variability in BC content in the upper surface snow layer during the spring period in Svalbard (Ny-Ålesund). A total of two different snow-sampling strategies were adopted during spring 2014 (from 1 April to 24 June) and during a specific period in 2015 (28 April to 1 May), providing the refractory BC (rBC) mass concentration variability on a seasonal variability with a daily resolution (hereafter seasonal/daily) and daily variability with an hourly sampling resolution (hereafter daily/hourly) timescales. The present work aims to identify which atmospheric variables could interact with and modify the mass concentration of BC in the upper snowpack, which is the snow layer where BC particles affects the snow albedo. Atmospheric, meteorological and snow-related physico-chemical parameters were considered in a multiple linear regression model to identify the factors that could explain the variations in BC mass concentrations during the observation period. Precipitation events were the main drivers of the BC variability during the seasonal experiment; however, in the high-resolution sampling, a negative association has been found. Snow metamorphism and the activation of local sources (Ny-Ålesund was a coal mine settlement) during the snowmelt periods appeared to play a non-negligible role. The statistical analysis suggests that the BC content in the snow is not directly associated to the atmospheric BC load.

Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

Due to recent climate change conditions, i.e. increasing temperatures and changing precipitation patterns, arctic snow cover dynamics exhibit strong changes in terms of extent and duration. Arctic amplification processes and impacts are well documented expected to strengthen in coming decades. In this context, innovative observation methods are helpful for a better comprehension of the spatial variability of snow properties relevant for climate research and hydrological applications.Microwave remote sensing provides exceptional spatial and temporal performance in terms of all-weather application and target penetration. Time-series of Synthetic Active Radar images (SAR) are becoming more accessible at different frequencies and polarimetry has demonstrated a significant advantage for detecting changes in different media. Concerning arctic snow monitoring, SAR sensors can offer continuous time-series during the polar night and with cloud cover, providing a consequent advantage in regard of optical sensors.The aim of this study is dedicated to the spatial/temporal variability of snow in the Ny-Ålesund area on the Br∅gger peninsula, Svalbard (N 78°55’ / E 11° 55’). The TerraSAR-X satellite (DLR, Germany) operated at X-band (3.1 cm, 9.6 GHz) with dual co-pol mode (HH/VV) at 5-m spatial resolution, and with high incidence angles (36° to 39°) poviding a better snow penetration and reducing topographic constraints. A dataset of 92 images (ascending and descending) is available since 2017, together with a high resolution DEM (NPI 5-m) and consistent in-situ measurements of meteorological data and snow profiles including glaciers sites.Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K0, K3, K4, and K7 are, respectively, the total intensity, phase ratio, intensity ratio, and shift between HH and VV phase center. Their interpretation allows analysing the structure of the snowpack linked to the near real time of in-situ measurements (snow profiles).The X-band signal is strongly influenced by the snow stratigraphy: internal ice layers reduce or block the penetration of the signal into the snow pack. The best R2 correlation performances between estimated and measured snow heights are ranging from 0.50 to 0.70 for dry snow conditions. Therefore, the use of the X-band for regular snow height estimations remains limited under these conditions.Conversely, this study shows the benefit of TerraSAR-X thanks to the Kennaugh matrix elements analysis. A focus is set on the Copolar Phase Difference (CPD, Leinss 2016) between VV and HH polarization: Φ CPD = Φ VV - Φ HH. Our results indicate that the CPD values are related to the snow metamorphism: positive values correspond to dry snow (horizontal structures), negative values indicate recrystallization processes (vertical structures).Backscattering evolution in time offer a good proxy for meteorological events detection, impacting on snow metamorphism. Fresh snowfalls or melting processes can then be retrieved at the regional scale and linked to air temperature or precipitation measurements at local scale. Polarimetric SAR time series is therefore of interest to complement satellite-based precipitation measurements in the Arctic.

Light-absorbing carbonaceous aerosols emitted by biomass or fossil fuel combustion can contribute to amplify Arctic climate warming by lowering the albedo of snow. The Svalbard archipelago, being near to Europe and Russia, is particularly affected by these pollutants, and improved knowledge of their distribution in snow is needed to assess their impact. Here we present and synthesize new data obtained on Svalbard between 2007 and 2018, comprising 324 measurements of elemental (EC) and organic carbon (OC) in snow from 49 sites. We used these data, combined with meteorological and aerosol data and snowpack modelling, to investigate the variability of EC and OC deposition in Svalbard snow across latitude, longitude, elevation and time. Overall, EC concentrations (CsnowEC) ranged from −1, while OC concentrations (CsnowOC) ranged from −1, with the highest values observed near Ny-Ålesund. Calculated snowpack loadings (LsnowEC, LsnowOC) in April 2016 were 0.1 to 16.2 mg m−2 and 1.7 to 320.1 mg m−2, respectively. The median CsnowEC and LsnowEC in the late 2015‒16 winter snowpack on glaciers were close to or lower than those found in earlier (2007–09), comparable surveys. Both LsnowEC and LsnowOCC increased exponentially with elevation and snow accumulation, with dry deposition likely playing a minor role. Estimated area-averaged snowpack loads across Svalbard were 1.8 mg EC m−2 and 71.5 mg OC m−2 in April 2016. An ~ 11-year long dataset of spring surface snow measurements from central Brøgger Peninsula was used to quantify the interannual variability of EC and OC deposition in snow. On average, CsnowEC and CsnowOC at Ny-Ålesund (50 m a.s.l.) were 3 and 7 times higher, respectively, than on the nearby Austre Brøggerbreen glacier (456 m a.s.l.), and the median EC/OC in Ny-Ålesund was 6 times higher, pointing to some local EC emission from Ny-Ålesund. While no long-term trends between 2011 and 2018 were found, CsnowEC and CsnowOC showed synchronous variations at Ny-Ålesund and Austre Brøggerbreen. Comparing CsnowEC at Austre Brøggerbreen with aerosol data from Zeppelin Observatory, we found that snowfall washout ratios between 10 and 300 predict a range of CsnowEC in agreement with that measured in surface snow. Together, results from this study and comparable surveys confirm the existence of a longitudinal gradient in EC deposition across the Arctic and sub-Arctic, with the lowest CsnowEC found in the western Arctic (Alaska, Yukon) and central Greenland, and the highest in northwestern Russia and Siberia.

Nearly all ice core archives from the Arctic and middle latitudes (such as the Alps), apart from some very high elevation sites in Greenland and the North Pacific, are strongly influenced by melting processes. The increases in the average Arctic temperature has enhanced surface snow melting even of higher elevation ice caps, especially on the Svalbard Archipelago. The increase of the frequency and altitude of winter “rain on snow” events as well as the increase of the length of the melting season have had a direct impact on the chemical composition of the seasonal and permanent snow layers due to different migration processes of water-soluble species, such as inorganic ions. This re-allocation along the snowpack of ionic species could significantly modify the original chemical signal present in the annual snow. This paper aims to give a picture of the evolution of the seasonal snow strata with a daily time resolution to better understand: a) the processes that can influence deposition b) the distribution of ions in annual snow c) the impact of the presence of liquid water on chemical re-distribution within the annual snow pack. Specifically, the chemical composition of the first 100 cm of seasonal snow on the Austre Broggerbreen Glacier (Spitsbergen, Svalbard Islands, Norway) was monitored daily from the 27th of March to the 31st of May 2015. The experimental period covered almost the entire Arctic spring until the melting season. This unique dataset gives us a daily picture of the snow pack composition, and helps us to understand the behaviour of cations (K+, Ca2+, Na+, Mg2+) and anions (Br−, I−, SO42−, NO3−, Cl−, MSA) in the Svalbard snow pack. We demonstrate that biologically related depositions occur only at the end of the snow season and that rain and melting events have different impacts on the snowpack chemistry.

Fe(II) is more soluble and bioavailable than Fe(III) species, therefore the investigation of their relative abundance and redox processes is relevant to better assess the supply of bioavailable iron to the ocean and its impact on marine productivity. In this context, we present a discrete chemiluminescence-based method for the determination of Fe(II) in firn matrices. The method was applied on discrete samples from a snow pit collected at Dome C (DC, Antarctica) and on a shallow firn core from the Holtedahlfonna glacier (HDF, Svalbard), providing the first Fe(II) record from both Antarctica and Svalbard. The method showed low detection limits (0.006 ng g−1 for DC and 0.003 ng g−1 for the HDF) and a precision ranging from 3% to 20% RSD. Fe(II) concentrations ranged between the LoD and 0.077 ng g−1 and between the LoD and 0.300 ng g−1 for the Antarctic and Arctic samples, respectively. The Fe(II) contribution with respect to the total dissolved Fe was comparable in both sites accounting, on average, for 5% and 3%, respectively. We found that Fe(II) correctly identified the Pinatubo/Cerro Hudson eruption in the DC record, demonstrating its reliability as volcanic tracer, while, on the HDF core, we provided the first preliminary insight on the processes that might influence Fe speciation in firn matrices (i.e. organic ligands and pH influences).

The Svalbard archipelago, between 74° and 81° N, is ∼60 % covered by glaciers and located at the Arctic sea ice edge. The region experiences rapid variations in atmospheric flow during the snow season (from late September to May) and can be affected by air advected both from lower and higher latitudes, which likely impact the chemical composition of snowfall. While long-term changes in Svalbard snow chemistry have been documented in ice cores drilled from two high-elevation glaciers, the spatial variability of the snowpack composition across Svalbard is comparatively poorly understood. Here, we report the results of the most comprehensive seasonal snow chemistry survey to date, carried out in April 2016 across 22 sites on 7 glaciers across the archipelago. At each glacier, three snow pits were sampled along altitudinal profiles and the collected samples were analysed for major ions (Ca2+, K+, Na+, Mg2+, NH+4, SO42−, Br−, Cl− and NO3−) and stable water isotopes (δ18O, δ2H). The main aims were to investigate the natural and anthropogenic processes influencing the snowpack and to better understand the influence of atmospheric aerosol transport and deposition patterns on the snow chemical composition. The snow deposited in the southern region of Svalbard was characterized by the highest total ionic loads, mainly attributed to sea salt particles. Both NO3− and NH4+ in the seasonal snowpack reflected secondary aerosol formation and post-depositional changes, resulting in very different spatial deposition patterns: NO3− had its highest loading in northwestern Spitsbergen, and NH4+ in the southwest. The Br− enrichment in snow was highest in northeastern glacier sites closest to areas of extensive sea ice coverage. Spatial correlation patterns between Na+ and δ18O suggest that the influence of long-range transport of aerosols on snow chemistry is proportionally greater above 600–700 m a.s.l.

Snow thickness observations from airborne snow radars, such as the NASA’s Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow thickness measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime, caused by deep snow on thin sea ice. During the Norwegian young sea ICE expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on March 19, 2015, during which ground truth data were collected during an OIB overflight. Snow and ice thickness measurements were obtained across a two dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snow pack were observed during the N-ICE2015 expedition, due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking and saturation into the basal snow layers, causing more diffuse scattering and influenced the airborne radar signal to detect the radar main scattering horizon well above the snow/sea ice interface, resulting in a subsequent underestimation of total snow thickness, if only radar-based information is used. The average airborne snow thickness was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow thickness, a 0.06 m underestimate in snow thickness by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow thickness distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that, with frequent flooding of the snow-ice interface in specific regions of the Arctic in the future, it may result in an underestimate of snow thickness or an overestimate of ice freeboard, measured from radar altimetry, thereby affecting the accuracy of sea ice thickness estimates.

Black Carbon (BC) is a major forcing agent in the Arctic but substantial uncertainty remains to quantify its climate effects due to the complexity of mechanisms involved. In this study, we provide unique information on processes driving the variability of BC mass concentration in surface snow in the Arctic. Two different snow-sampling strategies were adopted during spring 2014 and 2015, focusing on the refractory BC (rBC) mass Ny-Ålesund concentration daily/hourly variability on a seasonal/daily time scale (referred to as 80-days and 3-days experiments). Despite the low rBC mass concentrations (never exceeding 22 ng g−1), a daily variability of up to 4.5 ng g−1 was observed. Atmospheric, meteorological and snow-related physico-chemical parameters were considered in multiple statistical models to understand the factors behind the observed variation of rBC mass concentrations. Results indicate that the main drivers of the variation of rBC are the precipitations events, snow metamorphism (melting-refreezing cycles, surface hoar formation and sublimation) and the activation of local sources (wind resuspension) during the snow melting periods. The rBC in the snow seems de-coupled with the atmospheric BC load. Our results highlighted a common association of snow rBC with coarse mode particles number concentration and with snow precipitation events.

The Arctic and middle latitude (such as the Alps) ice core archives, except for the Greenland summit, are strongly influenced by melting processes, able to modify the original chemical signal of the annual snowfall. In the last decades, the increase of the average Arctic temperature has caused and enhanced surface snow melting in the higher ice cap, especially in the Svalbard Archipelago. The increase of the frequency and altitude of winter “rain on snow” events as well as the increase of the length of the melting season has a direct impact on the chemical composition of the seasonal and permanent snow layers due to different migration processes of water-soluble compounds, such as ionic species. The re-allocation along the snowpack of ionic species could significantly modify the original chemical signal present in the annual snow, making comprehensive interpretation of climate records difficult. The chemical composition of the first 100 cm of the seasonal snow at Austre Brøggerbreen Glacier (Spitsbergen, Svalbard Islands, Norway) was monitored daily from the 27th of March until to the 31st of May 2015. The experiment period covers almost the entire Arctic spring until the melting season. During the experiment, a rain event occurred on the 16th to 17th of April while from the 15th of May the snowpack reached an isothermal profile. The presented dataset is unique and helps to better understand the behaviour of cations (K+, Ca2+, Na+, Mg2+), anions (Br−, I−, SO42−, NO3−, Cl−, MSA) and two carboxylic acids (C2-glycolic and C5-glutaric acids) in the snowpack during this melting period. The results obtained from the experiment give us an overview of how the chemicals are remobilized in the snowpack during a rain event or due to the melting at the end of the spring season. The aim of this paper is to give a picture of the evolution of the seasonal snow strata with the aim to better understand the processes that can influence the chemical distribution in the annual snow. The results of the present work are unique and helpful for future analyses and interpretation of ice core paleoclimatic archives.

Due to its Arctic location at 79°N, Kongsfjorden in Svalbard experiences strong seasonality in light climate, changing from polar night to midnight sun. Sea ice conditions and the optical properties of seawater further modify the amount and the spectral composition of solar radiation penetrating into the water column, thus defining the underwater light climate in Kongsfjorden. Light represents one of the major shaping factors for the entire marine ecosystem. A number of studies focusing on implications of the underwater light for marine organisms have been conducted in Kongsfjorden, generating diverse datasets on seawater optical properties, scattered over time and space. This review synthesizes the fragmentary information available from the literature as well as presenting some unpublished data, and discusses the underwater light climate and its main controlling factors in Kongsfjorden. Furthermore, we provide a short synopsis about the relevance of light for different components of an Arctic marine ecosystem, exemplified by studies carried out in Kongsfjorden. Due to its year-round accessibility and its high-Arctic location, Kongsfjorden has become a prime fjord for studying how the strong seasonal changes in light availability, ranging from polar night to midnight sun, affect marine life with respect to primary production, behavioural aspects and synchronization of growth and reproduction.

International audience; Arctic snow cover dynamics offer a changing face in terms of temporal duration andwater equivalent, due to recent climate change conditions (Callaghan et al., 2011; Lemke & Jacobi,2012). Indeed, the Arctic is now experiencing some of the most rapid and severe climate change onearth. In this context, innovative and improved methods are helpful to enhance management of thesnow-pack resource for climate research, hydrology and human activities. The characteristics of Arcticsnow are different from “temperate” snow (i.e. the Alps), in terms of thickness, internal structure, thermalconductivity, and metamorphism. Ground observation often indicates wind slab at the snow surface,internal rounded grains, depth hoar at the bottom, and often internal ice layer or at the interfacewith ground surface (Dominé et al., 2016; Gallet et al., 2017, for spring snow). This work is part of the“Precip-A2” project (OSUG, Grenoble-France), focusing on snow and its interaction with the atmosphere,especially in terms of chemistry, radiative processes and precipitation. The application site isthe Brøgger peninsula, focused on Ny-Ålesund area, Svalbard, Norway (N 78°55’ / E 11° 55’). Onesub-task of the Precip-A2 project is dedicated to X-band radar measurements (ground and spaceborne)to retrieve physical properties of arctic snow.

Arctic winters have become increasingly warmer and rainier. Where permafrost prevails, winter rain (or rain-on-snow; ROS) is known to occasionally cause extensive ice layers at the snow/ground interface, i.e. "basal ice" or "ground-ice", with potentially large ecological and socio-economic implications. However, an overall lack of field data has so far restricted our predictive understanding of the environmental conditions shaping spatiotemporal variation in basal ice. Here, we use time-series of spatially replicated snowpack measurements from coastal (Ny-Ålesund area; 2000-2017) and central Spitsbergen (Nordenskiöld Land; 2010-2017), Svalbard, to analyze spatiotemporal patterns in basal ice and how they are linked with topography, weather, snowpack and climate change. As expected, both the spatial occurrence and thickness of basal ice increased strongly with the annual amount of winter rain. This effect was modified by accumulated snowfall; a deeper snowpack restricts ice formation following a minor rain event, but enhances ice formation following heavy rain due to an increased contribution of snowmelt. Accordingly, inter-annual variation in snow depth was negatively related to basal ice thickness. Annual fluctuations in basal ice thickness were strongly correlated in space (average correlation ρ = 0.40; 0-142 km distance between plots) due to strong spatial correlation in winter rain (ρ = 0.62; 14-410 km distance between meteorological stations). Models of basal ice based on meteorological time-series (1957-2017) suggested that ice-free winters (i.e. mean basal ice < 0.1 cm) had virtually not occurred since 1998, whereas such winters previously (1957-1998) occurred every three-four years on average. This detected cryosphere regime shift was linked to a parallel climate regime shift with increased winter rain amounts. Svalbard is regarded a bellwether for Arctic winter climate change. Our empirical study may therefore provide an early warning of future changes in high-arctic snowpacks. © 2019 The Author(s). Published by IOP Publishing Ltd under a CC BY 3.0 licence.

Black carbon (BC) particles emitted from incomplete combustion of fossil fuel and biomass and deposited on snow and ice darken the surface and reduce the surface albedo. Even small initial surface albedo reductions may have larger adjusted effects due to snow morphology changes and changes in the sublimation and snow melt rate. Most of the literature on the effect of BC on snow surface albedo is based on numerical models, and few in situ field measurements exist to confirm this reduction. Here we present an extensive set of concurrent in situ measurements of spectral surface albedo, BC concentrations in the upper 5 cm of the snowpack, snow physical parameters (grain size and depth), and incident solar flux characteristics from the Arctic. From this data set (with median BC concentrations ranging from 5 to 137 ng BC per gram of snow) we are able to separate the BC signature on the snow albedo from the natural snow variability. Our measurements show a significant correlation between BC in snow and spectral surface albedo. Based on these measurements, parameterizations are provided, relating the snow albedo, as a function of wavelength, to the equivalent BC content in the snowpack. The term equivalent BC used here is the elemental carbon concentration inferred from the thermo-optical method adjusted for the fraction of non-BC constituents absorbing sunlight in the snow. The first parameterization is a simple equation which efficiently describes the snow albedo reduction due to the equivalent BC without including details on the snow or BC microphysics. This can be used in models when a simplified description is needed. A second parameterization, including snow grain size information, shows enhanced correspondence with the measurements. The extracted parameterizations are valid for wavelength bands 400-900 nm, constrained for BC concentrations between 1 and 400 ng g(-1), and for an optically thick snowpack. The parameterizations are purely empirical, and particular focus was on the uncertainties associated with the measurements, and how these uncertainties propagate in the parameterizations. Integrated, the first parameterization (based only on the equivalent BC) gives a broadband (400-900 nm) snow albedo reduction of 0.004 due to 10 ng equivalent BC per gram of snow, while the effect is almost 5 times larger for BC concentrations 1 order of magnitude higher. The study shows that the reconstructed albedo from the second parameterization (including information on the snow grain size) corresponds better to the radiative transfer model Snow, Ice, and Aerosol Radiation albedo than the reconstructed albedo from the first parameterization (excluding grain size information).

Carbon-containing particles are associated with adverse health effects, and their light-absorbing fractions were recently estimated to be the second largest contributor to global warming after carbon dioxide. Knowledge on the spatiotemporal variability of light-absorbing carbon (LAC) particles in urban areas is relevant for air quality management and to better diagnose the population exposure to these particles. This work reports on the first mobile LAC mass concentrations (M LAC ) measured on-board four taxis in the Stockholm metropolitan area in November 2011. On average, concentrations were higher and more variable during daytime (median of 1.9 µg m −3 and median absolute deviation of 2.3 µg m −3 ). Night-time (21:00–05:00) measurements were very similar for all road types and also compared to levels monitored at an urban background fixed site (median of 0.9 µg m −3 ). We observed a large intra-urban variability in concentrations, with maxima levels inside road tunnels (median and 95th percentile of 7.5 and 40.1 µg m −3 , respectively). Highways presented the second ranked concentrations (median and 95th percentile of 3.2 and 9.7 µg m −3 , respectively) associated with highest vehicle speed (median of 65 km h −1 ), traffic rates (median of 62 000 vehicles day −1 and 1500 vehicles h −1 ) and diesel vehicles share (7–10%) when compared to main roads, canyon streets, and local roads. Multiple regression modelling identified hourly traffic rate and M LAC concentration measured at an urban background site as the best predictors of on-road concentrations, but explained only 25% of the observed variability. This feasibility study proved to be a time- and cost-effective approach to map out ambient M LAC concentrations in Stockholm and more research is required to represent the distribution in other periods of the year. Simultaneous monitoring of other pollutants, closely correlated to M LAC levels in traffic-polluted environments, and including video recording of road and traffic changes would be an asset. Keywords: black carbon, elemental carbon, mobile measurements, urban aerosols, air quality, multiple regression modelling, Sweden (Published: 9 April 2014) Citation: Tellus B 2014, 66 , 23533, http://dx.doi.org/10.3402/tellusb.v66.23533 To access the supplementary material to this article, please see Supplementary files under Article Tools online.

Light-absorbing aerosol – particularly elemental carbon (EC) – while mixed with snow and ice is an important climate driver from the enhanced absorption of solar radiation. Currently, considerable efforts are being made to estimate its radiative forcing on a global scale, but several uncertainties remain, particularly those regarding its deposition processes. In this study, concurrent measurements of EC in air and snow are performed for three years (2009–2012) at Changbai station, northeastern China. The scavenging ratio and the wet- and dry-deposition fluxes of EC over the snow surface are estimated. The mean EC concentration in the surface snow is 1000 ± 1500 ng g−1, ranging from 7 to 7640 ng g−1. The mean value of the scavenging ratio of EC by snow is 140 ± 100, with a median value of 150, which is smaller than that reported in Arctic areas. A non-rimed snow process is a significant factor in interpreting differences with Arctic areas. Wet-deposition fluxes of EC are estimated to be 0.47 ± 0.37 μg cm−2 month−1 on average over the three snow seasons studied. Dry deposition is more than five times higher, with an average of 2.65 ± 1.93 μg cm−2 month−1; however, only winter period estimation is possible (December–February). During winter in Changbai, 87% of EC in snow is estimated to be due to dry deposition, with a mean dry deposition velocity of 6.44 × 10−3 m s−1 and median of 8.14 × 10−3 m s−1. Finally, the calculation of the radiative effect shows that 500 ng g−1 of dry-deposited EC to a snow surface absorbs three times more incoming solar energy than the same mass mixed in the snow through wet deposition. Deposition processes of an EC-containing snow surface are, therefore, crucial to estimate its radiative forcing better, particularly in northeastern China, where local emission strongly influences the level and gradient of EC in the snowpack, and snow-covered areas are cold and dry due to the atmospheric general circulation. Furthermore, this study builds on the knowledge to characterize the conditions in the snow-laden Chinese rural areas better as well as to constrain transport of EC to the Arctic better.

Snow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N-ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid-June 2015 are presented here. Overall, the snow thickness was about 20 cm higher than the climatology for second-year ice, with an average of 55 ± 27 cm and 32 ± 20 cm on first-year ice. The average density was 350–400 kg m−3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.

Even though the specific surface area (SSA) and the snow area index (SAI) of snow are crucial variables to determine the chemical and climatic impact of the snow cover, few data are available on the subject. We propose here a novel method to measure snow SSA and SAI. It is based on the measurement of the hemispherical infrared reflectance of snow samples using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement). DUFISSS uses the 1310 or 1550 nm radiation of laser diodes, an integrating sphere 15 cm in diameter, and InGaAs photodiodes. For SSA2 kg−1, we use the 1310 nm radiation, reflectance is between 15 and 50% and the accuracy of SSA determination is 10%. For SSA>60 m2 kg−1, snow is usually of low density (typically 30 to 100 kg m−3), resulting in insufficient optical depth and 1310 nm radiation reaches the bottom of the sample, causing artifacts. The 1550 nm radiation is therefore used for SSA>60 m2 kg−1. Reflectance is then in the range 5 to 12% and the accuracy on SSA is 12%. We propose empirical equations to determine SSA from reflectance at both wavelengths, with that for 1310 nm taking into account the snow density. DUFISSS has been used to measure the SSA of snow and the SAI of snowpacks in polar and Alpine regions.

International audience; The specific surface area (SSA) of snow can be used as an objective measurement of grain size and is therefore a central variable to describe snow physical properties such as albedo. Snow SSA can now be easily measured in the field using optical methods based on infrared reflectance. However, existing optical methods have only been validated for dry snow. Here we test the possibility to use the DUFISSS instrument, based on the measurement of the 1310 nm reflectance of snow with an integrating sphere, to measure the SSA of wet snow. We perform cold room experiments where we measure the SSA of a wet snow sample, freeze it and measure it again, to quantify the difference in reflectance between frozen and wet snow. We study snow samples in the SSA range 12-37 m2 kg-1 and in the mass liquid water content (LWC) range 5-32%. We conclude that the SSA of wet snow can be obtained from the measurement of its 1310 nm reflectance using three simple steps. In most cases, the SSA thus obtained is less than 10 {%} different from the value that would have been obtained if the sample had been considered dry, so that the three simple steps constitute a minor correction. We also run two optical models to interpret the results, but no model reproduces correctly the water-ice distribution in wet snow, so that their predictions of wet snow reflectance are imperfect. The correction on the determination of wet snow SSA using the DUFISSS instrument gives an overall uncertainty better than 11%, even if the LWC is unknown. If SSA is expressed as a surface to volume ratio (e.g., in mm-1), the uncertainty is then 13% because of additional uncertainties in the determination of the volume of ice and water when the LWC is unknown.

The structure of the snowpack near Barrow was studied in March–April 2009. Verticalprofiles of density, specific surface area (SSA) and thermal conductivity were measuredon tundra, lakes and landfast ice. The average thickness was 41 cm on tundra and 21 cmon fast ice. Layers observed were diamond dust or recent wind drifts on top, overlayingwind slabs, occasional faceted crystals and melt-freeze crusts, and basal depth hoar layers.ThetoplayerhadaSSAbetween45and224m

International audience; The specific surface area (SSA) of snow determines in Part the albedo of snow surfaces and the capacity of the snow to adsorb chemical species and catalyze reactions. Despite these crucial roles, almost no value of snow SSA are available for the largest permanent snow expanse on Earth, the Antarctic. We have measured the first vertical profiles of snow SSA near Dome C (DC: 75°06' S, 123°20' E, 3233 m a.s.l.) on the Antarctic plateau, and at seven sites during the logistical traverse between Dome C and the French coastal base Dumont D'Urville (DDU: 66°40' S, 140°01' E) during the Austral summer 2008-2009. We used the DUFISSS system, which measures the IR reflectance of snow at 1310 nm with an integrating sphere. At DC, the mean SSA of the snow in the top 1 cm is 38 m2 kg‑1, decreasing monotonically to 14 m2 kg‑1 at a depth of 15 cm. Along the traverse, the snow SSA profile is similar to that at DC in the first 600 km from DC. Closer to DDU, the SSA of the top 5 cm is 23 m2 kg‑1, decreasing to 19 m2 kg‑1 at 50 cm depth. This is attributed to wind, which causes a rapid decrease of surface snow SSA, but forms hard windpacks whose SSA decrease more slowly with time. Since light-absorbing impurities are not concentrated enough to affect albedo, the vertical profiles of SSA and density were used to calculate the spectral albedo of the snow for several realistic illumination conditions, using the DISORT radiative transfer model. A preliminary comparison with MODIS data is presented for use in energy balance calculations and for comparison with other satellite retrievals. These calculated albedos are compared to the few existing measurements on the Antarctic plateau. The interest of postulating a submillimetric, high-SSA layer at the snow surface to explain measured albedos is discussed.

International audience; The specific surface area (SSA), defined as the surface area of ice per unit mass, is an important variable characterizing the complex microstructure of snow. Its application range covers the physical evolution of snow (metamorphism), photochemistry and optical and microwave remote sensing. This paper presents a new device, POSSSUM (Profiler Of Snow Specific Surface area Using SWIR reflectance Measurement), designed to allow the rapid acquisition of SSA profiles down to similar to 20 m depth and with an effective vertical resolution of 10-20 mm. POSSSUM is based on the infrared (IR) reflectance technique: A laser diode operating at 1310 nm illuminates the snow at nadir incidence angle along the face of a drilled hole. The reflected radiance is measured at three zenith angles (20 degrees, 40 degrees and 60 degrees) each for two azimuth angles (0 degrees and 180 degrees). A second laser operating at a shorter wavelength (635 nm), which is almost insensitive to SSA, allows the distance to the snow face to be estimated. The reflected IR radiance and the distance are combined to estimate bidirectional reflectances. These reflectances are converted into hemispherical reflectances and in turn into SSA using a theoretical formulation based on an asymptotic solution of the radiative transfer equation. The evaluation and validation of POSSSUM's SSA measurements took place in spring 2009 in the French Alps. The new method was compared with the methane adsorption technique and DUFISSS, another well-validated instrument based on the IR technique. The overall measurement error is in the range 10-15%.

International audience; Diamond dust (DD) refers to tiny ice crystals that form frequently in the Polar troposphere under clear sky conditions. They provide surfaces for chemical reactions and scatter light. We have measured the specific surface area (SSA) of DD at Barrow in March–April 2009. We have also measured its chemical composition in mineral and organic ions, dissolved organic carbon (DOC), aldehydes, H2O2, and the absorption spectra of water‐soluble chromophores. Mercury concentrations were also measured in spring 2006, when conditions were similar. The SSA of DD ranges from 79.9 to 223 m2 kg−1. The calculated ice surface area in the atmosphere reaches 11000 (±70%) μm2 cm−3, much higher than the aerosol surface area. However, the impact of DD on the downwelling and upwelling light fluxes in the UV and visible is negligible. The composition of DD is markedly different from that of snow on the surface. Its concentrations in mineral ions are much lower, and its overall composition is acidic. Its concentrations in aldehydes, DOC, H2O2 and mercury are much higher than in surface snows. Our interpretation is that DOC from the oceanic surface microlayer, coming from open leads in the ice off of Barrow, is taken up by DD. Active chemistry in the atmosphere takes place on DD crystal surfaces, explaining its high concentrations in aldehydes and mercury. After deposition, active photochemistry modifies DD composition, as seen from the modifications in its absorption spectra and aldehyde and H2O2 content. This probably leads to the emissions of reactive species to the atmosphere.

The specific surface area (SSA) of snow determines in Part the albedo of snow surfaces and the capacity of the snow to adsorb chemical species and catalyze reactions. Despite these crucial roles, almost no value of snow SSA are available for the largest permanent snow expanse on Earth, the Antarctic. We have measured the first vertical profiles of snow SSA near Dome C (DC: 75°06´ S, 123°20´ E, 3233 m a.s.l.) on the Antarctic plateau, and at seven sites during the logistical traverse between Dome C and the French coastal base Dumont D'Urville (DDU: 66°40´ S, 140°01´ E) during the Austral summer 2008–2009. We used the DUFISSS system, which measures the IR reflectance of snow at 1310 nm with an integrating sphere. At DC, the mean SSA of the snow in the top 1 cm is 38 m2 kg−1, decreasing monotonically to 14 m2 kg−1 at a depth of 15 cm. Along the traverse, the snow SSA profile is similar to that at DC in the first 600 km from DC. Closer to DDU, the SSA of the top 5 cm is 23 m2 kg−1, decreasing to 19 m2 kg−1 at 50 cm depth. This is attributed to wind, which causes a rapid decrease of surface snow SSA, but forms hard windpacks whose SSA decrease more slowly with time. Since light-absorbing impurities are not concentrated enough to affect albedo, the vertical profiles of SSA and density were used to calculate the spectral albedo of the snow for several realistic illumination conditions, using the DISORT radiative transfer model. A preliminary comparison with MODIS data is presented for use in energy balance calculations and for comparison with other satellite retrievals. These calculated albedos are compared to the few existing measurements on the Antarctic plateau. The interest of postulating a submillimetric, high-SSA layer at the snow surface to explain measured albedos is discussed.

High-accuracy measurements of snow Bidirectional Reflectance Distribution Function (BRDF) were performed for four natural snow samples with a spectrogonio-radiometer in the 500–2600 nm wavelength range. These measurements are one of the first sets of direct snow BRDF values over a wide range of lighting and viewing geometry. They were compared to BRDF calculated with two optical models. Variations of the snow anisotropy factor with lighting geometry, wavelength and snow physical properties were investigated. Results show that at wavelengths with small penetration depth, scattering mainly occurs in the very top layers and the anisotropy factor is controlled by the phase function. In this condition, forward scattering peak or double scattering peak is observed. In contrast at shorter wavelengths, the penetration of the radiation is much deeper and the number of scattering events increases. The anisotropy factor is thus nearly constant and decreases at grazing observation angles. The whole dataset is available on demand from the corresponding author.

Even though the specific surface area (SSA) of snow is a crucial variable to determine the chemical and climatic impact of the snow cover, few data are available on snow SSA because current measurement methods are not simple to use in the field or do not have a sufficient accuracy. We propose here a novel determination method based on the measurement of the hemispherical reflectance of snow in the infrared using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement). DUFISSS uses 1310 and 1550 nm radiation provided by laser diodes, an integrating sphere 15 cm in diameter, and InGaAs photodiodes. For SSA60 m2 kg−1, snow is usually of low to very low density (typically 30 to 100 kg m−3) and this produces artifacts caused by the e-folding length of light in snow being too long. We therefore use 1550 nm radiation for SSA>60 m2 kg−1. Reflectance is then in the range 5 to 12%, and the accuracy is 12%. No effect of crystal shape on reflectance was detected. We propose empirical equations to determine SSA from reflectance at both wavelengths, with that for 1310 nm taking into account the snow density. DUFISSS has been tested in the Alps to measure the snow area index (SAI) of the Alpine snowpack in a south facing area at 2100 m elevation. This was done by measuring the SSA, thickness and density of the seven main layers of the snowpack in just 30 min, and a value of 5350 was found, significantly greater than in Arctic and subarctic regions. DUFISSS can now be used to help study issues related to polar and Alpine atmospheric chemistry and climate.