Surface albedo is an essential variable to determine the Earth's surface energy budget, in particular for snow-covered areas where it is involved in one of the most powerful positive feedback loops of the climate system. In situ measurements of broadband and spectral albedo are therefore common. However they are subject to several artefacts. Here we investigate the sensitivity of spectral albedo measurements to surface slope, and we propose simple correction algorithms to retrieve the intrinsic albedo of a slope from measurements, as if it were flat. For this, we first derive the analytical equations relating albedo measured on a slope to intrinsic direct and diffuse albedo, the apportionment between diffuse and direct incoming radiation, and slope inclination and aspect. The theory accounts for two main slope effects. First, the slope affects the proportion of solar radiation intercepted by the surface relative to that intercepted by the upward-looking, horizontal, sensor. Second, the upward- and downward-looking sensors receive reduced radiation from the sky and the surface respectively and increased radiation from neighbouring terrain. Using this theory, we show that (i) slope has a significant effect on albedo (over 0.01) from as little as a ≈1∘ inclination, causing distortions of the albedo spectral shape; (ii) the first-order slope effect is sufficient to fully explain measured albedo up to ≈15∘, which we designate “small-slope approximation”; and (iii) for larger slopes, the theory depends on the neighbouring slope geometry and land cover, leading to much more complex equations. Next, we derive four correction methods from the small-slope approximation, to be used depending on whether (1) the slope inclination and orientation are known or not, (2) the snow surface is free of impurities or dirty, and (3) a single or a time series of albedo measurements is available. The methods applied to observations taken in the Alps on terrain with up to nearly 20∘ slopes prove the ability to recover intrinsic albedo with a typical accuracy of 0.03 or better. From this study, we derive two main recommendations for future field campaigns: first, sloping terrain requires more attention because it reduces the measurement accuracy of albedo even for almost invisible slopes (1–2∘). Second, while the correction of the slope effect is possible, it requires additional information such as the spectral diffuse and direction partitioning and if possible the actual slope inclination and aspect, especially when the absence of impurities can not be assumed.

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

Snow accumulation is the main positive component of the mass balance in Antarctica. In contrast to the major efforts deployed to estimate its overall value on a continental scale – to assess the contribution of the ice sheet to sea level rise – knowledge about the accumulation process itself is relatively poor, although many complex phenomena occur between snowfall and the definitive settling of the snow particles on the snowpack. Here we exploit a dataset of near-daily surface elevation maps recorded over 3 years at Dome C using an automatic laser scanner sampling 40–100 m2 in area. We find that the averaged accumulation is relatively regular over the 3 years at a rate of +8.7 cm yr−1. Despite this overall regularity, the surface changes very frequently (every 3 d on average) due to snow erosion and heterogeneous snow deposition that we call accumulation by “patches”. Most of these patches (60 %–85 %) are ephemeral but can survive a few weeks before being eroded. As a result, the surface is continuously rough (6–8 cm root-mean-square height) featuring meter-scale dunes aligned along the wind and larger, decameter-scale undulations. Additionally, we deduce the age of the snow present at a given time on the surface from elevation time series and find that snow age spans over more than a year. Some of the patches ultimately settle, leading to a heterogeneous internal structure which reflects the surface heterogeneity, with many snowfall events missing at a given point, whilst many others are overrepresented. These findings have important consequences for several research topics including surface mass balance, surface energy budget, photochemistry, snowpack evolution, and the interpretation of the signals archived in ice cores.

The presence of light-absorbing particles (LAPs) in snow leads to a decrease in short-wave albedo affecting the surface energy budget. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps and an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, a unique dataset collected at Col du Lautaret (2058 m a.s.l., above sea level, French Alps) for the two snow seasons 2016–2017 and 2017–2018 is presented. This dataset consists of spectral albedo measurements, vertical profiles of snow specific surface area (SSA), density and concentrations of different LAP species. Spectral albedos are processed to estimate SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of LAP concentrations and SSA measurements. Our dataset highlights, among others, large discrepancies between two measurement techniques of black carbon (BC) concentrations in snow (namely thermal-optical and laser-induced incandescence). Second, we present ensemble snowpack simulations of the multi-physics version of the detailed snowpack model Crocus, forced with in situ meteorological data, as well as dust and BC deposition fluxes from an atmospheric model. The temporal variations of near-surface LAP concentrations and SSA are most of the time correctly simulated. The simulated seasonal radiative forcing of LAPs is 33 % higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs is similar with 10 ± 5 and 11 ± 1 d for the first and the second snow seasons, respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance in accounting for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season (10 ± 5 d) also points out the sensitivity of model-based estimations of LAP impact on modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to the acceleration of the metamorphism by LAPs) is negligible for the 2 years considered here, which is contrary to what was found in previous studies for other sites.

We propose a system of analytical equations to retrieve snow grain size and absorption coefficient of pollutants from snow reflectance or snow albedo measurements in the visible and near-infrared regions of the electromagnetic spectrum, where snow single-scattering albedo is close to 1.0. It is assumed that ice grains and impurities (e.g., dust, black and brown carbon) are externally mixed, and that the snow layer is semi-infinite and vertically and horizontally homogeneous. The influence of close-packing effects on reflected light intensity are assumed to be small and ignored. The system of nonlinear equations is solved analytically under the assumption that impurities have the spectral absorption coefficient, which obey the Ångström power law, and the impurities influence the registered spectra only in the visible and not in the near infrared (and vice versa for ice grains). The theory is validated using spectral reflectance measurements and albedo of clean and polluted snow at various locations (Antarctica Dome C, European Alps). A technique to derive the snow albedo (plane and spherical) from reflectance measurements at a fixed observation geometry is proposed. The technique also enables the simulation of hyperspectral snow reflectance measurements in the broad spectral range from ultraviolet to the near infrared for a given snow surface if the actual measurements are performed at a restricted number of wavelengths (two to four, depending on the type of snow and the measurement system).