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The Snow, Ice, and Aerosol Radiative (SNICAR) model has been used in various capacities over the last 15 years to model the spectral albedo of snow with light-absorbing constituents (LACs). Recent studies have extended the model to include an adding-doubling two-stream solver and representations of non-spherical ice particles; carbon dioxide snow; snow algae; and new types of mineral dust, volcanic ash, and brown carbon. New options also exist for ice refractive indices and solar-zenith-angle-dependent surface spectral irradiances used to derive broadband albedo. The model spectral range was also extended deeper into the ultraviolet for studies of extraterrestrial and high-altitude cryospheric surfaces. Until now, however, these improvements and capabilities have not been merged into a unified code base. Here, we document the formulation and evaluation of the publicly available SNICAR-ADv3 source code, web-based model, and accompanying library of constituent optical properties. The use of non-spherical ice grains, which scatter less strongly into the forward direction, reduces the simulated albedo perturbations from LACs by ∼9%–31%, depending on which of the three available non-spherical shapes are applied. The model compares very well against measurements of snow albedo from seven studies, though key properties affecting snow albedo are not fully constrained with measurements, including ice effective grain size of the top sub-millimeter of the snowpack, mixing state of LACs with respect to ice grains, and site-specific LAC optical properties. The new default ice refractive indices produce extremely high pure snow albedo (>0.99) in the blue and ultraviolet part of the spectrum, with such values only measured in Antarctica so far. More work is needed particularly in the representation of snow algae, including experimental verification of how different pigment expressions and algal cell concentrations affect snow albedo. Representations and measurements of the influence of liquid water on spectral snow albedo are also needed.

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.

A snow survey was carried out to collect 13 surface snow samples (10 for fresh snow, and 3 for aged snow) and 79 subsurface snow samples in seasonal snow at 13 sites across northeastern China in January 2014. A spectrophotometer combined with chemical analysis was used to quantify snow particulate absorption by insoluble light-absorbing particles (ILAPs, e.g., black carbon, BC; mineral dust, MD; and organic carbon, OC) in snow. Snow albedo was measured using a field spectroradiometer. A new radiative transfer model (Spectral Albedo Model for Dirty Snow, or SAMDS) was then developed to simulate the spectral albedo of snow based on the asymptotic radiative transfer theory. A comparison between SAMDS and an existing model – the Snow, Ice, and Aerosol Radiation (SNICAR) – indicates good agreements in the model-simulated spectral albedos of pure snow. However, the SNICAR model values tended to be slightly lower than those of SAMDS when BC and MD were considered. Given the measured BC, MD, and OC mixing ratios of 100–5000, 2000–6000, and 1000–30 000 ng g−1, respectively, in surface snow across northeastern China, the SAMDS model produced a snow albedo in the range of 0.95–0.75 for fresh snow at 550 nm, with a snow grain optical effective radius (Reff) of 100 µm. The snow albedo reduction due to spherical snow grains assumed to be aged snow is larger than fresh snow such as fractal snow grains and hexagonal plate or column snow grains associated with the increased BC in snow. For typical BC mixing ratios of 100 ng g−1 in remote areas and 3000 ng g−1 in heavy industrial areas across northern China, the snow albedo for internal mixing of BC and snow is lower by 0.005 and 0.036 than that of external mixing for hexagonal plate or column snow grains with Reff of 100 µm. These results also show that the simulated snow albedos by both SAMDS and SNICAR agree well with the observed values at low ILAP mixing ratios but tend to be higher than surface observations at high ILAP mixing ratios.

Light absorbing impurities (LAI) initiate powerful snow albedo feedbacks, yet due to a scarcity of observations and measurements, LAI radiative forcing is often neglected or poorly constrained in climate and hydrological models. To support physically-based modeling of LAI processes, daily measurements of dust and black carbon (BC) stratigraphy, optical grain size, snow density and spectral albedo were collected over the 2013 ablation season in the Rocky Mountains, CO. Surface impurity concentrations exhibited a wide range of values (0.02–6.0 mg g−1 pptw) with 98% of mass being deposited by three episodic dust events in April. Even minor dust loading initiated albedo decline, and the negative relationship between dust concentrations and albedo was log-linear. As melt progressed, individual dust layers coalesced and emerged at the snow surface, with minimal mass loss to meltwater scavenging. The observations show that the convergence of dust layers at the surface reduced albedo to 0.3 and snow depth declined ~50% faster than other years with similar depth but less dust. The rapid melt led to an unexpected reduction in both grain size and density in uppermost surface layers. BC concentrations co-varied with dust concentrations but were several orders of magnitude lower (<1–20 ppb).

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite enables the accurate determination of atmospheric methane (CH4) and carbon monoxide (CO) abundances at high spatial resolution and global daily sampling. Due to its wide swath and sampling, the global distribution of both gases can be determined in unprecedented detail. The scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) has proven valuable in simultaneously retrieving the atmospheric column-averaged dry-air mole fractions XCH4 and XCO from TROPOMI's radiance measurements in the shortwave infrared (SWIR) spectral range.Here we present recent improvements of the algorithm which have been incorporated into the current version (v1.8) of the TROPOMI/WFMD product. This includes processing adjustments such as increasing the polynomial degree to 3 in the fitting procedure to better account for possible spectral albedo variations within the fitting window and updating the digital elevation model to minimise topography-related biases. In the post-processing, the machine-learning-based quality filter has been refined using additional data when training the random forest classifier to further reduce scenes with residual cloudiness that are incorrectly classified as good. In particular, the cloud filtering over the Arctic ocean is considerably improved. Furthermore, the machine learning calibration, addressing systematic errors due to simplifications in the forward model or instrumental issues, has been optimised. By including an additional feature associated with the fitted polynomial when training the corresponding random forest regressor, spectral albedo variations are better accounted for. To remove vertical stripes in the XCH4 and XCO data, an efficient orbit-wise destriping filter based on combined wavelet–Fourier filtering has been implemented, while optimally preserving the original spatial trace gas features. The temporal coverage of the data records has been extended to the end of April 2022, covering a total length of 4.5 years since the start of the mission, and will be further extended in the future.Validation with the ground-based Total Carbon Column Observing Network (TCCON) demonstrates that the implemented improvements reduce the pseudo-noise component of the products, resulting in an improved random error. The XCH4 and XCO products have similar spatial coverage from year to year including high latitudes and the oceans. The analysis of annual growth rates reveals accelerated growth of atmospheric methane during the covered period, in line with observations at marine surface sites of the Global Monitoring Division of NOAA's Earth System Research Laboratory, which reported consecutive annual record increases over the past 2 years of 2020 and 2021.

From the discovery that Venus has an atmosphere during the 1761 transit by M. Lomonosov to the current exploration of the planet by the Akatsuki orbiter, we continue to learn about the planet’s extreme climate and weather. This chapter attempts to provide a comprehensive but by no means exhaustive review of the results of the atmospheric thermal structure and radiative balance since the earlier works published in Venus and Venus II books from recent spacecraft and Earth based investigations and summarizes the gaps in our current knowledge. There have been no in-situ measurements of the deep Venus atmosphere since the flights of the two VeGa balloons and landers in 1985 (Sagdeev et al., Science 231:1411–1414, 1986 ). Thus, most of the new information about the atmospheric thermal structure has come from different remote sensing (Earth based and spacecraft) techniques using occultations (solar infrared, stellar ultraviolet and orbiter radio occultations), spectroscopy and microwave, short wave and thermal infrared emissions. The results are restricted to altitudes higher than about 40 km, except for one investigation of the near surface static stability inferred by Meadows and Crisp (J. Geophys. Res. 101:4595–4622, 1996 ) from 1  μ m observations from Earth. Little information about the lower atmospheric structure is possible below about 40 km altitude from radio occultations due to large bending angles. The gaps in our knowledge include spectral albedo variations over time, vertical variation of the bulk composition of the atmosphere (mean molecular weight), the identity, properties and abundances of absorbers of incident solar radiation in the clouds. The causes of opacity variations in the nightside cloud cover and vertical gradients in the deep atmosphere bulk composition and its impact on static stability are also in need of critical studies. The knowledge gaps and questions about Venus and its atmosphere provide the incentive for obtaining the necessary measurements to understand the planet, which can provide some clues to learn about terrestrial exoplanets.

Erect shrubs in the Arctic reduce surface albedo when branches protrude above the snow and modify snow properties, in particular specific surface area (SSA). Important consequences are changes in the land surfaceatmosphere energy exchange and the increase of snow melting in autumn, possibly inducing reduced soil thermal insulation and in turn permafrost cooling. Near Umiujaq (56.58N, 76.58W) in the Canadian low Arctic where dwarf birches (Betula glandulosa) are expanding, spectral albedo (400-1080 nm) under diffuse light and vertical profiles of SSA were measured in November and December 2015 at four sites: three with protruding branches and one with only snow. At the beginning of the snow season (8 November), shrub-induced albedo reductions were found to be wavelength dependent and as high as 55% at 500 nm and 18% at 1000 nm, which, integrated over the measurement range (400-1080 nm), corresponds to 70 W m 22 of additional absorbed energy. The impact of shrubs is not just snow darkening. They also affect snow SSA in multiple ways, by accumulating snow with high SSA during cold windy precipitation and favoring SSA decrease by inducing melting during warm spells. However, the impact on the radiation budget of direct darkening from shrubs likely dominates over the indirect change in SSA. Spectral albedo was simulated with a linear mixing equation (LME), which fitted well with observed spectra. The average root-mean-square error was 0.009. We conclude that LMEs are a suitable tool to parameterize mixed surface albedo in snow and climate models.

Radiative transfer models of snow albedo have usually assumed a spherical shape for the snow grains, using Mie theory to compute single-scattering properties. The scattering by more realistic nonspherical snow grains is less in the forward direction and more to the sides, resulting in a smaller asymmetry factor g (the mean cosine of the scattering angle). Compared to a snowpack of spherical grains with the same area-to-mass ratio, a snowpack of nonspherical grains will have a higher albedo, thin snowpacks of nonspherical grains will more effectively hide the underlying surface, and light-absorbing particles in the snowpack will be exposed to less sunlight. These effects are examined here for nonspherical snow grains with aspect ratios from 0.1 to 10. The albedo of an opaque snowpack with equidimensional (i.e., aspect ratio 1) nonspherical snow grains is higher than that with spherical snow grains by 0.032 and 0.050, for effective grain radii of 100 and 1000 μm, respectively. For an effective radius of 100 μm, the albedo reduction caused by 100 ng g−1 of black carbon is 0.019 for spherical snow grains but only 0.012 for equidimensional nonspherical snow grains. The albedo of a snowpack consisting of nonspherical snow grains can be mimicked by using a smaller grain of spherical shape; this is why radiative transfer models using spherical grains were able to match measurements of spectral albedo. The scaling factor for snow grain radius is different for nonspherical grains with different aspect ratios and is about 2.4 for equidimensional snow grains.

From April through June in 2019 and 2022, we monitored snow melt at three sites near Utqiaġvik, Alaska. Along 200‐m lines we measured snow depth, density, stratigraphy, snow‐covered area, and spectral albedo. Site 1 (ARM) was sloped tundra drained by water tracks. Site 2 (BEO) was flat polygonal tundra. Site 3 (ICE) was on undeformed landfast sea ice. All three sites were within a 6 km radius. Despite similar pre‐melt snow distributions and weather, the melt progression differed markedly between sites. In 2019, by mid‐melt, there was 40% less snow‐covered area at ARM versus ICE, and 34% less snow‐covered area at ARM versus BEO. The 2022 melt started 2 weeks later than in 2019 and was rapid, so smaller differences in snow‐covered areas developed. In both years meltout dates varied by up to 25 days between sites, and more than 20 days within sites, with melt rates at locations only meters apart differing by up to a factor of seven. This melt diachroneity led to highly heterogeneous meltout patterns at all three sites. Our measurements and observations indicate that, in addition to reductions in snow reflective properties and wind‐driven heat advection, the fate of meltwater plays a key role in producing melt diachroneity. We identify seven snow‐water mechanisms that can enhance or inhibit melt rates, all largely controlled by the local topography and the nature of the substrate. These mechanisms are important because the most rapid changes in albedo coincide with the peak of water‐snow melt interactions. Plain Language Summary We observed in detail snow melt on two different tundra locations and one sea ice location in Arctic Alaska in the spring of 2019 and 2022. During the melt season, these landscapes go from reflecting over 80% of the sunlight to less than 20%, a transition that impacts the global climate. Our observations showed that while warming weather is what ultimately controls snowmelt, the re‐freezing and flow of meltwater within the snowpack, plays a crucial role in controlling local melt rates of individual snow patches. These water‐related melt mechanisms, together with wind‐driven laterally advected air and the reflective properties of the snow, largely control the date individual snow patches disappear, and therefore, when the most rapid changes in solar reflectivity will take place. Key Points Water‐related processes in snow can promote or impede lateral advection of heat and mass, enhancing or inhibiting melt in the Arctic The composition and topography of the substrate govern the nature of the water‐snow interactions and thereby influence meltout patterns The most rapid and largest changes in surface albedo happen just prior to the snow‐free date when water‐snow melt mechanisms reach a peak

The Two-streAm Radiative TransfEr in Snow (TARTES) model computes the spectral albedo and the profiles of spectral absorption, irradiance, and actinic fluxes for a multi-layer plane-parallel snowpack. Each snow layer is characterized by its specific surface area, density, and impurity content, in addition to shape parameters. In the landscape of snow optical numerical models, TARTES distinguishes itself by taking into account different shapes of the particles through two shape parameters, namely the absorption enhancement parameter B and the asymmetry factor g. This is of primary importance as recent studies working at the microstructure level have demonstrated that snow does not behave as a collection of equivalent ice spheres, a representation widely used in other models. Instead, B and g take specific values that do not correspond to any simple geometrical shape, which leads to the concept of the “optical shape of snow”. Apart from this specificity, TARTES combines well-established radiative transfer principles to compute the scattering and absorption coefficients of pure or polluted snow, as well as the δ-Eddington two-stream approximation to solve the multi-layer radiative transfer equation. The model is implemented in Python, but conducting TARTES simulations is also possible without any programming through the SnowTARTES web application, making it very accessible to non-experts and for teaching purposes. Here, after describing the theoretical and technical details of the model, we illustrate its main capabilities and present some comparisons with other common snow radiative transfer models (AART, DISORT-Mie, SNICAR-ADv3) as a validation procedure. Overall the agreement on the spectral albedo, when in compatible conditions (i.e., with spheres), is usually within 0.02 and is better in the visible and near-infrared range compared to longer wavelengths.