Unraveling the optical shape of snow.

The reflection of sunlight off the snow is a major driver of the Earth's climate. This reflection is governed by the shape and arrangement of ice crystals at the micrometer scale, called snow microstructure. However, snow optical models overlook the complexity of this microstructure by using simple shapes, and mainly spheres. The use of these various shapes leads to large uncertainties in climate modeling, which could reach 1.2 K in global air temperature. Here, we accurately simulate light propagation in three-dimensional images of natural snow at the micrometer scale, revealing the optical shape of snow. This optical shape is neither spherical nor close to the other idealized shapes commonly used in models. Instead, it more closely approximates a collection of convex particles without symmetry. Besides providing a more realistic representation of snow in the visible and near-infrared spectral region (400 to 1400 nm), this breakthrough can be directly used in climate models, reducing by 3 the uncertainties in global air temperature related to the optical shape of snow.

Black carbon and dust alter the response of mountain snow cover under climate change.

By darkening the snow surface, mineral dust and black carbon (BC) deposition enhances snowmelt and triggers numerous feedbacks. Assessments of their long-term impact at the regional scale are still largely missing despite the environmental and socio-economic implications of snow cover changes. Here we show, using numerical simulations, that dust and BC deposition advanced snowmelt by 17 ± 6 days on average in the French Alps and the Pyrenees over the 1979-2018 period. BC and dust also advanced by 10-15 days the peak melt water runoff, a substantial effect on the timing of water resources availability. We also demonstrate that the decrease in BC deposition since the 1980s moderates the impact of current warming on snow cover decline. Hence, accounting for changes in light-absorbing particles deposition is required to improve the accuracy of snow cover reanalyses and climate projections, that are crucial for better understanding the past and future evolution of mountain social-ecological systems.

Permafrost cooled in winter by thermal bridging through snow-covered shrub branches.

Considerable expansion of shrubs across the Arctic tundra has been observed in recent decades. These shrubs are thought to have a warming effect on permafrost by increasing snowpack thermal insulation, thereby limiting winter cooling and accelerating thaw. Here, we use ground temperature observations and heat transfer simulations to show that low shrubs can actually cool the ground in winter by providing a thermal bridge through the snowpack. Observations from unmanipulated herb tundra and shrub tundra sites on Bylot Island in the Canadian high Arctic reveal a 1.21 °C cooling effect between November and February. This is despite a snowpack that is twice as insulating in shrubs. The thermal bridging effect is reversed in spring when shrub branches absorb solar radiation and transfer heat to the ground. The overall thermal effect is likely to depend on snow and shrub characteristics and terrain aspect. The inclusion of these thermal bridging processes into climate models may have an important impact on projected greenhouse gas emissions by permafrost.

Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements.

Monitoring the evolution of snow on the ground and lake ice-two of the most important components of the changing northern environment-is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

European In-Situ Snow Measurements: Practices and Purposes.

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called "A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction". Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

Comparison of commonly-used microwave radiative transfer models for snow remote sensing.

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (TB): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated TB at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (Dmax) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (Dmax), assuming non-sticky spheres for the DMRT models. Simulated TB sensitivity analysis using the same inputs shows relatively consistent TB behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground-based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated TB. Results using in-situ grain size measurements (SSA or Dmax, depending on the model) give a mean TB RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than Dmax measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

Bud-burst modelling in Siberia and its impact on quantifying the carbon budget.

Vegetation phenology is affected by climate change and in turn feeds back on climate by affecting the annual carbon uptake by vegetation. To quantify the impact of phenology on terrestrial carbon fluxes, we calibrate a bud-burst model and embed it in the Sheffield dynamic global vegetation model (SDGVM) in order to perform carbon budget calculations. Bud-burst dates derived from the VEGETATION sensor onboard the SPOT-4 satellite are used to calibrate a range of bud-burst models. This dataset has been recently developed using a new methodology based on the normalized difference water index, which is able to distinguish snowmelt from the onset of vegetation activity after winter. After calibration, a simple spring warming model was found to perform as well as more complex models accounting for a chilling requirement, and hence it was used for the carbon flux calculations. The root mean square difference (RMSD) between the calibrated model and the VEGETATION dataset was 6.5 days, and was 6.9 days between the calibrated model and independent ground observations of bud-burst available at nine locations over Siberia. The effects of bud-burst model uncertainties on the carbon budget were evaluated using the SDGVM. The 6.5 days RMSD in the bud-burst date (a 6% variation in the growing season length), treated as a random noise, translates into about 41 g cm-2 yr-1 in net primary production (NPP), which corresponds to 8% of the mean NPP. This is a moderate impact and suggests the calibrated model is accurate enough for carbon budget calculations. In addition to random differences between the calibrated model and VEGETATION data, systematic errors between the calibrated bud-burst model and true ground behaviour may occur, because of bias in the temperature dataset or because the bud-burst detected by VEGETATION is because of some other phenological indicator. A systematic error of 1 day in bud-burst translates into a 10 g cm-2 yr-1 error in NPP (about 2%). Based on the limited available ground data, any systematic error because of the use of VEGETATION data should not lead to significant errors in the calculated carbon flux. In contrast, widely used methods based on the normalized difference vegetation index from the advanced very high resolution radiometer satellite are likely to confuse snowmelt and vegetation greening, leading to errors of up to 15 days in bud-burst date, with consequent large errors in carbon flux calculations.