Snow Surface Research Articles

High Concentrations of Perfluoroalkyl Acids in Arctic Seawater Driven by Early Thawing Sea Ice.

Poly- and perfluoroalkyl substances are synthetic chemicals that are widely present in the global environment including the Arctic. However, little is known about how these chemicals (particularly perfluoroalkyl acids, PFAA) enter the Arctic marine system and cycle between seawater and sea ice compartments. To evaluate this, we analyzed sea ice, snow, melt ponds, and near-surface seawater at two ice-covered stations located north of the Barents Sea (81 °N) with the aim of investigating PFAA dynamics in the late-season ice pack. Sea ice showed high concentrations of PFAA particularly at the surface with snow-ice (the uppermost sea ice layer strongly influenced by snow) comprising 26–62% of the total PFAA burden. Low salinities (<2.5 ppt) and low δ18OH20 values (<1‰ in snow and upper ice layers) in sea ice revealed the strong influence of meteoric water on sea ice, thus indicating a significant atmospheric source of PFAA with subsequent transfer down the sea ice column in meltwater. Importantly, the under-ice seawater (0.5 m depth) displayed some of the highest concentrations notably for the long-chain PFAA (e.g., PFOA 928 ± 617 pg L–1), which were ≈3-fold higher than those of deeper water (5 m depth) and ≈2-fold higher than those recently measured in surface waters of the North Sea infuenced by industrial inputs of PFAAs. The evidence provided here suggests that meltwater arising early in the melt season from snow and other surface ice floe components drives the higher PFAA concentrations observed in under-ice seawater, which could in turn influence the timing and extent of PFAA exposure for organisms at the base of the marine food web.

Mercury in precipitated and surface snow at Dome C and a first estimate of mercury depositional fluxes during the Austral summer on the high Antarctic plateau

The role of deposition fluxes on the mercury cycle at Concordia station, on the high Antarctic plateau have been investigated over the Austral summer between December 2017 to January 2018. Wet/frozen deposition was collected daily from specially sited tables, simultaneously with the collection of surface (0–3 cm) and subsurface (3–6 cm) snow and the analysis of Hg0 in the ambient air. Over the course of the experiment the atmospheric Hg0 concentrations ranged from 0.58 ± 0.19 to 1.00 ± 0.33 ng m−3, surface snow Hg concentrations varied between (0–3 cm) 0.006 ± 0.003 to 0.001 ± 0.001 ng cm−3 and subsurface snow (3–6 cm) concentrations varied between 0.001 ± 0.001 to 0.003 ± 0.002 ng cm−3. The maximum daily wet deposition flux was found to be 23 ng m−2 d−1. Despite the low temporal resolution of our measurements combined with their potential errors, the linear regression of the Hg deposition fluxes against the snow accumulation rates allowed us to estimate the mean dry deposition rate from the intercept of the graph as −0.005 +- 0.008 ng m−2 d−1. From this analysis, we conclude that wet deposition accounts for the vast majority of the Hg deposition fluxes at Concordia Station. The number of snow events, together with the continuous GEM measurements have allowed us to make a first estimation of the mean snow scavenging factor at Dome C. Using the slope of the regression of mercury flux on snow accumulation we obtained a snow scavenging factor that ranges from 0.21 to 0.22 ± 0.02 (ngHg/g snow)/(ngHg/m3air). Our data indicate that the boundary layer height and local meteorological effects influence Hg0 reemission from the top of (0–3 cm) the snowpack into the atmosphere and into the deeper snowpack layer (3–6 cm). These data will help constrain numerical models on the behaviour of mercury in Antarctica.

Spectral degree of linear polarization and neutral points of polarization in snow and ice surfaces

The properties of light reflected from snow and ice surfaces are important for the understanding of light scattering theory and the interpretation of remote sensing in the cryosphere. Spectral measurements of the degree of linear polarization (DoLP) and its related Stokes parameters Pq=−Q/I and Pu=U/I of Stokes vector IS=[IQUV]T were made for various snow and ice surfaces in Hokkaido, Japan. The measurement results indicated that the angular dependence on both the viewing angle and azimuth angle in the DoLP, Pq and Pu was significant especially in the shortwave infrared (SWIR) regions. In addition, an important finding here is that the measurements revealed for the first time the existence of neutral points of Pq and Pu in the snow and bare ice surface. The SWIR polarization features can be explained by the single scattering properties of snow/ice or surface reflection associated with surface scattering. For the snow cases, the DoLP was represented by the measurement geometry and the scattering phase matrix while for the bare ice case, the DoLP was represented by the measurement geometry and the Fresnel reflection matrix. An additional remark is that the angular dependence of the neutral points was linked exclusively to the measurement geometry regardless of snow particle size, shape and scattering/absorbing inclusions in the ice. In contrast, the polarization in the visible regions was so small but remained detectable. However, the angular dependence of the neutral points was somewhat different from that in near infrared and SWIR regions. These results suggested that the neutral points depend on the polarization magnitude and the plane of polarization that related to the multiple scattering in the snow and atmosphere. The polarimetric measurements related to the neutral points are expected to be useful for the retrieval of new snow/ice physical parameters.

Avalanche danger level characteristics from field observations of snow instability

Abstract. Avalanche danger levels are described in qualitative terms that mostly are not amenable to measurements or observations. However, estimating and improving forecast consistency and accuracy require descriptors that can be observed or measured. Therefore, we aim to characterize the avalanche danger levels based on expert field observations of snow instability. We analyzed 589 field observations by experienced researchers and forecasters recorded mostly in the region of Davos (Switzerland) during 18 winter seasons (2001–2002 to 2018–2019). The data include a snow profile with a stability test (rutschblock, RB) and observations on snow surface quality, drifting snow, signs of instability and avalanche activity. In addition, observers provided their estimate of the local avalanche danger level. A snow stability class (very poor, poor, fair, good, very good) was assigned to each profile based on RB score, RB release type and snowpack characteristics. First, we describe some of the key snowpack characteristics of the data set. In most cases, the failure layer included persistent grain types even after a recent snowfall. We then related snow instability data to the local avalanche danger level. For the danger levels 1–Low to 4–High, we derived typical stability distributions. The proportions of profiles rated poor and very poor clearly increased with increasing danger level. For our data set, the proportions were 5 %, 13 %, 49 % and 63 % for the danger levels 1–Low to 4–High, respectively. Furthermore, we related the local avalanche danger level to the occurrence of signs of instability such as whumpfs, shooting cracks and recent avalanches. The absence of signs of instability was most closely related to 1–Low and the presence of them to 3–Considerable. Adding the snow stability class and the 3 d sum of new snow depth improved the discrimination between the lower three danger levels. Still, 2–Moderate was not well described. Nevertheless, we propose some typical situations that approximately characterize each of the danger levels. Obviously, there is no single easily observable set of parameters that would allow us to fully characterize the avalanche danger levels. One reason for this shortcoming is the fact that the snow instability data we analyzed usually lack information on spatial frequency, which is needed to reliably assess the danger level.

Ultraviolet germicidal irradiation of melted snow and ice samples: inactivation of microorganisms and effects on insoluble microparticles

AbstractThe long-term refrigerated storage of melted snow and/or ice samples for analyses of insoluble microparticles (hereafter, microparticles) may be limited by increases in the biological particle concentration caused by microbial growth after ~1–2 weeks. In this study, we examined an ultraviolet (UV) disinfection method for the storage of melted snow and/or ice samples and determined the effects of this method on microparticles. Surface snow obtained from Glacier No. 31 in the Suntar-Khayata Range, eastern Siberia, Russia was divided into two portions for UV treatment and untreated controls. Microparticle concentrations and size distributions (in the range of 0.52–12.0 μm) in the samples were measured using a Coulter counter. Whereas the microparticle concentration in untreated samples increased, no obvious increase was observed over 53 d in the samples subjected to UV treatment. Microbial growth was detected in only untreated samples using a viable particle counter. In addition, the original microparticle concentrations and size distributions were unaffected by UV treatment. Our results demonstrated that the microparticle size distribution in untreated melted water samples reflects the growth, decomposition and succession of microorganisms over time and further indicate that UV irradiation is effective for long-term storage for microparticle analysis.

Non-Binary Snow Index for Multi-Component Surfaces

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

Snow process monitoring using time-lapse structure-from-motion photogrammetry with a single camera

Snow-surface processes in high-mountain snow-covered regions can be both complicated and highly variable. It remains a challenge to monitor snow-surface processes due to its tough environment. In this study, a novel One-camera Time-lapse Structure-from-Motion photogrammetry system (O-T-SfM), which is achieved by mounted a camera on a slider to take seven images from different viewing angles, was build-up to monitor snow surface automatically. The novel O-T-SfM was installed next to the August-one ice cap in the Qilian mountains, northwestern China to monitor snow-surface processes by taking oblique digital photographs every three hours from 8:00 to 17:00 from August 24, 2019, to September 7, 2020. Agisoft Photoscan was used to process the images and to derive point clouds and plot 1.5 × 1.5 m scale digital elevation models (DEMs). The O-T-SfM measurements is highly consistent with three different methods such as manual measurement, checkpoints, and manual photogrammetry, which indicates that O-T-SfM photogrammetry can achieve centimeter-scale precision. We found that O-T-SfM photogrammetry was generally successful at monitoring snow surfaces and the performance varies with the variation of surface condition in different season. Best performance was reached with snow-free conditions and O-T-SfM has a good performance when snow bedforms were abundant from October to March. It is hard for O-T-SfM to capture the snow melting processes July to September and the smooth fresh snowfalls from April to June. Alignment achievement was greatest in the morning and declined throughout the day. We found that our digital DEMs could also be used to assess the settling characteristics of the snowpack (snow accumulation and ablation, snow-surface roughness). Our results suggest that remote O-T-SfM photogrammetry can be successfully used to monitor the snow processes.

Modeling spatially distributed snow instability at a regional scale using Alpine3D

AbstractAssessing the avalanche danger level requires snow stratigraphy and instability data. As such data are usually sparse, we investigated whether distributed snow cover modeling can be used to provide information on spatial instability patterns relevant for regional avalanche forecasting. Using Alpine3D, we performed spatially distributed simulations to evaluate snow instability for the winter season 2016–17 in the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to 100 m horizontal resolution and precipitation was scaled with snow depth measurements from airborne laser scanning. Modeled snow instability metrics assessed for two different weak layers suggested that the weak layer closer to the snow surface was more variable. Initially, it was less stable than the weak layer closer to the ground, yet it stabilized faster as the winter progressed. In spring, the simulated snowpack on south-facing slopes stabilized faster than on north-facing slopes, in line with the regional avalanche forecast. In the winter months January to March 2017, simulated instability metrics did not suggest that the snowpack on south-facing slopes was more stable, as reported in the regional avalanche forecast. Although a validation with field data is lacking, these model results still show the potential and challenges of distributed modeling for supporting operational avalanche forecasting.

A prediction model for roof snow distribution during long-period snowfall using Euler-Lagrangian method

This paper proposed a roof snow prediction model based on the Euler-Lagrangian method. The key objective is to improve the performance and robustness of the snow surface's evolution on roof during long-period snowfall. To this end, this paper implemented a COMSALT model expressed as a pseudo-transient form to simulate the erosion and deposition process of snow particles on a roof, and added a curvature-based smoothing algorithm to the update of the snow surface on the roof. As verification, a snowfall process around a stepped roof is reproduced using a physical time of about one day. The results are compared with data obtained from field measurements and previous CFD simulations, which demonstrate that the model proposed in this paper can use a large time step of several seconds or even hundreds of seconds to estimate the snow distribution on the building roof, and the snow surface can ensure a second-order continuity to maintain the quality of the CFD calculation.

Nanoplastics transport to the remote, high-altitude Alps

Plastic materials are increasingly produced worldwide with a total estimated production of >8300 million tonnes to date, of which 60% was discarded. In the environment, plastics fragment into smaller particles, e.g. microplastics (size < 5 mm), and further weathering leads to the formation of functionally different contaminants – nanoplastics (size <1 μm). Nanoplastics are believed to have entirely different physical (e.g. transport), chemical (e.g. functional groups at the surface) and biological (passing the cell membrane, toxicity) properties compared to the micro- and macroplastics, yet, their measurement in the environmental samples is seldom available. Here, we present measurements of nanoplastics mass concentration and calculated the deposition at the pristine high-altitude Alpine Sonnblick observatory (3106 MASL), during the 1.5 month campaigh in late winter 2017. The average nanoplastics concentration was 46.5 ng/mL of melted surface snow. The main polymer types of nanoplastics observed for this site were polypropylene (PP) and polyethylene terephthalate (PET). We measured significantly higher concentrations in the dry sampling periods for PET (p < 0.002) but not for PP, which indicates that dry deposition may be the preferential pathway for PET leading to a gradual accumulation on the snow surfaces during dry periods. Air transport modelling indicates regional and long-range transport of nanoplastics, originating preferentially from European urban areas. The mean deposition rate was 42 (+32/-25) kg km−2 year−1. Thus more than 2 × 1011 nanoplastics particles are deposited per square meter of surface snow each week of the observed period, even at this remote location, which raises significant toxicological concerns.

Spatial and Temporal Variations in Pigment and Species Compositions of Snow Algae on Mt. Tateyama in Toyama Prefecture, Japan.

Snow algae are photosynthetic microbes that inhabit the melting snow surface in alpine and polar regions. We analyzed the pigment and species composition of colored snow collected on Mt. Tateyama in Japan during the melting seasons of 2015 and 2016. High-performance liquid chromatographic analyses of the pigments extracted from the colored snow showed that their composition varied within the study area and were classified into four types: Type A (astaxanthin-monoester dominant), Type B (medium astaxanthin-monoester content), Type C (abundant primary carotenoids and free-astaxanthin), and Type D (abundant primary carotenoids and astaxanthin diesters). Types A and B were most commonly observed in the study area, whereas Types C and D appeared only at specific sites. Analysis of the 18S ribosomal RNA (18S rRNA) gene revealed six major amplicon sequence variants (ASVs) of snow algae, belonging to the Sanguina, Chloromonas, and Chlainomonas groups. The relative abundance of the algal ASVs showed that Sanguina was dominant (>48%) in both Types A and B, suggesting that the difference in astaxanthin abundance between the two types was caused by the production of pigments in the algal cells. The algal community structures of Types C and D differed from those of Types A and B, indicating that the primary carotenoids and astaxanthin diesters were derived from certain algal species in these types. Therefore, astaxanthin-rich Sanguina algae mostly induced the red snow that appeared widely in this alpine area; however, they were partially dominated by Chloromonas or Chlainomonas algae, causing different pigment compositions.

A novel meteorological method to classify wintertime cold-air pool events

Cold-air pools (CAPs) are common in mountain valleys throughout the world (e.g., western North America, Himalayas, Alps, etc.) during winter months. Weak surface winds, cold temperatures, high humidity, and snow cover presence are common characteristics of CAPs, and in populated areas there is an increase in air pollution concentrations. Previous methods for identifying CAP events and determining their strength often rely on a combination of, radiosonde data, air pollution concentrations, and/or surface meteorological datasets. Ambient air pollution concentrations vary by location based on the local emissions sources and continually change due to regulations and human activity patterns, therefore they are unreliable for consistent CAP quantification. Here, the bulk atmospheric stability is calculated as the valley heat deficit (VHD) using radiosonde data for 12 locations in the western U.S. over 16 winters. A new CAP classification method is developed and compared to three existing CAP classification methods. Results indicate that the new method agrees well with existing approaches but provides a more robust CAP classification because it is solely based on meteorology and not air quality. For all locations, 00Z (afternoon/early evening) radiosondes account for roughly 8–41% of all CAP occurrences (12Z and 00Z), independent of the method used. Meaning that the stable boundary layer persists throughout the daytime in these cases often leading to persistent CAP events (PCAP). While PCAP length varies across locations, they are a similar order of magnitude because synoptic conditions that span the entire western U.S. govern CAP onset and PCAP length. Additionally, several locations (e.g., Reno, Elko, Spokane, Riverton, and Grand Junction) release radiosondes from a site located at an elevation above the valley floor, often underestimating the bulk atmospheric stability by more than 30%. A method to incorporate surface station data in the bulk atmospheric stability calculation is given to reduce this underestimation.

Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai–Tibet Plateau

Abstract. Ground surface elevation changes, soil moisture, and snow depth are all essential variables for studying the dynamics of the active layer and permafrost. GPS interferometric reflectometry (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost areas. However, its applicability to estimating soil moisture in permafrost regions has not been assessed. Moreover, these variables were usually measured separately at different sites. Integrating their estimates at one site facilitates the comprehensive utilization of GPS-IR in permafrost studies. In this study, we run simulations to elucidate that the commonly used GPS-IR algorithm for estimating soil moisture content cannot be directly used in permafrost areas, because it does not consider the bias introduced by the seasonal surface elevation changes due to active layer thawing. We propose a solution to improve this default method by introducing modeled surface elevation changes. We validate this modified method using the GPS data and in situ observations at a permafrost site in the northeastern Qinghai–Tibet Plateau (QTP). The root-mean-square error and correlation coefficient between the GPS-IR estimates of soil moisture content and the in situ ones improve from 1.85 % to 1.51 % and 0.71 to 0.82, respectively. We also propose a framework to integrate the GPS-IR estimates of these three variables at one site and illustrate it using the same site in the QTP as an example. This study highlights the improvement to the default algorithm, which makes the GPS-IR valid in estimating soil moisture content in permafrost areas. The three-in-one framework is able to fully utilize the GPS-IR in permafrost areas and can be extended to other sites such as those in the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in the QTP, which fills a spatial gap and provides complementary measurements to ground temperature and active layer thickness.

Local and Remote Atmospheric Circulation Drivers of Arctic Change: A Review

Arctic Amplification is a fundamental feature of past, present, and modelled future climate. However, the causes of this “amplification” within Earth’s climate system are not fully understood. To date, warming in the Arctic has been most pronounced in autumn and winter seasons, with this trend predicted to continue based on model projections of future climate. Nevertheless, the mechanisms by which this will take place are numerous, interconnected. and complex. Will future Arctic Amplification be primarily driven by local, within-Arctic processes, or will external forces play a greater role in contributing to changing climate in this region? Motivated by this uncertainty in future Arctic climate, this review seeks to evaluate several of the key atmospheric circulation processes important to the ongoing discussion of Arctic amplification, focusing primarily on processes in the troposphere. Both local and remote drivers of Arctic amplification are considered, with specific focus given to high-latitude atmospheric blocking, poleward moisture transport, and tropical-high latitude subseasonal teleconnections. Impacts of circulation variability and moisture transport on sea ice, ice sheet surface mass balance, snow cover, and other surface cryospheric variables are reviewed and discussed. The future evolution of Arctic amplification is discussed in terms of projected future trends in atmospheric blocking and moisture transport and their coupling with the cryosphere. As high-latitude atmospheric circulation is strongly influenced by lower-latitude processes, the future state of tropical-to-Arctic teleconnections is also considered.

Microphysical Characteristics and Evolution of Seeded Orographic Clouds

AbstractThe spatial distribution and magnitude of snowfall resulting from cloud seeding with silver iodide (AgI) is closely linked to atmospheric conditions, seeding operations, and dynamical, thermodynamical, and microphysical processes. Here, microphysical processes leading to ice and snow production are analyzed in orographic clouds for three cloud-seeding events, each with light or no natural precipitation and well-defined, traceable seeding lines. Airborne and ground-based radar observations are linked to in situ cloud and precipitation measurements to determine the spatiotemporal evolution of ice initiation, particle growth, and snow fallout in seeded clouds. These processes and surface snow amounts are explored as particle plumes evolve from varying amounts of AgI released, and within changing environmental conditions, including changes in liquid water content (LWC) along and downwind of the seeding track, wind speed, and shear. More AgI did not necessarily produce more liquid equivalent snowfall (LESnow). The greatest amount of LESnow, largest area covered by snowfall, and highest peak snowfall produced through seeding occurred on the day with the largest and most widespread occurrence of supercooled drizzle, highest wind shear, and greater LWC along and downwind of the seeding track. The day with the least supercooled drizzle and the lowest LWC downwind of the seeding track produced the smallest amount of LESnow through seeding. The stronger the wind was, the farther away the snowfall occurred from the seeding track.

Quantifying the Stable Water Isotopologue Exchange Between the Snow Surface and Lower Atmosphere by Direct Flux Measurements

AbstractSurface processes in high latitudes play an important role in global climate and thus understanding the physics of these systems is critical for improving climate projections. The characterization of the stable water isotopologue flux between the surface and the atmosphere offers the potential to constrain parameterizations of these physical surface exchange processes in numerical models. In addition, observations of isotopologue surface fluxes allow the evaluation of surface fluxes as a process influencing the formation of the climate signal retrieved from ice core isotopologue records. Here, we present a record of stable water isotopologue surface fluxes measured in‐situ in the accumulation zone of the Greenland Ice Sheet at the East Greenland Ice Core Project site. We measured isotopologue fluxes above the snow surface directly by combining high‐frequency eddy covariance measurements with low‐frequency isotopologue measurements from a cavity ring‐down spectrometer (CRDS). We developed a method to correct for the high‐frequency loss of the CRDS by combining humidity measurements from both the CRDS and eddy covariance instruments. Using this approach our measurements provide the first direct observations of water isotopologue fluxes in polar areas. We observed a clear diurnal cycle in the fluxes of the different water isotopologues. The isotopic composition of the sublimation and deposition flux showed to be dependent on the snow and vapor isotopic composition, respectively. To a first order, the isotopic composition of the sublimation flux could be derived assuming equilibrium fractionation during sublimation.

Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain

Unmanned Aerial Vehicles (UAVs) offer great flexibility in acquiring images in inaccessible study areas, which are then processed with stereo-matching techniques through Structure-from-Motion (SfM) algorithms. This procedure allows generating high spatial resolution 3D point clouds. The high accuracy of these 3D models allows the production of detailed snow depth distribution maps through the comparison of point clouds from different dates. In this way, UAVs allow monitoring of remote areas that were not achievable previously. The large number of works evaluating this novel technique has not, to date, conducted a systematic evaluation of concurrent snowpack observations with different UAV devices. Taking into account this, and also bearing in mind that potential users of this technique may be interested in exploiting ready-to-use commercial devices, we conducted an evaluation of the snow depth distribution maps with different commercial UAVs. During the 2018–19 snow season, two multi-rotors (Parrot Anafi and DJI Mavic Pro2) and one fixed-wing device (SenseFly eBee plus) were used on three different dates over a small test area (5 ha) within Izas Experimental Catchment in the Central Pyrenees. Simultaneously, snowpack distribution was retrieved with a Terrestrial Laser Scanner (TLS, RIEGL LPM-321) and was considered as ground truth. Three different georeferencing methods (Ground Control Points, ICP algorithm over snow-free areas and RTK-GPS positioning) were tested, showing equivalent performances under optimum illumination conditions. Additionally, for the three acquisition dates, both multi-rotors were flown at two distinct altitudes (50 and 75 m) to evaluate impact on the obtained snow depth maps. The evaluation with the TLS showed an equivalent performance of the two multi-rotors, with mean RMSE below 0.23 m and maximum volume deviations of less than 5%. Flying altitudes did not show significant differences in the obtained maps. These results were obtained under contrasted snow surface characteristics. This study reveals that under good illumination conditions and in relatively small areas, affordable commercial UAVs provide reliable estimations of snow distribution compared to more sophisticated and expensive close-range remote sensing techniques. Results obtained under overcast skies were poor, demonstrating that UAV observations require clear-sky conditions and acquisitions around noon to guarantee a homogenous illumination of the study area.

INVERSING GLACIER ALBEDO IN HIGH MOUNTAIN ASIA BASED ON SENTINEL-3 SATELLITE DATA

Abstract. Surface albedo, defined as the ratio of the upward to downward solar irradiance, is an important climate parameter in the surface energy budget. Among them, glacier albedo is the link between glaciers and the hydration process of climate and cold zones, and it is also an important factor restricting the development of distributed glacier energy-material balance model. Changes in the albedo of ice and snow can alter the energy balance of the entire geo-gas system and can also cause local and even global climate change. Glaciers in High Mountain Asia have experienced heterogeneous rates of loss since the 1970s. The positive feedback effect of ice and snow makes its albedo an important indicator of ice and snow surface mass balance. This study used partly Sentinel-3 level-1b data of High Mountain Asia in 2017, RGI (Randolph Glacier Inventory) 6.0 and SPP (Snow Properties Processor) developed by the ESA’s team to extract the region's mountain glacier albedo in SNAP (Sentinel Application Platform) environment. At present, the products of the whole year of 2017 have been produced, with a time resolution of one month and a spatial resolution of 300 m.

Mapping the aerodynamic roughness of the Greenland Ice Sheet surface using ICESat-2: evaluation over the K-transect

Abstract. The aerodynamic roughness of heat, moisture, and momentum of a natural surface are important parameters in atmospheric models, as they co-determine the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available. In this study we adapt a bulk drag partitioning model to estimate the aerodynamic roughness length (z0m) such that it can be applied to 1D (i.e. unidirectional) elevation profiles, typically measured by laser altimeters. We apply the model to a rough ice surface on the K-transect (west Greenland Ice Sheet) using UAV photogrammetry, and we evaluate the modelled roughness against in situ eddy-covariance observations. We then present a method to estimate the topography at 1 m horizontal resolution using the ICESat-2 satellite laser altimeter, and we demonstrate the high precision of the satellite elevation profiles against UAV photogrammetry. The currently available satellite profiles are used to map the aerodynamic roughness during different time periods along the K-transect, that is compared to an extensive dataset of in situ observations. We find a considerable spatio-temporal variability in z0m, ranging between 10−4 m for a smooth snow surface and 10−1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

An Improved Algorithm Based on Wavelet Decomposition to Retrieve Snow Depth Using GNSS-R Signals

Objectives Snow depth retrieval using global navigation satellite system (GNSS) reflected signals has the characteristics of low cost, low power consumption and all-day data collection. Because the signal-to-noise ratio (SNR) observations of GNSS are greatly affected by the noise power, we aim to retrieve snow depth in low noise and analyze snow depth variations in different snow surface characteristics. Methods An improved algorithm is proposed to retrieve snow depth by introducing wavelet decomposition before spectral analysis, which can effectively separate the noise power from the signal power without changing the frequency composition of the original signal. This algorithm is verified by the SNR data collected at the Arctic Yellow River Station in the Winter and Spring of 2017. Furthermore, considering the complex variation of snow depth at the Yellow River Station, the applicability of this improved algorithm under different snow surface conditions was analyzed. Results Compared with the original algorithm, the maximum bias of the improved algorithm between the estimation and the measurement decreases from 13.71 cm to 9.43 cm, the root mean squared error (RMSE) decreases from 7.08 cm to 5.98 cm, the standard deviation decreases from 8.19 cm to 7.07 cm, and the data utilization rate increases from 82.60% to 89.31%. In the snow surface conditions of ablation, accumulation and stabilization, the RMSE of improved algorithm decreases from 9.02 cm, 10.30 cm, 7.59 cm to 5.82 cm, 5.64 cm, 7.17 cm, and mean absolute error decreases from 6.77 cm, 7.52 cm, 7.00 cm to 5.39 cm, 4.72 cm, 6.73 cm, respectively. Conclusions It can be seen that the proposed algorithm can provide more accurate and reliable snow depth even if in the conditions of ablation and accumulation.