Snow Density Profiles Research Articles

Meteorological, snow and soil data, CO2, water and energy fluxes from a low-Arctic valley of Northern Quebec

Abstract. As the vegetation in the Arctic changes, tundra ecosystems along the southern border of the Arctic are becoming greener and gradually giving way to boreal ecosystems. This change is affecting local populations, wildlife, energy exchange processes between environmental compartments, and the carbon cycle. To understand the progression and the implications of this change in vegetation, satellite measurements and surface models can be employed. However, in situ observational data are required to validate these measurements and models. This paper presents observational data from two nearby sites in the forest–tundra ecotone in the Tasiapik Valley near Umiujaq in Northern Quebec, Canada. One site is on a mixture of lichen and shrub tundra. The associated data set comprises 9 years of meteorological, soil and snow data as well as 3 years of eddy covariance data. The other site, 850 m away, features vegetation consisting mostly of tall shrubs and black spruce. For that location, 6 years of meteorological, soil and snow data are available. In addition to the data from the automated stations, profiles of snow density and specific surface area were collected during field campaigns. The data are available at https://doi.org/10.1594/PANGAEA.964743 (Domine et al., 2024).

Snow and meteorological conditions at Villum Research Station, Northeast Greenland: on the adequacy of using atmospheric reanalysis for detailed snow simulations

Reliable and detailed measurements of atmospheric and snow conditions in the Arctic are limited. While modern atmospheric reanalyses could potentially replace the former, the latter can be principally simulated by dedicated snow modelling. However, because the uncertainties of reanalyses and modelling are still exceptionally large at high latitudes, a thorough analysis of the performance of atmospheric reanalyses and the snow model simulations are required. Specifically, we aim to answer the following questions for Villum Research Station (VRS), northeast Greenland: (1) What are the predominant snow and meteorological conditions? (2) What are systematic differences between the modern atmospheric reanalysis ERA5 and in situ measurements? (3) Can the snow model Crocus simulate reliably snow depth and stratigraphy? We systematically compare atmospheric in situ measurements and ERA5 reanalysis (November 2015–August 2018) and evaluate simulated and measured snow depth (October 2014–September 2018). Moreover, modelled and measured vertical profiles of snow density and snow specific surface area (SSA) are analysed for two days where a survey had taken place. We found good agreement between in situ and ERA5 atmospheric variables with correlation coefficients >0.84 except for precipitation, wind speed, and wind direction. ERA5’s resolution is too coarse to resolve the topography in the study area adequately, leading presumably to the detected biases. Crocus can simulate satisfactorily the evolution of snow depth, but simulations of SSA and density profiles, whether driven by ERA5 or in situ measurements are biased compared to measurements. Unexpectedly, measured snow depth agrees better with ERA5 driven simulation than with simulation forced with in situ measurements (explained variance: 0.73 versus 0.23). This is due to differences in snowfall, humidity and air temperature between both forcing datasets. In conclusion, ERA5 has great potential to force snow models but the use of Crocus in the Arctic is affected by limitations such as inappropriate parametrisations for Arctic snowpack evolution, but also by lack of process formulations such as vertical water vapour transport. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.

Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada

Abstract. Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0 ∘C). Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Measuring cold content is a labour-intensive task as it requires extracting in situ snow temperature and density. Hence, few studies have focused on characterizing this snowpack variable. This study describes the multilayer cold content of a snowpack and its variability across four sites with contrasting canopy structures within a coniferous boreal forest in southern Québec, Canada, throughout winter 2017–2018. The analysis was divided into two steps. In the first step, the observed CC data from weekly snowpits for 60 % of the snow cover period were examined. During the second step, a reconstructed time series of modelled CC was produced and analyzed to highlight the high-resolution temporal variability of CC for the full snow cover period. To accomplish this, the Canadian Land Surface Scheme (CLASS; featuring a single-layer snow model) was first implemented to obtain simulations of the average snow density at each of the four sites. Next, an empirical procedure was used to produce realistic density profiles, which, when combined with in situ continuous snow temperature measurements from an automatic profiling station, provides a time series of CC estimates at half-hour intervals for the entire winter. At the four sites, snow persisted on the ground for 218 d, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (−2.62 MJ m−2) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was −0.47 MJ m−2, representing a site-to-site variability of 20 %. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable bulk estimates of snow water equivalent (SWE) (R2=0.64 and percent bias (Pbias) =-17.1 %), snow density (R2=0.71 and Pbias =1.6 %), and cold content (R2=0.93 and Pbias =-3.3 %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of layered cold content (R2=0.42 and Pbias =5.17 %). Thanks to these encouraging results, the reconstructed and continuous CC series could be analyzed at the four sites, revealing the impact of rain-on-snow and cold air pooling episodes on the variation of CC. The continuous multilayer cold content time series also provided us with information about the effect of stand structure, local topography, and meteorological conditions on cold content variability. Additionally, a weak relationship between canopy structure and CC was identified.

Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models

Abstract. Seasonal snow covers Arctic lands 6 to 10 months of the year and is therefore an essential element of the Arctic geosphere and biosphere. Yet, even the most sophisticated snow physics models are not able to simulate fundamental physical properties of Arctic snowpacks such as density, thermal conductivity and specific surface area. The development of improved snow models is in progress, but testing requires detailed driving and validation data for high Arctic herb tundra sites, which are presently not available. We present 6 years of such data for an ice-wedge polygonal site in the Canadian high Arctic, in Qarlikturvik valley on Bylot Island at 73.15∘ N. The site is on herb tundra with no erect vegetation and thick permafrost. Detailed soil properties are provided. Driving data are comprised of air temperature, air relative and specific humidity, wind speed, shortwave and longwave downwelling radiation, atmospheric pressure, and precipitation. Validation data include time series of snow depth, shortwave and longwave upwelling radiation, surface temperature, snow temperature profiles, soil temperature and water content profiles at five depths, snow thermal conductivity at three heights, and soil thermal conductivity at 10 cm depth. Field campaigns in mid-May for 5 of the 6 years of interest provided spatially averaged snow depths and vertical profiles of snow density and specific surface area in the polygon of interest and at other spots in the valley. Data are available at https://doi.org/10.5885/45693CE-02685A5200DD4C38 (Domine et al., 2021). Data files will be updated as more years of data become available.

Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures

The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes—including the effect of Arctic low vegetation and wind on snowpack—in the Crocus detailed snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of −0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979–2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5°, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of −0.33 cm dec−1. With the ERAi air temperature trend of +0.84 K dec−1 featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec−1 in winter. We show that the implemented snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec−1) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology.

Sensing Area‐Average Snow Water Equivalent with Cosmic‐Ray Neutrons: The Influence of Fractional Snow Cover

AbstractCosmic‐ray neutron sensing (CRNS) is a promising non‐invasive technique to estimate snow water equivalent (SWE) over large areas. In contrast to preliminary studies focusing on shallow snow conditions (SWE 130 mm), more recently the method was shown experimentally to be sensitive also to deeper snowpacks providing the basis for its use at mountain experimental sites. However, hysteretic neutron response has been observed for complex snow cover including patchy snow‐free areas. In the present study we aimed to understand and support the experimental findings using a comprehensive neutron modeling approach. Several simulations have been set up in order to disentangle the effect on the signal of different land surface characteristics and to reproduce multiple observations during periods of snow melt and accumulation. To represent the actual land surface heterogeneity and the complex snow cover, the model used data from terrestrial laser scanning. The results show that the model was able to accurately reproduce the CRNS signal and particularly the hysteresis effect during accumulation and melting periods. Moreover, the sensor footprint was found to be anisotropic and affected by the spatial distribution of liquid water and snow as well as by the topography of the nearby mountains. Under fully snow‐covered conditions the CRNS is able to accurately estimate SWE without prior knowledge about snow density profiles or other spatial anomalies. These results provide new insights into the characteristics of the detected neutron signal in complex terrain and support the use of CRNS for long‐term snow monitoring in high elevated mountain environments.

Revisiting Winter Arctic Ice Mass Balance Observations With a 1‐D Model: Sensitivity Studies, Snow Density Estimation, Flooding, and Snow Ice Formation

AbstractWe used a state‐of‐the art one‐dimensional snow and ice model (the LIM1D model), to simulate data collected in winter 2015 north of Svalbard with ice mass balance instruments. The quality of the simulations was assessed by comparing simulated temperature profiles and sea ice thicknesses with the data: The root‐mean‐square difference between observed and modeled temperature was 1.06 °C in snow and 0.19 °C in ice, and the root‐mean‐square difference between simulated and observed ice thickness was 2.0 cm (snow depth was prescribed). The long‐wave heat flux from the ERA‐I reanalysis was adequate to perform winter numerical simulations; in contrast, the ERA‐I air temperature induced large errors in the snow and ice temperature. Snow density had a direct impact on heat transfers and, thus, on the simulation. The joint use of the data and the simulations permitted the adjustment of the snow density profiles with a light (240 kg/m3) snow deposited on top of a denser (370 kg/m3) snow. The ice flooding, which occurred after a storm‐induced breakup of floes loaded with snow, was simulated by prescribing the observed lower limit of the snow. The simulations provided insights on the evolution of sea ice bulk salinity, brine fraction, and the amount of snow ice formed during the flooding event.

Evaluating the performance of coupled snow–soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

Abstract. Climate change projections still suffer from a limited representation of the permafrost–carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change.

Snow Density and Ground Permittivity Retrieved From L-Band Radiometry: A Retrieval Sensitivity Analysis

Aboveground dry snow influences the L-band ground–snow system emissivity as the result of impedance matching and refraction effects. Against this background, a retrieval scheme was proposed to estimate dry-snow density and ground permittivity from passive L-band measurements. In this study, the sensitivity of the recently proposed retrieval scheme with respect to surface roughness and snowpack density profile heterogeneities is investigated using synthetic brightness temperatures $T_{\rm B}^{p}$ . While the original retrieval algorithm proposed to use $T_{\rm B}^{p}(\theta)$ at observation angles θ and both polarizations p = “H” and p = “V”, the present analysis involves three polarization retrieval modes: “HV”, “H”, and “V” to identify the most robust one. The analyses based on synthetic $T_{\rm B}^{p}(\theta)$ suggest the use of exclusively vertical polarization (“V” retrieval mode) in case of low “instrumental noise” of $\sigma T_{\rm B}\,{ , as it yields most accurate results in the presence of heterogeneities in profiles and imperfect parametrization of ground surface roughness. The applied retrieval scheme still yields accurate results even in the presence of “instrumental noise” ( $\sigma T_{\rm B}\,\geq \,\text{0.5}$ K) in retrieval mode “HV”. Furthermore, it is shown that ground-permittivity retrievals are less affected than snow density retrievals by ground roughness variations or heterogeneities in snow density profiles. Altogether, our sensitivity investigations indicate the robustness of the retrieval scheme applied thereto with respect to snow density profile heterogeneities, which is expedient for its use with spaceborne radiometry data from, for example, “Soil Moisture and Ocean Salinity” or “Soil Moisture Active Passive” satellites.

The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime

Abstract. With climate warming, shrubs have been observed to grow on Arctic tundra. Their presence is known to increase snow height and is expected to increase the thermal insulating effect of the snowpack. An important consequence would be the warming of the ground, which will accelerate permafrost thaw, providing an important positive feedback to warming. At Bylot Island (73° N, 80° W) in the Canadian high Arctic where bushes of willows (Salix richardsonii Hook) are growing, we have observed the snow stratigraphy and measured the vertical profiles of snow density, thermal conductivity and specific surface area (SSA) in over 20 sites of high Arctic tundra and in willow bushes 20 to 40 cm high. We find that shrubs increase snow height, but only up to their own height. In shrubs, snow density, thermal conductivity and SSA are all significantly lower than on herb tundra. In shrubs, depth hoar which has a low thermal conductivity was observed to grow up to shrub height, while on herb tundra, depth hoar only developed to 5 to 10 cm high. The thermal resistance of the snowpack was in general higher in shrubs than on herb tundra. More signs of melting were observed in shrubs, presumably because stems absorb radiation and provide hotspots that initiate melting. When melting was extensive, thermal conductivity was increased and thermal resistance was reduced, counteracting the observed effect of shrubs in the absence of melting. Simulations of the effect of shrubs on snow properties and on the ground thermal regime were made with the Crocus snow physics model and the ISBA (Interactions between Soil–Biosphere–Atmosphere) land surface scheme, driven by in situ and reanalysis meteorological data. These simulations did not take into account the summer impact of shrubs. They predict that the ground at 5 cm depth at Bylot Island during the 2014–2015 winter would be up to 13 °C warmer in the presence of shrubs. Such warming may however be mitigated by summer effects.

Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

The annual accumulation and the physical properties of snow close to the surface on the Antarctic Plateau are characterized by a large decameter-scale variability resulting from snow drift that is not simulated by one-dimensional snow evolution models. Here, the detailed snowpack model Crocus was adapted to Antarctic conditions and then modified to account for this drift-induced variability using a stochastic snow redistribution scheme. For this, 50 simulations were run in parallel and were allowed to exchange snow mass according to rules driven by wind speed. These simple rules were developed and calibrated based on in situ pictures of the snow surface recorded for two years. The simulation performed with this new model shows three substantial improvements with respect to standard Crocus simulations. First, significant and rapid variations of snow height observed in hourly measurements are well reproduced, highlighting the crucial role of snow drift in snow accumulation. Second, the statistics of annual accumulation is also simulated successfully, including the years with net ablation which are as frequent as 15% in the observations and 11% in the simulation. Last, the simulated vertical profiles of snow density and specific surface area down to 50 cm depth were compared to 98 profiles measured at Dome C during the summer 2012–2013. The observed spatial variability is partly reproduced by the new model, especially close to the surface. The erosion/deposition processes explain why layers with density lower than 250 kg m−3 or specific surface area larger than 30 m2 kg−1 can be found deeper than 10 cm.

Observing System Simulation of Snow Microwave Emissions Over Data Sparse Regions— Part I: Single Layer Physics

A multilayer formulation of snow hydrological processes implemented in an existing snow hydrology-emission model (MLSHM-ML) was applied in observing system simulation mode (OSS) to two very different climatic and physiographic regions (Valdai, Russia and Colorado, USA) for both wet and dry snow regimes, and over multiple years. The results were evaluated against ground-based observations of snowpack physical properties and microwave radiometric observations from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) observations at 18-19-, 22-23-, and 36-37-GHz vertical and horizontal polarizations (V-pol and H-pol, respectively). Whereas snow water equivalent (SWE) results are similar to the results obtained with single-layer physics when the snow is dry, the multilayer physics have a better skill at capturing the overall temporal evolution of bulk density, snow temperature, and snow depth during the accumulation season, and at the onset and throughout the melting season. However, snow density profiles overestimate density at the bottom of the snowpack, consistent with the lack of an explicit representation of depth hoar in the rearrangement of mass and grain size distribution in the snowpack. Regarding the radiometric behavior, the multilayer SMMR OSS for Valdai shows improved results for nighttime simulations (descending SMMR paths, 11 P.M. LST) and H-pol (~ 3-5 K decrease in error statistics), particularly at 37 GHz. For daytime simulations (ascending SMMR paths, 11 A.M. LST), there are modest improvements at 18 (~ 1 K) and 37 GHz (~ 2-3 K) for H-pol, and generally loss of skill for V-pol at all frequencies. Systematic improvements at nighttime but not during daytime suggest that surface heterogeneities, including subgrid scale variability of transient melting, play an important role on surface emissivity. This is the case for cold land process experiment in Colorado, where spatial variability in fractional forest cover, geology, and complex topography explains the modest differences between the single and multilayer SSM/I OSS for H-pol, whereas significant gains (~ 4-8 K decrease in error statistics) were attained for V-pol at 37 GHz only. For AMSR-E, the multilayer OSS looses skill for H-pol, and only bias and mean absolute error improve for V-pol at all frequencies. These somewhat mixed results suggest that representation of snow stratigraphy alone is not sufficient to improve the OSS ability to describe the nonlinear interactions among hydrologic and electromagnetic processes. Chief among these are the temporal evolution of snow correlation length with depth and the representation of subgrid scale variability constrained by the spatial resolution and inherent uncertainty of the meteorological forcing. Nevertheless, the multilayer OSS improved performance at 37 GHz is an important finding toward reducing ambiguity in the sensitivity of 37-GHz H-pol brightness temperature to SWE in retrieval models.

Observing System Simulation of Snow Microwave Emissions Over Data Sparse Regions—Part II: Multilayer Physics

A multilayer formulation of snow hydrological processes implemented in an existing snow hydrology-emission model (MLSHM-ML) was applied in observing system simulation mode (OSS) to two very different climatic and physiographic regions (Valdai, Russia and Colorado, USA) for both wet and dry snow regimes, and over multiple years. The results were evaluated against ground-based observations of snowpack physical properties and microwave radiometric observations from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) observations at 18-19-, 22-23-, and 36-37-GHz vertical and horizontal polarizations (V-pol and H-pol, respectively). Whereas snow water equivalent (SWE) results are similar to the results obtained with single-layer physics when the snow is dry, the multilayer physics have a better skill at capturing the overall temporal evolution of bulk density, snow temperature, and snow depth during the accumulation season, and at the onset and throughout the melting season. However, snow density profiles overestimate density at the bottom of the snowpack, consistent with the lack of an explicit representation of depth hoar in the rearrangement of mass and grain size distribution in the snowpack. Regarding the radiometric behavior, the multilayer SMMR OSS for Valdai shows improved results for nighttime simulations (descending SMMR paths, 11 P.M. LST) and H-pol (~ 3-5 K decrease in error statistics), particularly at 37 GHz. For daytime simulations (ascending SMMR paths, 11 A.M. LST), there are modest improvements at 18 (~ 1 K) and 37 GHz (~ 2-3 K) for H-pol, and generally loss of skill for V-pol at all frequencies. Systematic improvements at nighttime but not during daytime suggest that surface heterogeneities, including subgrid scale variability of transient melting, play an important role on surface emissivity. This is the case for cold land process experiment in Colorado, where spatial variability in fractional forest cover, geology, and complex topography explains the modest differences between the single and multilayer SSM/I OSS for H-pol, whereas significant gains (~ 4-8 K decrease in error statistics) were attained for V-pol at 37 GHz only. For AMSR-E, the multilayer OSS looses skill for H-pol, and only bias and mean absolute error improve for V-pol at all frequencies. These somewhat mixed results suggest that representation of snow stratigraphy alone is not sufficient to improve the OSS ability to describe the nonlinear interactions among hydrologic and electromagnetic processes. Chief among these are the temporal evolution of snow correlation length with depth and the representation of subgrid scale variability constrained by the spatial resolution and inherent uncertainty of the meteorological forcing. Nevertheless, the multilayer OSS improved performance at 37 GHz is an important finding toward reducing ambiguity in the sensitivity of 37-GHz H-pol brightness temperature to SWE in retrieval models.

Measurements and modeling of the vertical profile of specific surface area of an alpine snowpack

The specific surface area of snow (SSA) is a useful variable to describe the physical and chemical properties of snow, including a quantitative link to snow metamorphism and the optical properties of snow. Here we present a series of 16 weekly profiles of snow physical properties including SSA measured using the DUFISSS instrument spanning the period from January to April 2010 at the Col de Porte field site in the French Alps near Grenoble. Measured SSA values for dry snow ranged between ca. 5 and 80m2kg−1, and generally decreased over time in a given snow layer. Wet snow conditions encountered towards the end of the snow season show SSA values between 3 and 10m2kg−1. This unique dataset is compared with simulations carried out using the Crocus snowpack model, using two parameterizations of snow SSA: one simply derived from the internal computation of the optical radius in Crocus, and the other one determined from density and snow type. Both parameterizations perform rather satisfactorily qualitatively and quantitatively, compared to the performance in terms of snow density profile. Ample room for improvement exists, in particular through the implementation of SSA as a fully fledged prognostic variable in Crocus, which is currently in progress.

Properties of snow overlying the sea ice off East Antarctica in late winter, 2007

The properties of snow on East Antarctic sea ice off Wilkes Land were examined during the Sea Ice Physics and Ecosystem Experiment (SIPEX) in late winter of 2007, focusing on the interaction with sea ice. This observation includes 11 transect lines for the measurement of ice thickness, freeboard, and snow depth, 50 snow pits on 13 ice floes, and diurnal variation of surface heat flux on three ice floes. The detailed profiling of topography along the transects and the d 18O, salinity, and density datasets of snow made it possible to examine the snow-sea-ice interaction quantitatively for the first time in this area. In general, the snow displayed significant heterogeneity in types, thickness (mean: 0.14±0.13 m), and density (325±38 kg m −3), as reported in other East Antarctic regions. High salinity was confined to the lowest 0.1 m. Salinity and d 18O data within this layer revealed that saline water originated from the surface brine of sea ice in 20% of the total sites and from seawater in 80%. From the vertical profiles of snow density, bulk thermal conductivity of snow was estimated as 0.15 W K −1 m −1 on average, only half of the value used for numerical sea-ice models. Although the upward heat flux within snow estimated with this value was significantly lower than that within ice, it turned out that a higher value of thermal conductivity (0.3 to 0.4 W K −1 m −1) is preferable for estimating ice growth amount in current numerical models. Diurnal measurements showed that upward conductive heat flux within the snow and net long-wave radiation at the surface seem to play important roles in the formation of snow ice from slush. The detailed surface topography allowed us to compare the air-ice drag coefficients of ice and snow surfaces under neutral conditions, and to examine the possibility of the retrieval of ice thickness distribution from satellite remote sensing. It was found that overall snow cover works to enhance the surface roughness of sea ice rather than moderate it, and increases the drag coefficient by about 10%. As for thickness retrieval, mean ice thickness had a higher correlation with ice surface roughness than mean freeboard or surface elevation, which indicates the potential usefulness of satellite L-band SAR in estimating the ice thickness distribution in the seasonal sea-ice zone.

SnowSTAR2002 Transect Reconstruction Using a Multilayered Energy and Mass Balance Snow Model

Abstract The lateral and vertical variability of snow stratigraphy was investigated through the comparison of the measured profiles of snow density, temperature, and grain size obtained during the Snow Science Traverse—Alaska Region (SnowSTAR2002) 1200-km transect from Nome to Barrow with model reconstructions from the Snow Thermal Model (SNTHERM), a multilayered energy and mass balance snow model. Model profiles were simulated at the SnowSTAR2002 observation sites using the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) as meteorological forcing. ERA-40 precipitation was rescaled so that the total snow water equivalent (SWE) on the SnowSTAR2002 observation dates equaled the observed values. The mean absolute error (MAE) of measured and simulated snow properties shows that SNTHERM was able to produce good simulations for snowpack temperature but larger errors for grain size and density. A spatial similarity analysis using semivariograms of measured profiles shows that there is diverse lateral and vertical variability for snow properties along the SnowSTAR2002 transect resulting from differences in initial snow deposition, influenced by wind, vegetation, topography, and postdepositional mechanical and thermal metamorphism. The correlation length in snow density (42 km) is quite low, whereas it is slightly longer for snow grain size (125 km) and longer still for snow temperature (130 km). An important practical question that the observed and reconstructed profiles allow to be addressed is the implications of model errors in the observed snow properties for simulated microwave emissions signatures. The Microwave Emission Model for Layered Snowpacks (MEMLS) was used to simulate 19- and 37-GHz brightness temperatures. Comparison of SNTHERM–MEMLS and SnowSTAR2002–MEMLS brightness temperatures showed a very good match occurs at 19 GHz [a root-mean-square error (RMSE) of 1.5 K (8.7 K) for vertical (horizontal) polarization] and somewhat larger [5.9 K (6.2 K) for vertical (horizontal) polarization] at 37 GHz. These results imply that the simulation of snow microphysical profiles is a viable strategy for passive microwave satellite–based retrievals of SWE.

Seasonal Snow Monitoring in Northeast China Using Space-borne Sensors: Preliminary Results

Snow and meteorological measurements collected at six sites in Northeast China were used to compare snow cover area (SCA) retrieved from the Moderate Resolution Imaging SpectroRadiometer (MODIS) and QuikSCAT and snow water equivalent (SWE) retrieved from the Advanced Microwave Scanning Radiometer (AMSR). The SCA retrieved from QuikSCAT and MODIS are in qualitative agreement with each other and with in situ measurements. Based on in situ data from stations Qingyu and Dehui, SWE retrieved from AMSR show a bias of 43% and a R2 value of 0.91 with in situ data for the early snow season. These results are consistent with previous estimates and point to the need to properly account for other snow properties such as snow density profile and grain size in the retrieval of regional snow parameters in Northeast China.

Measurements of blowing snow, part II: Mass and number density profiles and saltation height at Franklin Bay, NWT, Canada

Abstract Blowing snow is a frequent and significant winter weather event, and there is currently a need for more observations and measurements of blowing snow, especially in arctic and subarctic environments. This paper is the second part in a two part series studying blowing snow in Churchill, Manitoba, and Franklin Bay, NWT. In this part, the development and use of a camera system to measure the relative blowing snow density profile near the snow surface is described. This system has been used, along with standard meteorological instruments and optical particle counters, during a field campaign at Franklin Bay, NWT. A best-fit to the mass density profile in the saltation layer is derived, assuming a half-normal distribution of the vertical ejection velocity of saltating particles. Within the saltation layer, the observed vertical profile of mass density is found to be proportional to the function exp(− 0.61z/h), where h is the average height of the saltating particles. For the range of conditions studied, h varies from 1.0 to 10.4 mm, while the extent of the saltation layer varies from 17 to over 85 mm. There is a weak correlation between h and the square of friction velocity. There are weak negative correlations between h and temperature and relative humidity. No correlation is seen between h and the snow age. At greater heights, z > 0.2 m, the blowing snow density varies according to a power law (ρs∝z− γ), with a negative exponent 0.5

ASIRAS airborne radar resolves internal annual layers in the dry‐snow zone of Greenland

We present data from the European Space Agency's Airborne SAR/Interferometric Radar Altimeter System (ASIRAS), flown during the CryoVex 2004 field calibration/validation campaign, and new, high‐resolution depth profiles of snow density measured in the field by neutron scattering. We combine these data to calculate the depth of internal reflecting horizons in the ASIRAS data. The high resolution density data allow us to identify annual layers in the snow density profile, and correlate their peaks with the reflecting horizons. We use the thickness of the annual layers combined with the density profile to determine the spatial and temporal pattern of snow accumulation along the radar track, for a period of 6 years from 1995–2002. Our mean‐annual accumulation rate is 0.47 ± 0.09 ma−1 water equivalent, in agreement with the value determined from a core taken in this location in 1992. Similarly, our inter‐annual variability shows the same trends as recent model estimates over the entire ice sheet. Because ASIRAS was designed to mimic as closely as possible the characteristics of the SAR/Interferometric Radar Altimeter (SIRAL), the principal payload of CryoSat, the detection of internal layering with ASIRAS illuminates the possibility of detecting internal layers with a space‐borne radar, and thus the possible application of this technique to the dry‐snow zones of Antarctica, Greenland, and smaller ice bodies.

Application of the numerical snowpack model (SNOWPACK) to the wet-snow region in Japan

AbstractThe snow-cover model SNOWPACK was applied to the wet-snow areas of Japan. Simulated variations of snow type, snow depth and weight, profiles of snow density, temperature and liquid-water content were compared with snow-pit measurements. The snow-depth simulation during early winter agreed with the measurements, but the differences between the simulation and the measurements increased during the course of the melt season. These differences were caused by underestimation of the energy balance at the snow surface, mainly that regarding sensible-heat flux during the melt season. The underestimation was caused by the implicit numerical treatment of the heat-transport equation. Consistent with the underestimation of snowmelt, simulated metamorphosis of compacted particles into melt forms was slower than the change shown by the measurements, and faceted snow particles, which constitute a snow type not actually found in the study area, sometimes appeared in the model. The inaccurate melt treatment also influenced simulated densities, which were larger than the measurements at small densities, while they were smaller than the measurements at large densities. Greater accuracy was achieved when an empirical compressive viscosity formulation for wet snows in Japan was introduced. A new version of SNOWPACK, with an accurate treatment of melt processes, is available.