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

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.

Impact of radiation penetration on Antarctic surface melt and subsurface snow temperatures in RACMO2.3p3

This study investigates the sensitivity of modeled surface melt and subsurface heating on the Antarctic ice sheet to a new spectral snow albedo and radiative transfer scheme in the Regional Atmospheric Climate Model (RACMO2), version 2.3p3 (Rp3). We tune Rp3 to observations by performing several sensitivity experiments and assess the impact on temperature and melt by changing one parameter at a time. When fully tuned, Rp3 compares well with in situ and remote sensing observations of surface mass and energy balance, melt, temperature, albedo and snow grain specific surface area. Furthermore, the introduction of subsurface heating in Rp3 significantly improves the snow temperature profile. Near surface snow temperature is especially sensitive to the prescribed fresh snow specific surface area and fresh dry snow metamorphism. These processes, together with the refreezing grain size and subsurface heating, are important for melt around the margins of the Antarctic ice sheet. Moreover, small changes in the albedo and the aforementioned processes can lead to an order of magnitude overestimation of melt, locally leading to runoff and a reduced surface mass balance.

Snow Temperature Behind Sliding Skis as an Indicator for Frictional Meltwater

For many years, a frictional meltwater film has been assumed to be the reason for the low friction between skis and snow, but experimental studies have been inconclusive. Therefore, the aim of our study was to find indications or evidence for the presence of frictional meltwater. The friction between snow at −4°C and an XC ski as well as a flat ski was measured on a large-scale linear snow tribometer at realistic skiing speeds from 5 to 25 m/s. We used an infrared camera to analyze the snow temperature behind the skis. From the maximum snow surface temperature, we estimated the temperature at the spots where ski and snow contacted. Assuming that the contact spot temperature does not notably exceed 0°C, we calculated the relative contact area between ski and snow. Maximum snow surface temperatures were very close to 0°C. Given that not the entire snow surface is in contact with the ski, this finding is a strong indication for snow melting. Heat flow considerations led to the conclusion that there must be energy dissipation beyond the heat flow into ski and snow. The most obvious mechanism for the additional energy dissipation is snow melting. Presuming that the contact spot temperatures are at most slightly above 0°C, we calculated relative contact areas of 21–98%. Previous research has reported much lower values; however, most studies were conducted under conditions that are not realistic for skiing.

A Distributed Temperature Profiling System for Vertically and Laterally Dense Acquisition of Soil and Snow Temperature

Measuring soil and snow temperature with high vertical and lateral resolution is critical for advancing the predictive understanding of thermal and hydro-biogeochemical processes that govern the behavior of environmental systems. Vertically resolved soil temperature measurements enable the estimation of soil thermal regimes, freeze/thaw layer thickness, thermal parameters, and heat and/or water fluxes. Similarly, they can be used to capture the snow thickness and the snowpack thermal parameters and fluxes. However, these measurements are challenging to acquire using conventional approaches due to their total cost, their limited vertical resolution, and their large installation footprint. This study presents the development and validation of a novel Distributed Temperature Profiling (DTP) system that addresses these challenges. The system leverages digital temperature sensors to provide unprecedented, finely resolved depth-profiles of temperature measurements with flexibility in system geometry and vertical resolution. The integrated miniaturized logger enables automated data acquisition, management, and wireless transfer. A novel calibration approach adapted to the DTP system confirms the factory-assured sensor accuracy of +/−0.1 °C and enables improving it to +/−0.015 °C. Numerical experiments indicate that, under normal environmental conditions, an additional error of 0.01 % in amplitude and 70 seconds time delay in amplitude for a diurnal period can be expected, owing to the DTP housing. We demonstrate the DTP systems capability at two field sites, one focused on understanding how snow dynamics influence mountainous water resources, and the other focused on understanding how soil properties influence carbon cycling. Results indicate that the DTP system reliably captures the dynamics in snow thickness, and soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermal-hydrological regimes. Overall, the DTP system fulfills the needs for data accuracy, minimal power consumption, and low total cost, enabling advances in the multiscale understanding of various cryospheric and hydro-biogeochemical processes.

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

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). For any snowpack, fulfilling the cold content deficit is a pre-requisite before the onset of the snowmelt. Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Estimating the 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 2017–18. 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 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 days, 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 estimates of the snow water equivalent (SWE) (R2 = 0.64 and percent bias (Pbias) = −17.1 %), bulk snow density (R2 = 0.71 and Pbias = 1.6 %), and bulk cold content (R2 = 0.90 and Pbias = −2.0 %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of 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.

Avalanche flow regime transitions in a changing climate

<p>In the near future, climate change will impact the snow cover in Alpine regions. Higher precipitations and warmer temperatures are expected at lower altitude, leading to larger gradients of snow temperature, snow water content and snow depth between the top and the bottom of slopes. As a consequence, climate change will also indirectly influence the behavior of snow avalanches.</p><p>The present work aims to investigate how changes in snow cover properties will affect snow avalanches dynamics. Experimental studies allowed to characterize different avalanche flow regimes which result from particular combinations of snow physical and mechanical properties. In particular, expected variations of snow temperatures with elevation will cause more frequent and more extreme flow regime transitions inside the same avalanche. For example, a fast avalanche characterized by cold and low-cohesive snow in the upper part of the avalanche track will transform more frequently into a slow flow made of wet and heavy snow when the avalanche will entrain warm snow along the slope. A better understanding of these flow regime transitions, which have already been largely reported, is crucial, because it affects both daily danger assessment (e.g. forecasting services, road controls) and hazard mapping of avalanches.</p><p>To date, most avalanche modeling methods are not considering temperature effects and are then unable to simulate flow regime transitions and unprecedented scenarios. Our goal is then to develop a model capable of simulating reported flow regimes, flow transitions and the interactions between the snow cover and the flow (erosion, entrainment). Since these elements are not yet fully understood, we firstly model these mesoscopic processes using a 2D Discrete Element Model (DEM) in which varying particle cohesion and friction mimic the effect of changes in snow temperature. Flow regimes are simulated by granular assemblies put into motion by gravity on an inclined slope, which interact with a granular and erodible bed surface. Simulations are calibrated using experimental data coming from the avalanche test site located in Vallée de la Sionne, which record avalanches since more than 20 years. This modeling will then be used as an input to improve slope-scale models and make them more appropriate for avalanche risk management in the context of climate change.</p>

Subseasonal Forecast Skill of Snow Water Equivalent and Its Link with Temperature in Selected SubX Models

Accurate and skillful subseasonal forecasts have tremendous potential for sectors that are sensitive to hazardous weather and climate events. Analysis of prediction skill for snow water equivalent (SWE) and near-surface air temperature (T2m) is carried out for three (GEPS, GEFS, and FIM) global models from the subseasonal experiment (SubX) project for the 2000–14 period. The prediction skill of SWE is higher than the skill of T2m at week-3 and week-4 lead times in all models. The GEPS forecast tends to yield higher (lower) prediction skill of SWE (T2m) compared to the other two systems in terms of correlation skill score. The snow–temperature relationship in reanalysis is characterized by a strong negative correlation over most of the midlatitude regions and a weak positive correlation over high-latitude Arctic regions. All forecast systems reproduced well these observed features; however, the snow–temperature relationship is slightly weaker in the GEPS model. Despite the apparent lack of skill in temperature forecasts at week 4, all three models are able to predict the sign of temperature anomalies associated with extreme SWE conditions albeit with reduced intensity. The strength of the predicted temperature anomaly associated with extreme snow conditions is slightly weaker in the GEPS forecast compared to reanalysis and the other two models, despite having better skill in predicting SWE. These apparent disparities suggest that weak snow–temperature coupling strength in the model is one of the contributing factors for the lower temperature skill.

Seasonal variations of the backscattering coefficient measured by radar altimeters over the Antarctic Ice Sheet

Spaceborne radar altimeters are a valuable tool for observing the Antarctic Ice Sheet. The radar wave interaction with the snow provides information on both the surface and the subsurface of the snowpack due to its dependence on the snow properties. However, the penetration of the radar wave within the snowpack also induces a negative bias on the estimated surface elevation. Empirical corrections of this space- and time-varying bias are usually based on the backscattering coefficient variability. We investigate the spatial and seasonal variations of the backscattering coefficient at the S (3.2 GHz ∼ 9.4 cm), Ku (13.6 GHz ∼ 2.3 cm) and Ka (37 GHz ∼ 0.8 cm) bands. We identified that the backscattering coefficient at Ku band reaches a maximum in winter in part of the continent (Region 1) and in the summer in the remaining (Region 2), while the evolution at other frequencies is relatively uniform over the whole continent. To explain this contrasting behavior between frequencies and between regions, we studied the sensitivity of the backscattering coefficient at three frequencies to several parameters (surface snow density, snow temperature and snow grain size) using an electromagnetic model. The results show that the seasonal cycle of the backscattering coefficient at Ka frequency is dominated by the volume echo and is mainly driven by snow temperature evolution everywhere. In contrast, at S band, the cycle is dominated by the surface echo. At Ku band, the seasonal cycle is dominated by the volume echo in Region 1 and by the surface echo in Region 2. This investigation provides new information on the seasonal dynamics of the Antarctic Ice Sheet surface and provides new clues to build more accurate corrections of the radar altimeter surface elevation signal in the future.