Snow Surface Temperature Research Articles

Diurnal Scale Controls on C-Band Microwave Backscatter From Snow-Covered First-Year Sea Ice During the Transition From Late Winter to Early Melt

Diurnal observations of coincident in situ physical-, electrical-, and surface-based C-band microwave scattering properties of a 16-cm saline snow cover on smooth, moderately saline, first-year sea ice are presented for the transition period between late winter and early melt. Statistical regression analysis and backscatter modeling are employed to explore the scattering mechanisms within the snowpack and to assess associations between backscatter and snow properties for both periods. Our results demonstrate substantial variation in both measured snow properties and C-band microwave backscatter over the diurnal cycle during the late winter period when the difference between maximum and minimum air and snow surface temperature was approximately 5 °C. No such variation in snow properties and backscatter occurred for the early melt period when our case study exhibited a small diurnal variation (~1°C) in air and snow surface temperature. Statistical and modeled results show significant association between the microwave backscatter and snow properties for the top 8 cm of the snowpack. Basal snow properties do not contribute toward total backscatter in either case. As a result, we are certain that the sea ice surface was a negligible scattering interface during both cases. Correlations between backscatter and snow properties are incidence angle dependent, demonstrating the highest association at 50°. Diurnal backscatter from RADARSAT-2 synthetic aperture radar acquisitions support the influence of varying diurnal snow properties on C-band backscatter, showing a difference of ~4 dB for ${ \sigma }_{\vphantom {R_{j}}{\text {hh}}}^{o}$ and ${ \sigma }_{{\text {vv}}}^{o}$ during the late winter period. This difference is reduced to <1 dB for the early melt period.

A multiphysical ensemble system of numerical snow modelling

Abstract. Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

The effects of forest litter on snow energy budget in the Tianshan Mountains, China

AbstractForest litter exerts an impact on the energy budget of snow surfaces, which lie beneath forest canopies. In this study, we measured shortwave and longwave radiation levels, as well as quantities of Asian spruce (Picea schrenkinan) forest litter, over 3 snow study plots that representing an open environment, 20% forest canopy openness (20% FCO), and 80% forest canopy openness (80% FCO). The fractional litter coverage (lc) was obtained through the binarization of digital photographs of forest litter. The effects of forest litter on snow surface albedo (α), snow surface temperature (Ts), upward shortwave and longwave radiation (K↑ and L↑), and sensible heat flux (H) were then analyzed. According to our results, the energy budget over snow surface influenced by forest litter principally due to forest litter forcing α decrease and Ts increase. The effects of forest litter on the energy budget increased with time and lc. We found that forest litter exerted the most significant impact on K↑ and L↑ at daytime during the latter stages of the snowmelt period. The influence of forest litter on H was more apparent on windy days. The presence of forest litter increased gains in shortwave radiation and losses in longwave radiation and decreased gains in H. Compared to the simulated energy (K↑ + L↑ + H) over a snow surface without litter, the calculated energy decreased by −13.4 W/m2 and increased by 9.0 W/m2, respectively, at the 20% FCO and 80% FCO sites during the latter stages of the snowmelt period. Overall, forest litter facilitated snow surface energy gains at the 80% FCO site and impeded them at the 20% FCO site during the latter stages of the snowmelt period.

The Sensitivity of the Terrestrial Surface Energy and Water Balance Estimates in the WRF Model to Lower Surface Boundary Representations: A South Norway Case Study

Abstract A seasonal snow cover, expansive forests, a long coast line, and a mountainous terrain are features of Norway’s geography. Forests, ground snow, and sea surface temperature (SST) vary on time scales relevant for weather forecasting and climate projections. The mapping and model parameterization of these features vary in novelty, accuracy, and complexity. This paper investigates how increasing the influence of each of these features affects southern Norway’s surface energy and water balance in a regional climate model (WRF). High-resolution (3.7 km) experimental runs have been conducted over two consecutive hydrological years, including 1) heightening the boreal forest line (the Veg experiment), 2) increasing ground snow by altering the snow/rain criterion (the Snow experiment), or 3) increasing the SST (the SST experiment). The Veg experiment led to an increase in annual net radiation in the study area (by 3 W m−2), largely balanced out by an increase in latent heat flux. Moisture recycling increased, leaving only a negligible decrease in annual runoff. Surface temperature increased by 0.1°C, and its seasonal variability was dampened. Significant changes were also found outside the area of vegetation change. Snow decreased by 1.5 W m−2, despite slight increases in downward shortwave and longwave radiation. Both sensible heat flux and surface temperature decreased (by 1.3 W m−2 and 0.2°C, respectively), but the annual water balance remained mostly unchanged. The SST experiment led to increased downward and upward longwave radiation. Surface temperature was raised by 0.2°C. Advected oceanic moisture and thus both precipitation and runoff increased (by 2.5% and 2.8%, respectively).

MODIS-based estimates of strong snow surface temperature anomaly related to high altitude earthquakes of 2015

The high levels of uncertainty associated with earthquake prediction render earthquakes some of the worst natural calamities. Here, we present our observations of MODerate resolution Imaging Spectroradiometer (MODIS)-derived Land Surface Temperature (LST) anomaly for earthquakes in the largest tectonically active Himalayan and Andean mountain belts. We report the appearance of fairly detectable pre-earthquake Snow Surface Temperature (SST) anomalies. We use 16years (2000–2015) of MODIS LST time-series data to robustly conclude our findings for three of the most destructive earthquakes that occurred in 2015 in the high mountains of Nepal, Chile, and Afghanistan. We propose the physical basis behind higher sensitivity of snow towards geothermal emissions. Although the preliminary appearance of SST anomalies and their amplitudes vary, we propose employing a global-scale monitoring system for detecting and studying such spatio-temporal geophysical signals. With the advent of improved remote sensors, we anticipate that such efforts can be another step towards improved earthquake predictions.

Enhancing calculation of thin sea ice growth

The goal of the present study is to develop, generate, and integrate into operational practice a new model of ice growth. The development of this Sea Ice Growth Model for Arctic (SIGMA), a description of the theoretical foundation, the model advantages and analysis of its results are considered in the paper.The enhanced model includes two principal modifications. Surface temperature of snow on ice is defined as internal model parameter maintaining rigorous consistency between processes of atmosphere-ice thermodynamic interaction and ice growth. The snow depth on ice is naturally defined as a function of a local snowfall rate and linearly depends on time rather than ice thickness.The model was initially outlined in the Visible Infrared Radiometer Suite (VIIRS) Sea Ice Characterization Algorithm Theoretical Basis Document (Appel et al., 2005) that included two different approaches to retrieve sea ice age: reflectance analysis for daytime and derivation of ice thickness using energy balance for nighttime. Only the latter method is considered in this paper.The improved account for the influence of surface temperature and snow depth increases the reliability of ice thickness calculations and is used to develop an analytical Snow Depth/Ice Thickness Look up table suitable to the VIIRS observations as well as to other instruments.The applicability of SIGMA to retrieve ice thickness from the VIIRS satellite observations and the comparison of its results with the One-dimensional Thermodynamic Ice Model (OTIM) are also considered. The comparison of the two models demonstrating the difference between their assessments of heat fluxes and radical distinction between the influences of snow depth uncertainty on errors of ice thickness calculations is of great significance to further improve the retrieval of ice thickness from satellite observations.

Aerodynamic and Radiative Controls on the Snow Surface Temperature

Abstract The snow surface temperature (SST) is essential for estimating longwave radiation fluxes from snow. SST can be diagnosed using finescale multilayer snow physics models that track changes in snow properties and internal energy; however, these models are heavily parameterized, have high predictive uncertainty, and require continuous simulation to estimate prognostic state variables. Here, a relatively simple model to estimate SST that is not reliant on prognostic state variables is proposed. The model assumes that the snow surface is poorly connected thermally to the underlying snowpack and largely transparent for most of the shortwave radiation spectrum, such that a snow surface energy balance among only sensible heat, latent heat, longwave radiation, and near-infrared radiation is possible and is called the radiative psychrometric model (RPM). The RPM SST is sensitive to air temperature, humidity, ventilation, and longwave irradiance and is secondarily affected by absorption of near-infrared radiation at the snow surface that was higher where atmospheric deposition of particulates was more likely to be higher. The model was implemented with neutral stability, an implicit windless exchange coefficient, and constant shortwave absorption factors and aerodynamic roughness lengths. It was evaluated against radiative SST measurements from the Canadian Prairies and Rocky Mountains, French Alps, and Bolivian Andes. With optimized and global shortwave absorption and aerodynamic roughness length parameters, the model is shown to accurately predict SST under a wide range of conditions, providing superior predictions when compared to air temperature, dewpoint, or ice bulb calculation approaches.

Relating meteorological parameters to glide-snow avalanche activity

Glide-snow avalanches are notoriously difficult to predict, in part because we still do not fully understand the physical processes governing their release. Previous research has highlighted the presence of a wet basal layer as a prerequisite for glide-snow avalanche release. Different processes are believed to contribute to the formation of this basal layer in winter and in spring, since in winter the snowpack is usually cold and dry, while in spring it is warm and wet. We compared glide-snow avalanche activity to meteorological parameters of nearby weather stations for two winter seasons, the winter of 2008–2009 and the winter of 2011–2012. During both these winters, glide-snow avalanche activity was high in the Swiss Alps, posing a particular challenge to avalanche forecasting services. We used univariate and multivariate statistical methods to analyse the data for cold temperature events in winter and warm temperature events in spring, separately. For cold temperature events the minimum air temperature and the amount of new snow prior to avalanche release were the most significant variables, while for warm temperature events the most significant variables were air temperature, snow surface temperature derived from outgoing longwave radiation and decrease in snow height. Our results support the hypothesis that different processes result in the formation of glide-snow avalanches in winter and in spring. While the limited data set does not allow us to draw general conclusions, our results clearly indicate that treating cold and warm temperature events separately is of paramount importance to develop a widely applicable glide-snow avalanche forecasting model.

Dominant Modes of Interannual Variability in Eurasian Surface Air Temperature during Boreal Spring

Abstract This study investigates interannual variations of surface air temperature (SAT) over mid- and high latitudes of Eurasia during boreal spring and their association with snow, atmospheric circulation, and sea surface temperature (SST) changes. The leading mode of spring SAT variations is featured by same-sign anomalies over most regions. The second mode features a tripole anomaly pattern with anomalies over the central part opposite to those over the eastern and western parts of Eurasia. A diagnosis of surface heat flux anomalies suggests that snow change contributes partly to SAT anomalies in several regions mainly by modulating surface shortwave radiation but cannot explain SAT changes in other regions. Atmospheric circulation anomalies play an important role in spring SAT variability via wind-induced heat advection and cloud-induced surface radiation changes. Positive SAT anomalies are associated with anomalous westerly winds from the North Atlantic Ocean or with anomalous anticyclone and southerly winds. Negative SAT anomalies occur in regions of anomalous cyclone and northerly winds. Atmospheric circulation anomalies associated with the first mode have a close relationship to spring Arctic Oscillation (AO), indicating the impact of the AO on continental-scale spring SAT variations over the mid- and high latitudes of Eurasia. The atmospheric circulation anomalies associated with the second mode feature a wave pattern over the North Atlantic and Eurasia. Such a wave pattern is related to a tripole SST anomaly pattern in the North Atlantic Ocean, signifying the contribution of the North Atlantic Ocean state to the formation of a tripole SAT anomaly pattern over the mid- and high latitudes of Eurasia.

Short-term velocity variations at three rock glaciers and their relationship with meteorological conditions

Abstract. In recent years, strong variations in the speed of rock glaciers have been detected, raising questions about their stability under changing climatic conditions. In this study, we present continuous time series of surface velocities over 3 years of six GPS stations located on three rock glaciers in Switzerland. Intra-annual velocity variations are analysed in relation to local meteorological factors, such as precipitation, snow(melt), and air and ground surface temperatures. The main focus of this study lies on the abrupt velocity peaks, which have been detected at two steep and fast-moving rock glacier tongues ( ≥ 5 m a−1), and relationships to external meteorological forcing are statistically tested.The continuous measurements with high temporal resolution allowed us to detect short-term velocity peaks, which occur outside cold winter conditions, at these two rock glacier tongues. Our measurements further revealed that all rock glaciers experience clear intra-annual variations in movement in which the timing and the amplitude is reasonably similar in individual years. The seasonal decrease in velocity was typically smooth, starting 1–3 months after the seasonal decrease in temperatures, and was stronger in years with colder temperatures in mid winter. Seasonal acceleration was mostly abrupt and rapid compared to the winter deceleration, always starting during the zero curtain period. We found a statistically significant relationship between the occurrence of short-term velocity peaks and water input from heavy precipitation or snowmelt, while no velocity peak could be attributed solely to high temperatures. The findings of this study further suggest that, in addition to the short-term velocity peaks, the seasonal acceleration is also influenced by water infiltration, causing thermal advection and an increase in pore water pressure. In contrast, the amount of deceleration in winter seems to be mainly controlled by winter temperatures.

How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?*

Abstract Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperature Tsurf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234 mm (57% of peak SWE), mean differences up to 6.2°C in Tsurf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration.

Evaluation of VIIRS Land Surface Temperature Using CREST-SAFE Air, Snow Surface, and Soil Temperature Data

In this study, the Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) was evaluated against snow surface (T-skin) and near-surface air temperature (T-air) ground observations recorded at the Cooperative Remote Sensing Science and Technology Center—Snow Analysis and Field Experiment (CREST-SAFE), located in Caribou, ME, USA during the winters of 2013 and 2014. The satellite LST corroboration of snow-covered areas is imperative because high-latitude regions are often physically inaccessible and there is a need to complement the data from the existing meteorological station networks. T-skin is not a standard meteorological parameter commonly observed at synoptic stations. Common practice is to measure surface infrared emission from the land surface at research stations across the world that allow for estimating ground-observed LST. Accurate T-skin observations are critical for estimating latent and sensible heat fluxes over snow-covered areas because the incoming and outgoing radiation fluxes from the snow mass and T-air make the snow surface temperature different from the average snowpack temperature. Precise characterization of the LST using satellite observations is an important issue because several climate and hydrological models use T-skin as input. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Based on these results, empirical relationships to estimate T-air and T-skin for clear-sky conditions from remotely-sensed (RS) LST were derived. Additionally, an empirical formula to correct cloud-contaminated RS LST was developed.

Numerical simulation of extreme snowmelt observed at the SIGMA-A site, northwest Greenland, during summer 2012

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements as well as numerical modeling with a one-dimensional multi-layered physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. Above all, the fact that the SMAP model successfully reproduced frequently observed rapid increases in snow albedo under cloudy conditions highlights the advantage of the physically based snow albedo model (PBSAM) incorporated in the SMAP model. Using such data and model, we estimated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

Evaluating snow models with varying process representations for hydrological applications

AbstractMuch effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically based snow models. For the most part, these models are built on similar principles. The greatest differences are found in how each model parameterizes individual processes (e.g., surface albedo and snow compaction). Parameterization choices naturally span a wide range of complexities. In this study, we evaluate the performance of different snow model parameterizations for hydrological applications using an existing multimodel energy‐balance framework and data from two well‐instrumented alpine sites with seasonal snow cover. We also include two temperature‐index snow models and an intensive, physically based multilayer snow model in our analyses. Our results show that snow mass observations provide useful information for evaluating the ability of a model to predict snowpack runoff, whereas snow depth data alone are not. For snow mass and runoff, the energy‐balance models appear transferable between our two study sites, a behavior which is not observed for snow surface temperature predictions due to site‐specificity of turbulent heat transfer formulations. Errors in the input and validation data, rather than model formulation, seem to be the greatest factor affecting model performance. The three model types provide similar ability to reproduce daily observed snowpack runoff when appropriate model structures are chosen. Model complexity was not a determinant for predicting daily snowpack mass and runoff reliably. Our study shows the usefulness of the multimodel framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost.

Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance

AbstractThe forcing irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irradiances on simulations of snow is evaluated in idealized modeling experiments. Two snow models of contrasting complexity, the Utah Energy Balance Model (UEB) and the Snow Thermal Model (SNTHERM), are forced with irradiances with prescribed errors of the structure and magnitude representative of those found in methods for estimating the downwelling irradiances. Relatively modest biases have substantial impacts on simulated snow water equivalent (SWE) and surface temperature (Ts) across a range of climates, whereas random noise at the daily scale has a negligible effect on modeled SWE and Ts. Shortwave biases have a smaller SWE impact, due to the influence of albedo, and Ts impact, due to their diurnal cycle, compared to equivalent longwave biases. Warmer sites exhibit greater sensitivity to errors when evaluated using SWE, while colder sites exhibit more sensitivity as evaluated using Ts. The two models displayed different sensitivity and responses to biases. The stability feedback in the turbulent fluxes explains differences in Ts between models in the negative longwave bias scenarios. When the models diverge during melt events, differences in the turbulent fluxes and internal energy change of the snow are found to be responsible. From this analysis, we suggest model evaluations use Ts in addition to SWE.

Snowfall and Snowmelt Variability over Himalayan Region in Inter-annual Timescale

The Himalayan mountain system is the source of one of the world's largest supplies of freshwater, as all the major south Asian rivers originate in the Himalayas and their upper catchments are covered with snow and glaciers. There have been no studies describing large-scale pattern of snowmelt over this region. In order to develop a physically based distributed snowmelt-runoff model, a study on the variability of snowfall and snowmelt in the Himalayas is essential. Therefore, for the first time, a study on the variability of snow accumulation and ablation has been undertaken for this region. Remotely sensed snow water equivalent (SWE) from the National Snow and Ice data Centre (NSIDC) and surface temperature data from the Climate Forecast System Reanalysis (CFSR) have been used to study the snowmelt process in the Himalayas. It is seen that, in April, snowmelt mostly occurs in Afghanistan and northern parts of Jammu and Kashmir. Along the Pir Panjal range, snowfall continues in April. Maximum runoff from snowmelt in Kashmir, Himachal and Uttarakhand occurs in the months of June and July. The degree day factor (snowmelt coefficient) indicating snowmelt rate has been computed and it is found that there is large spatial variations in snowmelt rate over the Himalayas due to variations in surface temperatures, accumulated snow amount and elevation. The melt rate ranges from 0.02 to 0.6cm/oC/day in June. Around the Pir Panjal range and the Satluj basin, the melt rate is higher than other places.

Modeling the snow surface temperature with a one-layer energy balance snowmelt model

Abstract. Snow surface temperature is a key control on and result of dynamically coupled energy exchanges at the snow surface. The snow surface temperature is the result of the balance between external forcing (incoming radiation) and energy exchanges above the surface that depend on surface temperature (outgoing longwave radiation and turbulent fluxes) and the transport of energy into the snow by conduction and meltwater influx. Because of the strong insulating properties of snow, thermal gradients in snow packs are large and nonlinear, a fact that has led many to advocate multiple layer snowmelt models over single layer models. In an effort to keep snowmelt modeling simple and parsimonious, the Utah Energy Balance (UEB) snowmelt model used only one layer but allowed the snow surface temperature to be different from the snow average temperature by using an equilibrium gradient parameterization based on the surface energy balance. Although this procedure was considered an improvement over the ordinary single layer snowmelt models, it still resulted in discrepancies between modeled and measured snowpack energy contents. In this paper we evaluate the equilibrium gradient approach, the force-restore approach, and a modified force-restore approach when they are integrated as part of a complete energy and mass balance snowmelt model. The force-restore and modified force-restore approaches have not been incorporated into the UEB in early versions, even though Luce and Tartoton have done work in calculating the energy components using these approaches. In addition, we evaluate a scheme for representing the penetration of a refreezing front in cold periods following melt. We introduce a method to adjust effective conductivity to account for the presence of ground near to a shallow snow surface. These parameterizations were tested against data from the Central Sierra Snow Laboratory, CA, Utah State University experimental farm, UT, and subnivean snow laboratory at Niwot Ridge, CO. These tests compare modeled and measured snow surface temperature, snow energy content, snow water equivalent, and snowmelt outflow. We found that with these refinements the model is able to better represent the snowpack energy balance and internal energy content while still retaining a parsimonious one layer format.

PISTE: A Snow-Physics Model Incorporating Human Factors for Groomed Ski Slopes*

Abstract Accurately calculating snow-surface temperature and liquid water content for a groomed ski run, known as a ski piste, is crucial to the preparation of fast skis for alpine racing. Ski technicians can use forecasts of these variables to reduce ski–snow friction by applying layers of wax ahead of time. A new one-dimensional numerical Lagrangian snowpack model, Prognostic Implementation for Snow Temperature Estimation (PISTE), is presented that solves the heat-, liquid water–, and ice-budget equations to calculate these snow variables. In addition, the human effects of skiing and grooming are modeled. Meteorological measurements from a 5-day, clear-sky case study at a ski piste on Whistler Mountain, British Columbia, Canada, are prescribed to PISTE as boundary conditions. Because of a lack of interior snowpack measurements, PISTE was spun up from a dry, isothermal snowpack using repeated boundary conditions from 1 day of measurements. Initial conditions for the main model run that used the subsequent 4 days were taken from this spinup. Simulated and measured snow-surface temperatures show very good agreement, with slight cold daytime and warm nighttime biases (averaging 0.5° and 1°C, respectively). The modeled behavior of snowpack temperature and liquid water content profiles is consistent with previous literature having similar radiative boundary conditions. The case study indicates that PISTE is useful under simple conditions. It shows the potential to be developed into a more sophisticated model that can incorporate complex boundary conditions such as cloudiness and precipitation and can be driven by numerical weather prediction output.

Evolution of snow and ice temperature, thickness and energy balance in Lake Orajärvi, northern Finland

The seasonal evolution of snow and ice on Lake Orajärvi, northern Finland, was investigated for three consecutive winter seasons. Material consisting of numerical weather prediction model (HIRLAM) output, weather station observations, manual snow and ice observations, high spatial resolution snow and ice temperatures from ice mass balance buoys (SIMB), and Moderate Resolution Imaging Spectroradiometer (MODIS) lake ice surface temperature observations was gathered. A snow/ice model (HIGHTSI) was applied to simulate the evolution of the snow and ice surface energy balance, temperature profiles and thickness. The weather conditions in early winter were found critical in determining the seasonal evolution of the thickness of lake ice and snow. During the winter season (Nov.–Apr.), precipitation, longwave radiative flux and air temperature showed large inter-annual variations. The uncertainty in snow/ice model simulations originating from precipitation was investigated. The contribution of snow to ice transformation was vital for the total lake ice thickness. At the seasonal time scale, the ice bottom growth was 50–70% of the total ice growth. The SIMB is suitable for monitoring snow and ice temperatures and thicknesses. The Mean Bias Error (MBE) between the SIMB and borehole measurements was −0.7 cm for snow thicknesses and 1.7 cm for ice thickness. The temporal evolution of MODIS surface temperature (three seasons) agrees well with SIMB and HIGHTSI results (correlation coefficient, R=0.81). The HIGHTSI surface temperatures were, however, higher (2.8°C≤MBE≤3.9°C) than the MODIS observations. The development of HIRLAM by increasing its horizontal and vertical resolution and including a lake parameterisation scheme improved the atmospheric forcing for HIGHTSI, especially the relative humidity and solar radiation. Challenges remain in accurate simulation of snowfall events and total precipitation.

Evaluating the Utah Energy Balance (UEB) snow model in the Noah land-surface model

Abstract. Noah (version 2.7.1), the community land-surface model (LSM) of National Centers for Environmental Predictions-National Center for Atmospheric Research (NCEP-NCAR), which is widely used to describe the land-surface processes either in stand-alone or in coupled land-atmospheric model systems, is recognized to underestimate snow–water equivalent (SWE). Noah's SWE bias can be attributed to its simple snow sub-model, which does not effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow surface temperature and snowmelt outflow schemes. Snow surface temperature was estimated using the force-restore method and snowmelt event is regulated by accounting for the internal energy of the snowpack. The modified Noah's SWE simulations are compared with the SWE observed at California's NRCS SNOTEL stations for 7 water years: 2002–2008, while the model's snow surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snowmelt process.