Snow Surface Temperature Research Articles

A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites

Abstract. Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

AbstractData assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies.

Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes

Abstract. Winter near-surface air temperatures have important implications for ecosystem functioning such as vegetation dynamics and carbon cycling. In cold environments, the persistence of seasonal snow cover can exert a strong control on the near-surface temperatures. However, the lack of in situ measurements of both snow cover duration and surface temperatures over high latitudes has made it difficult to estimate the spatio-temporal variability in this relationship. Here, we quantified the fine-scale variability in winter near-surface air temperatures (+2 cm) and snow cover duration (calculated from temperature time series) using a total of 441 microclimate loggers in seven study areas across boreal and tundra landscapes in Finland during 2019–2021. We further examined the drivers behind this variation using a structural equation model and the extent to which near-surface air temperatures are buffered from free-air temperatures during winter. Our results show that while average winter near-surface temperatures stay close to 0 ∘C across the study domain, there are large differences in their fine-scale variability among the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatest variation in near-surface temperatures and in snow cover duration. In the tundra, for example, differences in minimum near-surface temperatures between study sites were close to 30 ∘C and topography was shown to be an important driver of this variability. In contrast, flat topography and long snow cover duration led to little spatial variation, as well as long periods of decoupling between near-surface and air temperatures. Quantifying and understanding the landscape-wide variation in winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal and tundra regions.

Spatiotemporal variations of atmospheric black carbon concentration and its correlation with meteorological and environmental factors in Xinjiang, China, from 2010 to 2022.

Black carbon (BC) is a pollutant produced by the combustion of fossil fuels and biomass fuels, which has a huge impact on regional climate, atmospheric environment, and human health. In this study, based on MERRA-2 reanalysis data and ground-based observation data, the Mann-Kendall (MK) test and random forest (RF) model were used to explore the spatiotemporal variation characteristics of atmospheric BC concentration in Xinjiang, China and its correlation with meteorological and environmental covariates in 2010-2022. The results showed that the use of MERRA-2 reanalysis data to explore the spatiotemporal variation characteristics of BC concentration in Xinjiang had high reliability (relative average deviation (RAD) = 0.65). From 2010 to 2022, the annual average concentration of atmospheric BC in Xinjiang was 195.40±15.55ng/m3, and the multi-year average change rate was -0.05%. The winter season had the highest atmospheric BC concentration (145.52 ± 39.31 ng/m3), followed by autumn (124.95±28.82 ng/m3), spring (74.05 ± 9.96 ng/m3), and summer (73.41 ± 5.69ng/m3). The atmospheric BC concentration had a significant spatial variation, showing two high-BC-concentration areas on the northern slope of the Tianshan Mountains (centered on Urumqi-Changji-Shihezi region) and the urban agglomeration around the Tarim Basin (centered on Kashgar). The RF model analysis showed that meteorological factors including snow depth, surface temperature, and humidity as well as environmental factors including NO2, PM10, and SO2 were the main factors affecting the BC concentration. This work is of great significance for clarifying the accumulation and spatial distribution characteristics of atmospheric BC in northwest China and the factors influencing the atmospheric BC concentration, and helps to raise public attention to the increasingly serious climate change and public health problems caused by BC.

Estimating Early Summer Snow Depth on Sea Ice Using a Radiative Transfer Model and Optical Satellite Data

Sea ice regulates the overall energy exchange and radiation budget of the Arctic region, and understanding this relationship requires an accurate determination of snow depth. However, methods for deriving snow depth have a large error through the annual winter and early spring periods due to the potential complexity of surface melting during early summer. In this study, we explore the potential of retrieving snow depth during the early summer using optical satellite imagery of the sea-ice cover. Measurements using VIS/IR (visible and infrared) usually feature much higher spatial resolution than L-band satellite data and can provide additional surface melting and leads information; in addition, considering the snow grain size–snow surface temperature interaction, there is co-variability between the observed sea-ice surface broadband albedo using an optical satellite sensor, the sea-ice surface temperature, and the retrieval target of snow depth on the spatial scale of optical imagery samples. We applied a surface classification procedure to optical satellite imagery and introduce an approach to derive snow depth from optical satellite imagery and ice surface temperature data using two solar radiation transfer models: the Delta-Eddington solar radiation model, which is the shortwave radiative scheme of the Los Alamos sea-ice model, and a simplified snow albedo scheme, which is tuned to the observational data of buoys. The snow depth was inversed from the model simulation results using a lookup-table-based method. For comparison with the observational data, using the Delta-Eddington solar radiation model, about 55% of the differences are below 5 cm, and thicker snowpack has a larger bias; using the simplified snow albedo scheme, a mean difference of 4.1 cm between retrieval and measurements was found, with 93% of the differences being smaller than 5 cm. This approach can be applied to optical satellite imagery acquired under clear-sky conditions and can serve as an addition to overcome the limitations of existing methods.

Inconsistency and correction of manually observed ground surface temperatures over snow-covered regions

Ground surface temperature (Ts) was manually measured using mercury thermometers in the early stage, and the snow surface temperature was substituted for Ts in areas with thick seasonal snow covers due to measurement difficulty. However, this practice produced Ts data that were inconsistent with more recent automatic measurements that determine Ts in the soil beneath the snow cover using modern instrumentation. In this study, we used observations from northeastern China to explore and correct this Ts inconsistency. Our results showed that the inconsistency varies from a few degrees to tens of degrees depending on the snow conditions and time periods considered. This inconsistency between earlier and modern measurements can lead to the overestimation of soil temperature warming rates in recent decades. We remove the Ts inconsistency using a numerical model that considers the effect of snow insulation on ground surface temperatures. This approach provides a means to improve Ts records that have similarly switched from manual to automatic measurements, which is critical for soil and climate investigations.

Cold waves still matter: characteristics and associated climatic signals in Europe

Few studies explore extreme wintertime European cold waves (CW) despite their huge economic, and social impacts and a recent decade punctuated by CW (February 2012, January 2017 or March 2018), while Europe bets on mild winter to avoid an energy crisis in 2023. Here, we investigate potential early CW warning signals and model biases over the winters (November-to-March) of the 1950–2005 period, calculating the atmospheric and surface conditions of key climate variables (e.g. snow, incoming radiation, cloudiness, pressure, winds and sea surface temperatures variables) both in reanalyses and climate models, before and during CW events. We show that global coupled climate models systematically overestimate the number of CW in the present-day period. Until 30 days before CW, some robust and significant early patterns emerge, both in models and reanalyses: weak atmospheric blocking, anomalously negative North Sea surface temperatures (SST) and a deficit of incoming longwave radiation. Downward shortwave radiation is anomalously positive during and before CW which weakens arguments for direct negative solar forcing on winter extreme cold events in Europe. Climate models share in their great majority (> 80%) these patterns (for dynamical and radiative-related variables, spatial correlation r > 0.7) and can correctly simulate the sign of climate variables anomalies during and at least 7 days before CW. We find that excess of European snow cover and snow depth are unlikely to cause occurrences of CW, but the advection of cold air masses (North-East) emerges as a potential precursor signal of a majority of CW. In a context of climate change, fossil fuels scarcity and increased uncertainty due to geopolitical events, it is crucial to study the sensitivity of the energy, health and agriculture sectors to compound extreme weather, including cold waves.

Estimation of Daily Arctic Winter Sea Ice Thickness from Thermodynamic Parameters Using a Self-Attention Convolutional Neural Network

Thermodynamic parameters play a crucial role in determining polar sea ice thickness (SIT); however, modeling their relationship is difficult due to the complexity of the influencing mechanisms. In this study, we propose a self-attention convolutional neural network (SAC-Net), which aims to model the relationship between thermodynamic parameters and SIT more parsimoniously, allowing us to estimate SIT directly from these parameters. SAC-Net uses a fully convolutional network as a baseline model to detect the spatial information of the thermodynamic parameters. Furthermore, a self-attention block is introduced to enhance the correlation among features. SAC-Net was trained on a dataset of SIT observations and thermodynamic data from the 2012–2019 freeze-up period, including surface upward sensible heat flux, surface upward latent heat flux, 2 m temperature, skin temperature, and surface snow temperature. The results show that our neural network model outperforms two thermodynamic-based SIT products in terms of accuracy and can provide reliable estimates of SIT. This study demonstrates the potential of the neural network to provide accurate and automated predictions of Arctic winter SIT from thermodynamic data, and, thus, the network can be used to support decision-making in certain fields, such as polar shipping, environmental protection, and climate science.

Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.

A Dynamics of Surface Temperature Forced by Solar Radiation

AbstractDue to lack of a unified description of the Earth surface temperature, a generic dynamic equation is postulated as an inference from the special case of snow. Solar radiation is explicitly included in the formulation for transparent media such as snow, ice and water while implicitly through (conductive) surface heat flux for non‐transparent media such as soil. The physical parameters of the equation are medium thermal inertia, thermal and radiative diffusivity. The equation for transparent media reduces to the familiar force‐restore model of soil surface temperature when the penetration depth of solar radiation tends to zero. Proof‐of‐concept validation for snow surface temperature as a paradigm of transparent media at three sites in the Arctic and Antarctica confirms the postulated equation as a generic description of the dynamics of surface temperature.

Study of climate change patterns of the Kashkadarya River basin based on GIS technologies

The article deals with the problem of studying changes in some climatic indicators (temperature, precipitation) in the Kashkadarya River basin. For this purpose, the analysis of land surface temperature and area of snow and ice cover on Landsat 8 images was used. Comparison of the obtained results with the results of analysis of archival data of Uzhydromet and water level data of gauging stations showed that the growth rate of average annual temperatures in Kashkadarya province is 0.21oC, and the rate of reduction of snow-ice cover area is 1% per year. in general, which has decreased over the past 15 years, by 75%. Such trends may lead to a significant reduction in the river flow of the Kashkadarya River in the coming years.

The Multiple Snow Data Assimilation System (MuSA v1.0)

Abstract. Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open-source, community-driven project dedicated to snow data assimilation, which makes it difficult to compare existing algorithms and fragments development efforts. Here we introduce a new data assimilation toolbox, the Multiple Snow Data Assimilation System (MuSA), to help fill this gap. MuSA was developed to fuse remotely sensed information that is available at different timescales with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e., the number of particles and their prior covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (central Pyrenees). Furthermore, we conducted a joint-assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single-cell experiment. In light of these experiments, we discuss the pros and cons of the assimilation algorithms, including their computational cost.

When and Where Are Multiple Snow Layers Important for Simulations of Snow Accumulation and Melt?

AbstractMany watershed‐scale and land surface models incorporate snowmelt modules with simplified representations of the snowpack with three or fewer layers. These modeling choices were traditionally made to reduce model complexity and computational demand while still being able to simulate large model domains. However, these simple snow layering schemes may not always simulate snow processes and the effects of climate change across a range of climatic and geographic conditions. Here we evaluate simple snow layering schemes (having two to five layers, commonly found in watershed‐scale and land surface models) against a synthetic benchmark with up to 100 layers at three locations with different climate conditions using the SUMMA modeling framework. We evaluate 10 different layering configurations of two to five layers with variable thicknesses and show that the effect of the layering scheme varies with site conditions. We find that the layer configuration is more important at a cold high elevation site in the Sierra Nevada, California (∼1.4°C annual average temperature), and at a warm site in the French Alps (∼6.5°C), and less important at a site in Idaho (∼5.0°C). The top layer thickness of the simpler snow layering configurations also influences the simulated snow surface and snowpack temperatures and timing of snowmelt. Our tests showed that the five‐layer model with thin layers near the surface was closest to the benchmark (median NSE = 0.99), and therefore we recommend using multiple snow layers for reliable simulations of snow accumulation and melt across a range of climates.

An 11-year record of wintertime snow-surface energy balance and sublimation at 4863 m a.s.l. on the Chhota Shigri Glacier moraine (western Himalaya, India)

Abstract. Analysis of surface energy balance (SEB) at the glacier/snow surface is the most comprehensive way to explain the atmosphere–glacier/snow interactions, but that requires extensive data. In this study, we have analysed an 11-year (2009–2020) record of the meteorological dataset from an automatic weather station installed at 4863 ma.s.l. (above sea level) on a lateral moraine of the Chhota Shigri Glacier, western Himalaya. The study was carried out over the winter months (December to April) to understand SEB drivers and snow loses through sublimation. Furthermore, this study examines the role of cloud cover on SEB and turbulent heat fluxes. The turbulent heat fluxes were calculated using the bulk-aerodynamic method, including stability corrections. The net short-wave radiation was the primary energy source. However, the turbulent heat fluxes dissipated a significant amount of energy. The cloud cover plays an important role in limiting the incoming short-wave radiation by about 70 %. It also restricts the turbulent heat fluxes by more than 60 %, resulting in lower snow sublimation. During winter, turbulent latent heat flux contributed the largest proportion (64 %) in the total SEB, followed by net radiation (25 %) and sensible heat flux (11 %). Sublimation rates were 3 times higher in clear-sky than overcast conditions, indicating a strong role of cloud cover in shaping favourable conditions for turbulent latent heat flux by modulating the near-surface boundary layer conditions. Dry air, along with high snow-surface temperature and wind speed, favours sublimation. Besides, we also observed that strong and cold winds, possibly through mid-latitude western disturbances, impede sublimation by bringing high moisture content to the region and cooling the snow surface. The estimated snow sublimation fraction was 16 %–42 % of the total winter snowfall at the study site. This study substantiates that the snow sublimation is an essential variable to be considered in glaciohydrological modelling at the high-mountain Himalayan glacierised catchments.

Effect of shadow on atmospheric and topographic processed NDSI values in Chenab basin, western Himalayas

The present study was undertaken in the Chenab basin, western Himalayas to explore the effect of shadow on atmospherically and topographically corrected normalized difference snow index (NDSI) values. The research was conducted using the Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) datasets of two time periods i.e., February 2015 and February 2018. Our results suggested that total snow cover area (SCA) computed using NDSI from raw satellite images, under estimated SCA (viz. 10,938.66 and 7739.97 Km2); relative to atmospherically corrected (AC) (14,819.01 and 11,199.7 Km2) and topographically corrected (TC) (14,882.07 and 11,188.18 Km2) images during both the time periods (i.e., 2015 and 2018, respectively). This is mainly due to the non-identification of snow pixels in the shadow regions of raw imageries. Maximum SCA lies in the higher NDSI range in case of AC and TC images, unlike raw image where maximum SCA lies in lower NDSI range for both sunlit and shadow test sites. In the sunlit and shadow terrains, minimum NDSI values were found to be higher for TC images as compared to AC images, except few observations which exhibited similar values for both AC and TC images. In case of maximum NDSI values, most of the observations exhibited similar values for both AC and TC images, except few observations which depicted higher maximum NDSI value in AC images as compared to TC images in both the terrains. This observation ascertains the narrow range of NDSI values for TC images as compared to AC images, which could be attributed to reduced background reflection and low atmospheric scattering in the TC images. Comparison among shadow and sunlit snow terrains, reveals low values of minimum and maximum NDSI in the shadow site, which is ascertained by significant (p < 0.001) t-values of means difference between shadow and sunlit terrains. Moreover, snow surface temperature (SST) values computed for both the time periods, reveal low SST values for shadow sites (t-values −5.1 and − 10.57 for minimum SST, and − 9.1 and − 12.61 for maximum SST for the years 2015 and 2018, respectively) as compared to sunlit sites, and this observation validates that minimum amount of solar radiation reaches the shadow sites. Thus, it is concluded that AC and TC are helpful for delineation of snow pixels under shadow, but shadow has a significant impact on the spectral reflectance of snow, even in the AC and TC images. The influence of shadow should be taken into account for accurate estimation of physical properties and broadband albedo of the snow.

Improving the Spatial Distribution of Snow Cover Simulations by Assimilation of Satellite Stereoscopic Imagery

AbstractMoutain snow cover is highly variable both spatially and temporally and has a tremendous impact on ecosystems and human activities. Numerical models provide continuous estimates of the variability of snow cover properties in time and space. However, they suffer from large uncertainties, for instance originating from errors in the meteorological inputs. Here, we show that the snow depth variability at 250 m spatial resolution can be well simulated by assimilating snow depth maps from satellite photogrammetry in a detailed snowpack model. The assimilation of a single snow depth map per snow season using a particle filter is sufficient to improve the simulated snow depth and its spatial variability, originally poorly represented due to missing physical processes and errors in the precipitation inputs. Assimilation of snow depth only is nevertheless not sufficient for both compensating for strong bias in precipitation and for selecting the most appropriate representation of the physical processes in the snow model. Regarding this limitation, combined assimilation of snow depths maps and other snow observations, such as snow cover area, surface temperature or reflectance, is a promising avenue for accurate simulations of mountain snow cover.

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.

Evaluation of the snow cover in the Soil, Vegetation, and Snow (SVS) land surface model

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at ten well-instrumented sites from the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force-restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

Bias Analysis and Correction of Ground Surface Temperature Observations across China

Based on the ground surface temperature (GST) and snow surface temperature (SST) measurements during the period of adjustment from manual to automatic observation systems in China, the influence of observation methods on GST and its relationship with snow cover is analyzed. GST is corrected by SST, and the correction effect is evaluated. The results show that, during the parallel observation period, the winter GSTs from automatic observations are generally higher than those from manual observations, with the automatically observed national daily GST 1.18°C higher. The adjustment has a greater impact on GSTs at 0200, 0800, and 2000 Beijing Time (BT) than at 1400 BT, and it has the greatest impact in Northeast and Northwest China, with deviations of 5.24 and 2.09°C, respectively. The GST deviation is closely related to the snow depth and annual snow totals. The average daily GST deviation increases at the rate of 0.66°C per 1-cm increase of snow depth when it is < 15 cm, while it tends to be stable at around 10°C for snow depth over 15 cm. The GST deviation at a station is affected by its winter snow totals in Northeast and Northwest China, where the largest deviations are found where snow totals are all above 1000 cm. After the correction with SST, the mean deviation between the automatic and manual observations as well as the false trend can be effectively reduced. Following the correction, the mean deviation of daily GST decreases by 5.8°C, and its trend decreases from 1.87 to 0.65°C decade−1.