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SWAP land surface model, developed by the authors of this article, was used to carry out many-year calculations of snow cover characteristics for forest and field areas in the main regions of Asian Russia: West Siberia, East Siberia, and Far East for a historic period (1967−2019). The comparison of simulation results with the appropriate data of route snow surveys at meteorological stations in the Asian part of RF showed that SWAP adequately reproduces the dynamics of snow water equivalent, snow depth and density in open areas and under forest canopy at the chosen sites. For all sites located in snow survey areas, climatic values of the characteristics of snow cover formation regime were obtained for two types of the land surface (forest and field) and two climatic periods (1967−1992 and 1993−2019) and used to identify trends in changes in these characteristics in the historic period. It was shown that the direction of changes in the climatic values of snow cover characteristics in forest and field areas in West Siberia, East Siberia, and Far East is the same and corresponds to projections for the XXI century derived from climate models. [ABSTRACT FROM AUTHOR]

Questions: We assessed interactions between climate change, bedrock types and snow cover duration on the trajectories of taxonomic and functional composition of subalpine plant communities. We predict (i) an increase in species richness on siliceous bedrock due to a reduced competition and a decrease in richness on calcareous bedrock due to increasing drought stress; (ii) decreasing snow cover duration should induce a higher shrub encroachment in hollows as compared to ridges; and (iii) increasing growing season temperature should induce taller sizes and more conservative growth traits, in particular in hollows. Location: Subalpine belt of the Grandes Rousses mountain range, southwestern Alps (France). Methods: 189 vegetation plots were sampled in 1997 and 2017–2018. The duration of snow cover was assessed during two years in 1995–1997 and five functional traits were measured on 108 species in 2021. We performed multivariate analyses, quantified community‐weighted means (CWM) of traits and used ANOVAs to detect responses to local‐scale factors and changes in snow cover, temperature and precipitation since 1997 according to a nearby meteorological station. Results: Overall, taxonomic composition weakly changed and changes were more dependent on the position of communities along the snow cover duration gradient than on their bedrock type. The abundance of drought‐tolerant species increased at the border of hollows and there was, over all communities, a slight increase in the abundance of dwarf shrubs and tall herbaceous species, a strong decrease in short herbaceous species and, thus, an overall decrease in species richness. There were important overall changes in CWM of size traits, in particular leaf area which increased the most in hollows irrespective of bedrock types. Conclusion: In this subalpine site the effects of decreasing snow cover duration overwhelmed the effects of bedrocks, which may explain the overall increase in competitive species and decrease in species richness. [ABSTRACT FROM AUTHOR]

Snow cover (SC) is a great indicator of climate change. It is highly related to temperature. Since the Polish climate faces warmer conditions, changes in the wintertime precipitation phase are observed. More frequent rainfall instead of snowfall is noticed at the end of the 20th and beginning of the 21st centuries. This work presents the spatial distribution, its changes, and variabilities of selected parameters that described snow cover in Poland from 1966/1967 to 2020/2021. The snow characteristics used in the study comprise of SC duration (SCD), the first and last day with SC in the season (SCbeg and SCend), the potential duration of SC season (PSCD), SC stability (SCS), average, maximum and accumulated SC depth (HS, HSmax and AHS). The changes in snow cover waves were analysed. Generally, the Polish climate is mild in the west, where the snow cover is finer and occurs relatively rarely and becomes more continental toward the north‐east, where the snow cover has a better condition to accumulate and preserve. The southern parts of the country are covered by mountain ranges of the Carpathians and Sudetes, where the snow cover remains the longest. The spatial distribution of the coefficient of variability is reversely proportional to SC characteristics—the highest in the north‐east (especially for the HS, HSmax and AHS). The most significant changes in SC are related to a decrease in SCD (5–7 days/decade), HSmax (1–2 cm/decade) and AHS (30–60 cm/decade). At the same time, the snow cover season becomes considerably shorter (especially in the western and central parts—about 10 days/decade). The trends in SCS are not yet significant in most of the country. This study reveals that the snow cover in Poland is under constant change, and the negative trends, which were hardly or not visible at the end of the 20th century, in the last decades have become statistically significant in a greater number of Polish stations. [ABSTRACT FROM AUTHOR]

This study is dedicated to the detection and attribution of changes in annual streamflow, maximum and mean winter snow water equivalent (SWE), start and end dates of seasonal snow cover, and its duration in three Arctic river basins (the Northern Dvina, Taz, and Indigirka) located in the European part of Russia, West, and East Siberia in different natural conditions. The observations of the above characteristics are rather scarce to detect statistically significant trends. At the same time, the available observations make it possible to calibrate the key parameters of the SWAP model, apply for hydrological simulations, and validate the model. Then, following the approach suggested within the framework of the international ISIMIP3a project, long-term simulations are performed for each basin using observational (factual) climate data, characterized by long-term changes, and counterfactual de-trended climate data. A comparison of factual and counterfactual simulations allows us to attribute the detected changes (in terms of trends) in the analyzed variables to climatic drivers. Statistically significant positive trends in streamflow are attributed to changes in annual precipitation for the Northern Dvina and Indigirka, and to the joint impact of increasing precipitation and warming, which resulted in permafrost thawing, for the Taz River. Negative trends in the basin-averaged end dates of snow cover and its duration as well as positive trends in winter and maximum SWE are detected for all basins and attributed to joint influence of changes in seasonal precipitation, air temperature, and solar radiation. The results highlight the vulnerability of Arctic river basins to climate change. [ABSTRACT FROM AUTHOR]

A procedure for calculating various characteristics of snow cover formation, based on the use of the land surface model SWAP was tested on forest areas of the European Russia for a historical period (1967−2019). The comparison of simulation results with appropriate observation data showed the good quality of reproduction of snow formation processes at these objects. Changes of the climatic values of snow cover formation characteristics in the historical period were analyzed to reveal tendencies in these changes in forest areas in the region. Thus, it was found that, despite the decrease in the duration of snow cover, there is increase in snowpack, in particular, in the maximal snow water equivalent. The difference between the characteristics of snow cover formation in the field and forest areas of European Russia was assessed. The mean values of the snow accumulation coefficient over the forest area relative to the field was found to be greater than 1.0. At the same time, the climatic changes in the historical period lead to a decrease of this characteristic in time. [ABSTRACT FROM AUTHOR]

A procedure for calculating various characteristics of snow cover formation, based on the use of the land surface model SWAP, was tested on field areas of the European Russia for a historical period (1967−2019). The comparison of simulation results with observation data showed the good quality of snow water equivalent reproduction at these objects. Variations of the climatic values of snow cover formation characteristics in the historical period were analyzed, revealing trends in changes of these characteristic in field areas in the period under consideration. [ABSTRACT FROM AUTHOR]

In the Alpine environment, snow plays a key role in many processes involving ecosystems, biogeochemical cycles, and human wellbeing. Due to the inaccessibility of mountain areas and the high spatial and temporal heterogeneity of the snowpack, satellite spatio-temporal data without gaps offer a unique opportunity to monitor snow on a fine scale. In this study, we present a random forest approach within three different workflows to combine MODIS and Sentinel-2 snow products to retrieve daily gap-free snow cover maps at 20 m resolution. The three workflows differ in terms of the type of ingested snow products and, consequently, in the type of random forest used. The required inputs are the MODIS/Terra Snow Cover Daily L3 Global dataset at 500 m and the Sentinel-2 snow dataset at 20 m, automatically retrieved through the recently developed revised-Let It Snow workflow, from which the selected inputs are, alternatively, the Snow Cover Extent (SCE) map or the Normalized Difference Snow Index (NDSI) map, and a Digital Elevation Model (DEM) of consistent resolution with Sentinel-2 imagery. The algorithm is based on two steps, the first to fill the gaps of the MODIS snow dataset and the second to downscale the data and obtain the high resolution daily snow time series. The workflow is applied to a case study in Gran Paradiso National Park. The proposed study represents a first attempt to use the revised-Let It Snow with the purpose of extracting temporal parameters of snow. The validation was achieved by comparison with both an independent dataset of Sentinel-2 to assess the spatial accuracy, including the snowline elevation prediction, and the algorithm's performance through the different topographic conditions, and with in-situ data collected by meteorological stations, to assess temporal accuracy, with a focus on seasonal snow phenology parameters. Results show that all of the approaches provide robust time series (overall accuracies of A1 = 93.4%, and A2 and A3 = 92.6% against Sentinel-2, and A1 = 93.1%, A2 = 93.7%, and A3 = 93.6% against weather stations), but the first approach requires about one fifth of the computational resources needed for the other two. The proposed workflow is fully automatic and requires input data that are readily and globally available, and promises to be easily reproducible in other study areas to obtain high-resolution daily time series, which is crucial for understanding snow-driven processes at a fine scale, such as vegetation dynamics after snowmelt. [ABSTRACT FROM AUTHOR]

This study investigated trends in snowpack for the period 1965–2014 in 40 catchments located in five mountain regions in Czechia. We analysed daily series of air temperature, precipitation, and snow water equivalent (SWE) that were simulated with a conceptual model. The Mann-Kendall test showed strong increasing trends in air temperature at all elevations, mostly at the end of the cold season. This increase caused a decrease in snowfall fraction and SWE. Maximum SWE decreased mainly in western parts of Czechia (by up to −45 mm/decade). The length of the snow-covered period decreased by up to −6.8 days/decade, mainly due to earlier melt-out. Snowpack was more sensitive to changes in air temperature at elevations below 900 m a.s.l., while precipitation had a larger impact on snowpack at elevations above 1200 m a.s.l. The relative importance of air temperature for snow variability increased at all elevations in the last few decades. [ABSTRACT FROM AUTHOR]

Abstract Depositions of light‐absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow‐atmosphere interaction in the present‐day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in‐situ measurements. Here, we develop a new model chain NHM‐Chem‐SMAP, which is composed of a detailed regional meteorology‐chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011–2012 winter. NHM‐Chem‐SMAP successfully reproduces the in‐situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition.

Depositions of light‐absorbing particles (LAPs), such as black carbon (BC) and dust, on the snow surface modulate the snow albedo; therefore, they are considered key factors of snow‐atmosphere interaction in the present‐day climate system. However, their detailed roles have not yet been fully elucidated, mainly due to the lack of in‐situ measurements. Here, we develop a new model chain NHM‐Chem‐SMAP, which is composed of a detailed regional meteorology‐chemistry model and a multilayered physical snowpack model, and evaluate it using LAPs concentrations data measured at Sapporo, Japan during the 2011–2012 winter. NHM‐Chem‐SMAP successfully reproduces the in‐situ measured seasonal variations in the mass concentrations of BC and dust in the surface snowpack. Furthermore, we find that LAPs from domestic and foreign sources played a role in shortening the snow cover duration by 5 and 10 days, respectively, compared to the completely pure snow condition. Plain Language Summary: Light‐absorbing particles (LAPs) such as black carbon (BC) and dust absorb sunlight. Therefore, BC and dust deposited on the snow surface can accelerate snow melting. However, their detailed qualitative and quantitative roles in the climate system have not yet been fully elucidated owing to the lack of in‐situ measurements. In Sapporo, Japan, it has been demonstrated that the depositions of LAPs on the snow surface in recent years have the potential to shorten the snow cover duration by approximately two weeks compared to the completely pure snow situation; however, the relative contributions of LAPs from domestic and foreign sources on the snow cover duration are unknown. Here, we develop a chain of models composed of a detailed aerosol chemical transport model and a multilayered physical snowpack model. Using the model chain, we find that LAPs from domestic and foreign sources had the potential to shorten the snow cover duration in Sapporo, Japan during the 2011–2012 winter by 5 and 10 days, respectively compared to the completely pure snow condition. Key Points: A model chain of a detailed aerosol chemical transport model and a multilayered physical snowpack model is evaluated at Sapporo, JapanThe model reproduces the measured seasonal evolution of the mass concentrations of light‐absorbing particles in the surface snowpackLight‐absorbing particles from domestic and foreign sources could shorten the snow cover duration by a maximum of 5 and 10 days, respectively [ABSTRACT FROM AUTHOR]

The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change. Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta. We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta. We extracted spring snow-cover duration determined by a latitudinal gradient. The ‘regular year’ snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta. We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed. In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021. The NDVI rise since 2018 is preceded by up to 4 °C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.

Climate warming influences highly on snow cover regime in the midlatitudes. Snow cover conditions, in turn, affect human activity very much. The aim of this study was (a) to analyse spatial and temporal variability of snow cover duration, (b) to analyse spatial and temporal variability of the start and end dates of the period with the permanent snow cover, (c) to describe spatial, temporal and seasonal variability of median and maximum snow depth in Estonia and (d) to determine the presence of long-term changes and trends in these parameters during the period 1950/51–2015/16. Time series of daily snow depth at 22 stations for that period were processed in order to obtain reliable estimates of changes in the snow regime. Snow cover data are non-normally distributed, therefore, median and quartile range were used to describe the mean state and variability of snow cover. Only these dates were included into the analysis when snow cover was observed at least on 50% of days in the time series. Trend analysis was made using the Mann-Kendall test and trend values were found using the Theil-Sen's method. A large spatio-temporal variability of snow cover duration was found. The median number of days with snow cover at the 22 stations was 112, varying between 61 and 130 days. In the coastal regions of Estonia and especially on the western coast of Saaremaa Island snow cover duration has been much lower than in the continental part. The longest snow cover period is observed on uplands in south-eastern and north-eastern Estonia. It was found that, in the average, the period with the permanent snow cover in the continental Estonia begins on 19 December and ends on 18 March. There was a negative trend in snow cover duration due to the earlier snow melting in spring at the majority of stations. The end date of the permanent snow cover has shifted earlier by 10–30 days in 66 years and its duration has decreased accordingly. The maximum snow depth has been recorded on uplands of south-eastern Estonia with the median value 38 cm. There was a decreasing multiannual trend in snow depth from January to the end of March. Changes in snow depth were largest in the end of February and in March when the trend was statistically significant. In the average, snow depth has decreased by 0.5–1.5 cm per decade, i.e. by 2–9 cm throughout the whole study period. [ABSTRACT FROM AUTHOR]

Aims Snow cover occupies large percentage of land surface in Tibetan Plateau. Snow cover duration (SCD) during non-growing seasons plays a critical role in regulating alpine vegetation's phenology by affecting the energy budgets of land surface and soil moisture conditions. Different period's snow cover during non-growing season may have distinct effect on the vegetation's phenology. Start of season (SOS) has been observed advanced under the ongoing climate change in the plateau, but it still remains unclear how the SCD alters the SOS. This study attempts to answer the following questions: (i) What is the pattern of spatial and temporal variations for SCD and grassland SOS? (ii) Which period's SCD plays a critical role in grassland's SOS? Methods The remote sensing datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) were utilized to compute the SOS and SCD on the Tibetan Plateau over 2003–15. The Asymmetric Gaussian function was applied to extract SOS. We also explored the spatial pattern and temporal variation of SOS and SCD. Then, by using linear correlation coefficients, we investigated the driving effects of different period's non-growing season SCD on SOS. Important Findings The non-growing season SCD slightly decreased during 2003–15, while SOS exhibited an overall advancing trend. Advanced trends in SOS were observed in the eastern plateau, and the delayed trends were mainly founded in western plateau. Snow cover area exhibited two separate peaks during autumn and late winter over the plateau. Extended SCD regions mainly distributed in middle-east of the plateau, while shrunken SCD distributed in other regions of the plateau. SCD of different seasons caused distinct effects on vegetation SOS. Lengthened autumn SCD advanced SOS over the eastern plateau. The slightly lengthened SCD postponed SOS over the western plateau. In the wet meadow regions, advanced SOS was positively associated with SCD during the entire non-growing season, whereas for the dry steppe, SCD over the preseason played a more dominant role. The SCD of previous autumn and winter also showed lag effect on SOS over meadow regions to a certain extent. This study confirmed the importance of SCD to phenological processes at the beginning of growing season and further suggested that role of SCD should be discriminated for different periods and for different heat-water conditions. With the lag effects and SCD's distinct effect of different seasons considered, predictions on the Tibetan Plateau's spring phenology could be improved. [ABSTRACT FROM AUTHOR]

In alpine tundra the interannual and seasonal variability of C and N forms in soil and lake water during the short snow-free season could be significant and related to climatic and pedoclimatic variables. The hypothesis that not only the climatic and pedoclimatic parameters recorded during the summer season but also the ones measured during the previous snow-covered season could contribute to explaining the C and N dynamics in soil and surface water was tested along 10 snow-free seasons in 3 sites in the alpine tundra in the north-western Italian Alps (LTER site Istituto Mosso). Among the considered parameters, the snow cover duration (SCD) exerted a primary control on soil N-NH4 +, DOC, Cmicr, Nmicr and DOC:DON ratio, with an inverse relationship. A long SCD might cause the consumption of all the subnival substrata by the soil microorganisms, determining a C starvation during the subsequent snow-free season. An opposite trend was observed for the lake water, where a longer SCD corresponded to a higher content of inorganic N forms. Among the pedoclimatic indices, the number of soil freeze/thaw cycles (FTC) recorded during the snow-covered season had a positive relation with most of soil C and N forms and N-NO3 - in lake water. Only the soil DON showed an inverse pattern, and this result is consistent with the hypothesis that FTC released soil DON, subsequently decomposed and mineralized. Only N-NO3 - had a significant intraseasonal variability, reaching the highest values in September both in soil and water, revealing a significant slowdown of the contribution of soil N immobilization processes. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1) and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel’s position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence) for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179–311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/.

The Tizinafu watershed has a complex mountainous terrain in the western Kunlun Mountains; little study has been done on the spatial and temporal characteristics of snow cover in the region. Daily snow cover data of 10 hydrological years (October 2002 to September 2012) in the watershed were generated by combining MODIS Terra (MOD10A1) and Aqua (MYD10A1) snow cover products and employing a nine-day temporal filter for cloud reduction. The accuracy and window size of the temporal filter were assessed using a simulation approach. Spatial and temporal characteristics of snow cover in the watershed were then analyzed. Our results showed that snow generally starts melting in March and reaches the minimum in early August in the watershed. Snow cover percentages (SCPs) in all five elevation zones increase consistently with the rise of elevation. Slope doesn’t play a major role in snow cover distribution when it exceeds 10°. The largest SCP difference is between the south and the other aspects and occurs between mid-October and mid-November with decreasing SCP, indicating direct solar radiation may cause the reduction of snow cover. While both the mean snow cover durations (SCDs) of the hydrological years and of the snowmelt seasons share a similar spatial pattern to the topography of the watershed, the coefficient of variation of the SCDs exhibits an opposite spatial distribution. There is a significant correlation between annual mean SCP and annual total stream flow, indicating that snowmelt is a major source of stream runoff that might be predictable with SCP.

Daily snow data during 1961-2013 at the 105 meteorological stations in Xinjiang, China were used to investigate the spatiotemporal variations of several parameters, including starting and ending dates, duration, annual and monthly average and maximum snow depths. The modified Mann-Kendall test, empirical mode decomposition, empirical orthogonal function (EOF), and the inverse distance weight interpolation were applied. Snow lasted for 71 to 120 days. Snow depth decreased from north to south. Daily snow depth had periodical variations and were classified as four typical types, i.e., flat peak, multi-peak, sharp single-peak, and right-skewed. After daily snow depth was decomposed into 17 intrinsic mode functions (IMFs), IMF9, IMF10, and IMF11 over 189, 302, and 437 days of scales accounted for 79% of the total spatiotemporal variance in snow depth. Both annual starting and ending day numbers had decreasing trends, while the duration in days had an increasing trend. The average and maximum snow depth increased in most sites whether considering the seasonality in December, January, February, or annual values. EOF1 accounted for 70% of spatial variability and the temporal coefficient EC1 varied periodically. The spatiotemporal analysis of snow properties provides a basis for snowmelt understanding. [ABSTRACT FROM AUTHOR]

Abstract: Timing and accumulation of snow are among the most important phenomena influencing land surface phenology in mountainous ecosystems. However, our knowledge on their influence on alpine land surface phenology is still limited, and much remains unclear as to which snow metrics are most relevant for studying this interaction. In this study, we analyzed five snow and phenology metrics, namely, timing (snow cover duration (SCD) and last snow day), accumulation of snow (mean snow water equivalent, SWEm), and mountain land surface phenology (start of season and length of season) in the Swiss Alps during the period 2003–2014. We examined elevational and regional variations in the relationships between snow and alpine land surface phenology metrics using multiple linear regression and relative weight analyses and subsequently identified the snow metrics that showed strongest associations with variations in alpine land surface phenology of natural vegetation types. We found that the relationships between snow and phenology metrics were pronounced in high‐elevational regions and alpine natural grassland and sparsely vegetated areas. Start of season was influenced primarily by SCD, secondarily by SWEm, while length of season was equally affected by SCD and SWEm across different elevational bands. We conclude that SCD plays the most significant role compared to other snow metrics. Future variations of snow cover and accumulation are likely to influence alpine ecosystems, for instance, their species composition due to changes in the potential growing season. Also, their spatial distribution may change as a response to the new environmental conditions if these prove persistent. [ABSTRACT FROM AUTHOR]

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of

The Qinghai-Tibetan Plateau (QTP) snow cover information acquisition of the high precision spatial and temporal characteristics is of great significance for the research on its land surface atmosphere coupled system and global climate change effects. The Moderate Resolution Imaging Spectro-radiometer (MODIS) daily snow cover products (MOD10A1 and MYD10A1) have been widely used in long time series of spatial and temporal variation analysis, but they are limited to be used because of high cloud cover ratio. In this paper, a 7-day rolling combination algorithm was presented to eliminate cloud obscuration, and the whole cloud amount falls below 7 %. The ground station in situ measurements verify that the overall precision is more than 90 %. The presented algorithm guaranteed the same spatial resolution and temporal resolution, and has higher precision than products MOD10A1 and MYD10A1. The MODIS 7-day rolling combination snow cover datasets products were obtained between 2003 and 2014 in the QTP, and the snow cover area of spatial and temporal variation was analyzed. The change characteristics of snow cover duration was also studied combining with the Digital Elevation Model data. Results show that the snow cover area of the whole QTP has a slowly decreased trend, but increases in autumn. Thus, the snow cover proportion of annual periodic and unstable in different elevations has the highest correlation with area of the elevation. [ABSTRACT FROM AUTHOR]

Helicoverpa armigera causes serious damage to most crops around the world. However, the impacts of snow thickness on the H. armigera overwintering pupae are little known. A field experiment was employed in 2012-2015 at Urumqi, China. At soil depths of 5, 10, and 15 cm, overwintering pupae were embedded with four treatments: no snow cover (NSC), snow cover (SC), increasing snow thickness to 1.5 times the thickness of SC (ISSC-1), and to two times the thickness of SC (ISSC-2). Results suggested that snow cover and increasing snow thickness both significantly increased soil temperatures, which helped to decrease the mortality of overwintering pupae (MOP) of H. armigera. However, the MOP did not always decrease with increases in snow thickness. The MOPs in NSC and ISSC-1 were the highest and the lowest, respectively, though ISSC-2 had much thicker snow thickness than ISSC-1. A maximum snow thickness of 60 cm might lead to the lowest MOP. The longer the snow cover duration (SCD) at a soil depth of 10 cm in March and April was, the higher the MOP was. A thicker snow cover layer led to a higher soil moisture content (SMC) and a lower diurnal soil temperature range (DSTR). The highest and the lowest MOP were at a depth of 15 and 10 cm, respectively. The SMC at the depths of 10 and 15 cm had significant effects on MOP. A lower accumulated temperature (≤0 °C) led to a higher MOP. The DSTR in March of approximately 4.5 °C might cause the lowest MOP. The largest influence factor for the MOPs at depths of 5 and 10 cm and the combined data were the SCDs during the whole experimental period, and for the MOPs at a depth of 15 cm was the soil temperature in November. [ABSTRACT FROM AUTHOR]

A long-term Northern Hemisphere (NH) daily 5-km snow cover extent (SCE) product (JASMES) was developed by the application of a consistent objective snow cover mapping algorithm to data from historical optical sensors on polar orbiting satellites from 1978 to 2015. A conventional decision tree algorithm with multiple threshold tests was employed to analyze radiances for the five spectral bands available across the full analysis period. The accuracies of the analyzed SCE maps were evaluated against in-situ snow data measured at ground stations along with the SCE maps from the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) product. The evaluation showed the JASMES product to have a more temporally stable producer's accuracy (PA; 1–omission error) than NOAA, which is a key factor in the analysis of long-term SCE trends. Comparison of seasonal NH SCE trends from the two products showed NOAA to have opposite trends to those of JASMES in the fall and winter seasons, and to have overestimated SCE decreasing trends in the spring and summer. These tendencies are consistent with the increasing spatial and temporal resolutions of information over time, which were used in generating the NOAA snow analysis. An estimation of unbiased SCEs based on the accuracies of SCE maps also endorses the long-term trends of the JASMES product. The JASMES NH seasonal SCE exhibited negative slopes in all seasons but was only statistically significant in the summer (JJA) and fall (SON). Delayed snow cover onset was observed to be the main driver of decreasing annual snow duration (SCD) trends. The spatial pattern of annual SCD trends exhibited noticeable asymmetry between continents, with the largest significant decreases observed over western Eurasia with relatively few statistically significant decreases over North America. [ABSTRACT FROM AUTHOR]

Mean snow cover duration was derived for the entire continent of Europe based on the MODIS daily snow cover products MOD10A1 and MYD10A1 for the period from 2000 to 2011. Dates of snow cover start and snow cover melt were also estimated. Polar darkness north of ~62°N and extensive cloud coverage affected the daily snow cover, preventing a direct derivation of the desired parameters. Combining sensor data from both MODIS platforms and applying a temporal cloud filter, cloud coverage and polar darkness were removed from the input data and accuracy remained above 90% for 87% of the area. The typical snow cover characteristics of the whole continent are illustrated and constitute a unique dataset with respect to spatial and temporal resolution. Abnormal events, glacier inventories or studies on possible impacts of climate change on snow cover characteristics are only some examples for applications where the presented results may be utilized.

Knowledge of the duration and distribution of seasonal snow cover is important for understanding the hydrologic regime in mountainous regions within semi-arid climates. In the headwater of the semi-arid Sugnugur catchment (in the Khentii Mountains, northern Mongolia), a spatial analysis of seasonal snow cover duration (SCD) was performed on a 30 m spatial resolution by integrating the spatial resolution of Landsat-7, Landsat-8, and Sentinel-2A images with the daily temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) snow products (2000–2017). Validation was achieved using in situ time series measurements from winter field campaigns and distributed surface temperature loggers. We found a mean increase of SCD with altitude at approximately +6 days/100 m. However, we found no altitude-dependent changes in snow depth during field campaigns. The southern exposed valley slopes are either snow free or covered by intermittent snow throughout the winter months due to high sublimation rates and prevailing wind. The estimated mean SCD ranges from 124 days in the lower parts of the catchment to 226 days on the mountain peaks, with a mean underestimation of 12–13 days. Snow onset and melt dates exhibited large inter-annual variability, but no significant trend in the seasonal SCD was evident. This method can be applied to high-resolution snow mapping in similar mountainous regions.

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Numerous studies have reported significant declines in snow resources in Finland and elsewhere during the 20th century. To identify the main climate factors controlling these declines in Finland, this study evaluated long-term variations and trends in wintertime climate, snowpack hydrological processes (SHPs) and continuous snow cover duration (CSCD), and their links to atmospheric circulation patterns (ACPs). Analyses were conducted using observed daily climatological time series and simulated SHPs at three stations in southern (Kaisaniemi), central (Kajaani) and northern (Sodankylä) Finland with about 100 years of data. The Mann-Kendall nonparametric test was used to detect significant trends, the Pearson's coefficient (r) to identify relationships within snow-related variables, and Spearman's coefficient (ρ) to measure correlations of these variables with ACPs. Sensitivity of the snow-related variables with projected changes in temperature and precipitation was assessed. The results showed increases in wintertime temperature only at Kaisaniemi, but decreases in wintertime precipitation, snowfall and snow water equivalent (SWE) and shorter CSCD at all stations. In general, variations in wintertime temperature were positively associated with the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). However, wintertime precipitation showed significant relationships with the East Atlantic/West Russia (EA/WR), AO and West Pacific (WP) patterns in southern, central and northern Finland, respectively. SHPs and CSCD in southern Finland were associated with the same ACP influencing wintertime temperature (AO), and those in central and northern areas with the patterns influencing wintertime precipitation (EA, EA/WR and AO). Thus, declines in snow resources in Finland are mainly the result of reductions in snowfall owing to both wintertime warming and decreased precipitation at Kaisaniemi, while only to decreases in wintertime precipitation at Kajaani and Sodankylä stations. However, increase in precipitation (up to 30%) plays an important role in offsetting effects of temperature warming (up to 4 °C) on snow resource decline in Finland. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1) and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel's position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence) for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179-311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/. [ABSTRACT FROM AUTHOR]

Linear trends in few surface weather variables such as air temperatures (maximum temperature, minimum temperature), snow and rainy days, snowfall and rainfall amounts, rainfall contribution to seasonal total precipitation amount, seasonal snow cover depth and snow cover days (duration) are examined from winter-time observations at 11 stations located over the Northwest Himalayas (NWH). This study indicates that snowfall tends to show a decline in this region, while the rainfall tends to increase during the winter months. Seasonal snow cover depth and seasonal snow cover days also tend to show a decline over the NWH. Decrease in seasonal snow cover depth and duration have reduced the winter period in terms of availability of seasonal snow cover over the NWH during the last 2-3 decades. Other surface weather variables also exhibited significant temporal changes in recent decades. Observed trends in temperature and precipitation over the NWH in recent decades are also supported by long data series of temperature over the western Himalayas (WH), north mountain India (NMI) rainfall data and reanalysis products. [ABSTRACT FROM AUTHOR]

• Temperature increases favour tree growth in Andean mesic and wet treelines. • Tree growth is negatively affected by temperature increases at dry treelines. • Relationships between tree growth and temperature at treelines are not linear. • Interactions between temperature and snow-cover duration regulates tree growth. The high sensitivity of Nothofagus pumilio growth to climate variations at upper treelines provides a unique opportunity to document changes in tree responses to a warmer climate in the Patagonian Andes. In the context of significant recent temperature and precipitation changes across Patagonia, we conducted a study along the precipitation gradient in the Río de las Vueltas basin, southern Patagonian Andes, to: (1) document differences in N. pumilio growth trends at upper treelines, (2) determine changes in climate-growth relationships along the precipitation gradient, and (3) estimate future growth responses to simulated 21st century warming. For the past 100 years, mean tree-ring width increases progressively from wet to dry treelines in response to less abundant precipitation and less persistent snow cover into the growing season. Mountain aspect regulates snow cover duration and hence growing season length, thereby also influencing tree-ring width. On interannual scale, temperature directly modulates tree-growth variations in wet and mesic treelines, but is inversely related to growth in dry sites. Growth trends show that the approximate 0.56°C temperature increment since the mid-1970's in the Patagonian Andes dramatically enhanced the recorded long-term increasing growth rates at mesic, but to a lesser extent at humid treelines, suggesting nonlinear interactions between temperature and snow persistence on tree growth. Contrarily, growth rates at dry treelines decreased over the past 100 years. Our predictive statistical models indicate sustained decadal increases in current radial growth rates at wet and mesic sites and decreases at dry treelines towards the end of the 21st century under the simulated future warming scenarios for the southern Andes. However, the nonlinear relationships between warming, snow cover and tree growth, combined with unreliable estimates of precipitation for the region during the 21st century, suggest that these simulated changes in tree growth should be viewed with caution. [Display omitted] [ABSTRACT FROM AUTHOR]

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p< 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact. [ABSTRACT FROM PUBLISHER]

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ∼4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation. [ABSTRACT FROM AUTHOR]

Snow cover protects alpine plants from winter frost damage, keeping them under warmer and more stable temperatures than if there were no snow. Future climate scenarios predict less snow cover and earlier snow melt due to warming, causing paradoxically colder winters in a warmer climate. We compared intraspecific variation in cold tolerance between early snow melt (ESM) and late snow melt (LSM) populations of Aciphylla glacialis. Seedlings grown under common conditions were found to differ in cold tolerance consistent with their habitat of origin. ESM seedlings were more frost resistant and had a greater capacity to increase frost resistance in response to low temperatures than LSM seedlings. These results emphasise the relevance of microclimatic heterogeneity in driving physiological differences that might buffer some effects of climate change. Loss of snow cover could increase vulnerability of A. glacialis to lethal freezing in LSM sites whereas plants with greater frost tolerance in adjacent colder habitats (ESM sites) would be protected. Thus, intraspecific differentiation in tolerance of climatic stresses in combination with microclimatic refuges provided by topographic variation could enhance survival of some alpine species as climate warming progresses. [ABSTRACT FROM AUTHOR]

• Temperature was measured at 93 sites on the Qinghai-Tibet Plateau from 2012 to 2018. • Regional difference in spatial-temporal variation of permafrost was revealed. • Snow cover duration controlled the active layer thickness variation in the northern permafrost regions on the QTP. • Freezing index was the dominant factor in the southern regions. Due to the complex topography and localized climate, active layer thickening and permafrost warming varied distinctly in different regions on the Qinghai-Tibet Plateau (QTP). Based on the borehole-temperature data at 93 sites from 2012 to 2018, we analyzed the temporal and spatial characteristics of active layer thickness, permafrost temperature, and relevant climatic factors in 8 typical geomorphological units on the QTP. The active layer thickened at 86 sites and thinned at 7 sites. The permafrost warmed at 89 sites and cooled at 4 sites. The median values of the annual increase rate of active layer thickness were from 0.04 to 0.13 m/a for the monitored regions. The highest rate reached 0.46 m/a, indicating severe permafrost degradation in local areas. The mean annual soil temperatures at a 6-m depth generally increased faster for cold permafrost, and the active layer thickened more significantly in warm permafrost sites. Among these regions, Kekexili Mountains showed a lower increase rate of active layer thickness, and the temperature rise of permafrost in the Fenghuoshan Mountains was more significant. The temporal change of snow cover duration was closely related to the active layer thickness variation in the northern permafrost regions on the QTP (Kunlunshan Mountains and Chumaerhe High Plain). In contrast, the temporal variation of freezing index was the dominant factor in the southern regions (Wuli Basin, Tongtianhe Basin, and Tanggula Mountains). No linear correlation between the temporal variations of climatical factors and active layer thickness variation was found for the regions in the middle of QTP (Kekexili Mountains, Beiluhe Basin, and Fenghuoshan Mountains). The comprehensive effects of freezing index and snow cover duration result in the different relationships between air temperature variation and permafrost change in different regions on the QTP. These findings are beneficial for understanding the relationship between climate change and permafrost evolution. [ABSTRACT FROM AUTHOR]

• Peak SWE dominates the interannual variability of the snow duration. • The melt rates dominates the duration of the snowpack in the lower elevations. • The importance of the melt rates due to warmer conditions will increase. The duration of the seasonal snowpack determines numerous aspects of the water cycle, ecology and the economy in cold and mountainous regions, and is a balance between the magnitude of accumulated snow and the rate of melt. The contribution of each component has not been well quantified under contrasting topography and climatological conditions although this may provide useful insights into how snow cover duration could respond to climate change. Here, we examined the contribution of the annual peak snow water equivalent (SWE) and the seasonal melt rate to define the duration of the snowpack over temperate mountains, using snow data for mountain areas with different climatological characteristics across the Iberian Peninsula. We used a daily snowpack database for the period 1980–2014 over Iberia to derive the seasonal peak SWE, melt rate and season snow cover duration. The influence of peak SWE and melt rates on seasonal snow cover duration was estimated using a stepwise linear model approach. The stepwise linear models showed high R-adjusted values (average R-adjusted = 0.7), without any clear dependence on the elevation or geographical location. In general, the peak SWE influenced the snow cover duration over all of the mountain areas analysed to a greater extent than the melt rates (89.1%, 89.2%, 81.6% 93.2% and 95.5% in the areas for the Cantabrian, Central, Iberian, Pyrenees and Sierra Nevada mountain ranges, respectively). At these colder sites, the melt season occurs mostly in the spring and tends to occur very fast. In contrast, the areas where the melt rates dominated snow cover duration were located systematically at lower elevations, due to the high interannual variability in the occurrence of annual peak SWE (in winter or early spring), yielding highly variable melt rates. However, in colder sites the melt season occurs mostly in spring and it is very fast in most of the years. The results highlight the control that the seasonal precipitation patterns, in combination with temperature, exert on the seasonal snow cover duration by influencing the peak SWE and suggest a future increased importance of melt rates as temperatures increase. Despite the high climatological variability of the Iberian mountain ranges, the results showed a consistent behaviour along the different mountain ranges, indicating that the methods and results may be transferrable to other temperate mountain areas of the world. [ABSTRACT FROM AUTHOR]

Changes in vegetation and snow cover may lead to feedbacks to climate through changes in surface albedo and energy fluxes between the land and atmosphere. In addition to these biogeophysical feedbacks, biogeochemical feedbacks associated with changes in carbon (C) storage in the vegetation and soils may also influence climate. Here, using a transient biogeographic model (ALFRESCO) and an ecosystem model (DOS-TEM), we quantified the biogeophysical feedbacks due to changes in vegetation and snow cover across continuous permafrost to non-permafrost ecosystems in Alaska and northwest Canada. We also computed the changes in carbon storage in this region to provide a general assessment of the direction of the biogeochemical feedback. We considered four ecoregions, or Landscape Conservations Cooperatives (LCCs; including the Arctic, North Pacific, Western Alaska, and Northwest Boreal). We examined the 90 year period from 2010 to 2099 using one future emission scenario (A1B), under outputs from two general circulation models (MPI-ECHAM5 and CCCMA-CGCM3.1). We found that changes in snow cover duration, including both the timing of snowmelt in the spring and snow return in the fall, provided the dominant positive biogeophysical feedback to climate across all LCCs, and was greater for the ECHAM (+3.1 W m ^−2 decade ^−1 regionally) compared to the CCCMA (+1.3 W m ^−2 decade ^−1 regionally) scenario due to an increase in loss of snow cover in the ECHAM scenario. The greatest overall negative feedback to climate from changes in vegetation cover was due to fire in spruce forests in the Northwest Boreal LCC and fire in shrub tundra in the Western LCC (−0.2 to −0.3 W m ^−2 decade ^−1 ). With the larger positive feedbacks associated with reductions in snow cover compared to the smaller negative feedbacks associated with shifts in vegetation, the feedback to climate warming was positive (total feedback of +2.7 W m ^−2 decade regionally in the ECHAM scenario compared to +0.76 W m ^−2 decade regionally in the CCCMA scenario). Overall, increases in C storage in the vegetation and soils across the study region would act as a negative feedback to climate. By exploring these feedbacks to climate, we can reach a more integrated understanding of the manner in which climate change may impact interactions between high-latitude ecosystems and the global climate system.

An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951-2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971-2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40-80 % by mid century relative to 1971-2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000-2,500 m, SWE reductions amount to 10-60 % by mid century and to 30-80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century. [ABSTRACT FROM AUTHOR]

ABSTRACT The spatial variables which affect the surface thermal regime are explored in a valley in a high-altitude catchment of the Andes of Santiago. Two one-year (2009-10 and 2010-11) ground surface temperature (GST) time series are analysed separately and linear mixed-effects models are used to quantify the effects of site characteristics on mean GST (MGST) and ground surface thermal regimes. The effect of snow cover onset and disappearance dates on MGST is further examined in a sensitivity analysis. Elevation has the strongest effect on MGST (1°C/100 m), 30 additional days of snow cover suppress MGST by an estimated 0.1 to 0.6°C and openwork boulder surfaces are cooler by an estimated 0.6 to 0.8°C. The sensitivity analysis corroborates the effect of late snow cover in the linear models, which can overwhelm the spatial differences in radiative effects. A positive MGST found on active rock glaciers would suggest negative thermal offsets probably related to the presence of coarse blocky material at the surface, and which may also be present outside rock glaciers. We suggest that spatial patterns of MGST can serve as a proxy for spatial patterns in the lower limit of permafrost occurrence. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mean snow cover duration was derived for the entire continent of Europe based on the MODIS daily snow cover products MOD10A1 and MYD10A1 for the period from 2000 to 2011. Dates of snow cover start and snow cover melt were also estimated. Polar darkness north of ∼62°N and extensive cloud coverage affected the daily snow cover, preventing a direct derivation of the desired parameters. Combining sensor data from both MODIS platforms and applying a temporal cloud filter, cloud coverage and polar darkness were removed from the input data and accuracy remained above 90% for 87% of the area. The typical snow cover characteristics of the whole continent are illustrated and constitute a unique dataset with respect to spatial and temporal resolution. Abnormal events, glacier inventories or studies on possible impacts of climate change on snow cover characteristics are only some examples for applications where the presented results may be utilized. [ABSTRACT FROM AUTHOR]