Comment on egusphere-2022-715

Responses of spring soil moisture of different land use types to snow cover in Northeast China under climate change background

Analysis of satellite and model datasets for variability and trends in Arctic snow extent and depth, 1948–2006

Evaluation of modelled snow depth and snow water equivalent at three contrasting sites in Switzerland using SNOWPACK simulations driven by different meteorological data input

Seasonal snow cover has an important impact on the difference between soil- and air temperature because of the insulation effect, and is therefore a key parameter in ecosystem models. However, it is still uncertain how specific variations in soil moisture, vegetation composition, and surface air warming, combined with snow dynamics such as compaction affect the difference between soil- and air temperature. Here, we present an analysis of 8 years (2012–2020) of snow dynamics in an Arctic ecosystem manipulation experiment (using snow fences) on Disko Island, West Greenland. We explore the snow insulation effect under different treatments (mesic tundra heath as a dry site and fen area as a wet site, snow addition from snow fences, warming using open top chambers, and shrub removal) on a plot-level scale. The snow fences significantly changed the inter-annual variation in snow depths and -phenology. The maximum annual mean snow depths were 90 cm on the control side and 122 cm on the snow addition side during all study years. Annual mean snow cover duration across 8 years was 234 days on the control side and 239 days on the snow addition side. The difference between soil- and air temperature was significantly higher on the snow addition side than on the control side of the snow fences. Based on a linear mixed-effects model, we conclude that the snow depth was the decisive factor affecting the difference between soil- and air temperature in the snow cover season (p < 0.0001). The change rate of the difference between soil- and air temperature, as a function of snow depth, was slower during the period before maximum snow depth than during the period between the day with maximum snow depth until snow ending day. During the snow-free season, the effects of the open top chambers were stronger than the effects of the shrub removal, and the combination of both contributed to the highest soil temperature in the dry site, but the warming effect of open top chambers was limited and shrub removal warmed soil temperature in the wet site. The warming effects of open top chambers and shrub removal were weakened on the snow addition side, which indicates a lagged effect of snow on soil temperature. This study quantifies important dynamics in soil-air temperature offsets linked to both snow and ecosystem changes mimicking climate change and provides a reference for future surface process simulations.

Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.

Spatiotemporal changes in snow cover over China during 1960–2013

Research on solid precipitation and snow cover, especially in mountainous areas, suffers from problems related to the lack of on-site observations and the low reliability of measurements, which is often due to instruments that are not suitable for the environmental conditions. In this context, the study area is the Monti Sibillini National Park, and it is no exception, as it is a mountainous area located in central Italy, where the measurements are scarce and fragmented. The purpose of this research is to provide a characterization of the snow cover with regard to maximum annual snow depth, average snow depth during the snowy period, and days with snow cover on the ground in the Monti Sibillini National Park area, by means of ground weather stations, and also analyzing any trends over the last 30 years. For this research, in order to obtain reliable snow cover data, only data from weather stations equipped with a sonar system and manual weather stations, where the surveyor goes to the site each morning and checks the thickness of the snowpack and records, it were collected. The data were collected from 1 November to 30 April each year for 30 years, from 1991 to 2020; six weather stations were taken into account, while four more were added as of 1 January 2010. The longer period was used to assess possible ongoing trends, which proved to be very heterogeneous in the results, predominantly negative in the case of days with snow cover on the ground, while trends were predominantly positive for maximum annual snow depth and distributed between positive and negative for the average annual snow depth. The shorter period, 2010–2022, on the other hand, ensured the presence of a larger number of weather stations and was used to assess the correlation and presence of clusters between the various weather stations and, consequently, in the study area. Furthermore, in this way, an up-to-date nivometric classification of the study area was obtained (in terms of days with snow on the ground, maximum height of snowpack, and average height of snowpack), filling a gap where there had been no nivometric study in the aforementioned area. The interpolations were processed using geostatistical techniques such as co-kriging with altitude as an independent variable, allowing fairly precise spatialization, analyzing the results of cross-validation. This analysis could be a useful tool for hydrological modeling of the area, as well as having a clear use related to tourism and vegetation, which is extremely influenced by the nivometric variables in its phenology. In addition, this analysis could also be considered a starting point for the calibration of more recent satellite products dedicated to snow cover detection, in order to further improve the compiled climate characterization.

Despite the importance of snow in alpine regions, little attention has been given to the homogenization of snow depth time series. Snow depth time series are generally characterized by high spatial heterogeneity and low correlation among the time series, and the homogenization thereof is therefore challenging. In this work, we present a comparison between two homogenization methods for mean seasonal snow depth time series available for Austria: the standard normal homogeneity test (SNHT) and HOMOP. The results of the two methods are generally in good agreement for high elevation sites. For low elevation sites, HOMOP often identifies suspicious breakpoints (that cannot be confirmed by metadata and only occur in relation to seasons with particularly low mean snow depth), while the SNHT classifies the time series as homogeneous. We therefore suggest applying both methods to verify the reliability of the detected breakpoints. The number of computed anomalies is more sensitive to inhomogeneities than trend analysis performed with the Mann–Kendall test. Nevertheless, the homogenized dataset shows an increased number of stations with negative snow depth trends and characterized by consecutive negative anomalies starting from the late 1980s and early 1990s, which was in agreement with the observations available for several stations in the Alps. In summary, homogenization of snow depth data is possible, relevant and should be carried out prior to performing climatological analysis.

Mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana) covers large areas in arid regions of western North America. Climate-change models predict a decrease in the range of sagebrush, but few studies have examined details of predicted changes on sagebrush growth and the potential impacts of these changes on the commu- nity. We analyzed effects of temperature, precipitation, and snow depth on sagebrush annual ring width for 1969 to 2007 in the Gunnison Basin of Colorado. Temperature at all times of year except winter had negative correlations with ring widths; summer temperature had the strongest negative relationship. Ring widths correlated positively with precipita- tion in various seasons except summer; winter precipitation had the strongest relationship with growth. Maximum snow depth also correlated positively and strongly with ring width. Multiple regressions showed that summer temperature and either winter precipitation or maximum snow depth, which recharges deeper soil horizons, are both important in controlling growth. Overall, water stress and perhaps especially maximum snow depth appear to limit growth of this species. With predicted increases in temperature and probable reduced snow depth, sagebrush growth rates are likely to decrease. If so, sagebrush populations and cover may decline, which may have substantial effects on community com- position and carbon balance.

The main objective of this study was to monitor wet snow conditions from Sentinel-1 over a season, to examine its variation over time by cross-checking wet snow with independent snow and weather estimates, and to study its distribution taking into account terrain characteristics such as elevation, orientation, and slope. One of our motivations was to derive useful representations of daily or seasonal snow changes that would help to easily identify wet snow elevations and determine melt-out days in an area of interest. In this work, a well-known approach in the literature is used to estimate the extent of wet snow cover continuously over a season and an analysis of the influence of complex mountain topography on snow distribution is proposed taking into account altitude, slope, and aspect of the terrain. The Sentinel-1 wet snow extent product was compared with Sentinel-2 snow products for cloud free scenes. We show that while there are good agreements between the two satellite products, differences exist, especially in areas of forests and glaciers where snow is underestimated. This underestimation must be considered alongside the areas of geometric distortion that were excluded from our study. We analysed retrievals at the scale of our study area by examining wet snow Altitude–Orientation diagrams for different classes of slopes and also wet snow Altitude–Time diagrams for different classes of orientations. We have shown that this type of representation is very useful to get an overview of the snow distribution as it allows to identify very easily wet snow lines for different orientations. For an orientation of interest, the Altitude–Time diagrams can be used to track the evolution of snow to locate altitudes and dates of snow loss. We also show that ascending/descending Sentinel-1 image time series are complementary to monitor wet snow over the French alpine areas to highlight wet snow altitude ranges and identify melt-out days. Links have also been made between Sentinel-1 responses (wet snow) and snow/meteorological events carefully listed over the entire 2017–2018 season.

It has repeatedly been reported that snow cover is a dominating factor in determining the presence or absence of permafrost in the discontinuous and sporadic permafrost regions. The temperature at the snow-soil interface by the end of winter, known as the bottom temperature of winter snow (BTS) method, has been used to detect the existence of permafrost in European alpine regions when the maximum snow depth is about 1.0 m or greater. A critical snow thickness of about 50 cm or greater can prevent the development of permafrost in eastern Hudson Bay, Canada. The objective of this study is to investigate the impact of snow cover on the presence or absence of permafrost in cold regions through numerical simulations. A one-dimensional heat transfer model with phase change and a snow cover regime is used to simulate energy exchange between deep soils and the atmosphere. The model has been validated against the in situ data in the Arctic. The simulation results indicate that both snow depth and the onset date of snow cover establishment are important parameters in relation to the presence or absence of permafrost. Early establishment of snow cover can make permafrost disappear, even with a relatively thin snow cover. Permafrost may survive when snow cover starts after the middle of December even with a snow thickness >1.0 m. This effect of snow cover on the ground thermal regime can be explained with reference to the pattern of seasonal temperature variation. Early establishment of snow cover enhances the insulating impact over the entire cold season, thus warming and eventually thawing the permafrost. The insulating effect is substantially reduced when snow cover starts relatively late and snowmelt in the spring creates a huge heat sink, resulting in a favorable combination for permafrost existence.

Snow patterns in ice-free areas of Greenland play important roles in ecosystems. Within a changing climate, a comprehensive understanding of the snow responses to climate change is of interest to anticipate forthcoming dynamics in these areas. In this study, we analyze the future snowpack evolution of a polar maritime Arctic location, Qeqertarsuaq (Disko Island, Central-Western Greenland). A physically-based snow model (FSM2) is validated and forced with CMIP6 projections for SSP2–4.5 and SSP5–8.5 greenhouse gasses emission scenarios, using two models: CanESM5 and MIROC6. The future snowpack evolution is assessed through four key seasonal (October to May) snow climate indicators: snow depth, snow days, snowfall fraction and ablation rate. Comparison against the observed air temperature for the reference climate period demonstrates superior accuracies for MIROC6 SSP2.4–5, with anomalies at 19 %, compared to CanESM5 SSP5.8–5 (25 %) and CanESM5 SSP2.4–5 (78 %). In terms of precipitation, CanESM5 SSP2.4–5 and SSP2.4–5 exhibit smaller anomalies against the observed data (5 %) in contrast to MIROC6 SSP2.4–5 (15 %) and MIROC6 SSP2.8–5 (17 %). Results demonstrate distinct snowpack responses to climate change depending on the model and emission scenario. For CanESM5, seasonal snow depth anomalies with respect to the reference period range from – 38 % (SSP2–4.5, 2040–2050 period) to – 74 % (SSP5–8.5, 2090–2100 period). MIROC6 projects lower snowpack reductions, with a decrease ranging from – 38 % (SSP2–4.5, 2040–2050 period) to – 57 % (SSP5–8.5, 2090–2100 period). Similar reductions are anticipated for snowfall and snow days. Changes in the snowpack evolution are primarily driven by positive trends in downwelling longwave radiation and air temperature. The projected increase in precipitation by the mid to late 21st century will lead to more frequent rain-on-snow events, intensifying snowpack melting. These findings help enhance the comprehension of future snow dynamics in the ice-free zones of Greenland, as well as the associated hydrological and ecological changes.

Frost weathering is a fundamental process controlling bedrock fracturing and rockfall activity in alpine environments, and its understanding requires evaluating the influence of snow cover on the thermal distribution at the bedrock surface (Matsuoka and Murton, 2008; Eckerstorfer et al., 2012; Haberkorn et al., 2017). In the Japanese Alps, characterized by heavy rainfall and snowfall, rock temperature data remain limited compared to the European Alps, which lie in the same mid-latitude zone as Japan. Moreover, long-term monitoring in the Japanese Alps has been confined to areas with relatively low snow accumulation (e.g., Matsuoka, 2019). In this study, we aim to clarify the conditions favorable for frost cracking in the heavy snow region by monitoring rock temperature, maximum snow depth, and snow-cover duration under various snow conditions on Mt. Hakuba (2,932 m a.s.l.) and Mt. Shakushi (2,812 m a.s.l.), located in the northern Japanese Alps.From October 2021 to September 2025, we continuously monitored rock temperatures at depths mainly of 2 cm, 20 cm, and 40 cm, and locally down to a maximum depth of 120 cm. In addition, aerial surveys were conducted using a Cessna aircraft and UAVs. 3D point cloud models of rock slopes for different seasons were generated from aerial images using SfM/MVS analysis. Maximum snow depth at each sensor location was obtained from point cloud distance calculations between models representing the maximum snow accumulation and the snow-free period when bedrock is exposed. From the rock temperature data, several indices related to frost cracking were calculated, including annual freeze–thaw cycles (Matsuoka, 2002), freezing degree days, the duration of temperatures within the frost cracking window, and thermal gradient conditions (Kellerer-Pirklbauer, 2017). These indices were compared with interannual variations in snow depth and snow-cover duration.In the results, snow-cover duration varied substantially between years, resulting in pronounced differences in frost cracking indices at each site. In addition, the height of snow cornices formed along ridges was greater than in heavy snow years, even when the overall snow cover duration was short, highlighting strong spatial heterogeneity in snow conditions near ridges. Although longer snow covers tended to raise winter rock temperatures, fractured bedrock sites showed lower temperatures and high freezing degree days. Future changes in frost cracking indices were calculated for each site based on various air-temperature warming scenarios, revealing markedly different trends depending on the scenario. These findings provide valuable insights for assessing present and future frost cracking potential in snow-rich alpine regions of Japan.

The Tibetan Plateau (TP) has the largest area of snow in the mid-latitude regions, and is strongly affected by the climate change. We examine the temporal variability of winter snow depth and number of days of snow cover at 69 Chinese Meteorological Administration stations above 2000 m a.s.l. in the eastern and central TP during 1961-2005. Snow depth is positively correlated with the number of snow days (R = 0.89, p < 0.0001). Regional mean winter (DJF) depth and days of snow cover increase at rates of 0.32 mm decade -1 and 0.40 d decade -1 from 1961 to 1990, but at rates of -1.80 mm decade -1 and -1.59 d decade -1 (i.e. decrease) between 1991 and 2005. The long term trends are weakly positive, but unrepresentative of shorter time periods. Thus snow depth and cover change depends on the timescale examined and cannot be attributed solely to increased greenhouse gas forcing. The decreasing snow depth in recent years will influence hydrological processes and water resources on the plateau and downstream. Both snow depth and duration have positive correlations with the winter Arctic Oscillation/North Atlantic Oscillation (AO/NAO) index and Nio-3 region (5N-5S, 150-90W) sea surface temperature (SST). During high AO/NAO index years, both a deeper India-Burma trough and an intensified cyclonic circulation near Lake Baikal bring more snowfall to the TP, consistent with a higher water vapor flux. The opposite is true in low AO/NAO years. Thus secular changes of snow depth and duration in the TP are not independent of changes in the macro-scale atmospheric circulation.