Snow Cover Depth Research Articles

Automatic meteorological measuring systems for microclimate monitoring

The paper presents a description of the atmospheric-soil measuring complex (ASMC) intended for mobile and stationary long-term automatic measurements of the basic parameters of the atmosphere, soil and water. The device developed by the Institute of Monitoring of Climate and Ecological Systems of the SB RAS has a number of differences from analogues. To substantiate the possibility of using the device as a meteorological value meter, the data of measurements of air temperature and humidity, soil temperature, snow cover depth were verified. The data obtained by ASMC were compared with standard meteorological instruments. As a result of the analysis, high correlation coefficients between the time series were obtained. The average error of air temperature in the ASMS measurement is less than 0.2°C, and 91% of the measurements are within ±1° C. The average air humidity error is 5%, 70% of the measurements are within 5% of the error, and 95.5% of the measurements are within 10% of the error. The average error in soil temperature measurements during the warm period is 0.5°C, during the cold period is 1.5°C. The average error in measuring the snow cover depth is 5 cm and 90% of the measurements do not exceed it.

Spatial variability of the snow depth on mountain slope in Svalbard

The study was carried out to estimate the spatial variability of snow cover depths in avalanche centers of the mountain slopes of Svalbard. Accounting for the variability is necessary for monitoring the snow cover depths in the avalanche centers and evaluating the snow cover stability on the slope. The main tasks of the work were to evaluate the variability parameters and compare them with similar estimates obtained in other regions. In contrast to conventional snow surveys, thickness measurements were carried out more frequently (with resolution of every 1 m) in profiles (eight profiles in total), not exceeding the characteristic linear size of the avalanche origin zone (up to 100 m2). Spatial variations of snow cover thickness in each profile are considered as the realization of a random process. Data of the spring measurements of 2015 were used to estimate the mathematical expectations, variances, and autocorrelation functions of the snow cover depth on the Mount Olav slopes. Comparison of parameters of variability with those obtained in different mountain regions of Russia with the similar underlying surface, shows that the variability on Svalbard are the most similar to the variability in the Khibiny Mountains. Although the scattering and coefficients of variation obtained in the Khibiny Mountains are slightly larger, the spatial coherence of the snow cover depths is the lowest on Svalbard. Estimates of the correlation radii are within the range of 2–6 m. With such variability any deterministic estimation of spatial snow accumulation with the help of remote measurement stakes is impossible. The obtained parameters of the spatial statistical structure of the snow cover thickness allow using statistical modeling for the interpretation of point measurements. In that case, uncertainty of snow cover thickness data in places where measurements were not made will be reflected in their probabilistic estimation.

The Desna River Daily Multi-Site Streamflow Modeling Using SWAT with Detail Snowmelt Adjustment

The Ukrainian Government started the process of EU water Directives implementation aimed at developing of the River Basin Management Plan for 9 main river catchments. The program SWAT was tested to simulate the water flow of a large plain river with a predominant snow supply. River discharges (12 gauges), snow cover depth (13 stations), and the soft data, including graphically defined surface runoff and MODIS evapotranspiration, were used to calibrate the model. The calibration flowchart, along with a detailed source data description, is proposed to aid with streamflow simulation for the snowmelt-driven watersheds and fill the existing gap of distributed hydrological modeling in the region. The main issues with snow simulation and climate data coverage are discussed. The program SUFI-2 in SWAT-CUP package was used for performing calibration/validation and uncertainty analysis. The statistics (R2, NSE, PBIAS) showed good agreement (NS > 0.7, R2 > 0.75, PBIAS < ±10%) for a major part of the gauges and satisfactory for all of them (NS > 0.5, R2 > 0.6, PBIAS < ±15%), except of two linked upstream outlets. This study provides the basis for further research on water resources, the impacts of climate change, and water quality issues.

Recent evolution of glaciers in Western Asia in response to global warming: the case study of Mount Ararat, Turkey

We here investigated the recent (1976–2014) evolution of the Ararat mountain glaciers, paradigmatic of the evolution of ice bodies in Western Asia and the Caucasus. We gathered ice cover maps, including debris cover from different sources, to depict glaciers’ extension, and its variation under recent climate patterns. We then gathered data of (daily/monthly) weather variables (temperature, precipitation, snow cover depth) from two local stations managed by Turkish State Meteorological Service, which we subsequently analyzed to assess the presence of significant trends. We used the recently developed, weather-driven glaciological model Poly-Ice, able to mimic distributed ice and snow melt, mass budget, and gravity-driven ice flow of glaciers, to reproduce recent evolution of the Ararat ice bodies. We found a measurable decrease of the area (− 2.38 km2, − 30% of the initial area, − 0.06 km2 year−1) of the Ararat glaciers, including loss of ice under debris covered tongue (− 1.99 km2, − 70% of the initial area), driven by significantly increasing temperature especially in spring (+ 0.05 °C year−1). Using our Poly-Ice model, we could (i) mechanistically reproduce the response of the glaciers to the changing climate patterns, (ii) confirm faster downwasting ever since the 1990s under increasing temperature, and (iii) highlight decreased winter snow cover at thaw at the highest altitudes ever since the 1990s, further driving ice melt. Such physically based tool will further allow to project forward the dynamics of these glaciers under future climate. Our results are fully consistent with the present know how of glaciers’ retreat from Europe to Caucasus, and Central and Southern Asia, and contribute to the ongoing discussion about retreating glaciers worldwide.

Winter Ecosystem Respiration and Sources of CO2 From the High Arctic Tundra of Svalbard: Response to a Deeper Snow Experiment

AbstractCurrently, there is a lack of understanding on how the magnitude and sources of carbon (C) emissions from High Arctic tundra are impacted by changing snow cover duration and depth during winter. Here we investigated this issue in a graminoid tundra snow fence experiment on shale‐derived gelisols in Svalbard from the end of the growing season and throughout the winter. To characterize emissions, we measured ecosystem respiration (Reco) along with its radiocarbon (14C) content. We assessed the composition of soil organic matter (SOM) by measuring its bulk‐C and nitrogen (N), 14C content, and n‐alkane composition. Our findings reveal that greater snow depth increased soil temperatures and winter Reco (25 mg C m−2 d−1 under deeper snow compared to 13 mg C m−2 d−1 in ambient conditions). At the end of the growing season, Reco was dominated by plant respiration and microbial decomposition of C fixed within the past 60 years (Δ14C = 62 ± 8‰). During winter, emissions were significantly older (Δ14C = −64 ± 14‰), and likely sourced from microorganisms decomposing aged SOM formed during the Holocene mixed with biotic or abiotic mineralization of the carbonaceous, fossil parent material. Our findings imply that snow cover duration and depth is a key control on soil temperatures and thus the magnitude of Reco in winter. We also show that in shallow Arctic soils, mineralization of carbonaceous parent materials can contribute significant proportions of fossil C to Reco. Therefore, permafrost‐C inventories informing C emission projections must carefully distinguish between more vulnerable SOM from recently fixed biomass and more recalcitrant ancient sedimentary C sources.

SNOW THICKNESS ON AUSTRE GRØNFJORDBREEN, SVALBARD, FROM RADAR MEASUREMENTS AND STANDARD SNOW SURVEYS

Summary Comparison of two methods of measurements of snow cover thickness on the glacier Austre Grønfjordbreen, Svalbard was performed in the spring of 2014. These methods were the radar (500 MHz) observations and standard snow surveys. Measurements were conducted in 77 different points on the surface of the glacier. A good correlation (R 2 = 0.98) was revealed. In comparison with the data of snow surveys, the radar measurements show a similar but more detailed pattern of the distribution of the snow cover depth. The discrepancy between the depths of snow cover on maps plotted from data of both methods did not exceed 30 cm in most parts of the glacier. The standard error of interpolation of the radar data onto the entire glacier surface amounts, on average, to 18 cm. This corresponds to the error of radar measurements of 18.8% when an average snow depth is about 160 cm and 9.4% at its maximum thickness of 320 cm. The distance between the measurement points at which the spatial covariance of the snow depth disappears falls between 236 and 283 m along the glacier, and between 117 and 165 m across its position. We compared the results of radar measurements of the pulse-delay time of reflections from the base of the snow cover with the data of manual probe measurements at 10 points and direct measurements of snow depth and average density in 12 snow pits. The average speed of radio waves propagation in the snow was determined as V cr = 23.4±0.2 cm ns −1 . This magnitude and the Looyenga and Kovacs formulas allowed estimating the average density of snow cover ρ L = 353.1±13.1 kg m −3 and ρ K = 337.4±12.9 kg m −3 . The difference from average density measured in 12 pits ρ av.meas = 387.4±12.9 kg m −3 amounts to −10.8% and −14.8%. In 2014, according to snow and radar measurements, altitudinal gradient of snow accumulation on the glacier Austre Grønfjordbreen was equal to 0.21 m/100 m, which is smaller than the average values (0.35 m/100 m). According to the results of snow measurements of 2011–2014, the average thickness of the snow cover on the glacier Austre Grønfjordbreen was by 17 cm greater than in 1979. In the very snowy year 2012, it was higher by 21.5 cm in comparison with the year 1979, and its spatial variability (standard deviation σН) had increased by 25.6 cm. Estimates of spatial and temporal variability of snow cover depth will be used to analyze the hydrothermal state of the glacier and its changes with regard to revealed features and climatic trends.

Atmospheric Nitrogen Deposition and the Properties of Soils in Forests of Vologda Region

Twenty plots (20 m2 each) were selected in coniferous and mixed forests of the industrial Vologda district and the Vytegra district without developed industries in Vologda region. In March, snow cores corresponding to the snow cover depth were taken on these plots. In August, soil samples from the 0- to 20-cm layer of litter-free soddy-podzolic soil (Albic Retisol (Ochric)) were taken on the same plots in August. The content of mineral nitrogen (Nmin), including its ammonium (NH+4) and nitrate (NO-3) forms, was determined in the snow (meltwater) and soil. The contents of total organic carbon, total nitrogen, and elements (Al, Ca); pH; particle size distribution; and microbiological parameters―carbon of microbial biomass (Cmic) and microbial respiration (MR)―were determined in the soil. The ratio MR/Cmic = qCO2 (specific respiration of microbial biomass, or soil microbial metabolic quotient) was calculated. The content of Nmic in meltwater of two districts was 1.7 mg/L on the average (1.5 and 0.3 mg/L for the NH+4 and NO–3 forms, respectively). The annual atmospheric deposition was 0.6–8.9 kg Nmin/ha, the value of which in the Vologda district was higher than in the Vytegra district by 40%. Reliable correlations were found between atmospheric NH+4 depositions and Cmic (–0.45), between NH+4 and qCO2 (0.56), between atmospheric NO-3 depositions and the soil NO-3 (–0.45), and between NO-3 and qCO2 (–0.58). The content of atmospheric Nmin depositions correlated with the ratios C/N (–0.46) and Al/Ca (–0.52) in the soil. In forests with the high input of atmospheric nitrogen (>2.0 kg NH+4/(ha yr) and >6.4 kg Nmin/(ha yr)), a tendency of decreasing Cmic, C/N, and Al/Ca, as well as increasing qCO2, was revealed, which could be indicative of deterioration in the functioning of microbial community and the chemical properties of the soil.

Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: a review

ABSTRACTWe review recent climate changes over the Tibetan Plateau (TP) and associated responses of cryospheric, biospheric, and hydrological variables. We focused on surface air temperature, precipitation, seasonal snow cover, mountain glaciers, permafrost, freshwater ice cover, lakes, streamflow, and biological system changes. TP is getting warmer and wetter, and air temperature has increased significantly, particularly since the 1980s. Most significant warming trends have occurred in the northern TP. Slight increases in precipitation have occurred over the entire TP with clear spatial variability. Intensification of surface air temperature is associated with variation in precipitation and decreases in snow cover depth, spatial extent, and persistence. Rising surface temperatures have caused recession of glaciers, permafrost thawing, and thickening of the active layers over the permafrost. Changing temperatures, precipitation, and other climate system components have also affected the TP biological system. In addition, elevation‐dependent changes in air temperature, wind speed, and summer precipitation have occurred in the TP and its surroundings in the past three decades. Before projecting multifaceted interactions and process responses to future climate change, further quantitative analysis and understanding of the change mechanisms is required.

Observed soil temperature trends associated with climate change in the Tibetan Plateau, 1960\u20132014

Soil temperature, an important indicator of climate change, has rarely explored due to scarce observations, especially in the Tibetan Plateau (TP) area. In this study, changes observed in five meteorological variables obtained from the TP between 1960 and 2014 were investigated using two non-parametric methods, the modified Mann–Kendall test and Sen’s slope estimator method. Analysis of annual series from 1960 to 2014 has shown that surface (0 cm), shallow (5–20 cm), deep (40–320 cm) soil temperatures (ST), mean air temperature (AT), and precipitation (P) increased with rates of 0.47 °C/decade, 0.36 °C/decade, 0.36 °C/decade, 0.35 °C/decade, and 7.36 mm/decade, respectively, while maximum frozen soil depth (MFD) as well as snow cover depth (MSD) decreased with rates of 5.58 and 0.07 cm/decade. Trends were significant at 99 or 95% confidence level for the variables, with the exception of P and MSD. More impressive rate of the ST at each level than the AT indicates the clear response of soil to climate warming on a regional scale. Monthly changes observed in surface ST in the past decades were consistent with those of AT, indicating a central place of AT in the soil warming. In addition, with the exception of MFD, regional scale increasing trend of P as well as the decreasing MSD also shed light on the mechanisms driving soil trends. Significant negative-dominated correlation coefficients (α = 0.05) between ST and MSD indicate the decreasing MSD trends in TP were attributable to increasing ST, especially in surface layer. Owing to the frozen ground, the relationship between ST and P is complicated in the area. Higher P also induced higher ST, while the inhibition of freeze and thaw process on the ST in summer. With the increasing AT, P accompanied with the decreasing MFD, MSD should be the major factors induced the conspicuous soil warming of the TP in the past decades.

The Arctic System Reanalysis, Version 2

AbstractThe Arctic is a vital component of the global climate, and its rapid environmental evolution is an important element of climate change around the world. To detect and diagnose the changes occurring to the coupled Arctic climate system, a state-of-the-art synthesis for assessment and monitoring is imperative. This paper presents the Arctic System Reanalysis, version 2 (ASRv2), a multiagency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polar-optimized version of the Weather Research and Forecasting (Polar WRF) Model and WRF three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000–12. New features in ASRv2 compared to version 1 (ASRv1) include 1) higher-resolution depiction in space (15-km horizontal resolution), 2) updated model physics including subgrid-scale cloud fraction interaction with radiation, and 3) a dual outer-loop routine for more accurate data assimilation. ASRv2 surface and pressure-level products are available at 3-hourly and monthly mean time scales at the National Center for Atmospheric Research (NCAR). Analysis of ASRv2 reveals superior reproduction of near-surface and tropospheric variables. Broadscale analysis of forecast precipitation and site-specific comparisons of downward radiative fluxes demonstrate significant improvement over ASRv1. The high-resolution topography and land surface, including weekly updated vegetation and realistic sea ice fraction, sea ice thickness, and snow-cover depth on sea ice, resolve finescale processes such as topographically forced winds. Thus, ASRv2 permits a reconstruction of the rapid change in the Arctic since the beginning of the twenty-first century–complementing global reanalyses. ASRv2 products will be useful for environmental models, verification of regional processes, or siting of future observation networks.

Tree-Ring Widths and Snow Cover Depth in High Tauern

The aim of the study is to examine the correlation of Norway spruce tree-ring widths and the snow cover depth in the High Tauern mountains. The average standardized tree-ring widths indices for Nowary spruce posted by Bednarz and Niedzwiedz (2006) were taken into account. Increment cores were collected from 39 Norway spruces growing in the High Tauern near the upper limit of the forest at altitude of 1700-1800 m, 3 km from the meteorological station at Sonnblick. Moreover, the maximum of snow cover depth in Sonnblick (3105 m a.s.l.) for each winter season in the period from 1938/39 to 1994/95 (57 winter seasons) was taken into account. The main results of the research are as follows: (1) tree-ring widths in a given year does not reveal statistically significant dependency on the maximum snow cover depth observed in the winter season, which ended this year; (2) however, the tested relationship is statistically significant in the case of correlating of the tree-ring widths in a given year with a maximum snow cover depth in a season of previous year. The correlation coefficient for the entire period of the study is not very high (r=0.27) but shows a statistical significance at the 0.05 level; (3) the described relationship is not stable over time. 30-year moving correlations showed no significant dependencies till 1942 and after 1982 (probably due to the so-called divergence phenomenon). However, during the period of 1943-1981 the values of correlation coefficient for moving 30-year periods are statistically significant and range from 0.37 to 0.45; (4) the correlation coefficient between real and calibrated (on the base of the regression equation) values of maximum snow cover depth is statistically significant for calibration period and not significant for verification one; (5) due to a quite short period of statistically significant correlations and not very strict dependencies, the reconstruction of snow cover on Sonnblick for the period before regular measurements seems to be not reasonable.

Different winter soil respiration between two mid-temperate plantation forests

Abstract Larch and Chinese pine plantation forests are important carbon (C) sinks in the temperate regions, especially in China. However, their soil respiration in winter is still poorly studied. Here we explored the different microbial characteristics and winter soil respiration in larch and Chinese pine plantation forests in northeastern China, which has similar climate and basic soil characteristics. Results showed that both mean and cumulative winter soil CO2 fluxes were significantly higher in Chinese pine forest (0.45 µmol m−2 s−1 and 46.39 g C m−2, respectively) than in larch forest (0.25 µmol m−2 s−1 and 25.92 g C m−2, respectively). Snow depth and inorganic nitrogen (N) could not explain the differences in winter soil respiration between the two sites. Instead, Chinese pine forest had higher soil microbial biomass, fungi abundance, F/B (ratio of fungi to bacteria), and extracellular enzymatic activities (EEAs) than larch forest, which could lead to higher winter soil respiration in Chinese pine forest than in larch forest. Our findings indicated that the thermal insulation effect of litter cover was important to winter soil respiration, especially when the snow cover depth was less than 30 cm. Soil microbes played a more important role in soil respiration than soil nutrient status and should be carefully considered for better estimation of the C budget in different forest ecosystems. Although soil respiration was higher in Chinese pine forest, soil organic C content was also higher, suggesting its better C sequestration capacity than larch forest.

Эффективность технологического процесса лесозаготовок и степень влияния технологических и природных факторов

When implementing the technological process in the forest for logging operations, various factors influence, as well as taxation indicators such as (average diameter, height of the stand, reserve per hectare, availability of growth, completeness, etc.), natural climatic factors (Soil type carrying capacity of soils, depth of snow cover, etc.), degree of operator training, etc. One of the main tasks of rational justification of the technological process of cutting areas development is to find and combine controlled factors, at which performance indicators would be extreme. The most suitable variant of the technological development of the cutting area assumes what should be the intensity of logging, the layout of the cutting area, the number of loading points, and the productivity of harvesting machines. This task will be solved taking into account the influence of logging equipment and technology on the forest environment. The state of the stand left for growing after selective felling and its further development depends on the machine systems used in logging operations. The most important factor in the development of felling areas is the degree of intensity of felling. Since the quality of logging operations will depend on the method of cutting in the development of the cutting area (from the point of view of the impact on the natural environment, the soil cover, the left part of the stand, the adolescent).

Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing

Abstract. Snow cover on the Qinghai–Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

Slow community responses but rapid species responses 14 years after alpine turf transplantation among snow cover zones, south–central New Zealand

Alpine ecosystems are particularly vulnerable to the impact of global climate change. Depth and duration of seasonal snow cover are major drivers of variation in alpine plant community composition, so a reduction in snow cover as a result of climate change would expose plants that are currently protected by snow in winter and spring to greater extremes of temperature and increased risk of frost damage. We reciprocally transplanted 64 intact 60 cm × 60 cm × 10 cm (minimum depth) turves of alpine vegetation among four topographic zones on the Rock and Pillar Range, south-central South Island, New Zealand to investigate how shifts along a snow cover gradient affected plant growth, survival and community composition. The four zones: late-melting snowbed and early-melting snowbed in depressions, moderately exposed leeward upper slopes dominated by herbfield, and extremely exposed summit plateau dominated by cushionfield, differed in winter and spring snow cover. As expected, the highest species losses occurred in turves transplanted to very different zones e.g. late snowbed to summit plateau and vice versa. However many snowbed species still survived on the summit plateau seven years following transplantation. The degree to which turves had been colonised after seven years was significantly related to transplant zone rather than turf origin or original species richness; turves transplanted to the most species-rich zones were affected most by colonisation. Measurements of leaf production over three years in three focal Celmisia species (Asteraceae), characteristic of the herbfield on leeward slopes, and early and late snowbeds, showed that the late snowbed specialist suffered significantly reduced growth when transplanted to more exposed sites, but its survival was more affected by invertebrate herbivory rather than the direct effects of exposure. The cosmopolitan focal lichen species Thamnolia vermicularis, monitored over 14 years, rapidly colonised turves transplanted to cushionfield on the exposed summit plateau, where this and other lichens are abundant, but equally rapidly declined in turves transplanted to snowbeds. These findings add to a growing body of evidence that biotic interactions and species-specific traits will be critical drivers of alpine vegetation change under future climate scenarios.

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

Snow-atmosphere coupling and its impact on temperature variability and extremes over North America

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981–2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40–60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

Measuring the effects of reduced snow cover on Australia's alpine arthropods

AbstractSnow is one of the most important factors in the ecology of alpine ecosystems. In Australia, both the depth and duration of snow cover have declined significantly in recent decades and this trend is projected to continue with global warming. Many small arthropods remain active throughout the winter, within a space beneath the snowpack (subnivean) where the snow's insulation creates a thermally stable environment. Using field surveys and experimental manipulation of snow depth at two locations in the Australian alpine region, we explored the diversity of winter‐active arthropods and their response to reduced snow. Individuals from 18 arthropod Orders were detected beneath the snow during winter, with Collembola, Araneae, Acari and Coleoptera accounting for 95–98% of the individuals collected. The subnivean taxa represented a distinct subset of those active outside the winter months. Removal of the snow layer increased daily temperature fluctuations, increased the number of days below freezing and raised the mean surface temperatures. Community composition was altered by snow removal, driven by changes in the numbers of two abundant springtail taxa at each location. We found a strong reduction in the abundances of both taxa at one study site, and contrasting responses (one strong positive and one strong negative) to snow removal at the second study site. Subnivean arthropod communities in Australia thus appear sensitive to snow conditions at small spatial scales.

Tendencies in current climate change and atmospheric circulation variability in the Arctic region of West Siberia

The main goal of this study is to carry out the investigation of the climatic parameters variability and the role of global atmospheric circulation in their trends over the Arctic region of West Siberia (60-70°N, 60-90°E) using reanalysis data. The characteristics of spatial and temporal variability of meteorological parameters (surface air temperature and soil temperature, atmospheric pressure, snow depth and surface albedo) were calculated using ERA-Interim reanalysis data over the period of 1979−2015. It was established that in the beginning of XXI century, there is an air and soil temperature decrease in winter and autumn and its statistically significant increase in spring and summer. The tendency to permafrost area degradation is observed for the Arctic region. The maximal changes are observed in low-temperature permafrost soils than in soils with higher temperature. This trend is accompanied by the decrease in snow cover depth and surface albedo. Global circulation indices variability, its relationships with meteorological parameters in West Siberia and with sea ice cover extent in the Arctic Seas indicate that atmospheric blocking processes, which are responsible for anticyclonic type of weather, were developed in the region during last decades.

Monitoring and projecting snow on Hawaii Island

AbstractThe highest mountain peaks on Hawaii Island are snow covered for part of almost every year. This snow has aesthetic and recreational value as well as cultural significance for residents and visitors. Thus far there have been almost no systematic observations of snowfall, snow cover, or snow depth in Hawaii. Here we use satellite observations to construct a daily index of Hawaii Island snow cover starting from 2000. The seasonal mean of our index displays large interannual variations that are correlated with the seasonal mean freezing level and frequency of trade wind inversions as determined from nearby balloon soundings. Our snow cover index provides a diagnostic for monitoring climate variability and trends within the extensive area of the globe dominated by the North Pacific trade wind meteorological regime. We have also conducted simulations of the Hawaii climate with a regional atmospheric model. Retrospective simulations for 1990–2015 were run with boundary conditions prescribed from gridded observational analyses. Simulations for the end of 21st century employed boundary conditions based on global climate model projections that included standard scenarios for anticipated anthropogenic climate forcing. The future projections indicate that snowfall will nearly disappear by the end of the current century.