Snow Cover Variability Research Articles

Northern Hemisphere Snow Cover Variability and Change, 1915–97

Abstract Historical and reconstructed snow cover data from stations in Canada, the United States, the former Soviet Union, and the People’s Republic of China were used to reconstruct monthly snow cover extent (SCE) fluctuations over midlatitudinal (∼40°–60°N) regions of North America (NA) and Eurasia back to the early 1900s using an areal snow index approach. The station distribution over NA allowed SCE to be reconstructed back to 1915 for 6 months (November–April), along with estimates of monthly mean snow water equivalent (SWE) from gridded daily snow depth data. Over Eurasia, SCE was able to be reconstructed back to 1922, but major gaps in the station network limited the approach to 3 months (October, March, and April). The reconstruction provided evidence of a general twentieth century increase in NA SCE, with significant increases in winter (December–February) SWE averaging 3.9% per decade. The results are consistent with an observed increasing trend in winter snow depth over Russia and provide furth...

Interannual and decadal variations of snow cover over Qinghai-Xizang Plateau and their relationships to summer monsoon rainfall in China

Interannual and decadal variations of winter snow cover over the Qinghai-Xizang Plateau (QXP) are analyzed by using monthly mean snow depth data set of 60 stations over QXP for the period of 1958 through 1992. It is found that the winter snow cover over QXP bears a pronounced quasi-biennial oscillation, and it underwent an obvious decadal transition from a poor snow cover period to a rich snow cover period in the late 1970’s during the last 40 years.

The seasonal variation of urban air quality in northern European conditions

Meteorological conditions in northern Europe vary widely with the seasons. Consequently, the seasonal variation of urban air pollution is, to a large extent, determined by those meteorological variables which are important for air quality. We present first results on the yearly variation of atmospheric stability (and the occurrence of inversion layers), wind velocity, ambient temperature, solar radiation, precipitation and snow cover. The seasonal variations of the guideline exceedences for CO, NO2, SO2, TSP and PM10 in urban areas in Finland are then interpreted in the light of the meteorological variation.

Modeling interdecadal variations of lake-ice thickness and sensitivity to climatic change in northernmost Alaska

AbstractA physically based finite-element heat-transfer model with phase change is used to simulate ice growth and thickness variability on shallow, thaw lakes on the North Slope of Alaska during the period 1947–97. The basic inputs to the model are air temperature and snow depth as recorded at the U.S. National Weather Service station, Barrow, Alaska. The simulated long-term mean maximum ice thickness was 1.91 ±0.21 m with a range from 1.33 m (1962) to 2.47 m (1976). Variations in the seasonal snow cover played a much greater role than air temperatures in controlling ice-thickness variability during the 50 year simulation period. The sensitivity of lake-ice growth to extremes of snow depth, air temperature and snow bulk thermal conductivity is investigated. This study shows that lake-ice thickness has varied significantly from year to year in northern Alaska. Continued variability combined with potential climate change could affect the area of ice that freezes completely to the bottom of lakes each winter, resulting in changes in water storage and availability, permafrost thermal regime and talik dynamics beneath lakes, and methane efflux and energy fluxes to the atmosphere. It is concluded that quantification and a full understanding of these potential effects will require systematic and continuous field measurements that will provide better forcing and validation fields for improved models.

Temporal and spatial variability of North American prairie snow cover (1988–1995) inferred from passive microwave‐ derived snow water equivalent imagery

Estimates of regional snow water equivalent (SWE) are essential for hydrological prediction, climatological analysis, and meteorological forecasting. Passive microwave‐derived estimates of snow cover have unique benefits such as all‐weather imaging, rapid scene revisit capabilities, and the ability to provide these quantitative SWE data. For this study the available time series of special sensor microwave/imager (SSM/)) brightness temperatures in the equal area SSM/I Earth grid projection were processed with the Canadian Atmospheric Environment Service dual‐channel SWE algorithm for a ground‐validated North American prairie region. Seven winter seasons (December, January, and February) of SWE imagery spanning 1988–1995 and averaged for 5 day intervals were subjected to a rotated principal components analysis (PCA) performed individually for each season. A final PCA considering all 7 winter seasons was performed in order to investigate the degree to which snow cover patterns reappear from one season to the next. Results indicate that modes of snow cover in the North American prairies are most persistent during the late winter (February) and exhibit a greater degree of variability during December than the other winter months. Two snow cover regimes are identified for the study region, with the winters of 1988/1989–1991/1992 characterized in a manner that is unique in both temporal and spatial aspects from the winters of 1992/1993–1994/1995.

Vagaries of the summer monsoon rains

Summer monsoonal rains are very important for agriculture in our country. Fluctuations in monsoonal performance often create wide variations in crop production. In the present paper, the predictability of monsoon rains in the context of Professor Lorenz's recent work has been discussed. Variations in sea-surface temperature and snow cover and their impact on monsoonal rains are also discussed. Recent work on, the ENSO event and monsoons is described. The article covers the statistical methods for long-range prediction of rains. The limitations of the current 16-parameter regression equation are mentioned. In conclusion, the cost benefits, which can be assessed by methods based on probability are discussed.

Snowpack controls on nitrogen cycling and export in seasonally snow-covered catchments

Here we provide an overview of current research activities on nitrogen (N) cycling in high-elevation catchments of the Colorado Front Range. We then use this information to develop a conceptual model of how snow cover controls subnivial (below snowpack) microbial processes and N leachate from the snow-soil interface to surface waters. This model is based on research that identifies subnivial processes as a major control on the leaching loss of N from soil during snowmelt. These subnivial soil processes are controlled by the development of the seasonal snow pack that insulates soil from cold air temperatures and allows heterotrophic microbial activity in the soil to immobilize N. In this model the duration of snow-cover is divided into four snowpack regimes; zone I is characterized by shallow-short duration snowpacks. zone II is characterized by high interannual variability in snow depth and duration, zone III is characterized by early developing, continuous snow cover, and zone IV is characterized by deep, long-duration snow cover verging on perennial snowpacks. In zone I, soils remain frozen and there is little microbial activity and N leachate is high. In zone II, total microbial activity is highly variable and the amount of N leachate is highly variable. In zone III, total microbial activity is high and there is little N leachate. In zone IV, microbial activity is reduced because of carbon limitation and N leachate is high. This model suggests that a portion of the spatial and temporal variability observed in N export from these seasonally snow-covered systems is due to variability in winter snow cover across landscape types and inter-annually within a landscape type.

Scaling issues in snow hydrology

The concept of scale can be used to quantify characteristic lengths of (a) a natural process (such as the correlation length of the spatial snow water equivalent (SWE) variability); (b) a measurement (such as the size of a snow density sample or the footprint of a satellite sensor), and (c) a model (such as the grid size of a distributed snow model). The different types of scales are denoted as process scale, measurement scale and model scale, respectively. Interpolations, extrapolations, aggregations, and disaggregations are viewed as a change in model scale and/or measurement scale. In a first step we examine, in a linear stochastic analysis, the effect of measurement scale and model scale on the data and the model predictions. It is shown that the ratio of the measurement scale and the process scale, and the ratio of the model scale and the process scale are the driving parameters for the scale effects. These scale effects generally cause biases in the variances and spatial correlation lengths of satellite images, field measurements, and simulation results of snow models. It is shown, by example, how these biases can be identified and corrected by regularization methods, At the core of these analyses is the variogram. For the case of snow cover patterns, it is shown that it may be difficult to infer the true snow cover variability from the variograms, particularly when they span many orders of magnitude. In a second step we examine distributed snow models which are a non-linear deterministic approach to changing the scale. Unlike in the linear case, in these models a change of scale may also bias the mean over a catchment of snow-related variables such as SWE There are a number of fundamental scaling issues with distributed models which include subgrid variability, the question of an optimum element size, and parameter identifiability. We give methods for estimating subgrid variability. We suggest that, in general, an optimum element size may not exist and that the model element scale may in practice be dictated by data availability and the required resolution of the predictions. The scale effects in distributed non-linear models can be related to the linear stochastic case which allows us to generalize the applicability of regularization methods. While most of the paper focuses on physical snow processes, similar conclusions apply and similar methods are applicable to chemical and biological processes.

Remote Sensing of Seasonal Snow Cover in the Mountains of New Zealand Using Satellite Imagery

This paper reviews the use of satellite imagery for mapping seasonal snow cover in New Zealand. Despite the highly variable nature and importance of seasonal snow, there have been few studies, which attempt to derive snow information using remote sensing in New Zealand. These studies are restricted to analyses of hard copy images or of digital analogues of limited parts of the country. There is no regular satellite monitoring programme. Three case studies are presented and explore the effectiveness of using AVHRR imagery to obtain snow covered area and snow line elevation. The studies highlight major problems of separating cloud from snow and the lack of available cloud free images. This probably means that by itself, satellite monitoring is not sufficient to provide a complete snow climatology, but can give valuable and low cost monthly checks as to the variability of snow cover. In combination with snow models, the area and volume of seasonal snow cover can then be obtained.

Snowmelt runoff models

Models that predict meltwater runoff at a daily timescale are important in water resource management, flood hazard assessment and climate-change impact studies. This article identifies four basic components of such models: meteorological extrapolation, snowmelt estimation at a point, snow-cover depletion and runoff routing. Alternative ways of handling these are discussed, with emphasis on the contrasting treatments in two widely used models: HBV and SRM. Many of the issues in meltwater modelling reflect wider debates in hydrological and environmental modelling, including problems of complexity vs. simplicity, the appropriate level of spatial disaggregation, parameter identification and calibration, and internal validation. In reviewing current trends emphasis is placed on the potential and limitations of fully distributed models, problems in using energy-balance rather than temperature-index melt models at basin scale, ways to deal with spatial variability in snow cover, and the value and limitations of earth observation data.

Corrections to “Eurasian snow cover variability and northern hemisphere climate predictability”

Table 1. Eurasian snow cover and SSTs as predictors (SON) and concurrent (DJF) of hemispheric and regional climate indices. Observed climate indices are shown in rows all climate indices are for DJF. Climate indices are, ZMW, NAO, Scandinavia or Eurasian One Pattern (SCA), West Pacific Pattern (WPP), Pacific/North American Pattern (PNA). NAO and SCA are dominant modes of variability in North Atlantic and PNA and WPP are dominant modes of variability in North Pacific in winter /Barnston and Livezey, 1987]; also shown in square brackets are the autocorrelations of each DJF climate index with its own preceding SON index. Statistical significance greater than 95% confidence level in italics and greater than 99% confidence level in bold. The correlation coefficient included in parentheses corresponds to the statistic excluding E1-Nifio years 1972, 1982, 1986, 1991. We also computed the correlations between the detrended time-series of snow cover and the zonal mean wind, NAO and SCA indices and no changes were found in the statistical significance of the correlations from those presented in the table.

Eurasian snow cover variability and northern hemisphere climate predictability

We present observational evidence demonstrating dynamic interactions and feedbacks between multi‐seasonal snow cover and winter‐time circulation anomalies over mid‐high latitudes. The cooling effect of snow cover is associated with a strengthened and more expansive Siberian high with more frequent, topographically constrained intrusions west and north. Early‐season snow cover variability leads to altered general circulation patterns consistent with the dominant mode of winter variability observed in the Northern Hemisphere troposphere. The implications of the surface‐atmosphere coupling for seasonal to interannual predictability are also discussed.

Winter snow cover variability on East Antarctic sea ice

Analysis of the first detailed data set of snow characteristics collected over East Antarctic sea ice in winter confirms that on small scales, snow on Antarctic sea ice is highly variable in both thickness and properties. High‐amplitude cyclical variability in atmospheric forcing related to the passage of storms is responsible for the high degree of textural heterogeneity observed. Changes in snow properties were examined over a 3‐week period, during which a largely icy snow cover, formed at near‐freezing temperatures, metamorphosed to snow in which facetted crystals and depth hoar dominated, as the air temperature plummeted. Even on flat ice, significant localized thickening of snow occurs in the form of barchan dunes. Although we observed great variability in snow thickness and properties on local scales, overall snow thickness distribution and the complex textural assemblage of snow types are similar from region to region. Similar observations were made by Sturm et al. [1998] in West Antarctica. Large‐scale similarities are also apparent in mean snow density, grain size, and bulk snow salinity, although high variability is again found across individual floes. Rapid depth hoar formation is a ubiquitous process that greatly affects the density, texture, grain size, and effective thermal conductivity of the snow cover. The observed heterogeneity results in varying snow effective thermal conductivities. The mean bulk effective thermal conductivity, computed from the proportion of observed snow types, is 0.164 W m−1 K−1, significantly lower than values typically used in large‐scale sea ice modeling but similar to that derived by Sturm et al. [1998] in a near‐simultaneous experiment in the Bellingshausen and Ross Seas. It varies from 0.097 to 0.383 W m−1 K−1 in different snow pits. The findings support those of Sturm et al. [1998] that periodic flooding and subsequent snow ice formation, which are also ubiquitous processes, effectively diminish the degree to which basal snow processes create inhomogeneities in the snow pack.

Snow ablation modelling in a mature aspen stand of the boreal forest

Snow ablation modelling at the stand scale must account for the variability in snow cover and the large variations of components of energy transfer at the forest floor. Our previous work successfully predicted snow ablation in a mature jack pine stand by using a one-dimensional snow process model and models predicting radiation below forest canopies. This work represents a second test of our basic modelling scenario by predicting snow ablation in a leafless, deciduous aspen stand and verifying the results with field data. New modifications to the snow model accounted for decreased albedo owing to radiation penetration through optically thin snowpacks. A provisional equation estimates litter fall on the snowpack, thereby reducing the areal averaged albedo. We showed that subcanopy radiation measurements can be used with a canopy model to estimate a branch area index for defoliated aspen as an analogue to the foliage area index used for conifers. Modelled incoming solar and long-wave radiation showed a strong correlation with measurements, with r2=0·96 and 0·91 for solar and long-wave radiation, respectively. Model results demonstrate that net radiation overwhelms turbulent exchanges as the most significant driving force for snowmelt in aspen forests. Predicted snow ablation in the aspen stand compared very favourably with available data on snow depth. © 1998 John Wiley & Sons, Ltd.

European Snow Cover and Its Influence on Spring and Summer Temperatures

European surface temperatures in late Spring and early Summer were examined in relation to European snow cover using satellite-derived monthly snow-cover data and station temperature anomalies. Maximum annual snow extent and March snow extent were correlated with temperature anomalies from April to July. Maximum annual snow-cover extent, which occurred in January or February, was significantly negatively correlated with temperature in the Spring and early Summer. Correlations were generally in the range -0.4 to -0.7. Three distinct regions emerged as being correlated with variation in maximum annual snow cover. These were Central Europe, Western Europe and Eastern Europe in April, May andJuly respectively. The correlation with March snow cover was significant over an extensive area in April but was no longer significant by June. Analysis of other possible causes of temperature anomaly in these areas in the months in question suggested that snow cover was probably significant in the Central European region in April. It is also possible that indirect snow-cover effects are significant inJuly.

Modelling runoff using optical satellite remote sensing data in a high mountainous alpine catchment of Italy

Development of hydrological models for seasonal and real-time runoff forecast in rivers of high alpine catchments is useful for management of water resources. The conceptual models for this purpose are based on a temperature index and/or energy budget and can be either lumped or distributed over the catchment area. Remote sensing satellite data are most useful to acquire near real-time geophysical parameters in order to input to the distributed forecasting models. In the present study, integration of optical satellite remote sensing-derived information was made with ground meteorological and hydrological data, and predetermined catchment morphological parameters, to study the feasibility of application of a distributed temperature index snowmelt runoff model to one of the high mountainous catchments in the Italian Alps, known as Cordevole River Basin. Five sets of Landsat Multispectral Scanning System (MSS) and Thematic Mapper (TM) computer-compatible tapes (CCTs) were processed using digital image processing techniques in order to evaluate the snow cover variation quantitatively. Digital elevation model, slope and aspect parameters were developed and used during satellite data processing. The satellite scenes were classified as snow, snow under transition and snow free areas. A second-order polynomial fit has been attempted to approximate the snow depletion and to estimate daily snow cover areal extent for three elevation zones of the catchment separately. Model performance evaluation based on correlation coefficient, Nash–Sutcliffe coefficient and percentage volume deviation indicated very good simulation between measured and computed discharges for the entire snowmelt period. The use of average temperature values computed from the maximum and minimum temperatures into the model was studied and a suitable algorithm was proposed. © 1997 John Wiley & Sons, Ltd.

Some important characteristics of surface albedo of China in recent years

Based on the radiation data from 14 representive stations from 1986 to 1990 and the distribution situations of vegetation and soil, some new characteristics of surface albedo in China are analysed. In different regions, surface albedo is different due to differences of surface features. Even though in the same region, surface albedo varies with seasonal variation. However, in the seasons and regions without snow cover, the long-term and interannual variation of surface albedo is not obvious. On the viewpoint of climate, the values of surface albedo mainly depend on whether there is snow cover or not. The interannual variation of surface albedo is greater to the north of 50°N, with a variation range of 5%. The influences of geographical conditions, such as latitude, elevation and air pollution, are also analysed.

THE IMPACT OF CLIMATE CHANGE ON THE SNOW COVER PATTERN IN ESTONIA

The paper deals with problems of temporal and spatial variability of snow cover duration, of correlation between snow cover and winter mean air temperature patterns and of the impact of climate change on the snow cover pattern in Estonia. Snow cover fields are presented in form of IDRISI raster images. Snow cover duration measured at ca 100 stations and observation points have been interpolated into raster cells. On the base of time series of raster images, a map of mean territorial distribution of snow cover duration is calculated. Estonia is characterized by a great spatial variability of snow cover mostly caused by the influence of the Baltic Sea. General regularities of snow cover pattern are determined. A 104-year time series of spatial mean values of snow cover duration is composed and analyzed. A decreasing trend and periodical fluctuations have detected. Standardized principal component analysis is used for the time series of IDRISI raster images. It enables to study the influence of different factors on the formation of snow cover fields and territorial extent of coherent fluctuations. Correlation between snow cover duration and winter mean air temperature fields is analyzed. A spatial regression model is created for estimation of the influence of climate change on snow cover pattern in Estonia. Using incremental climate change scenarios (2 °C, 4 °C and 6 °C of warming in winter) mean decrease of snow cover duration in different regions in Estonia is calculated. According to results of model calculation, the highest decrease of snow cover duration will be take place on islands and in the coastal region of West Estonia. A permanent snow cover may not form at all. In the areas with maximum snow cover duration in North-East and South-East Estonia, that decrease should be much lower.

HISTORICAL SNOW COVER VARIABILITY IN THE GREAT PLAINS REGION OF THE USA: 1910 THROUGH TO 1993

The duration of seasonal (September–May) snow cover over the central and northern Great Plains region of the USA has varied considerably during this past century. Annual and decadal fluctuations are embedded in a statistically significant trend toward greater seasonal snow cover, from 29 days in the early part of the century to 38 days in recent decades. The variability of snow cover duration also increased throughout the century, exhibiting the largest changes in a north-west-trending zone bisecting the region. Seasonal snowfall, temperature, and precipitation show fluctuations similar to those of snow cover; however, only snowfall has increased significantly since 1910. A post-1970 shift toward longer autumn (September–November) snow cover duration and shorter spring (March–May) duration has been accompanied by decreases in autumn maximum and minimum temperatures and increases in spring minimum temperatures. The reliance on snow cover for hydrological and agricultural purposes in this part of the country, the sensitivity of the boundary layer climate to snow cover conditions in the Plains, and the projections by global climate models for this region to become warmer and dryer as a result of increasing atmospheric concentrations of greenhouse gases, necessitate increased study and hence a better understanding of the variability of snow cover over the Plains.

HISTORICAL SNOW COVER VARIABILITY IN THE GREAT PLAINS REGION OF THE USA: 1910 THROUGH TO 1993

The duration of seasonal (September–May) snow cover over the central and northern Great Plains region of the USA has varied considerably during this past century. Annual and decadal fluctuations are embedded in a statistically significant trend toward greater seasonal snow cover, from 29 days in the early part of the century to 38 days in recent decades. The variability of snow cover duration also increased throughout the century, exhibiting the largest changes in a north-west-trending zone bisecting the region. Seasonal snowfall, temperature, and precipitation show fluctuations similar to those of snow cover; however, only snowfall has increased significantly since 1910. A post-1970 shift toward longer autumn (September–November) snow cover duration and shorter spring (March–May) duration has been accompanied by decreases in autumn maximum and minimum temperatures and increases in spring minimum temperatures. The reliance on snow cover for hydrological and agricultural purposes in this part of the country, the sensitivity of the boundary layer climate to snow cover conditions in the Plains, and the projections by global climate models for this region to become warmer and dryer as a result of increasing atmospheric concentrations of greenhouse gases, necessitate increased study and hence a better understanding of the variability of snow cover over the Plains.