Snow Cover Variability Research Articles

Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach

Abstract. Avalanche hazard assessment requires a very precise estimation of the release area, which still depends, to a large extent, on expert judgement of avalanche specialists. Therefore, a new algorithm for automated identification of potential avalanche release areas was developed. It overcomes some of the limitations of previous tools, which are currently not often applied in hazard mitigation practice. By introducing a multi-scale roughness parameter, fine-scale topography and its attenuation under snow influence is captured. This allows the assessment of snow influence on terrain morphology and, consequently, potential release area size and location. The integration of a wind shelter index enables the user to define release area scenarios as a function of the prevailing wind direction or single storm events. A case study illustrates the practical usefulness of this approach for the definition of release area scenarios under varying snow cover and wind conditions. A validation with historical data demonstrated an improved estimation of avalanche release areas. Our method outperforms a slope-based approach, in particular for more frequent avalanches; however, the application of the algorithm as a forecasting tool remains limited, as snowpack stability is not integrated. Future research activity should therefore focus on the coupling of the algorithm with snowpack conditions.

Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning

PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2016WR018896 Key Points: A machine-learning algorithm for optimizing snow sensor placements is compared to expert placements in an existing sensor network The spatial and temporal transferability of the algorithm is then assessed in 14 total LiDAR surveys in two uninstrumented catchments The accuracy of the snow depth estimated from the sensor measurements is higher than expert and randomized placements in each of the surveys Correspondence to: C. A. Oroza, coroza@berkeley.edu Citation: Oroza, C. A., Z. Zheng, S. D. Glaser, and D. Tuia (2016), Optimizing embedded sensor network design for catchment- scale snow-depth estimation using LiDAR and machine learning, Water Resour. Res., 52, 8174–8189, doi:10.1002/2016WR018896. Received 11 MAR 2016 Accepted 23 SEP 2016 Accepted article online 27 SEP 2016 Published online 22 OCT 2016 Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning Carlos A. Oroza 1 , Zeshi Zheng 1 , Steven D. Glaser 1 , Devis Tuia 2 , and Roger C. Bales 1,3 Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA, 2 Department of Geography, University of Zurich, Zurich, Switzerland, 3 Sierra Nevada Research Institute, University of California, Merced, California, USA Abstract We evaluate the accuracy of a machine-learning algorithm that uses LiDAR data to optimize ground-based sensor placements for catchment-scale snow measurements. Sampling locations that best represent catchment physiographic variables are identified with the Expectation Maximization algorithm for a Gaussian mixture model. A Gaussian process is then used to model the snow depth in a 1 km 2 area sur- rounding the network, and additional sensors are placed to minimize the model uncertainty. The aim of the study is to determine the distribution of sensors that minimizes the bias and RMSE of the model. We com- pare the accuracy of the snow-depth model using the proposed placements to an existing sensor network at the Southern Sierra Critical Zone Observatory. Each model is validated with a 1 m 2 LiDAR-derived snow-depth raster from 14 March 2010. The proposed algorithm exhibits higher accuracy with fewer sensors (8 sensors, RMSE 38.3 cm, bias 5 3.49 cm) than the existing network (23 sensors, RMSE 53.0 cm, bias 5 15.5 cm) and randomized placements (8 sensors, RMSE 63.7 cm, bias 5 24.7 cm). We then evaluate the spatial and temporal transferability of the method using 14 LiDAR scenes from two catchments within the JPL Airborne Snow Observatory. In each region, the optimized sensor placements are determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys is then com- pared to 100 configurations of sensors selected at random. We find the error statistics (bias and RMSE) to be more consistent across the additional surveys than the average random configuration. 1. Introduction A major challenge of hydrologic science in montane regions relates to estimating the spatial variability of snow cover [Bales et al., 2006; Trujillo and Lehning, 2015; McCreight et al., 2014; Guan et al., 2013]. Multiple independent variables govern the distribution of snow cover, including elevation, slope, aspect, and the dis- tribution of canopy [Faria et al., 2000; Musselman et al., 2008; Lehning et al., 2011; Helfricht et al., 2014]. Non- stationary effects such as climate warming and changes in vegetation structure may significantly alter the timing and magnitude of storage and runoff in these watersheds [Goulden and Bales, 2014; Flanner et al., 2009]. Existing regression-based hydrologic models, which use statistical relations from historical hydro- graphs to predict runoff and inform allocation decisions [Perkins et al., 2009; Rosenberg et al., 2011; Rango and Martinec, 1995] will have limited accuracy as conditions deviate from historical norms and thus may prove to be inadequate for predictions in water management. C 2016. American Geophysical Union. V All Rights Reserved. OROZA ET AL. Recent research has focused on improving hydrologic models by assimilating remote-sensing and in situ measurements with distributed energy-balance models to better estimate storage and runoff [Guan et al., 2013]. These methods use well-developed remote-sensing [Painter et al., 2003; Rosenthal and Dozier, 1996; € lli et al., 2002; Egli et al., 2012] and energy-balance models [Marks et al., 1992; Link and Marks, Dozier, 1989; P a 1999; Brubaker et al., 1996] to estimate snow and snowmelt processes across basins. In situ measurements for these methods are presently limited to snow pillows and snow courses, which largely sample flat, open terrain [Molotch and Bales, 2006], yet the distribution of snow cover can vary considerably as a function of topographic features. To address this, in situ sensor measurements can be deployed to capture the mean and variance of the snow depth, which can be used to inform models that use these statistics as inputs [e.g., Essery and Pomeroy, 2004]. Alternatively, individual sensor measurements can be used together with OPTIMIZING SNOW SENSOR PLACEMENTS

Spatiotemporal patterns of snow cover retrieved from NOAA-AVHRR LTDR: a case study in the Tibetan Plateau, China

ABSTRACTSnow cover plays an important role in hydrological processes and seasonal water balance. Especially in the Tibetan Plateau (TP), snow cover is an important source of the Yangtze River, Yellow River and Lancang River (SRYYL), which greatly influences regional water balance. In this study, we quantified the temporal trend and spatial variation of snow cover across the TP by calibrating and developing the Advance Very High Resolution Radiometer (AVHRR) Long Term Data Record (LTDR)-derived snow cover products during 1982-2011. We also examined the relationship of snow cover with temperature and precipitation over the TP during 1982–2011. The results indicate that seasonal snow cover generally starts to accumulate from central plateau in October, while significant melting starts to occur from the southeastern plateau in May of following year. The long-term variability of snow cover is characterized by the tendency for a slight decrease in the mean snow coverage during the period of hydrological year (HY) 1982–1993 and a slight increase from HY2001 to 2011, but the total snow cover area remains relatively stable over the past 30 years. The results also show that temperature plays a critical role in controlling the snow cover days.

Distribution and variability of total mercury in snow cover-a case study from a semi-urban site in Poznań, Poland.

In the present paper, the inter-seasonal Hg variability in snow cover was examined based on multivariate statistical analysis of chemical and meteorological data. Samples of freshly fallen snow cover were collected at the semi-urban site in Poznań (central Poland), during 3-month field measurements in winter 2013. It was showed that concentrations of atmospherically deposited Hg were highly variable in snow cover, from 0.43 to 12.5 ng L−1, with a mean value of 4.62 ng L−1. The highest Hg concentration in snow cover coincided with local intensification of fossil fuel burning, indicating large contribution from various anthropogenic sources such as commercial and domestic heating, power generation plants, and traffic-related pollution. Moreover, the variability of Hg in collected snow samples was associated with long-range transport of pollutants, nocturnal inversion layer, low boundary layer height, and relatively low air temperature. For three snow episodes, Hg concentration in snow cover was attributed to southerly advection, suggesting significant contribution from the highly polluted region of Poland (Upper Silesia) and major European industrial hotspots. However, the peak Hg concentration was measured in samples collected during predominant N to NE advection of polluted air masses and after a relatively longer period without precipitation. Such significant contribution to the higher Hg accumulation in snow cover was associated with intensive emission from anthropogenic sources (coal combustion) and atmospheric conditions in this area. These results suggest that further measurements are needed to determine how the Hg transformation paths in snow cover change in response to longer/shorter duration of snow cover occurrence and to determine the interactions between mercury and absorbing carbonaceous aerosols in the light of climate change.

Influences of surface air temperature and atmospheric circulation on winter snow cover variability over Europe

ABSTRACTThe relationships between snow cover (SC) variability in Europe, the local surface air temperature (SAT), and the associated atmospheric circulation changes are studied. This investigation indicates that the European winter SC is closely correlated with SAT. Higher (lower) SC is coincident with strong and large‐scale surface cooling (warming). Similar but weaker temperature signals are observed in the middle and upper troposphere. Periods of enhanced (reduced) SC are characterized by surface heat loss (gain), partly due to dampened (enhanced) sensible heat fluxes towards the ground surface, which is in turn related to the lower (higher) SAT. Higher (lower) SC is also accompanied by reduced (enhanced) downward longwave irradiance. Consistent with previous studies, our analysis demonstrates that variations in the atmospheric circulation in the North Atlantic/European sector, including those associated with the North Atlantic Oscillation, are accompanied by changes in horizontal heat advection and SC over Europe. The circulation changes modulate the water vapour transport towards the European continent, and thereby influence the available water vapour there and lead to fluctuations in downward longwave irradiance and cloud cover. The wind anomalies associated with these variations also drive surface heat flux changes in the North Atlantic, which in turn lead to well‐defined sea surface temperature (SST) tendencies. The above characteristic patterns exhibit notable variability in different calendar months of the winter season. The monthly averaged circulation anomalies are evidently related to changes in the tracks of atmospheric disturbances with synoptic time scales. Overall, there is no strong evidence supporting a principal role for the North Atlantic SST or the El Niño Southern Oscillation in driving inter‐annual SC variability over Europe.

Unraveling the Teleconnection Mechanisms that Induce Wintertime Temperature Anomalies over the Northern Hemisphere Continents in Response to the MJO

Abstract Significant extratropical surface air temperature variations arise as a result of teleconnections induced by the Madden–Julian oscillation (MJO). The authors elucidate the detailed physical processes responsible for the development of temperature anomalies over Northern Hemisphere continents in response to MJO-induced heating using an intraseasonal perturbation thermodynamic equation and a wave activity tracing technique. A quantitative assessment demonstrates that surface air temperature variations are due to dynamical processes associated with a meridionally propagating Rossby wave train. Over East Asia, a local Hadley circulation causes adiabatic subsidence following MJO phase 3 to be a main driver for the warming. Meanwhile, for North America and eastern Europe, horizontal temperature advection by northerlies or southerlies is the key process for warming or cooling. A ray-tracing analysis illustrates that Rossby waves with zonal wavenumbers 2 and 3 influence the surface warming over North America and a faster wavenumber 4 affects surface temperature over eastern Europe. Although recent studies demonstrate the impacts of the Arctic Oscillation, Arctic sea ice melting, and Eurasian snow cover variations on extremely cold wintertime episodes over the NH extratropics, the weather and climate there are still considerably modulated through teleconnections induced by the tropical heat forcing. In addition, the authors show that the MJO is a real source of predictability for strong warm/cold events over these continents, suggesting a higher possibility of making a skillful forecast of temperature extremes with over 1 month of lead time.

Linking snow depth to avalanche release area size: measurements from the Vallée de la Sionne field site

Abstract. One of the major challenges in avalanche hazard assessment is the correct estimation of avalanche release area size, which is of crucial importance to evaluate the potential danger that avalanches pose to roads, railways or infrastructure. Terrain analysis plays an important role in assessing the potential size of avalanche releases areas and is commonly based on digital terrain models (DTMs) of a snow-free summer terrain. However, a snow-covered winter terrain can significantly differ from its underlying, snow-free terrain. This may lead to different, and/or potentially larger release areas. To investigate this hypothesis, the relation between avalanche release area size, snow depth and surface roughness was investigated using avalanche observations of artificially triggered slab avalanches over a period of 15 years in a high-alpine field site. High-resolution, continuous snow depth measurements at times of avalanche release showed a decrease of mean surface roughness with increasing release area size, both for the bed surface and the snow surface before avalanche release. Further, surface roughness patterns in snow-covered winter terrain appeared to be well suited to demarcate release areas, suggesting an increase of potential release area size with greater snow depth. In this context, snow depth around terrain features that serve as potential delineation borders, such as ridges or trenches, appeared to be particularly relevant for release area size. Furthermore, snow depth measured at a nearby weather station was, to a considerable extent, related to potential release area size, as it was often representative of snow depth around those critical features where snow can accumulate over a long period before becoming susceptible to avalanche release. Snow depth – due to its link to surface roughness – could therefore serve as a highly useful variable with regard to potential release area definition for varying snow cover scenarios, as, for example, the avalanche hazard assessment for transport routes or ski resorts.

An evaluation of high‐resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow‐albedo feedback

AbstractThe snow‐albedo feedback (SAF) strongly influences climate over midlatitude mountainous regions. However, over these regions the skill of regional climate models (RCMs) at simulating properties such as snow cover and surface albedo is poorly characterized. These properties are evaluated in a pair of 7 year long high‐resolution RCM simulations with the Weather Research and Forecasting model over the central Rocky Mountains. Key differences between the simulations include the computational domain (regional versus continental) and land surface model used (Noah versus Noah‐MP). Simulations are evaluated against high‐resolution satellite estimates of snow cover and albedo from the Moderate Resolution Imaging Spectroradiometer. Both simulations generally reproduce the observed seasonal and spatial variability of snow cover and also exhibit important biases. One simulation substantially overpredicts subpixel fractional snow cover over snowy pixels (by up to 0.4) causing large positive biases in surface albedo, likely due in part to inadequate representation of canopy effects. The other simulation exhibits a negative bias in areal snow extent (as much as 19% of the analysis domain). Surface measurements reveal large positive biases in snow albedo (exceeding 0.2) during late spring caused by neglecting radiative effects of impurities deposited onto snow. Semi‐idealized climate change experiments show substantially different magnitudes of SAF‐enhanced warming in the two simulations that can be tied to the differences in snow cover in their control climates. More confident projections of regional climate change over mountains will require further work to evaluate and improve representation of snow cover and albedo in RCMs.

Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts

Abstract. Distributed snowpack simulations in the French and Spanish Pyrenees are carried out using the detailed snowpack model Crocus driven by the numerical weather prediction system AROME at 2.5 km grid spacing, during four consecutive winters from 2010 to 2014. The aim of this study is to assess the benefits of a kilometric-resolution atmospheric forcing to a snowpack model for describing the spatial variability of the seasonal snow cover over a mountain range. The evaluation is performed by comparisons to ground-based measurements of the snow depth, the snow water equivalent and precipitations, to satellite snow cover images and to snowpack simulations driven by the SAFRAN analysis system. Snow depths simulated by AROME–Crocus exhibit an overall positive bias, particularly marked over the first summits near the Atlantic Ocean. The simulation of mesoscale orographic effects by AROME gives a realistic regional snowpack variability, unlike SAFRAN–Crocus. The categorical study of daily snow depth variations gives a differentiated perspective of accumulation and ablation processes. Both models underestimate strong snow accumulations and strong snow depth decreases, which is mainly due to the non-simulated wind-induced erosion, the underestimation of strong melting and an insufficient settling after snowfalls. The problematic assimilation of precipitation gauge measurements is also emphasized, which raises the issue of a need for a dedicated analysis to complement the benefits of AROME kilometric resolution and dynamical behaviour in mountainous terrain.

Shortwave Radiative Fluxes on Slopes

AbstractSnow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.

Case study of spatial and temporal variability of snow cover, grain size, albedo and radiative forcing in the Sierra Nevada and Rocky Mountain snowpack derived from imaging spectroscopy

Abstract. Quantifying the spatial distribution and temporal change in mountain snow cover, microphysical and optical properties is important to improve our understanding of the local energy balance and the related snowmelt and hydrological processes. In this paper, we analyze changes of snow cover, optical-equivalent snow grain size (radius), snow albedo and radiative forcing by light-absorbing impurities in snow and ice (LAISI) with respect to terrain elevation and aspect at multiple dates during the snowmelt period. These snow properties are derived from the NASA/JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from 2009 in California's Sierra Nevada and from 2011 in Colorado's Rocky Mountains, USA. Our results show a linearly decreasing snow cover during the ablation period in May and June in the Rocky Mountains and a snowfall-driven change in snow cover in the Sierra Nevada between February and May. At the same time, the snow grain size is increasing primarily at higher elevations and north-facing slopes from 200 microns to 800 microns on average. We find that intense snowmelt renders the mean grain size almost invariant with respect to elevation and aspect. Our results confirm the inverse relationship between snow albedo and grain size, as well as between snow albedo and radiative forcing by LAISI. At both study sites, the mean snow albedo value decreases from approximately 0.7 to 0.5 during the ablation period. The mean snow grain size increased from approximately 150 to 650 microns. The mean radiative forcing increases from 20 W m−2 up to 200 W m−2 during the ablation period. The variability of snow albedo and grain size decreases in general with the progression of the ablation period. The spatial variability of the snow albedo and grain size decreases through the melt season while the spatial variability of radiative forcing remains constant.

A new water-resistant snow index for the detection and mapping of snow cover on a global scale

ABSTRACTAn up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.

Distribution, attribution, and radiative forcing of snow cover changes over China from 1982 to 2013

Unlike large-scale snow cover variation and its effects on the Earth’s climate system, regional-scale snow cover and its impacts on surface–atmosphere interaction and the Earth’s energy budget have received little attention. This study aims to quantify the distribution and attribution of snow cover changes over mainland China and the associated snow radiative forcing from 1982 to 2013 by using satellite observations at high spatial and temporal resolutions. Driven by decreased temperature and increased precipitation in the accumulation season, the snow cover fraction (SCF) over mainland China shows an increasing trend at 0.29 % decade−1 during 1982–2013, which is significant at the 0.05 level. The spatial distribution of changes in the SCF over the 32-year study period exhibits a maximum positive change of 31.08 % in the south-eastern Tibetan Plateau (TP) zone and a maximum negative change of 27.49 % in the northwestern Xinjiang arid zone as well as in the western margin area of the TP zone. Induced by overall increased SCF over mainland China, the snow radiative forcing (RFsnow) in clear-sky conditions is shown to have strengthened by 0.21 ± 0.01 Wm−2 decade−1 at the 0.05 significance level during 1982–2013, which indicates that the cooling effects caused by snow cover over mainland China have strengthened since 1982.

Extensive forests and persistent snow cover promote snowshoe hare occupancy in Wisconsin

Range boundaries are a manifestation of species' tolerances to environmental factors, including climate and human disturbance. As such, studying populations persisting along range boundaries can provide important insights into species' sensitivity to environmental change. Because multiple environmental influences may contribute to a range limit, studies focusing on range boundaries can guide management and conservation of these critical populations. To this end, we quantified the environmental factors associated with snowshoe hare (Lepus americanus) occurrence at their southern range limit in Wisconsin. We surveyed 199 locations across a regional southern range boundary of snowshoe hares and analyzed the data within an occupancy modeling framework to test the hypothesis that this range boundary results from an interaction between winter climate and land cover. We found that dense vegetation structure and increasing forest and snow cover duration had positive effects on snowshoe hare occupancy along their southern range boundary, supporting our prediction that a suite of abiotic factors is associated with the southerly distribution of this winter-adapted mammal. These effects were additive and we found limited support for climate–land cover interactions, forest composition, winter snow depth, or predator occurrence. Spatial predictions of occupancy using only forest and snow cover variables denote a sharp range boundary, further illustrating the importance of these variables in shaping this species distribution. Our results highlight an important vulnerability of snowshoe hares to future climate change, and options for mediating this vulnerability through the maintenance of early successional habitat and forested landscapes along their southern range boundary. © 2016 The Wildlife Society.

Variation in the snow cover on the Qilian Mountains and its causes in the early 21st century

ABSTRACTBased on MODIS data, we analyzed the spatial distribution and the intra- and inter-annual variations of the snow cover in the Qilian Mountains. In addition to discussing regional climate, we discussed the reasons for the snow cover pattern variations. The spatial distribution of the snow cover in the Qilian Mountains is uneven, and the snow cover in the west is significantly greater than that in the east. The difference in the intra-annual variations of the snow cover at different altitudes is large, and the general seasonal cycle exhibits two maxima and one minimum. The snow cover and precipitation in the Qilian Mountains were consistently increasing from 2000 to 2005, but they generally decreased from 2008 to 2013. The influences of temperature, wind speeds and precipitations on snow cover variation at different altitudes vary greatly, with the snow cover being more sensitive to precipitation in the winter, while temperature and precipitation are the major factors that affect the snow cover in the spring.

The combined influences of autumnal snow and sea ice on Northern Hemisphere winters

AbstractPast studies have demonstrated a significant relationship between the phase and amplitude of the Northern Annular Mode (NAM) and both Arctic sea ice and high‐latitude snow cover during boreal autumn. However, those studies have considered these forcings separately. Here we consider the collective effect of Arctic sea ice and snow cover variability for producing skillful subseasonal forecasts for Northern Hemisphere (NH) winter conditions. We find that these two cryospheric elements interact with the extratropical atmosphere differently through the cold season. Sea ice extent minima during November play a role in stratospheric and tropospheric circulation anomalies during November/December with a secondary maximum in late January/February. October snow cover anomalies, however, have impacts on the NAM primarily during middle to late winter. These timing differences are likely tied to differences in anomalous wave driving between the two cases, though other processes may be in play. We exploit these different influences to produce a skillful forecast model of subseasonal NH surface temperatures using both sea ice and snow cover as predictors, with large gains in skills in January. Overall, our study suggests that the Arctic has a demonstrable and detectable influence on midlatitude winter weather in the present and likely future climate.

Spatiotemporal analysis of snow cover variations at Mt. Kilimanjaro using multi-temporal Landsat images during 27 years

The Landsat TM and ETM+ images have been acquired for the long period from the 1980s until the present with the temporal resolution of a 16-day repeat cycle from the visible, near infrared (NIR), short wave infrared (SWIR) and thermal infrared (TIR) bands. The Landsat multi-temporal images have been successfully used to monitor variations of the Earth surface during 27 years. In this paper, we observe the variations of (1) the snow cover area, (2) the snowline height and (3) the land surface temperature (LST) lapse rate at Mt. Kilimanjaro using a total number of 15 Landsat-5 TM and Landsat-7 ETM+ images from June 1984–July 2011. Segmentation of normalized difference snow index (NDSI) images with a threshold of 0.6 is used to extract snow cover. Snowline altitude is then determined by combining the snow cover classification maps with a digital elevation model (DEM). And the LST lapse rate is also calculated from the TIR band in the forest area. The results from this study show that (1) the snow cover area largely decreases from 10.1km2 to 2.3km2 during about 27 years, which corresponds to a 77.2% reduction, (2) the snowline height rose from 4760m to 5020m by about 260m, and (3) the LST lapse rate shifted from −5.2°C/km to −2.7°C/km. This study demonstrates that multi-temporal Landsat images can be successfully used for the spatiotemporal analysis of long-term snow cover changes.

Eurasian snow cover variability in relation to warming trend and Arctic Oscillation

Two distinct modes of snow cover variability over Eurasia are investigated using cyclostationary empirical orthogonal function (CSEOF) analysis. The first mode of Eurasian snow cover extent (SCE) represents a seasonally asymmetric trend between spring and fall. The spring SCE shows a decreasing trend, while the fall SCE particularly in October exhibits a clear increasing trend. This seasonally asymmetric trend of SCE is closely linked to Arctic sea ice decline accompanied by warming in the northern Eurasia. The decreased SCE during spring is primarily attributed to the warm air temperature anomalies, while the increased SCE in October results from the loss of sea ice and the ensuing moisture transport to the atmosphere, which is realized as increased snow in October. The second mode of Eurasian SCE, on the other hand, is closely related to Arctic Oscillation (AO), which is a dominant mode of Northern Hemisphere atmospheric variability. The snow cover variability over Europe during winter is largely affected by AO variability, rather than the warming signal represented by the first CSEOF mode. Detailed descriptions of the two distinct modes of Eurasian SCE and their interactions with oceanic and atmospheric variables are presented along with possible implications for future climate.

Snow cover dynamics and geohazards: a case study of Bhilangna watershed, Uttarakhand Himalaya, India

This study examines snow covered area (SCA) and associated geohazards in the Bhilangna watershed using the Normalized Difference Snow Index. Two Landsat images from 1990 and 2010 were analyzed. In order to estimate the average elevation of the snowline, a digital elevation model from the Shuttle Radar Topography Mission was used. In 1990, 124 km2 (9 % of the watershed) was snow covered. In 2010, 96 km2 (7 %) was snow covered. Therefore, during the study period (i.e., 1990–2010) SCA decreased by 28 km2 (2 %). Four snow types were identified and mapped: frost, fine, medium and coarse snow. In 1990, 38 km2 (30 % of SCA) was covered by frost snow, 86 km2 (69 %) was covered by fine snow, < 1 km2 (<1 %) was covered by medium granular snow, and an insignificant area was covered by coarse snow. In 2010, frost and fine snow covered 19 km2 (20 %) and 76 km2 (79 %) respectively, medium snow covered 1 km2 (1 %), and coarse snow was not traced. The snowline shifted from 4611 m in 1990 to 4698 m in 2010. These observations show the variability of snow cover in the Bhilangna watershed.

Snow density and ground permittivity retrieved from L-band radiometry: Application to experimental data

Abstract The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between − 6 kg m − 3 and 15 kg m − 3 for the forest clearing site, and between 37 kg m − 3 and 90 kg m − 3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈ 45 km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.