Snow Depth Data Research Articles

Mapping snow depth across the West Midlands using social media‐generated data

WeatherVolume 68, Issue 3 p. 82-82 Research Article Mapping snow depth across the West Midlands using social media-generated data Catherine L. Muller, Corresponding Author Catherine L. Muller Birmingham Urban Climate Laboratory (BUCL), School of Geography, Earth and Environmental Sciences, University of Birmingham Correspondence to: Catherine L. Muller c.l.muller@bham.ac.ukSearch for more papers by this author Catherine L. Muller, Corresponding Author Catherine L. Muller Birmingham Urban Climate Laboratory (BUCL), School of Geography, Earth and Environmental Sciences, University of Birmingham Correspondence to: Catherine L. Muller c.l.muller@bham.ac.ukSearch for more papers by this author First published: 26 February 2013 https://doi.org/10.1002/wea.2103Citations: 12Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume68, Issue3March 2013Pages 82-82 RelatedInformation

チベット高原におけるAVNIR-2・MODISに基づく積雪深の評価手法の考案

Seasonal frozen ground area on the Tibetan plateau has particular land surface condition with frozen ground and patchy snow distribution. The existing satellite microwave remote sensing was therefore unable to estimate the snow quantity exactly. An evaluation method of snow area proportion based on AVNIR-2 and MODIS over the seasonal frozen ground area on the Tibetan plateau was therefore devised in this study, and it was introduced to estimated snow depth based on Tsutsui and Koike (2012) 's snow estimation method. In the result, we were able to compare the estimated snow depth with the in situ snow depth data, and the relatively good agreement was recognized. Simultaneously the availability of the Tsutsui and Koike (2012)'s snow estimation method and the evaluation method of snow area proportion based on AVNIR-2 and MODIS was recognized.

Evaluation of remotely sensed snow cover product in Central Asia

Central Asian countries depend highly on water resources from snow and glacier melt, which has to be studied thoroughly to estimate water availability. However, the observation network in Central Asia is poor to carry out such studies in detail. Observations from space using remote sensing techniques might fill this observation gap, which needs to be validated. Therefore, this study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product in Central Asia. For the evaluation, in situ snow depth data from 30 meteorological stations and higher resolution Landsat data are used. The results show an overall snow agreement between MODIS and ground observations of 93.1 and 92.7% for MODIS Terra and MODIS Aqua snow products, respectively. The agreement between MODIS and Landsat is 91.9% when considering snow and land agreements. The snow fraction product from MODIS is also validated using Landsat data, and varying accuracies are obtained. The main disadvantage of the MODIS snow cover product are the cloud induced gaps. Therefore, cloud covered pixels are eliminated using the ModSnow algorithm. Using the in situ data, the snow agreement of cloud removed snow cover data is checked, and an accuracy of 84.4% is achieved.

Evaluating global snow water equivalent products for testing land surface models

This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is paid to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE>80–150mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20+ days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1–6days) although the spread was still on the order of three weeks.During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to corrector avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.

Initialisation of Land Surface Variables for Numerical Weather Prediction

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.

Assimilating MODIS and AMSR-E Snow Observations in a Snow Evolution Model

Abstract In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.

Bidirectional Reflectance for Multiple Snow-Covered Land Types From MISR Products

Bidirectional reflectance factors (BRFs) play a key role in land surface studies. Snow has a significant influence on vegetative surface BRF. To evaluate the surface reflectance behaviors of snow-covered regions, a surface BRF database has been constructed from Multi-angle Imaging SpectroRadiometer BRF products for five biomes in the mid-high latitude regions of the U.S. (evergreen needleleaf forests, shrublands, grasslands, croplands, and urban areas). Using corresponding surface snow depth data from 26 meteorological stations, BRF signatures with snow cover are derived from the database to show the effect of snow on the BRF of vegetation. Five bidirectional reflectance distribution function models' abilities of capturing vegetation-snow mixed BRF shape are evaluated by fitting all the BRF data with snow. The results show that the Rahman model, Ross-Li model, and Walthall model perform well in fitting forest, grassland, and cropland BRFs when the surface is covered by snow. The Rahman model, Ross-Li model, and Roujean model fit visible reflectance well for mixed surfaces. The Rahman model best captures the BRF shapes, followed by the Ross-Li model.

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today’s optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf’s initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km × 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States

In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed.

Influence factors of snow change in Lake Bosten Basin

积雪是西北干旱地区河流的主要补给源,是绿洲的生命线.积雪的时空变化是全球变化的区域响应敏感因子之一,同时也是影响西北干旱地区地表水资源变化的主要因子之一.本研究利用MODIS雪盖产品、地表温度、SSM/I雪深、DEM等数据,通过GIS空间分析及地统计分析功能,系统分析了博斯腾湖流域雪盖、雪深的时空变化规律及其与影响因素之间的关系.研究表明,研究区雪深和雪盖多年月平均值从8月份到1月份达到最大值,到7月份降到最低值.但月最大雪深却出现在3月份.雪盖、雪深与地温相关系数分别达到-0.878、-0.853,与分布高程均值相关系数分别达到-0.626和-0.791.雪深最大值受海拔影响有明显的陡坎效应.从12月到8月份随着时间的推移雪的深度在降低,陡坎向高海拔方向移动.9-11月份雪深在加深,陡坎向低海拔方向移动.同一高程段雪深的变幅反应坡向对雪深的影响,变幅越宽坡向影响越大.并且变幅也有先从低海拔到高海拔移动,然后再回到低海拔的特点.本研究对了解该研究区积雪特性的研究有很大作用,可为在该地区开展融雪径流模拟等研究提供重要的参考信息.;Snow is the major water source of rivers in the arid areas in the northwest. Its temporal and spatial variation is one of the sensitive factors responses to global change in the lake. This paper, using MODIS Snow Cover, Surface Temperature, SSM/I Snow Depth, DEM and other data with the help of spatial and statistics analysis function of GIS, analyzes the temporal and spatial variation regularity of snow cover and snow depth of Lake Bosten Basin, and studies their relationships with the influencing factors. The results show that monthly average of snow cover and snow depth start to rise from August, reach a maximum until January, and then decline to a minimum in July, while the maximum snow depth appears in March. The correlation coefficients of snow cover, snow depth and surface temperature, are -0.878 and -0.853, respectively, while the coefficients are -0.626 and -0.791 between snow cover, snow depth and elevation. The maximum snow depth is significantly affected by the altitude with a steep step effect. From December to August, the snow depth is reducing with time, and the steep step is moving towards the high altitude, while the situation is totally opposite from September to November. The snow depth amplitude of one segment elevation response to the impact of aspect on snow depth, and the influence is greater while the amplitude is wider. Simultaneously, the amplitude, as a characteristic, moves from low altitude to high and then to low again. This study plays an important role in understanding characteristics of snow in Lake Bosten Basin. It will also provide important references for other studies, such as the simulation of snowmelt runoff.

Winter severity index using widely available weather information

Context Changes in global climate and evidence of species’ responses to these changes have increased interest in relationships between climate variables and species demography and distributions. Although an important tool for many ecological questions, large-scale climate indices fail to provide the spatial resolution necessary to investigate drivers of change across small spatial scales. Climate variables that describe yearly climate variation at large spatial extents and small spatial grain are needed. Aim Here we develop a model for snow depth using snow water equivalent (SWE) data, which are readily available in a number of formats, to be included in a more general climate index. We use an existing winter severity index (WSI) for white-tailed deer to test the performance of the model. Methods We obtained data for 13 weather stations from north-western Canada, reporting both SWE and snow depth. We accumulated a snowpack from daily SWE of snowfall and then tested two methods for converting the SWE of the snowpack into the snow portion of the WSI. We then generalised the model for application to the northwest forest climate region. Key results Coefficients of determination (R2) relating the actual and predicted snow depth portion of the WSI ranged from 0.41 to 0.78, with only three stations being below 0.50. Coefficients of determination (R2) relating the actual and predicted WSI for the northwest climate region ranged from 0.58 to 0.88. Conclusions The SWE model predicts the snow portion of the WSI well for most stations and, when incorporated into the full WSI, provides a good measure of relative winter severity across space and time for most stations. Implications The method developed here could be applied elsewhere, where snow depth is an important factor in species ecology. The benefit of this approach is a comparatively simple method that maximises the use of widely available SWE data in place of snow-depth data.

Spatio-temporal measurements and analysis of snow depth in a rock face

Abstract. Snow in rock faces plays a key role in the alpine environment for permafrost distribution, snow water storage or runoff in spring. However, a detailed assessment of snow depths in steep rock walls has never been attempted. To understand snow distribution in rock faces a high-resolution terrestrial laser scanner (TLS), including a digital camera, was used to obtain interpolated snow depth (HS) data with a grid resolution of one metre. The mean HS, the snow covered area and their evolution in the rock face were compared to a neighbouring smoother catchment and a flat field station at similar elevation. Further we analyzed the patterns of HS distribution in the rock face after different weather periods and investigated the main factors contributing to those distributions. In a first step we could show that with TLS reliable information on surface data of a steep rocky surface can be obtained. In comparison to the flatter sites in the vicinity, mean HS in the rock face was lower during the entire winter, but trends of snow depth changes were similar. We observed repeating accumulation and ablation patterns in the rock face, while maximum snow depth loss always occurred at those places with maximum snow depth gain. Further analysis of the main factors contributing to the snow depth distribution in the rock face revealed terrain-wind-interaction processes to be dominant. Processes related to slope angle seem to play a role, but no simple relationship between slope angle and snow depth was found. Further analyses should involve measurements in rock faces with other characteristics and higher temporal resolutions to be able to distinguish individual processes better. Additionally, the relation of spatial and temporal distribution of snow depth to terrain – wind interactions should be tested.

Catchment flow estimation using Artificial Neural Networks in the mountainous Euphrates Basin

Summary Streamflow prediction has a great significance in hydrology, water resources planning and management studies. Either long term or short term predictions of streamflow are necessary to optimise the operation of water resources systems. An Artificial Neural Network (ANN) is a nonlinear black-box modelling approach which has the capability to model complex nonlinear hydrological processes without physical expression. It is an alternative modelling approach that is inspired by brain and nervous systems to conventional hydrological models. This paper describes implementation of an ANN to predict catchment flows in a snow dominated mountainous basin named Karasu Basin, which is in the headwater of the Euphrates Basin in Turkey. Due to the non-availability of accurate snow data, catchment flows were predicted using only basic meteorological data, and a best meteorological data set was investigated to achieve best performance of the model. The ANN model was calibrated with recorded runoff data available for a period of 12 years. Calibrated model was further used for a period of another 10 years. To achieve model accuracy, the model was simulated with both the whole-year (annual) data and part-year (seasonal) data. It was found that model accuracy increased significantly when it was used for part-year (seasonal) data. The model results are encouraging and model accuracy can be further increased when the simulations include snow depth data and/or snow water equivalent data.

Linear mixed modelling of snow distribution in the central Yukon

AbstractRemote sensing estimates of snow water equivalent (SWE) in mountainous areas are subject to large uncertainties. As a prerequisite for testing passive microwave algorithm estimations of SWE, this study aims to collect snow depth (SD) data and provide an understanding of its complex spatial structure as part of the Canadian International Polar Year observations theme. Snow accumulation, redistribution and ablation are controlled by processes that depend on a variety of topographic factors as well as land surface characteristics, which leads us to modelling SD as a function of proxy variables derived from digital elevation model and Landsat data. Field measurements were performed at 3924 locations compromising 184 sites in 50 transects over 2 years. These measurements were used to predict SD over the study area using a spatial linear mixed‐effects model, a model type capable of handling the hierarchical structure of the field data.The model, built using stepwise variable selection, uses as predictor variables transformed elevation, slope, the logarithm of slope, potential incoming solar radiation and its transform; the normalized difference vegetation index, and a transformed tasseled cap brightness from Landsat imagery. A second, simpler model links SD with density giving SWE. The cross‐validated root mean squared error of the SD distribution model was 14 cm around an overall mean of 80 cm over a domain of 250 × 250 km.This instantaneous end‐of‐season peak‐accumulation snow map will enable the validation of satellite remote sensing over a generally inaccessible area. Copyright © 2011 John Wiley & Sons, Ltd.

Reconstructing historical snow depth surfaces to evaluate changes in critical demographic rates and habitat components of snow‐dependent and snow‐restricted species

Summary1. Climate change models consistently predict snow depth declines across the Northern Hemisphere. Snow depth has been linked to the demography of numerous species, and snow depth reduction is expected to affect the demography of some northern species. As many demographic studies depend on long‐term population data that extend back beyond available sources of spatially continuous snow depth data, reliable hindcasting of snow depth surface maps is needed.2. We developed a two‐stage regression modelling approach to reconstructing historic snow depths using an existing and readily available spatiotemporal data set of daily meteorological variables across a large, heterogeneous landscape in North America from 1982 to 2003. The final model accounted for ecoregional differences and predicted snow depth as a function of elevation and total snowfall accumulation.3. Model validations showed that the estimated snow depths were spatially and temporally accurate.4. We demonstrate how to apply this model with ArcGIS to hindcast monthly and yearly sequences of high‐resolution, spatially continuous surfaces of historic snow depths. We also illustrate the utility of the modelled snow depth surfaces for informing predictions of how changes in snow depth may influence the demography and habitats of snow‐dependent and snow‐restricted species by assessing the spatiotemporal stochasticity of snow conditions that potentially benefit wolverine Gulo gulo and the winter ranges, net energy costs and population growth of mule deer Odocoileus hemionus.5. This model can be used to hindcast snow depth surfaces across similar regions; our simple two‐stage modelling approach can be applied to other regions where equivalent climate data are available. The resulting spatiotemporal snow depth surfaces estimated from this model can be linked to existing long‐term wildlife population data sets to investigate the extent to which snow depth declines may regulate or limit populations.

Long-term changes in annual maximum snow depth and snowfall in Switzerland based on extreme value statistics

Mountain snow cover is an important source of water and essential for winter tourism in Alpine countries. However, large amounts of snow can lead to destructive avalanches, floods, traffic interruptions or even the collapse of buildings. We use annual maximum snow depth and snowfall data from 25 stations (between 200 and 2,500 m) collected during the last 80 winters (1930/31 to 2009/2010) to highlight temporal trends of annual maximum snow depth and 3-day snowfall sum. The generalized extreme value (GEV) distribution with time as a covariate is used to assess such trends. It allows us in particular to infer how return levels and return periods have been modified during the last 80 years. All the stations, even the highest one, show a decrease in extreme snow depth, which is mainly significant at low altitudes (below 800 m). A negative trend is also observed for extreme snowfalls at low and high altitudes but the pattern at mid-altitudes (between 800 and 1,500 m) is less clear. The decreasing trend of extreme snow depth and snowfall at low altitudes seems to be mainly caused by a reduction in the magnitude of the extremes rather than the scale (variability) of the extremes. This may be caused by the observed decrease in the snow/rain ratio due to increasing air temperatures. In contrast, the decreasing trend in extreme snow depth above 1,500 m is caused by a reduction in the scale (variability) of the extremes and not by a reduction in the magnitude of the extremes. However, the decreasing trends are significant for only about half of the stations and can only be seen as an indication that climate change may be already impacting extreme snow depth and extreme snowfall.

The median winter snowline in the Alps

The relative duration of snow cover in a season is a number between zero and unity; here it represents the probability to encounter, at a given station, snow of at least 5 cm depth. We use routine station data of snow depth for the winters 1961-2000 to explore the pattern of relative snow duration in the Alps. A horizontal isoline is drawn across all stations that exhibit 50 % snow duration; we consider this isoline the median snowline. We further introduce the mountain temperature as linear expansion of the Central European temperature with respect to station coordinates; it separates the large-scale European temperature from the local-scale vertical lapse rate and serves as substitute for the station temperature. The mountain temperature allows to condense the snow data of all stations and years into one analytical curve, the state function of snow duration. This curve yields every desired snowline; the median snowline coincides with the altitude of maximum sensitivity of snow duration to European temperature. The median snowline in winter is located at an average altitude of 641 m and slightly slopes downward towards the eastern Alps. The average altitude varies considerably from winter to winter under the influence of European temperature fluctuations; it shifts upward by about 123 m per °C climate warming.

A paradox of cooling winter soil surface temperatures in a warming northeastern United States

Climate model projections indicate that wintertime temperatures will warm and change snowfall patterns in the northeastern U.S. Snow provides insulation for soils from air temperature fluctuations. Therefore, these snowfall changes will have consequences, particularly for soil surface temperatures. Changes in minimum soil surface temperatures were investigated using a heat flow soil temperature model, driven using daily average air temperature and snow depth data. Two scenarios of three coupled atmosphere-ocean circulation models were used to run the soil temperature model. Modeled projections of minimum soil surface temperatures in the northeastern U.S. indicate that warming will occur over the majority of the region over the 2000–2099 period. In contrast, soil surface temperature projections in the northernmost and snowiest regions of the Northeast indicate minimum soil surface temperatures will be colder. In these northern regions, the coldest soil surface temperatures are also projected to occur later in winter, but show little change in other regions. Most trends throughout the Northeast are enhanced using the higher emission scenario A1fi. Changes to snow depth drive the changes in the minimum soil surface temperatures where snow persists during winter, whereas average air temperatures drive changes in the rest of the northeastern U.S.

Snowmelt and Atmospheric Heating Processes over Eastern Mongolia

A shallow snowpack, such as that seen in Mongolia, easily melts through heating by the atmosphere. In turn, a snow-free surface without much meltwater heats the atmosphere quickly through heat fluxes at the surface. The present study investigated the snowmelt processes over eastern Mongolia during 2005−2007, using ground-observed temperature, snow depth, albedo, and radiation data. Moreover, we explored atmospheric influences on the snowmelt through the heat budget analysis of reanalysis data provided by the National Centers for Environmental Prediction. The results show that atmospheric heating over eastern Mon- golia during the snowmelt season is caused by the combination of vertical adiabatic heating in a traveling anticyclone and one-day- lagging horizontal advection after the passage of the anticyclone. These synoptic processes lead to a drastic increase in temperature and finally to substantial snowmelt under certain atmospheric conditions: the daily-mean surface air temperature exceeding −10 °C and downward longwave radiation exceeding 194 W m −2 under clear sky conditions.