Modelling the snow distribution at two high arctic sites at Svalbard, Norway, and at an alpine site in central Norway

2004 ◽  
Vol 35 (3) ◽  
pp. 191-208 ◽  
Author(s):  
Oddbjørn Bruland ◽  
Glen E. Liston ◽  
Jorien Vonk ◽  
Knut Sand ◽  
Ånund Killingtveit
Keyword(s):  
Snow Cover ◽  
Frequency Distribution ◽  
Land Surface ◽  
Snow Depth ◽  
Rank Order ◽  
Model Performance ◽  
The Arctic ◽  
Major Influence ◽  
Model Parameters ◽  
Snow Distribution

In Arctic regions snow cover has a major influence on the environment both in a hydrological and ecological context. Due to strong winds and open terrain the snow is heavily redistributed and the snow depth is quite variable. This has a significant influence on the snow cover depletion and the duration of the melting season. In many ways these are important parameters in the climate change aspect. They influence the land surface albedo, the possibilities of greenhouse gas exchange and the length of the plant-growing season, the latter also being important for the arctic terrestrial fauna. The aim of this study is to test to what degree a numerical model is able to recreate an observed snow distribution in sites located in Svalbard and Norway. Snow depth frequency distribution, a snow depth rank order test and the location of snowdrifts and erosion areas were used as criteria for the model performance. SnowTran-3D is the model used in this study. In order to allow for occasions during the winter with milder climate and temperatures above freezing, a snow strengthening calculation was included in the model. The model result was compared to extensive observation datasets for each site and the sensitivity of the main model parameters to the model result was tested. For all three sites, the modelled snow depth frequency distribution was highly correlated to the observed distribution and the snowdrifts and erosion areas were located correspondingly by the model to those observed at the sites.

Snow Distribution at a High Arctic Site at Svalbard

2001 ◽  
Vol 32 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Oddbjørn Bruland ◽  
Knut Sand ◽  
Ånund Killingtveit
Keyword(s):  
Snow Depth ◽  
High Arctic ◽  
The Arctic ◽  
Major Influence ◽  
Differential Gps ◽  
Detailed Measurement ◽  
Distribution Mapping ◽  
Survey System ◽  
Snow Distribution ◽  
Strong Winds

In the Arctic regions snow cover has a major influence on the environment both in a hydrological and ecological context. Due to strong winds and open terrain the snow is heavily redistributed and the snow depth is quite variable. This has a significant influence on the duration of the melting season, on the possibilities of greenhouse gas exchange, the plant growing season and therefore the arctic terrestrial fauna. The aim of this study is to describe the snow depth variability by detailed measurement of snow distribution in a 3 km2 site near to Ny-Ålesund at 79° north of Svalbard and to link this to topography and climate at the location. The measurements were carried out in a grid of 100 m by 100 m cells using the SIR-2 Georadar from Geophysical Survey System Inc. (GSSI). Differential GPS was used to create a detailed Digital Elevation Model (DEM) and the snow depth data were correlated to topographic data. The average snowdepth in the area was about 70 cm with a standard deviation of 40 cm. Statistical distribution and spatial correlation for the snow depths were found. The method was found acceptable for snow distribution mapping. The main observation was the major accumulation in the west facing slopes due to easterly winds that are dominant in this area.

scholarly journals On the role of Eurasian autumn snow cover in dynamical seasonal predictions 

2021 ◽  
Author(s):  
Paolo Ruggieri ◽  
Marianna Benassi ◽  
Stefano Materia ◽  
Daniele Peano ◽  
Constantin Ardilouze ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
General Circulation ◽  
Dominant Role ◽  
General Circulation Models ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Eurasian Snow

<p>Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.</p>

scholarly journals High resolution snow distribution data from complex Arctic terrain: a tool for model validation

2002 ◽  
Vol 2 (3/4) ◽  
pp. 147-155 ◽  
Author(s):  
Ch. Jaedicke ◽  
A. D. Sandvik
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Numerical Models ◽  
The Arctic ◽  
Distribution Data ◽  
Blowing Snow ◽  
Data Set ◽  
Meteorological Model ◽  
Drift Model ◽  
Snow Distribution

Abstract. Blowing snow and snow drifts are common features in the Arctic. Due to sparse vegetation, low temperatures and high wind speeds, the snow is constantly moving. This causes severe problems for transportation and infrastructure in the affected areas. To minimise the effect of drifting snow already in the designing phase of new structures, adequate models have to be developed and tested. In this study, snow distribution in Arctic topography is surveyed in two study areas during the spring of 1999 and 2000. Snow depth is measured by ground penetrating radar and manual methods. The study areas encompass four by four kilometres and are partly glaciated. The results of the surveys show a clear pattern of erosion, accumulation areas and the evolution of the snow cover over time. This high resolution data set is valuable for the validation of numerical models. A simple numerical snow drift model was used to simulate the measured snow distribution in one of the areas for the winter of 1998/1999. The model is a two-level drift model coupled to the wind field, generated by a mesoscale meteorological model. The simulations are based on five wind fields from the dominating wind directions. The model produces a satisfying snow distribution but fails to reproduce the details of the observed snow cover. The results clearly demonstrate the importance of quality field data to detect and analyse errors in numerical simulations.

scholarly journals The influence of climate and hydrological variables on opposite anomaly in active-layer thickness between Eurasian and North American watersheds

2013 ◽  
Vol 7 (2) ◽  
pp. 631-645 ◽  
Author(s):  
H. Park ◽  
J. Walsh ◽  
A. N. Fedorov ◽  
A. B. Sherstiukov ◽  
Y. Iijima ◽  
...  
Keyword(s):  
Layer Thickness ◽  
Active Layer ◽  
North American ◽  
Land Surface ◽  
Snow Depth ◽  
The Arctic ◽  
Surface Model ◽  
Active Layer Thickness ◽  
Hydrological Variables ◽  
Permafrost Regions

Abstract. This study not only examined the spatiotemporal variations of active-layer thickness (ALT) in permafrost regions during 1948–2006 over the terrestrial Arctic regions experiencing climate changes, but also identified the associated drivers based on observational data and a simulation conducted by a land surface model (CHANGE). The focus on the ALT extends previous studies that have emphasized ground temperatures in permafrost regions. The Ob, Yenisey, Lena, Yukon, and Mackenzie watersheds are foci of the study. Time series of ALT in Eurasian watersheds showed generally increasing trends, while the increase in ALT in North American watersheds was not significant. However, ALT in the North American watersheds has been negatively anomalous since 1990 when the Arctic air temperature entered into a warming phase. The warming temperatures were not simply expressed to increases in ALT. Since 1990 when the warming increased, the forcing of the ALT by the higher annual thawing index (ATI) in the Mackenzie and Yukon basins has been offset by the combined effects of less insulation caused by thinner snow depth and drier soil during summer. In contrast, the increasing ATI together with thicker snow depth and higher summer soil moisture in the Lena contributed to the increase in ALT. The results imply that the soil thermal and moisture regimes formed in the pre-thaw season(s) provide memory that manifests itself during the summer. The different ALT anomalies between Eurasian and North American watersheds highlight increased importance of the variability of hydrological variables.

scholarly journals Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

2016 ◽  
Author(s):  
Rafael Pimentel ◽  
Javier Herrero ◽  
María José Polo
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Scale Effects ◽  
Control Area ◽  
Sigmoid Function ◽  
Snow Cover Fraction ◽  
Snow Distribution ◽  
Snow Model ◽  
Snow Conditions ◽  
High Level

Abstract. Subgrid variability introduces non-negligible scale effects on the GIS-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow depletion curves (DCs). In this study, terrestrial photography (TP) of a cell-sized area (30 x 30 m) was used to define local snow DCs at a Mediterranean site. Snow cover fraction (SCF) and snow depth (h) values obtained with this technique constituted the two datasets used to define DCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting DCs were able to capture certain physical features of the snow, which were used in a decision-tree and included in the point snow model formulated by Herrero et al. (2009). The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this DC-snow model was found to have a high level of accuracy with global RMSE values of 84.2 mm for the average snow depth and 0.18 m2 m-2 for the snow cover fraction in the control area. The use of DCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

scholarly journals High-resolution snow depth prediction using Random Forest algorithm with topographic parameters and an ecosystem map: a case study in the Greiner Watershed, Nunavut

2021 ◽  
Author(s):  
Julien Meloche ◽  
Alexandre Langlois ◽  
Nick Rutter ◽  
Don McLennan ◽  
Alain Royer ◽  
...  
Keyword(s):  
Random Forest ◽  
Snow Cover ◽  
Snow Depth ◽  
The Arctic ◽  
Topographic Position Index ◽  
Depth Prediction ◽  
Position Index ◽  
First Time ◽  
Topographic Parameters

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.

scholarly journals A Global Intercomparison of Modeled and Observed Land–Atmosphere Coupling*

2012 ◽  
Vol 13 (3) ◽  
pp. 749-784 ◽  
Author(s):  
Craig R. Ferguson ◽  
Eric F. Wood ◽  
Raghuveer K. Vinukollu
Keyword(s):  
Coupling Strength ◽  
Land Surface ◽  
Rank Order ◽  
Spatial Scales ◽  
Model Performance ◽  
Infiltration Capacity ◽  
Earth Observing System ◽  
The North ◽  
Capacity Model ◽  
Global Land

Abstract Land–atmosphere coupling strength or the degree to which land surface anomalies influence boundary layer development—and in extreme cases, rainfall—is arguably the single most fundamental criterion for evaluating hydrological model performance. The Global Land–Atmosphere Coupling Experiment (GLACE) showed that strength of coupling and its representation can affect a model’s ability to simulate climate predictability at the seasonal time scale. And yet, the lack of sufficient observations of coupling at appropriate temporal and spatial scales has made achieving “true” coupling in models an elusive goal. This study uses Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) soil moisture (SM), multisensor remote sensing (RS) evaporative fraction (EF), and Atmospheric Infrared Sounder (AIRS) lifting condensation level (LCL) to evaluate the realism of coupling in the Global Land Data Assimilation System (GLDAS) suite of land surface models (LSMs), Princeton Global Forcing Variable Infiltration Capacity model (PGF–VIC), seven global reanalyses, and the North American Regional Reanalysis (NARR) over a 5-yr period (2003–07). First, RS and modeled estimates of SM, EF, and LCL are intercompared. Then, emphasis is placed on quantifying RS and modeled differences in convective-season daily correlations between SM–LCL, SM–EF, and EF–LCL for global, regional, and conditional samples. RS is found to yield a substantially weaker state of coupling than model products. However, the rank order of basins by coupling strength calculated from RS and models do roughly agree. Using a mixture of satellite and modeled variables, a map of hybrid coupling strength was produced, which supports the findings of GLACE that transitional zones tend to have the strongest coupling.

scholarly journals Impact of Surface Albedo Assimilation on Snow Estimation

2020 ◽  
Vol 12 (4) ◽  
pp. 645 ◽  
Author(s):  
Sujay Kumar ◽  
David Mocko ◽  
Carrie Vuyovich ◽  
Christa Peters-Lidard
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Snow Accumulation ◽  
High Plains ◽  
Surface Model ◽  
Fractional Snow Cover ◽  
The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

scholarly journals Simulation of Northern Eurasian Local Snow Depth, Mass, and Density Using a Detailed Snowpack Model and Meteorological Reanalyses

2013 ◽  
Vol 14 (1) ◽  
pp. 203-219 ◽  
Author(s):  
Eric Brun ◽  
Vincent Vionnet ◽  
Aaron Boone ◽  
Bertrand Decharme ◽  
Yannick Peings ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Northern Eurasia ◽  
Surface Model ◽  
Monthly Precipitation ◽  
Blowing Snow ◽  
Snow Water

Abstract The Crocus snowpack model within the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface model was run over northern Eurasia from 1979 to 1993, using forcing data extracted from hydrometeorological datasets and meteorological reanalyses. Simulated snow depth, snow water equivalent, and density over open fields were compared with local observations from over 1000 monitoring sites, available either once a day or three times per month. The best performance is obtained with European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim). Provided blowing snow sublimation is taken into account, the simulations show a small bias and high correlations in terms of snow depth, snow water equivalent, and density. Local snow cover durations as well as the onset and vanishing dates of continuous snow cover are also well reproduced. A major result is that the overall performance of the simulations is very similar to the performance of existing gridded snow products, which, in contrast, assimilate local snow depth observations. Soil temperature at 20-cm depth is reasonably well simulated. The methodology developed in this study is an efficient way to evaluate different meteorological datasets, especially in terms of snow precipitation. It reveals that the temporal disaggregation of monthly precipitation in the hydrometeorological dataset from Princeton University significantly impacts the rain–snow partitioning, deteriorating the simulation of the onset of snow cover as well as snow depth throughout the cold season.

scholarly journals Benchmarking of a Physically Based Hydrologic Model

2017 ◽  
Vol 18 (8) ◽  
pp. 2215-2225 ◽  
Author(s):  
Andrew J. Newman ◽  
Naoki Mizukami ◽  
Martyn P. Clark ◽  
Andrew W. Wood ◽  
Bart Nijssen ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Hydrologic Model ◽  
Complex Model ◽  
Model Parameters ◽  
Infiltration Capacity ◽  
Large Sample ◽  
Variable Infiltration Capacity Model ◽  
Capacity Model ◽  
Physically Based

Abstract The concepts of model benchmarking, model agility, and large-sample hydrology are becoming more prevalent in hydrologic and land surface modeling. As modeling systems become more sophisticated, these concepts have the ability to help improve modeling capabilities and understanding. In this paper, their utility is demonstrated with an application of the physically based Variable Infiltration Capacity model (VIC). The authors implement VIC for a sample of 531 basins across the contiguous United States, incrementally increase model agility, and perform comparisons to a benchmark. The use of a large-sample set allows for statistically robust comparisons and subcategorization across hydroclimate conditions. Our benchmark is a calibrated, time-stepping, conceptual hydrologic model. This model is constrained by physical relationships such as the water balance, and it complements purely statistical benchmarks due to the increased physical realism and permits physically motivated benchmarking using metrics that relate one variable to another (e.g., runoff ratio). The authors find that increasing model agility along the parameter dimension, as measured by the number of model parameters available for calibration, does increase model performance for calibration and validation periods relative to less agile implementations. However, as agility increases, transferability decreases, even for a complex model such as VIC. The benchmark outperforms VIC in even the most agile case when evaluated across the entire basin set. However, VIC meets or exceeds benchmark performance in basins with high runoff ratios (greater than ~0.8), highlighting the ability of large-sample comparative hydrology to identify hydroclimatic performance variations.

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