A comparison of four snow models using observations from an alpine site

Essery, Richard; Martin, E.; Koshiro, Tsuyoshi; Fernández, Alberto; Brun, E.

doi:10.1007/s003820050302

Cited by 74 publications

(83 citation statements)

References 10 publications

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“…Biases may be due to uncertainty of the boundary conditions from the ERA-Interim reanalysis (Chaudhuri et al, 2013) since errors from the WRF downscaling model are difficult to quantify in mountain areas (Gutmann et al, 2012), and uncertainties that typically result from simulations of snow mass and energy balance from meteorological data (Essery et al, 1999(Essery et al, , 2013Magnusson et al, 2015). The use of the standard air temperature lapse rate could also be a source of uncertainty.…”

Section: Gridded Snow Dataset: Applications and Limitationsmentioning

confidence: 99%

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Alonso‐González

López‐Moreno

Gascoin

et al. 2018

Earth Syst. Sci. Data

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Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

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Section: Gridded Snow Dataset: Applications and Limitationsmentioning

confidence: 99%

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Alonso‐González

López‐Moreno

Gascoin

et al. 2018

Earth Syst. Sci. Data

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“…Turbulent fluxes of latent and sensible heat between the snow surface and the atmosphere are the third important parameter for the thermal budget of the snowpack. In the CRO-CUS model, these fluxes are parameterized with a transfer coefficient C H for the turbulent fluxes using the bulk Richardson number (Essery et al, 1999):…”

Section: Snowpack Modelingmentioning

confidence: 99%

Simulation of the specific surface area of snow using a one-dimensional physical snowpack model: implementation and evaluation for subarctic snow in Alaska

et al. 2010

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Abstract. The specific surface area (SSA) of the snow constitutes a powerful parameter to quantify the exchange of matter and energy between the snow and the atmosphere. However, currently no snow physics model can simulate the SSA. Therefore, two different types of empirical parameterizations of the specific surface area (SSA) of snow are implemented into the existing one-dimensional snow physics model CROCUS. The parameterizations are either based on diagnostic equations relating the SSA to parameters like snow type and density or on prognostic equations that describe the change of SSA depending on snow age, snowpack temperature, and the temperature gradient within the snowpack. Simulations with the upgraded CROCUS model were performed for a subarctic snowpack, for which an extensive data set including SSA measurements is available at Fairbanks, Alaska for the winter season 2003/2004. While a reasonable agreement between simulated and observed SSA values is obtained using both parameterizations, the model tends to overestimate the SSA. This overestimation is more pronounced using the diagnostic equations compared to the results of the prognostic equations. Parts of the SSA deviations using both parameterizations can be attributed to differences between simulated and observed snow heights, densities, and temperatures. Therefore, further sensitivity studies regarding the thermal budget of the snowpack were performed. They revealed that reducing the thermal conductivity of the snow or increasing the turbulent fluxes at the snow surfaces leadsCorrespondence to: H.-W. Jacobi (jacobi@lgge.obs.ujf-grenoble.fr) to a slight improvement of the simulated thermal budget of the snowpack compared to the observations. However, their impact on further simulated parameters like snow height and SSA remains small. Including additional physical processes in the snow model may have the potential to advance the simulations of the thermal budget of the snowpack and, thus, the SSA simulations.

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“…Such models have been operated at grid resolutions of a few metres (e.g. Essery et al, 1999;Liston and Elder, 2006;Schneiderbauer and Prokop, 2011) to hundreds of meters (e.g. Bernhardt et al, 2009;Bavay et al, 2009Bavay et al, , 2013MacDonald et al, 2010;Magnusson et al, 2010).…”

mentioning

confidence: 99%

“…Wind plays a major role for the deposition and redistribution of snow (e.g. McKay and Gray, 1981;Pomeroy and Gray, 1995;Essery et al, 1999;Trujillo et al, 2007;Lehning et al, 2008). Moreover, snow can be relocated by avalanches and sloughing (Blöschl and Kirnbauer, 1992;Gruber, 2007;Bernhardt and Schulz, 2010), and the local radiation and energy balance influence the spatially varying ablation processes (Cline et al, 1998;Pomeroy et al, 1998Pomeroy et al, , 2004Pohl et al, 2006;Mott et al, 2011).…”

mentioning

confidence: 99%

Statistical modelling of the snow depth distribution in open alpine terrain

Grünewald¹,

Stötter

Pomeroy

et al. 2013

Hydrol. Earth Syst. Sci.

122

142

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Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91 % of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23 % of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

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A comparison of four snow models using observations from an alpine site

Cited by 74 publications

References 10 publications

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Simulation of the specific surface area of snow using a one-dimensional physical snowpack model: implementation and evaluation for subarctic snow in Alaska

Statistical modelling of the snow depth distribution in open alpine terrain

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