Influence of stress, temperature and crystal morphology on isothermal densification and specific surface area decrease of new snow

Schleef, S.; Löwe, Henning; Schneebeli, Martin

doi:10.5194/tc-8-1825-2014

Cited by 37 publications

(40 citation statements)

References 39 publications

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“…The SSA decay is also consistent with current understanding and parameterizations of SSA evolution (e.g. Carmagnola et al, 2014;Flanner and Zender, 2006;Schleef et al, 2014).…”

Section: Vertical Representativeness Of Optimal Impurity Contentsupporting

confidence: 65%

In situ continuous visible and near-infrared spectroscopy of an alpine snowpack

Dumont¹,

et al. 2017

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Abstract. Snow spectral albedo in the visible/near-infrared range has been continuously measured during a winter season at Col de Porte alpine site (French Alps; 45.30 • N, 5.77 • E; 1325 m a.s.l.). The evolution of such alpine snowpack is complex due to intensive precipitation, rapid melt events and Saharan dust deposition outbreaks. This study highlights that the resulting intricate variations of spectral albedo can be successfully explained by variations of the following snow surface variables: specific surface area (SSA) of snow, effective light-absorbing impurities content, presence of liquid water and slope. The methodology developed in this study disentangles the effect of these variables on snow spectral albedo. The presence of liquid water at the snow surface results in a spectral shift of the albedo from which melt events can be identified with an occurrence of false detection rate lower than 3.5 %. Snow SSA mostly impacts spectral albedo in the near-infrared range. Impurity deposition mostly impacts the albedo in the visible range but this impact is very dependent on snow SSA and surface slope. Our work thus demonstrates that the SSA estimation from spectral albedo is affected by large uncertainties for a tilted snow surface and medium to high impurity contents and that the estimation of impurity content is also affected by large uncertainties, especially for low values below 50 ng g −1 black carbon equivalent. The proposed methodology opens routes for retrieval of SSA, impurity content, melt events and surface slope from spectral albedo. However, an exhaustive accuracy assessment of the snow black properties retrieval would require more independent in situ measurements and is beyond the scope of the present study. This time series of snow spectral albedo nevertheless already provides a new insight into our understanding of the evolution of snow surface properties.

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“…The SSA decay is also consistent with current understanding and parameterizations of SSA evolution (e.g. Carmagnola et al, 2014;Flanner and Zender, 2006;Schleef et al, 2014).…”

Section: Vertical Representativeness Of Optimal Impurity Contentsupporting

confidence: 65%

In situ continuous visible and near-infrared spectroscopy of an alpine snowpack

Dumont¹,

et al. 2017

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“…2.2) and the constants a η = 0.1 K −1 , b η = 0.023 m 3 kg −1 , and T η = 5 K. The viscosity dependence on snow temperature is limited according to Schleef et al (2014) who pointed out that the impact of snow temperature on snow densification becomes negligible at low temperatures. The last dimensionless function, f W , describes the decrease of viscosity in the presence of liquid water.…”

Section: Snow Compactionmentioning

confidence: 99%

Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model

et al. 2016

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Abstract. In this study we analyzed how an improved representation of snowpack processes and soil properties in the multilayer snow and soil schemes of the Interaction SoilBiosphere-Atmosphere (ISBA) land surface model impacts the simulation of soil temperature profiles over northern Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over northern Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile, and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

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“…Furthermore, the compaction velocity actually has a high dependence to snow microstructure (Lehning et al, 2002). This complex dependence cannot be described in Crocus by the visco-elastic concept and microstructure-dependent models of compaction are only available for very specific conditions (Schleef et al, 2014). These limitations partly explain the errors of simulated HN identified 15 by Champavier et al (2018) in combination with the known errors and underdispersion of precipitation input.…”

Section: Crocus Snowpack Modelmentioning

confidence: 99%

Statistical post-processing of ensemble forecasts of the height of new snow

Nousu¹,

Lafaysse²,

Vernay³

et al. 2019

Preprint

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Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts 5 than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km 2 ). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two 10 training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs 15 long reforecasts as homogeneous as possible with the operational systems.30 2018) are considerably lower than errors in solid precipitation measurements.The goal of this study is to test the ability of a nonhomogeneous regression method to improve the ensemble forecasts of HN from the PEARP-S2M ensemble snowpack modelling system. More precisely, the regression method of Scheuerer andHamill (2015) based on the Censored Shifted Gamma Distribution was chosen in this work for the advantages identified by the authors in the case of precipitation forecasts. In particular, this method allows to extrapolate the statistical relationship 35 3 https://doi.between predictors and predictands from common events to more unusual events. Considering the specificities of the available datasets in terms of predictands and predictors, two other scientific questions are considered: (1) can statistical postprocessing be applied at a larger spatial scale than the observation points? (2) what are the requirements of a robust training forecast dataset for statistical postprocessing?The structure of the paper is as follows. Section 2 describes the model components of the PEARP-S2M system, the obser-5 vation and forecast datasets used in this study, the nonhomogeneous regression method chosen for post-processing and the evaluation me...

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Influence of stress, temperature and crystal morphology on isothermal densification and specific surface area decrease of new snow

Cited by 37 publications

References 39 publications

In situ continuous visible and near-infrared spectroscopy of an alpine snowpack

In situ continuous visible and near-infrared spectroscopy of an alpine snowpack

Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model

Statistical post-processing of ensemble forecasts of the height of new snow

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