Review article: Parameterizations of snow-related physical processes in land surface models

Abstract. Snow on land surface plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. Since the snow cover affects the snow albedo and the ground and soil heat fluxes, it is crucial to detect snow cover changes accurately. It is challenging to acquire observation data for snow cover, snow albedo, and snow depth; thus, an excellent alternative is to use the simulation data produced by the LSMs that calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. Thus, the synthetic intercomparisons of the snow physics in the LSMs will help the improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, snow albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the Earth System Model or operational forecasting systems. We examined single layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM 5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). First, we identified that BATS, Noah-MP, JULES, and ISBA reflect the snow depth and roughness length to parameterize snow cover fraction, and CLM 5 accounts for the standard deviation of the elevation value for the snow cover decay function. Second, CLM 5 and BATS are relatively complex, so that they explicitly take into account the solar zenith angle, black carbon, mineral dust, organic carbon, and ice grain size for the determinations of snow albedo. Besides, JULES and ISBA are also complicated model which concerns ice grain size, solar zenith angle, new snow depth, fresh snowfall rate, and surface temperature for the albedo scheme. Third, HTESSEL, CLM 5, and ISBA considered the effects of both wind and temperature in the determinations of the new snow density. Especially, ISBA and JULES considered internal snow characteristics such as snow viscosity, snow temperature, and vertical stress for parameterizing new snow density. The future outlook discussed geomorphic and vegetation-related variables for the further improvement of the LSMs. Previous studies clearly show that spatio-temporal variation of snow is due to the influence of altitude, slope, and vegetation condition. Therefore, we recommended applying geomorphic and vegetation factors such as elevation, slope, time-varying roughness length, vegetation indexes, or optimized parameters according to the land surface type to parameterize snow-related physical processes.

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Optimization of snow density parameter of Noah Land Surface Model using micro-genetic algorithm for estimating snow depth

10.5194/egusphere-egu21-7848 ◽

2021 ◽

Author(s):

Ebony Lee ◽

Seon Ki Park

Keyword(s):

Genetic Algorithm ◽

Land Surface ◽

Snow Depth ◽

Fitness Function ◽

Land Surface Model ◽

Surface Model ◽

Density Parameter ◽

Snow Density ◽

Noah Land Surface Model ◽

Micro Genetic Algorithm

<p>The Noah Land Surface Model (Noah LSM) estimates snow depth using snow water equivalent and snow density. The snow density is determined by snow compaction, snowmelt water storing, and density of fresh snowfall. The Noah LSM usually underestimates snow depth compared to the ground observations in Korea, which occurs from the beginning of snowfall. We performed an optimal estimation of parameters related to the density of fresh snowfall, using micro-genetic algorithm (&#956;-GA) that uses the evolution process concept through natural selection and mutation mechanism. Ground observations from 36 sites of the Korea Meteorological Administration, for the recent 10 years (May 2009 &#8211; April 2019), are used for offline forcing of the Noah LSM and evaluating the fitness function in &#956;-GA. Optimized parameters reduced the density of fresh snowfall, and improved the simulated snow depth. The root-mean-square error of snow depth decreased from 8.1 cm to 7.1 cm.</p>

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Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations

Remote Sensing ◽

10.3390/rs12183101 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3101

Author(s):

Donghang Shao ◽

Wenbo Xu ◽

Hongyi Li ◽

Jian Wang ◽

Xiaohua Hao

Keyword(s):

Remote Sensing ◽

Land Surface ◽

Spectral Reflectance ◽

Satellite Remote Sensing ◽

Land Surface Model ◽

Integrated Model ◽

Surface Model ◽

Snow Surface ◽

Snow Albedo ◽

Surface Models

Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.

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Impact of Surface Albedo Assimilation on Snow Estimation

Remote Sensing ◽

10.3390/rs12040645 ◽

2020 ◽

Vol 12 (4) ◽

pp. 645 ◽

Cited By ~ 1

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-012.1 ◽

2013 ◽

Vol 14 (1) ◽

pp. 203-219 ◽

Cited By ~ 79

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.

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Evaluation of the snow cover in the Soil, Vegetation, and Snow (SVS) land surface model

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0249.1 ◽

2021 ◽

Author(s):

Gonzalo Leonardini ◽

François Anctil ◽

Vincent Vionnet ◽

Maria Abrahamowicz ◽

Daniel F. Nadeau ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Snow Water Equivalent ◽

Weather Prediction ◽

Land Surface Model ◽

Late Winter ◽

Surface Model ◽

Lower Performance ◽

Snow Water

AbstractThe Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at ten well-instrumented sites from the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force-restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

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On the Ability of LIDAR Snow Depth Measurements to Determine or Evaluate the HRU Discretization in a Land Surface Model

Hydrology ◽

10.3390/hydrology7020020 ◽

2020 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

Michael Weber ◽

Moritz Feigl ◽

Karsten Schulz ◽

Matthias Bernhardt

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Water Cycle ◽

Land Surface Model ◽

Surface Model ◽

Discretization Scheme ◽

Dominant Component ◽

Alpine Research ◽

Alpine Catchments

To find the adequate spatial model discretization scheme, which balances the models capabilities and the demand for representing key features in reality, is a challenging task. It becomes even more challenging in high alpine catchments, where the variability of topography and meteorology over short distances strongly influences the distribution of snow cover, the dominant component in the alpine water cycle. For the high alpine Research Catchment Zugspitze (RCZ) a new method for objective delineation of hydrological response units (HRUs) using a time series of high resolution LIDAR derived snow depth maps and the physiographic properties of the RCZ is introduced. Via principle component analysis (PCA) of these maps, a dominant snow depth pattern, that turned out to be largely defined during the (winter) accumulation period was identified. This dominant pattern serves as a reference for HRU delineations on the basis of cluster analyses of the catchment’s physiographic properties. The method guarantees for an appropriate, objective, spatial discretization scheme, which allows for a reliable and meaningful reproduction of snow cover variability with the Cold Regions Hydrological Model — at the same time avoiding significant increase of computational demands. Different HRU schemes were evaluated with measured snow depth and the comparison of their model results identified significant differences in model output and best performance of the scheme which best represents measured snow depth distribution.

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Representation of vegetation effects on the snow-covered albedo in the Noah land surface model with multiple physics options

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3197-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3197-3218

Author(s):

S. Park ◽

S. K. Park

Keyword(s):

East Asia ◽

Land Surface ◽

Surface Albedo ◽

Land Surface Model ◽

Surface Model ◽

Vegetation Types ◽

Area Index ◽

Snow Albedo ◽

Noah Land Surface Model ◽

Vegetation Effect

Abstract. Snow albedo plays a critical role in calculating the energy budget, but parameterization of the snow surface albedo is still under great uncertainty. It varies with snow grain size, snow cover thickness, snow age, forest shading factor and other variables. Snow albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, the vegetation effect rarely exerts on the surface albedo in winter in East Asia with only these parameters. Thus, we investigated the vegetation effects on the snow-covered albedo from observations and evaluated the model improvement by considering such effect. We found that calculation of albedo without proper reflection of the vegetation effect is mainly responsible for the large positive bias in winter. Therefore, we developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the winter albedo. As a result, the Noah-MP's performance in albedo has been significantly improved – RMSE is reduced by approximately 73%.

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An Improved Snow Scheme for the ECMWF Land Surface Model: Description and Offline Validation

Journal of Hydrometeorology ◽

10.1175/2010jhm1249.1 ◽

2010 ◽

Vol 11 (4) ◽

pp. 899-916 ◽

Cited By ~ 160

Author(s):

Emanuel Dutra ◽

Gianpaolo Balsamo ◽

Pedro Viterbo ◽

Pedro M. A. Miranda ◽

Anton Beljaars ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Land Surface Model ◽

Model Description ◽

Surface Model ◽

Negative Bias ◽

Snow Density ◽

Snow Cover Fraction ◽

Basin Scale ◽

Wide Range

Abstract A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2) and global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and by ECMWF Re-Analysis ERA-Interim. The new scheme reduces the end of season ablation biases from 10 to 2 days in open areas and from 21 to 13 days in forest areas. Global GSWP2 results are compared against basin-scale runoff and terrestrial water storage. The new snow density parameterization increases the snow thermal insulation, reducing soil freezing and leading to an improved hydrological cycle. Simulated snow cover fraction is compared against NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) with a reduction of the negative bias of snow-covered area of the original snow scheme. The original snow scheme had a systematic negative bias in surface albedo when compared against Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The new scheme reduces the albedo bias, consequently reducing the spatial- and time-averaged surface net shortwave radiation bias by 5.2 W m−2 in 14% of the Northern Hemisphere land. The new snow scheme described in this paper was introduced in the ECMWF operational forecast system in September 2009 (cycle 35R3).

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Assimilation of Satellite-Observed Snow Albedo in a Land Surface Model

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-0125.1 ◽

2012 ◽

Vol 13 (3) ◽

pp. 1119-1130 ◽

Cited By ~ 19

Author(s):

M. Jahanzeb Malik ◽

Rogier van der Velde ◽

Zoltan Vekerdy ◽

Zhongbo Su

Keyword(s):

Land Surface ◽

Snow Depth ◽

Prediction Models ◽

Weather Prediction ◽

Land Surface Model ◽

Surface Energy Budget ◽

Shortwave Radiation ◽

Surface Model ◽

Snow Albedo ◽

The Impact

Abstract This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.

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Land-atmosphere interactions in sub-polar and alpine climates in the CORDEX FPS LUCAS models: I. Evaluation of the snow-albedo effect

10.5194/tc-2021-290 ◽

2021 ◽

Author(s):

Anne Sophie Daloz ◽

Clemens Schwingshackl ◽

Priscilla Mooney ◽

Susanna Strada ◽

Diana Rechid ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Radiative Forcing ◽

Climate Models ◽

Dominant Role ◽

Land Surface Model ◽

Surface Model ◽

Sensitivity Index ◽

Snow Albedo ◽

Albedo Effect

Abstract. In the Northern Hemisphere, the seasonal snow cover plays a major role in the climate system via its effect on surface albedo and fluxes. The parameterization of snow-atmosphere interactions in climate models remains a source of uncertainty and biases in the representation of the local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled to different land surface models to simulate the snow albedo effect over Europe, in winter and spring. We use a previously defined index, the Snow Albedo Sensitivity Index (SASI), to quantify the radiative forcing due to the snow albedo effect. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over mid- and high-latitudes in Europe. The choice of parameterizations with first and foremost the choice of the land surface model but also the convection scheme and the planetary boundary layer, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover correctly. The agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that the choice of the land surface model is more critical for the representation of SASI than the atmospheric model during this time period.

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