An advanced snow parameterization for the models of atmospheric circulation
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An advanced snow parameterization for the models
of atmospheric circulation
Ekaterina E. Machul’skaya¹, Vasily N. Lykosov
¹Hydrometeorological Centre of Russian Federation, Moscow, Russia
²Moscow State University, Russia³Institute for Numerical Mathematics, Russian
Academy of Sciences, Moscow, Russia
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Introduction• Numerous observational studies and model simulations have shown
that snow cover affects atmospheric circulation, air temperature, and the hydrologic cycle, due to its especial properties (high albedo, reduced roughness etc.)
• Snow is related to a number of feedbacks, the most obvious being the snow albedo feedback:
a positive temperature bias
larger snow melt, faster snow cover depletion
decrease of surface albedo
more absorption of solar radiation
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Snow models description (1)
•Heat conduction•Melting when snowtemperature > 0°C orwhen soil surface temperature > 0°C
•Heat conduction•Liquid water transport•Gravitational compaction +metamorphosis •Solar radiation penetration
1 layerArbitrary number of layers,
in this study 5
Numerical schemes
Implemented processes
COSMO INM
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Implemented processes (2)
- snow temperature, - snow liquid water content,
- snow density, - snow specific heat content,
- snow heat conductivity,
- latent heat for freezing/melting,
- melting rate, - refreezing rate, - infiltration rate due to gravity
Water percolation:
- snow hydravlic conductivity, - snow water holding capacity,
- snow porosity
Heat and water transport
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Gravitational compaction and metamorphosis
describes the gravity effectWhere
member
member describes the snow metamorphosis, = 75 Pa
- the snow compaction viscosity
Solar radiation penetration
Implemented processes (3)
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Data (1)Yakutsk
Russia, East Siberia
62 N, 130 E
boreal coniferous forest zone
grassland site
ValdaiRussia, European part
58 N, 33 E
boreal mixed forest zone
grassland site
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Data (2)
Atmospheric forcing
snow water-equivalent depth
Valdai: 1966 – 1983Yakutsk: 1971 – 1973
In winter: every 10 daysIn spring: more often
Every 3 hours:
air temperatureair pressureair humiditywind speed at 10 mprecipitation rate
Estimated:shortwave radiationlongwave radiation
at 2 m
Valdai: 1966 – 1983Yakutsk: 1937 – 1984
Evaluation data
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Correlation coefficient between
time series of observed and
simulated SWE
(N = 221, p<0.0001)
Mean error(± standard deviation)
in the time of the snow complete ablation (days)
COSMO 0.81 14 (±2)
INM 0.90 1 (±1)
Results discussion (1)
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Results discussion (2): SWE in Yakutsk
observationsCOSMOINM
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observationsCOSMOINM
Results discussion (3): SWE in Valdai
Days from Jan. 1st, 1966
1966/671967/68
1977/78 1978/79 1979/80
1968/69
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COSMO TSINM TS
COSMO SWEINM SWE
Results discussion (3): Impact on the surface temperature (TS)
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Summary• A new advanced snow parameterization is suggested, implemented and
tested by means of long-term data.
• This multilayered scheme takes into account the latent heat of the phase transfer of water and the interaction with radiative fluxes in the snowpack.
• In comparison with the more simple model incorporated in COSMO at present, the new more physical scheme represents the snow evolution more realistically, particularly during melting period.
• The implementation of the new scheme in COSMO is recommended since it can improve the quality of the surface air temperature prediction, particularly in spring.
• Results of the long-term continues integration with a real forcing data can be used as initial approximation fields for reanalysis of the surface temperature, snow mask and albedo for the adjustment of initial conditions of weather forecast model.
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Futher possible directions of the study
• The Valdai observational data set includes data related to the snow density and albedo, as well as to the snow cover fraction.
• It is known that fractional snow cover, snow albedo, and their interplay have a considerable effect on the energy available for ablation (Slater et al., 2001; Luce et al., 1998). In alpine environment, elevation, aspect, and slope exert a major control on snow distribution affecting snow accumulation and snowmelt energetics (Pomeroy et al. (2003)) .
• Different data sets that are obtained at present from different field experiments and regular observations (in mountain regions as well), allow to further evaluate the COSMO snow model and to understand to what extent the adequate simulation of different variables is important, in order to improve the prediction of snow evolution and surface air temperature.