An%20advanced%20snow%20parameterization%20for%20the%20models%20of%20atmospheric%20circulation

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An advanced snow parameterization for the models
of atmospheric circulation

• Ekaterina E. Machulskaya¹, Vasily N. Lykosov
• ¹Hydrometeorological Centre of Russian
  Federation, Moscow, Russia
• ²Moscow State University, Russia
• ³Institute 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

larger snow melt, faster snow cover depletion
a positive temperature bias
decrease of surface albedo
more absorption of solar radiation
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Snow models description (1)
INM
COSMO
Implemented processes

• Heat conduction
• Liquid water transport
• Gravitational compaction
• metamorphosis
• Solar radiation penetration

• Heat conduction
• Melting when snow
• temperature gt 0C or
• when soil surface
• temperature gt 0C

Numerical schemes
Arbitrary number of layers, in this study 5
1 layer
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Implemented processes (2)
Heat and water transport
- snow temperature,
- snow liquid water content,
- snow heat conductivity,
- latent heat for freezing/melting,
- snow density,
- snow specific heat content,
- melting rate,
- refreezing rate,
- infiltration rate due to gravity
Water percolation
- snow hydravlic conductivity,
- snow water holding capacity,
- snow porosity
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Gravitational compaction and metamorphosis
Implemented processes (3)
Where member
describes the gravity effect
member
describes the snow metamorphosis, 75 Pa
- the snow compaction viscosity
Solar radiation penetration
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Data (1)
Yakutsk Russia, East Siberia 62 N, 130 E boreal
coniferous forest zone grassland site
Valdai Russia, European part 58 N, 33 E boreal
mixed forest zone grassland site
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Data (2)
Atmospheric forcing
Evaluation data
Valdai 1966 1983 Yakutsk 1937 1984
snow water-equivalent depth Valdai 1966
1983 Yakutsk 1971 1973 In winter every 10
days In spring more often
Every 3 hours air temperature air pressure air
humidity wind speed at 10 m precipitation
rate Estimated shortwave radiation longwave
radiation
at 2 m
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Results discussion (1)
Correlation coefficient between time series of observed and simulated SWE (N 221, plt0.0001) Mean error ( standard deviation) in the time of the snow complete ablation (days)
COSMO 0.81 14 (2)
INM 0.90 1 (1)
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Results discussion (2) SWE in Yakutsk
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Results discussion (3) SWE in Valdai
1967/68
1966/67
1968/69
1977/78
1978/79
1979/80
Days from Jan. 1st, 1966
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Results discussion (3) Impact on the surface
temperature (TS)
COSMO TS INM TS
COSMO SWE INM SWE
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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.