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An advanced snow parameterization for the models of atmospheric circulation

An advanced snow parameterization for the models of atmospheric circulation. Ekaterina E. Machul’skaya ¹ , Vasily N. Lykosov ¹ Hydrometeorological Centre of Russian Federation, Moscow, Russia ² Moscow State University, Russia

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An advanced snow parameterization for the models of atmospheric circulation

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  1. 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, Russia ³Institute for Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

  2. 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 decrease of surface albedo a positive temperature bias more absorption of solar radiation

  3. 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 > 0°C or • when soil surface • temperature > 0°C Numerical schemes Arbitrary number of layers, in this study 5 1 layer

  4. 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

  5. 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

  6. Data (1) Valdai Russia, European part 58 N, 33 E boreal mixed forest zone grassland site Yakutsk Russia, East Siberia 62 N, 130 E boreal coniferous forest zone grassland site

  7. 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

  8. Results discussion (1)

  9. observations COSMO INM Results discussion (2): SWE in Yakutsk

  10. observations COSMO INM Results discussion (3): SWE in Valdai 1967/68 1966/67 1968/69 1977/78 1978/79 1979/80 Days from Jan. 1st, 1966

  11. Results discussion (3): Impact on the surface temperature (TS) COSMO TS INM TS COSMO SWE INM SWE

  12. 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.

  13. 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.

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