andrey by yaofenji

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									Parameterization of snow vertical
structure and representation of the
corresponding snow properties in
      local climate modeling
          Andrey B. Shmakin
        (Institute of Geography,
     Russian Academy of Sciences)
          Sergey A. Sokratov
      (Moscow State University)
      INTAS-funded project 03-51-5296
    «Influence of snow vertical structure on
    hydrothermal regime and snow-related
   economical aspects in Northern Eurasia»

- Technical University, Vienna
- Institute of Geography, Moscow
- Moscow State University
- Hydrometeorological Center of Russia, Moscow
- Charles University, Prague
- Swiss Federal Institute for Snow and Avalanches
Research, Davos
- Institute of Geology and Geophysics, Tashkent, Uzbekistan
- Institute of Hydrology, Slovakia
  Quantitative                  Qualitative
  methods for      The       approaches for
   evaluation     project       description
 of snow cover              of snow structure,
   parameters                its stratigraphy,
in mathematical             crystal formation,
    modeling                        etc.
RESEARCH OBJECTIVES

-Creation of a data set of snow stratigraphy in Northern Eurasia;

-Development of classification of the snow cover and its effective
properties for Northern Eurasia;

-Development of parameterization schemes of the snow cover
stratigraphy for climate and hydrological models, and their testing
against the observed data;

-Estimation of the influence of the snow cover stratigraphy on
hydrothermal regime in the Northern Eurasia and extreme events;

- Estimation of the economic effect of the snow cover spatial and
temporal variability in the Northern Eurasia, and its projection for
certain future climate change scenarios.
Changes in time of the snow vertical structure (from SNOWPACK model
and observations), Weissfluhjoch, February – April, 1999. The colors
correspond to different types of snow crystals with certain surface
curvature and other characteristics.

Basically, each type carries a set of effective parameters (density,
thermal conductivity, liquid water holding capacity, etc.). Then, integral
effective parameters for the entire snow profile are calculated and used
in local model of energy/water exchange on the land.
The heat conductivity evaluation:
combining continuity of flux and energy conservation at the surface
                                         M
                                                                                         f pM
 ksn   1   1  di    da  ki ad   1    di   1  da    k p  k pht  id
                                       fi
                                       ic                                                ac
 where  is the dimensionless snow porosity; di is the fraction of discontinuous ice, da is
 the same for discontinuous air, both varying from 0 to 1; ki is the heat conductivity of ice;
 kp is the same for the mechanisms of the heat conduction and convection in the pore
 space; kpht is the “equivalent heat conductivity” of the process of alternating latent heat
 release/gain; fMi and fMp are macroscale gradient enhancements for ice and pores, χadic and
 χidac are structure factors for continuous ice with dispersed pores and for continuous air
 with dispersed ice.

Previously, data on internal geometry of the snow crystals were
unavailable, and there was no the equation combining the porous
media with anisotropic matter.

  At the next stage, liquid water holding capacity of the snow will be modified
  according to the crystal type. Also, possibility of multiple snow layers will be
  allowed in the model.
Testing site
Dukant, Uzbekistan (41°09’N, 70°04’E, 2000 and 2250 m a.s.l.),
Jan. – Apr. 1990
(Courtesy of Dr.Maxim Petrov and his team from the INTAS project)
          Daily average air temperature (°C) at Dukant
                     in January-April, 1990
20
15
10
    5
    0




        101
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         96
          1
          6




 -5
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                                                              The atmospheric
-15                                                           forcing parameters also
                                                              include:
         Daily precipitation sums (cm, blue) and average      - wind speed,
        effective cloudiness (fraction, brown) at Dukant in   - air humidity,
                        January-April, 1990
                                                              - daily range of air
5                                                             temperature.
4
3
2
1
0
                                                101
                                                106
                                                111
                                                116
    11
    16
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                      36
                      41
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                      66
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                      76
                      81
                                                 86
                                                 91
                                                 96
     1
     6
               Snow depth (cm) at Dukant in January-April, 1990, according to
              observations (blue), calculations with heat conductivity by Sturm
                        (1992; green), and by the new method (red)

120

100

 80

 60

 40

 20

  0
      1   6    11   16   21   26   31   36   41   46   51   56   61   66   71   76   81   86   91   96   101   106   111   116
      Snow depth (cm) at Point 28 in January-April, 1990,
          according to observations (blue diamonds),
      calculations with heat conductivity by Sturm (1992;
              green), and by the new method (red)

200
150
100
50
 0




      101
      106
      111
       11
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       96
        1
        6
       Soil surface temperature (°C) at Point 28 in January-
           April, 1990, according to observations (blue
        diamonds), calculations with heat conductivity by
        Sturm (1992; green) and by the new method (red)

   0




       101
       106
       111
       116
        11
        16
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        31
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        91
        96
         1
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-0,5
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Conclusions

- Parameterization of the snow heat conductivity is
developed, based on the structure of snow crystals, their
density and temperature. Information of main types of the
snow (fluffy, small-grain, depth hoar, etc.) fit to the procedure.

- Snow depth can be sensitive to the method of snow heat
conductivity evaluation, depending on the weather conditions.
At Dukant, it provides higher accuracy in modeling of the
snow depth dynamics. Climate modeling with the new snow
scheme is expected to benefit from the new procedure.

- The new method results in more accurate soil surface
temperature calculation. This will allow better evaluation of
snow-related processes (soil thermal regime, hydrology, etc.)
in the project.
           FURTHER DEVELOPMENT OF THE PROJECT

At the next stage, the snow types recognition will be formalized
according to the evolution of atmospheric conditions during winter
season, and landscape features (presence of tall vegetation, etc.).

Then, the heat/water exchange blocks of SPONSOR model will be
interactively incorporated in global climate models as parameterization
schemes. The snow cover vertical structure will appear as a result of
weather variations, and influence them in turn.

A series of numerical experiments will be carried out for contemporary
climate conditions and some future scenarios. The results will be
analyzed from the viewpoint of both average regime and extreme
events.

Projections of the economical effects in Northern Eurasia due to
climate change and corresponding snow properties change will be
made.
Thank you!

								
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