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

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

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

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

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)

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

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

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)

Results discussion (2): SWE in Yakutsk

observationsCOSMOINM

observationsCOSMOINM

Results discussion (3): SWE in Valdai

Days from Jan. 1st, 1966

1966/671967/68

1977/78 1978/79 1979/80

1968/69

COSMO TSINM TS

COSMO SWEINM SWE

Results discussion (3): Impact on the surface temperature (TS)

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.

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.