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EUINTAS03515296 Influence on Snow Vertical Structure on Hydrothermal Regime and Snow Related Economi

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Title: EUINTAS03515296 Influence on Snow Vertical Structure on Hydrothermal Regime and Snow Related Economi


1
EU-INTAS-03-51-5296 Influence on Snow Vertical
Structure on Hydro-thermal Regime and Snow
Related Economical ImpactsNATO ESP CLG 981942
"Snow/Saftety/Economy" in support of INTAS
project 03-51-5296
A dynamic classification for a parameterization
of snow cover in GCM
Sergey Gromov, Konstantin Rubinstein Hydrometeorol
ogical Centre of Russia, Moscow
2
AMIP II models snow cover reproduction RHMC GCM
snow cover modeling 
3
Introduction 
What is snow classification system? physically
based classification system for seasonal snow
covers (Sturm et al, 1995) based on observable
properties each snow class is defined in terms
of typical sequences of snow layers, the
thickness and density of these layers crystal
morphology and grain characteristics within the
layers class determination upon attributes of
the snow cover change with time snow classes
related to the winter climate of a given location
4
Classes attributes 
Each class is combination of textures, layers
and lateral variability into the group with
properties that have close affinity or are found
to be recurrent in nature. ? Stratigraphic
and textural attributes of each class of snow
cover as they would appear in middle to late
winter.
Fig. taken from Sturm et al., 1995
5
Why to use this classification? 
Why to use this snow classification? Concrete
physical snow properties assigned to a snow cover
in parameterization scheme An ability to
derive a snow class directly from atmospheric
conditions instead of physical snow properties
Fig. taken from Sturm et al., 1995
6
Classes properties 
7
Snow classes distribution built using ERA data 
8
Snow classes distribution built using NCEP2 data 
9
Classes coverage 
10
Dynamic snow classification 
For the ability to use the classification in
parametrization the building algorithm changed
classes alter during the snow setup, deposition
and spring season
11
AMIP II models snow cover reproduction Dynamic
snow classification 

Monthly snow cover integral change over NH
Average Year Snow Cover Integral over NH
12
Dynamic snow classification 
Dynamic classification annual snow classes change
ERA40
Snow classes distribution as it would appear in
middle to late winter (Sturm)
13
Snow albedo change correspondingly snow cover
class experiment 
An experiment with first implementation of
dynamic snow cover classification in Russian
Hydrometcentre GCM snow parameterization carried
in the reference scheme the resulting albedo
during the snow existence on the ground
calculated using the background land albedo aL
which is modified by snow albedo aS and snow
depth sn in the radiation scheme       where
the albedo of the snow aS is 0.8 and sncr is a
critical snow value of 0.01 m of equivalent
liquid water. New scheme include the maximum snow
albedo aS changing according the snow class at
given location  
14
Snow albedo change correspondingly snow cover
class experiment 
15
Snow albedo change correspondingly snow cover
class experiment 
16
Conclusions 
The algorithm to derive the Sturm snow
classification is examined on alternative data
sources and the results shows that it applicable
to be used with reanalysis data and GCM The
dynamic snow cover classification is developed,
it adequately describes the snow classes changes
during the snow cover setup and deposition
periods A step taken to improve the GCM snow
cover parameterization - a dynamic time and
spatial changing concrete physical properties of
snow (such as thermal conductivity and capacity,
density, etc.) are to be assigned to the model
snow layer Snow albedo change with snow cover
class experiment reproduction revealed more
perceptible response effect of snow albedo
changes on radiation balance and surface
temperature than the snow reproduction There
are still uncertainties connected with special
cases such as complex topography and
mountains This study has been carried under
partial support of the INTAS Project 03-51-5296,
RFBR grants 04-05-65099, 04-05-64151 and NATO ESP
CLG 981942 Project
17

Thank you!
INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
18

INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
19
Datasets 
Data used For the original Sturm classification
following datasets were used Legates-Willmott
surface air temperature precipitation Gridded
global earth-surface classification of Olson et
al. (1985) (Vegetation is used as a proxy for
wind data) Three reanalysis datasets used for
reconstructing the classification European
Centre for Medium-Range Weather Forecast
(ERA40) US National Center for Atmospheric
Research (NCAR/NCEP) ver. 1, 2
20
Sturm snow classificationIntroduction 
Historical seasonal snow classification
systems Formozov (1946) Roch (1949) Rikhter
(1954) Espenshade Jr. and Schytt (1956) Gold and
Williams (1957) Bilello (1957, 1969, 1984) Benson
(1959, 1962, 1967, 1969, 1982) Potter
(1965) Pruitt Jr. (1970, 1984, 1970) McKay and
Findlay (1971) McKay (1972) Irwin (1979) McKay
and Gray (1981)
Basis for the classification systems climatic
zones density depth duration ecological
zones grain and crystal hardness liquid
water mountain climate size and shape
snowshoe design temperature time-density
curves trafficability vegetation zones
The given classification can be used to obtain
the information on snow cover physical properties
to improve the modeling calculations and results.
INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
21
Sturm snow classificationSnow classes map 
INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
22
Sturm snow classificationSnow classes 
Seasonal snow covers divided into six classes
Tundra Taiga Maritime Ephemeral Prairie
Mountain Mountain snow is a special class for
the regions where the snow cover is highly
variable
Fig. taken from Sturm et al., 1995
INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
23
Building Sturm snow classificationWinter
changing classification 
The algorithm of the classification building can
be used to create a winter changing
classification using the winter climate seasonal
course or current modeled data. It is performed
summing CDM and precipitation from first winter
month to current.
INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
24
Critical values 
Since the data used in binary classification
system completely defines the classification, the
modification of critical values can be used for
the closest approximation of derived
classification to ethalon (Sturm). Because of
low resolution of wind data (it cannot describe
the wind speed gradients near the surface
correctly) the vegetation dataset was taken to
represent the wind conditions. As the experiment
shows, the precipitation critical value (Pd) has
the most influence on classes distribution. The
modification of the temperature critical value
(Td) did not used in approximation because of
close reproduction of temperature climate in all
datasets.
INTAS 03-51-5296 Workshop Zvenigorod, Moscow
region, October 23-26, 2005
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