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International Conference and Young Scientists School on Computational Information Technologies for
Environmental Sciences: “CITES-2005”Novosibirsk, Russia, March 13-23, 2005
Atmosphere-Sea Hydrodynamic-Ecosystem model study in the sea
Rein Tamsalu (University of Tartu)
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Introduction
Today the environmental science are very much coupled with everyday life. Management policies need answer to concrete questions concerning the response of nature to both natural and manmade changes in enviromental forcing factors and loding.
Numerical Hydro-Ecological modelling is an important tool for a better undrstanding of relations between the processes, and for forecasting these responses.
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Atm.-Sea-Hydro-ecological forecasting modelling
The goal of our activities is to answer to concrete questions concerning the
response of nature to both natural and man-made changes
in marine environment.
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Concrete Questions
The influence of the Port of Tallinn reconstruction
to the Muuga Bay marine environment
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The influence of the Port of Tallinn reconstruction to the Muuga Bay marine environment
Baltic Sea =3’ =6’ Gulf of Finland =1’ =2’
Talsingi =0.25’ =0.5’ Gulf of Muuga =0.05’ =0.1’
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Atm.-Hydro-Ecological forecasting modelling
In the Atmosphere-Sea-Hydro-Ecological
modelling system are coupled several
sub-models:
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Atm.-Sea-Hydro-Ecological Models System
FRESCO (Finnish Russian EStonian COoperation)
Weather Forecasting Model HIRLAM (FMI)Weather Forecasting Model HIRLAM (FMI)
Atm. Boundary Layer ModelS. Zilitinkevich (HU)
Atm. Boundary Layer ModelS. Zilitinkevich (HU)
Atm. Poll. Trans. ModelM. Hongisto (FMI)
Atm. Poll. Trans. ModelM. Hongisto (FMI)
Wind Wave ModelV.Zakharov (IO)
M. Zaslavskii (IO)J. Kabachenko (SOI)
Wind Wave ModelV.Zakharov (IO)
M. Zaslavskii (IO)J. Kabachenko (SOI)
Circulation ModelV. Zalesny (INM)R. Tamsalu(UT)
Circulation ModelV. Zalesny (INM)R. Tamsalu(UT)
Oil Spill ModelS. Ovsienko (SOI)Oil Spill Model
S. Ovsienko (SOI)
EMHI-Est. Meteor. And Hyd. Inst.FMI-Finnish Meteorol. Inst.HU -Helsinki UniversityINM-Inst. of Num. Math. (Moscow)OI -Oceanological Inst. (Moscow)SOI-State Ocean. Inst.(Moscow)TU -University of Tartu
Ecological ModelR. Tamsalu (UT)
H. Kuosa (HU)
Ecological ModelR. Tamsalu (UT)
H. Kuosa (HU)
Meso-scale Atm. ModelR. Rõõm, A. Männik (UT)
Ivar Ansper (EMHI)
Meso-scale Atm. ModelR. Rõõm, A. Männik (UT)
Ivar Ansper (EMHI)
k-( )Model
R. Tamsalu(UT)
k-( )Model
R. Tamsalu(UT)
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Meso-Scale Atmosph. Model
TU-EMHI modelETA model is based on the reference HIRLAM model, ETB is the non-hydrostatic version.
A pre-operational version of nonhydrostatic HIRLAM is used at the Estonian Meteorological Hydrological Institute (EMHI) to test the non-hydrostatic kernel of the model. Two modelling domains, as illustrated in Figure 1, are in use. Grid size of the larger domain ETA is 11km and the smaller domain ETB 3km. As the limited area models require boundary fields from larger models, the ETA model is nested to the FMI operational HIRLAM and ETB to the ETA.
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Meso- Scale Atmospheric Model
ETA-6 * 6 nm
ETB-1.5*1.5 nm
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Marine Circulation Models
There are many different models
barotropicbaroclinichydrostaticnonhydrostatic.......................
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Measured Temperaure Vertical Stucture in the Muuga Bay
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Velocity Measurements In Muuga Bay
Recording Doppler Current Profiler RDCP 600 ( Aandera Instruments AS, Bergen, Norway.)
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Marine Circulation Model
It is clear that we need
Baroclinic Nonhydrostatic Circulation Model
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Marine Circulation Model The governing equations of the circulation model are:
Momentum equation for velocity vector U (u,v,w)Continuity equation for incompressible fluid
Transport-diffusion equations for: Salinity S
Temperature T
State equation for buoyancy b=f(T,S)Two-equation turbulent model for Kinetic energy k and generic length scale quantity or
sea level fluctuation , hydrostatic pressure p* and nonhydrostatic pressure p’
Pressure components
are calculated
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Wind wave calculation
Surface wind waves are an integrated effect, in space and time, of driving wind fields.The wind wave model computes the two-dimensional wave action spectra through integration of the transport equation, where the right hand side consists of several terms describing different evolution mechanisms, such as
•energy input from wind ;•the non-linear transfer of energy through the spectrum ;•different kinds of dissipation mechanisms.
Interactive atmospheric input term is used in the Miles’ form.In this model the so-called narrow-directional approximation is used. This approximation is based on well-known fact that wind waves have a narrow angular spreading function of spectra. The latter circumstance plays a key role in parameterization of nonlinear term.
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Size-Dependent Pankton Community ModelSize- dependent plankton community food web is formed by
autotrophs (Ai ) i=1,2,…,NP
heterotrophs (Hi ) i=1,2,…,NP
bacterioplankton (B )
This plankton community forms the NP triplet stucture.
Size- dependent plankton community food web is formed by autotrophs (Ai ) i=1,2,…,NP
heterotrophs (Hi ) i=1,2,…,NP
bacterioplankton (B )
This plankton community forms the NP triplet stucture.
Zooflag.Zooflag.
Microzoopl.Microzoopl.
DIP
DIN
DIC
DIP+DOP
DIN+DON
DOC
Picophyto.Picophyto. Bacteriopl.Bacteriopl.
ESD (m)
0.2 - 2I
Phytoflag.Phytoflag.
Nanozoopl.Nanozoopl.
II 2 - 10
Nanophyto.Nanophyto.III 10 - 50
Netphyto.Netphyto.
Mesozoopl.Mesozoopl.
IV50 - 250
250 – 1250
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Growth reactions
There are two energy flows to the plankton community
The first one is the uptake of dissolved inorganic nutrients by autotrophy
and it is directed from the autotrophy toward heterotrophy trough grazing .
The other is the uptake of dissolved organic and inorganic nutrients by
bacterioplankton and it is directed from bacterioplankton towards
heterotrophy trough predation.
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Loss reactions
Energy is lost through
autotrophy exudation , mortality and respiration
heterotrophy excretion, mortality and respiration
bacterioplankton excretion and respiration
detritus decay
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Different grid resolutions
Baltic Sea Gulf of Finland
Talsinki area
Muuga Bay
open boundary
I - 3.0 * 3.0 nmII - 1.0 * 1.0 nmIII- ¼ * ¼ nmIV- 1/20 * 1/20 nm
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Horizontal velocity on the surface layer
Muuga Bay
Muuga Bay
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HORIZONTAL VELOCITY IN THE BOTTOM LAYER
Muuga Bay
Muuga Bay
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Velocity on the cross-section
Muuga Bay
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Wind Waves In TALSINKI area during SW Storm
24.50 24.60 24.70 24.80 24.90 25.00 25.10 25.20 25.30
59.40
59.50
59.60
59.70
59.80
59.90
60.00
60.10
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Ecological compounds calculation
Autotrophs in the beginning of May
Heterotrophs in the beginning of May
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Suspended Material Calculation
Spawning place
Reconstruction area
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Oil Spill calculation
Stranding of oil and shoreline interaction
The following oil spill processes are modeled:
Transport and deformation of an oil slick due to time and spatially varying winds and currents
•Diffusion and dispersion of oil on the sea surface and in the water column
•Evaporation of a multi-component mixture of oil
Sinking of oil in water, and consequent sedimentation
Formation of oil-in-water emulsion
Weathering of oil, resulting in changes in density, viscosity, and water content, due to evaporation and emulsification processes
Oil spreading at the sea surface due to positive buoyancy
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Oil Spill calculation
The probability of the oil accident consequence in the NW part of the island
Saaremaa in the summer time during three months.
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The influence of the Wind Waves
to theBaroclinic Circulation
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Surface Temperature after 30 days calculation
No Wind Waves
Wind Waves are included
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Bottom Temperature after 30 days calculationNo Wind Waves Tmax=10Tmin=6.5
Wind Waves are included Tmax=10Tmin=6.5
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Temperature profile after 30 days calculation
No Wind Waves Tmax=10 Tmin=6.5
Wind Waves are included Tmax=10 Tmin=6.5
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Eddy Viscosity profile after 30 days calculationNo Wind Waves
Wind Waves are included Kmax=100cm2/s. Kmin=0.1 cm2/s.
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Surface velocity after 30 days calculationNo Wind Waves
Wind Waves are included
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Bottom Velocity after 30 days calculationNo Wind Waves
Wind Waves are included