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Modelling deep ventilation of Lake Baikal: a plunge into the abyss of the world's deepest lake. Department of Civil and Environmental Engineering University of Trento - Italy. Swiss Federal Institute of Aquatic Science and Technology Kastanienbaum - Switzerland. - PowerPoint PPT Presentation
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1/30Modelling deep ventilation of Lake Baikal
Swiss Federal Institute of Aquatic Science and Technology
Kastanienbaum - Switzerland
Modelling deep ventilation of Lake Baikal:a plunge into the abyss of the world's deepest lake
Department of Civil and Environmental Engineering University of Trento - Italy
Kastanienbaum, Switzerland, October 17th 2011
Group of Environmental Hydraulics and Morphodynamics, Trento
PhD Candidate:Sebastiano Piccolroaz
Supervisor:Dr. Marco Toffolon
2/30Modelling deep ventilation of Lake Baikal
The lake of records
Lake Baikal - Siberia (Озеро Байкал - Сибирь)
The oldest, deepest and most voluminous lake in the world
Lake Baikal - introduction
3/30Modelling deep ventilation of Lake Baikal
Main characteristics:Volume: 23 600 km3
Surface area: 31 700 km2
Length: 636 kmMax. width: 79 kmMax .depth: 1 642 mAve. Depth: 744 mShore Length: 2 100 kmSurf. Elevation: 455.5 mAge: 25 million yearsInflow rivers: 300Outflow rivers: 1 (Angara River)World Heritage Site in 1996
Lake Baikal in numbers
Divided into 3 sub-basins:South BasinCentral BasinNorth Basin
1461 m
Lake Baikal formed in an ancient rift valley tectonic origin
Lake Baikal - introduction
4/30Modelling deep ventilation of Lake Baikal
Some curiosities
The largest freshwater basin in the world: 20% of the world’s total unfrozen fresh water reserve
Great Lakes are almost 8 times more extended than Lake Baikal
but
taken all together, have the same volume of water!
Surface area: 244 160 km2
Surface area: 31 700 km2
Lake Baikal - introduction
Freezes annually:Jan - May in the South Basin Dec - June in the North Basin
5/30Modelling deep ventilation of Lake Baikal
Baikal oilfish (Golomyanka): a translucent abyssal fish famous for decomposing almost instantly to fat and
bones when exposed to the sun
The Pearl of Siberia 1/2
Singular, sometimes extreme, environmental conditions: – enormous depth– several months of ice cover– high oxygen concentration– low nutrient concentration
gave rise to a unique ecosystem: more than 1000 endemic species(diatoms, sponges, salmonid fish and the Baikal freshwater seal)
Baikal seal or Nerpa (Pusa sibirica): the only exclusively freshwater pinniped species
An amphipod regards a diver from a sponge forest in Lake Baikal. It has been estimated that the biomass of crustaceans in the lake exceeds 1,800,000 tons
Lake Baikal - introduction
6/30Modelling deep ventilation of Lake Baikal
The bathymetry
Lake Baikal - introduction
An impressive bathymetry: average depth at 744 m
flat bottom steep sides
7/30Modelling deep ventilation of Lake Baikal
Strong external forcing
Deep ventilation
The physical mechanism
Deep ventilation
Phenomenon triggered by thermobaric instability (Weiss et al., 1991):
− density depends on T and P (equation of state: Chen and Millero, 1976)
− T of maximum density decreases with the depth (P=Patm Tρmax ≈ 4°C)
Temperature [°C]
De
pth
[m
]
1 2 3 40
500
1000
1500
Tρmax
Temperature profile
Reference parcelρparcel< ρlocal
Weak external forcing
ρparcel = ρlocal
ρparcel > ρlocal Compensation depth - hc
8/30Modelling deep ventilation of Lake Baikal
Deep ventilation
wind
sinking volume of water
A simplified sketch
The main effects:
− deep water renewal;
− a permanent, even if weak, stratified temperature profile.
− high oxygen concentration up to the bottom;
Presence of aquatic life down to huge depths!
deep ventilation at the shore
9/30Modelling deep ventilation of Lake Baikal
Deep ventilation
The state of the art− Observations and data analysis:
Weiss et al., 1991; Killworth et al., 1996; Peeters et al., 1997, 2000; Wüest et al., 2005; Schmid et al., 2008
− Downwelling periods (May – June and December – January)
− Downwelling temperature (3 ÷ 3.3 °C)
− Downwelling volumes estimations (10 ÷ 100 per year)
− Numerical simulations:
Akitomo, 1995; Walker and Watts, 1995; Tsvetova, 1999; Botte and Kay, 2002; Lawrence et al., 2002
− 2D or 3D numerical models
− Simplified geometries or partial domains
− Main aim: understand the phenomenon (triggering factors/conditions)
Wal
ker a
nd W
atts,
199
5
10/30Modelling deep ventilation of Lake Baikal
A simplified 1D numerical model
A simplified 1D model
The aims− simple way to represent the phenomenon (at the basin scale)
− just a few input data required (according to the available measurements)
− suitable to predict long-term dynamics (i.e. climate change scenarios)
The model in three parts
− simplified downwelling mechanism (wind energy input vs energy required to reach hc)
− lagrangian vertical stabilization algorithm (looking for unstable regions)
− vertical diffusion equation solver with source terms (for temperature, oxygen and other solutes)
11/30Modelling deep ventilation of Lake Baikal
Volume [km3]
Dep
th [m
]
Hypsometric curve
The downwelling mechanism
Vi = 5 km3
1272 sub-volumes
Constant-volume discretization scheme
Temperature [°C]
Dep
th [m
]
3 3.50
200
1500
4
1
Vi
Vi+1
Vi-1
Tρmax
Procedural steps:– assign Vd and ew
– compute Td
– calculate hc – compute the energy required to move Vd
– move the Vd until zd
Two cases:1.Shallow downwelling2.Deep downwelling
Td
dT/dz|ad
hc
A simplified 1D numerical model
12/30Modelling deep ventilation of Lake Baikal
The lagrangian vertical stabilization algorithm
The profile is unstable: the sinking Vd is heavier (lighter) than the
surrounding water
We need to stabilize the temperature profile
mixing factor included to take into account the exchanges occurring during the sinking of Vd
Remark: the stabilization is computed on the temperature profile, but all the other tracers
follow the same re-arrangement!
Temperature [°C]
Dep
th [m
]
3 3.50
200
1500
Tρmax
dT/dz|ad
hc
A simplified 1D numerical model
13/30Modelling deep ventilation of Lake Baikal
The vertical diffusion equation solver
The diffusion equation is solved for any tracer C
given the boundary conditions at the surface:– surface water temperature– oxygen saturation concentration– evolution of the CFC concentration
and along the shores:– geothermal heat flux– oxygen consumption rate
geothermal heat flux
coolingTemperature [°C]
Dep
th [m
]
3 3.50
200
1500
Tρmax
A simplified 1D numerical model
geot
herm
al h
eat fl
ux
14/30Modelling deep ventilation of Lake Baikal
The input data
The main input data of the model− seasonal cycle of surface water temperature - Tsurf
− energy input from the external forcing - ew
− sinking water volume - Vd
wind parameters unknowns equationswind speed W specific energy input ew ew=ξCD
0.5W
wind duration Δtw downwelling volume Vd Vd=ηCDW2Δtw
CD is the drag coefficientξ and η are the calibration parameters (mainly dependent on the geometry)
The wind forcing
The input data
Thanks to:Chrysanthi Tsimitriprof. Alfred Wüestdr. Martin Schmid
15/30Modelling deep ventilation of Lake Baikal
0.6620.645
Poor wind data seasonal probabilistic curves of W and Δtw
from: Rzheplinsky and Sorokina, 1997 Atlas of wave and wind action in Lake Baikal (in Russian)
(Атлас волнения и ветра озера Байкал)
Stochastic reconstruction of wind forcing
Wsummer IV-IX
Δtw
winter X-IIIΔtw
0.1540.7850.5860.9450.4540.5710.6460.231
7
0.571
0.1540.7850.5860.9450.4540.829
0.829
8-12
h
1st random extraction 2nd random extraction
The input data
16/30Modelling deep ventilation of Lake Baikal
Parameters to be calibrated:
− ξ (for the energy input)
− η (for the downwelling volume)
− vertical profile of the “effective” diffusivity
− mixing factor
Calibration of the model
Calibration parameters and procedure
Calibration of the model
Calibration procedure:
Medium term simulations in the period 1945-2000:
− formation of the CFC profile (1988-1996) no reactive, no decay rate
− comparison of simulated temperature and oxygen profiles with measured data
17/30Modelling deep ventilation of Lake Baikal
Calibration of the model
Numerical solution and the probabilistic approach
The numerical solution depends on the random seed used for the stochastic reconstruction of winds and the definition of surface temperature
Problems for the medium term simulations: we want to numerically reproduce a specific condition of the lake during a particular historical period (1980s- 1990s).
Possible solutions:
− Set of simulations having different random seeds average the solution over all the runs
− Use re-analysis data for wind and surface temperature.
Past observations of the main meteorological variables are analyzed and interpolated onto a system of grids, giving the meteorological conditions really occurred.
Thanks to Samuel Somot (Meteo France)
18/30Modelling deep ventilation of Lake Baikal
Re-analysis data 1/2
ERA-40 re-analysis dataset (53.375°N, 108.125°E) wind speed and air temperature every 6
hours from 1958 to 2002
Large interpolation grid:– the data are not representative of the real conditions at the lake surface– the data can give a good description of the historical sequence of events
Calibration of the model
to be suitably rescaled to the observed values
19/30Modelling deep ventilation of Lake Baikal
Calibration of the model
Re-analysis data 2/2
1. at every time step the wind speed value (occourred) is extracted from the series
2. The probability associated to that wind speed is extracted from the reanalysis cdf
3. The adjusted wind speed is calculated through the observational-based cdf
Dec 1974
0.91 0.91
6 14
20/30Modelling deep ventilation of Lake Baikal
Some results
Some results
15th of February: average over the last 15 years simulation
21/30Modelling deep ventilation of Lake Baikal
Some results
Validation of the model 1/2
Long term simulation (1000 years) same boundary conditions (wind, surface temperature, geothermal heat flux etc.) different initial condition of the temperature: T = const = 4°C
The aim:– validate the model comparing numerical solution with observations;– investigate the general behavior of Lake Baikal;– characterize deep ventilation (i.e.statistically estimate the typical downwelling volume and temperature).
Mean temperature after 150 years3.36 °C
22/30Modelling deep ventilation of Lake Baikal
Some results
Validation of the model 2/2
15th of February 15th of September
features of downwelling eventsVdM [km3] TdM [°C]
Present model 60 ± 43 3.22 ± 0.08
Peeters et al. [2000] 110 -
Wüest et al., [2005] 10÷30 3.15÷3.27
Schmid et al., [2008] 50÷100 (winter season) 3.03÷3.28
VdM = mean annual sinking volume
TdM = typical downwelling temperature range
23/30Modelling deep ventilation of Lake Baikal
15 August
Current condition analysis
Temporal distribution of downwelling events
Late spring Betweenfall and winter
15 April 15 June 15 December
24/30Modelling deep ventilation of Lake Baikal
Current condition analysis
Energy demand
Energy demand is higher in winter
Annual probability of downwelling
Period Prob. (>1300 m)
Winter 0.82
Summer 0.55
25/30Modelling deep ventilation of Lake Baikal
Climate change scenarios
The scenarios
Climate change
Simplified scenarios changing the main external forcing
Spring Winter
Wind: increasing/decreasing of the winds Temperature trend: global warming +4°C in summer, +2°C in autumn (Hampton et al., 2008)reduction of ice-covered period (Magnuson et al., 2000)
iceice
iceice
+4°C
+2°C 5 days 11 days
26/30Modelling deep ventilation of Lake Baikal
Calm wind
Current condition vs climate change 1/2
VdM = 24 ± 22 km3
TdM = 3.34 ± 0.12 °C
VdM = 60 ± 43 km3
TdM = 3.22 ± 0.08 °C
Strong wind
VdM = 83 ± 72 km3
TdM = 3.02 ± 0.06 °C
Warming+4°C; +2°C
VdM = 59 ± 46 km3
TdM = 3.20 ± 0.09 °C
Warming and strong wind+4°C; +2°C
VdM = 83 ± 76 km3
TdM = 3.02 ± 0.06 °C
Climate change scenarios
Current condition:
27/30Modelling deep ventilation of Lake Baikal
iceice
iceice
+4°C
+2°C
5 days 11 days
Warming+4°C; +2°C
Warming and strong windy+4°C; +2°C
Current condition vs climate change 2/2
Climate change scenarios
The favorable periods are only shifted in time, not significantly modified in duration.
28/30Modelling deep ventilation of Lake Baikal
ConclusionsModelling results:
−simplified model suitable to simulate deep ventilation
−analyse downwelling dynamics statistically
Physical results:
−downwelling volume is estimated as 60 ± 43 km3/year
−wind forcing and the duration of the favourable downwelling periods are the most important factors
−surface temperature warming in summer does not strongly influence the downwelling mechanism
Conclusions
Further activities
−construct more realistic/robust scenarios
−use a 3D model to better investigate the initiation of thermobaric instability
−investigate the periodical turnover of Lake Garda
29/30Modelling deep ventilation of Lake Baikal
− M. Toffolon, C. Carlin, S. Piccolroaz, G. Rizzi, Can turbulence anisotropy suppress horizontal circulation in lakes?, 7th International Symposium on Stratified Flows (ISSF), Roma (Italy), 22-26 August 2011
− A.Zorzin, S. Piccolroaz, M. Toffolon, M. Righetti, On the reduction of thermal destratification by a horizontal ciliate jet, 7th International Symposium on Stratified Flows (ISSF), Roma (Italy), 22-26 August 2011
Parallel works
Conclusions
Altered colors
30/30Modelling deep ventilation of Lake Baikal
Conclusions
Thank [email protected]
Mysterious ice circles in the world’s deepest lake