1/40 Modelling deep ventilation of Lake Baikal Deep ventilation in Lake Baikal: a simplified model for a complex natural phenomenon Trento, April 19 th

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  • 1/40 Modelling deep ventilation of Lake Baikal Deep ventilation in Lake Baikal: a simplified model for a complex natural phenomenon Trento, April 19 th 2013 Department of Civil, Environmental and Mechanical Engineering University of Trento Group of Environmental Hydraulics and Morphodynamics, Trento PhD Candidate: Sebastiano Piccolroaz Supervisor: Dr. Marco Toffolon
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  • 2/40 Modelling deep ventilation of Lake Baikal Part 1 - A plunge into the abyss of the world's deepest lake Lake Baikal and deep ventilation A simplified 1D model Calibration, validation, sensitivity analysis and main results Climate change scenarios Outline Part 2 Back to the surface A simple lumped model to convert T a into surface T w Conclusions
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  • 3/40 Modelling deep ventilation of Lake Baikal Part 1 A plunge into the abyss of the world's deepest lake
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  • 4/40 Modelling deep ventilation of Lake Baikal The lake of records Lake Baikal - Siberia ( - ) The oldest, deepest and most voluminous lake in the world Lake Baikal
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  • 5/40 Modelling deep ventilation of Lake Baikal Main characteristics: Volume: 23 600 km 3 Surface area: 31 700 km 2 Length: 636 km Max. width: 79 km Max.depth: 1 642 m Ave. Depth: 744 m Shore Length: 2 100 km Surf. Elevation: 455.5 m Age: 25 million years Inflow rivers: 300 Outflow rivers: 1 (Angara River) World Heritage Site in 1996 Lake Baikal in numbers Divided into 3 sub-basins: South Basin Central Basin North Basin 1461 m Lake Baikal formed in an ancient rift valley tectonic origin Lake Baikal
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  • 6/40 Modelling deep ventilation of Lake Baikal Lake Baikal An impressive bathymetry: maximum depth at 1642 m average depth at 744 m flat bottom steep sides Source: The INTAS Project 99-1669 Team. 2002. A new bathymetric map of Lake Baikal. Open-File Report on CD-Rom Bathymetry
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  • 7/40 Modelling deep ventilation of Lake Baikal 1 bar 10 m water depth 250 m depth 1000 m depth 2000 m Density [kg m -3 ] Temperature T [C] http://www.engineeringtoolbox.com e w >e hc DEEP DOWNWELLING e w local strong hchc
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  • 8/40 Modelling deep ventilation of Lake Baikal 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 Deep ventilation
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  • 9/40 Modelling deep ventilation of Lake Baikal Observations and data analysis: Weiss et al., 1991; Shimaraev et al., 1993; Hohmann et al., 1997; Peeters et al., 1997, 2000; Ravens et al., 2000; West et al., 2000, 2005; Schmid et al., 2008; Shimaraev et al., 2009, 2011a,b, 2012 Downwelling periods (May June, December January) Downwelling temperature (3 3.3 C) Downwelling volumes estimations (10 100 km 3 per year) Numerical simulations: Akitomo, 1995; Walker and Watts, 1995; Killworth et al., 1996; Tsvetova, 1999; Peeters et al., 2000; 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) Putin turns submariner at Lake Baikal MIR: Deep Submergence Vehicle Field measurement campaign (photo credit: C. Tsimitri) Deep ventilation The state of the art Walker and Watts, 1995
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  • 10/40 Modelling deep ventilation of Lake Baikal The input data surface water temperature (measurements + reanalysis) wind speed and duration (observations + reanalysis) Courtesy of Prof. A. West and his research team (EAWAG) ERA-40 reanalysis dataset, thanks to Clotilde Dubois and Samuel Somot (Meteo France) Rzheplinsky and Sorokina, 1977 ERA-40 reanalysis dataset, thanks to Clotilde Dubois and Samuel Somot (Meteo France) 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 site South Basin of Lake Baikal South Basin
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  • 11/40 Modelling deep ventilation of Lake Baikal Required energy e hc The model in three parts A simplified 1D numerical model 1.simplified downwelling algorithm (wind energy input vs energy required to reach h c ) specific energy input e w e w =C D 0.5 W downwelling volume V d Vd=CDW2twVd=CDW2tw Wind - based parameterization: Available energy (downwelling volume) and : main calibration parameters of the model e w e hc DEEP DOWNWELLING (mainly dependent on the geometry)
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  • 12/40 Modelling deep ventilation of Lake Baikal The model in three parts 2. Lagrangian vertical stabilization algorithm (re-arrange unstable regions, move the sinking volume) z re-sorting starting form the pair of sub-volumes showing the higher instability the mixing exchanges are accounted for at every switch where is the generic tracer and the mixing coeff. Stable Unstable C T max A simplified 1D numerical model
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  • 13/40 Modelling deep ventilation of Lake Baikal 3. vertical diffusion equation solver with source (reaction) terms (for temperature, oxygen and other solutes) The model in three parts C z DO the diffusion equation is solved for any tracer given the BC at the surface and R along the water column. cooling higher sat. conc. T max geothermal heat flux oxygen consumption flux source A simplified 1D numerical model
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  • 14/40 Modelling deep ventilation of Lake Baikal it is a matter of feedback Lacustrine systems are regulated by a complex network of feedback loops, controlled by the external forcing Self-consistent procedure to dynamically reconstruct D z A simplified 1D numerical model
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  • 15/40 Modelling deep ventilation of Lake Baikal Calibration Thanks to S. Somot and C. Dubois (Meteo France) Calibration procedure (, , c mix and D z,r ) Medium term simulations during the second half of the 20 th century: comparison of simulated temperature and oxygen profiles with measured data formation of the CFC profile (1988-1996) unambiguous tracer: non-reactive, high chemical stability [e.g. England, 2001] Objective: numerically reproduce particular conditions of the lake during a specific historical period (1980s- 1990s). Available data: reanalysis dataset the reprocessing of past climate observations combining together data assimilation techniques and numerical modeling (GCMs) ERA-40 datasets: wind speed (W) and air temperature (T a ) every 6 hours from 1958 to 2002
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  • 16/40 Modelling deep ventilation of Lake Baikal Calibration Reanalysis data: limitations reanalysis horizontal resolution is too coarse ( 100 km x 100 km) for the purpose of many practical applications (mismatch of spatial scales) reanalysis data are often affected by inconsistencies due to the lack of fundamental feedback between the numerous natural processes air temperature is available, but the model requires surface water temperature Post-processing (downscaling) is necessary
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  • 17/40 Modelling deep ventilation of Lake Baikal Calibration Statistical downscaling Transfer function approach: establishes a relationship between the cumulative distribution functions (CDFs) of observed local climate variables (predictands) and the CDFs of large-scale GCMs outputs (predictors) Quantile mapping method [Panofsky and Brier, 1968]: assumption x r = generic climatic variable of re-analysis (W, T a ) X r,adj = generic climatic variable adjusted CDF r = cumulative distribution function of re-analysis data CDF o = cumulative distribution function of observations Drawbacks: it does not include information of future climate patterns it is stationary in the variance and skew of the distribution, and only the mean changes it is not indicated to be applied for climate change analysis [e.g. Minville et al.,2008; Diaz-Nieto and Wilby, 2005; Hay et al., 2000]
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  • 18/40 Modelling deep ventilation of Lake Baikal Quantile-mapping approach Wind: seasonal CDFs Temperature: daily CDFs WrWr W r,adj T a,r T w,adj Calibration From reanalysis (large scale) to observations (local scale)
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  • 19/40 Modelling deep ventilation of Lake Baikal 15 th of February15 th of September Calibration Temperature profiles
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  • 20/40 Modelling deep ventilation of Lake Baikal CFC and dissolved oxygen profiles Calibration Mean annual
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  • 21/40 Modelling deep ventilation of Lake Baikal Sensitivity analysis Aimed at evaluating the robustness of the calibration and the role played by each of the main parameters of the model. Procedure: a new set of 40-year simulations, changing , and c mix (one by one) within the interval of 50% of the calibrated value. Results: an evident deviation from measurements and calibrated solution suggesting that a proper calibration has been achieved no dramatic changes are observed in the behavior of the limnic system indicating the suitability and robustness of the fundamental algorithms
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  • 22/40 Modelling deep ventilation of Lake Baikal Validation Validation procedure Limited amount of available information a classical validation of this model with an independent set of data is not possible Indirect validation: long-term simulation, starting from arbi