1. International Conference on Environmental Observations, Modeling and Information Systems ENVIROMIS-2004 17-25 July 2004, Tomsk, Russia International Conference on Environmental Observations, Modeling and Information Systems ENVIROMIS-2004 17-25 July 2004, Tomsk, Russia Representation of lakes in numerical Representation of lakes in numerical models for environmental applications models for environmental applications (INTAS grant 01-2132) (INTAS grant 01-2132) V.N. Lykosov, S.D. Golosov,V.K. Makin, V.N. Lykosov, S.D. Golosov,V.K. Makin, D.V. Mironov, V.L. Perov, A.Yu. Terzhevik D.V. Mironov, V.L. Perov, A.Yu. Terzhevik

2. Research Teams Research Teams German Weather Service, Germany Dr. Dmitrii Mironov, Co-ordinator Swedish Meteorological and Hydrological Institute, Sweden Dr. Veniamin Perov Royal Netherlands Meteorological Institute, The Netherlands Prof. Vladimir Makin Northern Water Problems Institute, Russia Prof. Nikolai Filatov Institute of Limnology, Russia Dr. Sergei Golosov Institute for Numerical Mathematics, Russia RAS Corresponding Member Vasily Lykosov

3. OBJECTIVES OBJECTIVES To develop a physically sound and computationally efficient lake model capable of the predicting the lake surface temperature on the diurnal to seasonal time scales, including a module to predict the vertical temperature structure of lake, a module to predict the evolution of the ice and snow cover, and a module to describe the interaction of the lake water and bottom sediments To develop a physically sound and computationally efficient lake model capable of the predicting the lake surface temperature on the diurnal to seasonal time scales, including a module to predict the vertical temperature structure of lake, a module to predict the evolution of the ice and snow cover, and a module to describe the interaction of the lake water and bottom sediments To develop a physically sound and computationally efficient atmospheric surface layer parameterization scheme that accounts for specific features of the surface layer over lakes To develop a physically sound and computationally efficient atmospheric surface layer parameterization scheme that accounts for specific features of the surface layer over lakes To better understand and quantify the effect of lakes on the surface temperature and humidity and on the surface fluxes of momentum, heat and water vapor in numerical modelling systems for environmental applications To better understand and quantify the effect of lakes on the surface temperature and humidity and on the surface fluxes of momentum, heat and water vapor in numerical modelling systems for environmental applications

4. Parameterization of Lakes in NWP: Description of a Lake Model and Single-Column Tests Dmitrii Mironov German Weather Service, Offenbach am Main, Germany Frank Beyrich and Erdmann Heise (German Weather Service) Arkady Terzhevik (Northern Water Problems Research Institute, Petrozavodsk, Russia)

5. Lake Parameterizations for NWP and Climate Modeling Systems (e.g. Ljungemir et al. 1996, Goyette et al. 2000, Tsuang et al. 2001) One-layer models, complete mixing down to the bottom Neglect stratification  large errors in the surface temperature Turbulence closure models, multi-layer (finite-difference) Describe the lake thermocline better  expensive computationally A compromise between physical realism and computational economy is required A two layer-model with a parameterized vertical temperature structure

6. Schematic representation of the temperature profile in the mixed layer and in the thermocline Here, θ s (t) is the temperature of the mixed layer of depth h(t), and θ b (t) is the temperature at the lake bottom, z=h+Δh.

7. The Concept Put forward by Kitaigorodskii and Miropolsky (1970) to describe the temperature structure of the oceanic seasonal thermocline. The essence of the concept is that the temperature profile in the thermocline can be fairly accurately parameterised through a “universal” function of dimensionless depth, using the temperature difference across the thermocline, Δθ=θ s (t)-θ b (t), and its thickness, Δh, as appropriate scales of temperature and depth:

8. Dimensionless temperature profile in the lake thermocline. Curves show a polynomial approximation (Kirillin 2002).

9. Lake Ryan. April – November 1990. Water surface temperature Measured vs. modelled ? (sensor malfunction)

10. Numerical simulation of heat and moisture transport in the “air – (snow) – (ice) – water – ground” system V.N. Lykosov, V.M. Stepanenko V.N. Lykosov 1,2, V.M. Stepanenko 2 Institute for Numerical Mathematics, 1) Institute for Numerical Mathematics, Russian Academy of Sciences Russian Academy of Sciences Scientific Research Computer Center, 2) Scientific Research Computer Center, Moscow State University

11. Snow “Upper” ice Water Ground “Lower” ice U H,LE EsEs EaEa S Thermodynamics of shallow reservoir 1) One-dimensional approximation. 2) On the upper boundary: fluxes of momentum, sensible and latent heat, solar and long-wave radiation are calculated On the lower boundary: fluxes are prescribed 3) Water and ice: heat transport Snow and ground: heat- and moisture transport U – wind velocity H – sensible heat flux LE – latent heat flux S – shirt-wave radiation E a – incoming long-wave radiation E s – outgoing long-wave radiation

12. Mathematical formulation - for water and ice:, - heat conductivity - for snow : - temperature - liquid water - for ground : - temperature - liquid water - ice

14. Boundary Conditions - ice-water: - water phase changes - snow-ice - temperature and heat flux continuity - water-soil - temperature and heat flux continuity - maximal water content - lower ground boundary: zero heat- and moisture fluxes

15. Atmospheric forcing - air-water interface: - empirical formula for radiative fluxes - sensible and latent heat fluxes from the Monin-Obukhov similarity theory Routine observational data set on meteorological stations is used

16. Syrdakh Lake

18. Conclusions Lake models are developed that offer both physical realism and computational efficiency Lake models are developed that offer both physical realism and computational efficiency Models do not require (re-)tuning Models do not require (re-)tuning Single-column tests of models (including the surface air layer parameterization scheme) show promising results Single-column tests of models (including the surface air layer parameterization scheme) show promising results Future work: Future work: implementation and testing of models in a 3D NWP environment and climate modeling EU Commissions, Project INTAS-01-2132.

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