Parameterization of Lakes in NWP and Climate Models

Transcription

1 Parameterization of Lakes in NWP and Climate Models Dmitrii Mironov and Jürgen Helmert German Weather Service, Offenbach am Main, Germany Hermann Asensio, Erdmann Heise, Ekaterina Machulskaya, Bodo Ritter (German Weather Service, Offenbach am Main, Germany) Sergey Golosov (Institute for Lake Research, Russian Academy of Sciences, St. Petersburg, Russia) Georgy Kirillin (Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany) Ekaterina Kourzeneva (Finnish Meteorological Institute, Helsinki, Finland, and Russian State Hydrometeorological University, St. Petersburg, Russia) Arkady Terzhevik (Northern Water Problems Institute, Russian Academy of Sciences, Petrozavodsk, Russia)

2 Outline Parameterization of lakes in NWP and climate models the problem The lake parameterization scheme FLake FLake in COSMO (ICON) FLake performance Conclusions and outlook

3 Parameterization of Lakes in NWP and Climate Models A Twofold Problem (1a) The interaction of the atmosphere with the underlying surface is strongly dependent on the surface temperature and its time-rate-of-change. (Most) NWP models assume that the water surface temperature can be kept constant over the entire forecast period. The assumption is doubtful for small-to-medium size relatively shallow lakes, where the diurnal variations of the surface temperature reach several degrees. A large number of such lakes will become resolved-scale features as the horizontal resolution is increased. (1b) Apart from forecasting the lake surface temperature, its initialization is also an issue. (2) Lakes strongly modify the structure and the transport properties of the atmospheric surface layer. A major outstanding question is the parameterization of the water-surface roughness with respect to wind (e.g. limited fetch) and to scalar quantities.

4 Lake Regions: Finland, Karelia

5 Lake Regions: Khanty-Mansiisk region (middle Ob river) Lake Regions: Canada

6 Schematic lake temperature stratification Summer θ (t) θ (t) b s θ S (t) Snow Ice Winter θ I (t) θ s (t) θ b (t) -H(t)-H I -H(t) I h(t) Water h(t) D θ H (t) D H(t) Sediment H(t) L θ H (t) θ L θ L L

7 Lake Parameterization Schemes for NWP and Climate Models (e.g. Croley 1989, 1992, Croley and Assel 1994, Hostetler and Bartlein 1990, Hostetler 1991, Hostetler et al. 1993, 1994, Barrette and Laprise 2005, Bates et al. 1993, 1995, Ljungemir et al. 1996, Goyette et al. 2000, Tsuang et al. 2001, Song et al. 2004, León et al. 2005, 2007, Long et al. 2007, Mackay 2005, Mackay et al. 2009, Stepanenko and Lykosov 2005, Stepanenko 2007, Stepanenko et al. 2010, Subin et al. 2012) 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

8 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 parameterized through a universal function of dimensionless depth, using the temperature difference across the thermocline, θ=θ s -θ b, and its thickness, h, as appropriate scales of temperature and depth: θ s ( t) θ ( z, t) = ϑ ( ς ), ς = z h( t). θ ( t) h( t)

9 Analogy with the Mixed-Layer Concept Using θ s (t) and h(t) as appropriate scales of temperature and depth, the temperature profile in the upper mixed layer is represented as θ ( z, t) θ ( t) s = Φ( ξ ), ξ = z. h( t) Since the layer is well mixed, the universal function Φ(ξ) is simply a constant equal to 1. Then, integrating the heat transfer equation (partial differential equation in z, t) θ t w θ = z over z from 0 to h(t), reduces the problem to the solution of an ordinary differential equation for θ s (t), dθs Qs Q( h) = dt h

10 The Lake Model FLake The model is based on the idea of self-similarity (assumed shape) of the evolving temperature profile. That is, instead of solving partial differential equations (in z, t) for the temperature and turbulence quantities (e.g. TKE), the problems is reduced to solving ordinary differential equations for time-dependent parameters (variables) that specify the temperature profile. These are (optional, modules can be switched off) the mean temperature of the water column, the surface temperature, the bottom temperature, the mixed-layer depth, the shape factor with respect to the temperature profile in the thermocline, the depth within bottom sediments penetrated by the thermal wave, and the temperature at that depth. In case of ice-covered lake, additional prognostic variables are the ice depth, the temperature at the ice upper surface, the snow depth, and the temperature at the snow upper surface. Important! The model does not require (re-)tuning.

11 Schematic representation of the evolving temperature profile θ b (t) θ s (t) h(t) C θ (t) D H(t) L θ H (t) θ L (a) (a) The evolving temperature profile is characterised by several time-dependent variables, namely, the temperature θ s (t) of the mixed layer, its depth h(t), the bottom temperature θ b (t), and the temperature-profile shape factor C θ (t). Optionally, the depth H(t) within bottom sediments penetrated by the thermal wave and the temperature θ H (t) at that depth can be computed.

12 θ S (t) Snow θ I (t) θ s (t) θ b (t) -H (t)-h (t) I S -H (t) I Ice h(t) Water C θ (t) Sediment (b) θ H (t) θ L D H(t) L (b) For ice-covered lakes, additional variables are the temperature θ I (t) at the ice upper surface and the ice thickness H I (t), and (optionally) the temperature θ S (t) at the snow upper surface and the snow thickness H S (t).

13 FLake in NWP and Climate Models: External Parameters geographical latitude (easy) lake fraction of the NWP model grid-box (not so easy) lake depth (not easy at all, e.g. for lack of data) typical wind fetch optical characteristics of lake water (extinction coefficients with respect to solar radiation) depth of the thermally active layer of bottom sediments, temperature at that depth (cf. soil model parameters) Default values of the last four parameters can be used.

14 Lake Fraction Lake-fraction external-parameter field for the COSMO-EU numerical domain of the COSMO model (ca. 7 km horizontal mesh size).

15 Lake Depth Lake depths for Northern Europe region of the COSMO-EU numerical domain (ca. 7 km horizontal mesh size).

16 Lake Depth Lake depths based on COSMO-DE external-parameter field (ca. 2.8 km horizontal mesh size).

17 FLake in COSMO (ICON): Configuration bottom sediment module is switched off (heat flux through the waterbottom sediment interface is zero) snow above the lake ice is not considered explicitly, the effect of snow is accounted for implicitly through the temperature dependence of the ice surface albedo (Mironov and Ritter 2003, 2004, Mironov et al. 2012) turbulent fluxes at the surface are computed with the current COSMO/ICON surface-layer scheme (Raschendorfer 2001); optionally, the new surface-layer scheme (Mironov et al. 2003) can be used 2D fields of lake fraction and of lake depth based on data (Kourzeneva 2009, 2010, Kourzeneva et al. 2012), default values of other lake-specific parameters

18 FLake in COSMO: Results from Parallel Experiment January 31 December 2006 Lake Hjälmaren, Sweden (mean depth = 6.1 m) Black lake surface temperature from the COSMO SST analysis Green lake surface temperature computed with FLake

19 FLake in COSMO: Results from Parallel Experiment January 31 December 2006 Lake Balaton, Hungary (mean depth = 3.3 m) Black lake surface temperature from the COSMO SST analysis Green lake surface temperature computed with FLake

20 FLake in COSMO: Results from Parallel Experiment January 31 December 2006 Freeze-up: 10 January Ice melting: very beginning of March Lake Balaton, Hungary (mean depth = 3.3 m). Ice thickness computed with COSMO-FLake.

21 FLake in COSMO: Results from Parallel Experiment January 31 December 2006 Lough Neagth, UK (mean depth = 8.9 m) Black lake surface temperature from the COSMO SST analysis Green lake surface temperature computed with FLake

22 FLake in NWP and Climate Models As a lake parameterization scheme, FLake is implemented, or on the way, into a number of NWP and climate models (COSMO, ICON, HIRLAM, UK Met Office Unified Model, NWP model suite of Meteo France, ECMWF IFS, CLM, RCA, Canadian Regional Climate Model), used as a lake parameterization module in the surface schemes TESSEL, SURFEX, and JULES, used operationally at the German Weather Service within COSMO-UE/DE, implemented into ICON, used operationally at the Finish Meteorological Institute within HIRLAM

23 Conclusions and Outlook FLake is implemented into the COSMO model (set llake=.false. to run without FLake) Since / Flake is operational at DWD within COSMO- EU/DE (ca. 7 km and ca. 2.8 km mesh size, respectively) Documentation and synopsis of FLake routines are ready FLake is implemented into ICON Monitor operational results, assess the effect of lake parameterization scheme on the overall NWP/climate model performance Update external-parameter fields Use FLake within a tiled surface scheme Long term prospective: explicit treatment of snow over lake ice, three-layer temperature profile, salinity, data on optical properties of lake water

24 Info FLake Web Page (mirror c/o Georgiy Kirillin and Arkady Terzhevik Online FLake version at (take a look and have fun!) References Kirillin, G., J. Hochschild, D.Mironov, A. Terzhevik, S.Golosov, and G. Nützmann, 2011: FLake-Global: Online lake model with worldwide coverage. Environ. Modell. Softw., 26, Kourzeneva, E., 2010: External data for lake parameterization in Numerical Weather Prediction and climate modeling. Boreal Env. Res., 15, Kourzeneva, E., H. Asensio, E. Martin, and S. Faroux, 2012: Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modelling. Tellus A, 64, doi: /tellusa.v64i Mironov, D. V., 2008: Parameterization of lakes in numerical weather prediction. Description of a lake model. COSMO Technical Report, No. 11, Deutscher Wetterdienst, Offenbach am Main, Germany, 41 pp. Mironov, D., E. Heise, E. Kourzeneva, B. Ritter, N. Schneider, and A. Terzhevik, 2010: Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Env. Res., 15, Mironov, D., B. Ritter, J.-P. Schulz, M. Buchhold, M. Lange, and E. Machulskaya, 2012: Parameterization of sea and lake ice in numerical weather prediction modelsof the German Weather Service. Accepted for publication in Tellus A. Further references at

25 Thank you for your attention! Acknowledgements: Frank Beyrich, Michael Buchhold, Ulrich Damrath, Günther Doms, Jochen Förstner, Helmut Frank, Thomas Hanisch, Jürgen Helmert, Peter Meyring, Van Tan Nguyen, Ulrich Schättler, Christoph Schraff (DWD), Andrey Martynov (UQAM), Burkhardt Rockel (GKSS), Patrick Samuelsson (SMHI), Laura Rontu (FMI), Zachary Subin (UC Berkeley). EU Commissions, Projects INTAS and INTAS ; Nordic Research Board through the Nordic Networks on Fine-Scale Atmospheric Modelling (NetFAM) and Towards Multi- Scale Modelling of the Atmospheric Environment (MUSCATEN).