Implementation of Lake Model FLake into HIRLAM

HIRLAM Newsletter no 54, December 2008

E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

Implementation of Lake Model FLake into HIRLAM E. Kourzeneva, Russian State Hydrometeorological University (kourzeneva,rshu.ru) P. Samuelsson, Swedish Meteorological and Hydrological Institute (patrick.samuelsson,smhi.se) G. Ganbat, Russian State Hydrometeorological University (gtuya,yahoo.com) D. Mironov, Deutscher Wetterdienst (dmitrii.mironov,dwd.de)

1 Introduction In some regions, such as Finland, Scandinavia and Karelia, lakes may cover a sizable part of the territory. Lakes affect the surface fluxes of heat, water vapour and momentum and therefore the structure of the atmospheric surface layer. The surface physics and the interaction with the atmosphere are quite different for the ice covered and ice free lake surfaces, respectively. The moments of ice freeze-up and of ice break-up are crucial points for the forecast. Besides, in seasurface temperature (SST) analysis products only little attention is paid to the inland water bodies. This may lead to considerable errors in lake surface temperature. These problems become more pronounced for high resolution models, in particular, when tiling approach is used. Hence there is a need for a lake parameterization not only for climate models but also for numerical weather prediction (NWP) models. To be coupled with an atmospheric model, climate or NWP, a lake model should be first of all computationally cheap, should incorporate most of essential physics, should not require tuning for a particular lake, and should require a minimum set of specific lake parameters. All these restrictions are quite strong. A Lake model FLake (Mironov, 2006; Mironov et al., 2007), which is used as a basis for the lake parameterization in HIRLAM, meets these requirements. FLake is based on a two-layer self-similar parametric representation (assumed shape) of the evolving temperature profile and on the integral budgets of entropy and of turbulence kinetic energy for the layers in question. Temperature profile in lakes is assumed to consist of a vertically homogeneous mixed layer underlined by a stably stratified thermocline. Using the concept of self-similarity, the temperature profile is described by a number of time-dependent quantities: the mixed layer temperature, the mixed layer depth, the shape factor with respect to the temperature profile in the thermocline, and the bottom temperature. The same concept is used to describe the temperature structure of the thermally-active upper layer of bottom sediments and of the ice and snow cover. The temperature profile within the bottom sediments is assumed to have one extremum, and the temperature profiles within ice and snow are assumed to be linear. The mixed-layer depth is computed prognostically, using different evolution equations for cases of convectivemixing and wind mixing in stable or neutral stratification. The evolution equations for the ice and snow depths are based on simple thermodynamic arguments. The volumetric character of the solar radiation heating is accounted for. The sensitivity tests (Kourzeneva et al., 2005) have shown that, as far as the lake surface temperature is concerned, the only lake parameter the model is sensitive to is lake depth.

2 Experience with a regional climate model Coupling of a climate model with the lake model provides a good experience. As an illustration, some model results and verification are presented here. As a lake parameterization scheme, the lake model FLake was incorporated into the Rossby Centre regional atmospheric model, RCA. In order to obtain fluxes in the surface layer, the classical

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HIRLAM Newsletter no 54, December 2008

E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

aerodynamic formulations (referred to as the standard Louis algorithm) with drag coefficients dependent on the thermal stability in the atmosphere are used in RCA. We use them for the lake surface as well. The RCA is interfaced with the underlying surface as follows: the atmospheric model provides surface fluxes and the surface scheme (including lake model FLake) provides the surface temperature. FLake, as part of the surface scheme, that handles the surface type ”lake” is called every time step. The RCA numerical domain covers Europe, and the horizontal mesh size is ca. 50 km. Data on the lake depth from the national lake database of Sweden are used. Most of the European lakes except some large lakes have the ”reference” mean depth of 10 m. Some large lakes such as Lake Ladoga, have their actual mean depth. The tiling (and even sub-tiling) approach is used. Lakes are divided into 3 classes: shallow lakes with the depth of 3 m, medium lakes with the depth of 7 m and deep lakes with their actual depth (these external-parameter data are taken from the lake database of Sweden). The climate model experiments should start in autumn because of some specific features of hydrological cycle. Fortunately, a constant temperature from the lake surface to the lake bottom can be used as a physically realistic initial condition for most European lakes during autumn. The open-boundary conditions and the SST for the RCA experiments are provided by ERA40 reanalysis. Here we present the simulation results for the year 1985. The model runs are evaluated against the climatological mean observational data on the surface temperature for a number of lakes. The data are provided by International Lake Environment Committee (WLD, 2002). Lakes of various depth that are located in different parts of Europe and have quite different regimes of ice cover are considered. Besides, a comparison is made with the results obtained from lake model PROBE (Ljungemyr et al., 1996) which is a one-dimensional finite-difference lake model (among other things, PROBE is more expensive computationally than FLake). We also compared the model results with the surface temperature provided by ERA40 reanalysis. There is a good agreement between observational data and model results obtained with FLake, see Fig. 1. RCA-FLake coupled system reproduces the annual cycle of the lake surface temperature and the dates of ice freeze-up and breakup quite satisfactory. At some points, the model results are very different from ERA40, even for large lakes where the grid-box mean surface temperature should be equal to the lake surface temperature. For these points, the simulated temperature is generally closer to the observations, such as, for example, for lakes Lake Ladoga and Lake Vattern. This means that the SST analysis procedure used to provide the grid-box data for ERA40 could be erroneous with respect to the lake surface. As the procedure of the same kind and the same SST products are used in NWP, errors with respect to the lake surface temperature are expected in NWP forecasts as well.

3 Critical issues Experience gained through the stand-alone model tests and through the climate simulations suggests a number of issues to be addressed with respect to parameterization of lakes for climate modelling and for NWP. The first issue is a strong need for external-parameter data, namely, data on lake depth and on lake fraction. The lake-depth external parameter field is necessary in any case. In case the tiling approach is used, or in case a lake mask without tiling should be developed, the lake-fraction field is required as well. Within the framework of the INTAS innovation project 05-1000007-431 and in cooperation with COSMO (Consortium for Small-scale Modeling), a database of lake external parameters was developed, first for Europe and then several major world lakes were added. The sources of data for this database are (i) a hydrological lake database and (ii) a dataset for ecosystems (at present, GLCC dataset (GLCC, 2005) with 1 km resolution is used). The hydrological lake database was developed using data from national lake databases and from water cadastres of different European countries, including Norway, Sweden, Finland, Russia (and former USSR), Poland, Germany, Austria,

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Figure 1: Annual cycle for the lake surface temperature from the RCA model run for 1985 with the lake model FLake (blue)and PROBE (magenta) included, annual cycle of the surface temperature from ERA40 (green), and annual cycle (climate) for the lake surface temperature from observations (red).

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HIRLAM Newsletter no 54, December 2008

E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

Figure 2: Raw data used to develop the externalparameter database: (i) the fragment of the hydrological lake database (ii) the the fragment of set for ecosystems (deep blue – land, blue – sea water, green – inland water)

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HIRLAM Newsletter no 54, December 2008

E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

Switzerland, among others. Different organizations kindly provided data, mainly through personal communication. The opportunity to acknowledge all of them in this publication should not be missed. As compared to Europe, data for the rest of the world are much poorer; they were taken from the web-site of the International Lake Environment Committee (WLD, 2002). The hydrological lake database presently contains about 9500 lakes and provides geographical coordinates of the lake in question (of a point at the water surface), the lake name and the name of the country where it is located, the lake mean depth, and the lake maximum depth and lake area (if available). Figure 2 gives the examples of raw data: the hydrological lake database and the dataset for ecosystems with 1 km resolution. To combine these sources of data and to provide the external-parameter fields for a (virtually) arbitrary target grid and domain of an atmospheric model, the interface (software package) was developed. The system accounts for various sources of uncertainty in the raw data, errors both in the hydrological lake database and in the ecosystems dataset. To aggregate data from the onekilometre grid of the dataset for ecosystems to the coarser target grid of an atmospheric model, the empirical PDFs were used. An example of the output from the software package mentioned above is given in Fig. 3. The second issue related to the use of a lake parameterization scheme within NWP models is a need for the initial fields of lake-related variables for the cold start. The fields of the prognostic lake variables, (the mixed-layer temperature, the mixed-layer depth, the bottom temperature, etc.) are necessary. Some experience in this area was gained through the development of the cold-start dataset for the COSMO model (COSMO-EU configuration run operationally at the GermanWeather Service). The perpetual-year runs of the lake model were used to develop that dataset. The lake model was forced by “climatological mean” atmospheric data to obtain a pseudo-periodic regime (a “climatological mean” annual cycle of the lake-model prognostic variables). Data from the NCEP

Figure 3: Example of the output from the lake database for HIRLAM grid. Left panel – modal (most probable) lake depth, right panel – lake fraction.

Figure 4: A perpetual-year annual cycle of the prognostic lake variables for Lake Ladoga (31 E, 61 N, mean depth is 50 m). Left panel: ice thickness times 10 – blue, mixed-layer depth – magenta. Right panel: mean temperature of the water column – magenta, bottom temperature – yellow, lake surface temperature (i.e. the temperature of the ice surface or of the water surface) – brown, 2 m air temperature – grey. Mixed-layer temperature and temperature of the ice surface are not shown.

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HIRLAM Newsletter no 54, December 2008

E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

reanalysis are used as an atmospheric forcing. For every grid box of the coarse NCEP grid, perpetualyear solutions for several lakes that differ in terms of mean depth (in the depth range from 1 m to 50) are obtained. An example of the perpetual-year solution for Lake Ladoga is shown in Fig. 4.

4 First runs with the coupled HIRLAM-FLake system As a lake parameterization scheme, the lake model FLake was implemented into the HIRLAM prediction system (“newsnow” version). The lake database is partly incorporated as well while the system for generation the coldstart initial fields is not yet incorporated. In order to initialize the lake-model variables for cold start in the present configuration, all lakes in the model domain are assumed to be mixed down to the bottom (for Europe, this is physically realistic only for middle or late autumn conditions only). At the cold start, the mixed-layer temperature is set equal to the SST taken from the ECMWF pseudo-observations. The first results obtained with the coupled HIRLAMFLake system are presented here, first of all, to demonstrate that the system works technically (some preliminary conclusions are drawn as well). The atmospheric model domain covers a part of Finland with Saima region, a small part of Sweden, the Baltic countries with Lake Peipsi, and the northern part of Russia with Lake Ladoga and Lake Onego. The resolution is 11 km (which is quite coarse). The experiment included the 6-hour cycling with the 3D-VAR analysis for all variables except the lake-model variables. The lateral boundary conditions are specified through the ECMWF analysis. Parallel runs are performed with (experiments FLK) and without (experiments REF) the lake parameterization scheme (FLake). A “short-term” experiment was initialized on November 2, 2006, it included 6-hour assimilation cycles for 1 day. A significant warm SST anomaly was observed at that moment. The initial surface temperature field and the final fields for experiments REF and FLK are presented in Fig. 5. The sensitivity to the inclusion of the lake parameterization scheme is quite large. The surface temperature for Lake Ladoga differs by 2 K between experiments REF and FLK. The difference in the surface temperature between the initial field and the forecast field is much larger for experiment REF than for experiment FLK. This means that the SST analysis procedure is very active just after the cold start, especially in the situation of high SST anomaly. FLake is not that active – the surface temperature changes rather slowly in the model. The second “long-term” experiment was initialized on October 1, 2006, it included 6-hour assimilation cycles for 31 days. The SST analysis procedure in experiment REF was not so active

Figure 5: Left panel – the initial surface temperature (cold start) on November 2, 2006, 00 UTC, middle panel – the surface temperature on November 3, 2006, 00 UTC as the result of 6-hour assimilation cycles for 1 day for experiment REF, right panel – the same as middle panel, but for experiment FLK.

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HIRLAM Newsletter no 54, December 2008

E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

Figure 6: The surface temperature on October 30, 2006 (last day of numerical experiment) from the “longterm” experiment with the 6-hour assimilation cycles, initialized on October 1, 2006. Left panel – experiment REF, right panel – experiment FLK.

towards the end of the simulation period. However, the difference between the two experiments with respect to the surface temperature is still quite large, see Fig. 6. For Lake Ladoga, for example, the surface temperature is 2-4 K warmer in experiment FLK than in experiment REF. In could be noted in brackets that in FLake all the prognostic variables are of double precision, but in HIRLAM are of ordinary precision. But lake variables are so slow, tendencies could be so small that changes for short HIRLAM time step could often be neglected if lake variables in HIRLAM are kept with ordinary precision. So, the FLake variables should be kept of double precision even when they go up from FLake to HIRLAM.

5 Conclusion The lake model FLake is implemented into HIRLAM as a lake parameterization scheme. The lake database is partly included as well. Results from first numerical experiments with the HIRLAMFLake coupled system show that the simulated lake surface temperature is quite sensitive to the lake parameterization. More experiments to study the lake-atmosphere system behavior in different situations are needed. The lake-related variables appear to be quite slow. The initialization of the forecast model in the situation of high surface-temperature anomaly should be avoided whenever possible. Data assimilation of the lake temperature and the ice characteristics is highly desirable. Future plans include an update of the system to generate the cold-start initial data on lake-related variables and its incorporation into HIRLAM.

Acknowledgments The authors thank Laura Rontu from the FinnishMeteorological Institute, Stefan Gollvik from the Swedish Meteorological and Hydrological Institute and Mariken Homleid from the Norwegian Meteorological Institute for useful discussions. Useful contacts with COSMO are gratefully acknowledged. The work was partially supported by the EU Commissions through the project INTAS-05-1000007-431, the NetFAM/NordForsk (http://netfam.fmi.fi), and the Swedish Institute, project 01753/2004.

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E. Kourzevena, P. Samuelsson, G, Ganbat and D. Mironov

References GLCC, 2005. Global land cover characteristics data base. Available at http://edcsns17.cr.usgs.gov/glcc/globdoc2 0.html. Kourzeneva K., and D. Braslavsky, 2005: Lake model FLake, coupling with atmospheric model: first steps. Fourth SRNWP/HIRLAMWorkshop on Surface Processes and Assimilation of Surface Variables jointly with HIRLAM Workshop on Turbulence,Workshop report, June 2005, SMHI, Norrkoping, Sweden, 43-53. Ljungemyr, P., N. Gustafsson and A. Omstedt, 1996: Parameterization of lake thermodynamics in a high resolution weather forecasting model. Tellus, 48A, 608-621. Mironov, D. V., 2006: Parameterization of lakes in numerical weather prediction. Part 1: Description of a lake model. GermanWeather Service, Offenbach amMain, Germany, 41 pp. (manuscript is available from the author) Mironov, D., E. Heise, E. Kourzeneva, B. Ritter, and N. Schneider, 2007: Parameterization of lakes in numerical weather prediction and climate models. Proc. of the 11th Workshop on Physical Processes in NaturalWaters, L. Umlauf and G. Kirillin, Eds., Berichte des IGB, Vol. 25, Berlin, Germany, 101-108. WLD, 2002.World Lake Database. Available at http://www.ilec.or.jp/database/database.html.

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