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Coupling of a High-Resolution Atmospheric Model and an Ocean Model for the Baltic Sea NILS GUSTAFSSON, LEIF NYBERG,
AND
ANDERS OMSTEDT
Swedish Meteorological and Hydrological Institute, Norrkoping, Sweden (Manuscript received 21 July 1997, in final form 15 December 1997) ABSTRACT The coupling between a high-resolution weather forecasting model and an ocean model is investigated. It is demonstrated by several case studies that improvements of short-range weather forecasting in the area of the Baltic Sea require an accurate description of the lower boundary condition over sea. The examples are taken from summer situations without sea ice as well as from winter situations with extreme sea ice conditions. It is shown that the sea state variables used in the model influence the weather forecast both directly on the local scale due to the local impact of surface fluxes of latent and sensible heat and on regional and larger scales. The convective snowbands during winters with cold airmass outbreaks over the open water surfaces of the Baltic Sea are extreme examples of the influence of sea state variables on a regional scale. It is furthermore demonstrated that the sea state conditions may change considerably within forecasting periods up to 48 h. This implies the necessary application of ocean models, two-way interactively coupled to the weather forecasting model. The coupling of an advanced 2.5-dimensional ice–ocean model to the operational Swedish Meteorological and Hydrological Institute (SMHI) weather forecasting model HIRLAM is described. The ice– ocean model includes two-dimensional, horizontally resolved ice and storm surge models and a one-dimensional, vertically resolved ocean model applied to 31 Baltic Sea regions. The coupled model system is applied operationally in a data assimilation system at the SMHI. No data assimilation is applied in the operational ocean component; manual modifications to the sea state variables are introduced a few times every winter season. The application of this operational coupled model data assimilation system to the mesoscale reanalysis for the Baltic Sea Experiment (BALTEX) shows that it is necessary to apply data assimilation for the sea state variables in order to avoid drift of the coupled model system toward less realistic model states. A successful application of a simple assimilation of SST observations is presented. The observed SSTs are first subject to a horizontal filter in order to minimize the effects of observational errors and to restrict the influence to a larger horizontal scale. Then the differences between these filtered temperature observations and the model SSTs are used to construct a modified sensible heat flux that is applied as a form of a ‘‘nudging’’ term to the ocean model. It turns out that this ‘‘nudging’’ is successful in avoiding the drift away from realistic sea state conditions. The described atmosphere and ocean data assimilation scheme has been applied in a rerun of the BALTEX mesoscale reanalysis for the cold winter 1986/87. The quality of this reanalysis was assessed through the successful simulation of the convective snowbands in January 1987.
1. Introduction Coupled atmospheric and ocean models have most frequently been applied in climate modeling studies and in long range and seasonal weather forecasting. For shorter-range forecasting, that is, for two weeks or shorter, it has generally been considered satisfactory to initialize the weather forecasting model with sea surface temperature (SST) and sea ice conditions according to observations and to keep these ocean parameter conditions fixed during the time integration of the weather forecasting model. The implicit assumptions are that timescales of significant changes in the ocean conditions
Corresponding author address: Dr. Nils Gustafsson, Swedish Meteorological and Hydrological Institute, S-601 76 Norrko¨ping, Sweden. E-mail:
[email protected]
q 1998 American Meteorological Society
are much longer than the timescales of atmospheric change and that consistent information on SST and sea ice are available at least every second day. However, in shallow semienclosed seas, such as the Baltic Sea, the changes of properties in the upper layers of the sea and in the sea ice cover are often rapid. This is due to large variability in the meteorological forcing, and to the presence of coastlines and islands that causes divergent motions in the upper layers of the sea. Therefore, there are oceanographic processes that act on timescales of the order of a day, which also influence weather conditions on these shorter timescales. The development of high-resolution meteorological models now makes it possible to resolve the meteorological impact of many of the specific features of the Baltic Sea geometry such as subbasins and straits. There is yet another important argument in favor of applying coupled oceanographic models within the framework of
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numerical weather prediction. The ocean models will provide SST and sea ice with high resolution in time and space, information that is not easily available from observations only (Omstedt et al. 1997). By also applying data assimilation in operational oceanographic models, it is possible to obtain an improved and consistent initial state of the ocean surface parameters to be used as initial values of the lower boundary condition for the atmospheric model. This is made possible through the abilities of the ocean models to accurately simulate processes such as cooling or warming of surface temperatures, sea ice formation, ice drift, and ice melting. This argument for introduction of ocean model data assimilation is the same as was used when data assimilation for atmospheric models was introduced several decades ago and should be even stronger for the ocean models as ocean data are sparse compared with atmospheric data. The need for utilization of coupled ocean models in numerical weather prediction will be further discussed through several examples in section 2 of this paper. Different strategies for coupling of the HIgh Resolution Limited Area Model (HIRLAM) to ocean models is discussed in section 3. This is followed by a description of the specific coupling between HIRLAM and the ocean model system Bohai and Baltic Sea Ice Forecasting System—Program for Boundary Layers in the Environment (BOBA–PROBE) in section 4. Results from application of this coupled system to a BALTEX reanalysis for the cold winter 1986/87 are described in section 5. Concluding remarks are given in section 6. 2. The need for coupled ocean models in numerical weather prediction—Examples Here we will give a few examples of situations where the rapidly changing sea surface conditions in the Baltic Sea have profound effects on weather developments. We will restrict ourselves to the atmospheric effects of changes in ice concentrations and SSTs, since we consider these effects to be most important for weather developments in the Baltic Sea area. Also, ocean surface waves may be important in the development of weather systems due to their effects on the turbulent transport processes—for example, friction—in the lower part of the atmosphere. Such effects will not be considered here. a. Changing ice boundaries The sea ice conditions in the Baltic Sea have strong seasonal and interannual variations (Omstedt and Nyberg 1996). During mild winters only the Bothnian Bay is ice covered, whereas during severe winters the Baltic Sea is more or less completely covered with ice. These variations of the sea ice conditions strongly influence the fluxes between the Baltic Sea and the atmosphere. Open water surfaces have, for example, distinctly dif-
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ferent evaporation, sensible heat flux, radiation, surface roughness, and albedo characteristics than ice-covered water surfaces, and this will influence local weather conditions like low-level air temperature and humidity, visibility, low-level clouds, and low-level winds. The early part of the winter 1993/94 was dominated by cold and stable weather with weak surface wind conditions in the Baltic Sea area. The Bothnian Bay was rapidly covered with ice—the ice cover was almost complete on 28 December 1993 (Fig. 1). During the following two days there was a change in the weather conditions, the temperature increased and strong southerly winds were established. Due to this, a significant part of the ice cover over the Bothnian Bay disappeared. The SST and sea ice conditions for 30 December 1993 are given in Fig. 2. From a numerical weather prediction perspective, this means a drastic change in the lower boundary conditions within the time range of a 48-h forecast. During operational forecasting conditions, such changes to the lower boundary conditions over ocean surfaces can be forecasted only through ocean models, two-way coupled to the atmospheric forecast model. For the particular forecast between 28 and 30 December 1993, Omstedt and Nyberg (1995) have shown that an operational coupled ice and ocean model can be utilized to achieve the required forecast performance. Changing ice concentrations do not only influence the local weather conditions, but may also influence the weather conditions on a regional or even on a larger scale. During winters with cold airmass outbreaks over the open water surfaces of the Baltic Sea, convective snowbands may occur and these snowbands have a profound effect on the weather in certain coastal areas of the Baltic Sea region. One example of such a situation with convective snowbands is given in the infrared satellite picture of Fig. 3. Notice the impressive convective snowband that starts from the western part of the Gulf of Finland and ends up on the east coast of Sweden. Another impressive convective snowband starts over the ¨ land, passes Bornholm, and ends up on Ru¨island of O gen. Andersson and Gustafsson (1994) studied the origin of these convective snowbands and the sensitivity of the snowbands to various external conditions and internal processes by simulation experiments with a mesoscale version of the HIRLAM model. With reference to the snowband starting in the Gulf of Finland, and ending up with very heavy snowfalls on the east coast of Sweden, they found that the upstream geometry of the coastline and the ice borders along the Finnish east coast and in the Finnish archipelago (‘‘coast of departure’’) as well as the coastline and the ice borders along the Swedish east coast (‘‘coast of arrival’’) were the most important factors influencing the structure, intensity, and position of the major snowbands. In particular, details of the ice borders in the mouth of the Gulf of Finland were crucial for the formation of the snowbands. The main physical
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FIG. 1. Observed ice and SST conditions on 28 December 1993.
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FIG. 2. Observed ice and SST conditions on 30 December 1993.
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FIG. 3. Infrared NOAA-9 satellite image 11 January 1987 1235 UTC.
process involved was secondary atmospheric circulations—that is, land breeze circulations—forced by the thermal differences between the warm water surfaces on one hand and the cold ice and land surfaces on the other. We may conclude that an accurate knowledge of the Baltic Sea ice borders is needed for the forecasting of these convective snowbands. The effect of the ice borders is thus not only felt locally, rather the most significant effects of the ice borders in the eastern part of the Baltic Sea occur as heavy snowfalls on the east coast of Sweden. With regard to these convective snowbands of January 1987, it should also be said that the ice cover of the Baltic Sea was formed during a few very cold days in the middle of January 1987. Finally, it is very difficult
to reconstruct the very detailed ice conditions seen in Fig. 3 by objective analysis techniques from observations only. Thus, coupling of an ice and ocean model to the weather forecasting and data assimilation system is the obvious step toward making the forecasting of these extreme weather events possible. b. Changing SSTs The SSTs in a shallow sea, with very inhomogeneous distribution of the land and water surfaces like the Baltic Sea, have rapid variations on small spatial and temporal scales and these variations are important for the interactions with the atmosphere. One summertime example of the importance of using correct SSTs for the calcu-
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lation of latent heat fluxes in a mesoscale atmospheric forecast model is given in Figs. 4–6. The late part of the summer 1995 was unusually warm in the Baltic Sea area. By the 24th of August the SSTs in the southern Baltic Sea had reached above 1208C (Fig. 4). This was much above the corresponding climatological SSTs (Alexander and Mobley 1976), of the order of 1158 to 1178C, that are used by many operational weather forecasting models. On 29 August there was a rapid change in the weather situation with a cold airmass outbreak from the northeast over the Baltic Sea. Due to the very large differences between the warm SSTs and the low-level air temperatures of the cold air mass, the latent and sensible heat fluxes from water surfaces of the southern Baltic Sea reached extremely high values during the initial period of this cold airmass outbreak. The maximum parameterized latent heat flux reached values of the order of 700 W m22 in the mesoscale meteorological model for the forecast 29 August 1995 118 h (Fig. 5a), if observed SSTs were utilized. If climatological SSTs were utilized, the corresponding maximum parameterized latent heat flux reached more modest values of the order of 350 W m22 (Fig. 5b). We may conclude that it is important to utilize realistic SSTs to get the fluxes from the sea surface correct in such situations with a cold airmass outbreak. It is also interesting to note that due to the cold airmass outbreak and the strong northerly winds until the end of August 1995, the sea state conditions changed very quickly. The SSTs for 4 September 1995 are given in Fig. 6. Notice in particular the drop in SSTs by approximately 158C along the west coast of the island of Gotland, caused by upwelling of cold seawater. Again, we may conclude that a coupled oceanographic model is needed; in this particular case the coupled model should be able to handle the upwelling process.
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operational ocean models that are available today and to implement a two-way coupling of the different ocean model components to the atmospheric model step-bystep. If needed, associated ocean model data assimilation schemes should also be introduced. One should start with as simple solutions as possible and move toward more complex solutions. In doing so, however, it is important to pay attention to the software interfaces between the different coupled models—these should be constructed in such a way that atmosphere or ocean model components should be possible to replace with more complex ones at a later stage. A different strategy for introduction of a coupled ocean model would be to directly introduce a three-dimensional ocean circulation model to be coupled to the atmospheric model. This would certainly be a research and development effort that would last over several years, in particular since it is considered necessary to introduce data assimilation schemes together with the coupled ocean model (see section 5 below). The Swedish Meteorological and Hydrological Institute (SMHI) has chosen to take the first strategy for introduction of coupled ocean models. The main argument for the selection of this strategy was the possibility to obtain useful operational results with relatively small development efforts, at the same time as these early experiences could help in the more longterm efforts to couple the atmospheric model to a general three-dimensional ocean model. The main argument against this strategy is that certain oceanographic processes, in particular coastal upwelling and ocean eddies, are not described by the ice–ocean model selected. 4. The coupling between HIRLAM and a 2.5-dimensional ice–ocean model a. The HIRLAM system
3. Strategies for coupling of ocean models to HIRLAM The main short-range weather forecasting tool for the weather services in the Nordic countries is the HIRLAM forecasting system. An important aim of the weather services is to improve the quality of operational weather forecast products. We have seen in the discussion above that short-range numerical forecasts in the Baltic Sea area in certain weather situations are highly sensitive to the specification of sea state parameters that enter the atmospheric forecast model through the lower boundary conditions. In this discussion we also provided arguments for introduction of two-way coupled atmosphere– ocean models in order to have the best possible sea state information as input to the atmospheric model, and also to make it possible to describe the time evolution of the sea state parameters within the forecast range of 48 h. There are several possible strategies for introduction of coupled ocean models into the operational practice of a weather service. One obvious strategy is to take
The HIRLAM forecasting and data assimilation system has been developed by the international HIRLAM project. This is a joint effort among the weather services in the Nordic countries, Ireland, the Netherlands, and Spain. The HIRLAM system is applied for operational purposes by most of the weather services participating in the project. A detailed description of the HIRLAM forecast model is given by Ka¨lle´n (1996). Here we give only a brief overview of the model formulation together with literature references and some details of relevance for the coupling to the ocean model. The HIRLAM model is based on the primitive equations with horizontal velocity components, temperature, and surface pressure as prognostic variables. The vertical coordinate is the terrain-following hybrid coordinate with sigma levels at the surface and pressure levels at the top (Simmons and Burridge 1981). The horizontal grid is a spherical rotated coordinate system with the equator going through the center of the integration area. The model is written on the Arakawa C-grid with sec-
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FIG. 4. SSTs on 24 August 1995.
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ond-order spatial accuracy (Arakawa 1966; Sadourny 1975) and the time scheme is a three-time-level semiimplicit scheme (Simmons and Burridge 1981). Horizontal diffusion is carried out by a linear fourth-order scheme. A semi-implicit semi-Lagrangian time integration scheme has also been developed by McDonald and Haugen (1993), and this version of the model has been utilized for the experiments described in the present paper. The vertical diffusion scheme affects the horizontal wind components, dry static energy, and specific humidity. It is based on first-order turbulence closure and it follows closely the scheme described by Louis (1979). Surface fluxes affect the prognostic values in the lowest model layer. They are determined by means of a drag coefficient formulation, using Monin and Obukhov (1954) similarity theory for the atmospheric surface layer. For each grid point a fraction of land and a fraction of sea ice are defined. The roughness length over the fraction of the grid area covered by open sea is computed by Charnock’s formula, whereas the roughness length over land and ice is constant in time. For each grid point the surface fluxes are computed separately over land (including the ice-covered part of the sea) and over open sea (including lakes). The distinction between ‘‘land plus sea ice’’ and ‘‘open sea’’ applies only to the surface fluxes and not to the fluxes above the lowest model layer. The calculation of fluxes above the lowest model layer is based on a mixing length formulation, using exchange coefficients, which depend on static stability and wind shear, described by means of a Richardson number. HIRLAM includes, in addition, a scheme for parameterization of condensation, cloud, and precipitation processes based on an explicit treatment of cloud water content as a prognostic variable. The HIRLAM data assimilation includes a three-dimensional multivariate statistical interpolation of massand windfield observations, a three-dimensional univariate statistical interpolation of humidity observations, a two-dimensional analysis of surface observations, and an implicit nonlinear normal mode initialization. b. The BOBA–PROBE ocean model The BOBA ice forecasting system has been developed within a Chinese–Finnish and a Swedish–Finnish winter research program. The BOBA system is applied for operational purposes in the Bohai Sea and in the Baltic Sea. A detailed description of the operational system for the Baltic Sea is given by Omstedt et al. (1994) and Omstedt and Nyberg (1995) and for the Bohai Sea by Zhang and Wu (1994). Below, only a brief overview of the model formulation is given. The BOBA system is based on coupled ice–ocean models that treat the ice drift as a horizontal two-dimensional continuum flow, the water levels and vertical mean currents as a horizontal two-dimensional flow, and the thermodynamics
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as a vertical one-dimensional boundary layer flow. The ice momentum equation uses a steady state approximation and the ice thickness is described with a threeparameter approach (open water, level ice, and ridged ice). The mechanical deformation describing closing and opening of leads and ridging is modeled as in Leppa¨ranta (1981) and the ice constitutive law follows the plastic model by Hibler (1979). The combined ice dynamic model was first presented by Wu and Leppa¨ranta (1988) and the first results from the Baltic Sea by Leppa¨ranta and Zhang (1992). The operational version of BOBA was first presented by Omstedt et al. (1994), where the dynamic couplings were discussed. The numerical calculations of the operational BOBA are made in a horizontal grid with a grid size of 10 n mi (18 km). In a later paper by Omstedt and Nyberg (1995), the BOBA model was coupled to a vertically resolved transient boundary layer model (PROBE) including full thermodynamics (water cooling, ice formation, ice growth/decay, and water warming). The ocean boundary layer model is a one-dimensional model, resolved only in the vertical, which is applied to different thermodynamic regions of the Baltic Sea such as different archipelagoes, coastal zones, and open subbasin areas (Fig. 7). The boundary layer is treated as a transient Ekman flow influenced by salinity and temperature stratification. The calculation of the eddy viscosity is due to a two-equation turbulence model, the so-called k–e model. The BOBA–PROBE model regions follow depth contours and also consider climatological borders of sea ice in the Baltic Sea (see Fig. 7). As compared to the operational BOBA–PROBE, see Omstedt and Nyberg (1995), the number of regions for this study was increased from 19 to 31, mainly to obtain a better horizontal description of coastal and archipelago areas. c. The coupling between the models The two-way coupling between the atmospheric model HIRLAM and the ocean model system BOBA– PROBE is illustrated in Fig. 8a. The ocean model system is forced by forecasts of surface pressure, 10-m winds, 2-m temperature, 2-m humidity, and total cloudiness from the atmospheric model. The ocean model system provides forecasts of SST and/or sea ice concentration to be used as the lower boundary condition during the time integration of the atmospheric model. Three different versions of the coupling have been applied. These versions differ only in the treatment of SST from the Baltic Sea in the coupled model system: Version I: Observed Baltic Sea SSTs are utilized in a surface analysis that prepares the initial lower boundary condition for HIRLAM. Only the initial sea ice distribution is determined from the ocean model system. This version is utilized operationally at the SMHI. Manually selected (bogus) SST observations are prepared by the marine forecaster on
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FIG. 5. Latent heat flux (Wm22 ) from SMHI Mesoscale HIRLAM, 29 August 1995 118 h: (a) with observed SST and (b) with climatological SSTs.
duty. These bogus observations are based on satellite images in addition to ship and coastal observations. Version II: No assimilation of SST observations from the Baltic Sea was applied in a first trial with the atmospheric mesoscale reanalysis of the winter 1986/87 for BALTEX. SST data for the HIRLAM initial lower boundary condition were obtained from the BOBA–PROBE short-range forecast. They were kept constant during the HIRLAM forecasts. Version III: As in version II but SST observations from the Baltic Sea were assimilated in the ocean model system in a second trial to carry out the atmospheric mesoscale reanalysis of the winter 1986/87 for BALTEX. The coupling between the atmospheric and ocean
models described in Fig. 8a could, in principle, be applied at every time step in the integration of the coupled models. This would have made it necessary, however, to merge the computer codes of all the models involved. Considering the physical problem at hand, such a complicated coupling certainly is an ‘‘overkill.’’ The timescales of the processes simulated by the coupled model system are of the order of hours with regard to changes of the atmospheric forcing for the ocean model and of the order of a day with regard to the response of the ocean model to this atmospheric forcing. Considering these timescales of the processes involved, we introduced a time scheme for the coupling as outlined in Fig. 8b where the HIRLAM model produces 13 h, 16 h, 19 h, 112 h, 115 h, 118 h, 121 h, and 124 h forecasts starting from 0600 UTC initial data every day. These 3-hourly atmospheric forecasts
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FIG. 5. (Continued)
are used to force the ocean model system in a 124 h time integration starting from the same 0600 UTC initial time every day. Output from the ocean model system in the form of SSTs and sea ice concentration for the Baltic Sea is produced at 16 h, 112 h, 118 h, and 124 h. These sea state variable forecasts are subsequently used as the initial lower boundary condition over the Baltic Sea for the HIRLAM forecasts starting from 1200 UTC, 1800 UTC, 0000 UTC as well as from the next 0600 UTC initial time. Since data assimilation for HIRLAM is carried out at 6-hourly intervals, and since we do not change these sea state variables during the 148 h HIRLAM forecasts, we may say that we have coupled the HIRLAM and the BOBA-PROBE models through the HIRLAM 6-hourly data assimilation cycle only. The coupling between the data assimilation of the atmospheric model HIRLAM and the ocean model sys-
tem BOBA-PROBE described above raises several questions: R Is it also necessary to control the ocean part of a coupled atmosphere–ocean model system through assimilation of ocean state observations? Version III of the coupled system includes a direct assimilation of SST observations in the ocean model. The use of SST observations in the atmospheric model in version I implies a (weak) control of the low-level atmospheric temperatures that force the ocean model, whereas version II includes no such control of the ocean state variables in the Baltic Sea. R The coupling of the ocean model system to the atmospheric model is carried out through forecasts of surface pressure, cloudiness, temperature, humidity, and wind produced by the atmospheric model. These
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FIG. 6. SSTs 4 September 1995.
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FIG. 7. The 31 thermodynamic regions of the BOBA–PROBE ocean model system.
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forecasts are used by the ocean model system to estimate fluxes of sensible heat, latent heat, radiation heat, and momentum through the ocean–atmosphere interface. Such estimated fluxes are, however, also directly available in the atmospheric model and are utilized for the calculation of the low-level forecast variables used for the coupling. It is certainly a longterm objective to utilize surface fluxes computed by the atmospheric model directly for the coupling between the models. There is also scope for improving the flux calculations in both model systems. R Another long-term objective is to utilize the coupling between the ocean model and HIRLAM instead of using only the initial SSTs kept constant in time throughout the 148 h HIRLAM forecasts. Output data from the ocean model forecasts are available and experimentation should start with sensitivity experiments. Outside the model domain of the BOBA–PROBE (see Fig. 7), initial SST and sea ice cover fields are influenced by SHIP (weather messages from commercial ships) and coastal station observations via the surface parameter analysis. In addition to the coupling between HIRLAM and the ocean model system BOBA–PROBE, a twoway coupling between HIRLAM and a lake model has also been introduced for the BALTEX atmospheric reanalysis effort (Ljungemyr et al. 1996). 5. The impact of a simple scheme for assimilation of SSTs in the ocean model The operational coupling (version I) between HIRLAM and the BOBA-PROBE ocean model system was first introduced as a one-way coupling from the atmospheric model to the ocean model, thus the output from the atmospheric model was used as a forcing for the ocean model system only. This coupled model system turned out to perform well; high quality SST and sea ice concentration forecasts were obtained by this relatively simple ocean model system during the first winter of operational application (Omstedt and Nyberg 1995). On the other hand, difficulties were experienced in trials to obtain initial sea ice concentration fields for the HIRLAM model by objective analysis techniques. The drift of the sea ice in the Baltic Sea may, for example, create narrow ice leads along one coastline while the ice masses are converging toward the opposite coastline. Objective analysis techniques with isotropic analysis structure functions are not able to reconstruct such narrow features. Thus, it was a natural step to introduce operationally a two-way coupling simply by utilizing the ice
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concentration forecast from the coupled ocean model system as initial data for the HIRLAM model. Since objective analysis of the SST field in the Baltic Sea turned out to be less problematic, this feature was kept in the operational application of the coupled model system. As was pointed out above, this implies a weak control of the HIRLAM low-level temperature forecasts that are important for the forcing of the ocean model. a. Drift in the Baltic Sea SST and sea ice conditions without ocean model data assimilation Within the framework of the BALTEX research program, SMHI has undertaken the task of carrying out mesoscale reanalysis of the atmospheric state for two winter periods, 15 December 1986–15 February 1987 as well as 1 December 1992–31 January 1993. The reanalysis for the first period has already been completed. This period was characterized by extreme cold weather in the Baltic Sea area due to a cold airmass outbreak from the northeast. In the beginning of the period the Baltic Sea was almost without any ice, but due to the extremely cold weather almost the whole Baltic Sea became covered with sea ice during the 2-month period of the reanalysis. A mesoscale version of the HIRLAM forecast model system, with a horizontal grid resolution of 20 km and with 24 vertical levels, is utilized for the reanalysis effort. Since presence or absence of sea ice will have a profound effect on the weather systems simulated by the mesoscale HIRLAM (Andersson and Gustafsson 1994), it was considered important to have access to accurate SST and sea ice data for the reanalysis. No such data were available in digital form; twice-weekly manual SST and sea ice maps were available from the SMHI marine forecasting office as hard copies only. Considering the promising experiences from the operational coupled HIRLAM/BOBA–PROBE data assimilation system, in addition to the lack of SST and sea ice data in digital form, we decided to try to run the coupled model system for the BALTEX reanalysis of the winter 1986/87 without any direct input of SST and sea ice observations (version II). To start from a realistic sea state valid for the 15 December 1986, the BOBA– PROBE ocean model system was ‘‘spun up’’ during a 1.5-month integration started from 1 November 1986. The atmospheric forcing for the spinup integration was obtained from SYNOP (weather messages from land surface stations) and SHIP data interpolated horizontally to a 18 3 18 grid. The initial profiles of temperature and salinity were estimated from oceanographic measure-
← FIG. 8. (a) Coupling between HIRLAM and BOBA–PROBE; I 5 operational coupling, II 5 coupling for the BALTEX reanalysis without SST assimilation, and III 5 coupling for the BALTEX reanalysis with SST assimilation. (b) Flow diagram outlining the coupling between different HIRLAM and BOBA–PROBE runs: AOBS 5 atmospheric observations; OOBS 5 oceanic observations; HIRLAM output is given by full lines, BOBA–PROBE output by dotted lines, and input of oceanic observations by dashed lines.
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ments. Since only a few measurements were available, climate data were also used. The quality of the initial profiles was studied during the model spinup integration by comparing calculated and observed SSTs. The initialization of any ocean model is a major problem, as often few ocean measurements are available. In addition, the stratification spinup time in the Baltic Sea is long, indicating that the initial profiles will influence the model results for a long time. The initial salinity profiles will, for example, influence the salinity stratification during the whole winter season. The sea state as provided by the spinup of the ocean model system was quite realistic at the start of the reanalysis period and the simulated sea state remained realistic during approximately three weeks of reanalysis model integrations. By mid-January 1987, however, the extreme cold airmass outbreak occurred and the Baltic Sea very quickly started to be covered with sea ice. The freezing of sea ice in the coupled model system turned out to be slower than in reality. For example, a delay in freezing of sea ice of approximately 10 days was experienced around 608N in the Baltic Sea. The simulated SSTs were less than 18C in error, but the freezing of sea ice is very sensitive to even small errors when the temperature is close to the freezing point. We may simply conclude that the idea of running a coupled atmosphere–ocean model system for reanalysis purposes, as well as for operational purposes, without any control of the sea state variables by assimilation of sea state observations, is a poor idea. The drift of the coupled model sea state away from a realistic sea state may have several explanations. The flux calculations in the atmospheric model, as well as in the ocean model, may not be accurate enough. Either of the two models may have systematic errors that can cause a significant ‘‘climate drift’’ close to the surface of the Baltic Sea after a few weeks of model integrations. Spinup or spindown of, for example, processes associated with moisture and clouds in the atmospheric model may also contribute to such a drift, since the coupling between the two model systems is mainly carried out through very short-range model integrations. b. A simple scheme for assimilation of SST observations in the coupled ocean model From the first trial with the BALTEX reanalysis for the winter 1986/87 we learned that it is necessary to control the evolution of sea state variables by assimilation of sea state observations. The only available source of sea state data for the winter 1986/87 is twiceweekly SST and sea ice maps produced manually by the SMHI marine forecasting office. Due to the difficulties with objective analysis of the sea ice fields, we decided to assimilate SST observations only in the coupled ocean model. The hypothesis is that the ocean model will be able to simulate the freezing, the drift, and
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the melting of the sea ice, provided the modeled SSTs are accurate. Sea surface temperature observations are needed for each of the 31 subareas of the PROBE model and were simply extracted manually from the marine analysis maps. Such manually extracted SST observations will certainly be associated with errors, due to errors in the manually produced SST maps and errors introduced during the manual extraction. In addition, the SST values from the analysis map may not be representative for the PROBE subareas. As a rough estimate, we may assume the standard deviation of the error of the manually introduced SST observations is of the order 0.58C—satellite image data and ship data were utilized to produce the manual analysis maps. To minimize possible negative effects of these errors, the SST values to be introduced into the PROBE model were obtained by means of a spatial filtering of observation increments in several subareas:
O W (T N
FC T AN sea (k) 5 T sea (k) 1
kl
OBS sea
(l) 2 T FC sea (l)),
(1)
l51
OBS AN where T FC sea, T sea , and T sea denote forecasted, observed, and analyzed (filtered) SST values, respectively. Index k denotes the PROBE subarea to be analyzed and index l any of the neighboring PROBE subareas, including subarea k itself. Ideally, neighboring subareas should be given filtering weights in accordance with the statistical relationship between forecast errors in neighboring subareas, as it is done in, for example, the statistical interpolation analysis technique. Such statistical forecast error information has to be accumulated over long application periods and was not available for the present study. The filtering weights W kl were therefore assigned subjectively on the basis of the distances between the subareas and physical similarities between the subareas (sea depths, etc.). As mentioned above PROBE is a one-dimensional vertically resolved ocean model. It includes parameterization of vertical turbulent transports and interaction with the atmosphere in the form of vertical fluxes of sensible heat and latent heat, radiative heating/cooling, and vertical fluxes of momentum through wind during open water conditions and ice drift during ice conditions. This means that a realistic vertical temperature profile is established in the uppermost ocean layer. It would certainly be meaningless just to replace the PROBE surface temperature by the analyzed surface temperature. Rather it is necessary to influence the whole temperature profile in the uppermost ocean layer in such a way that the modified temperature profile will have the observed surface temperature at the same time as the profile is a model solution. This is achieved by adding an extra sensible heat flux term to the PROBE model equations. We may call this procedure ‘‘sensible heat nudging’’ since the extra surface sensible heat flux
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FIG. 9. SST variations in three PROBE subareas during the period November 1986–May 1987. Observed values (full line), simulated values without data assimilation (dashed line), and simulated values with data assimilation (dotted line). (a) Central Bothnian Bay (region ˚ land archipelago (region 8), and (c) eastern Baltic proper (region 25). 3), (b) the A
F AN is added to the model equations over a certain fixed s nudging time period Dt: F AN 5 s
Dd C r (T AN 2 T FC sea ), Dt pw w sea
(2)
where C pw is the specific heat of water at constant pressure, r w the density of water, and Dd the depth of the vertical column of water over which the extra input sensible heat is assumed to be distributed. For practical reasons, the time period Dt for the sensible heat nudging was set to 3 h and Dd to 10 m in this study. It was noticed during testing of the assimilation scheme, however, that a longer nudging period would have been better in a few cases of very large differences between the analyzed and forecast SSTs. In such cases, unreasonable vertical temperature profiles may temporarily be established in the uppermost ocean layer. The impact of the SST assimilation in the BOBA– PROBE ocean model system was first tested by running the ocean model without the coupling to the atmospheric
model. The ocean model was forced by the same SYNOP and SHIP data interpolated horizontally to a 18 3 18 grid, as were used for the model spinup. Two ocean model runs, with and without data assimilation, were carried out for the period November 1986–May 1987. Both model runs were verified against the manually extracted SST observations. The verification was carried out at model forecast times (0600 UTC 124 h) just prior to the start of the observation nudging periods (0600 UTC 10 h to 13 h). Verification was done when the twice-weekly SST and sea ice maps were available. The root-mean-square (rms) verification scores calculated over all 31 PROBE subareas and the whole 7month period show that the ocean model data assimilation has a significantly positive impact on the SST simulations. The rms verification score decreases from 0.748C without data assimilation to 0.348C with data assimilation. Both of these rms values are certainly rather low, compared to the estimated accuracy of the manually introduced SST values. Three examples of the time
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evolution of SSTs, with and without data assimilation, in three individual subareas, are given in Figs. 9a–c. The first example, in Fig. 9a, shows the time development of SST in subarea 03 of the central Bothnian Bay (see Fig. 7), an open sea area. The simulations are here quite accurate, with or without SST data assimilation. The second example, in Fig. 9b, is from subarea ˚ land archipelago, where a 08 with many islands, the A nice improvement by the data assimilation can be noticed. A significantly positive impact of the data assimilation can also be seen in Fig. 9c, which is taken from region 25 in the eastern part of the Baltic proper. Here the data assimilation helps to enhance the cooling of the seawater, which is too slow in the model run without data assimilation. One can also notice that the time evolution in the ocean model run with data assimilation is smoother than the curve with the observered values, an indication of the filtering effects of the data assimilation procedure. We may conclude that the simple data assimilation introduced here has a positive impact on the simulated SSTs. To optimize the data assimilation scheme, however, some efforts have to be spent on, for example, development of quality control algorithms and spatial filtering techniques, as well as coefficients and time periods of the sensible heat nudging scheme. c. Rerun of the BALTEX mesoscale reanalysis including SST assimilation Considering its successful implementation and test in a stand-alone version of the BOBA–PROBE ocean model, the SST assimilation was added to the BALTEX mesoscale reanalysis system. The reanalysis for the period 15 December 1986–15 February 1987 was repeated. In this second trial, the drift of the simulated sea state variables away from the observed sea state was avoided. Figure 10 illustrates the simulated sea and lake ice coverage for 1, 11, 21, and 31 January 1987. Most of the freezing of the sea ice in the Baltic Sea occurred during this period. The lake model was applied only to the lakes in Sweden; lake ice in other areas was taken according to a (poor) lake ice climatology. This explains the artificial lake ice gradient along the Swedish–Norwegian border and the small area in southern Finland with ice-free lakes. The development of the sea ice conditions in the Baltic Sea during January 1987, as illustrated in Fig. 10, is in good agreement with available manual marine analysis maps. By 1 January 1987 the Bothnian Bay is more or less frozen and the freezing of the Gulf of Finland has started. Ten days later on 11 January, the Gulf of Finland is frozen with the exeption of a small area in ˚ land the western part. Also the water surfaces in the A archipelago have now been covered with ice and there is an almost unbroken east–west border line between open sea and land–ice areas starting from the mouth of the Gulf of Finland all the way to the archipelago of
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Stockholm. This is an important prerequisite for the formation of convective snowbands over the Baltic Sea, as discussed by Andersson and Gustafsson (1994). By 21 January the northern part of the Baltic Sea is completely covered with sea ice and, with regard to the lakes in Sweden, only the deep Lake Va¨ttern remains almost ice-free. Toward the end of January 1987, the freezing of sea ice is less rapid. Considering that the presented sea ice concentration maps are obtained from model variables in BOBA–PROBE that are unaffected by any direct assimilation of sea ice observations, the results must be regarded as satisfactory. The efficient horizontal resolution depends on the 31 thermodynamic regions of the BOBA–PROBE model, and these were selected partly on the basis of sea depth distribution and partly on the basis of sea ice climatology. As the model advects ice horizontally, the thermodynamic regions only control ice growth and ice decay. Parameters related to the energy and water budgets of the Baltic Sea area are of major importance with regard to the output data from the BALTEX reanalysis. Mean surface sensible heat flux and mean surface latent heat flux for January 1987 are given in Figs. 11 and 12, respectively. The accumulated precipitation for January 1987 is given in Fig. 13. The averaging and the accumulation have been carried out for forecasts from 1 to 31 January 1987 at 0000, 0600, 1200, and 1800 UTC. Forecasts between 16 h and 112 h were used since these should be less affected by model spinup effects than forecasts between 10 h and 16 h. The time averaging of the surface fluxes was done for every time step of the model integrations. A separate verification showed that the accumulated precipitation was indeed sensitive to the selection of the forecast accumulation period, whereas the surface sensible and latent heat fluxes were insensitive to the corresponding averaging period. Several interesting features related to the coupling between the atmospheric and ocean models can be noticed in Figs. 11–13. Maximum monthly mean values of the sensible heat flux occur over open water surfaces just downstream of land areas or sea ice areas during the cold airmass outbreak from the northeast, while monthly mean values in the range 200–220 W m22 occur outside the coasts of Estonia, Latvia, and Lithuania and in the mouth of the Gulf of Finland. Another maximum of about 160 W m22 is present over the Skagerrak. It is also of interest to note the local maximum of 120 W m22 over the eastern parts of the Bothnian Sea. The modest values of this local maximum may be understood from the fact that the freezing of the sea ice occurred in the middle of the period of the cold airmass outbreak around 15 January 1987. Clearly the presence of several different border lines between land–sea ice areas and open water surfaces during the cold airmass outbreak are reflected in strong gradients of the average sensible heat flux. The structure of the monthly mean latent heat flux is
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FIG. 10. Sea and lake ice coverage simulated by the coupled ocean and lake models: 1 January 1987 (upper left), 11 January 1987 (upper right), 21 January 1987 (lower left), and 31 January 1987 (lower right).
very similar to the structure of the monthly mean sensible heat flux, but the maximum values reach only 100– 120 W m22 . This corresponds to an evaporation of 110– 130-mm water from the Baltic Proper surface during January 1987. As a validation of the calculated evaporation rates by the HIRLAM/BOBA–PROBE model we compare the
results with two other independent estimates. H.-J. Isemer (1997, personal communication) used ship observations from the Baltic proper and a bulk parameterization of the latent heat flux. He estimated the evaporation rate to be 75.0 mm month21; see Table 1. With regard to the Baltic proper west, the estimated evaporation rate of 75.5 mm month21 was the highest cal-
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FIG. 11. Monthly mean surface sensible heat flux (W m22 ) for January 1987, calculated by averaging of forecasts between 16 h and 112 h for initial data at 1–31 January 1987 0000, 0600, 1200, and 1800 UTC.
culated January monthly mean for the whole period 1964–94 studied by Isemer. Omstedt et al. (1997) applied an ocean model, in which the Baltic Sea was treated as 13 horizontally coupled subbasins. Each subbasin was modeled with a high vertical resolution. SSTs as well as sea ice were calculated and verified. In the model, the latent heat flux was calculated according to a bulk formula parameterization. The January 1987 evaporation rate for the Baltic proper was estimated to 70.4 mm month21 . The two studies mentioned above come close in estimating the evaporation rates, but with values considerably lower than the present model calculations, possibly indicating that the HIRLAM parameterization scheme overestimates the latent heat flux. Large evaporation rates calculated by HIRLAM were also found by C. Fortelius (1997, personal communication) when he compared
evaporation rates from the whole HIRLAM reanalysis period with calculations based on ship observations and the corresponding rates calculated by Isemer. Also, evaporation rates for the Baltic proper estimated from the European Centre for Medium-Range Weather Forecasts reanalysis data by P. Ka˚llberg (1997, personal communication), with maximum values of the order of 90.0 mm month21 , seem to support the hypothesis that the evaporation rates by HIRLAM are overestimated. A recent investigation of the HIRLAM surface parameterizations over sea surfaces has indeed confirmed that the sensible heat and moisture exchange coefficients need to be reformulated in strong surface wind conditions (V. Makin and V. Perov 1997, personal communication). Several interesting features can also be noticed from the accumulated precipitation map for January 1987 (Fig. 13). There are local maxima of the order 80–100
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FIG. 12. Monthly mean surface latent heat flux (W m22 ) for January 1987, calculated by averaging of forecasts between 16 h and 112 h for initial data at 1–31 January 1987 0000, 0600, 1200, and 1800 UTC.
mm, close to some of the areas with the maxima of the surface latent heat flux, for example, outside the coasts of Latvia and Lithuania. But, more importantly, maxima of the accumulated precipitation also occur downstream on the other side of the open water surfaces. Note, for example, the elongated maximum above 120 mm over the east coast of Sweden and similar elongated maxima in the southern Baltic Sea toward Ru¨gen and over the North Sea downstream of Skagerrak. All these maxima occur in the same areas as the convective snowbands, observed as well as simulated by Andersson and Gustafsson (1994), during the cold airmass outbreak in midJanuary 1987. The dominance of these convective snowbands for the accumulated precipitation patterns during January 1987 indicates the importance of the corresponding physical processes, for example, mesoscale atmospheric secondary circulation systems, for the wa-
ter budget of the Baltic Sea area during such cold airmass outbreaks. d. Validation of the simulation of convective snowbands with BALTEX reanalysis data Andersson and Gustafsson (1994) carried out a successful simulation of the convective snowbands on 11 January 1987 over the Baltic Sea. This was achieved only after a very tedious manual procedure to analyze and digitize the sea ice and SST initial conditions for the model simulation. The coupled HIRLAM and BOBA–PROBE data assimilation system, including assimilation of SST observations, provides a technical framework to carry out the same model simulation in an objective and reproducable manner. Starting from BALTEX reanalysis initial data, and using BALTEX
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FIG. 13. Accumulated precipitation (mm) for January 1987, calculated from forecasts between 16 h and 112 h for initial data at 1–31 January 1987 0000, 0600, 1200, and 1800 UTC.
reanalysis data also for the lateral boundary conditions, the simulation experiment of Andersson and Gustafsson (1994) was repeated with the same model resolution and the same physical parameterizations as was used in the original study. Andersson and Gustafsson presented the TABLE 1. Monthly mean evaporation rates (in mm month 21) for the Baltic proper during January 1987.
Evaporation (mm month21) Baltic Baltic Baltic Baltic
proper proper proper proper
north south west total
H.-J. Isemer (1997, perHIRLAM/ sonal BOBA– communicaPROBE tion) 105.2 112.2 89.1 106.1
76.1 73.9 75.5 75.0
Omstedt et al. (1997) 68.8 84.4 58.0 70.4
simulated accumulated precipitation between 16 h and 118 h based on initial data from 11 January 1987 0000 UTC. The repeated precipitation simulation forecast is reproduced in Fig. 14. The simulated precipitation pattern is, in general, very similar to the precipitation pattern in the original study. The forecast precipitation amounts agree well with observed precipitation amounts from the few available SYNOP stations, for example, along the east coast of Sweden and on the Islands of ¨ land. Gotland and O The sensible heat flux and the latent heat flux as simulated by the HIRLAM model for 11 January 1987 0000 UTC 112 h are presented in Figs. 15 and 16. Notice the impressive total surface heat flux of the order of 1000 W m22 from the open water surfaces of the Baltic Sea outside the coasts of Estonia, Latvia, and Lithuania as well as over the Skagerrak. The significant contri-
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FIG. 14. Forecast of accumulated precipitation (mm) for 11 January 1987 0000 UTC, 16 h to 118 h.
bution of the surface heat fluxes from the extreme event of the cold airmass outbreak to the monthly mean surface heat fluxes in Figs. 11 and 12 is obvious. 6. Discussion and concluding remarks The necessity of applying two-way coupled atmospheric and ocean models for improving data assimilation and short-range weather forecasting in the Baltic Sea area has been discussed in this paper. Several examples in support of this hypothesis from the summer season, with the presence of upwelling, as well from the winter season, with the presence of sea ice, have been presented. It is shown that the modeled sea state variables have a direct local impact upon the weather forecasts through the sensitivity of the surface fluxes of sensible and latent heat to these sea state variables. In addition, there may also be significant impact on re-
gional or larger scales upon the weather forecasts through, for example, secondary atmospheric circulation systems that are forced by gradients in these surface heat fluxes. The coupling between the mesoscale weather prediction model and the 2.5-dimensional ocean model BOBA–PROBE has been described together with the application of this coupled model system to the mesoscale atmospheric reanalysis of the cold winter 1986/87 for BALTEX. From this application of a coupled atmosphere–ocean model, several important conclusions can be drawn: R It is necessary to apply assimilation of sea state observations in order to avoid a drift of the modeled sea state variables away from realistic sea states. R It is sufficient to apply assimilation of SST observations and to rely on the ocean model for the sim-
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FIG. 15. Forecast surface sensible heat flux (W m22 ) for 11 January 0000 UTC 112 h.
ulation of the sea ice conditions. Sensible heat nudging turned out to be an efficient technique for input of the SST information to the ocean model. R Accurate sea state lower boundary conditions, necessary for the forcing of convective snowbands during cold airmass outbreaks over the Baltic Sea, can be obtained from the coupled ocean model. R In connection with cold air outbreaks, mesoscale atmospheric secondary circulation systems, as well as associated convective snowbands, are important not only for the simulation of isolated weather events but also for the calculation of monthly based water and energy budgets in the Baltic Sea area. This implies, in principle, that BALTEX water and energy budgets must be estimated with the aid of coupled atmosphere and ocean models at mesoscale spatial resolutions. In addition, assimilation of observations must be applied for the atmospheric as well as for the ocean component of the coupled model system. For a reliable estimation
of these water and energy budgets by the coupled model system presented here, however, the boundary layer and surface parameterization schemes need to be reviewed and if needed improved. The present coupled atmosphere–ocean model system is only a first step in the development of a future coupled model. Further development steps will include the following. R Improved coupling between the atmospheric and ocean model components by introduction of consistent flux calculations to be applied in model components and calculation of albedo and roughness length from available information on sea ice structure. R Improvement of the spatial resolution of the ocean model PROBE by refinement of the thermodynamic regions. R Input of SST observations, and possibly also sea ice
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FIG. 16. Forecast surface latent heat flux (W m22 ) for 11 January 0000 UTC 112 h.
concentration observations, derived from satellite image data by an image processing system. The present coupled model system certainly is associated with specific as well as general limitations. The horizontal resolution is limited by the thermodynamic regions of the PROBE model. A refinement of these regions is one possibility but the long-term objective should be to introduce a three-dimensional, horizontally resolved, ocean model to be coupled to the mesoscale atmospheric model. We would like to stress, however, that the present coupling between the mesoscale atmospheric model HIRLAM and the 2.5-dimensional ocean model BOBA– PROBE has allowed us to benefit from the advantages of coupling several years in advance of the future coupling between HIRLAM and a general three-dimensional ocean model. For such a coupled system the problem of data assimilation has yet to be solved. We are,
however, convinced that the simplified ocean model data assimilation introduced here, sensible heat nudging, can also be utilized for a three-dimensional ocean model. Acknowledgments. We would like to express our gratitude to Lars Meuller, who carried out the mesoscale reanalysis calculations for the winter 1986/87. We would also like to thank Dr. Carl Fortelius for his support in validation of the reanalysis data and Dr. HansJo¨rg Isemer for providing evaporation rates for January 1987 based on ship observations. Prof. Bennert Machenhauer and an anonymous reviewer provided us with useful suggestions, that have helped to improve this manuscript. This work was supported by the Swedish Meteorological and Hydrological Institute, European Commission Contract ENV4-CT95-0072i, and the Swedish Environmental Protection Agency.
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