A multiprocessor coupled ice-ocean model for the Baltic Sea: Application to salt inflow

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. C8, 3273, doi: /2000jc000521, 2003 A multiprocessor coupled ice-ocean model for the Baltic Sea: Application to salt inflow H. E. Markus Meier and Ralf Döscher Rossby Centre, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden Torgny Faxén National Supercomputer Centre, Linköping University, Linköping, Sweden Received 5 July 2000; revised 30 November 2001; accepted 23 April 2003; published 22 August [1] Within the Swedish Regional Climate Modeling Program, SWECLIM, a threedimensional (3-D) coupled ice-ocean model for the Baltic Sea has been developed to simulate physical processes on timescales of hours to decades. The code has been developed based on the massively parallel version of the Ocean Circulation Climate Advanced Modeling (OCCAM) project of the Bryan-Cox-Semtner model. An elasticviscous-plastic ice rheology is employed, resulting in a fully explicit numerical scheme that improves computational efficiency. An improved two-equation turbulence model has been embedded to simulate the seasonal cycle of surface mixed layer depths as well as deepwater mixing on decadal timescale. The model has open boundaries in the northern Kattegat and is forced with realistic atmospheric fields and river runoff. Optimized computational performance and advanced algorithms to calculate processor maps make the code fast and suitable for multi-year, high-resolution simulations. As test cases, the major salt water inflow event in January 1993 and the stagnation period , have been selected. The agreement between model results and observations is regarded as good. Especially, the time evolution of the halocline in the Baltic proper is realistically simulated also for the longer period without flux correction, data assimilation, or reinitialization. However, in particular, smaller salt water inflows into the Bornholm Basin are underestimated, independent of the horizontal model resolution used. It is suggested that the mixing parameterization still needs improvements. In addition, a series of process studies of the inflow period 1992/1993 have been performed to show the impact of river runoff, wind speed, and sea level in Kattegat. Natural interannual runoff variations control salt water inflows into the Bornholm Basin effectively. The effect of wind speed variation on the salt water flux from the Arkona Basin to the Bornholm Basin is minor. INDEX TERMS: 4215 Oceanography: General: Climate and interannual variability (3309); 4243 Oceanography: General: Marginal and semienclosed seas; 4255 Oceanography: General: Numerical modeling; KEYWORDS: Baltic Sea, ice-ocean modeling, multiprocessor model, model validation, regional climate, saltwater inflow Citation: Meier, H. E. M., R. Döscher, and T. Faxén, A multiprocessor coupled ice-ocean model for the Baltic Sea: Application to salt inflow, J. Geophys. Res., 108(C8), 3273, doi: /2000jc000521, Introduction [2] The estuarian circulation and hydrography of the Baltic Sea is determined mainly by four factors [cf. Mälkki and Tamsalu, 1985]: (1) water exchange through the Danish Straits, (2) bottom topography, (3) river discharge, and (4) atmosphere-ice-ocean interaction. The water exchange between Baltic and North Sea is restricted by the narrows and sills of the Danish Straits (Figure 1). The width of the narrowest part of the Öresund (Sound) near Helsingør- Helsingborg amounts to approximately 4 km. Darss Sill, having a depth of about 18 m, separates the Belt Sea from Copyright 2003 by the American Geophysical Union /03/2000JC the Arkona Basin. The Öresund has a sill depth of only 8 m at its southern entrance (Drogden). [3] The highly variable bottom topography separates the water masses into various basins, delimited by high sills or bays (Figure 1). The mean water depth amounts to 52 m and the maximum depth at Landsort Deep is 459 m. [4] The water exchange through the Danish Straits and the river runoff into the Baltic Sea determine the stratification of the water masses into a homogeneous upper layer and a stratified lower layer. The mean annual river discharge to the Baltic Sea is 15,310 m 3 s 1 for the period [Bergström and Carlsson, 1994]. The river flow is highly variable through the year and there are large interannual variations. The lowest (11,132 m 3 s 1 in 1976) and highest (18,660 m 3 s 1 in 1981) annual values differ from the mean 29-1

2 29-2 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL Figure 1. Bottom topography of the Baltic Sea including Kattegat and Skagerrak (data from Seifert and Kayser [1995]). The model domain of RCO is limited with open boundaries in the northern Kattegat (dashed line). Selected monitoring positions (see text) and the section from Figure 3 are depicted additionally. See color version of this figure at back of this issue. value by 27% and +22%, respectively. In addition, surface freshwater fluxes (i.e., precipitation minus evaporation) have to be considered. For the period , Omstedt et al. [1997] calculated the total mean atmospheric freshwater inflow to be 1986 m 3 s 1. The interannual variability is large as well, with values ranging from 502 m 3 s 1 (in 1983) to 4069 m 3 s 1 (in 1987). Similar results are reported by Meier and Döscher [2002]. Today only rough estimates of mean transports through the Danish Straits are possible. To achieve improved water and salt budgets, accurate modeling and monitoring of the highly variable inflow and outflow through the narrow Danish Straits are required over long time periods. [5] The fourth factor, important for the Baltic Sea circulation and hydrography, is the interaction with the atmosphere. Owing to the stratification, the atmosphere influences mainly the homogeneous upper layer directly. During the summer, the upper layer is heated and a seasonal thermocline is established. During late autumn and winter, vertical convection erodes the thermal stratification, resulting in the characteristic salinity-dominated two-layer structure of the Baltic Sea. Half of the water column is

3 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL 29-3 dominated by the seasonal signal directly because mixed layer depths are typically m at a mean water depth of only 52 m. [6] An essential feature of the Baltic is the seasonal sea ice cover. Sea ice acts as a relatively rigid insulating lid between the air and the sea which modifies air-sea exchange of momentum, heat, and matter, and influences local meteorological conditions. With respect to the ocean, sea ice influences the temperature and salinity characteristics of the water masses and the circulation of the Baltic Sea. Normally, the ice season lasts 5 7 months, from November to May. The interannual variability of the ice extent is large. During a mild winter, ice occurs only in the Bothnian Bay, but in a cold winter the entire Baltic Sea becomes ice-covered [see Swedish Meteorological and Hydrological Institute/Finnish Institute of Marine Research, 1982]. As surface albedo changes drastically with ice conditions, sea ice in the Baltic is regarded as a key element in the north European climate system. [7] So far, several models have been used to study the dynamics of the Baltic Sea on decadal timescale. Most of them were process-oriented models resolving the horizontal variations by dividing the area into a number of subbasins [e.g., Stigebrandt, 1983; Omstedt and Axell, 1998; Gustafsson, 2000a, 2000b]. These models were utilized to simulate the Baltic Sea climate and to perform sensitivity studies. Recently, first multi-year three-dimensional (3-D) simulations for the Baltic Sea have been performed taking also the sub-basin scale into account. The Rossby Center Ocean model (RCO) which is introduced in this paper has been used by Meier [2001] to test different mixing parameterizations during the hindcast period He showed that the seasonal thermocline and the stratification are simulated realistically using an embedded k-e turbulence model. During a stagnation period without major salt water inflows, deepwater salinity in the Baltic proper decreases as observed. The same model has been used also for regional ocean climate change scenarios with a horizontal resolution of 6 nautical miles [Meier, 2002a, 2002b]. A regional shelf sea model for the North Sea and the Baltic Sea has been developed by Schrum et al. [2000]. They simulated the period also with a horizontal resolution of 6 nautical miles and concluded that their model is able to give reliable estimates of the natural variability. A 10-year simulation ( ) using an eddy-permitting Baltic Sea model with a horizontal resolution of approximately 5 km has been recently presented by Lehmann et al. [2002]. They focused on the effects of remote and local atmospheric forcing on circulation and upwelling in the Baltic Sea but did not show the temporal evolution of the vertical stratification. [8] In this study, two hindcast periods have been defined to evaluate the model performance of RCO. The first period, May 1992 to June 1993, covers a major salt water inflow event in January Such inflows are important for long climate integrations as they set the conditions in the Baltic Sea deepwater for decades. A second, longer evaluation period ( ) has been chosen to test long-term behavior and interannual variability. For example, sea ice seasons with mild, normal, and severe winters are included in the test period as well as one major salt water inflow in January 1993 at the end of the period. The 16-year stagnation period between 1976 and 1992 serves as an excellent test for deepwater mixing parameterizations. Thereby, important requirements are to keep the stratification during the integration stable and to avoid artificial erosion of the halocline due to numerical inaccuracies. Problems with discretization and parameterization schemes are often hidden in short simulations but will show up clearly during long-term integrations. The long test period is further characterized by high availability of homogeneous observational data sets for atmospheric variables, river runoff, and ocean data of sufficient quality. The latter is used for initialization and verification. [9] Once a state-of-the-art performance for RCO is established in hindcast runs, the sensitivity of the system needs to be tested. The horizontal resolution is important to determine the volume and salinity transport through the Danish Straits. The highest resolution we can technically utilize in decade-long simulations is 2 nautical miles, which is considered eddy permitting but not eddy resolving, considering baroclinic Rossby radii of 5 7 km in the Baltic proper [Fennel et al., 1991]. We aim at examining the consequences of different resolutions for salt water inflows. Meier [2001] analyzed results of a coarse grid (6 nautical miles) version of RCO and found that the salt transport into the Bornholm Basin is underestimated. [10] Another important factor for salt water inflows is given by the river runoff to the Baltic Sea. It is thought to be efficient in controlling inflow by affecting horizontal pressure gradients. A similar role is assumed for the sea level north of the Danish Straits and the large-scale wind speed. The distribution of inflowing salinity is affected by the vertical mixing. The relative importance of these forcing and model components are assessed in this paper and compared to earlier studies using process-oriented models by Stigebrandt [1983] and Gustafsson [2000b]. [11] Summarizing, the scientific goals of this presentation are (1) to evaluate the performance of a 3-D high-resolution, coupled ice-ocean model for the Baltic Sea in decade-long integrations; (2) to explore whether increased horizontal resolution improves simulated salt water inflows into the Baltic Sea; and (3) to investigate the sensitivity of simulated salt water inflows to changes in river runoff, wind speed, and sea level in Kattegat. [12] The paper is organized as follows: In the second and third sections, the ocean and the sea ice models are described briefly. A model verification is carried out, regarding the inflow event 1993 (section 4) and the longer period, May 1980 to October 1993 (section 5). In the sixth section, sensitivity studies of the inflow event 1992/1993 are presented. The paper ends with a discussion and conclusions. 2. Ocean Circulation Model [13] From the factors listed in the introduction, it is evident that 3-D Baltic Sea modeling requires sophisticated coupled turbulence and sea ice models and atmospheric forcing of high quality to perform realistic multi-year hindcast simulations. In addition, high vertical and horizontal resolution with a corresponding short time step is essential to resolve the bottom topography and small-scale processes with impact on the large scale. Thus integrations over the longest timescale of the system, i.e., the diffusive timescale

4 29-4 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL of about years, requires a state-of-the-art super computer which was available for the Swedish Regional Climate Modeling Program, SWECLIM ( ), in the form of a CRAY T3E-600 with 272 processors at the Swedish National Supercomputer Center (NSC). When SWECLIM started in 1997, none of the available Baltic Sea models including sea ice was suitable for parallel computing. Hence, the Rossby Center Ocean Model (RCO) has been developed using the version of the Ocean Circulation Climate Advanced Modeling (OCCAM) project [Webb et al., 1997] of the Bryan-Cox-Semtner primitive equation ocean model [Bryan, 1969; Semtner, 1974; Cox, 1984] with a free surface [Killworth et al., 1991]. The OCCAM model includes improved vertical and horizontal advection schemes [Webb, 1995; Webb et al., 1998], a quadratic law for bottom friction [Cox, 1984], harmonic horizontal viscosity and diffusivity, and a third-order polynomial approximation [Bryan and Cox, 1972] for the equation of state [U.N. Educational, Scientific, and Cultural Organization, 1981]. [14] As the model domain of RCO is limited (contrary to the global OCCAM) with open boundaries in the northern Kattegat (Figure 1), open boundary conditions, as developed by Stevens [1990, 1991] for the Bryan-Cox-Semtner model, have been re-implemented. In case of inflow, temperature, and salinity values at the boundaries are nudged towards observed climatological profiles. In case of outflow, a modified Orlanski radiation condition is utilized [Orlanski, 1976]. Sea level elevations at the boundaries are prescribed from hourly tide gauge data. [15] In RCO, a two-equation turbulence closure, the k-e model [Svensson, 1978; Rodi, 1980], is embedded [Meier, 2001]. The effect of breaking surface gravity waves is taken into account, which results in a turbulence enhanced surface layer [Craig and Banner, 1994]. The k-e model is extended to include a parameterization for breaking internal waves [Stigebrandt, 1987]. Penetrating solar radiation is parameterized using two extinction lengths according to Paulson and Simpson [1977]. [16] The model depths are based on realistic bottom topography data [Seifert and Kayser, 1995] as shown in Figure 1. RCO is making use of 41 levels with layer thicknesses from 3 m close to the surface to 12 m near the bottom. The maximum depth in RCO is 250 m. The horizontal resolution is 2 nautical miles corresponding to j =2 0 (j = latitude) and l =4 0 (l = longitude). [17] Initial fields for temperature and salinity are generated for 14 basins. The borders have been chosen according to topographic features. The hydrography of each sub-basin is assumed to be homogeneous. Profiles from the Swedish Oceanographic Data Center (SHARK) at the Swedish Meteorological and Hydrological Institute (SMHI) have been selected to compile initial conditions for May 18, 1992 (test case 1) and for May 26, 1980 (test case 2). Initial sea level and current velocities have been set to zero. [18] The atmospheric forcing is developed at SMHI and based on 3-hourly gridded observations of sea level pressure, geostrophic wind components, 2-m air temperature, 2-m relative humidity, precipitation, and total cloud cover. Data from all available synoptic stations (about 700 to 800) covering the entire Baltic Sea drainage basin are interpolated on a 1 regular horizontal grid. A two-dimensional univariate optimum interpolation scheme is used. As only geostrophic wind fields are available, a boundary layer parameterization is utilized to calculate wind speeds in 10 m height [Bumke et al., 1998]. [19] Standard bulk formulae are used to calculate sea surface fluxes of momentum [Large and Pond, 1981], sensible and latent heat [Large and Pond, 1982], shortwave incoming radiation [Bodin, 1979], and longwave incoming radiation [Maykut and Church, 1973] [see Meier, 2002a, Appendix A]. The albedo for the open water surface is calculated from Fresnel s formula. [20] The haline surface boundary condition is formulated as a flux of salt including precipitation/evaporation, ice freezing/melting, and river runoff. In combination with advection, the free surface scheme conserves volume but is not strictly density conserving, due to neglecting the surface elevation in the tracer equations. This problem is described by Griffies et al. [2001]. [21] Monthly river runoff data has been taken from the Baltic Sea Experiment (BALTEX) Hydrological Data Center (BHDC) at SMHI [Bergström and Carlsson, 1994]. [22] For a more detailed model description of RCO the reader is referred to Meier et al. [1999]. The computational performance of RCO has been investigated by Meier and Faxén [2002]. The RCO code is optimized for massively parallel computer architectures with horizontal partitioning. An improved domain partitioning technique minimizes load imbalance. 3. Sea Ice Model [23] A first version of the sea ice model of the OCCAM project has been adopted and significantly modified to simulate seasonal ice in the Baltic using two levels (open water and ice). The equations of the ice model are discretized on the same Arakawa-B-grid as used for the ocean Dynamics [24] An extension of the viscous-plastic rheology [Hibler, 1979] with an elastic component [Hunke and Dukowicz, 1997] leads to a fully explicit numerical scheme that improves computational efficiency, particularly on highresolution grids, and adapts easily to parallel computer architectures. For the first time, the elastic-viscous-plastic approach is applied to the seasonal ice of the Baltic Sea. [25] The elastic-viscous-plastic rheology differs from the traditional viscous-plastic approach only by adding a term representing elastic waves of elasticity E (Young s modulus): 1 ij þ 2h s ij þ h z 4hz s kkd ij þ P 4z d ij ¼ _ ij : Here s ij (i, j = 1, 2) denotes the internal ice stress tensor, t time, d ij Kronecker delta (d ij =1ifi = j and 0 if i 6¼ j), _ ij strain rate, and z and h bulk and shear viscosity, respectively. The internal ice pressure P is a function of ice concentration A and ice thickness h, ð1þ C 1 A P ¼ P*Ahe ð Þ ; ð2þ with constants P* and C. [26] Within each time step, the dynamic component needs to be subcycled several times to damp elastic waves. As

5 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL 29-5 Table 1. Standard Parameters for the Elastic-Viscous-Plastic Ice Dynamics Used in RCO Parameter Symbol Value Young s modulus E Number of subcycles N 40 Ice strength P * Nm 2 Strength reduction constant C 20 Yield curve aspect ratio e 2 Horizontal diffusion of ice A ice m 2 s 1 described by Hunke and Zhang [1999], the elastic term initially makes a prediction for the stress s, which is then corrected toward the viscous-plastic solution by means of subcycling. By choosing the number of subcycles N, a compromise has to be made between an energetic solution that quickly adjust during rapidly changing forcing conditions (small N ) and a solution which not significantly differs from viscous-plastic on longer timescales (high N ). We found that N = 40 is a good compromise between numerical efficiency and sufficient damping of elastic waves in the Baltic Sea. [27] In addition to ice velocity, prognostic variables of the sea ice model are ice thickness, ice concentration, snow thickness, heat content of brine, surface temperature, temperature of snow, and ice layers. These quantities are transported using an upstream advection scheme. If the water depth is smaller than 10 m, motionless fast ice is assumed. Horizontal harmonic diffusion is necessary to prevent numerical instabilities but can be kept as small as given by Haapala and Leppäranta [1996]. Table 1 lists the standard parameters for the ice dynamics used in RCO. [28] According to Hibler [1979] the continuum approximation for ice models is valid if the grid size in the Arctic exceeds 50 km. As the ice floe size scale is roughly proportional to ice thickness, 50 km in the Arctic would correspond to 10 km in the Baltic. Such realistic grid sizes are supported by several other model studies [e.g., Haapala and Leppäranta, 1996; Leppäranta et al., 1998; Haapala, 2000]. The information finer than the continuum scale should not be used for result analysis. A comparison of two different grid resolutions (2 and 6 nautical miles) showed that the elastic-viscous-plastic ice rheology does not generate unrealistic small-scale features with impact on larger-scale ice variations Thermodynamics [29] The thermodynamic part is built on Semtner s layer models [Semtner, 1976]. In RCO, thick ice consists of one or two ice layers and thick snow consists of one snow layer. The reason for the discrimination between thick and thin ice/snow is numerical stability. The multi-layer ice model tends to become unstable for snow of less than 15 cm thickness and ice of less than 25 cm thickness. Ice and snow temperatures are governed by one-dimensional diffusion equations. Interfacial temperatures result from the steadiness of heat fluxes across boundaries. The zero -layer models for ice and snow are based on simple heat budgets. [30] Atmospheric surface and bottom heat fluxes are based on standard bulk formulae [see Meier, 2002a; Appendix B]. Bottom ablation and accretion are calculated from flux differences. The bottom temperature right below the ice is always given by the salinity dependent freezing point temperature according to Millero [1978]. The surface temperature can be approached by assuming it to be an equilibrium value that results if the internal snow and ice profile adjusts instantaneously to the applied forcing [see Semtner, 1976]. [31] Ten percent of the incoming solar radiation penetrates the ice [Sahlberg, 1988] and 17% is stored in brine pockets [Semtner, 1976]. Sea ice releases brine to the sea; however, some brine remains in pockets. These brine pockets are able to store heat. If the upper ice layer temperature drops below the melting point, the energy from the brine pockets is converted to force temperature back toward the melting point, thereby parameterizing release of heat through refreezing of brine pockets. In polar oceans this parameterization would lead to delayed top ice melting in summer and delayed internal cooling in fall. In the Baltic Sea, however, where no ice exists in summer, the brine pocket storage smoothes warming-cooling events. [32] Four different surface albedos are applied in RCO, according to Perovich [1996] for dry (0.7) and wet ice (0.3), and for dry (0.87) and wet snow (0.77). [33] Precipitation over ice is converted to snow. Snow is converted to snow-ice if flooding occurs. According to Saloranta [2000], negative freeboard conditions in the Baltic fast ice area last up to months. Hence, a negative freeboard of up to 5 cm is allowed. Newly formed ice volume in leads is laterally added to pre-existing ice according to the method of Harvey [1988]. 4. Test Case 1: The Inflow Event 1992/1993 [34] Horizontal advection of North Sea water over the sills of the Danish Straits and down into Bornholm and Gotland Basin is the only mechanism to renew the Baltic Sea deepwater and to improve oxygen conditions. It is essential that climate models are able to simulate these overflows without underestimating salt transports. From coarse resolution z-coordinate models of the North Atlantic it is known that they have problems with overflow if the bottom boundary layer is not resolved explicitly [e.g., Beckmann and Döscher, 1997; Döscher and Beckmann, 2000]. In the model, overflow is simulated as sequence of successive horizontal advection and vertical convection processes between neighboring grid boxes. In case of not resolving the frictional bottom boundary layer, too much of the inflowing salt water is diluted and will not reach the bottom of the basins. Therefore, the major inflow event in January 1993 is used as a model performance test. [35] Measurements of this event were described and analyzed in several publications [e.g., Håkansson et al., 1993; Matthäus et al., 1993; Jakobsen, 1995; Matthäus and Lass, 1995]. At the end of December 1992 and during the first days in January 1993, a distinct outflow situation is established with low salinities in the Danish Straits. After the onset of strong westerly winds the salinity fronts are moved towards Arkona Basin and the Belt Sea is filled up with salty water. Between January 6 and 27, a mean salinity of psu was observed at Drogden Sill [Håkansson et al., 1993]. At Darss Sill, vertical mean maximum salinities of about 22 psu were observed between January 26 and 28 [Matthäus et al., 1993].

6 29-6 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL [36] In general, the overflow is simulated correctly compared to observations from the sills. During the inflow, maximum current velocities of about 2 m s 1 are simulated. At Drogden Sill, model results between January 9 and 26 show a mean salinity of 21.5 psu (maximum 26 psu). The high-saline water arrived later at Drogden Sill than in observations and retracts temporarily on January 19 and 20. This strong outflow was not observed. Obviously, the too low salinities are caused by wrong wind forcing during the inflow period. At Darss Sill, we found the culmination at the same time as in observations with vertical mean salinities of 20.1 psu. Although during that period maximum bottom salinities were 23.5 psu (compared to only 21.5 psu in the observations), the vertical mean salinity is lower because the outflow in the surface layer occurred a few days earlier than observed causing the surface salinity to decrease. [37] Observations of Liljebladh and Stigebrandt [1996] casted between February 6 to 8 in the Arkona Basin confirm the existence of a thick bottom pool of deepwater separated by a halocline from the surface water of Baltic origin. Approaching the northern coastal boundary from the central parts of the pool, the halocline sank by about 20 m before hitting the bottom. At the end of January the amount of inflowing salty water across Drogden and Darss Sill into Arkona Basin is greater than the outflow through Bornholm Channel into Bornholm Basin. Hence the halocline in the Arkona Basin is lifted above the level of Darss Sill by the end of the inflow event [Matthäus and Lass, 1995]. On January 29 again outflow occurred. [38] In RCO, we found also a thick bottom pool of salty water in the Arkona Basin. Simulated maximum bottom salinities at Arkona Deep are 22.1 psu (Figure 2a), somewhat lower than in observations (about psu). The inflowing water sank to the bottom of Bornholm Basin penetrating below the old bottom water which was lifted up (Figure 2b). [39] Figure 3 shows a cross section of salinity from Fehmarn Belt to Stolpe Channel through Arkona and Figure 3. Cross section of salinity (in psu) from Fehmarn Belt to Stolpe Channel through Arkona and Bornholm Basin, casted between February 14 and 17, 1993: (a) observations, (b) k-e model, and (c) k model (see Meier [2001]). Contour interval: 1 psu. The position of the section is depicted in Figure 1. Figure 2. Simulated isohaline depths (in psu) from May 18, 1992, to June 21, 1993, at (a) Arkona Deep (BY2) and (b) Bornholm Deep (BY5). Contour interval: 1 psu. Positions of BY2 and BY5 are depicted in Figure 1. Bornholm Basin between February 14 and 17, Simulated maximum bottom salinities at Bornholm Deep are 18 psu whereas psu were observed. The model underestimates the amount of saline water flowing downslope from the Arkona to the Bornholm Basin (Figure 3b). Strikingly good is the correspondence between model results and data concerning the gradients across the halocline. The halocline is very sharp in Arkona Basin separating the 10-m-thick bottom pool from the surface water, whereas in Bornholm Basin the observed (simulated) gradient is much smoother with the 9 psu isoline in 50 m depth and the 18 psu isoline in 80 m (90 m) depth. [40] From observations [e.g., Jacobsen, 1980; Fischer and Matthäus, 1996; Jakobsen and Trébuchet, 2000] and from high-resolution model studies [e.g., Meier, 1996], it is believed that the Öresund to Belt Sea volume flow ratio amounts to about 3:8, on average. Mattsson [1996] argued that the ratio is only 2:8. In RCO, during the inflow event in January 1993 the volume flow ratio between Öresund and Belt Sea amounts to 3:7 (Table 2). Even more unknown than the volume flow ratio is the ratio of salt transports, on

7 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL 29-7 Table 2. Comparison of Inflow Volumes in January 1993 at Great Belt (GB), Öresund (S), Little Belt (LB), and Over Both Sills, i.e., Darss Sill and Öresund (DS + S) a V d,km 3 V,km 3 References GB S LB DS + S GB S LB Sum Håkansson et al. [1993] Matthäus et al. [1993] Matthäus and Lass [1995] 135 Jakobsen [1995] b Gustafsson [2000b] This paper a V d is calculated during the inflow period with S > 17 psu for Darss Sill and Öresund and with S > 24.5 psu for Great Belt and Little Belt [cf. Jakobsen, 1995]. V is calculated during precursory and inflow period (S > 0). b The estimate of total inflowing high-saline water into the Arkona Basin of 154 km 3 given by Jakobsen [1995] is discussed in section 7. average. Meier [1996] has calculated the salt transport ratio for the inflow event in January 1993 to 4:3. However, as pointed out by Fischer and Matthäus [1996], far more salt enters the Baltic across Darss Sill, usually. They used longterm observations of salinity and currents made at lightships Gedser Rev and Drogden in the sill areas to evaluate the role of the Drogden Sill in major inflow events between 1897 and Fischer and Matthäus [1996] found that the inflow 1993 is an exception, with equally large salt transports across Darss and Drogden Sill. [41] Although the question about volume and salt flow ratios is not solved yet, it is evident from the earlier studies that the role of the Öresund with respect to water exchange on climatological timescales might be as important as the Great Belt. On the other hand the topography in the Öresund is not well resolved within RCO. Hence the validation of volume flows through the Öresund is important. RCO volume flows are compared with results from the simplified model presented by Håkansson [2003] (Figure 4). Using sea level data from the two tide gauge stations, Viken (x V ) and Klagshamn (x K ), located in the northern and southern entrance of the Öresund, respectively, and assuming a balance between along-strait pressure gradient and bottom friction term within the vertically integrated momentum equation, the following expression for the volume transport Q M can be derived easily: [42] The agreement between RCO volume flows (Q RCO ) and transports calculated with equation (3), the observed volume flows (Q M ), is good (Figure 4). The scatterplot (Figure 4b) shows that in the range m 3 s 1 < Q M m 3 s 1 the transports Q RCO and Q M are well correlated. However, large transports (e.g., the inflow event in January 1993 between days 234 and 255; see Figure 4a) are overestimated. For the whole period, the correlation coefficient is 0.93 and the explained variance is Håkansson [2003] used observed volume flows obtained from ADCP ship-borne transects from the period August and October 1993 [Håkansson, 2003, Figure 4] and a number of moored Aanderaa current meters across the sill area between July 1 and August 24, 1993 [Håkansson, 2003, Figure 5] to calibrate and validate volume flows Q M. His data are limited within the range m 3 s 1 < Q M m 3 s 1 as shown by the two dashed lines in Figure 4b. Thus it is not possible at this time to decide whether RCO indeed overestimates large transports through the Öresund or whether the formula of the simplified model (equation (3)) cannot be used in case of large transports. Further investigations are necessary. 5. Test Case 2: The Stagnation Period [43] A 13-year high-resolution model run (HR0) has been carried out to examine long-term behavior of sea level, sea ice, temperature, and salinity Sea Level [44] RCO sea levels are compared with observations at 17 Swedish and Finnish tide gauge stations. The raw data are qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Q M ¼ K jx 0 K x V j x 0 K x V jx 0 K x V j ; ð3þ with the resistance coefficient K = 74,770 m 5/2 s 1 according to Håkansson [2003]. Håkansson [2003] pointed out that the sea level difference between Viken and Klagshamn in case of inflow has to be corrected because Klagshamn station is located south of the sill in a harbor with a 500-mlong pier to the south, which can have profound influence on the local sea level by blocking the current, resulting in a pressure head and therefore a local rise in the sea level. His correction technique is based on the following equation: x 0 K x V ¼ ð1 þ dgþ x 0 K x V ; ð4þ with d =1ifx K < x V, d =0ifx K x V and g = Figure 4. (a) Time records of hourly simulated volume flows through the Öresund (in m 3 s 1 ) obtained from RCO (solid line) and from the simplified model ( observed transports, dotted line) between August 25, 1992, and January 22, Outflow is counted positive. Both records are low-pass filtered to eliminate the semi-diurnal and diurnal tides. (b) Scatterplot of modeled versus observed volume flows for the whole integration period (May 1992 to June 1993). The straight solid line indicates perfect agreement and the vertical dotted lines show the range of available observed volume flows obtained from ADCP ship-borne transects across the sill area used by Håkansson [2003] to calibrate the simplified model.

8 29-8 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL Figure 5. Mean sea surface height (in cm) for the period June 1980 to May The numbers at selected tide gauge positions indicate model results (left) and geodetic solutions with geostrophic closure of Ekman and Mäkinen [1996] (right). Contour interval: 2 cm. detrended to account for the land uplift in Northern Europe using correction rates from Ekman [1996]. Thereafter, the local mean values are replaced by the geodetic values from the Nordic Height system (NH60) according to Ekman and Mäkinen [1996]. This procedure makes simulation results comparable to observations, as the model is forced according to NH60 conventions at its open boundary. Figure 5 shows simulated 12-year mean sea surface height for the HR0 run. The mean sea level increases from Kattegat to the Gulf of Bothnia and to the Gulf of Finland with about 25 to 35 cm. The slope is caused by freshwater supply of rivers located mainly in the northern and eastern parts of the Baltic Sea. In addition, the mean wind speed from the southwest direction contributes to the slope of the sea surface height. We found a mean wind speed of 2 m s 1 at the station Landsort, which is in agreement with observations. There is also a general agreement between mean sea surface height in RCO and the geodetic results of Ekman and Mäkinen [1996]. However, mean sea levels in RCO are always higher. The largest differences occur in the Gulf of Finland. The reason might be an overestimation of the mean wind speed in the atmospheric data set. As the spreading of juvenile fresh water is expected on the northern and western side of the Gotland Basin, the higher simulated sea level at the eastern coast might also be caused by the too strong mean wind from the southwest direction. In addition, our test period might be not representative for the long-term mean sea levels as calculated by Ekman and Mäkinen [1996]. [45] Time series comparisons are carried out for two different time ranges: A short comparison period covers the time between May 18, 1992, and September 30, A long comparison period covers May 26, 1980, to October 26, Even sub-intervals of long runs are used for the comparison with short runs (e.g., HR0short is a part of HR0long). In addition, we show results from the only short, high-resolution run HR2 (2 nautical miles, initial conditions for May 18, 1992; see section 4) and from a low-resolution run LR1 (6 nautical miles). Basic statistics based on twodaily values are given in Table 3. The uncertainty of leveling is proportional to the square root of the distance between two stations. For example, the mean sea level difference between Varberg in Sweden and Frederikshavn in Denmark is 9.7 cm with an error of 2.5 cm (M. Ekman, personal communication, 1994). To reduce the uncertainty between Stockholm and the Åland Islands, Ekman and Mäkinen [1996] estimated a mean current between the two positions and assumed geostrophy. In Table 3, RCO results are compared to both data sets, pure geodetic (gd) and geodetic with geostrophic closure (st). In the following, we refer to the first solution because the correction based on the geostrophy assumption might be overestimated. Mean errors of all stations range from 1.7 to 2.9 cm. The rms errors are below 10 cm for most of the cases. [46] The sea level at Landsort during the inflow period 1992/1993 is shown as an example showing generally good agreement (Figure 6a). Using coarse resolution lowers the mean sea level by about 3 cm (for the long case LR1 compared to HR0). In addition, coarse resolution worsens rms errors. They are generally 1 2 cm higher. We found that this is due to too large transports through the Danish Straits, leading to increased efficiency of wind events. [47] The seasonal variability of sea level height is reproduced very well as seen in a time series of 2-monthly averages for all stations (Figure 6b). Contrary to observations, the model shows a small downward trend ( 0.5 cm y 1 ). Possible reasons are a trend in the wind forcing or a trend in the river runoff forcing. These forcing issues cannot be resolved in this paper and thus need more exploration. [48] A comprehensive verification of modeled sea level heights is given by a coherence analysis. The average squared coherency (Figure 6c) is a measure of frequencydependent correlation. For long periods, the coherence curve approaches unity (0.95 for a period of 64 days). Coherency decreases monotonously with shorter periods (0.78 for a period of 4.2 days). A correlation of 0.9 is reached at about 20 days. Even the low-resolution case Table 3. Basic Statistics for Short (May 18, 1992, to September 30, 1993) and Long (May 26, 1980, to October 26, 1993) Comparison Periods Based on Two-Daily Sea Levels a Run RMSE (gd) RMSE (st) ME (gd) ME (st) Mean RCO Mean Data(gd) Mean Data(st) LR1long LR1short HR0long HR0short HR2short a Here gd is pure geodetic results; st is geodetic results with geostrophic closure; ME is mean error; and RMSE is root mean square error). Units are centimeters.

9 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL 29-9 Figure 6. (a) Hourly sea level at Landsort, (b) two-monthly average sea level for all stations, and (c) average squared coherency of long simulations LR1 and HR0. (LR1, dotted curve in Figure 6b) shows good correlation (0.75 for a period of 4.2 days; 0.9 is reached at about 30 days). Only the very short periods less than 4.2 days show distinctly reduced skill with correlations of about Sea Ice [49] Figure 7a shows simulated total ice coverage with an ice concentration larger than 0.1 compared with observed maximum ice extent regularly published by the Finnish Institute of Marine Research (FIMR). In addition, calibrated gridded weekly ice area data from the USA National Ice Center (NIC) are depicted [cf. Schrum et al., 2000]. Table 4 lists the errors compared to these calibrated observations. The two data sets, from FIMR and from NIC, have been compared by Schrum et al. [2000]. The errors of their Hamburg Shelf Ocean Model, HAMSOM, and the errors of RCO are smaller than the discrepancy between the data sets. In RCO, the ice cover during mild winters is generally overestimated. [50] In Figures 7b and 7c, simulated ice and snow thickness results are compared to measurements at the coastal station Kemi-Ajos in the northern Bothnian Bay (Figure 1). The agreement is regarded as very good, although the ice thickness is overestimated during the severe winter 1985/1986. During some mild winters the ice thickness is also underestimated slightly. However, one has to keep in mind that the utilized snow-ice model is quite simple. Ice and snow thicknesses are very sensitive against changes in the snow-ice model. [51] In the low-resolution run, LR1, the ice-covered area is generally larger, with thicker ice and more snow on top compared to HR0. The ice extent bias is significantly larger (Table 4). Especially during mild winters, ice concentration in the high-resolution run, HR0, is lower compared to LR1. This behavior is more realistic compared to available SSM/I data (not shown). As the lead parameterization depends on the time step and as the fraction of newly formed ice depends on the area of the model grid box, differences of the ice model response for different resolutions are expected Temperature and Salinity Profiles [52] In Figure 8, median profiles of temperature and salinity for the period May 1980 to October 1993 are shown at four stations. These are from north to south SR5 in the Bothnian Sea, BY15 (Gotland Deep) in the eastern Gotland Basin, BY5 (Bornholm Deep) in the Bornholm Basin, and BY2 (Arkona Deep) in the Arkona Basin. The model profiles have been selected at the same dates as the data to avoid a comparison between high-frequent model results and undersampled observations in time. There is good agreement between observations and model results. The mean temperature profiles and their variability are simulated quite well. Thus it is concluded that the seasonal heat cycle is captured by the model. However, the depth of the summer thermocline is slightly underestimated as can be seen from the third quartiles. [53] The model reproduces salinity gradients from north to south as well as from the surface to the bottom. The halocline is not eroded during the almost 5000-day-long integration. However, a problem with overflow is visible in results from Bornholm Basin. The continuous inflow of salt water from the Kattegat into the Baltic Sea is underestimated, resulting in a too strong decrease of bottom layer salinity. The deficiencies are significant after about 3 years of integration, i.e., the renewal timescale of the Bornholm

10 29-10 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL Table 4. Model Errors of Weekly Mean Ice Cover for the Period for Two RCO Versions With Different Horizontal Resolution, LR1 and HR0 a Run ME, 10 9 m 2 RMSE, 10 9 m 2 R VAR LR HR HAMSOM a For comparison, the results of HAMSOM for [Schrum et al., 2000] are tabulated. ME is mean error; RMSE is root mean square error; R is the correlation coefficient; and VAR is explained variance. the Bothnian Sea compared to observations and compared to LR1 [Meier, 2001, Figure 7]. Figure 7. (a) Simulated ice covered area (in 10 9 m 2 )for the period July 1980 to June 1993 (run HR0, solid line). The Baltic Sea area (including Kattegat) of about m 2 is shown as dashed horizontal line. Squares denote observed maximum ice extent. The dashed line denotes calibrated data [Schrum et al., 2000]. The difference between model results and observations is shown on top of the panel. (b) Simulated ice thickness (in cm) and (c) snow thickness (in cm) at the monitoring station Kemi-Ajos (run HR0, solid line). Plus signs denote observations from Finnish Marine Research [1982], Kalliosaari and Seinä [1987], Seinä and Kalliosaari [1991], Seinä and Peltola [1991] and Seinä et al. [1996]. For winter 1990/1991 data are not available for this paper. The position of Kemi-Ajos in the Bothnian Bay is shown in Figure 1. Basin. Therefore the median of the simulated bottom salinity is about 2 psu smaller than observed. [54] In Table 5, the errors of the simulated median temperature and salinity profiles at five stations are listed and compared with results of the PROBE-Baltic model [Omstedt and Axell, 1998]. For temperature, we found the smallest rms errors using the coarser grid version of RCO (LR1). In all three model runs, the largest error occurs at Bornholm Deep (BY5). [55] For salinity, the best results are obtained with HR0 at Arkona Deep (BY2), with PROBE-Baltic or HR0 at Bornholm Deep (BY5) and at Gotland Deep (BY15), and with all three model runs at Landsort Deep (BY31). The worst results are obtained using PROBE-Baltic at BY2 and LR1 at BY5. HR0 shows somewhat fresher conditions in 6. Sensitivity Studies of the Inflow Event 1992/1993 [56] In this section, sensitivity studies of the inflow event in January 1993 using the high-resolution version of RCO are presented. The reference run is HR2 (see section 4). [57] The volume transport through the Danish Straits is a function of the sea level difference between the Kattegat and the western Baltic Sea. For the Öresund, the relationship is given by equation (3), approximately. Thus salt water inflows are quite sensitive to changes of the sea level amplitude prescribed at the open boundary in Kattegat. We found that no salt water inflow occurs if sea level observations from another than the inflow period 1992/1993 are used. In addition, the wind field over the Baltic Sea is important because the sea level variations in the Kattegat and in the western Baltic Sea must be out-of-phase to make inflow possible. In a sensitivity experiment, this dependency on the local wind has been tested. The wind forcing has been replaced by the wind fields of the winter 1991/1992. Although the sea level at the open boundary is prescribed as in the reference experiment, no salt water inflow occurs in January 1993 because the sea level differences are too small to allow an inflow event. [58] Although salt water inflows occur on longer time scales of about days, barotropic waves modify the salt transports. During the main inflow period, the Öresund transport shows large oscillations (even reversals, see Figure 4) having an impact on mixing and on the total salt transport. [59] The impact of freshwater supply, wind, and mixing on the inflow of salt is quantified by calculating the volumes of high-saline water (S > 17 psu) in the Arkona and in the Bornholm Basin (Figure 9). In the Arkona Basin, two maxima are visible on January 26 (137 km 3 ) and on March 25 (31 km 3 ). The latter results from another smaller inflow event which had no impact on the Bornholm Basin deepwater. After April 24, almost no high-saline water (<2 km 3 ) is left in Arkona Basin. In the Bornholm Basin, the volume of high-saline water increased rapidly between January 26 and February 21 to the maximum of 34 km 3. Thereafter, a slow decrease due to mixing is calculated. Only 25% of the high-saline water in the Arkona Basin is advected into the Figure 8. (opposite) Observed (solid line) and simulated (dotted line) median, first and third quartile profiles for temperature (in C) and salinity (in psu) for the period May 1980 to October 1993 at (a, b) SR5, (c, d) BY15, (e, f ) BY5, and (g, h) BY2. Positions are shown in Figure 1.

11 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL 29-11

12 29-12 MEIER ET AL.: MULTIPROCESSOR BALTIC SEA MODEL Table 5. Model Errors of Median Temperature (T ) and Salinity (S) Profiles for the Period for Two RCO Versions With Different Horizontal Resolution, LR1 and HR0, and for the PROBE-Baltic Model, PRO [Omstedt and Axell, 1998] a ME T, C RMSE T, C ME S, psu RMSE S, psu Basin LR1 HR0 PRO LR1 HR0 PRO LR1 HR0 PRO LR1 HR0 PRO BY BY BY BY SR a ME is mean error, and RMSE is root mean square error. Bornholm Basin. The remainder leaves the Baltic Sea through the Danish Straits. [60] As an annual mean runoff range of 27% to +22% is observed, we performed sensitivity experiments with increased and reduced runoff of ±22%. In the Arkona Basin (Bornholm Basin), the volume of high-saline water is then reduced by 10% (47%) or increased by 8% (44%) at the maximum (Figure 9). Thus the model response is slightly asymmetric. The relative impact of changing runoff is larger for the Bornholm Basin than for the Arkona Basin. [61] In earlier publications, the impact of river regulation on the decreased frequency of major inflows as observed during the 1980s and 1990s was discussed [e.g., Matthäus and Schinke, 1999]. River regulation redistributes runoff between summer and winter months and gives rise to higher values during autumn and winter. Rödel [2001] calculated that compared to the period before 1970, today about 7 8% more freshwater enters the Baltic Sea between November and January due to river regulation. Between January and March 1993, he found an increase of even 9% compared to the unregulated case. However, the impact on the major inflow in January 1993 is relatively small. Assuming unregulated runoff according to Rödel [2001], the volume of high-saline water in the Arkona Basin (Bornholm Basin) increased by only 2% (12%) (Figure 9). [62] Twenty percent increased wind speeds cause higher inflow into the Arkona Basin but show almost no impact on the Bornholm Basin because also vertical mixing has increased in the Arkona Basin (Figure 9). This is in agreement with results by Gustafsson [2000b]. [63] In the experiment with 22% increased runoff, the annual mean sea surface height is increased compared to the reference run by about cm in the Baltic proper and by about 1 cm in the coastal areas of the northern and northwestern Gulf of Bothnia and of the northeastern Gulf of Finland (Figure 10). The signal of the additional freshwater input propagates as internal Kelvin waves from the river mouths in the Gulf of Bothnia and in the Gulf of Finland along the coasts in anti-clockwise direction. The transport path of juvenile freshwater is illustrated by the largest sea level rises in Figure 10. The outflowing surface layer water from the Baltic Sea is psu lower after Figure 9. Volume (in km 3 ) of high-saline water (S > 17 psu) in (left) Arkona Basin and (right) Bornholm Basin: reference run with k-e model and observed runoff (solid line), 22% increased runoff (dash-dotted line), 22% reduced runoff (long-dashed line), unregulated runoff (short-dashed line), 20% increased wind speed (dotted line), and k model with observed runoff (dash-triple-dotted line). The time axis starts on January 2, 1993.