Validation of Two Numerical Ocean Models in Skagerrak

UNIVERSITY OF GOTHENBURG Department of Earth Sciences Geovetarcentrum/Earth Science Centre

Validation of two

Numerical Ocean

Lisa Svenningsson

ISSN 1400-3821 B712 Master of Science (Two Years) thesis Göteborg 2012

Mailing address Address Telephone Telefax Geovetarcentrum Geovetarcentrum Geovetarcentrum 031-786 19 56 031-786 19 86 Göteborg University S 405 30 Göteborg Guldhedsgatan 5A S-405 30 Göteborg SWEDEN Abstract A new high-resolution numerical ocean model over the north eastern Skagerrak region, with focus on the Koster fjord national park area, is in need of lateral boundary conditions. The suitability of two different 30-year long model simulations have been examined, namely (i) the BaltiX model, a NEMO based configuration developed by SMHI (Swedish Meteorological and Hydrological Institute, Norrköping, Sweden) and (ii) a MIKE3 model by DHI (Danish Hydrological Institution). A comparison of these two models to observational data is made to see which one of these models is more suitable as outer boundary condition for the high-resolution model. Since the salinity field is known to be of great importance for the dynamics in Skagerrak, this study has been focused on the vertical salinity distribution. The conclusions are that both models have lower salinity than observations but apart from that seem to capture many characteristics of the observed stratification. The surface variability of both models also seems reasonable in comparison to statistics of observations. The BaltiX model suffers from low salinity in the entire water column in the beginning of the year and seems to have some problem transporting the low salinity surface water out from Skagerrak during the later months of the year. MIKE3 is the stronger candidate of the two in most comparisons but still has extensive problems with low salinity. It is also shown that both models simulate less concentrated costal currents than the observed.

Table of Contents 1. Introduction ...... 3 1.1 Study area ...... 3 1.1.1 Hydrography ...... 4 2. Models and Validation Methods ...... 4 2.1 BaltiX ...... 4 2.2 MIKE3 by DHI ...... 6 2.3 Validation method ...... 6 2.3.1 Salinity profiles ...... 8 2.3.2 Freshwater height ...... 8 2.3.3 Single depth qualitative comparison ...... 8 2.2.4 Short-term variability comparison ...... 8 3. Results ...... 9 3.1 Salinity profiles ...... 9 3.2 Freshwater height ...... 9 3.3 Single depth qualitative comparison ...... 10 3.4 Comparison of short-term variability between the models ...... 12 4. Discussion ...... 13 5. Conclusions ...... 14 6. Acknowledgement ...... 14 7. References ...... 15 Appendix I ...... 16 Appendix II ...... 21

1. Introduction The Skagerrak and the North Sea region is a well investigated area and has for a long time been the subject of many marine research projects in the surrounding countries. Several numerical oceanographic model studies have been carried out over the North Sea area and the Skagerrak region throughout the years. A new high-resolved three-dimensional model study is now planned for the north eastern Skagerrak region with focus on the Koster fjord national park area. The purpose is to study the spreading of free floating particles such as cod fish eggs and planktonic larvae. Since mesoscale structures is known to influence the dispersion pattern of free floating substances, a resolution high enough to resolve eddy structures is important (Albretsen and Røed 2010).

Several comprehensive validation projects of model simulations have been carried out in the Skagerrak area over the years. Some examples of such are the Skagex project 1995 (Svendsen et al. 1995), a validation experiment by Gothenburg university in collaboration with the Swedish environmental protection agency, the Norwegian metrological institute and Danish hydraulic institute (Gustafsson et al. 2000) and a comprehensive investigation concerning simulations of mesoscale structures by Albretsen and Røed, 2010. The most interesting of these, regarding particle dispersion, is the latter since it focuses on finding a resolution sufficient to capture mesoscale structures, such as eddies, current jets and filaments to simulate pathways and the statistical properties of the observed patterns.

A numerical oceanographic model, like the one planned for the Koster fjord national park area, is in need of forcing data on the open boundaries. For this, two 30-year long model simulations with a computational domain over the North and the Baltic Sea are available. The BaltiX model, a NEMO based configuration developed by SMHI (Swedish Meteorological and Hydrological Institute, Norrköping, Sweden) and a MIKE3 model by DHI (Danish hydrological institution). The computational domain as well as the area of the high-resolved model is shown in figure 1. To investigate which one of these models is best suitable to use as forcing in the highly-resolved model, they need to be evaluated in comparison to observations.

The aim of this thesis is to compile existing knowledge and data of the hydrographic conditions in the Skagerrak area and to examine model runs from BaltiX and MIKE3 which are candidates for being used as outer boundary forcing datasets for the high-resolved study.

1.1 Study area The Skagerrak is located between the Danish Jutland, the Swedish west coast and southern Norway. In the south it borders to Kattegat, which in turn is connected to the Baltic Sea through the Danish straits. In the west, Skagerrak has a border to the North Sea (see figure 1). The basin is significantly deeper than the surrounding areas with a maximum depth of approximately 700m and an average depth of 210m (Rodhe, 1996). The most prominent topographical feature of the region is the Norwegian trench outside the southern tip of Norway. A more detailed description of the topographical features of Skagerrak is given by Rodhe (1996).

1.1.1 Hydrography The main characteristic feature of the hydrography of Skagerrak is a strong salinity stratification in the upper layer. The fresh surface layer, 10-100m deep, with salinity <35 psu, is a result of three freshwater sources. The most important of them is the north bound Baltic current of low saline water flowing northwards along the Swedish cost carrying about 15 000 m3s-1 of freshwater from the Baltic Sea (Svansson, 1975). The other two freshwater sources are river run-off from local areas, corresponding to 2 500 m3s-1, and low salinity surface water from the southern North Sea that circles the basin (Rydberg et al. 1996). The low salinity in the southern North Sea is due to continental river discharge. The circulation of the upper freshwater influenced layer has a mean cyclonic movement, as does the deep water. The volume of the low-salinity flow from Kattegat has been shown to increase about eight times before leaving the Skagerrak along the Norwegian coast. This illustrates how important the diapycnal mixing mechanism is for the characteristics of the freshwater influenced layer in the Skagerrak (Gustafsson and Stigebrant, 1996).

The northbound Baltic current that follows the Swedish west coast is, as mentioned, the main reason for the strong salinity stratification in the Skagerrak. The current emerges at the Skagerrak-Kattegat front where a strong easterly-westerly current often can be detected along the southern side of the front and this current continues northward along the Swedish coast. When turning westward in the north-eastern corner of Skagerrak, the Baltic current change name to the Norwegian coastal current. In the north-eastern corner of Skagerrak there is substantial accumulation of low salinity water. The main reason for this accumulation is the large amount of freshwater run-off in the Oslo fjord area. The high run-off results in an even stronger stratification in this part of Skagerrak compared to the rest of the region and an additional shallow surface layer is often found here. The low salinity water also becomes trapped in a large scale eddy, probably due to topographical effects, which causes the extensive accumulation of low salinity water in this area (Gustafsson and Stigebrant, 1996).

Another major current in the Skagerrak is the Jutland current which flows along the Danish coast. The Jutland current carries water from the North Sea in to the Skagerrak Basin. It consists of two water masses, dense water of North Sea or Atlantic origin that forms the deep water of Skagerrak and water of lower salinities from the southern North Sea. The water with lower salinity flows close to the Danish coasts while the denser water mass follows a steeper slope further offshore. Outside the Danish coast the high saline inflow occasionally extends all the way up to the surface (Rydberg, et al. 1996). This dense inflow of water from the North Sea or the Atlantic Ocean is the origin of the largest body of water residing in the Skagerrak, the Deep water (Rodhe, 1987).

2. Models and Validation Methods

2.1 BaltiX The BaltiX model is a NEMO based configuration for the Baltic Sea and North Sea developed by SMHI. NEMO (Nucleus for European Modeling of the Ocean) is a modeling framework for oceanographic research consisting of several modules providing numerical solutions for the physics of ocean, sea- ice, tracers and biochemistry. It is a free software available under the CeCILL license. For more details about the configuration and NEMO ocean engine, see Hordoir et al. (2012) and NEMO official webpage (http://www.nemo-ocean.eu/).

The BaltiX has a grid with 52 vertical levels. The vertical resolution is 2 m close to the surface decreasing to 16 m at the bottom level. The horizontal resolution is 2 nautical miles. The model computational domain covers the entire Baltic Sea, the English Channel and the North Sea with open boundaries in the English Channel and between the north-eastern tip of Scotland and the Norwegian west coast (see figure 1). Along these boundaries temperature, salinity and baroclinic velocities are prescribed by the World Ocean Atlas by Levitus, 1994. The barotropic velocities at the open boundary are given by the Oregon State University Tidal Inversion Model, a global model of ocean tides defining both sea surface height and velocities. The atmospheric forcing is generated by the RCA3 model (Rossby Centre Regional Climate model) (Hordoir et al. 2012).

River run-off is based on climatological monthly means. Here different databases have been used for The Baltic Sea and the North Sea. The climatological data and outflows for the Skagerrak region are presented in figure 13 and table 5 in Appendix II.

The temporal resolution of the output data generated by the model is every third hour but for this study only daily output has been available.

Figure 1. Map over the North Sea, Skagerrak and Baltic Sea. The red line marks the area of the new high-resolved model. The blue area shows the computational domain of BaltiX and MIKE3. 2.2 MIKE3 by DHI MIKE3 by DHI is commercial software and therefore detailed information about the forcing is not publicly available. However, for this study access to some information of the forcing has been provided though a DHI internal report; Baltic Sea - North Sea Transition Area. Environmental Status Year 2004 (2006).

The MIKE3 hydrodynamic module calculates the flow velocities, surface elevations, salinities and water temperatures as well as dispersion properties. It has a Cartesian grid with a horizontal resolution of 3 nautical miles and a vertical resolution of 2 m. The computational domain is the same as for the BaltiX model, including the entire Baltic Sea and the North Sea with a transition area between Stavanger (Norway) and Scotland and another open boundary in the English Channel (see figure 1). The forcing consists of tides, corrected for atmospheric pressure effects, and climatological values of temperature and salinity at the open boundaries. For the run-off, actual monthly values are used for Danish streams and German rivers discharging into the North Sea. For the remaining rivers climatological data are used. Atmospheric forcing consists of hourly wind, air pressure and temperature fields of actual values and climatological values of net precipitation.

The temporal resolution of the output data generated by the MIKE3 model is hourly.

2.3 Validation method Since the salinity field is known to be of great importance for the dynamics in the Skagerrak the focus in this model validation has been on the vertical salinity distribution. For the new high-resolved model the most important currents are the Baltic current and the Norwegian costal current which flows perpendicular through the open boundaries of the model. The freshwater supply by Glomma river is also of great importance for the circulation in the area. Based on these criteria, 13 observation stations with good temporal resolution have been selected for analysis. Six of these stations constitutes a transect outside Smögen, Swedish west coast. Another transect outside Arendal, Norway, contains five stations and two stations are located outside the mouth of the Oslo fjord (see figure 2).

The hydrographic observational data are from the database of the International Council for the Exploration of the Sea (ICES). The ICES database is a publically available database mainly consisting of spatially and temporally irregularly distributed CTD casts. The observed hydrographic data used for validation has the depth resolution 0, 5, 10, 20, 30, 50, 76 and 101 meters. Based on the availability of hydrographic ICES data in the area of interest the year 2000 was chosen as the validation period.

Figure 2. Map of the Skagerrak region. The Koster fjord national park area, the area of interest for the new high-resolved model, is marked with a dashed rectangle. The other boundaries for the high-resolved model are marked with a darker gray line. All observation locations are marked with red markers and station names.

2.3.1 Salinity profiles Simultaneously observed and modeled vertical salinity profiles have been assembled for the selected observation locations. As a further validation tool, monthly means and standard deviations computed from a 10-20 year period of monthly observations have been plotted together with the instantaneously observed and modeled vertical salinity profiles.

2.3.2 Freshwater height Freshwater height is a frequently used integrated measure of the salinity stratification that has been used in several validation studies of numerical ocean models, for example Gustafsson et. al 2000. Gustafsson (1999) also states that “Being proportional to the total buoyancy of the surface water (if temperature effects are neglected) the amount of freshwater in a vertical column is a variable of great dynamic significance in the Skagerrak”.

Freshwater height, F, is defined by Gustafsson (1999) as

∫

Where S0 is the reference salinity defined by the salinity below the freshwater influenced layer, S(z) is the salinity at the depth z and D is the maximum integration depth; in this case 85 m. Negative contributions to the freshwater height is avoided by setting the minimum value to zero.

2.3.3 Single depth qualitative comparison The single depth qualitative comparison has been made to investigate if the models may have a time bias in the response to the metrological forcing. The differences in simultaneously observed and modeled salinity, and observed and modeled salinity with 48 and 96 hour forward displacement in time respectively are compared. In addition it also gives a compact result containing information about how well each model simulates the salinity at a given depth. This has been done for the depths, 5, 10, 30 and 76 m for all selected stations and observation occasion. The results have been assembled in four graphs for each model.

2.2.4 Short-term variability comparison The variability of the salinity and the stratification is important when modeling areas with fjords and archipelagos. If the model doss not simulates fluctuations in salinity of reasonable magnitude it will not be able to simulate water exchanges in basins and fjords in the right way.

The short-term variability comparison will reveal how well the two models correlate and respond to climatological forcing in relation to each other. By viewing some sparse observations from a corresponding location and depth together with time series from both models we will also see how the models relate to these.

Some basic statistics of the time series and of observations made during a 5-20 years period at the corresponding location has also been computed. The computed standard deviation will expose if the magnitude of the fluctuations in the surface layers simulated by the models is reasonable.

3. Results

3.1 Salinity profiles The salinity stratification in Skagerrak is, as mentioned earlier, of great importance to the dynamics of the baroclinic circulation in the area. For the high-resolved model to be able to capture the nature of mesoscale structures and water exchanges it is essential for the forcing dataset to give a fairly correct representation of the vertical stratification. Vertical salinity profiles of both models plotted together with observations are shown in Appendix I.

Both models can be seen to generally give a considerably lower salinity than observed at all depths and throughout the entire year, BaltiX even more so than MIKE3. However both models capture the main characteristics of the observed stratifications at many occasions, this occurs more often for MIKE3. In the beginning of the year, January to March, BaltiX suffers from very low salinity through the entire water column while MIKE3 mostly has problems with the deep water during this period. This can be seen most clearly in the profiles of the A transect outside Arendal, Norway (figure 6 Appendix I). From April to October both models often give a fair representation of the shape of the stratification even if they suffer from low salinity in comparison with observations. Towards the end of the year, November and December, the BaltiX model often simulates a deep reaching low salinity surface layer which is not consistent with the observed stratification. During this period MIKE3 gives much too low salinity values but captures the characteristics of the stratification in a better way.

Outside the mouth of the Oslo fjord, at stations G1 and G2, the MIKE3 model gives quite an impressive resemblance with the observations. The BaltiX model also gives a fair representation on the stratification at these locations, except during the first three months of the year, when the salt content is much too low, (see figure 12 Appendix I).

3.2 Freshwater height A yearly mean of the freshwater height calculated from the vertical salinity profiles presented above and in Appendix I are shown in table 1. The freshwater height verifies the discrepancies seen in the salinity profiles. Both model shows significantly larger freshwater height than the observations.

The Baltix model has an annual total mean freshwater height of 8.43 m which is more than double compared to 3.81 m in the observed profiles. The freshwater height of MIKE3 is 6.77 m, which is also much higher than what is observed. For the observed profiles we also see that the freshwater height decreases with the distance from the coast in the two transects, Arendal and Smögen (see figure 2) much faster than for the two models. This indicates that the modeled coastal currents are more wide-ranging than the observed which flows more concentrated along the coast (see table 1).

A1 A2 A3 A4 A5 S6 S5 S4 S3 S2 S1 G1 Total ● ● Observed 3.81 5,37 4,67 3,58 2,68 2,47 4,82 3,83 3,07 2,30 2,71 1,78 8,46 BaltiX 8.43 8,06 7,96 7,47 6,83 6,42 8,73 9,70 9,41 8,91 9,16 8,90 9,62 MIKE3 6.77 6,39 6,45 6,64 5,92 5,62 7,88 7,42 6,97 6,81 6,87 6,86 7,45 Table 1. Freshwater height, a yearly mean for each station analyzed through salinity profiles, see figure 6-12 in appendix I (except G2). A black point marks the stations closest to the coast in both transect, Arendal and Smögen, see figure 2. The total means for each model and observations are shown in the column to the far right in the table.

3.3 Single depth qualitative comparison By comparing the model-data differences at various depths (figure 3 and figure 4), we see that, for the three shallower depths, 5, 10 and 30m, MIKE3 gives differences slightly more spread out on both sides of the zero line than BaltiX, that more often shows a positive bias. This means that MIKE3 more frequently simulates salinity values both lower and higher than the observed compared to BaltiX which tends to generate low salinity more regularly. From the 76 m depth it is not possible to see any distinct differences between the two models.

In order to determine if any of the models do suffer from a time bias in the response to metrological forcing, the mean difference for each model, depth and time displacement (0, 48, and 96 hours) has been computed, see table 2. None of the models can, from this, be seen to have any problem with time lag in the response to forcing. The BaltiX has a mean difference of 3.9 at 5 m depth for all three time displacements and MIKE3 has a corresponding mean difference of about 2.8. Discrepancies between observation and model decreased with depth, which is expected because of the larger variations in the surface. MIKE3 has lower mean difference than BaltiX at 5, 10 and 30 m depth, 76 m depth. However, BaltiX has the lowest mean difference of the two. All mean differences calculated are shown in table 2 below.

BaltiX MIKE3 simultaneous 48 hour 96 hour simultaneous 48 hour 96 hour 5 m 3,95 3,95 3,91 2,83 2,85 2,78 10 m 3,53 3,55 3,62 2,64 2,50 2,53 30 m 2,42 2,47 2,60 1,58 1,81 1,86 76 m 0,60 0,54 0,50 0,84 0,86 0,84 Table 2. Mean difference for both models, all depths and time displacement (0, 48, and 96 hours) plotted in figure 3 and 4 above are here shown together.

Figure 3. Difference between simultaneously observed and BaltiX generated salinity is compared with 48 respectively 96 hour forward time displacement of model salinity. The difference between simultaneously observed and modeled salinity are marked with a black marker. Difference between observed salinity and modeled with a 48 hour forward displacement in time is marked with red marker and the difference between observed and modeled with 96 hour forward displacement in time are marked with blue markers. Panel A shows differences at 5 m depth, panel B differences at 10 m depth, panel C differences at 30 m depth and panel D differences at 76 meters depth. A marker of each color is marked out for every observation made at any of the 13 observational locations during year 2000.

Figure 4. Difference between simultaneously observed and MIKE3 generated salinity is compared with 48 respectively 96 hour forward time displacement of model salinity. The difference between simultaneously observed and modeled salinity are marked with a black marker. Difference between observed salinity and modeled with a 48 hour displacement in time is marked with red marker and the difference between observed and modeled with 96 hour displacement in time are marked with blue markers. Panel A shows differences at 5 m depth, panel B differences at 10 m depth, panel C differences at 30 m depth and panel D differences at 76 meters depth. A marker of each color is marked out for every observation made at any of the 13 observational locations during year 2000.

3.4 Comparison of short-term variability between the models The short-term variability comparison between the models shows how the two models correlate and respond in relation to each other. There is no possibility to fully compare these time series with observations since high frequent observational time series do not exist. However, the sparse observations that do exist are marked out to show how the models relate to these.

At station S4 (see figure 5 upper panel) both models can be seen to follow each other very well for the first six months. Thereafter, the BaltiX model is turning a bit lower in salinity and varies a bit more in relation to MIKE3 but the correlation between them is still there most of the time. At station A4 outside the Norwegian cost, see middle panel, the models follow each other quite well during most of the year. The BaltiX model is typically lower in salt content than MIKE3 and has a greater variance. See also table 2 for numerical values of statistical properties. The coherence between the models is quite good for station A4 as well, but not as good as for S4.

For the station near the Glomma outlet (station G1) the Baltix model is significantly lower in salinity compared to MIKE3. The two models seem in general to have a rather poor correlation to each other at this location. MIKE3, however, relates very well to five of the eight occasions that can be associated with observations. Note again that the temporal resolution is hourly for MIKE3 and daily for BaltiX. This might be the reason for the stronger high frequency variability of MIKE3, which is clearly seen during October.

In table 3, some basic statistics of the time series shown in figure 5 have been assembled. Since there is no temporally high resolved observations of surface salinity, the mean and standard deviation for observations has been computed based on 5-20 years of observations.

Figure 5. Modeled surface salinity for both models over the entire investigation period, year 2000, for station S4 (upper panel), A4 (middle panel) and G1 (lower panel), are visualized together with sparse observations of the corresponding location.

S4 A4 G1 mean std mean std mean std Observed 28,65 4,19 29,65 3,66 25,28 3,63 (no. obs.) (155) (155) (223) (223) (11) (11) BaltiX 24,92 4,16 27,13 2,73 23,58 3,64 MIKE3 27,25 4,29 29,78 2,04 29,78 2,04 Table 3. Mean value and standard deviation for each model, calculated from the time series visualized in figure 5. The values for observations are calculated from measurements made between year 1990 and 2009. Numbers of observations included in calculations are shown in parenthesis below the observed values. 4. Discussion It is important that the open boundaries forcing datasets gives a fairly accurate representation of the salinity stratification and variability, this is essential when modeling areas with fjords and archipelagos, like Koster fjord national park area in this case, which will be the focus area for a new high-resolved MIKE3 simulation. If the halocline, for example, is constantly located too deep in relation to sills in the costal area the model will not be able to correctly simulate intermediate water exchanges. It is also important that the model is able to simulate the variability of the salinity stratification which is a major forcing mechanism of the water exchange at many coastal areas along the Swedish west coast.

Both MIKE3 and BaltiX are considerably low in salinity, a fact that is supported by all results generated in this study. Both models also give less concentrated costal currents than the observed. However, both models seem to capture many events of the scarcely observed stratification. Also, The variability in the surface layer appears to be reasonable.

MIKE3 had been shown to be the model that gives the most correct salinity stratification with the most realistic characteristics throughout the year. It has some problems with a bias towards low salinity though.

The BaltiX model seems to have some problem transporting the low salinity surface water out from the Skagerrak area during the later months of the year. Extensive accumulation of freshwater in the surface can be seen in the salinity profiles. This might be connected with the very low salinity in the entire water column in the beginning of the year. However to be able to verify such a connection we must study longer timespan than one year.

Another thing that is questionable for the BaltiX model is the applied climatological run-off for Skagerrak and Kattegat. The total yearly mean run-off for the entire Skagerrak and Kattegat used in the model is about 1700 m3s-1. This is much lower than the 3000 m3s-1 estimated by Svansson (1975) for the same area. Yet the model has much lower salinity than observations. Also, Glomma outlet seems to be placed at an incorrect location and has no annual variations incorporated (see freshwater source marked R3 in figure 13 and table 5 in Appendix II). Glomma River has the highest average water flow in Scandinavia and extensive annual variations in water discharge (Nationalencyklopedin, 2012). However, since the focus here is whether the model is suitable to use for forcing along the open boundaries of the new high-resolved model and since Glomma is located quite far from these boundaries, this fact may not be that important. This is however something that needs to be investigated further.

It is unfortunately not possible to discuss MIKE3 in relation to its climatological forcing, as no detailed information of the forcing has been available for this study.

To further evaluate the two models suitability as forcing dataset the following analysis is suggested:

 Comparison of current measurement to model currents if possible.  Comparison with high frequency salinity data if relevant observational time series can be found.  Comparison of the width of the modeled Baltic current and Norwegian current through vertical interpolated isoline plots with observations.  Cost function analysis, like the one used by Kari Eilola (2011), to summarize long term statistical characteristics of the two models. For this analysis, decadal long time series from the models and corresponding observational time series are needed.  Computation of monthly values of freshwater height.  Computation of a water balance through the volume conservation principle for both models and compare it to, by observation estimated, mean salinities of the Skagerrak. See Omstedt and Nohr (2004).

5. Conclusions  Both MIKE3 and BaltiX are generally low in salinity compared to observations but seem to capture many events of the scarcely observed stratification.  The surface salinity variability seems reasonable in both models.  BaltiX has very low salinity in the entire water column in the beginning of the year and also seems to have some problem transporting the low salinity surface water out from Skagerrak during the later months of the year.  MIKE3 is the stronger candidate in most comparisons made. It captures many of the characteristics of the observed salinity profiles but has some problems with generally low salinity.

6. Acknowledgement The author would like to thank Göran Björk, who has been supervising this project and Christin Eriksson at DHI, assisting supervisor, for great support with this work. Thanks are also required to Per Jonsson at Sven Lovén Centre for Marine Sciences at Tjärnö for giving me the chance participate in this project. Anders Engqvist for help with the BaltiX data. Malin Ödalen and Victor Veiderpass for their interest, opinions, and input for which I am grateful.

7. References Albertsen, J. and L. P. Røed, 2010: Decadal long simulations of mesoscale structures in the northern North Sea/Skagerrak using two ocean models. Journal of Ocean Dynamics 60: 933–955

Eilola, K., B.G. Gustaffson, I. Kuznetsov, H.E.M. Meier, T. Neumann and O.P. Savchuk, 2011: Evaluation of biogeochemical cycles in an ensemble of three state-of-the-art numerical models of the Baltic Sea. Journal of Marine Systems 88: 267–284

Gustafsson, B., G. Björk, S. Evans, B. Hackett and O. Petersen, 2000: Validation experiments of hydrodynamic models applied to the Skagerrak. Copenhagen, Nordic council of ministers, Report 1: 1-63

Gustafsson, B., 1999: High frequency variability of the surface layers in the Skagerrak during SKAGEX. Continental Shelf Research 19: 1021-1047

Gustafsson, B. and A. Stigebrandt, 1996: Dynamics of the freshwater-influenced surface layers in the Skagerrak. Journal of Sea Research 35: 39–53

Levitus, S., and T. P. Boyer, 1994: World Ocean Atlas 1994, vol. 5, Salinity, NOAA Atlas NESDIS, U.S.Gov. Print O., Washington, D.C.

Omstedt A. and C. Nohr, 2004: Calculating the water and heat balances of the Baltic Sea using the ocean modeling and available metrological, hydrological and ocean data. Tellus 56A: 400-414

Rodhe, J., 1987: The large-scale circulation in the Skagerrak, interpretation of some observations. Tellus 39A: 245–253

Rodhe, J., 1996: On the dynamics of the large-scale circulation of the Skagerrak. Journal of Sea Research 35: 9– 21

Rydberg, L., J. Haamer and O.Liungman, 1996: Fluxes of water and nutrient within and into Skagerrak. Journal of Sea Research 35: 23–38

Svansson, A., 1975. Physical and chemical oceanography of the Skagerrak and the Kattegat, 1. Open sea conditions. Fishery Board of Sweden, Marine Research Institute, Report 1: 1-88.

Svendsen, E., J. Berntsen, M. Skogen B. Ådlandsvik and E. Martinsen, 1995: Model simulation of the Skagerrak circulation and hydrography during Skagex. Journal of Marine Systems 8: 219-236

NEMO official webpage, http://www.nemo-ocean.eu/, June 4, 2012

Nationalencyklopedins official webpage, http://www.ne.se/lang/glomma, June 4, 2012

Not publically available references Hordoir, R., B.W. An, J. Haapala and H.E.M. Meier, 2012: A 3D Ocean Modelling Configuration for Baltic & North Sea Exchange Analysis. SMHI Oceanografisk forskning, Folkborgsvägen 1, 601 76 Norrköping, Sweden, internal report

Sehested Hansen, I., A. Friis-Cheistensen, 2006: Baltic Sea - North Sea Transition Area. Environmental Status Year 2004, Bansai project. DHI Water and Environment, Agern Allé 5, DK-2970 Hørsholm, Denmark, internal report

Appendix I Simultaneously observed and modeled vertical salinity profiles for all 13 stations analyzed are here presented. Figure 5-7 shows transect A outside Arendal, Norway, station A1 is here located closest to the coast. Figure 8-10 presents transect S outside Smögen, Sweden, Here station S6 is closest to the cost. Figure 11 presents the two station G1 and G2 located outside the mouth of the Oslo fjord and close to the Koster fjord National park area.

Figure 6. Salinity profiles for transect A outside Arendal, see figure 2, January to April. The light gray shading shows the standard deviation of the monthly mean profile calculated from 20 years (1990-2009) of observations, see table 4 below for details. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Figure 7. Salinity profiles for transect A outside Arendal, Norway, see figure 2, May to August. The light gray shading shows the standard deviation of the monthly mean profile calculated from 20 years (1990-2009) of observations, see table 4 below for details. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Figure 8. Salinity profiles for transect A outside Arendal, Norway, see figure 2, September to December. The light gray shading shows the standard deviation of the monthly mean profile calculated from 20 years (1990-2009) of observations, see table 4 below for details. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Figure 9. Salinity profiles for transect S outside Smögen, see figure 2, February to May. The light gray shading shows the standard deviation of the monthly mean profile calculated from 20 years (1990-2009) of observations, see table 4 below for details. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Figure 10. Salinity profiles for transect S outside Smögen see figure 2, June to August. The light gray shading shows the standard deviation of the monthly mean profile calculated from 20 years (1990-2009) of observations, see table 4 below for details. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Figure 11. Salinity profiles for transect S outside Smögen, see figure 2, September to December The light gray shading shows the standard deviation of the monthly mean profile calculated from 20 years (1990-2009) of observations, see table 4 below for details. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Figure 12. Salinity profiles for Station G1 and G2, see figure 2, 8 occasions between January 13 and September 13. The dashed profile is observed at the date given to the right of each panel. Larger dots marks the observation depths. Simultaneously modeled salinity profiles from the grid cell comprising the observation location of each station are plotted dark grey for BaltiX and black for MIKE3.

Jan Feb Mar Apr Maj Jun Jul Aug Sep Okt Nov Dec A1 15 13 13 15 15 16 17 16 15 18 14 14 A2 23 18 19 20 24 28 20 22 22 21 17 21 A3 17 14 14 14 18 26 16 17 16 16 12 13 A4 15 14 13 15 17 22 16 16 16 18 12 13 A5 16 14 15 15 18 22 16 16 16 17 12 12 S2 10 12 10 12 11 13 14 17 10 10 8 11 S3 8 10 9 11 10 12 13 15 11 9 10 11 S4 8 10 9 11 10 12 13 16 11 10 9 11 S5 8 10 9 11 10 12 13 16 9 10 8 11 S6 10 13 9 11 10 12 12 16 12 10 10 11 Table 4. The table shows the number of observations from which the monthly mean and standard deviation shown in the figure 6-11 above is calculated from.

Appendix II Information about the climatological forcing applied to the BaltiX model. Monthly climatological values for Skagerrak and Kattegat are shown in table 5. All outflow sources are shown in figure 13.

Figure 13. Map over the North Sea, Skagerrak and Baltic Sea. All run-off sources applied to the BaltiX model are marked. For the Skagerrak and Kattegat region, the outflows has been given an index for which corresponding climatological values is shown in table 7.

Jan. Feb. Mar. Apr. Maj. Jun. Jul. Aug. Sep. Oct. Nov. Dec. R1 25 19 19 39 63 23 14 26 36 46 50 37 R2 27 22 20 40 227 250 95 81 85 86 62 40 R3 696 696 696 696 696 696 696 696 696 696 696 696 R4 53 40 39 436 236 75 31 31 274 39 46 85 R5 1 103 928 702 618 374 295 302 278 332 462 996 1 171 R6 121 191 119 618 243 74 55 34 55 46 86 98 Table 5. Climatological run-off applied to the BaltiX for Skagerrak and Kattegat region. Values are given in m3s-1. Se figure 13 for locations of R1-R6.