Ocean Dynamics (2013) 63:901–921DOI 10.1007/s10236-013-0634-0
The future of the western Baltic Sea: two possible scenarios
Ulf Grawe · Rene Friedland · Hans Burchard
Received: 2 August 2012 / Accepted: 5 June 2013 / Published online: 3 July 2013© Springer-Verlag Berlin Heidelberg 2013
Abstract Globally coupled climate models are generallycapable of reproducing the observed trends in the glob-ally averaged atmospheric temperature. However, the globalmodels do not perform as well on regional scales. Here, wepresent results from four 100-year, high-resolution oceanmodel experiments (resolution less than 1 km) for the west-ern Baltic Sea. The forcing is taken from a regional atmo-spheric model and a regional ocean model, imbedded intotwo global greenhouse gas emission scenarios, A1B and B1,for the period of 2000 to 2100 with each two realisations.Two control runs from 1960 to 2000 are used for valida-tion. For both scenarios, the results show a warming with anincrease of 0.5–2.5 K at the sea surface and 0.7–2.8 K below40 m. The simulations further indicate a decrease in salinityby 1.5–2 practical salinity units. The increase in water tem-perature leads to a prolongation of heat waves based onpresent-day thresholds. This amounts to a doubling or eventripling of the heat wave duration. The simulations showa decrease in inflow events (barotropic/baroclinic), whichwill affect the deepwater generation and ventilation of thecentral Baltic Sea. The high spatial resolution allows usto diagnose the inflow events and the mechanism that willcause future changes. The reduction in barotropic inflowevents correlates well with the increase in westerly winds.The changes in the baroclinic inflows can be consistentlyexplained by the reduction of calm wind periods and thusa weakening of the necessary stratification in the westernBaltic Sea and the Danish Straits.
Responsible Editor: Aida Alvera-Azcarate
U. Grawe (�) · R. Friedland · H. BurchardDepartment of Physical Oceanography and Instrumentation,Leibniz Institute for Baltic Sea Research,Warnemunde, Germanye-mail: [email protected]
Keywords Regional ocean models · Climate change ·Baltic Sea · Baltic inflow
1 Introduction
Climate change and variability affect the coastal zone, themarine ecosystem and fisheries in several ways. First, tem-perature has a direct influence on metabolism and growth;see, for example, Jobling (1996). Climate may also havesecondary effects, affecting a species by changes in foodavailability, competitors, or predators. For the North Sea andBaltic Sea, there are several recent studies on the effects ofclimate change on fish stock and plankton (Clark et al. 2003;Isla et al. 2008; Margonski et al. 2010). Temperature andsalinity changes may also act as proxies for other climatemechanisms such as circulation alterations and changes invertical mixing and stratification.
The Baltic Sea is a marginal, semi-enclosed water body,with a highly stratified water column. The salinity con-tent and stratification in the Baltic Sea is controlled bythe excess of riverine freshwater, the vertical mixing andepisodic inflows of North Sea water (Reißmann et al. 2009).These events transport saline, oxygen-rich North Sea waterinto the Baltic Sea and are an important climate variable(Omstedt et al. 2004; Meier 2007). In addition to the influ-ence on the salinity, these inflows are also a source ofnutrients and zooplankton (Feistel et al. 2008). Due tothis highly variable environment, life in the Baltic Sea isstrongly adapted and often reaches its physiological limits(e.g., Flinkman et al. 1998; Koster et al. 2003). Moreover,large-scale variability in the atmospheric forcing, and hencechanges in local climate, might lead to a significant increasein coastal erosion due to changes in storm surges or waveaction (Meyer et al. 2008; Zhang et al. 2010).
902 Ocean Dynamics (2013) 63:901–921
The climate changes projected to occur within the next100 years will have a considerable impact on the physi-cal conditions of the Baltic Sea (BACC 2008). The pro-jected warming varies between 3 and 5 K, with a tendencytowards a reduced salinity. To study the implications ofthe projected climate change (Meier et al. 2004, 2006;Wang et al. 2008; van Roosmalen et al. 2010), a vari-ety of scenarios of future climate are needed. Such sce-narios are produced by globally coupled atmosphere–ocean circulation models. However, for shallow seaslike the western Baltic Sea, the present generation (butalso the next generation) of such global models do nothave the necessary resolution to properly resolve thecomplex topography. Typically, they also lack impor-tant shelf sea physical processes like turbulent mix-ing, overflows and fronts. Hence, there is an increasingneed to use regional ocean models to provide valuable,high-resolution information to governments, stakeholdersand coastal engineers (Adlandsvik and Bentsen 2007;Melsom et al. 2009; Holt et al. 2010; Grawe and Burchard2012; Olbert et al. 2012).
This paper uses dynamical downscaling to regionalisefuture global climate scenarios for the western Baltic Sea.Model studies on the western Baltic Sea allow to seecumulative effects of the climate change in the Baltic Sea,because all water masses that enter or leave the Baltic haveto pass the Danish Straits (Great Belt and Øresund; seeFig. 1). The modelling is done by forcing a well-calibratedhigh-resolution local ocean model (Burchard 2009) with
atmospheric forcing and open ocean lateral boundarydescription from a regional atmospheric and a regionalocean model. The final spatial resolution of the local modelis less than 1 km, which allows for a realistic descriptionof topographic features like sills, sounds and coastlines. Itwill be shown that such a high spatial resolution is neces-sary for properly reproducing inflow events into the westernBaltic Sea. The forcing used in the present study are twogreenhouse gas emission scenarios proposed by the Inter-governmental Panel on Climate Change (IPCC 2007), A1Band B1, with each two realisations. The latter scenario isthe more optimistic one, with less greenhouse gas emis-sions. The presented transient simulations range from 1960to 2100 and are a novel feature in regionalised ocean cli-mate modelling, because they do not rely on the assumptionthat the underlying system has reached a dynamical steadystate (see also Neumann 2010). Meier and Kauker (2003b)discussed that for the Baltic Sea with an average exchangetime of 35 years, the usual 30-year time slice experimentsmust fail, especially for salinity scenarios. The memory ofthe Baltic Sea is longer than the simulations itself would be.Thus, only the sudden transition phase could be observedand not the response of the Baltic Sea to a slowly changeclimate.
The outline of the paper is as follows: In the next sec-tion, we briefly recall the hydrodynamics of the westernBaltic Sea and the driving mechanisms of salt inflows intothe Baltic Sea. In Section 3, we explain our modelling strat-egy and the necessity of using a high-resolution model for
Fig. 1 Model domain of thelocal model, open boundariesand location of the westernBaltic Sea. The colouringindicates the depth below meansea level in metre. Upper panel:a map of the whole Baltic Seashowing the location of themodel domain. The location ofobservation stations are asfollows: KB, Kiel buoy; DSB,Darss Sill buoy; OBB, OderBank buoy; and ROS Rostock.Model output is used at GRT,Gedser Rev transect;ABB-Arkona Basin buoy; andBBS, Bornholm Basin station.Further, GB denotes the GreatBelt and OS, the Øresund. Theblue circles denote the positionof river mouths. The thick bluelines indicate the location ofopen boundaries
Kattegat
KB
OBB
ABB
BBS
DSB
ROS
GRT
OS
GB
Bornholm Basin
Arkona Basin
Odra lagoonGermanyGermany
Sweden
Denmark
Denmark
Poland
0 50 100 km50
10° E 12° E 14° E 16° E
54° N
55° N
56° N
57° N
−100
−80
−60
−40
−20
0
0° 10° E 20° E
55° N
60° N
65° N
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downscaling. We give details on the forcing scenarios andthe performed numerical experiments. Moreover, we intro-duce diagnostic measures like heat wave duration, potentialenergy anomaly, salt and volume fluxes to quantify changesin the climate projections. Section 4 deals with the vali-dation of present-day conditions and sets the stage for thediscussion of changes in the climate runs which are anal-ysed in Section 5. Here, a special focus is put onto shifts inthe inflow dynamics. Finally, we end the paper with a shortconclusion in Section 6.
2 Dynamics of the western Baltic Sea
The transition region between the Kattegat and the BalticSea, the western Baltic Sea (Fig. 1), is characterised byshallow and narrow straits (the Øresund and Great Belt,also called Danish Straits; OS and GB in Fig. 1), flow-limiting sills (Darss Sill; GRT in Fig. 1 and Drogden Sillat the southern end of the Øresund), but also several basinswith depths of 50–100 m (Arkona Basin and BornholmBasin) (Fennel and Sturm 1992; Siegel et al. 2005). Thewater exchange through the western Baltic Sea can be sep-arated into three components. At first, there exist a nearlypermanent outflow of approximately 15,000 m3/s of brack-ish Baltic Sea water with a salinity of 7 practical salinityunits (psu). This flow directing into the Kattegat is causedby the excess of freshwater input from rivers discharginginto the Baltic and the net precipitation (Hordoir and Meier2010). Secondly, the baroclinic pressure gradient across theDanish Straits, fed by the salinity difference of 20–25 psubetween the Kattegat (34 psu) and the Arkona Basin (7 psu),causes episodic inflows of saline water into the Baltic Sea(Sellschopp et al. 2006; Reißmann et al. 2009). Finally, thebarotropic sea level differences between the Kattegat andthe Baltic Sea can additionally trigger the inflow of Katte-gat water into the western Baltic Sea (Matthaus and Franck1992). The abovementioned Baltic inflows manifest as bot-tom gravity currents. These inflow events occur irregularly,from repeated events within a single year to stagnation peri-ods lasting for a decade (Matthaus 2006). What they havein common is that they only take the route via the GreatBelt and Darss Sill. The significant impact of such eventson the physical, chemical and biological status of the BalticSea has intensively been investigated in the past 50 years.A recent review on these studies was given by Matthaus(2006). These inflow events have the potential for deepwaterventilation of the Gotland Basin (Feistel et al. 2008).
To characterise the barotropic inflows, also called “majorBaltic inflows” (MBIs), Matthaus and Franck (1992) usedan indicator time series of the bottom salinity at GedserRev (GRT, see Fig. 1). Matthaus and Schinke (1994)showed that as a preconditioner, the water level within
the Baltic has to be lowered by easterly winds, lasting for2–4 weeks. These easterly winds have to be followed bystrong westerly winds to create a sea level difference inthe order of 1 m between the Kattegat and the ArkonaBasin.
The baroclinic inflow events are driven by baroclinicpressure gradients, especially horizontal salinity differencesbetween the Kattegat and Bornholm Basin (Feistel et al.2004, 2008). They appear during persistently calm windconditions lasting for more than 14 days (usually in the latesummer) and are characterised by a significant stratifica-tion at Darss Sill. Whereas the driving mechanism of thebaroclinic inflows is well understood, long-term statistics ofthese events are missing. Based on recent observations andmodelling studies (Feistel et al. 2004; Meier et al. 2004),it is estimated that the salt transport associated with baro-clinic inflows is at least a factor of 5–10 smaller comparedto MBIs. Moreover, Burchard et al. (2005) showed that thebalance in gravity-driven plumes in the Arkona Sea is notonly between pressure gradients and the Coriolis acceler-ation, but also the bottom friction plays a substantial role.They concluded that the local topography plays an importrole in balancing the gravity currents.
Besides the barotropic dynamics and the saline flow, thewestern Baltic Sea shows a summer temperature stratifi-cation leading to a three-layer system with a well-mixedseasonal thermocline in the upper 20 m and a permanenthalocline through inflows and the dense bottom water poolin the deeper basins.
Concluding, due to the narrow channels and sills incombination with the thermohaline dynamics, the westernBaltic Sea is a challenging region to model. Thus, high spa-tial and vertical resolution is required to capture verticalstratification and fronts due to inflows (Umlauf et al. 2007).
3 Methods and data
3.1 Modelling strategy
In this study, we use a high-resolution local ocean modelthat is the last part of a model chain. The downscaling startswith a global climate model and subsequent nestings of aregional ocean and atmospheric model. Finally, the Gen-eral Estuarine Transport Model (GETM) is used as the innermodel (local model) forced by the regional scale models.The usage of the high-resolution local model is motivatedby the following reasons: Fischer and Matthaus (1996) andLintrup and Jakobsen (1999) showed the importance of theØresund for the water exchange of the Baltic Sea. Thisnarrow strait (a width of 3 km at the narrowest position)is difficult to resolve, even in regional-scale ocean mod-els (Meier 2006; Neumann 2010). For instance, the 5-km
904 Ocean Dynamics (2013) 63:901–921
(3-nm) Modular Ocean Model (MOM) setup of Neumann(2010) with its B-grid, needs at least two grid cells to obtaina velocity cell, so that a channel has a minimal broadnessof 10 km. This also implies that the cross-sectional areaof the Danish Straits is changed (keeping the depth con-stant), or the depth of the straits has to be changed (keepingthe cross-sectional area constant). Both changes will alterthe stratification characteristics, the flow structure and thevolume transport in the Danish Straits.
An advantage of GETM is its usage of bottom-following coordinates. Although σ -style coordinate systemsare known to cause problems due to discretisation errorsof the internal pressure gradient (Haney 1991), they areadvantageous for modelling gravity currents, and they donot need an additional overflow/gravity currents parameter-isation like in geopotential ocean models (Beckmann andDoscher 1997). Further, Griffies et al. (2000) and Ezer andMellor (2004) (and references therein) discussed the prob-lems of artificial numerical mixing using z-level coordinatesin modelling overflows/gravity currents. Due to this addi-tional mixing in z-level models, the salt flux through theDanish Straits needs to be increased for geopotential modelsto match the long-term salinity content in the deeper basinsof the Baltic. GETM is used as a nested model; there is noneed to have this artificially higher salt transport throughthe Danish Straits. A detailed validation of the salt transportis discussed in Section 4.3. To further stress the importanceof a high-resolution setup, we have to refer to the findingsof Grawe and Burchard (2012). With the identical setup,the authors study the changes in storm surges in the west-ern Baltic Sea. The validation of the water levels show thatthe 30- and 50-year storm surges could be reproduced withan error of ±5 %. The simulations of Meier et al. (2004),using a less fine grid, could reproduce surge heights formost parts of the Baltic Sea. The only region lacking agree-ment was the western Baltic Sea (underestimation of 50 %).The water and salt exchange through the Danish Straits ismostly controlled by the barotropic pressure gradient. Thus,the water level difference over the Danish Straits is a sec-ond key ingredient to properly model inflow events into theBaltic Sea. As shown by Grawe and Burchard (2012), thepresented setup is able to do so.
Additionally, Reißmann (2005) and Osinski et al. (2010)estimated the first baroclinic Rossby radius of deforma-tion in the southern/western Baltic Sea to vary between3 and 6 km. Assuming that at least 4-5 grid points areneeded to resolve this important length scale, the presentgeneration of regional climate models for the Baltic Seaas used by Meier et al. (2004, 2006) or Neumann (2010)are unable to resolve the baroclinic Rossby radius in theArkona Basin. This length scale is important to prop-erly resolve the filament structure caused by upwelling(Lehmann and Myrberg 2008) or the dynamics of gravity
currents in the Arkona Basin (Burchard et al. 2005; Umlaufet al. 2007). van der Lee and Umlauf (2011) showed theimportance of internal waves on mixing in the BornholmBasin. With the presented model setup here with a resolutionof 900 m, we were able to resolve the first modes of internalwaves and thus did not need internal wave mixing param-eterisations. Hence, the current setup is able to reproducemesoscale and submesoscale features.
3.2 The forcing scenarios and downscaling
The simulations are based on the Intergovernmental Panelon Climate Change (IPCC) scenarios A1B and B1 (IPCC2007). Here, the B1 scenario represents a moderate sce-nario with a slow increase in CO2 emissions, whereas theA1B scenario mimics a more pessimistic future with a rapidincrease in CO2 emissions until the mid of the twenty-firstcentury. The forcing data set covers the period from 1960 to2100. It is divided into the reference period (C20) coveringthe years 1960–2000 and the two greenhouse gas emissionscenarios, A1B and B1 (2001–2100). For each referenceperiod and scenario, two realisations are available. Theserealisations follow the same assumptions; however, they dif-fer in their initial conditions. Thus, due to deterministicchaos, they show different temporal dynamics. This can beused to estimate the natural variability. Combining all sce-narios and the reference runs, 480 years of simulation dataare accessible.
Since our simulations are the last part in a downscalingchain, we shortly describe the driving models. The globalmodel used for our simulations is the ECHAM5/MPI-OM(Roeckner et al. 2003; Jungclaus et al. 2006) of the Max-Planck-Institute for Meteorology in Hamburg, Germany.The regionalised meteorological forcing for our simulationswas provided by the dynamic downscaling carried out withthe CLM (Hollweg et al. 2008), the climate version of theoperational weather forecast model of the German WeatherService. The horizontal resolution of the CLM is about18 km (this is high enough to capture the effect of land/seatransition), and the time resolution is taken as 3 h for allnecessary meteorological variables (10-m wind, air temper-ature, dew point temperature, cloud cover, air pressure andprecipitation). Details of the CLM runs as model domain,forcing etc., or validation are described in detail by Hollweget al. (2008) and Jaeger et al. (2008).
The oceanic boundary conditions are taken from the tran-sient simulations of Neumann (2010) with MOM (Griffieset al. (2001)). The model covers the entire Baltic Sea andparts of the North Sea and has a horizontal resolution ofapproximately 5 km and 77 vertical (geopotential) grid lay-ers, with a near-surface resolution of 2 m, increasing withdepth. At the open boundaries, the model of Neumann(2010) is forced with the data from ECHAM5/MPI-OM.
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3.3 The local ocean model
The GETM (Burchard 2009), which has been used forthe present numerical study, combines the advantages ofbottom-following coordinates with the turbulence moduleof the general ocean turbulence model (Umlauf et al. 2006).GETM has been successfully applied to several coastal,shelf sea and limnic scenarios, for turbulent flows in theWadden Sea (Stanev et al. 2007), for dynamics in the NorthSea (Staneva et al. 2009), for a basin-exchange study in theLake of Geneva (Umlauf and Lemmin 2005) or mixing stud-ies in a Danish fjord (Hofmeister et al. 2009). Moreover,Hofmeister et al. (2011) recently showed that GETM wellreproduces inflow events into the Central Baltic Sea, if thenewly developed vertical adaptive coordinate mechanism isused (Hofmeister et al. 2010) (even with a “coarse” 1 nmmodel setup). Furthermore, GETM has shown its capabil-ities to simulate inflow events into the western Baltic Sea(Burchard et al. 2005; Burchard 2009). The latter ones aredone with the identical setup as used in this work.
GETM is a three-dimensional free-surface primitiveequation model using the Boussinesq and boundary layerapproximations. Vertical mixing is parameterised by meansof a two-equation k − ε turbulence model coupled to analgebraic second-moment closure (Canuto et al. 2001). Theatmospheric variables as provided by CLM are used tocompute fluxes using bulk formulas (Kondo 1975). Forhorizontal discretisation, a high-resolution bathymetry hasbeen used with a grid spacing of approximately 900 m.Since numerical mixing is significant even at that resolution(Burchard and Rennau 2008), explicit horizontal diffusion isneglected. Further, bottom- and surface-fitted vertical coor-dinates with 35 vertical layers and a horizontally uniformbottom layer thickness of 0.4 m are applied, such that theflow can smoothly advect along the bed. A detailed vali-dation of hindcast simulations with the current setup andforcing by MOM is given by Rennau and Burchard (2009),Burchard (2009) or Rennau et al. (2012). Additionally,Burchard et al. (2005) could reproduce with the identi-cal setup in idealised studies the horizontal and verticalstructure of inflows originating from the Øresund.
To force the high-resolution local model along the openboundaries, mean profiles (averaged over 4 h) of temper-ature and salinity are extracted from the MOM simula-tions. Additionally, the sea surface elevation and the depth-averaged currents are prescribed with a temporal resolutionof 1 h. The global sea level rise, seen in the boundary con-ditions, is estimated to be in the order of 50 cm for the A1Bscenarios and of 25 cm for the B1 runs. These projectionsagree with the estimations of the IPCC, where the possiblerange for the A1B scenario is given as 21 to 50 cm and forthe B1 scenario as 18 to 38 cm. Especially for the A1Bscenarios, Landerer et al. (2007) gave a detailed discussion
on the different contributions to the sea level rise in theECHAM5/MPI-OM model for northern Europe.
To properly simulate the fresh water discharge, six riversare included in the model domain. The time series of riverdischarge are also taken from the simulations of Neumann(2010). The most important is the Oder (mean discharge600 m3/s), which directly discharges into the Oder Lagoon(see Fig. 1). For consistency, both ocean models use thesame CLM atmospheric forcing on the same spatial grid.Due to the two open boundaries at which sea levels fromthe MOM Baltic Sea model (Neumann 2010) were pre-scribed, the net flow through the western Baltic Sea had tobe fitted. This procedure is necessary, because both modelshave different bottom roughness parameterisations, differ-ent vertical coordinates and therefore a different hydraulicresistance in the Danish Straits. This adjustment was doneby increasing the barotropic pressure difference betweenboth open boundaries. Here, we had to add a constant offsetof 2 cm for the sea surface elevation at the eastern bound-ary. This procedure is also described in detail by Burchard(2009). Although we used an ocean model with a wettingand drying algorithm, no changes in the coastline are con-sidered. Especially the loss of land due to flooding is nottaken into account.
3.4 Diagnostics
To quantify changes in the western Baltic Sea, daily meanfields of temperature, salinity and velocity are statisticallyanalysed.
3.4.1 Consecutive heat days
To estimate changes in the Baltic Sea temperature andhence possible changes in the habitat conditions for biol-ogy, we compute the consecutive heat days per year, whichis defined as the highest number of consecutive days wherethe water temperature is higher than a certain threshold.As threshold, we use the 30-year mean (1971–2000) of theannual 99th percentile of the daily mean temperature. Thisthreshold is exceeded on average on only 5 days per yearand can be regarded as an extreme. Moreover, we apply thismeasure only to the bottom temperature.
3.4.2 Quantifiying Baltic Sea inflows
To characterise the barotropic inflows (MBIs, see alsoSection 2), Matthaus and Franck (1992) used as indicatortime series of the bottom salinity at Gedser Rev (GRT, seeFig. 1). According to Matthaus and Franck (1992), a majorinflow event is defined if the bottom salinities at GRT areabove 17 psu for more than five consecutive days. More-over, the stratification G at GRT has to be nearly destroyed.
906 Ocean Dynamics (2013) 63:901–921
Matthaus and Franck (1992) defined as a measure for thestratification G = (1 − SS/SB), with SS the surface salin-ity and SB the bottom salinity. Stratification is defined to beabsent for G < 0.2. We use the above-defined criteria toquantify inflows in our numerical setup.
To count baroclinic inflows, we apply the same criteriaas mentioned above for MBIs, except that the stratifica-tion measure G is neglected. Therefore, in combination,both criteria can be used to count the duration and computethe associated transport of the barotropic/baroclinic inflowevents. If two events are separated by less than 5 days, theyare counted as one event.
In Table 1, a summary of the used criteria to distinguishbaroclinic and barotropic inflows are given.
A further possibility to transport salt and hence North Seawater into the Baltic Sea are inflows through the Øresund.Lintrup and Jakobsen (1999) discussed the importance ofthe Øresund for the water exchange of the Baltic Sea andconcluded that the salt transport (during single events) couldexceed the transport through the Great Belt. However, itis assumed that the transport is distributed as 7:3 betweenthe Great Belt and the Øresund (Mattsson 1996). To quan-tify the salt transport through the Øresund, we follow theprocedure of Lintrup and Jakobsen (1999) and integrate allmass/salt flux if salinities in the Øresund are above 13 psuand pointing into the Baltic Sea (Lintrup and Jakobsen(1999) showed that the separation point between outflowand inflow occurs for a salinity of approximately 13 psu).To quantify the overall mass flow, the advective salt flux QS
into the Baltic Sea is computed as follows:
QS(t) =∫
A
u(x, y, z, t) ·S(x, y, z, t) ·ρ(x, y, z, t)dA, (1)
with u as the velocity perpendicular to the transects(Fig. 1, positive if directed into the Baltic Sea); S, thesalinity; A, the cross-sectional area; and ρ, the density ofwater. To quantify the volume flow, the volume flux QV iscomputed:
QV (t) =∫
A
u(x, y, z, t)dA. (2)
By doing the temporal integration over the period of aninflow event, the total salt content QStot and the volumeQVtot can be computed.
Table 1 Criteria to identify inflows at GRT and to define the salt fluxthrough the Øresund
G Salinity (psu) Duration
Barotropic inflow (MBIs) <0.2 >17 >5
Baroclinic inflow >0.2 >17 >5
Øresund – >13 –
3.4.3 Stratification
Potential energy arguments have been found to be an excel-lent measure to study the competition of stratification andmixing. To quantify stratification, Simpson et al. (1977)considered changes in potential energy relative to the mixedcondition and defined a scalar parameter φ, the potentialenergy anomaly,
φ(x, y, t) = 1
D(x, y, t)
∫ η
−H(x,y)
(ρ(x, y, t) − ρ(x, y, z, t)))gzdz (3)
where D = η+H is the total water depth; η, the sea surfaceelevation; H , the mean water level; ρ, the depth-averageddensity; ρ(z)), the vertical density profile over the watercolumn; g, the gravitational acceleration; and z, the verti-cal coordinate. For a given density profile, φ (in joule percubic metre) is approximately the amount of work requiredto bring about complete vertical mixing per unit of vol-ume. This integral measure can only be used to quantifythe strength of the stratification. One has to note that thedepth of the halocline/pycnocline and the potential energyanomaly are independent parameters—the depth of the pyc-nocline can change with constant potential energy and viceversa.
3.5 Data
It is common for climate simulations to use 30 years of sim-ulations to compute the appropriate statistics (Christensenet al. 1997). Whereas long-time observations for the atmo-sphere are available, this is not the case for oceanographicobservations. Moreover, the distribution of measurementstations in the ocean is rather sparse. Because our obser-vations of high-resolution time series (temporal resolution1 h) of atmospheric forcing and temperature/salinity in andover the Baltic Sea cover only 14 years (Table 2), we havecompared the statistics of 14-year slices and the full refer-ence period. Omstedt et al. (2004) showed that for the BalticSea, 90 % of the variance in the observations is found ontime scales shorter than 15 years. Therefore, they suggestedthat 15 years is an appropriate climate averaging time forthe Baltic Sea. Thus, the limited lengths of observations arestill challenging but already allow for a validation. Table 2summarises the available observation data and the data cov-erage. As it can be seen, the data coverage is relatively high,especially for Darss Sill. Although the data coverage for theother stations is lower, no seasonal data bias is inherent inthe data. Temperature/salinity and atmospheric variables aremeasured hourly, but, for comparison with the control run,a daily average is computed.
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Table 2 Summary of availableobservation stations and datacoverage (see also Fig. 1).Numbers in parenthesesindicate the data availability
Station Water depth (m) Atmosphere (%) Ocean (%)
Oder Bank buoy (OBB) 12 – 1997-2009 (77)
Darss Sill buoy (DSB) 19 1995–2009 (91) 1995–2009 (89)
Kiel buoy (KB) 13 1995–2009 (86) 1995–2009 (69)
AVHRR – – 1987–2009 (96)
Rostock (ROS) – 1971–2000 (94) –
To validate the spatial pattern of sea surface temperature,satellite-based advanced very high-resolution radiometerdata (AVHRR) are used (McClain et al. 1985). These dataare provided by NOAA as monthly mean fields of day andnight pass.
4 The present-day climate 1971-2000
4.1 Atmospheric forcing
In Table 3, the comparison of the 2-m air temperaturebias �TA for CLM and the observations is shown (� =simulation-observation). It is clearly visible that the CLMdata have a cold bias varying around 1.5 K. This cold biasis known in the literature (Jacob et al. 2007), but the cli-mate change signals of the CLM results are robust in theensemble mean of different RCMs. Moreover, the CLMresults underestimate the variability by up to 0.6 K. Com-pared to the annual standard deviation of the air temperatureof roughly 6 k, this bias can be regarded as small. Althoughthere is an ongoing debate on bias correction of data sets(Piani et al. 2010; Seguı et al. 2010), we do not apply a sta-tistical bias correction to the CLM data. At first, we have tobe consistent with the large-scale driving model (Neumann2010); secondly, the underlying assumption on such a cor-rection are uncertain (Piani et al. 2010); and third, not fullyconsistent: a change in temperature would cause a changein moisture, which would also affect the cloud cover. Espe-cially the latter one is difficult to correct in a consistent way.A second approach to circumvent the cold bias is to use the
� approach (Meier 2006). Here, differences inferred fromRCM, between a modelled future period and a present-dayperiod are computed. Such derived changes in tempera-ture, precipitation, etc. are used to modify observed climatedata and thereafter to run impact models. Although it isby definition bias-free, it can only reproduce the present-day statistic with a shifted mean prescribed by the climatechange signal �. Thus, completely new atmospheric con-ditions or extremes (long-lasting heat waves, changes inannual cycle, extreme winds (Kjellstrom 2004; Christensenand Christensen 2007) cannot be modelled by this approach.
For a detailed discussion of the validity of the atmo-spheric CLM hindcast runs (driven by ERA40 boundaryconditions), the interested reader is referred to Jaeger et al.(2008). However, in summary, the CLM hindcast simula-tions appear to perform similarly to other state-of-the-artRCMs in Europe (e.g., global climate model (GCM)-driven PRUDENCE simulations; Jacob et al. (2007)), whichindicate that the bias problem in the CLM climate sim-ulations is introduced by the large-scale forcing modelECHAM5/MPI-OM.
Meier et al. (2011) or Neumann et al. (2012) showedin an ensemble of downscaled atmospheric forcing fieldsfor the Baltic Sea that biases, especially in air temperature,are common. However, Meier et al. (2011) discussed thatit is more important that the gradients are correct. As gra-dients, they defined the differences between the northernBaltic Sea and the Danish Straits or the east–west differ-ences (Kattegat and Gulf of Finland). Meier et al. (2011)concluded that all atmospheric models were able to repro-duce these gradients. Note that the absolute values matter
Table 3 Validation statistics for the reference period
Station �TA (K) �TS (K) �TB (K) �SB (psu) STD(SB ) (psu)
Oder Bank buoy (OBB) −1.46 (−0.58) −1.55 (−0.08) −1.05 (−0.32) −0.12 (0.05) 0.75
Darss Sill buoy (DSB) −1.35 (−0.37) −0.95 (−0.26) −1.38 (0.23) 0.31 (−0.21) 3.30
Kiel buoy (KB) −1.52 (−0.41) −1.52 (−0.42) −1.27 (0.49) 0.35 (0.12) 2.51
AVHRR − −1.43 (−0.36) − – –
Bias (sim.-obs.) in 2-m air temperature �TA, sea surface temperature �TS , bottom temperature �TB and bottom salinity �SB for the observationstations (see also Fig. 1). Values in italics mark significant deviations at the 5 % significance level. Values in parentheses indicate the bias in thestandard deviation. In the last column, we depict the natural variability (standard deviation) of the bottom salinity. The bias for AVHRR representsa domain average
908 Ocean Dynamics (2013) 63:901–921
(although they are important), but the pattern needs to bereproduced.
The cold bias in the atmospheric forcing is in the rangeof the expected warming for the middle of this century butvaries over the seasons and stations (Table 4). It is visiblethat there is no clear pattern in the seasonal bias of air tem-perature. Peak values can be seen for Kiel buoy (KB) duringautumn and winter, whereas Oder Bank buoy (OBB) showsthe highest bias during spring. Thus, it can be assumed thatthe temperature bias is nearly uniform during the wholeyear. Further, the two realisations of both scenarios showsimilar results.
In Fig. 2, a comparison of the annual cycle of the zonaland meridional component of the wind speed for Rostock(ROS, Fig. 1) is shown. The results at the other observa-tion stations are similar, although the data coverage is lessoptimal (not shown). The CLM data set can reproduce theannual cycle in pattern and magnitude. The annual cyclein the two control simulations is mostly within the naturalvariability as indicated by the monthly standard deviationsin Fig. 2. Only the reversal of the meridional componentduring summer is not captured. Comparing both referenceruns, it is noticeable that C202 is more intense, especiallyduring January and February. Possible consequences will bediscussed at a later stage. Additionally, at present, it is diffi-cult to assess/quantify the errors in the ocean model causedby errors introduced in the atmospheric model. The trig-gering of MBIs is well understood (Nehring et al. 1995;Matthaus et al. 1999). However, it is still unclear whatis causing the long stagnation period during the last twodecades (Feistel et al. 2008). It is hypothesised that subtlechanges in the large-scale forcing can cause these changes.Therefore, it is difficult to assess whether the deviationsas seen in Fig. 2 can have a severe impact onto the entiresystem.
For a detailed comparison of the CLM data and anintercomparison of different RCMs, the interested readeris referred to Jacob et al. (2007) or Christensen andChristensen (2007). One has to note that the presentedresults here are only valid for a rather small region (westernBaltic Sea), whereas Jacob et al. (2007) or Christensen andChristensen (2007) provided results for much larger regions(e.g. central Europe, Scandinavia).
Table 4 Bias in 2-m air temperature in K for different seasons of theyear
Station DJF MAM JJA SON
OBB −0.8, −1.0 −1.8, −1.4 −1.4, −1.5 −1.7, −1.6
DSB −1.0, −1.2 −1.1, −1.3 −1.9, −1.9 −1.3, −1.2
KB −1.9, −2.0 −0.9, −1.0 −1.2, −1.1 −1.8, −1.9
The two numbers indicate the different realisations of the control runs
2 4 6 8 10 12−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
2.5
3
Month
Win
d sp
eed
[m/s
]
VObs
VC20
1
VC20
2
UObs
UC20
1
UC20
2
Fig. 2 Validation of CLM forcing at Rostock station. Shown are theannual cycle of the zonal and meridional components of observationand CLM. The vertical bars indicate the monthly standard deviations.For clarity, only every second error bar is plotted
4.2 Water temperature
To show that GETM can represent the present-day statistics,Table 3 gives the bias in mean and standard deviation forKB, Darss Sill buoy (DSB) and OBB. All stations show asystematic cold bias in surface temperature, �TS , which iscaused by the too low air temperature. The cold bias in airtemperature �TA does not affect only the SST, but also thewhole water column, as is can be seen by the too low bottomtemperatures �TB . Table 3 further indicates that the meantemperature at all observation points is underestimated, butthat the deviations in the variability have varying sign. Themagnitude of the bias in the mean and standard deviationsare in the range as that for the air temperature. For DSBand KB, the variability is overestimated. Since these twostations are strongly influenced by MBIs, a different tem-poral behaviour of the modelled inflows, in comparison tothe real ones, might be the reason. GETM slightly overes-timates the occurrence of MBIs for the present climate (seeSection 5) and thus also the resulting variability. This couldalso explain why the bias is not constant with depth, an indi-cation that means stratification is slightly different betweenthe model and observations. A reason for the differences in�TS of OBB, KB and DSB might be the different landmaskused in the atmospheric model. Since OBB and KB are closeto the coast, they see a slightly different forcing than DSB,which is a true open water station. The land sea transitioncan explain these deviations.
Figure 3a shows the annual mean sea surface tem-perature (SST) from the control runs (1971–2000, meanof both realisations) in comparison with the mean SSTfrom AVHRR satellite observations (1987–2009) (Fig. 3b).
Ocean Dynamics (2013) 63:901–921 909
a)
SST: Simulations
10° E 12° E 14° E
54° N
55° N
7
7.5
8
8.5
9
b)
SST: Observations
10° E 12° E 14° E
54° N
55° N
8.5
9
9.5
10
10.5
Fig. 3 Mean sea surface temperature in degree Celsius for a C20(1971–2000, both realisations) and b AVHRR satellite data (1987–2009). Note that we have shifted the colour scale to reflect theagreement in pattern despite the cold bias
GETM can reproduce the spatial pattern with the high-est temperatures in the Oder Lagoon and along the Ger-man Coast and the cooler water in the northeast of thedomain. The cooler water at the Swedish coast is causedby coastal upwelling due to the prevailing westerly winds(Lehmann and Myrberg 2008). The comparison revealsagain the cold bias in the SST by 1.4 K (Table 3).
4.3 Salinity
The comparison of measured and modelled salinity distri-butions at KB, OBB and DSB (Fig. 4) indicates that GETMcan reproduce the salinity statistics. Only for OBB, GETMpredicts slightly too high extremes, which are not observed.Moreover, for all three stations, GETM underestimates thelow salinity events, most noticeable for KB. One still hasto keep in mind that we do a statistical comparison. Forinstance, it is not clear whether the larger salinity valuesfor station OBB (Fig. 4b) are an artefact of a single real-isation of a climate control run, or if these deviations arecaused by a too low skill of the model system. Neverthe-less, Table 3 indicates that a statistical significant deviationexists only for DSB. The signs of the bias in standard devi-ation are varying, indicating no systematic error. Looking atthe natural variability as given in the last column of Table 3,
one can clearly see the high variability with values of upto 3.3 psu at Darss Sill. Thus, although there exists a salin-ity bias of 0.31 psu, it is still small compared to the naturalvariability.
For comparison, the salinity pdf at Darss Sill calculatedby MOM is given in Fig. 4. It shows that the salinity is over-estimated during inflow events, with values of up to 30 psu,which are far from being observed. These higher salinitieslead to an overestimation of the density. Since MOM over-estimates the density, interleaving of water masses in theArkona Basin and Bornholm Basin might be suppressed inthe simulations of Neumann (2010). This is a clear indica-tion for the necessity of downscaling the inflow dynamicsfrom the regional to the local level with a high-resolutionmodel (see also Section 3.1).
4.4 Major Baltic inflows
Matthaus and Franck (1992) counted the occurrences of theMBIs (Table 5) for the period of 1897–1976. Although theobservation period spans 90 years, only 80 years of data areavailable (observations during World War I and World WarII are missing). The long-term statistics indicate nearly oneMBI per year, lasting for 10 days. The average volume trans-port associated with these events is approximately 70 km3,and the average salt transport is less than 2×109 t (Matthausand Franck 1992). The hindcast simulations of Meier andKauker (2003b) yield much higher numbers of MBIs andhigher transport. These higher transports might be causedby the coarse resolution of the Danish Straits and thereforeby the mechanism discussed in Section 3.1. However, thevalues are of the same magnitude. For the comparison of thelong-term inflow statistics of Matthaus and Franck (1992)and Meier and Kauker (2003b) with our simulations, it hasto be taken into account that those cover nearly one century,whereas our reference simulations have a length of 40 years.Therefore, the long-term statistics might be biassed by theongoing climate change.
The higher transports of Meier and Kauker (2003b) arealso seen in the results of Neumann (2010) (Table 5). How-ever, the results for the MOM run might be underestimated,because they are computed based on 6-daily mean trans-port fields, but they should give a good estimate of thesalt transport in the regional forcing model. In Table 5,the statistics of the GETM C20 runs indicate similar num-bers compared to the observations of Matthaus and Franck(1992). However, one still has to keep in mind that nei-ther the measurements of the MBIs (estimations from sealevel differences) nor the output of the model chain are the“truth”. Having that in mind, we conclude that the wholemodel chain (AOGCM, RCMs and GETM) is able to repro-duce with a reasonable accuracy the present-day statistics ofinflow events.
910 Ocean Dynamics (2013) 63:901–921
10 15 20 2510
−4
10−3
10−2
10−1
Salinity [psu]
PD
F
[1/(
psu)
]
a)
Obs
C201
C202
5 10 1510
−4
10−3
10−2
10−1
Salinity [psu]
b)Obs
C201
C202
10 20 3010
−4
10−3
10−2
10−1
Salinity [psu]
c)
Obs
C201
C202
MOM1
Fig. 4 Validation of the probability distribution function (PDF) of daily averaged bottom salinity at a KB , b OBB and c DSB. For comparison,the salinity distribution at DSB as calculated by MOM is given. For better visualisation, the logarithm of the salinity PDF is plotted
Finally, in Section 4.1, we discussed that the second ref-erence run (C202) showed more intense wind fields (Fig. 2).A slight impact on the inflows can also be seen in Table 5,where the C202 runs shows a lower number of inflowswhich are, however, more intense.
5 Climate projections for the western Baltic Sea2021–2100
In the following section, the impact of the projected cli-mate change on the western Baltic Sea hydrodynamics isdiscussed.
5.1 Atmospheric forcing
In Fig. 5a, annual mean time series of projected 2-m airtemperature (only above water points) are given. Here, theincrease by 2–3.5 K at the end of the century is visible.Interesting to note is that the deviations between the two
scenarios start to emerge not until the middle of the cen-tury. In Fig. 5b, we present the changes in the extremewind as represented by the 99th percentile of the windspeed. Clearly, a high annual variability is visible which isalready seen in the reference period. At a first glance, allwind extremes show a positive trend. To check whether sta-tistical significant changes can be deduced from the timeseries, a Mann–Kendall trend test was used (Mann 1945;Kendall 1975). The results are given in Table 6. Especiallyfor the A1B simulations, a statistical significant increasein the mean and strong wind events can be seen, with anincrease of up to 5 % (0.15 m/s for mean wind speedand 0.7 m/s for the 99th percentile). This is not the casefor the B1 scenarios. However, in the climate commu-nity, there is no overall agreement whether the frequencyand intensity of storms will increase in future climate(Raisanen et al. 2004; Christensen and Christensen 2007;Kjellstrom et al. 2011), although the A1B scenarios pointin that direction. The possible increase might have impli-cations on the occurrence and intensity of storm surges,
Table 5 Statistics of MBIs
1897–1976 1902–1998 1960–2000 1960–2000
Matthaus and Franck (1992) Meier and Kauker (2003b) Neumann (2010) C20
MBIs 90 180 57 54, 50
MBIs/year 1.12 1.87 1.49 1.37, 1.25
〈Duration〉 (days) 10 – 13 14, 15⟨QStot
⟩(109 t) < 2 2.3 2.13 1.74, 1.95⟨
QVtot
⟩(km3) 70 105 117 102, 108
For comparison, the measured data of Matthaus and Franck (1992), the hindcast simulations of Meier and Kauker (2003b) and the estimationbased on the first realisation of the MOM run (Neumann 2010) are given. Data in parentheses indicate average values. In the last column, the twonumbers indicate the results of the two GETM realisations
Ocean Dynamics (2013) 63:901–921 911
7
8
9
10
11
Tem
pera
ture
[°C
]a)
A1B1
A1B2
B11
B22
2000 2050 210017.5
18
18.5
19
19.5
20
20.5
Win
dspe
ed [m
/s]
Year
b)
Fig. 5 a Projected annual 2-m air temperature over the western BalticSea, and b projected annual 99th percentile of the wind speed. Thedata are smoothed by using a 5-year running mean
but also due to the additional mixing on the depth of thehalocline/thermocline.
5.2 Water temperature
In Fig. 6, changes in the annual mean SST are visualised.The climate projections show a warming of the sea surface.Here, the increase of temperature in the surface layer rangesfrom 0.7 to 2.8 K for the A1B scenario and 0.5–1.9 K forthe B1 scenario. For both scenarios and periods, a gradientin surface warming between the Kattegat and the BornholmBasin is clearly visible. A similar pattern is visible for thebottom temperatures (not show). The largest changes canbe seen in the Arkona Basin and Bornholm Basin. Thispronounced warming is consistent with the findings of sim-ulations for the entire Baltic Sea (Meier 2006; Neumann2010; Neumann et al. 2012). A feature that is visible in the
C20 runs is the temperature difference between the Swedishcoast and the Oder Lagoon with an average value of �T ≈2 K. This gradient will be weaker at the end of the cen-tury with �T ≈ 1.4 K for A1B and �T ≈ 1.7 K for B1.Therefore, the SST will be spatially more homogeneous.
To understand if the climate changes only affect the sur-face layer, Table 7 gives a description of the warming inrelation to C20 for the whole water column. The changesat the bottom (depth >40 m) are the strongest; however,the values indicate that the whole water column is shiftedtowards higher temperatures. Similar results with a slightdestratification were also found by Meier (2006).
5.3 Heat wave duration
To understand the impact of a changed climate on the habi-tat conditions at the sea floor, we computed the numberof consecutive heat days based on the bottom temperaturethreshold (Fig. 7a) as defined in Section 3.4.1. The high-est bottom temperature threshold can be seen in the OderLagoon and in the surrounding waters with values wellabove 16 ◦C. The bottom temperature threshold for theArkona Basin are 15 and 10 ◦C for the Bornholm Basin. InFig. 7b, the number of consecutive heat days based on thebottom temperature threshold is shown for the control sim-ulations. Highest values can be seen in the Arkona Basin.This feature is caused by the inflow events and the persis-tent pools of stagnant water at the bottom (Sellschopp et al.2006). The climate projections for 2021–2050 indicate forboth scenarios an increase in the duration of heat waves.Especially for the Arkona Basin, this amounts to 20 days(B1) and 30 days (A1B). This is a doubling of the presentconditions. At the end of the century, the model predicts thatthe present-day extremes will persist for nearly 2 monthsin both scenarios (Fig. 7e,f), which is a tripling of thepresent-day conditions. Neumann et al. (2012) investigatethe exceedance probability of the SST for values higher than18 ◦C in the Baltic. Their results only indicate a doubling ofthe occurrence of critical SST values in the southern BalticSea. These deviations can be explained by two reasons. Atfirst, due to the higher spatial resolution in our setup, we canbetter reproduce the effects of stratification and suppressionof vertical mixing. Thus, we can better resolve vertical gra-dients. Secondly, due to changes in the inflow statistics (see
Table 6 Linear trends in mean wind speed (〈u〉) and the 99th percentile for the period of 2001–2100
A1B1, % (m/s) A1B2, % (m/s) B11, % (m/s) B12, % (m/s)
〈u〉 4 (0.1) 5 (0.1) 3 (0.07) 2 (0.05)
99th percentile 5 (0.7) 3 (0.4) 2 (0.3) 2 (0.3)
Italic values indicate a significant trend at the 5 % level, based on a Mann–Kendall trend test. Values in parentheses indicate the projected changeover the century
912 Ocean Dynamics (2013) 63:901–921
a) 54° N
55° N
0.7
0.8
0.9
1
b) 10° E 12° E 14° E
54° N
55° N
2.3
2.4
2.5
2.6
2.7
c)0.35
0.4
0.45
0.5
0.55
d) 10° E 12° E 14° E
1.6
1.7
1.8
1.9
2
Fig. 6 Deviation of the annual mean sea surface temperature in Kelvin from the period of 1971–2000 for a A1B 2021–2050, b A1B 2071–2100,c B1 2021–2050 and d B1 2071–2100. Note the different colour scales to emphasise details. Shown is the average of both realisations
Section 5.5), the exchange time of the Arkona Basin bottomwater is increased, and thus the residence time is increased.This leads to higher values of the heat wave duration.
In summary, the prolongation of the heat wave durationwill give additional stress for species adapted to present-day conditions, if they are not able to evolute within this100 years.
According to the solubility of oxygen in seawater (Weiss1970), the projected changes of temperature (+3 K for theA1B-scenario) and salinity (−1.5 psu, see Sec. 5.4) willlead to a decrease of the maximal dissolved oxygen ofabout 0.4 mg/l until the end of the twenty-first century (thiscorresponds to a reduction of nearly 6 %). This lower avail-ableness of oxygen will increase the hypoxic areas and willhave serious impacts on the Baltic ecosystem (Conley et al.2009), since all oxygen-consuming organisms have to adaptor to mitigate (Diaz and Rosenberg 1995). Thereby, thebottom organisms, which cannot move upwards (e.g. fisheggs), are endangered, since their adaptations may fail due
to the longer durations of heat waves and possible hypoxia.Moreover, Baker-Austin et al. (2012) show that the risk ofa Vibrio infections increases with a rise in water tempera-ture. They concluded that the key risk factors for the BalticSea are water temperature exceeding 19 oC for 3 weeks ormore. These values can already be seen for the region northof the Oder Lagoon (Fig. 7a). For the end of the century, theheat wave duration can clearly exceed 4–5 weeks (Fig. 7ef)in this region. Thus, the likelihood of a Vibrio infection willincrease in the near future.
5.4 Salinity
The projected changes in the bottom salinity at DSB aregiven in Fig. 8a. For both scenarios, a decrease in salin-ity is visible, mainly caused by the increase in freshwatersupply (Meier and Kauker 2003a). The reduction in salin-ity is consistent with the findings of Meier and Kauker(2003a), Meier et al. (2006) and Neumann et al. (2012), with
Table 7 Summary of temperature changes for the air temperature, the SST, bottom temperature (depth >40 m) and the whole water column inKelvin
Station A1B B1
2021–2050 2071–2100 2021–2050 2071–2100
Air temperature 1.1, 1.5 2.7, 3.0 0.7, 1.1 1.7, 2.1
SST 0.9, 1.3 2.7, 2.9 0.6, 1.2 1.8, 2.0
Bottom temperature 1.0, 1.2 2.8, 3.0 0.7, 1.0 2.0, 2.1
Whole water body 0.9, 1.2 2.7, 3.0 0.6, 1.1 1.9, 2.0
The two numbers indicate both realisations
Ocean Dynamics (2013) 63:901–921 913
a) 54° N
55° N
10
15
20
b) 10° E 12° E 14° E
54° N
55° N
0
5
10
15
20
c)0
10
20
30
40
d) 10° E 12° E 14° E
0
10
20
30
40
e)20
40
60
80
f) 10° E 12° E 14° E
20
40
60
80
o
Fig. 7 a Bottom temperature threshold (in degrees Celsius) of the con-trol simulations C20 (1971–2000). Mean number of consecutive daysabove the bottom temperature threshold: b C20 (1971–2000), c A1B
(2021–2050), d B1 (2021–2050), e A1B (2071–2100) and f B1 (2071–2100). Note the different colour scales. The contour interval is 5 days.Shown is the average of both realisations
a decrease of approx 1.5 psu for the B1 scenario and 2 psufor the A1B scenario. It is interesting to note the differenceof 1.0 psu between the two control runs, which is indicatingthe high natural variability.
In Fig. 8b, the difference between bottom and surfacesalinity for DSB is shown. For both stations, no trend inthe salinity difference is visible. Thus, the salinity profile ofthe whole water column is simply shifted to lower values.Therefore, the shape of the salinity profile does not change.
9
10
11
12
13
Sal
inity
[ps
u]
a)
A1B1
A1B2
B11
B12
1975 2000 2025 2050 2075 21002
3
4
Δ S
[ps
u]
b)
Years
Fig. 8 a Projected changes in annual mean bottom salinity at DarssSill (DSB). b Bottom–surface salinity difference (�S) for DSB. Thedata are smoothed by using a 5-year running mean
The scenario simulations of Meier et al. (2006) showedsimilar results.
This shift in the salinity profile can also be seen forthe Bornholm Basin. In Fig. 9a, the changes for BBS aredepicted. Especially in the upper 20 m of the water col-umn, a clear shift is visible. The surface salinity decreases
a)
b)
Fig. 9 a Annual mean salinity profile for station BBS and b annualmean vertical density gradient ∂zρ. The reference C20 profiles arecomputed as mean for 1971–2000. The grey-shaded area indicates themean value of annual standard deviations. The projected profiles arecomputed as mean for 2071–2100
914 Ocean Dynamics (2013) 63:901–921
by approximately 1.5 psu for both scenarios. For thebottom salinity, the decrease is shadowed by the highervariability as indicated by the standard deviation. Whereasthe reduction in surface salinity is similar for both scenar-ios and realisations, differences at the bottom are visible.The A1B2 and B12 runs show a stronger reduction than theA1B1 and B11 realisations. This behaviour can also be seenin Fig. 8a.
The main reason for the reduction of salinity is a changein the freshwater supply due to river discharge. An analy-sis of the freshwater runoff entering the Baltic Sea revealedthat both scenarios show an increase in runoff of about 10 %by the end of the century, compared to present-day values.A Mann–Kendall test confirmed that these changes aresignificant. Based on sensitivity experiments, Meier et al.(2006) concluded that in future scenarios due to changesin freshwater inflow, the Baltic Sea basin averaged salin-ity can vary between −31 and +9 %. In their experimentswith the largest changes, the salinity in the Bornholm Basinwas as low as in the Bothnian Bay. Although we do notsee such extreme changes, our results are within the possi-ble spread as discussed by Meier et al. (2006). In Fig. 10,we show the spatial changes in salinity distribution com-pared to the reference period. Clearly, for both scenarios,the isolines are shifted towards the Kattegat. Most notice-able are the changes of the 7 psu isoline. Whereas thisisoline is located at present close to Bornholm, in both
b)
10° E 12° E 14° E
54° N
55° N
5
10
15
20
a) 54° N
55° N
1971−2000 − 7 psu2071−2100 − 7 psu1971−2000 − 13 psu2071−2100 − 13 psu
5
10
15
20
Fig. 10 Mean surface salinity distribution in psu of the reference runsC20 (colour coded). The thick lines indicate the shift of the 7 and13 psu isoline for a A1B and b B1
scenarios, it is moved to Darss Sill and the entrance ofthe Øresund. This is a change in position by approxi-mately 100 km. For the 13 psu isoline, the shift is only60 km. However, for the A1B scenario, this isoline is nearlymoved out of the Danish Straits into the Kattegat. Thisimplies that the exiting salinity gradient is shifted to thenorth.
Schrum (2001) hypothesised that a second reason forthe salinity reduction could be the changes of the windspeed and direction. In Fig. 11, the deviations of the zonalwind speed compared to the mean of the control runs areshown. Especially for autumn, higher mean wind speedscould occur. This additional wind stress hampers the out-flow of freshwater out of the Baltic and reduces the inflowof saline North Sea water (Schrum 2001; Meier 2006).Due to the additional mixing in the Danish Straits, strat-ification is reduced, and the inflowing saline water andthe outflowing brackish Baltic Sea water are mixed. Thisreduces the density and thus the potential for deepwatergeneration.
5.5 Baltic inflows
In Fig. 12, the durations of the MBIs for the present climateand the projections A1B1 and B11 are given. A feature notvisible in the simulations is the present stagnation periodof MBIs (Nehring et al. 1995; Matthaus et al. 1999). Dur-ing the last two decades, only three MBIs were observed.
2 4 6 8 10 12−0.5
0
0.5
1
1.5
2
2.5
3
Month
Dev
iatio
n in
zon
al w
ind
spee
d [m
/s]
A1B1
A1B2
B11
B12
Fig. 11 Projected deviations from the annual cycle of C20 zonal windspeed (1971–2000) to A1B and B1 (2071–2100). The plus sign indi-cates significant changes at the 5 % significance level compared to theC20 runs
Ocean Dynamics (2013) 63:901–921 915
Fig. 12 Duration of MBIs. aScenario A1B1. b Scenario B11
10
20
30
40
50
60
dura
tion
[day
s]
a)
1980 2000 2020 2040 2060 2080
10
20
30
40
50
60
Dur
atio
n [d
ays]
Years
b)
The resulting long stagnation periods are not reproducedby the C20 simulations; however, it is not expected thata control simulation with forcing from a GCM is able toreproduce the long stagnation periods due to the chaoticbehaviour of the atmosphere. More important, the long-term statistics match with observations (Table 5). Figure 12indicates the occurrence of stagnation periods for bothscenarios. It is also noticeable that the spread of inflow dura-tions becomes wider. The simulations show inflows lastingnearly 2 months and a clustering of short events, which isnot visible in the present climate. Similar results can beseen for A1B2 and B12 (not shown). Additionally, the B11
run (Fig. 12) shows inflows with durations of 50–60 days,which are not observed in the reference runs. Matthausand Franck (1992) already differentiated the intensity ofinflows in weak, moderate, strong and very strong. The 50–60 days inflows seen in the B11 run can be categorisedas very strong. Since inflows are “extreme events” andtherefore sparse in time by definitions, we cannot estimatewhether these changes are significant. Certainly, these verystrong events will lead to massive deepwater exchange inthe Gotland Basin.
Table 8 indicates that at the end of the century, the occur-rence of MBIs/year, in both scenarios, is reduced by a factorof two. The mean duration with 10–15 days is similar to theconditions in the present climate.
To show the effect of the changes in MBI duration,Fig. 13a depicts the cumulative salt transport associatedwith the inflow events. Whereas one of the B1 simulationsfollows the climatology, both A1B simulations show a sig-nificant deviation from the climatological salt transport. TheA1B scenarios show for the period of 2070–2100 a decreaseof up to 50 % in cumulative salt transport compared to the
present-day climate. For the B1 runs, the mean reductionis slightly lower with 30 %. However, as the inflows in theC20 runs match with long-term observations (Table 5), thesenegative trends are robust. The reduction in salt transportcan also be seen in the total salt content of the Baltic Seaand therefore in the changes of the salinity at DSB (Fig. 8a).
To figure out the reasons for this change in salt transport,at first we counted the duration of consecutive east-winddays over the Bornholm Basin, which can act as a proxy forthe preconditioning of MBIs (Matthaus and Schinke 1994).Table 9 indicates that the mean duration of consecutive east-wind days is 13 days and does not change significantlywithin the next hundred years. Only the variability shows aslight increase. Since the numbers of consecutive east-winddays stay nearly constant, the necessary preconditioner isstill given in the near future. Therefore, large-scale changesin atmospheric pattern that promote easterly winds can beruled out as cause for the decrease in salt transport.
Lass and Matthaus (1996) discussed that in years withoutmajor Baltic inflows, the prevailing easterly winds duringthe preconditioning are reduced. Moreover, they found ananomalous west-wind component over the western Balticfor seasons without major Baltic inflow, compared to sea-sons with inflow events. The annual cycle of zonal wind(Fig. 11) indicates such a shift in wind pattern. We seea strengthening of the westerly component, especially forA1B 2071–2100, of up to 2.5 m/s during autumn. Thisincrease in west-wind strength can be seen for nearly allscenarios. Therefore, this west-wind anomaly can indeedchange the occurrence of MBIs and thus change the salttransport: at first by additional mixing in the Danish Straitsand secondly by changing the filling state of the Baltic.The two above-discussed mechanisms reduce the potential
916 Ocean Dynamics (2013) 63:901–921
Table 8 Projected statistics of inflows and salt flux QS through the Øresund
1960–2000 2010–2050 2060–2100
C20 A1B B1 A1B B1
Barotropic inflows
MBIs 54, 50 30, 42 43, 39 26, 23 30, 40
MBIs/year 1.37, 1.25 0.73, 1.05 1.07, 0.97 0.65, 0.57 0.73, 0.75
〈Duration〉 (days) 14, 15 11, 13 16, 15 14, 15 12, 10⟨QStot
⟩(109 t) 1.74, 1.95 1.51, 1.25 1.14, 1.96 0.79, 1.06 2.03, 1.92⟨
QVtot
⟩(km3) 102, 108 96, 90 74, 109 93, 111 56 , 63
Baroclinic inflows
Inflows/year 1.1, 1.7 0.7, 1.3 1.3, 1.4 0.4, 1.6 0.8, 1.0
〈Duration〉 (days) 18 , 16 15, 18 19, 19 26, 13 18, 19⟨QStot
⟩(109 t) 0.28, 0.16 0.20, 0.21 0.30, 0.21 0.41, 0.16 0.29, 0.25⟨
QVtot
⟩(km3) 19, 11 15, 17 18, 14 18, 19 17, 16
Øresund
〈QS〉 (ton/s) 48.4, 42.7 42.5, 41.2 45.9, 41.2 43.0, 38.9 43.8, 37.6
The two numbers represents the single realisations
inflow volume as indicated by Schrum (2001) or Meier(2006).
An additional mechanism to transport salt into the Balticare baroclinic inflows. In Table 8, a statistical descrip-tion is given. The baroclinic inflows occur on averageonce a year with a duration of less than 3 weeks. Theassociated salt transport for the period of 1961–2000 iswith 0.29×109 t/year approximately a factor of 8 smallerthan for MBIs (2.3×109 t/year). Table 8 further indicatesa decrease in occurrence within the next hundred years.Although the frequency is reduced, the simulations indicatea slight intensification of the associated salt transport at theend of the century. Moreover, the total baroclinic salt trans-port for the end of the century stays nearly constant foreach event (0.28×109 t/year for A1B and 0.24×109 t/yearfor B1). Since the average salt content of individual baro-clinic inflow events stays constant, but the total numberof baroclinic inflows decreases, the total salt transport isreduced.
In Fig. 13b, the cumulative salt transport of baroclinicinflows is shown. The integrated salt transport for the A1B
scenario indicates a significant deviation from the clima-tology (1961–2000). Feistel et al. (2008) discussed thehypothesis that in a future climate with a less saline BalticSea, the increased baroclinic pressure gradient between theKattegat and the Arkona Basin might lead to an intensi-fication of baroclinic inflows. For the A1B scenarios, thishypothesis seems not to be valid. The simulations indicatea decrease of baroclinic salt transport by 30 % for A1Band 15 % for B1 at the end of the century (2061–2100,Table 8). Looking onto the total cumulative baroclinic trans-port for the whole twenty-first century (Fig. 13), a reductionby roughly 45 % for A1B and 17 % for B1 are estimated.However, one has to recall the preconditions for baroclinicinflows. These events require a calm wind period of at least14 days such that stratification in the Danish Straits canbuild up. In Table 10, the annual average of consecutivenumber of calm wind days at Darss Sill is given. For theperiod of 2021–2050, the B11 scenario shows a positiveanomaly. This slight increase in calm wind days (U<6 m/s),compared to the C20 runs, might be the reason for theincreased salt transport. The decrease in baroclinic inflow
Table 9 Mean annualconsecutive east-wind daysover the Bornholm Basin
Ueast >0 m/s Ueast >10 m/s
C20 12.8, 13.1 (3.2, 3.6) 3.0, 3.1 (1.2, 1.2)
A1B (2021-2050) 13.5, 12.9 (5.2. 4.3) 2.7, 2.8 (1.3, 1.1)
A1B (2071-2100) 13.6, 13.9 (4.3, 6.0) 3.2, 2.5 (2.6, 1.5)
B1 (2021-2050) 13.5, 14.2 (4.1, 4.7) 2.9, 3.0 (1.1, 1.3)
B1 (2071-2100) 14.3, 12.9 (4.4, 3.5) 3.1, 2.9 (1.2, 1.1)
Values in parentheses indicatethe mean annual standard devia-tion. The two numbers representthe two realisations
Ocean Dynamics (2013) 63:901–921 917
0
50
100
150
200
250
Cum
ulat
ive
QS [1
09 t]
Barotropic inflowsa)
2000 2050 21000
20
40
60
80
Cum
ulat
ive
QS [1
09 t]
Years
Baroclinic inflowsb)
A1B1
A1B2
B11
B12
C20−fit
Fig. 13 Projected cumulative salt transport QStot at GRT duringinflow events: a barotropic and b baroclinic inflows. The climatologyis based on a linear fit of the averaged C20 runs
events for the A1B scenario can be caused by the decreasein calm wind periods from 13 days at present to less than10 days in a future climate (Table 10).
A third mechanism to transport salt into the Baltic Seaare inflows through the Øresund as discussed by Fischer andMatthaus (1996) or Lintrup and Jakobsen (1999). Althoughthese inflows do not have the potential for deepwater gen-eration, they act as a constant supply of salty, oxygen-richNorth Sea water to the western Baltic Sea. The projectedchanges (Table 8) indicate a reduction in salt transportthrough the Øresund by the end of the century. However,these changes are with a decrease of 10 % much smaller thanthe changes in transport via Darss Sill. These deviations canbe explained by geometrical reasons. Figure 10 indicatesthat the isolines of salinity are shifted into the direction of
the Kattegat. Whereas the salinity of north of the DanishStraits nowadays is close to 34 psu, these values will belower in a future climate. This implies that the water enter-ing the Øresund will be less saline and thus reducing thetotal salt flux.
At present, the triggering of MBIs is well understood(Matthaus and Franck 1992). However, it is still unclearwhat is causing the long stagnation period during thelast two decades (Nehring et al. 1995; Matthaus et al.1999; Feistel et al. 2008). It is hypothesised that sub-tle changes in the large-scale forcing can cause thesechanges. Although the projected changes in calm winddays (Table 10) or the changes in east-wind days (Table 9)are small (in general less than 10 %), these changes cancause the change in inflow dynamics as seen in Fig. 13 orTable 8.
5.6 Stratification
In Fig. 14, time series of the potential energy anomaly andits standard deviation for the Bornholm Basin are shown.For both scenarios, no significant trend in the mean strati-fication is visible (Fig. 14a). Only a high natural variabilityin the different runs can be seen. The time series for BBSindicate an increase in the annual variability (Fig. 14b) forall realisations. This change in variability is mainly causedby the increase in wind speeds (Table 6). The rise in strat-ification variability but also the change in the range ofthe variability correlates well with the time series of theprojected annual 99th percentile of the wind speed. Thesestronger winds will lead to an enhanced vertical mixingand thus a partially homogenisation of the water column.Since the mean stratification does not change a processthat causes a re-stratification needs to be found. A possi-ble cause are the MBIs. Although they are at the end ofthe century not intense enough to reach the Gotland Basin,they still can re-stratify the water column in the Born-holm Basin. Thus, the mean stratification is restored. Theincrease in stagnation periods will prolong the time betweenthe renewing of the saline bottom pool in the ArkonaBasin and the Bornholm Basin. This directly translatesinto an increase in variability of the potential energyanomaly.
Table 10 Mean annualconsecutive calm wind daysover Darss Sill
Uwest <3 m/s Uwest <6 m/s
C20 2.3, 2.5 (0.5, 0.4) 13.6, 13.3 (2.7, 2.4)
A1B (2021–2050) 2.2, 2.0 (0.4, 0.4) 11.4, 12.3 (1.9, 3.0)
A1B (2071–2100) 2.1, 2.2 (0.3, 0.4) 9.9, 10.7 (2.3, 2.1)
B1 (2021–2050) 2.2, 2.1 (0.6, 0.5) 14.9, 13.5 (3.6, 3.4)
B1 (2071–2100) 2.3, 2.2 (0.6, 0.4) 10.9, 11.8 (1.9, 3.0)
Values in parentheses indicatethe mean of annual standarddeviation over the appropriatetime periods. The two numbersrepresent each realisation
918 Ocean Dynamics (2013) 63:901–921
450
500
550
600
650
700
750
φ [J
/m3 ]
a)
A1B1
A1B2
B11
B12
2000 2050 2100
60
80
100
120
140
std(
φ) [
J/m
3 ]
b)
Years
Fig. 14 Projected changes in potential energy anomaly for BBS: aannual mean potential energy anomaly and b standard deviation ofpotential energy anomaly. The data are smoothed by using a 5-yearrunning mean
The sensitivity experiments of Meier and Kauker (2003b)indicated that the halocline in the central Baltic Sea is nearlypersistent at 60–100 m, independent of moderate changesin freshwater supply. Our findings (Fig. 14) are similar forthe Bornholm Basin, indicating that the mean stratificationis nearly independent of the freshwater supply. Moreover,because the salinity profiles are only shifted (Fig. 9a) andsimilarly the temperature profiles (Table 7), there is no rea-son for major changes of the mean stratification in the basinsof the western Baltic Sea. To further strengthen our find-ings, we show in Fig. 9b the vertical density gradient ∂zρ.It is clearly visible that the shape of the density profile doesnot change in the different scenarios. Thus, the position ofthe pycnocline varies not significantly, and the climate pro-jections are within the natural variability of the referenceruns.
These findings are in agreement with Hordoir andMeier (2012) who reported that the spring and sum-mer stratification at the bottom of the mixed layer willbe small in the southern Baltic Sea but will increasetowards the North of the Baltic Sea. They could showthat the effect of the temperature on the maximum densityis responsible for these changes. Since thermal convec-tion is not an important process in the western BalticSea, the changes in stratification are considered to besmall.
6 Conclusion
In this paper, transient climate simulations, covering theperiod of 1960–2100 using a high-resolution ocean model(GETM) for the western Baltic Sea are discussed and anal-ysed. These simulations are based on the IPCC scenariosA1B and B1. Despite the fact that this study is only based onboundary conditions from one regional atmospheric model(CLM) and one regional ocean model (MOM) and not afull ensemble, this analysis can offer valuable informationof a changing environment. Due to restrictions in comput-ing power, only two realisations for each scenario werecomputed. Therefore, uncertainty estimates, based on fullensemble prediction (Jacob et al. 2007; Meier et al. 2006)cannot be given.
With the help of a high-resolution local model, we couldovercome some of the limitations of regional climate mod-els. The use of GETM leads to a much better representationof the salinity PDF’s at Darss Sill and thus to an improve-ment in the statistical reproduction of Baltic Sea inflows.This is achieved due to a much better representation of thetopography and the Danish Straits. The usage of higher spa-tial resolution gave also lower numerical mixing (Burchardand Rennau 2008; Hofmeister et al. 2011).
The findings of this study can be summarised asfollows:
1. The model results indicate a warming, with an increaseof 0.5–2.5 K at the sea surface and 0.7–2.8 K below40 m. The response of the water column is nearly linearto changes in the air temperature.
2. The simulations show a decrease in salinity by 1.5–2 psu for both scenarios, with a slightly lower decreasefor B1. Although this is consistent with simulations ofthe whole Baltic Sea (Meier 2006; Neumann 2010),uncertainties associated with changes in salinity arehigh (Meier et al. 2006). The bottom–surface salin-ity difference is nearly constant in the projections,indicating a shift in the salinity profiles.
3. Based on present-day bottom temperature extremes(99th percentile), we see a prolongation of heat wavesbased on this threshold. Especially for the ArkonaBasin, the projections show that for the end of thecentury, these extremes (present-day duration 10 days)will last for 50–60 days (A1B) and 30 days (B1),respectively.
4. The mean stratification (measured by the potentialenergy anomaly and the vertical density profile) in thedeep basins is not influenced by the climate changes.This persistence is caused by not only a shifted salin-ity profile but also a shifted temperature profile, whereboth effects cancel each other. Due to higher windspeeds, there is a tendency for higher variability of the
Ocean Dynamics (2013) 63:901–921 919
stratification. The persistence of the stratification andthe associated location of the halocline/thermocline isconsistent with the findings of Meier (2006). Althoughthe model results a reduction and weakening of MBIs,they are still intense enough to restore the stratificationin the Bornholm Basin.
5. An analysis of the occurrence of MBIs showed a sig-nificant decrease in salt and volume transport. Thisdecrease in transport is mainly caused by the increaseof westerly winds. The projections indicate a reductionof MBIs, but also weakening. Nevertheless, the averageduration of these inflow events does not change in theprojections.
6. Although the freshening of the Baltic Sea will increasethe baroclinic pressure gradient over the Danish Straits,the simulations indicate no increase of baroclinicinflows (B1) or even a decrease (A1B). The changes inbaroclinic inflow events can be consistently explainedby the changes in calm wind periods, which are anecessary preconditioner.
7. The simulations indicate a reduction of up to 10 % insalt transport through the Øresund.
Finally, we would like to comment on future climate modeldevelopment for the Baltic Sea. At present, there is noneed to do climate downscaling with a baroclinic eddyresolving model (resolution better than 500 m; Reißmann(2005) or Osinski et al. (2010)). Since one is mostly inter-ested in statistical measures (change in mean temperature,change in total biomass or change in the mean stratifi-cation), a coarse and thus cheap climate model might beenough. However, in the Baltic Sea, the Danish Straits arethe bottleneck to get the water/volume exchange right. Thecrucial point is that with a coarse model, the water/volumeexchange in the western Baltic Sea is overestimated (Fig. 4;see also the discussion in Section 3.1). Thus, a coarseclimate model will give results that are not very reli-able for that region. A possible solution would be to useeither a grid refinement in the western Baltic Sea or touse two-way nesting for the Danish Straits as shown byFu et al. (2011).
Acknowledgments The Baltic Monitoring Programme (COM-BINE) and the stations of the German Marine Monitoring Network(MARNET) in the Baltic Sea are conducted by the Leibniz Institute forBaltic Sea Research Warnemunde (IOW) on behalf of the Bundesamtfur Seeschifffahrt and Hydrographie, financed by the Bundesminis-terium fur Verkehr, Bau-und Wohnungswesen. Supercomputing powerwas provided by “The North-German Supercomputing Alliance”. Thiswork was supported by the Bundesministerium fur Bildung, Wis-senschaft, Forschung und Technologie (BMBF) of Germany in theproject RA:dOst through grant number 01LR0807B. We are gratefulto Karsten Bolding (Asperup, Denmark) for the code maintenance ofGETM.
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