Report on the biogeochemical model setup for the Baltic Sea and its applications.
Date: 2020-06-15
Institutions: Joint Research Centre
Subjects: Baltic Sea, environmental impact, environmental research, eutrophication, marine environment, ocean, oceanography, quality of the environment, remote sensing, research report, simulation

Report on the biogeochemical model 
setup for the Baltic Sea and its 
applications  

Pärn, O; Friedland, R.; Garcia-Gorriz, E; Stips, A. 

2020 

EUR 30252 EN 

 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output 
expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person 
acting on behalf of the Commission is responsible for the use that might be made of this publication. For information on the 
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on the maps do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal 
status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. 

Contact information 
Name: Ove Pärn    
Address: Via E. Fermi, TP270, Ispra, Varese, Italy 
Email: jrc-mmf@ec.europa.eu; Ove.PARN@ext.ec.europa.eu  

EU Science Hub 
https://ec.europa.eu/jrc 

JRC120378 

EUR 30252 EN 

PDF 

ISBN 978-92-76-19448-4 

ISSN 1831-9424  

doi:10.2760/672255 

Luxembourg: Publications Office of the European Union, 2020 

© European Union, 2020 

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How to cite this report: Pärn, O., Friedland, R., Garcia Gorriz, E. and Stips, A., Report on the biogeochemical model setup for the 
Baltic Sea and its applications, EUR 30252 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-
19448-4, doi:10.2760/672255, JRC120378. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ..................................................................................................................................... 4 

1 

Introduction........................................................................................................................ 5 

2  Material and Method ......................................................................................................... 6 

2.1 

Study area ................................................................................................................................................... 6 

2.2  Model set-up ............................................................................................................................................... 7 

2.3  Air temperature during the melting period, simple ice model .................................................. 9 

2.4  Riverine nutrient inputs provided by GREEN ................................................................................. 10 

2.5  Observation data used for model forcing and validation ......................................................... 12 

3  Validation.......................................................................................................................... 13 

3.1  Comparison of satellite and model SST data ................................................................................ 13 

3.2 

Sea ice extent ........................................................................................................................................... 15 

3.3 

Sea surface and bottom salinity at stations .................................................................................. 18 

3.4 

Sea surface elevation ............................................................................................................................. 20 

3.5  Chlorophyll a ............................................................................................................................................ 21 

3.6 

Bottom oxygen ......................................................................................................................................... 29 

3.7  Nitrate ........................................................................................................................................................ 31 

3.8  Phosphate .................................................................................................................................................. 38 

3.9 

Impact of sea ice on phytoplankton spring bloom ...................................................................... 45 

3.10  Modelling of the Baltic Sea ecosystem to assess management scenarios ....................... 46 

4  Summary ........................................................................................................................... 47 

References ............................................................................................................................... 48 

Annex 1. List of abbreviations and definitions .................................................................... 50 

3 

 
 
 
 
Abstract  

Various human-induced environmental pressures such as eutrophication affect the Baltic Sea. A 
coupled hydrodynamic-biogeochemical model was used to assess the response of the Baltic Sea’s 
ecosystem to climate change and nutrient reduction. This model was run with different settings and 
observation data was used for data analysis and model validation. The validation confirmed that the 
results for the model are reliable and allow its usage for simulations involving hindcast and forecast 
scenarios. Hydrodynamic features such as salinity and temperature were well reproduced. Among the 
modelled eutrophication indicators, chlorophyll-a, oxygen, nitrate and phosphorus followed the 
seasonal cycle. The presence of ice affected the central variables of the biogeochemical model 
substantially. Changing ice conditions are one of the key factors directly affecting the timing and 
composition of spring bloom within the Baltic Sea, with cascading consequences for nutrient transfer 
and ecological dynamics. Moreover, the modelling results indicated that the assumed nutrient 
reductions based on the implementation of various measures in the catchment area could lead to a 
slight improvement in the health of the Baltic Sea as there was an approximate 2% increase in the 
improvement of the D5C1 indicators. But longer time series are required to observe a substantial 
change in the Baltic Sea’s environmental conditions and that stronger nutrient input reduction 
measures are necessary to achieve an improved environmental status. 

4 

 
 
 
 
 
1 

Introduction 

The Baltic Sea is suffering from severe effects of eutrophication for several decades, and its ecosystem 
has become vulnerable to climate change. Eutrophication resulting from the anthropogenic 
enrichment of nutrients is a major environmental concern around the Baltic Sea (HELCOM, 2018). Long 
nutrient residence times and a high buffer capacity of the ecosystem necessitate long-term 
environmental management of the sea along with strong administrative actions. The Baltic Sea Action 
Plan (BSAP) of the Helsinki Commission (HELCOM) was adopted by all coastal countries of the Baltic 
Sea and by the European Community (HELCOM, 2007, 2013, 2018). Fighting eutrophication is the most 
crucial component of the BSAP, including specific goals with respect to water quality targets as well as 
maximal allowable nutrient inputs for the various sub-basins to achieve a ‘healthy’ Baltic by 2021. To 
achieve these, nutrient reduction goals were allocated to countries around the sea. 
The southern part of the Baltic Sea is located in the temperate zone while the northern part has a 
subarctic climate. The Baltic Sea ice season lasts up to 5–7 months (from November to May). During a 
mild winter, ice occurs only in the Bothnian Bay and the easternmost Gulf of Finland, but during a cold 
winter the most parts of the Baltic Sea are covered with ice (Vihma and Haapala, 2009). The maximum 
extent of the ice cover in the Baltic Sea is normally reached towards late February or early March. In 
the southern part of the Baltic Sea, ice conditions vary extensively from one year to another. In 1987, 
the ice cover was so large that only a small area in the southern Baltic Sea remained ice-free 
(according to remote sensing data). However, during mild winters, no ice forms in the middle and 
southern part of the sea. 
The Baltic Sea ecosystem has severely changed over the last decades due to eutrophication, climate 
change and other anthropogenic pressures (HELCOM 2017). Since the late 1980s, a dramatic decrease 
of the diatom standing stock and a change to the dominance of autotrophic dinoflagellates in the 
Baltic has been observed (Hjerne et al. 2019; Wasmund et al. 2017, Spilling et al. 2018). During spring 
blooms the largest amount of annual phytoplankton biomass is produced (Spilling et al. 2018), which 
is strongly affecting all biogeochemical cycles in the Baltic Sea. The diatoms and dinoflagellates are 
dominant in the phytoplankton spring bloom and they play a decisive role as the food web for the 
higher trophic levels. When the spring bloom is dominated by flagellates, they promote a pelagic food 
web, while benthic organisms are benefitting from faster-sinking diatoms. The flagellates are dominant 
in areas where sea ice is present and diatoms dominate in areas with no ice (Pärn at al. 2020), but also 
the bloom timing is influenced by the ice period (Klais et al. 2011; Hjerne et al. 2019; Pärn et al. 2020).  
The physical‐biogeochemical processes in the ice, water and sediment are complex, making it difficult 
to predict specific effects of sea ice or nutrient reduction. Hence, model studies are needed to 
investigate possible impacts by applying fully coupled three-dimensional models. As ice cover heavily 
influences the Baltic Sea ecosystem, the sea ice model is an essential part of Baltic Sea ecosystem 
model framework. The JRC Baltic Sea model is based on the General Estuarine Transport Model (GETM) 
and the Ecological ReGional Ocean Model (ERGOM). GETM was selected because its hydrodynamical 
engine is in a methodological agreement with the other regional JRC models and because GETM is 
well suited for modelling the Baltic Sea (Stips et al., 2005; Lessin et al., 2014; Gräwe et al., 2015; Placke 
et al., 2018).    
The coupled hydrodynamic–biogeochemical model has been applied to investigate the following 
processes: 
The impact of sea ice on spring bloom in the Baltic Sea and therefore the importance of the influence 
of the ice model on the ecosystem simulation (Pärn et al., 2020). 
The influence of the already occurred climate change and the effects of sea ice on dinoflagellate and 
diatom concentrations during the spring bloom in the Baltic Sea (Pärn et al., 2020) 
Modelling of the Baltic Sea ecosystem to assess nutrient reduction scenarios for their management 
(Friedland et al. 2020)  

5 

 
 
 
 
 
2  Material and Method 

2.1 

Study area 

The Baltic Sea is a semi-enclosed brackish water mass with a surface of 415,000 km2 and a volume of 
21,700 km3. It is characterised by a strong salinity gradient extending from marine salinity (30 PSU) in 
the entrance to nearly freshwater (Fig 1) (Leppäranta and Myrberg, 2009). Water exchange between 
the Baltic Sea and North Sea is restricted due to the shallow and narrow Danish Straits that lie between 
the two seas. The sea is quite shallow (Fig. 2), with a mean depth of only 54 m and maximum depth of 
459 m. The deepest point of the Baltic Sea is located in the deep trench of the Landsort Deep (BY31, 
Fig 1). The Gotland Deep (BY15), which has a maximum depth of 250 m in the central Baltic Proper, is a 
dynamic deep area that considerably influences the hydrographic and biogeochemical fields of the 
Baltic Sea. The inflows of saline and oxygenated water into the Baltic Sea, especially the major Baltic 
inflows, only occur intermittently (Mohrholz, 2018).  
The waters of the Baltic Sea are generally cold, and the water in the northern areas of the sea freezes 
every winter. However, the temperature of the surface water increases in the summer to more than    
20 °C. The temperature and salinity gradients in the Baltic Sea cause large differences in the ecological 
conditions along the sea, with the biodiversity declining sharply with the salinity (Ojaveer et al. 2010). 
The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle 
driven by the annual variations in solar radiation (Leppäranta and Myrberg, 2009). The maximum water 
temperatures occur in July and August. The minimum temperatures occur during February, when the 
Baltic Sea becomes partially frozen. 
Upwelling (vertical displacement of the water body and mixing) is a typical and characteristic process 
in the Baltic Sea (Myrberg et al. 2019).  During the thermal stratified period, upwelling can lead to a 
strong sea-surface temperature drop of more than 10 °C changing drastically the thermal balance and 
stability conditions at the sea-surface (Lehmann and Myrberg 2008). Upwelling is replenishing the 
euphotic zone with the nutritional components necessary for biological productivity, when the surface 
layer is depleted of nutrients.   

Figure 1. January mean sea surface salinity. The model initial field of salinity and locations of the stations used for 
comparing the modelled values. 

6 

 
 
 
 
 
Figure 2. Bottom topography of the Baltic Sea: the model domain with open boundaries in the northern Kattegat. 
GoB: Gulf of Bothnia, GF: Gulf of Finland, GoR: Gulf of Riga, BP: Baltic Proper.  

2.2  Model set-up 

Simulations were performed using a coupled three-dimensional model system. It consists of a 
hydrodynamic model GETM (https://getm.eu/; Burchard, 1999; Burchard and Bolding, 2002; Stips et al., 
2004) and a biogeochemical model (ERGOM; www.ergom.net), which is based on the model described 
by Neumann (2000) and used by several marine modelling groups around the Baltic Sea, e.g. Tallinn 
University of Technology (Estonia), Aarhus University (Denmark) or Leibniz-Institute for Baltic Sea 
Research Warnemünde and Federal Maritime and Hydrographic Agency (both in Germany) (Friedland 
et al. 2020) . GOTM (General Ocean Turbulence Model; www.gotm.net) is coupled to GETM to resolve 
vertical mixing (Umlauf and Burchard, 2005) and ice existence. GETM was coupled with the ERGOM by 
using the Framework for Aquatic Biogeochemical Models (FABM 2020; Bruggeman and Bolding, 2014), 
which is a general framework that allows the operational communication and data exchange between 
hydrodynamic and biogeochemical models. 
The ERGOM version applied in this study included several groups of phytoplankton, namely large and 
small phytoplankton cells (representing diatoms and dinoflagellates, respectively) and cyanobacteria; 
bulk zooplankton group; nitrate, ammonium, and phosphate as most important dissolved nutrients; 
dissolved oxygen; pelagic and benthic detritus; and iron-bound phosphorus in water. The 
corresponding set-up was previously developed and used by Lessin et al. (2014). Our implementation 
of the model for the Baltic Sea (Fig 2) had a horizontal resolution of 2 × 2 nm and included 25 vertical 
σ layers with an open boundary in the northern Kattegat. Hourly sea level data were interpolated from 
gauge measurements at Kattegat and no horizontal currents were explicitly prescribed at the open 
boundary. 

7 

 
 
 
 
 
 
 
 
 
Initial distributions of water temperature and salinity (Fig 1) were interpolated to the model grid from 
monthly climatological data (Janssen, 1999). The prescribed salinity and temperature distributions at 
the open boundary were interpolated using monthly climatological data. Typical concentrations of 
biogeochemical variables were prescribed uniformly within the model domain.  
The six-hourly meteorological forcing data from ERA-Interim and ERA5 reanalysis obtained from the 
European Centre for Medium-Range Weather Forecasts (http://www.ecmwf.int) were applied to the 
model. The model considered the land-based run-off and nutrient loads incorporated into 20 major 
rivers (Lessin et al., 2014) for task 1 and 2 and provided by JRC´s catchment model GREEN (Section 2.3) 
for task 3. The atmospheric deposition of nutrients was considered constant over the entire modelling 
period and area: surface phosphate flux—0.0015 mmol P/m2/d, nitrate flux—0.083 mmol N/m2/d, and 
ammonium flux—0.06 mmol N/m2/d (Bartnicki et al., 2016). 
The Winton ice model (part of GOTM) is modified; the options added are described in section 3.1. In 
the presence of ice: 

 
 
 

the surface temperature of the water is equal to the freezing point 
the wind stress that causes circulation is 0  
light conditions change following Lei et al. (2011) and photosynthetically active radiation 
entering the water is reduced by 30% (in the case of ‘thin sea ice’) or by 60% (for ‘thick ice’).  

The following sea ice models were used in the simulations (see tab. 1 for details): 

a.  Simulation with observed ice 
b.  A simple approach was implemented for modelling ice conditions. Because the model did not 
include the dynamic ice model, a minimal thermodynamic ice approximation was assumed. 
When the sea surface temperature (SST) was equal to the freezing temperature, the model grid 
cell was assumed to be ice covered. 

c.  Simulation with modified Winton ice model.  

Table 1. Ice model used for scenarios simulations 

Task 

nr. 

Subject 

Ice model 

Runoff 

Atmospheric 

Simulation 

forcing 

period 

1. 

2. 

3. 

Impact of ice model quality on 

phytoplankton spring bloom 

run 1 - a 

(observed ice) 

& run 2 – b 

20 rivers 

ERA5 

1982-2018 

(simple ice 

model) 

run 1 - a 

Climate change impact on 

(observed ice) 

plankton species composition 

& run 2 - no 

20 rivers 

ERA-interim 

1982-2018 

Nutrient reduction scenario 

simulations 

ice model 

run 1 – c 

(modified 

GREEN 

ERA-interim 

2005-2012 

Winton) 

8 

 
 
 
 
 
 
 
2.3  Air temperature during the melting period, simple ice model 

The average (1982-2018) air temperature during the spring melting period is shown in Fig 3. Air 
temperatures over the Bothnian Bay, Gulf of Finland and Gulf of Riga were selected for this study. 
According to the ice data, the average end of the sea ice melting period was 19 May in BB, 3 May in 
GoF and 17 April in GoR. At those times, the daily average sea surface temperature was 3-3.7°C. At the 
end of the ice melting period, the daily average air temperature reaches about 5°C (Fig. 3) in all the 
considered areas. A simplified conclusion drawn from the temperature data is that the ice formation 
starts for water temperatures at -0.3°C and the ice melting period ends when the air temperature 
reaches 5°C. 

Figure 3. Daily average (1982-2018) air temperature and sea surface temperature. ERA-Interim air temperature 
over the Bothnian Bay (BB, 65° N, 22.7°), Gulf of Finland (GoF, 60° N, 28° E), Gulf of Riga (GoR, 57.9° N, 23.4° E) and 
Baltic Proper (BP, 55.3° N, 20° E). The sea ice cut-off dates are indicated by coloured dot.  

An empirical ice model based on this result provides the daily ice extent, which had a good correlation 
(0.86) with observed ice extent for winters 1982-2018. As an example, the simple ice model result is 
presented for winter 2010 (Fig 4). The correlation coefficient between SMHI ice product and simple ice 
model was 0.91, which is significant correlation. The critical value of the Pearson correlation coefficient 
is 0.26 (p=0.01). This simple approximation is used partly in our ice model. 

Figure 4. Daily ice extent [km2] in winter 2010. The yellow line represents the simple ice model whereas the blue 
line represents the SMHI ice product. The correlation coefficient between the model and observed data is 0.91. 

9 

 
 
 
 
 
 
 
 
 
2.4  Riverine nutrient inputs provided by GREEN 

Riverine nutrient inputs (see fig. 5) were provided by JRC’s catchment model GREEN (Grizzetti et al., 
2012, 2019). The freshwater run-off and nutrient loads of this model were only available on an annual 
basis. We used climatological cycles computed from the catchment model HYPE to downscale the 
inputs to a monthly temporal resolution. The annual freshwater inflow was the highest in 2010 and the 
lowest in 2006 (Fig. 5) 

Figure 5. Total inflow and catchment area in 2005–2012. 

Figure 6. Annual freshwater run-off into the Baltic Sea. 

10 

 
 
 
 
 
 
 
The highest nutrient inputs were to the Baltic Proper (Fig. 7), while the lowest riverine total nitrogen 
(TN) load was into the Gulf of Bothnia (GoB), whereas the lowest total phosphorus (TP) load was into 
the Gulf of Riga (GoR) (Fig. 7). Because the loads are only available as annual TN and TP inputs, the 
biogeochemical model conversion factors for required inorganic nutrients are estimated on the basis 
of observations (NO3 = 0.75 TN, NH4 = 0.05 TN and PO4 = 0.5 TP). 

Figure 7. Total phosphorus (TP) and total nitrogen (TN) load to the basins of the Baltic Sea. GoB, GoF, GOR, BP. 

The river scenarios based on the actual state of pressure on freshwater resources (REF (reference) 
scenario) and a list of potential measures to be undertaken with respect to water usage, point-source 
nutrient pollution and diffusive-source nutrient pollution (MTFR scenario). The total nutrient input to 
the Baltic Sea can vary depending on rainfall conditions. The nutrient inputs were highest in 2010 (Fig 
8), when abundant rainfall was reported in the southern part of the Baltic Sea catchment area. 

Figure 8. Annual TP and TN load over the simulation period to the whole Baltic Sea for both REF and BLUE2 
scenarios. 

11 

 
 
 
 
 
 
 
 
 
2.5  Observation data used for model forcing and validation  

The six-hourly meteorological forcing (ERA-interim reanalysis) data obtained from the European 
Centre for Medium-Range Weather Forecast (http://www.ecmwf.int) were applied to the model. 
The sea ice observation data, the daily ice concentrations over the Baltic Sea were provided by the 
Copernicus Marine Environment Monitoring Service 
(https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view=deta
ils&product_id=SST_BAL_SST_L4_REP_OBSERVATIONS_010_016). The ice concentration data originated 
from the Swedish Meteorological and Hydrological Institute (SMHI); the radiometric satellite 
observations were obtained through the sensors AVHRR, ATSR-1, ATSR-2, and AATSR. 
The following observational data sets were used to validate the model: 
1 

The Danish Meteorological Institute reanalysed the daily SST data on to the grid of 0.03° times 
0.03°. The horizontal resolution combined the Pathfinder AVHRR satellite data record, the Along-
Track Scanning Radiometer (ATSR) Reprocessing for Climate data set, and in situ observations. 
The validation against an independent set of in situ observations revealed an extremely stable 
performance of the reanalysed data set, with the mean deviation of −0.06°C and the standard 
deviation of 0.46°C with respect to the data of moored buoys (Høyer et al., 2016). 
The data set comprising the monthly average time series of basic hydrographic and 
biogeochemical properties from 1970 to 2008 (Gustafsson and Medina 2011, Eilola et al. 2011), 
which is available in the Baltic Environmental Database at the Baltic Nest Institute 
(http://nest.su.se). Due to the lack of data at certain stations, the data in the Baltic Environmental 
Database (BED) have been used for comparison. In the figures, this data is marked "seasonal". 
The Baltic Sea data products of EMODNET chemistry (https://ec.oceanbrowser.net/emodnet/) 
covering chlorophyll-a, nitrate and phosphate. 
Chlorophyll-a concentrations based on satellite data from COPERNICUS following the 
methodology of D'Alimonte et al. (2012) 
(https://resources.marine.copernicus.eu/?option=com_csw&task=results?option=com_csw&view
=details&product_id=OCEANCOLOUR_BAL_CHL_L3_REP_OBSERVATIONS_009_080)  
The latest eutrophication indicators published by HELCOM (https://helcom.fi/baltic-sea-
trends/indicators/), which were assessed in 2018 and whose status (ES values) is provided on the 
HELCOM Map and Data Service (http://maps.helcom.fi/website/mapservice/).  

2 

3 

4 

5 

6  Data regarding all basic hydrographic and biogeochemical properties provided by the World 

Ocean Database (WOD; https://www.nodc.noaa.gov/OC5/WOD/pr_wod.html) and the ICES 
database (https://ocean.ices.dk/Helcom/Helcom.aspx?Mode=1) were assessed on monthly basis.  

12 

 
 
 
 
3  Validation 

3.1  Comparison of satellite and model SST data 

Figure 9. Bias (model—satellite SST) and correlation between model and satellite monthly mean data in 2005–
2012 

A comparison of the modelled SST with satellite SST data (Fig. 9) revealed a mostly good agreement. 
The averaged bias over space and time was 0.86°C (ERAin). The bias was reduced to 0.7°C, when using 
the ERA5 meteo data. In the blue-marked area the model underestimates and in the red areas the 
model overestimates the temperature values. The highest bias was found in the eastern Gulf of Finland 
(1.7°C) and northeast of the Gulf of Bothnia (1.5°C), while along the South and East coast of Sweden 
and around Gotland the model SST was up to 1.5 K too low. A mean correlation coefficient of 0.99 was 
obtained. 
The multi-annual monthly average temperature values from the model predictions coincided well with 
the measurements (ICES) at the stations (Fig. 10); a RMSE of 0.76°C was obtained. 

13 

 
 
 
 
 
 
 
 
 
 
 
Figure 10. Comparison of surface and bottom temperatures [°C] from the model (green and black) and observed 
ones (blue and grey: data extracted from ICES) 

14 

 
 
 
 
 
3.2 

Sea ice extent 

The total extent of sea ice was fairly well described by the model. See Fig. 11 for the total sea ice 
extent. Compared to the observed extent by the Copernicus Marine Environment Monitoring Service, a 
correlation coefficient of 0.9 was obtained. Furthermore, the model is well able to reproduce the 
interannual spatial variability as it observed (see fig. 12, tab. 2). For 90% of the area, the predicted 
occurrence (or not occurrence) of ice fits with the observations.  

Figure 11. Sea ice extent in 2005–2012. 

Figure 12. Cont. 

15 

 
 
 
 
 
 
 
 
 
Figure 12. Cont. 

16 

 
 
 
 
 
 
Figure 12. Comparison of sea ice extent at 1st March of each year: Red: ice is observed and occurred in the model; 
blue: no ice observed and no ice in the model; yellow: no ice observed, but ice occurred at the model; green: ice 
was observed, but no ice in the model.  

Table 2. Distribution in percentage of the whole Baltic Sea area (color refer to fig. 12).  

Blue 

Red 

Yellow 

Green 

Year 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

62 

48 

71 

87 

75 

46 

42 

77 

mean 

63,5 

3 

5 

5 

1 

5 

8 

6 

4 

4,6 

2 

2 

6 

2 

3 

3 

18 

11 

5,9 

33 

45 

18 

10 

17 

43 

34 

8 

26 

17 

 
 
 
 
 
 
 
 
 
3.3 

Sea surface and bottom salinity at stations 

A comparison of the modelled salinity with data for the stations representing the main Baltic basins 
(Fig. 1) revealed a mostly good agreement on the sea surface (Figs. 13–15, red colour). In the bottom 
layer, the agreement was good; however, some stations differed in measured and modelled values by 
more than 2 PSU. Partly, a difference in salinity between the observed and modelled bottom layers 
existed because the topography used in the model did not match the natural topography. Table 3 
shows station depths and RMSE at sea surface and bottom (BED). The development of a strong 
halocline is a central hydrodynamic feature of the Baltic Sea, preventing the vertical ventilation and 
resulting in permanently low oxygen conditions. This strong vertical salinity gradient is well captured 
by the model (Fig. 16). 

Table 3. Stations of observations (BED), root-mean-square error (RMSE) of sea surface and bottom salinity 

Station 

Depth (m) 

Salinity RMSEsurf  

Salinity RMSEbottom 

F9 

SR5 

F1 

LL7 

BY28 

BY31 

BY15 

BY5 

125 

126 

75 

80 

180 

445 

240 

90 

0.64 

3.4 

1.1 

1.7 

1.7 

0.89 

1.2 

0.9 

0.7 

0.7 

0.6 

0.7 

0.5 

0.58 

0.6 

0.36 

18 

 
 
 
 
 
 
 
 
 
 
Figure 13. Sea surface salinity and bottom salinity at Gulf of Bothnia.  

Figure 14. Sea surface salinity and bottom salinity at Gulf of Finland.  

Figure 15. Sea surface salinity and bottom salinity at the Baltic Proper.  

19 

 
 
 
 
 
 
 
 
 
Figure 16. Salinity profile at stations BY15 and BY2 averaged between July 2005 and July 2007. RMSEBY15 = 0.25 
PSU and RMSEBY2 = 0.2 PSU. Model1 refers to the simulation, forcing by the 20 biggest rivers, while for model2 
riverine inputs from GREEN were used. 

3.4 

Sea surface elevation 

The modelled water level changes follow the natural fluctuations measured at Landsort (BY31) station 
strongly (Fig 17), indicating that the JRC Baltic Sea model is well able to reproduce the gradients of the 
sea surface elevations, which are mostly induced by wind conditions and changes of the atmospheric 
pressure gradients. 

Figure 17. Sea level elevation at Landsort (BY31) measured data from Permanent Service for Mean Sea Level 
(PSMSL). RMSE=198 mm. 

20 

 
 
 
 
 
 
 
 
 
 
 
3.5  Chlorophyll a 

Observed and simulated mean annual cycle of chlorophyll-a for the simulation period (2005–2012) is 
shown for 2 stations (figs. 18, 19). In addition, the multi-annual monthly averages of the long-term 
data series and the model are shown for representative stations (fig. 20), the representative statistics 
are summarized in Table 4. Chlorophyll-a concentrations follow a strong seasonal cycle all over the 
Baltic Sea with a first phytoplankton bloom in spring and a second more pronounced one in summer. 
This is captured well by the model for all stations. Also the clear water phase in June (e.g. at station LL7 
in Gulf of Finland; fig. 20) is well reproduced, while a further quantitative comparison is hampered by 
the strong variations between the observational data sets.  
While the peak chlorophyll-a concentration values are underestimated at most parts of the Central 
Baltic Sea (e.g. stations BY15 or BY5), an overestimation takes place at LL7 (Gulf of Finland). 
Chlorophyll-a concentration values in summer are estimated accurately, but underestimated in spring 
and early winter at station BY5 (Fig 20). At station F9 (Bothnian Bay), the phytoplankton growth starts 
latest (due to the long-lasting ice formation). The growing season is restricted to April to October, 
what is well captured by the model, as well as the much lower overall concentrations compared to the 
other parts of the Baltic Sea. Chlorophyll-a concentration values are underestimated at station BY15. 
Chlorophyll-a concentration values in summer are estimated accurately, but underestimated in spring 
and early winter at station BY5 (Fig 18). 

Figure 18. Comparison of chlorophyll a values [mg m-3] from the model (green) and observation (blue: data 
extracted from BED and ICES; red: satellite data) at station F9 (left: full time series; right: multi-annual monthly 
averages). RMSE=0.9 mg m-3 full time series between model and ICES. 

Figure 19. Comparison of chlorophyll a values [mg m-3] from the model (green) and observation (blue: data 
extracted from BED and ICES; red: satellite data) at station BY5 (left: full time series; right: multi-annual monthly 
averages). RMSE=1.8 mg m-3 full timeseries between model and ICES. 

21 

 
 
 
 
 
 
Comparing the surface chlorophyll-a concentration of the whole Baltic Sea from the model simulation 
with the different available data sets, revealed further, that strongest overestimation in the model takes 
place in Gulf of Finland and along the southern shore, where Curonian, Oder or Vistula Lagoon are 
missing. This results in too high chlorophyll-a concentrations near shore. On the other hand, especially 
in the Central Baltic Sea and Gulf of Riga the model is underestimating the chlorophyll-a 
concentration. Taking out Gulf of Finland from the statistical evaluation, resulted in substantially lower 
RMSE values (Fig. 27). Although the strongest deviation between model and observed values occurred 
in summer and autumn season (Figs. 21 – 27), the mean chlorophyll-a concentration at the different 
offshore seas, used by HELCOM, is well reproduced by the model (Fig. 22).   

Figure 20. Comparison of multi-annual monthly averages chlorophyll a values from the model (green) and 
observation (blue: data extracted from ICES; red: satellite data and black from BED). 

22 

 
 
 
 
 
 
 
 
 
a)

c)

b)

d)

Figure 21. Average surface concentration of Chlorophyll-a [mg m-3] provided by satellite data (a) and from JRC 
Baltic Sea model (b); root mean square deviation between both (c) and correlation coefficients (d) 

.
t
p
e
S
o
t
e
n
u
J

-

]
3
-
m
g
m

[
a
-
l
l
y
h
p
o
r
o
h
C

l

20

15

10

5

0

HELCOM data
Model data

Kattegat

Great
Belt

The
Sound

Kiel
Bay

Bay of
Mecklen-
burg

Arkona
Basin

Bornholm
Basin

Gdansk
Basin

Eastern
Gotland
Basin

Western
Gotland
Basin

Gulf of
Riga

Northern
Baltic
Proper

Gulf of
Finland

Åland
Sea

Bothnian
Sea

The
Quark

Bothnian
Bay

Figure 22. Average surface concentration of Chlorophyll-a [mg m-3] from June to September provided by HELCOM 
and from JRC Baltic Sea model on the scale of the offshore basins (SEA001-SEA017) used by HELCOM 

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
a)

b)

c) 

 d)

e) 

Figure 23. Average surface concentration of Chlorophyll-a [mg m-3] during the winter season (December to 
February) from JRC Baltic Sea model (a) and from different data sources: b) satellite data; c) EMODNET chemistry; 
d) World Ocean Database; e) ICES database 

24 

 
 
 
 
 
 
 
 
a)

b)

c)

d)
e)
Figure 24. Average surface concentration of Chlorophyll-a [mg m-3] during the spring season (March to May) from 
JRC Baltic Sea model (a) and from different data sources: b) satellite data; c) EMODNET chemistry; d) World Ocean 
Database; e) ICES database 

25 

 
 
 
 
 
  
  
a)

b)

 d)

c)

e)

Figure 25. Average surface concentration of Chlorophyll-a [mg m-3] during the summer season (June to August) 
from JRC Baltic Sea model (a) and from different data sources: b) satellite data; c) EMODNET chemistry; d) World 
Ocean Database; e) ICES database 

26 

 
 
 
 
 
 
  
a)

b)

 d)

c)

e)

Figure 26. Average surface concentration of Chlorophyll-a [mg m-3] during the autumn season (September to 
November) from JRC Baltic Sea model (a) and from different data sources: b) satellite data; c) EMODNET 
chemistry; d) World Ocean Database; e) ICES database 

27 

 
 
 
 
 
 
  
a)

b)
Figure 27. Correlation (a) and root mean square deviation divided by the standard deviation from the observations 
(b); assessed for the near-surface Chlorophyll-a concentrations taken from JRC Baltic Sea model and different data 
sources (blue: EMODNET chemistry; orange: World Ocean Database WOD; green: ICES database; purple: satellite 
data), evaluated for the single seasons. Results without Gulf of Finland are given separately.  

28 

-0,10,00,10,20,30,40,50,60,70,8winterspringsummerautumnall seasonsCorrelation coeffcientCHL compared to Emodnet chemistryCHL compared to Emodnet chemistry (without GoF)CHL compared to WODCHL compared to WOD  (without GoF)CHL compared to ICES dataCHL compared to ICES data (without GoF & coastal)CHL compared to sat dataCHL compared to sat data (without GoF)CHL all statistics combinedCHL all statistics combined (without GoF)051015202530winterspringsummerautumnall seasonsRoot mean square deviation / standard dev. of observationsCHL compared to Emodnet chemistryCHL compared to Emodnet chemistry (without GoF)CHL compared to WODCHL compared to WOD  (without GoF)CHL compared to ICES dataCHL compared to ICES data (without GoF & coastal)CHL compared to sat dataCHL compared to sat data (without GoF)CHL all statistics combinedCHL all statistics combined (without GoF) 
 
 
 
 
Table 4. Stations of observations (see fig. 1), root-mean-square error (RMSE) of MSFD eutrophication indicators 

Station  CHL RMSE  Oxygen RMSE 

Nitrate RMSE 

Phosphate RMSE 

F9 

SR5 

F1 

LL7 

0.9 

0.9 

4.2 

4 

BY2 

1.2 

BY31 

1 

BY15 

BY5 

1.8 

0.9 

1.6 

5 

1.8 

3.5 

2 

2.9 

0.25 

0.3 

0.8 

0.7 

1.7 

2.7 

0.9 

0.9 

1.9 

1.4 

0.2 

0.2 

1 

1.2 

0.1 

0.2 

0.15 

0.2 

3.6  Bottom oxygen 

Observed and simulated mean annual cycles of bottom oxygen are shown for the stations in fig. 28. 
The sea depth map used in the model does not exactly coincide with the natural topography. Due to 
the strong vertical stratification, the oxygen values of the bottom layer are strongly related to the 
topography of the model. For station BY15 the bottom oxygen values are shown: “model” at the 
geographical location of the station and “model_b” selecting a nearby model location with the same 
depth values as the station. 

29 

 
 
 
 
 
 
 
 
 
 
30 

 
 
 
 
 
Figure 28. Comparison of multi-annual monthly averages bottom oxygen values from the model (green) and 
observation (blue: data extracted from ICES and black from BED). Model data extracted for station BY15 (model_b, 
red line) has been shifted from 57.33°N to 57.05°N. 

3.7  Nitrate 

Observed and simulated mean annual cycles of nitrate for the main stations (fig. 1) are shown in Fig 30 
and accompanied by the spatial maps for the seasonal averages in Figs. 30 - 35. Overall, the annual 
cycle with high concentrations during winter and low values during summer is well reproduced for all 
stations. Again the comparison with HELCOM indicator (only evaluated for winter season) showed a 
good agreement (Fig. 29), although the model mostly underestimated concentration in the southern 
basins. Opposite to Chlorophyll-a and Phosphate, the model quality worsened, when Gulf of Finland 
was excluded from the evaluation (Fig. 35). 

Figure 29. Average surface concentration of Dissolved Inorganic Nitrogen (DIN) [mmol m-3] from December to 
February provided by HELCOM and from JRC Baltic Sea model (Ammonium + Nitrate) on the scale of the offshore 
basins (SEA001-SEA017) used by HELCOM eutrophication assessment 2018  
(http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/1c5cc76d-0631-4c24-80f8-
ebec20dcea8f) 

31 

 
 
 
 
 
 
 
 
 
Figure 30. Comparison of multi-annual monthly averages nitrate values from the model (green) and observation 
(blue: data extracted from ICES, black from BED). 

32 

 
 
 
 
 
a)

b)

c)

d)
Figure 31. Average surface concentration of Ammonium + Nitrate [mmol m-3] during the winter season 
(December to February) from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) 
World Ocean Database; d) ICES database 

33 

 
 
 
 
 
  
 
a)

b)

c)

d)
Figure 32. Average surface concentration of Ammonium + Nitrate [mmol m-3] during the spring season (March to 
May) from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) World Ocean 
Database; d) ICES database 

34 

 
 
 
 
 
  
 
a)

b)

c)

d)
Figure 33. Average surface concentration of Ammonium + Nitrate [mmol m-3] during the summer season (June to 
August) from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) World Ocean 
Database; d) ICES database 

35 

 
 
 
 
  
  
 
a)

b)

c)

d)
Figure 34. Average surface concentration of Ammonium + Nitrate [mmol m-3] during the autumn season 
(September to November) from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; 
c) World Ocean Database; d) ICES database  

36 

 
 
 
 
 
  
  
a) 

b) 
Figure 35. Correlation (a) and root mean square deviation divided by the standard deviation from the observations 
(b); assessed for the near-surface DIN concentrations taken from JRC Baltic Sea model and different data sources 
(blue: EMODNET chemistry; orange: World Ocean Database WOD; green: ICES database), evaluated for the single 
seasons 

37 

0,00,10,20,30,40,50,6winterspringsummerautumnall seasonsCorrelation coeffcientDIN compared to Emodnet chemistryDIN compared to Emodnet chemistry (without GoF)DIN compared to WODDIN compared to WOD  (without GoF)DIN compared to ICES dataDIN compared to ICES data (without GoF & coastal)DIN all statistics combinedDIN all statistics combined (without GoF)0123winterspringsummerautumnall seasonsRoot mean square deviation / standard dev. of observationsDIN compared to Emodnet chemistryDIN compared to Emodnet chemistry (without GoF)DIN compared to WODDIN compared to WOD  (without GoF)DIN compared to ICES dataDIN compared to ICES data (without GoF & coastal)DIN all statistics combinedDIN all statistics combined (without GoF) 
 
 
 
 
   
3.8  Phosphate 

Observed and simulated mean annual cycles of phosphate for the main stations is shown in Fig 37 and 
accompanied by the spatial maps for the seasonal averages in Figs. 38 - 42. Overall, the annual cycle 
with high concentrations during winter and spring and low values during late summer (when 
cyanobacteria blooms occur) is well reproduced for all stations. Again the comparison with HELCOM 
indicator (only evaluated for winter season) showed a good agreement (Fig. 36), although the model 
mostly underestimated concentration in the southern basins, while Gulf of Finland is strongly 
overestimated. Like seen for Chlorophyll-a, the model quality statistics improved, when Gulf of Finland 
was excluded from the evaluation (Fig. 42). 

Figure 36. Average surface concentration of Phosphate [mmol m-3] from December to February provided by 
HELCOM and from JRC Baltic Sea model on the scale of the offshore basins (SEA001-SEA017) used by HELCOM 
eutrophication assessment 2018 
(http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/2dac4f17-5331-4f8b-8073-
4206fccfe0b0) 

38 

 
 
 
 
 
 
 
 
Figure 37. Multi-annual monthly average phosphate at stations (fig. 1). Model results in the years 2005–2012. 

39 

 
 
 
 
 
 
a)

b)

c)

d)
Figure 38. Average surface concentration of Phosphate [mmol m-3] during the winter season (December to 
February) from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) World Ocean 
Database; d) ICES database 

40 

 
 
 
 
 
  
 
a)

b)

c)

d)
Figure 39. Average surface concentration of Phosphate [mmol m-3] during the spring season (March to May) from 
JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) World Ocean Database; d) 
ICES database 

41 

 
 
 
 
 
  
  
 
a)

b)

c)

d)
Figure 40. Average surface concentration of Phosphate [mmol m-3] during the summer season (June to August) 
from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) World Ocean Database; 
d) ICES database 

42 

 
 
 
 
  
  
   
a)

b)

c)

d)
Figure 41. Average surface concentration of Phosphate [mmol m-3] during the autumn season (September to 
November) from JRC Baltic Sea model (a) and from different data sources: b) EMODNET chemistry; c) World 
Ocean Database; d) ICES database 

43 

 
 
 
 
 
  
  
a) 

b) 
Figure 42. Correlation (a) and root mean square deviation divided by the standard deviation from the observations 
(b); assessed for the near-surface phosphate concentrations taken from JRC Baltic Sea model and different data 
sources (blue: EMODNET chemistry; orange: World Ocean Database WOD; green: ICES database), evaluated for 
the single seasons 

44 

-0,10,00,10,20,30,40,50,60,70,8winterspringsummerautumnall seasonsCorrelation coeffcientDIP compared to Emodnet chemistryDIP compared to Emodnet chemistry (without GoF)DIP compared to WODDIP compared to WOD  (without GoF)DIP compared to ICES dataDIP compared to ICES data (without GoF & coastal)DIP all statistics combinedDIP all statistics combined (without GoF)01234567winterspringsummerautumnall seasonsRoot mean square deviation / standard dev. of observationsDIP compared to Emodnet chemistryDIP compared to Emodnet chemistry (without GoF)DIP compared to WODDIP compared to WOD  (without GoF)DIP compared to ICES dataDIP compared to ICES data (without GoF & coastal)DIP all statistics combinedDIP all statistics combined (without GoF) 
 
 
 
 
  
3.9 

Impact of sea ice on phytoplankton spring bloom 

The coupled hydrodynamic–biogeochemical model was applied to two simulations, one with observed 
ice (a) and one using the simple ice model (b). From these simulations six severe winter seasons 
(1986/87, 1995/96, 2002/03, 2009/10, 2010/11 and 2012/13) were selected and analysed to estimate 
the effects of sea ice on phytoplankton spring bloom. We could show that the spring dynamics of 
diatoms depended on the selected ice model (Fig. 46). The correlation coefficient of the diatom 
concentration between 25 March and 15 May was in the range from 0.13–0.89. The presence of ice 
significantly affected the predicted values of the biogeochemical model even in the southern part of 
the Baltic Sea. Pärn et al. (2020) analysed the study results in detail. 
Furthermore, the results of the realistic reference run (in the presence of sea ice) was compared with a 
simulation assuming the absence of sea ice (possibly as a result of global warming). In the absence of 
sea ice, diatoms dominated the phytoplankton biomass, whereas in the presence of thin sea ice, 
dinoflagellates dominated the biomass. The DIA/DINO index (Wasmund et al., 2017) was calculated as 
an indicator of ecosystem changes. The index reflected dominance patterns in the biomass of 
phytoplankton spring bloom; the index values were 0.5 with ice and 0.97 without ice in the GoF and 
0.61 with ice and 0.97 without ice in the GoR (index values of <0.5 indicated flagellate dominance, 
whereas values of >0.5 indicated diatom dominance), respectively. 
Diatom-only blooms were observed in the ice-free sea in the spring. In regions of the sea with thin ice, 
both diatoms and dinoflagellates developed; however, dinoflagellates gained a competitive advantage 
and grew in the surface layer. In regions with thick ice, neither diatoms nor dinoflagellates developed. 
In the near future, with the thinning of the sea ice, dinoflagellate predominance could be expected, 
whereas in the long-term, with a nearly ice-free situation, diatoms could become dominant. Pärn et al. 
(2020) analysed the study results in detail. 

Figure 46. Diatom concentrations at stations LL7 and BY5 from 1 March to 30 April in 2013 

45 

 
 
 
 
 
3.10  Modelling of the Baltic Sea ecosystem to assess management scenarios 

The JRC Baltic Sea model got provided two nutrient load scenarios (2005–2012), varying substantially 
in the riverine loads (see sect. 2.4). The hydrological modelling team at the JRC developed both river 
scenarios on the basis of the actual state of pressure on freshwater resources (reference scenario - REF) 
and a list of potential measures to be implemented with respect to water usage, point-source nutrient 
pollution, and diffusive-source nutrient pollution reduction (BLUE2/MTFR scenario). The marine model 
was driven with both input scenario and the results for the Baltic Sea highlight a general improvement 
in marine eutrophication indicators such as nutrient, chlorophyll a, and bottom oxygen concentrations 
in the BLUE2/MTFR scenario compared with the REF scenario. The relative change in these indicators is 
most marked in the inflow areas of large rivers. Locally, in some areas, the improvement can be up to 
10% (Fig. 48). However, the change in concentration throughout the sea (2005–2012) was observed as 
follows: chlorophyll a = −0.34%, Nitrate = −0.55%, Phosphate = −0.01%, and bottom oxygen = 1.8%. 
The main conclusion from this experiment is that longer time series are required to observe a 
substantial change in the Baltic Sea environmental conditions and that stronger reduction measures 
are necessary to achieve an improved environmental status, as demanded by the Baltic Sea Action 
Plan. 

(a) 

(b) 

(c) 

(d) 

Figure 47. Relative change (%) from REF run to BLUE2/MTFR scenario in concentrations of nitrate (a), 
phosphate (b), chlorophyll a (c), and bottom oxygen in 2005–2012. 

46 

 
 
 
 
 
 
 
 
 
 
4  Summary 

Based on the performed simulations and validations, we can conclude that hydrodynamic features 
such as salinity and temperature were reproduced well; however, the modelled salinity values in some 
areas need to be improved (e.g. by using better initial conditions). The model is further well able to 
reproduce the ice-covered areas as well as the interannual variability. But it needs some more 
fundamental improvements, e.g. by modelling the ice thickness directly and to get able to adapt the 
albedo and underwater light regime under ice to the thickness, as the light availability is vital for all 
biological processes under ice. 
All modelled eutrophication indicators, chlorophyll-a, oxygen, nitrate and phosphate follow the 
dominant seasonal cycles. However, the model underestimates the phytoplankton biomass 
(chlorophyll-a concentration) in most parts of the Baltic Sea and overestimates the oxygen content 
compared to the observations, what might be a consequence of the too low phytoplankton biomass. 
The Nitrate concentration was overestimated by the model when compared to BED data (except for 
station F9), but underestimated when compared to HELCOM data. Phosphate was overestimated in the 
Gulf of Bothnia and the Gulf of Finland, however, it was underestimated in the rest of the Baltic Sea. 
The excess phosphate in Golf of Finland is quite probably causing the too high Chlorophyll-a 
concentrations there, which are largely made by cyanobacteria in summer. Although, the nutrient 
inputs (especially by the Neva, the largest river flowing into the Baltic Sea) are validated against 
observation (Grizzetti et al., subm.), strong uncertainties remain regarding the riverine inputs and must 
be evaluated in depth. Furthermore, especially for chlorophyll-a there is a strong mismatch between 
model estimates and observations, resulting in high RMSE values (fig. 27). These statistical values could 
be improved by taking out Gulf of Finland from the evaluation, but some deviations are further caused 
by an insufficient representation of the coastal waters in the JRC Baltic Sea model, e.g. the Darss-
Zingst-Bodden-Chain in Germany has extremely high chlorophyll-a concentrations (above 100 mg m-
3) and it is only divided from the open Baltic Sea by a thin peninsula. Comparing the chlorophyll-a 
concentrations from the inner coastal water and the open Baltic outside the peninsula must result in 
insufficient results. Including the big Baltic lagoons (like Curonian, Vistula or Oder Lagoon) and as far 
as possible coastal waters like Darss-Zingst-Bodden-Chain into the JRC Baltic Sea model, might be 
therefore an important step forward to improve the model quality. This would also lead to an 
improved representation of the nutrient dynamics, as coastal waters are important places for the 
nutrient removal, e.g. by denitrification. Voss et al. (2010) have shown that the nutrient export from 
Oder Lagoon into the open Baltic Sea is substantially below the riverine inputs. On the other hand, the 
computational effort will increase drastically to get able to include the coastal waters in a sufficient 
way, a horizontal resolution of at least 1 nm (like done at the Baltic Sea model at Tallinn University of 
Technology) or even finer would be necessary, what would result in drastically increased number of 
grid cells (and a smaller internal time step for the simulation to stay numerically stable). Alternatively 
nesting with high resolution regional grids could be considered.  
It could be shown, how important it is for the modelling of the ecosystem dynamics to include the sea 
ice in the model, as the used ice model is significantly affecting the predicted biogeochemical values, 
as well as the phytoplankton spring bloom. In the ice-free sea diatom blooms dominate during spring, 
while in the regions with thin ice, both diatoms and dinoflagellates grew; however, dinoflagellates 
gained a competitive advantage and grew on the surface layer. In the near future, with the further 
thinning of sea ice due to climate change, dinoflagellate predominance could be expected, whereas in 
the long-term, with nearly ice-free conditions, diatoms could become dominant. Hence, changing ice 
conditions are one of the key factors directly affecting the timing and composition of spring bloom 
within the Baltic Sea, with cascading consequences for nutrient transfer and ecological dynamics. 
In conclusion the overall objective of developing a coupled hydrodynamic–biogeochemical model 
capable of simulating the main features of the Baltic Sea as barotropic salt water inflows, anoxic deep 
basins, seasonal nutrient dynamics, spring and summer blooms as well as costal and estuary 
eutrophication has been achieved.  

47 

 
 
 
 
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Principle and Handling Instruction. Frontiers in Marine Science, https://doi.org/10.3389/fmars.2017.00022 

49 

 
 
 
 
 
 
 
Annex 1. List of abbreviations and definitions  

BSAP 
BP  
ECMWF 
ERA5 
ERAin 
ERGOM 
GETM 
GoB 
GoF 
GoR 
GREEN 
HELCOM 
JRC 
LTL 
MTFR 
PSU 
REF 
RMSE 
SST 
TN 
TP 

The Baltic Sea Action Plan 
The Baltic Proper 
European Centre for Medium term Weather Forecasting 
New climate reanalysis ECMWF 
Old ERA-interim climate reanalysis ECMWF  
The Ecological ReGional Ocean Model 
The General Estuarine Transport Model 
The Gulf of Bothnia 
The Gulf of Finland 
The Gulf of Riga 
The JRC’s catchment model GREEN 
The Helsinki Commission 
The Joint Research Centre 
The model of the lower trophic levels 
The Maximum technically feasible reduction 
Practical Salinity Unit 
The reference scenario 
Root-mean-square error 
The sea surface temperature 
Total nitrogen 
Total phosphorus 

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doi:10.2760/672255 

ISBN: 978-92-76-19448-4