Report on the biogeochemical model of the North-Western European Shelf.
Date: 2020-04-17
Institutions: Joint Research Centre
Subjects: North Sea, environmental research, eutrophication, marine environment, oceanography, quality of the environment, remote sensing, research report, simulation

Report on the biogeochemical model of 
the North-Western European Shelf 

Friedland, R., Stips, A., Grizzetti, B., de Roo, A., 
Lessin, G.  

2020 

EUR 30168 EN 

 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
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Contact information 
Name: René Friedland 
Address: Via E. Fermi, TP270, Ispra, Varese, Italy 
Email: jrc-mmf@ec.europa.eu, rene.friedland@ec.europa.eu 
Tel.: +39-0332-78947 

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

JRC120213 

EUR 30168 EN 

PDF 

ISBN 978-92-76-17866-8 

ISSN 1831-9424 

doi:10.2760/78173 

Luxembourg: Publications Office of the European Union, 2020 

© European Union, 2020 

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How to cite this report: Friedland, R., Stips, A., Grizzetti, B., de Roo, A. and Lessin, G., Report on the biogeochemical model of the North-
Western European Shelf, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-17866-8, doi:10.2760/78173, 
JRC120213. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

1 

Introduction..................................................................................................................................................................................................................................................... 2 

2  Material and method ............................................................................................................................................................................................................................. 5 

2.1  Model setup ...................................................................................................................................................................................................................................... 5 

2.2  Riverine nutrient and freshwater inputs ............................................................................................................................................................. 10 

2.3  Validation data sets for hydrographical properties ................................................................................................................................. 17 

2.4  Validation data sets for biogeochemical properties ............................................................................................................................... 20 

3  Results .............................................................................................................................................................................................................................................................. 28 

3.1  Sea surface height compared to gauge data................................................................................................................................................ 28 

3.2  Sea surface temperature (compared to satellite data) ....................................................................................................................... 29 

3.3  Sea surface salinity (compared to satellite data) ..................................................................................................................................... 30 

3.4  Salinity and temperature compared to climatologies (KNSC and GINS) ............................................................................. 31 

3.5  Mixing zones and current fields.................................................................................................................................................................................. 32 

3.6  State of MSFD eutrophication indicators ........................................................................................................................................................... 33 

3.7  Seasonal climatologies ....................................................................................................................................................................................................... 37 

3.8  Model results compared to satellite data ......................................................................................................................................................... 42 

3.9 

ICES COMPEAT data ............................................................................................................................................................................................................... 47 

3.10 Assessment for AMM7 regions ................................................................................................................................................................................... 49 

3.11 Station data ................................................................................................................................................................................................................................... 53 

4  Summary ........................................................................................................................................................................................................................................................ 55 

References ............................................................................................................................................................................................................................................................. 56 

i 

 
 
 
 
 
 
1 

Introduction 

Marine  and  coastal  waters  all  around  Europe  and  the  globe  are  heavily  threatened  by  eutrophication,  the 

excessive accumulation of nutrients in the water resulting in potentially toxic algal blooms and regions with 

nearly no oxygen in the water, preventing any  underwater life (Nixon,  1995).  Hence,  clean oceans and  seas 

are  addressed  by  the  2030  Agenda  for  Sustainable  Development  of  the  United  Nations  (1)  as  well  as  by 

Directive 2008/56/EC of the  European Parliament and of  the European Council (Marine Strategy Framework 

Directive (2008/56/EC)). 

The  accumulation  of  (inorganic)  nutrients  in  the  Greater  North  Sea  region  has  already  been  reported  for 

decades, for example a first target to half the riverine nutrient loads was set in 1988 (PARCOM, 1988). Using 

the years 2006 to 2014, the third eutrophication status assessment (OSPAR, 2017) of the Convention for the 

Protection  of  the  Marine  Environment  of  the  North-East  Atlantic  (OSPAR)  led  to  the  identification  of  non-

problematic,  potential  problematic  and  problematic  areas  (see  Figure  1).  All  problematic  and  potential 

problematic areas are located along the southern and eastern parts of the North Sea and along the shoreline 

of France. 

Currently,  strong  efforts  are  being  made  to  update  the  eutrophication  assessment,  especially  within  the 

OSPAR  working  group  on  Hazardous  Substances  and  Eutrophication  (HASEC  ( 2)),  leading  to  revised 

assessment  units.  A  first,  yet  not  finalised  approach,  is to  implement  and  apply  the  ICES  COMPEAT  tool  (3) 

(see  Figure  2).  The  preliminary  assessment  to  compare  the  present  state  with  set  targets,  resulted  in  a 

worsened  classification  as  more  regions  are  now  assessed  as  ‘Not  Good’.  These  regions  are  again  mostly 

located  in  the  southern  and  eastern  North  Sea,  but  now  also  many  (coastal)  waters  around  UK  and  in  the 

Celtic and Irish Sea are classified as ‘Not Good’ (see Figure  2). 

To support the integrated assessment and develop the ability to test future threads or suitable measures to 

improve  the  water  quality, the  Marine  Modelling  Framework  (MMF)  of  JRC  was extended  by  a  fully  coupled 

three-dimensional  model  of  the  Greater  North  Sea,  Celtic  Sea  and  Irish  Sea.  The  developed  model  was 

supposed  to  incorporate  the  dominating  hydrodynamical  features  as  well  as  a  sophisticated  model  of  the 

lower trophic levels (LTL), suitable to allow scenario simulations and validated using up-to-date observations. 

While  a  wide  branch  of  coupled  3d-models  for  the  North  Sea  region  already  exists,  e.g.  shown  in  Moll  and 

Radach (2003), the newly developed JRC model was chosen to base on GETM and ERSEM. GETM was selected 

as  hydrodynamical  engine  of  the  model  to  be  in  a  methodologial  agreement  with  the  other  LTL  models of 

MMF and because it was already shown that GETM is well suitable for the modelling of the Greater North Sea 

(e.g. Stips et al. (2016), Pätsch et al. (2017), Gräwe et al. (2015)). Further, it allows easy incorporation of up-

to-date  LTL  models  like  ERSEM,  which  was  already  successfully  used  for  the  simulation  of  the 

biogeochemistry  of  the  Greater  North  Sea  (Butenschön  et  al.,  2016).  Nevertheless,  many  other  LTL  models 

were  also  applied  to  the  region,  e.g.  ERGOM  (Maar  et  al.,  2011),  ECOSMO  (Daewel  and  Schrum,  2013), 

ECOHAM (Große et al., 2016), ERSEM-BFM (van Leeuwen et al., 2015) or even combined to a model ensemble 

(Lenhart et al., 2010). 

(1) https://sustainabledevelopment.un.org/sdgs 
(2) https://www.ospar.org/work-areas/hasec 
(3) https://ocean.ices.dk/core/compeat?assessmentperiod=20062014_Test 

2 

 
                                           
Figure 1: Result of the integrated eutrophication assessment  (4) by (OSPAR, 2017) 

(4) https://odims.ospar.org/layers/geonode:ospar_eut_status_2017_06_001/metadata_detail 

3 

 
 
                                           
Figure 2: Result of the eutrophication assessment, computed with ICES COMPEAT 

4 

  
2  Material and method 

2.1  Model setup 

Based  on  a  collaboration  with  PML  and  NIOZ  a  3d-model  system  of  the  North-Western  European  Shelf 

(NWES) was developed. It covers the region between  46.4° N-63° N and 17.5° W-13.1° E (see Figure  3) and 

includes the Greater North Sea and surrounding areas (Skagerrak and Kattegat in the east, Norwegian Sea in 

the  North,  English  Channel  and  Celtic  Sea  in  the  South,  Irish  Sea  and  parts  of  deep  Atlantic  Ocean  in  the 

West).  The  model  system  is  a  fully  coupled  3d-hydrodynamical-biogeochemical  model  of  the  lower  trophic 

web.  The  hydrographical  part  is  based  on  the  General  Estuarine  Transport  Model  (GETM  (5)).  By  using  the 

Framework  for  Aquatic  Biogeochemical  Models  (FABM;  Bruggeman  and  Bolding  (2014)),  the  European 

Regional  Seas  Ecosystem  Model  (ERSEM;  Butenschön  et  al.  (2016))  was  coupled  dynamically  to  the 

hydrodynamic model engine. The model simulations covered 8  years (2005-2012), restricted by the riverine 

inputs. 

The horizontal grid cells of the model region are equidistant with a resolution of 0.08° in x-direction and 0.05° 

in  y-direction,  resulting  in  a  cell  size  of  4.04-6.13  km  (x-direction)  and  5.56  km  (y).  For  the  vertical 

decomposition,  adaptive  coordinates  were  chosen,  which  adjust  the  layer  height  depending  on  the  vertical 

stratification. To avoid numerical instabilities, the minimal height of the surface and bottom  cell was set to 

0.5  m. It has been shown that adaptive coordinates are advantageous for the simulation of stratified waters 

like the North or Baltic Sea (Gräwe et al., 2015). The bathymetry of the model domain (Figure  3a) was initially 

taken from North-West European Shelf Operational Oceanographic System (NOOS  (6); Stips et al. (2016)), but 

was  adjusted    to  include  some  shallow  parts  of  the  Wadden  Sea.  A  spatially  variable  bottom  roughness 

(Figure  3b) was chosen, which was used as optimisation parameter to enhance the match of modelled and 

observed tides. 

(a)

(b)

Figure 3: Used topography (a; m) and bottom roughness (b; m) of the NWES-model 

The used version of ERSEM is freely available  (7) (upon registration), a previous version of ERSEM was used 

already  by  JRC  MMF  (Garcia-Gorriz  et  al.,  2016).  It  consists  of  dissolved  inorganic  nutrients  (ammonium, 

nitrate, phosphate, carbon, silicate) and has a sophisticated growth model for phytoplankton, which is divided 

(5) http://www.getm.eu 
(6) http://www.noos.cc 
(7) https://gitlab.ecosystem-modelling.pml.ac.uk/stable/ERSEM 

5 

 
 
 
                                           
into  four  functional  groups  (see  Figure  4).  Each  group  has  its  specific  ecological  niche  and  requirements. 

Nutrients  are  uptaken  in  a  variable  ratio  (and  not  following  the  Redfield  ratio  like  in  most  other 

biogeochemical models). This allows them to adapt their growth to changes of the nutrient limitations. While 

previous  studies  (Ford  et  al.,  2017)  have  shown  a  good  agreement  of  the  different  modelled  groups  with 

observations,  the  analyses  within  this  report  are  focused  on  the  integrated  Chlorophyll-a  content  of  all 

phytoplankton  groups,  as  MSFD  is  using  this  as  a  central  eutrophication  indicator  (D5.C2).  ERSEM  further 

incorporates  three  functional  zooplankton  groups  and  several  benthic  models  of  different  complexities.  For 

the present study, the benthic model with the lowest complexity  (8), the so-called ‘benthic returns’, was used 

to reduce the computational efforts. 

Figure 4: Schematic overview of the European Regional Seas Ecosystem Model (ERSEM; taken from  (9)) 

Atmospheric  forcing  fields  (10)  were  taken  from  the  European  Centre  for  Medium  Range  Weather  Forecast 

(ECMWF, using ERA5  (11)) in agreement with the other marine model of JRC  (12). Sea level data and surface 

currents at the open  boundaries were generated in temporal resolution of 30  minutes from the tide  model 

provided by Oregon State University  (13). They were enhanced by the daily deviation from the long-term mean 

using HYCOM  (14) (Cummings and Smedstad, 2013). The 2d-boundaries are following the spatial gradients of 

(8) Following fabm-ersem-15.06-L4-noben-docdyn-iop.yaml 
(9) https://www.pml.ac.uk/Modelling_at_PML/Models/ERSEM 
(10) 10 m winds, 2 m temperature, 2 m dewpoint temperature, cloud cover, mean sea level pressure, 

precipitation 

(11) https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 
(12) Taken from /ACQUA/COMMONDATA/ECMWF/ncdf_era5 
(13) http://volkov.oce.orst.edu/tides/AO.html 
(14) http://www.hycom.org 

6 

 
 
 
                                           
the sea surface height from the global model (see Figure  5), especially with a higher water level at the Baltic 

Sea boundary (due to the nature of the Baltic Sea, where freshwater inputs exceed evaporation, so that a net 

export  of  water  into  the  North  Sea  must  take  place).  Dominated  by  the  Gulf  Stream,  along  the  western 

boundary  of  NWES  the  positive  U-currents  indicate  an  inflow  into  the  region,  while  a  major  part  is  leaving 

again through the northern boundary, following the positive V-currents (see Figure  5). Using the salinity and 

water temperature fields from HYCOM, the climatological means were computed at the open boundaries. This 

approach allows to use the open boundaries for later scenario simulations without adjusting them further. On 

the other hand, the simulation results  got  slightly  worse along the  boundaries, as the short-term  variability 

was  not  included.  The  fields  along  the  southern  boundary  had  to  be  adjusted,  using  the  mean  values from 

World Ocean Atlas 2018 (WOA2018  (15)). 

Using  observational  data  covering  the  inorganic  nutrients  (phosphate,  nitrate,  ammonium  and  silicate), 

dissolved  oxygen,  dissolved  inorganic  carbon  and  alkalinity  from  WOA2018,  climatological  values  (in  a 

monthly resolution) were computed and processed to be used along the open boundaries. Furthermore, winter 

values  from  WOA2018  were  processed  to  generate  the  necessary  initial  conditions  (referring  to  early 

January). Thereafter, the values were fitted to the model bathymetry and vertically adjusted to be  utilised as 

model input. Using these fields as first initial conditions, a full simulation of NWES covering a five-year period 

was  conducted.  At  the  end  of  this  spin-up  phase,  stable  annual  biogeochemical  cycles  were  recognisable. 

Afterwards, hydrographical and biogeochemical variables (Figures 6 and 7) were selected from the end of the 

spin-up phase and processed to provide initial conditions for the later simulations. 

Figure 5: Sea Surface Height (black), U- and V-components of the currents (orange and gray) along the open boundaries 
averaged over the complete simulation period. 

(15) https://www.nodc.noaa.gov/OC5/woa18 

7 

 
 
                                           
(a)

(b)

(c)

(d)
Figure 6: Initial fields of water temperature (a, b) and salinity (c, d) of the NWES setup, shown are surface values (a, c) and 
vertical averages (b, d) of both 

(a)

(b)

(c)

 (d)

8 

  
 
 
Figure 7: Initial fields of dissolved inorganic nitrogen (a), phosphate (b), dissolved inorganic carbon (c) and silicate (d) of 
the NWES setup, only surface values are shown 

9 

 
 
2.2  Riverine nutrient and freshwater inputs 

Riverine  nutrient  and  freshwater  inputs  were  provided  by  JRC’s  catchment  models  GREEN  (Grizzetti  et  al., 

2012,  Grizzetti  et  al.,  2019)  and  LISFLOOD  (De  Roo  et  al.,  2000,  Van  Der  Knijff  et  al.,  2010).  GREEN  is  a 

statistical  GIS-based  model  covering  the  whole  of  Europe,  including  the  nutrient  loads  entering  the  North-

Western  European  Shelf  (Figures  8-10).  The  nutrient  and  freshwater  loads  were  provided  at  the  land-sea-

interface, following the  classification of outflow  points from GREEN,  which incorporated the freshwater flux 

provided  by  the  LISFLOOD  model.  LISFLOOD  is  a  grid-based  rainfall-runoff-water  resources  model,  which 

simulates  the  daily  water  balance  of  Europe  using  5  km  grid  cells.  It  takes  water  abstractions  and  return 

flows  for  irrigation,  livestock,  industry,  energy  cooling,  public  water  usage,  and  ecosystem  constraints  into 

account. The output of both  catchment models  is  provided with a spatially very high resolution,  resulting in 

roughly  15  000  inflow  points  for  the  NWES  setup.  These  were  merged  into  the  nearest  grid  cell  of  the 

hydrodynamic  model.  As  only  annual  values  of  freshwater  runoff  and  nutrient  loads  could  be  provided,  a 

temporal  downscaling  was  conducted,  using  climatological  cycles  computed  from  the  publicly  available 

catchment  model  HYPE  (16).  As  the  biogeochemical  model  needs  the  inorganic  nutrient  loads  (Ammonium, 

Nitrate,  and  Phosphate),  Total  Nitrogen  and  Phosphorus  provided  by  GREEN  were  used  to  estimate  the 

inorganic  loads  (17).  Constant  conversion  factors  were  assumed,  computed  from  the  ICG  EMO  nutrient  load 

database. 

To validate the freshwater runoff and riverine nutrient loads, a variety of observed data for the biggest rivers 

was merged, provided by: 

●  Global Runoff Data Centre  (18); 

●  Global Nutrient Export from WaterSheds 2 (Mayorga et al., 2010); 

●  European Environment Agency  (19); 

●  Publicly  available  databases  (OSPAR  Data  and  Information  Management  System  (20);  UK 
National River Flow Archive  (21); Hydro-Data provided by the Irish Office of Public Works  (22); 

●  OSPAR Intersessional Correspondence Group on Eutrophication Modelling (ICG EMO  (23)); 

●  Literature sources (see Table  1). 

The  riverine  freshwater  runoffs  and  nutrient  loads  provided  by  LISFLOOD and  GREEN  fit  very  well  with  the 

observations  (see  Figures  11-13).  Correlations  between  reported  and  modelled  riverine  nutrient  loads  are 

overall high and the linear regression is close to 1. 

Table 1: Used literature sources for the validation of the riverine loads 

Reference 

River 

(16) https://hypeweb.smhi.se/explore-water/historical-data/europe-time-series 
(17) Ammonium and Nitrate are 5 % and 75 % of Total Nitrogen, while Phosphate is 50 % of Total Phosphorus. 
(18) https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html 
(19) https://www.eea.europa.eu/data-and-maps/data/waterbase-rivers-10 
(20) https://odims.ospar.org 
(21) https://nrfa.ceh.ac.uk/data/search 
(22) http://waterlevel.ie/hydro-data/list.html# 
(23)  Provided  by  Sonja  v  Leeuwen  (NIOZ,  pers.  comm.),  see  Lenhart  et  al.  (2010);  when  forcing  the  NWES 
simulation  with  ICG  EMO  the  freshwater  inputs  were  scaled  by  a  factor  of  0.9  to  improve  the  salinity 
gradients. 

10 

 
                                           
Hartmann et al. (2011) 

Hesse and Krysanova (2016) 

Howden et al. (2010) 

Ménesguen et al. (2019) 

Minaudo et al. (2015) 

Mockler et al. (2017)  

Rhine 

Elbe 

Thames 

Loire, Seine 

Loire 

Shannon 

Passy et al. (2013), Thieu et al. (2010) 

Scheldt, Seine, Somme 

Passy et al. (2016) 

Tockner et al. (2009) 

Seine 

Elbe, Loire, Rhine 

Radach and Pätsch (2007)  

Elbe, Ems, Rhine, Weser 

Romero et al. (2013) 

Loire, Scheldt, Seine, Somme, Vilaine 

11 

 
(a)

(b)

(c)

Figure 8: Freshwater runoff [m3 s-1] into the European seas (a), zoomed into the NWES region (b) and summed per 
country (c, highlighted are the biggest rivers)  

12 

 
 
 
 
 
(a)

(b)

(c) 
Figure 9: Riverine Total Nitrogen Load [kt a-1] into the European seas (a), zoomed into the NWES region (b) and summed 
per country (c, highlighted are the biggest rivers) 

13 

 
 
 
(a)

(b)

(c)

Figure 10: Riverine Total Phosphorus Load [kt a-1] into the European seas (a), zoomed into the NWES region (b) and 
summed per country (c), highlighted are the biggest rivers) 

14 

 
 
 
Figure 11: Comparison of reported freshwater runoff [m3 s-1] with LISFLOOD/GREEN model 

Figure 12: Comparison of reported Total Nitrogen load [t a-1] with GREEN model  

15 

 
 
Figure 13: Comparison of reported Total Phosphorus load [t a-1] with GREEN model 

16 

 
 
 
2.3  Validation data sets for hydrographical properties 

For the calibration and validation of the NWES model, a wide range of observational data sets was used. First, 

sea  gauge  data  for  stations  along  all  coasts  (see  Figure  14)  was  taken  from  EMODnet  (24)  and  further 

processed.  Two  sets  of  Sea  Surface  Temperature  (SST)  fields  derived  from  satellite  data  were  taken  from 

COPERNICUS. The gap-free ‘High Resolution L4 Sea Surface Temperature’, reprocessed by IFREMER  (25) was 

used with a very high temporal (daily) and spatial resolution (0.04°, see Figure  15a), as well as the ESA CCI 

SST  (26) data set based on OSTIA, which has a slightly coarser spatial resolution (0.05°, see Figure  15b). ESA 

CCI  (27) (Boutin et al., 2019) is further providing satellite data for the sea surface salinity (see Figure  16). The 

KLIWAS  North  Sea  Climatology  (KNSC  (28);  (Bersch  et  al.,  2013))  was  further  incorporated.  The  data  set 

contains the monthly means of water temperature and salinity on a 0.5  ×  0.25° grid (79 vertical layers; see 

Figure  17), but the number of usable observations varies strongly. As the NWES-model has a finer horizontal 

and  temporal  resolution,  the  monthly  mean  for  all  cells  with  a  KNSC  grid  box  was  computed.  Further,  the 

observations collected at the World Ocean Database (WOD  (29)) were processed on a daily basis for the grid 

cells  of  the  NWES-model  (Figure  18)  for  a  straightforward  comparison  with  the  model  results.  Further,  at 

WOD a climatology covering the years 2005-2012 for the Greenland, Iceland and Norwegian Seas (GINS  (30)) 

is provided on a quite fine horizontal grid of 0.1°. 

Following van Leeuwen et al. (2015), the North Sea can be divided into five regions according to their vertical 

density gradients (permanently stratified or mixed, seasonally stratified, intermittently stratified or dominated 

by freshwater influence). This classification was compared with a model-based estimation of the frequency of 

stratification (Figure  32). 

(a)

(b)

Figure 14: Used gauge stations taken from EMODnet (left) and computed tidal range [m] (right) 

(24) http://www.emodnet-physics.eu/Portal 
(25)http://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=SST_NWS_SST_L4_REP

_OBSERVATIONS_010_023 

(26) http://marine.copernicus.eu/services-portfolio/access-to-

products/?option=com_csw&view=details&product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_024 

(27) http://cci.esa.int/salinity 
(28) https://icdc.cen.uni-hamburg.de/1/daten/ocean/knsc-hydrographic.html 
(29) https://www.nodc.noaa.gov/OC5/WOD/pr_wod.html 
(30) https://www.nodc.noaa.gov/OC5/regional_climate/gin-seas-climate 

17 

 
 
                                           
(a)
(b)
Figure 15: Average SST derived from satellite data processed by IFREMER (a) and ESA SST CCI project using OSTIA (b) 

(a)

(b)

Figure 16: Average SSS (a) and standard deviation (b) derived from satellite data provided by ESA SST CCI 

(a)

(b)
Figure 17: Average Sea Surface Salinity (a) computed from KNSC and number of observations per grid box (b) 

18 

 
 
 
 
(a)

(c)

(b)

(d)

Figure 18: Water temperature (a) and salinity (b) taken from WOD averaged over all depth layers together with number of 
observations per grid point for water temperature (c) and salinity (d) 

19 

 
 
 
2.4  Validation data sets for biogeochemical properties 

For  the  calibration  and  validation  of  the  biogeochemical  part  of  the  NWES  model,  a  wide  range  of 

observational data sets was used. Only variables related to indicators of MSFD descriptor  5 (eutrophication) 

were selected. The used data sets span from single station data, like the WCO long-term monitoring station 

L4  (31)  and  Helgoland  Roads  (Wiltshire  et  al.,  2008)  taken  from  PANGAEA  (32),  over  publicly  available 

databases, providing raw observations (like ICES database  (33) or WOD  (34), which both vary substantially in 

the  distribution  of  included  data, see  Figure  20).  As the  observation  provided  by  the  two  databases  is  very 

scattered, they were merged for  13  regions (AMM7, see  Figure  19), as suggested by  Edwards et al. (2012) 

and Wakelin et al. (2012). 

Further,  the  North  Sea  Biogeochemical  Climatology  (NSBC  (35);  Hinrichs  et  al.  (2017);  see  Figure  21)  was 

used,  which  provides  monthly  averages  for  every  year  (level  2  data),  as  well  as  an  optimally  interpolated 

monthly climatology (level  3 data). Emodnet Chemistry  (36) provides another seasonal climatology (but only 

for  the  North  Sea  area,  see  Figure  22).  Two  satellite  data  sets  were  used  to  validate  the  Chlorophyll-a 

concentrations  (see  Figure  23).  Therefore,  monthly  averaged  data  provided  at  Environmental  Marine 

Information  System  (EMIS  (37))  and  daily  data  from  the  Copernicus  Marine  environment  monitoring 

service  (38)  was  used.  Due  to  the  different  temporal  resolution  of  the  two  satellite  data  sets,  they  vary 

strongly  in  the  data  availability  for  the  model  validation  (see  Figures  23e, f).  The  comparison  with  satellite 

data was extended by including the light attenuation (kd490 taken from EMIS  (39), see Figure  25), as well as 

two  estimations  of  the  net  primary  productivity  (see  Figure  24)  taken  from  COPERNICUS  (40)  and  Ocean 

Productivity  (41)  (VGPM,  CBPM,  EPPLEY).  Both  data  sets  have  a  decreasing  data  availability  towards  North. 

Finally, the observations  gathered within ICES COMPEAT  tool (Figures  2,  26) were used, which are  currently 

applied to assess the ecological state of the OSPAR regions. 

Following  the  methods  of  Edman  and  Omstedt  (2013),  the  model  quality  was  classified  into  three  groups 

(excellent,  good  and  improvable).  Therefore,  the  correlation  coefficient  (R)  between  observed  and  modelled 

concentrations was computed, as well as the root mean square deviation (RMSD), which was divided by the 

standard deviation from the observation to calculate the value of the cost function (see Figure  34). 

(31) https://westernchannelobservatory.org.uk/l4_nutrients.php 
(32) https://www.pangaea.de 
(33) https://ocean.ices.dk/HydChem/HydChem.aspx?plot=yes 
(34) https://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html 
(35) https://icdc.cen.uni-hamburg.de/1/daten/ocean/knsc-hydrographic0/; 
(36) http://ec.oceanbrowser.net/emodnet-combined/#0 
(37) http://emis.jrc.ec.europa.eu/satellite/2km/EMIS_A_CHLA.xml 
(38) http://marine.copernicus.eu/services-portfolio/access-to-products 
(39) https://data.jrc.ec.europa.eu/dataset/f43226c2-c346-4e3c-9828-86428a16335f 
(40) http://marine.copernicus.eu/services-portfolio/access-to-

products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_082 

(41) http://sites.science.oregonstate.edu/ocean.productivity/index.php 

20 

 
                                           
Figure 19: Subdivisions of the NWES region used for the model validation 

21 

  
 
 
(a)

(c)

(b)

(d)

(e)

(f)
Figure 20: Number of observations for different biogeochemical variables (Chlorophyll-a, Nitrate and Phosphate) from 
ICES data base (a, c, e) and World Ocean Database (b, d, f) 

22 

 
 
 
(a)

(b)

(c)

(d)

(e)

(f)
Figure 21: Near-surface concentrations of Chlorophyll-a (a, b), Dissolved Inorganic Nitrogen (c, d) and Phosphate (e, f) 
taken from NSBC level 2 data (a, c, e) from 2005 to 2012 and from NSBC level 3 climatology (b, d, f) 

23 

  
  
 
 
(a)

(b)

(d)
(c)
Figure 22: Mean observed Chlorophyll-a concentration (a) and Phosphate (c) taken from Emodnet Chemistry together with 
the frequency of available observations (b, d) at Emodnet 

24 

 
 
 
 
(a)

(c)

(b)

(d)

(e)
(f)
Figure 23: Mean observed concentrations (a, b), standard deviation (c, d) and data availability between 2005 and 2012 (e, 
f) of Chlorophyll-a taken from two satellite data products (a, c, e from EMIS, b, d, f from COPERNICUS data service)  

25 

 
 
 
 
 
(a)

(c)

(b)

(d)

(e)

(f)
Figure 24: Mean vertically averaged Net Primary Production [mg C m-2 d-1] estimated by COPERNICUS (a) and Ocean 
Productivity models: (c) VGPM; (e) CBPM; (f) EPPLEY), together with the data availability (b, d) 

26 

 
 
 
 
(a)

(b)
Figure 25: Mean diffuse attenuation coefficient [m-1] at 490  nm taken from EMIS (a) and data availability (b) 

Figure 26: Spatial distribution of the data points within the single assessment units of ICES COMPEAT 

27 

 
 
 
 
 
3  Results 

3.1  Sea surface height compared to gauge data 

Compared  with  the  gauge  data  from  EMODnet  (see  Figure  14),  a  good  agreement  between  observed  and 

modelled  sea  surface  height  (Figure  26)  was  revealed  for  the  stations,  which  are  dominated  by  ebb  and 

flood. Stations with a lower tidal range (e.g. along the Danish and Swedish coasts) showed lower correlations 

(Figure  27), indicating that the model system is well able to reproduce the dominating tides, while the small-

scale  variations  of  the  sea  surface  height  can  be  improved  in  the  model.  This  is  also  obvious  for  some 

stations,  especially  along  the  German  and  UK  coast,  which  are  suffering  from  the  coarse  horizontal  model 

resolution worsening the  model’s ability to reproduce complex systems, like Wadden Sea, Severn Estuary or 

Isle of Islay. 

(a)
(b)
Figure 26: Correlation coefficients (a) and RMSE scaled by the tidal range (b) between modelled and observed sea surface 
height at the gauge stations taken from EMODnet 

Figure 27: Correlation coefficients (grey) and RMSE scaled by the tidal range (red) as function of the tidal range 

28 

 
 
 
 
 
 
 
3.2  Sea surface temperature (compared to satellite data) 

Comparing the modelled SST with satellite data (Figure  15), resulted in a mostly good agreement (indicated 

by high correlation coefficients: 0.953 for IFREMER and 0.963 for OSTIA), although some regional differences 

between  the  two  data  sets  occurred  (Figure  28).  Lowest  correlations  occur  around  the  Faroe  Islands  and 

along the western coast of Norway. For both data sets, the mean difference averaged over the NWES region 

is negative (IFREMER: –  0.75K, OSTIA: –  0.40K), indicating that the modelled SST is too high. This is especially 

the case in the North Sea, Celtic Sea and Irish Sea, while in the deep ocean model SST is slightly too low. 

(a)

(b)

(c)
(d)
Figure 28: Correlation coefficients (a, b) and mean differences (c, d; in K) between modelled and observed SST taken from 
IFREMER (a, c) and OSTIA (b, d) accompanied by the mean differences (negative differences denote regions, where the 
model SST is too high) 

29 

 
 
 
 
 
3.3  Sea surface salinity (compared to satellite data) 

Comparing  the  modelled  SSS  with  satellite  data  (Figure  16),  resulted  in  a  mostly  good  agreement  (see 

Figure  29),  although  some  regional  differences  occur.  Correlation  is  0.65  and  mean  difference  is 

approximately  –  0.12,  indicating  that,  averaged  over  the  whole  region,  the  modelled  salinity  is  slightly  too 

high. This overestimation occurs especially in the Northern North Sea and the Celtic Sea, while salinity is too 

low along the east coast of UK and in the eastern part of the North Sea. 

(a)

(b)

Figure  29: Correlation  coefficients (a)  and mean  differences (b)  between  modelled  and observed  SSS  taken 

from ESA SSS CCI (negative differences denote regions, where the model SSS is too high) 

30 

 
 
 
 
3.4  Salinity and temperature compared to climatologies (KNSC and GINS) 

As already seen from the SSS comparison (Figure  29), the salinity, especially along the southern and eastern 

shore  of  the  North  Sea,  is  mostly  too  low  in  the  model.  This  is  mainly  caused  by  the  missing  estuaries  of 

Rhine, Elbe or Humber (due to the coarse model resolution), where a substantial mixing of fresh and marine 

waters takes place. Nevertheless, the overall correlation is still quite high (0.87 if all depths are used, 0.92 for 

only the surface values), while the mean differences of the vertically averaged salinity (0.15) as well as of the 

surface  salinity  (0.46)  are  positive  (see  Figure  31).  Dominated  by  the  strong  annual  cycle  (Figure  30),  the 

water temperature has an even better correlation (0.97 for KNSC and 0.96 for GINS). It has a small positive 

bias for both data sets (0.21  K for KNSC and 0.33  K for GINS), occurring mainly in the deep ocean but also in 

most parts of the North Sea, while modelled water temperature in the south-western North Sea, as well as in 

the Celtic and Irish Seas is slightly too high (Figure  30). Compared to spatially quite coherent results gained 

from the satellite data (Figure  28), the comparison with water temperature fields of KNSC is more uneven, as 

the  monthly  means  are  strongly  influenced  by  the  available  observations  for  every  month,  which  vary 

between the grid cells. 

(a)

(b)

(c)
Figure 30: Average sea surface temperature from KNSC data set (black) and NWES model (red) averaged over the region 
covered by KNSC (a) and GINS (b); and mean differences between observed SST from KNSC (c) and GINS (d) compared to 
NWES model (negative differences denote regions, where the model SST is too high) 

(d)

31 

  
 
(a) 

(( b) 
Figure 31: Mean differences between modelled and observed salinity, using data from all depths (a) and only for the 
surface (b); negative differences denote regions, where the model temperature or salinity are too high) 

3.5  Mixing zones and current fields 

Comparing  the  stratification  regions  as  identified  by  van  Leeuwen  et  al.  (2015)  with  the  model  results 

(Figure  32) reveals a good qualitative agreement, except for the seasonally stratified region in the northern 

part of the North Sea. On the other hand, the permanently mixed and intermittently stratified regions in the 

English Channel and Southern North Sea are matching  well. The prevailing, anti-clockwise current system is 

well reproduced by the NWES model (Figure  33). There is a strong inflow from the English Channel into the 

North Sea, as well as through the central northern boundary, while the outflow mainly takes place along the 

Norwegian coast. In the southern North Sea, the water from the English Channel is mixing with water masses 

that  have  been transported  along the eastern coast of  UK. In the  central part of the  North Sea, the Dooley 

current is especially visible in the vertically averaged fields. On the other hand, there is a strong current along 

the continental shelf west and north of Ireland. 

(a)

(b)

Figure 32: Classification of the North Sea to different stratification regions following van Leeuwen et al. (2015)  
((a) as published; (b) calculated from NWES model) 

32 

  
 
 
(a)

(b)

(c)

(d)
Figure 33: Scheme of the general circulation in the North Sea (a, taken from (OSPAR, 2000)) and mean currents from 
NWES model at different depth (b) surface, (c) vertically averaged, (d) near-bottom; colour-coded is current speed in m s-
1) 

3.6  State of MSFD eutrophication indicators 

In  Figure  34  the  NWES  model  quality  is  shown  for  the  validation  of  all  biogeochemical  variables  in 

comparison to all data sets presented afterwards. The results differ strongly for each variable and between 

the different observations. The best results are achieved for Phosphate and Chlorophyll-a, while Ammonium, 

Nitrate and Silicate are mostly improvable. 

The  average  over  the  whole  simulation  period  (2005-2012)  of  the  central  eutrophication  indicators 

Chlorophyll-a,  Dissolved  Inorganic  Nitrogen  (DIN,  sum  of  Ammonium  and  Nitrate)  and  Phosphate  are 

presented  in  Figures  35-37.  All  show  strong  spatial  gradients,  with  highest  values  along  the  southern  and 

eastern shore of the North Sea and along the UK coast. All three near-surface indicators follow strong annual 

cycles  (Figure  38).  While  the  nutrient  concentrations  reach  their  annual  maximum  values  during  winter 

season,  Chlorophyll-a  concentrations  are  highest  during  spring  (North  Sea)  and  summer  (Celtic  Sea).  The 

vertical averaged concentrations show less developed annual cycles. 

33 

 
 
 
 
Figure 34: Classification of the validation results for the main biogeochemical variables (colour-coded) following Edman et 
al. (2013) using all data sets 

(a)

(b)

(c)
(d)
Figure 35: Average Chlorophyll-a concentration [mg m-3] from NWES model near the surface (a) and vertically averaged 
(c) together with the standard deviation (b, d) 

34 

  
 
 
  
 
(a)

(b)

(c)

(d)
Figure 36: Average DIN concentration [mmol m-3] from NWES model near the surface (a) and vertically averaged (c) 
together with the standard deviation (b, d) 

(a)

(b)

(c)
Figure 37: Average Phosphate concentration [mmol m-3] from NWES model near the surface (a) and vertically averaged 
(c) together with the standard deviation (b, d) 

(d)

35 

 
 
 
 
(a)

(b)

(c)
Figure 38: Climatology of concentrations of Chlorophyll-a (a), Ammonium + Nitrate (b) and Phosphate (c) averaged for 
North Sea and Celtic Sea using only surface values and all depths 

36 

 
 
  
3.7  Seasonal climatologies 

Using  the  seasonal  climatologies  provided  by  Emodnet  Chemistry  to  compare  with  the  model  results  (see 

Figure  39),  revealed  a  good  agreement  for  Chlorophyll-a  and Phosphate, while  Ammonium and  Silicate  are 

improvable.  Comparing  to  the  model  results  with  observations  gathered  by  NSBC  level  3  data,  resulted  in 

high correlation coefficients for all assessed variables (Figures  40, 41, 42), indicating that the seasonal cycles 

are well reproduced. The only exception is Chlorophyll-a in the central North Sea, where correlations are quite 

low. The assessment revealed only little differences of the model quality if all  depths are evaluated or only 

near-surface  values.  Overall,  the  difference  between  observed  and  modelled  Chlorophyll-a  concentration  is 

positive (Figures  40(b), (d)). This indicates that the model values are too low, especially in the Irish Sea and 

the southern North Sea (this is opposite to the comparison result from Emodnet Chemistry, see Figure  39(a)). 

While the phosphate concentration difference is mostly around 0 (Figure  41), DIN concentrations in the model 

have  a  strong  bias  with  too  high  values  at  NWES  model.  This  is  especially  the  case  in  the  southern  and 

eastern North Sea, where DIN concentrations should be strongly reduced due to denitrification taking place in 

the sediments, which is missing in the present model setup. 

37 

(a)

(b)

(c)

(d)

Figure 39: Mean differences between observed concentrations (taken from Emodnet Chemistry) and modelled 

ones,  evaluated  for  Chlorophyll-a  (a),  Ammonium  (b),  Phosphate  (c)  and  Silicate  (d)  [negative  differences 

denote regions, where the model concentrations are too high] 

38 

 
 
(a)

(c)

(b)

(d)

Figure  40: Correlation (a, c) and mean differences (b, d) between observed Chlorophyll concentration (taken 

from NSBC level  3) and modelled ones, evaluated for near-surface values (a, b) and for all depth layers (c, d) 

[negative differences denote regions, where the model concentrations are too high] 

39 

 
 
 
(a)

(c)

(b)

(d)

Figure  41: Correlation  (a,  c)  and  mean  differences (b,  d)  between  observed  Phosphate  concentration  (taken 

from NSBC level  3) and modelled ones, evaluated for near-surface values (a, b) and for all depth layers (c, d) 

[negative differences denote regions, where the model concentrations are too high] 

40 

 
 
 
(a)

(c)

(b)

(d)

Figure  42:  Correlation  (a,  c)  and  mean  differences  (b,  d)  between  observed  DIN  (Nitrate  +  Ammonium) 

concentration (taken from NSBC level  3) and modelled ones, evaluated for near-surface values (a, b) and for 

all depth layers (c, d) [negative differences denote regions, where the model concentrations are too high] 

41 

 
 
 
 
3.8  Model results compared to satellite data 

Several satellite products covering the NWES regions were used for the  model  validation. The results differ 

substantially  between  the  individual  data  sets.  For  Chlorophyll-a  taken  from  EMIS  RMSD  between 

observations  and  model  is  quite  high  (1.14)  and  correlation  only  fair  (R  =  0.38).  Especially  in  the  southern 

and eastern North Sea, the Chlorophyll-a computed by the model is too high (see Figure  44). One problem is 

thereby  the  strong  underestimation  of  the  light  attenuation  (see  Figure  45),  due  to  the  missing  inorganic 

suspended matter (SPM, incorporated in the coupled 3d-model, e.g. by van der Molen et al. (2017)), which is 

dominating  the  availability  of  light  strongly  (Capuzzo  et  al.,  2013).  The  statistics  are  even  worse  for  the 

comparison with the COPERNICUS Chlorophyll-a data set (RMSD: 1.93; R  =  0.29; see Figure  43). The vertically 

integrated  Primary  Productivity  was  compared  with  different  data  sets  derived  from  satellite  data  (see 

Figures  46,  47).  The  model  results  are  strongly  underestimating,  resulting  in  high  RMSDs  (COPERNICUS: 

1  169, VGPM: 1  340, CBPM: 622.8, EPPLEY: 680.3  mg C m-2 d-1) and low correlations (COPERNICUS: 0.17, 

VGPM: 0.17, CBPM: 0.12, EPPLEY: 0.14). Nevertheless, the extremely high values from the satellite products of 

up  to  5  000  mg  C  m-2  d-1  seem  unrealistically  high  (Weston  et  al.  (2005)  reported  values  between  456-

1  015  mg  C  m-2  d-1;  Westberry  et  al.  (2008)  up  to  1  500  mg  C  m-2  d-1;  and  Skogen  and  Moll  (2000) 

reported also only values below 1  000  mg C m-2 d-1) and may cause the strong bias. 

(a)

(b)

(c)

(d)

Figure  43: Mean concentrations [mg C m-3] of observed (a; taken from COPERNICUS data service) and modelled (b) near-
surface Chlorophyll-a, correlation (c) and mean difference (d; negative differences denote regions, where the model 
Chlorophyll-a is too high) between both data sets 

42 

 
 
(a)

(b)

(d)
(c)
Figure  44: Mean concentrations [mg C m-3] of observed (a; taken from EMIS) and modelled (b) near-surface Chlorophyll-
a, correlation (c) and mean difference (d; negative differences denote regions, where the model Chlorophyll-a is too high) 
between both data sets  

43 

 
 
(a)

(b)

(c)

(d)
Figure  45: Mean attenuation [m-1] as observed (a; taken from EMIS) and modelled (b), correlation (c) and mean difference 
(d; positive differences denote regions, where the model attenuation is too low) between both data sets 

44 

 
 
 
(a)

(b)

(c)

(d)

(e)

(f)

Figure  46: Mean vertically averaged Net Primary Production [mg C m-2 d-1] estimated using VGPM (a); the ratio between 
VGPM and model values (b), correlation (c) and mean difference between both data sets (d; positive differences denote 
regions, where the model attenuation is too low) 

45 

        
 
  
 
(a)

(b)

(d)
(c)
Figure  47: Mean vertically averaged Net Primary Production [mg C m-2 d-1] taken from COPERNICUS (a) and modelled 
(b), correlation (c) and mean difference (d; positive differences denote regions, where the model attenuation is too low) 
between both data sets 

46 

 
 
 
 
3.9 

ICES COMPEAT data 

Following the subdivision proposed by the OSPAR working group ICG EMO and implemented by ICES COMPEAT 

tool, the model quality regarding Chlorophyll-a (see Figure  48), Dissolved Inorganic Nitrogen (Figure  49) and 

Phosphate (Figure  50) was assessed (based on the climatologies computed for every assessment unit) using 

the same classification system as in Figure  34.  

The classification resulted, for Phosphate, in an excellent model quality, except for the eastern North Sea and 

linked to it the Norwegian Trench and Skagerrak area. Further, Chlorophyll-a is also excellent in most coastal 

areas, while it is mostly improvable in the open sea parts. Dissolved Inorganic Nitrogen is classified only for 

one region as excellent, due to the too high values in most of the regions. 

Figure 48: Classification of Chlorophyll-a model quality compared to COMPEAT  

47 

 
 
 
Figure 49: Classification of Dissolved Inorganic Nitrogen model quality compared to COMPEAT 

Figure 50: Classification of Dissolved Inorganic Phosphorus (Phosphate) model quality compared to COMPEAT 

48 

 
 
 
3.10  Assessment for AMM7 regions 

Using  the  AMM7  region  (see  Figure  19),  model  results  and  observations  (using  ICES  data,  WOD  and  NSBC 

level  3) were spatially averaged and compared. In Figures  52 and 53 the computed climatologies for some 

regions of the North Sea and Celtic Sea are shown in detail. While the model shows a strong annual cycle, the 

observed concentrations differ strongly between the single data sets. 

Further, the model quality was assessed for all regions for the near-surface values and using all depth layers 

(see  Figure  51).  Overall  the  classification  results  in  the  best  results  for  the  comparison  with  NSBC.  For  all 

data  sets,  the  surface  values  showed  higher  correlations  compared  to  all  depths.  The  high  correlations 

indicate  that  the  dominating  annual  cycles  are  well  reproduced.  On  the  other  hand,  the  cost  function  for 

Nitrate had  mostly higher values, due to the overestimation of Nitrate at NWES  model (especially Southern 

and Central North Sea, Skagerrak and Norwegian Trench). 

Using  the  light  attenuation  from  the  NWES  model,  Secchi  Depth  (often  used  as  indirect  eutrophication 

indicator)  was  computed.  In  Figure  54  it  is  compared  to  observations  gathered  from  ICES  and  an  estimate 

using  the  attenuation  from  EMIS.  Due  to  the  underestimation  of  the  attenuation,  Secchi  Depth  is  mostly 

everywhere too high in the model, especially in the southern North Sea, but also in the Celtic or Irish Sea. Only 

during summer in the Central North Sea, does the model estimate meet the observation, as during the calm 

periods the suspended particles sink down, resulting in high Secchi Depth. 

(a)

(c)

(b)

(d)

(e)

(f)
Figure  51: Classification of the model quality for Chlorophyll-a (a, b), Nitrate (c, d) and Phosphate (e, f) for the AMM7 
regions (see Figure  19), evaluated only for near-surface values (a, c, e) and all depths (b, d, f) 

49 

 
(a)

(d)

(b)

(e)

(c)

(f)

(g)
Figure 52: Monthly climatologies of model values (black) and different observations (blue: WOD; grey: ICES data; red: NSBC) computed Chlorophyll-a (a-c), Nitrate (d-f) and Phosphate (g-i) 
for three AMM7 regions (see Figure  19) covering the North Sea 

(h)

(i)

50 

 
  
 
 
 
(a)

(d)

(b)

(e)

(c)

(f)

(g)
Figure 53: Monthly climatologies of model values (black) and different observations (blue: WOD; grey: ICES data; red: NSBC) computed Chlorophyll-a (a-c), Nitrate (d-f) and Phosphate (g-i) 
for three AMM7 regions (see Figure  19) covering the North Sea 

(h)

(i)

51 

 
 
 
 
 
 
 
(a)

(b)

(c)
(d)
Figure 54: Monthly climatologies of Secchi Depth [m] computed from the model values (black), observations provided by 
ICES (grey) and estimated from the attenuation (kd490) taken from EMIS (green; see Figure  45) 

52 

 
 
 
 
 
3.11  Station data 

Two  stations  (WCO  long-term  monitoring  station  L4  and  Helgoland  Reede)  were  selected  for  the  validation 

(see Figure  55), representing the English Channel (L4) and the German Bight next to Helgoland, respectively. 

Both stations have distinctive annual cycles with high levels of Nitrate and Phosphate during winter and low 

levels  during  summer.  This  is  reproduced  well,  although  the  strong  overestimation  of  Nitrate  in  the  south-

eastern  North  Sea  appears  again,  as  well  as  the  too  low  salinities.  An  opposite  annual  cycle  occurs  for 

Chlorophyll-a  with  lowest  in  values  (possibly  below  observed  values)  and  high  values  during  spring  and 

summer. While the annual maxima at L4 are well reproduced by the NWES model, at Helgoland they are too 

high, which might be caused by the too strong riverine influence, seen in the low salinity values. 

(a)

(c)

(e)

(b)

(d)

(f) 

(g)

(h)

Figure 55: Comparison of model values and observation taken from several data sets regarding Chlorophyll-a (a, b), 
Nitrate (c, d), Phosphate (e, f) and salinity (g, h) evaluated for WCO long-term monitoring station L4 (a, c, e, g) and around 
Helgoland (b, d, f, h) 

53 

 
 
 
   
 
54 

 
4  Summary 

A  coupled  three-dimensional  simulation  model  of  the  North-Western  European  Shelf  including  the 

hydrodynamics and a lower trophic level (biogeochemical) model was newly developed. Its results are  mostly 

reliable  and allow  to  apply  the  model  for  scenario  simulations.  All  major  hydrodynamic  features,  especially 

the tides, are well reproduced, as well as the dominating current fields (see Figure  33) or areas where mixing 

or stratification is mostly occurring (see Figure  32). The salinity gradients from central estuaries (like Rhine, 

Elbe or Humber) towards the marine waters are  overestimated in the model (see Figure  31), as low salinity 

region extends too far into the sea. This could  be improved  by  using a finer horizontal grid, which could be 

able to follow the complex topography of the estuaries in a more sophisticated way, so that a stronger mixing 

of sea water and riverine freshwater could take place already in the estuary. 

Main  biogeochemical  variables  used  as  eutrophication  indicators  (Chlorophyll-a,  Phosphate,  Nitrate  and 

Ammonium) are included in the NWES model and mostly follow the annual cycles, which are also visible in the 

observations.  On  the  other  hand,  the  comparison  of  the  model  results  with  the  large  variety  of  available 

observations revealed strong differences between the  individual data sets. This results in mixed signals, for 

example the simulated Chlorophyll-a concentration in the south-eastern North Sea is above the observations 

reported  by  Emodnet  Chemistry  (Figure  39),  satellite  data  (Figure  43)  or  EMIS  (Figure  44),  but  below  the 

NSBC  data  (Figure  40),  as  well  as  below  the  data  from  WOD  and  ICES  (Figure  52a).  Overestimating  the 

Chlorophyll-a  concentration  may  be  induced  by  too  low  light  attenuation  coefficients  (Figure  45),  as 

suspended inorganic particles (SPM) are missing, which lead especially in the southern and eastern North Sea 

to high attenuation coefficients. 

Another improvable  feature is that Nitrate and Ammonium (combined as Dissolved Inorganic  Nitrogen, DIN) 

are  in  the  NWES  model  mostly  above  the  observed  concentrations  (e.g.  visible  in  Figure  55d).  This  is  most 

likely  caused  by  the  lacking  denitrification  in  the  sediments,  which  is  the  central  process  to  convert  DIN  to 

atmospheric Nitrogen. Solving this issue requires the implementation of a more sophisticated benthic model, 

coupled  to  ERSEM.  Another  uncertainty  is  introduced  by  the  riverine  nutrient  loads.  The  monthly  DIN  loads 

were computed from the annual loads (assuming climatological means) and constant conversion factors from 

the  total  Nitrogen  load.  This  method  is  quite  simplified  and  might  be  extended  by  incorporating  conversion 

factors and seasonality from the riverine loads, where observations are available. The very same proceeding 

worked  quite  well  for  the  Phosphate  loads,  seen  in  a  quite  good  agreement  to  the  observed  Phosphate 

concentrations of the NWES region. 

Other  key  elements  of  the  biogeochemical  model  (like  Dissolved  Oxygen  or  Inorganic  Carbon)  were  also 

assessed  and  validated,  resulting  in  a  good  model  quality  (not  shown).  Silicate  is  included  in  some  of  the 

validation plots (e.g. Figure  39), revealing that model values especially in the southern and eastern North Sea 

are much too low. Due to its limitation, Diatoms are underestimated in the model simulation, what has only a 

limited influence on the overall Chlorophyll-a concentration, as the other Phytoplankton groups are not limited 

by Silicate and therefore their growth is enhanced. Nevertheless, improving the riverine Silicate loads seems 

important to achieve better model results, as rivers along the southern shore of the North Sea are the main 

source of Silicate to the NWES. 

55 

 
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