jan Leeuwen et al. 
Model domains 
0 | 
| 
3 
(X 
:Cefas, JRC 
Deltares 
UHH-HZG |, 
7 IFREMER 
Oldenburg "- 
RBINS 
3MHI 
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Longitude [deg. E] 
9° 
IGURE 1 
Model domains of the different models used to calculate pre- 
zutrophic marine conditions. Note that the JRC (Joint Research 
Centre, EU) and Cefas model domains are identical, though thei' 
acOsvstem models are different 
2.2 Available observational data 
Observations were obtained from the OSPAR Common 
Procedure Eutrophication Assessment Tool (COMPEAT, see 
GitHub - ices-tools-prod/COMPEAT, ICES, 2022) for validation 
and weighting purposes. This tool is applied in the OSPAR 
Comprehensive Procedure (COMP) for the determination of the 
gutrophication status of marine areas based on observational data 
submitted by each member country. The observations, which are 
che same as those used in the official eutrophication assessments, 
2ave a higher spatial and temporal coverage in coastal areas than in 
offshore waters (see Appendix B; Supplementary Materials). Thus, 
spatial averages of the observations over assessment areas that 
comprise coastal and more open waters may not be fully 
zepresentative of these assessment areas. Simulated area-averaged 
results will always be representative of the full area, complicating a 
direct comparison with the observational averages (see, e.g., Garcia- 
Garcia et al., 2019). Observations were available for DIN, DIP and 
Chl. Due to the low confidence in the in-situ Chl observations 
‘Appendix B; Supplementary Figure 3) related to data scarcity (both 
‚emporally and spatially), satellite data (a reanalysis product, Van 
der Zande et al., 2019; Lavigne et al., 2021) were added to the Chl 
observational database in COMPEAT. To calculate a combined 
seasonal mean concentration per assessment area from in-situ and 
Earth Observation (EO) data a weighting factor was applied, based 
on the OSPAR confidence rating of in-situ data availability which is 
implemented in the COMPEAT tool. Where the combined 
‚emporal and spatial confidence of in-situ Chl was high, 50:50 
“in-situ/EO) data was used to derive the assessment area mean. If 
in-situ confidence was moderate, 30:70 (in-situ/EO) was used, while 
“rontiers in Marine ı zie.17e 
10.3389/fmars.2023.1129951 
where in-situ confidence was low, 10:90 (in-situ/EO) was applied 
(OSPAR, 2022a). Coastal waters are optically challenging 
environments for satellite wavelength observations, due to shallow 
depths and high levels of suspended matter. Retrieving accurate Chl 
estimates from satellite data is therefore more challenging in coastal 
waters than in offshore waters (Lavigne et al., 2021). 
Observational time series were extracted for the stations with 
the most complete temporal coverage over the simulated period 
(available in the ICES COMPEAT tool), for validation purposes. 
Unfortunately, using these data results in a strong spatial bias, as 
most observations were obtained in near-shore waters of the 
southern North Sea. In addition, data from long-term observation 
stations were provided by Cefas (station Stonehaven) and PML 
(station WCO-L4). See Supplementary Figure 2 in the 
zupplementary materials for the locations of the used stations. 
2.3 Model validation 
Each model used in this exercise has been validated separately 
before this application (see Supplementary Materials Appendix D). A 
common validation procedure was applied using a subsection of the 
COMPEAT data, representing short-term and long-term time series, 
augmented with 2 additional long-term stations (Stonehaven, WCO- 
L4) to allow for a comparison of model performance (Figure 2). 
Following the approach of Eilola et al. (2011) and Edman and 
Omstedt (2013), we used a combination of correlation coefficient 
and the root mean square difference (RMSD) scaled by the standard 
deviation of the observations to assess the skills of the different 
ecosystem models compared to the observations. This comparison 
was done for each station individually and later the station results 
were combined to assess the overall skill for each model (Figure 2). 
Overall, most model systems have a good to acceptable model skill. 
| UHH-HZG 
SMHI 
RBINS 
Oldenburg 
‚RC 
+ DIP 
= DIN 
$ Chlorophyll-: 
| IFREMER 
eltares 
> as 
1 - Correlation coefficient 
FIGURE 2 
Model skill assessment for DIN, DIP and Chl for all participating 
models (in respective areas where the number of observations is the 
nighest). The horizontal axis shows 1 - the correlation coefficient 
oetween model values and observations, the vertical axis shows the 
‚oot mean square difference (RMSD) divided by the observational 
standard deviation (STD). The closer the values are to the origin, the 
zloser the model results are to observations. The inner field (x-axis: 
I-1/3, y-axis: 0—1) indicates good agreement and strong 
correlation between model and observations. The middle field (x- 
axis: 1/3—-2/3, y-axis: 1-2) indicates reasonable agreement and 
moderate correlation between model and observations. The outer 
Äeld indicates poor agreement between model and observations. 
SMHI values for DIN and DIP are off the scale at (0.35, 3.61) and 
(0.32,3.42), respectively. Negative correlations did not occur. 
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