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Full text: Deriving pre-eutrophic conditions from an ensemblemodel approach for the North-West European seas

jan Leeuwen et al. 
TABLE 3 Pre-eutrophic nutrient concentrations/loads? As percentage of 
:urrent values for the Droaden and Darss sills in the Baltic Sea 
gl 
NO3 
NH 
DON 
204 
86 
IS 
91% 
model results. We applied these weights to calculate ensemble 
model averages for the OSPAR areas defined for the COMP4 
assessment (section 2.4). Almroth and Skogen (2010) used this 
weighted ensemble approach to derive a better estimate of the 
current state, which was then assessed against the eutrophication 
criteria of the time. Here, we apply this method to obtain weights 
based on validation of current state results. We then applied these 
weights to the historic results and estimate the area’s pre- 
gutrophic state. 
The applied weighting method is given by Eq. 1-Eq. 4 and is 
based on model results for the current state and the available 
observations from the COMPEAT tool. It relies on observational 
concentrations being available in each area over the chosen COMP3 
veriod (2009-2014). As such, the weighting is applied to winter DIN 
and DIP and growing season mean Chl results. When observations 
were not available for a given area, we used the unweighted 
ensemble mean (ie. a classical averaging was applied), but DIN, 
DIP and Chl weights were also applied to Total N, Total P and Chl 
P90, respectively. For Chl, the observations involved both in situ 
and satellite observations. In any given area, the cost function C? 
(Eq. 1) was calculated for each model £ and parameter P (e.g. DIN), 
with P °S and P model S CS referring to the current state, 
observational and simulated values, respectively. Model results 
were averaged over the years 2009-2014 before application in the 
cost function: as such, a one-to-one comparison of individual 
stations is not included in the method. Individual model results 
for the cost function are shown in Appendix E, for the parameters 
DIN, DIP and Chl. Weights W were then calculated per model and 
per assessment area (Eq. 2) with B=0.1 an arbitrary constant to 
avoid division by small numbers in case of good model fits 
‚Almroth and Skogen, 2010). The weights were then normalized 
using all contributing models in the area for each parameter (Egq. 3, 
with N the number of contributing models). Normalized weights 
from the current state were then applied to the model results 
obtained from the historic scenario (Eq. 4). Note that the number 
of contributing models varies with area and parameter, with a 
maximum of 7 (Southern Bight of the North Sea) and a minimum 
of 2 (Gulf of Biscay). 
PP 
mean Pmodel 5 CS _ ynean pn 
std. pobs 
Eg. ] 
AT 
CE 
+ P 
Eqg. 2 
"rontiers in Marine Science 
10.3389/fmars.2023.1129951 
N 
Wnorm® = = Wf Eq 
De Wi 
1 N 
pP P del <= HS1 
WMAHsı = Zr X (Wnormi +meanPf"“ ) Eq. 4 
Si Wnorm; 1 
As the cost function is based only on the current state results, it 
‚nherently neglects differences in the individual model responses to 
che historic scenario, which is inevitable in absence of sufficient 
observations for the historic scenario. Weighted ensemble results 
are generally more robust than those of the individual members, as 
model strengths are enhanced and model weaknesses are reduced 
by the applied weighting. 
3 Resu!'- 
3.1 Annual results per model 
First we show results for 2 areas in more detail: the Channel 
Well Mixed Tidally Influenced area (Figure 5) and the Southern 
North Sea area (Figure 6) (see Figure 3 for the locations of these 
assessment areas). Annual values per individual model are shown, 
as well as the number of available observations per year and their 
mean annual value. The overall observational mean, ensemble 
model mean and the weighted ensemble model mean over the 
original COMP4 assessment period (2006-2014) are also provided. 
Additional selected areas are shown in (Appendix H; 
Supplementary Figures 9-15). All data presented here are spatially 
averaged over the area and temporally averaged over the 
individual years. 
Through the ensemble-weighted-mean method a more robust 
estimate can be made of nutrient and chlorophyll concentrations. 
Model estimates for specific variables in specific areas and years 
show large variability, with between-model variability generally 
larger than interannual variability within an area. The ensemble 
nodel mean (light-blue diamonds in Figures 5, 6) tends to be closer 
to the observed concentrations (black squares) than the individual 
model results. The weighted ensemble mean is even closer to the 
observed concentrations (red asterisks), but the ensemble model 
mean cannot get closer to the observations than the closest model 
result (e.g. Figure 6B, all models underestimate winter DIP 
concentrations in this area). The effectiveness of the weighted 
ensemble mean approach strongly depends on the availability and 
tepresentativeness of observation data per assessment area. This is 
illustrated by Figure 5, where for both DIN and DIP only one 
observation was available in 6 years, leading to large uncertainty in 
the observational data that the weights are based upon. In contrast 
there are many more observed data available for chlorophyll in the 
same area, thanks to earth observation data. Note that in-situ 
observations for Chl tend to be higher than the mean EO Chl data. 
In general, all models capture the yearly observational mean for 
DIN, DIP and Chl well for most areas. However, differences 
between models for each parameter exist. DIN results display 
high variability (overestimation as well as underestimation) but 
DIP is usually close to the observational range (Figures 5, 6; 
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