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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