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

‚an Leeuwen et al. 
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10.3389/fmars.2023.1129951 
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% Red. Winter DIP ([CS-HSVCS, %) 
“IGURE 12 
delative reduction of growing-season mean Chl as a function of the relative reduction of winter DIN (A) and winter DIP (B). Each dot indicates a 
narine area for one model and models are differentiated by colours 
Although the models deliver fairly good results for nutrient 
concentrations, the modelled Chl concentrations are often lower 
ıhan expected, and this must be considered carefully in any further 
application. In all applied models the Chl concentration is mainly 
determined by nutrient availability, light availability and grazing 
pressure. Differences can thus stem from the complexity of included 
nutrient recycling processes, hydrodynamic differences in nutrient 
and suspended particulate matter transport, inclusion of benthic 
storage and release of nutrients, inclusion of a separate sediment 
resuspension model and the complexity of zooplankton 
representation. For example, the Deltares model does not include 
zooplankton, while the SMHI model has the lowest number of 
pelagic state variables, indicating lower pelagic complexity 
‘Appendix E). Whether these model characteristics contribute to 
ihe observed high Chl concentrations from these models needs 
further careful analysis though. The same applies to those models 
:hat have consistently low Chl predictions compared to 
observations. Lack of phytoplankton species resolution in the 
models (usually 2-6 different functional groups) can also play a 
part in underestimating Chl levels (unlikely to capture a single 
species sudden bloom event well), as can the applied Chl:C ratio 
Ased to calculate the Chl concentrations in models based on the 
simulated phytoplankton biomass. Reappraisal of individual model 
results and possible model improvement is thus a key part of 
ensemble modelling. 
Structural diversity of the models, parametric uncertainties, 
differences in spatial resolution, in boundary conditions and in 
‚orcings will necessarily cause differences between model estimates. 
Although some of these issues have been solved in this exercise by 
applying identical loads, forcings and boundary conditions, there is 
still variability in model responses. This variability is desirable as it 
displays a range of possible outcomes, and ensemble modelling 
approaches are used to explore and quantify this diversity. Though 
parametric variation for each ensemble member would enhance 
confidence in the individual results even further, a separate 
parametric ensemble for each contribution to the overall 
ensemble is generally unfeasible due to computational and 
"rontiers in Marıne © 12... 
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financial restraints. Note that we applied the weighting method 
by Almroth and Skogen (2010) in a fundamentally different way 
{rom the original article: they used it to enhance the quality of the 
modelled current state in order to compare it against thresholds 
whereas in this study we applied it to a pre-eutrophic scenario 
which can be used to derive thresholds. 
An objective way to further reduce uncertainties is to resort to 
weight-averaged values, estimated from the comparison between 
model outputs and observations, and apply these weights to the 
individual model results before taking the ensemble average 
(Almroth and Skogen, 2010). The present exercise used this 
weighted-ensemble-mean method to provide pre-eutrophic 
values, or reference values, for the indicators of eutrophication in 
coastal and shelf areas. For this the availability of observational data 
in the COMPEAT tool was essential. More observational evidence 
would therefore also increase confidence in the weighted 
ensemble result. 
4.4 Ensemble modelling as a tool for 
marine management 
In the past several single model approaches have been used to 
estimate the pre-eutrophic state of marine systems (Schernewski 
and Neumann, 2005; Schernewski et al., 2015; Kerimoglu et al., 
2018), including using multiple single models to cover a larger area 
(Desmit et al., 2018). Ensemble modelling addresses the inherent 
uncertainties in single model results and is increasingly applied in 
marine response studies (Almroth and Skogen, 2010; Lenhart et al., 
2010; Eilola et al., 2011; Meier et al., 2019; Friedland et al., 2021; 
Stegert et al., 2021) despite the higher efforts involved. These efforts 
include the necessity to combine a variety of modelling groups and 
their individual models, as well as agreement to a common protocol 
to ensure comparable results, agreement on suitable scenarios and 
to a common analysis of the obtained scenario results. Individual 
funding issues can undermine this common approach, as can 
technical issues as demonstrated here (lack of results from one 
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