‚an Leeuwen et al.
100
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10.3389/fmars.2023.1129951
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U 40 60 80 100
% Red. Winter DIN ([CS-HSVCS, %)
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60
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Deltares
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RBINS
SMHI
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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...
arm
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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