Access to this full-text is provided by Springer Nature.
Content available from Communications Earth & Environment
This content is subject to copyright. Terms and conditions apply.
ARTICLE
Offshore wind farms are projected to impact
primary production and bottom water
deoxygenation in the North Sea
Ute Daewel 1✉, Naveed Akhtar 1, Nils Christiansen1& Corinna Schrum1,2
The wind wake effect of offshore wind farms affects the hydrodynamical conditions in the
ocean, which has been hypothesized to impact marine primary production. So far only little is
known about the ecosystem response to wind wakes under the premisses of large offshore
wind farm clusters. Here we show, via numerical modeling, that the associated wind wakes in
the North Sea provoke large-scale changes in annual primary production with local changes
of up to ±10% not only at the offshore wind farm clusters, but also distributed over a wider
region. The model also projects an increase in sediment carbon in deeper areas of the
southern North Sea due to reduced current velocities, and decreased dissolved oxygen inside
an area with already low oxygen concentration. Our results provide evidence that the ongoing
offshore wind farm developments can have a substantial impact on the structuring of coastal
marine ecosystems on basin scales.
https://doi.org/10.1038/s43247-022-00625-0 OPEN
1Institute for Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, D-21502 Geesthacht, Germany. 2Institute of
Oceanography, CEN, Universität Hamburg, Hamburg, Germany. ✉email: ute.daewel@hereon.de
COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 |www.nature.c om/commsenv 1
1234567890():,;
Content courtesy of Springer Nature, terms of use apply. Rights reserved
The North Sea is a shallow shelf sea system in which the
interactions between bathymetry, tides and a strong
freshwater supply at the continental coast foster a complex
frontal system, which separates well-mixed coastal waters from
seasonally stratified deeper areas. The shallow coastal areas and
sandbanks combined with stable wind resources make the North
Sea an ideal area for renewable energy production and have made
the North Sea a global hotspot for offshore wind energy
production1. The recently negotiated European Green Deal to
support the European target to phase out dependence on fossil
fuels will further accelerate the development of offshore renew-
able energy2and a substantial increase of installed capacity
(212 GW by 20503) is planned in the North Sea as a consequence
to Europe´s strategy to be carbon neutral by 2050. The size and
magnitude of the already installed (28 GW European offshore
wind farm capacity by 20214) and the planned offshore wind
farm (OWF) installation5has raised concerns about their impact
on the marine environment6and scientific efforts have increased
to understand and assess the implications of these large structures
for the marine system. In addition to impacts on the regional
atmosphere7, multiple physical8,9, biological6,10 and chemical11
impacts on the marine system have been identified. The under-
water structures, such as foundations and piles may cause tur-
bulent current wakes, which impact circulation, stratification,
mixing, and sediment resuspension12–14. Most studies conclude
that the direct hydrodynamic consequences of the windfarm
structures are mainly restricted to the area within the wind
farms15,16. However, some speculate also, that the cumulative
impacts of an increasing number of offshore installations might
result in substantial impacts on the larger scale stratification13,17.
Larger scale effects of offshore wind energy production, well
beyond the wind farm areas, are introduced to the atmosphere by
infrastructures above the sea level and the energy extraction
itself18. Atmospheric wakes appearing in the lee of wind farms
extend on scales up to 65 km and beyond, depending on atmo-
spheric stability, with a wind speed reduction of up to 43% inside
the wakes18 leading to upwelling and downwelling dipoles in the
ocean beneath19. Previous modeling studies9,19 showed that these
dipoles are associated with vertical velocities in the order of
meters per day and consequent changes in mixing, stratification,
temperature, and salinity. Recently, Floeter et al.20 provided
empirical evidence for the existence of these upwelling/down-
welling dipoles showing distinct structural changes in mixed layer
depth and potential energy anomaly inside the wind wake area of
OWFs in the summer stratified area of the southern North Sea. A
first assessment of the large-scale integrated impact of atmo-
spheric wakes from already existing OWFs on the hydrography of
the southern North Sea revealed the emergence of large-scale
oceanic structures with respect to currents, sea surface elevation,
and stratification8.
For the marine ecosystem the effects of OWFs might or might
not be severe, positive or negative. As van Berkel et al.16 explain,
the evaluation of ecosystem effects through BACI (before-after-
control-impact) surveys are challenging due to the spatio-
temporal variability of the natural system, regional and global
trends, as well as other anthropogenic impacts, such as changes
in fishing effort, eutrophication, and noise levels, while the focus
of investigations is on selected fish and seabird species. In the
literature we find, so far, a number of studies related to immediate
impacts of OWFs on marine fauna6, such as the artificial reefs
effect21,22 or the impacts of acoustic disturbances on fish and
marine mammals23,24. Indirect impacts are, however, likely even
more important, more complex, and more difficult to investigate.
This includes consequences of restricted fisheries inside the
OWFs25 as well as the impacts of the above-described modulation
of the physical environment on the structuring of the pelagic10
and benthic22 ecosystem. It is well known that modifications in
mixing and stratification also impacts nutrient availability in the
euphotic zone26,27, however, the picture of the ecosystem impacts
is less clear for some obvious reasons: (i) The changes in nutrient
concentration would start a cause-effect chain that translates into
changes in primary production and effectively alters the food
chain; (ii) In a dynamic system like the southern North Sea,
which is characterized by strong tidal and residual currents,
changes in the biotic and abiotic environment are exposed to
advective processes; (iii) The expected changes depend strongly
on the prevailing hydrodynamic conditions, which makes it dif-
ficult to disentangle natural from inflicted changes. Other than a
high-density suite of physical and biological observations,
numerical modeling studies are the only means to build BACI
studies as scenarios with and without the disturbance can be
simulated28. In a previous modeling study, van der Molen et al.28
proposed such an approach for an OWF at Dogger Bank, a
relatively shallow, well-mixed area of the North Sea using a
relatively coarse hydrodynamics-ecosystem model in combina-
tion with a wave model. Their study, however, was restricted to a
single OWF, which was parameterized simply as a reduction in
wind speed above the OWF.
Future OWF installations are planned to be far more
extensive29 and the consequences of accelerated deployment for
atmospheric dynamics and thermodynamics were shown to be
substantial and large scale in the area of the North Sea7. The
implications of these atmospheric changes for the future ocean
dynamics are still unclear. The question on how and to what
degree the emergent large-scale structural changes in atmosphere
and ocean7,8, under the premisses of large OWF clusters, might
affect marine ecosystem productivity remains yet unanswered.
Here we address this question while concentrating on the effects
of atmospheric wakes to the ocean. Mixing induced by the turbine
foundations in the ocean was neglected. For a future offshore
wind farm installation scenario, we consider the atmospheric
impact as simulated by a high-resolution (~2 km) atmospheric
model7to force a fully coupled physical-biogeochemical model
for the North Sea and Baltic Sea30. Different to earlier studies8we
employ an atmospheric model including a dynamical para-
meterization of OWFs, which takes into account the size of the
windfarm and the number of turbines7, and estimates impacts not
only on the wind field but on the entire atmospheric physics. The
experiment including OWFs (Exp. 1: OWF) follows the design
given in Akhtar et al.7that includes all existing and planned
OWFs in the North Sea area based on information available in
2015 (see Supplementary Fig. 1) and is compared to a reference
simulation (Exp. 2: REF) without OWFs. For our idealized sce-
nario simulation, wind farm parameterization for 5 MW turbines
and hub heights of 90 m are used, the rotor diameter was con-
sidered as 126 m. The density of installed turbines was chosen to
be comparable to currently used densities for similar turbine
types. For the spatial distribution of installed capacity all planned
wind farm areas (planning status 2015) were used to distribute
the turbines for the wind farm parameterization. The installed
capacity for this scenario amounts to 120 GW, which is between
the 2030 high scenario of 65 GW for the North Sea and the
recently agreed commitment of the four countries Denmark,
Netherlands, Germany and Belgium (Esbjerg declaration) to
install a capacity of at 150 GW by 205031 in the North Sea. The
EUs overall plan for the installed capacity in 2050 in the North
Sea amounts to 212 GW. Hence, our simplified scenario corre-
sponds approximately to the installed capacity reached in about
15 yrs, by 2037.
The scenario simulations provide evidence that the increasing
amount of future OWF installations will substantially impact and
restructure the marine ecosystem of the southern and central
ARTICLE COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0
2COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 | www.nature.com/commsenv
Content courtesy of Springer Nature, terms of use apply. Rights reserved
North Sea. Changing atmospheric conditions will propagate
through ocean hydrodynamics and change stratification intensity
and pattern, slow down circulation and systematically decrease
bottom shear stress. The model projects that wind wakes of large
OWF clusters in the North Sea provoke large scale changes in
annual primary production with local changes (increase/decrease)
of up to 10%, while region-wide averages in estimated annual
primary production remain almost unchanged. In addition, the
results show an increase in sediment carbon in deeper areas of the
southern North Sea with local increases of up to 10%, and
reduced dissolved oxygen at the Oyster Grounds, which is an area
where oxygen levels can occasionally fall below 3 mg l−132.
Results and discussion
Average system response to OWFs. Our results confirm the
direct ocean response identified by earlier studies8,9to the
alterations in the wind field (Supplementary Fig. 2) with clearly
defined upwelling and downwelling dipoles in the vicinity of the
OWF clusters. However, none of the earlier studies could show
the systematic, large-scale, time-integrated response of the ocean
to large OWF clusters as they are planned to be implemented in
the southern North Sea. As a consequence of the substantial
amount of energy that is extracted from the lower atmosphere7,
the ocean responds with a clear and systematic change in strati-
fication, both in strength of stratification (Supplementary Fig. 3)
and depths of the seasonal mixed layer. The latter was estimated
to be, on average, 1–2 m shallower in and around the OWF
clusters (Fig. 1a). This effect occurred most clearly in the deeper
stratified German Bight area and around the Dogger Bank region.
For OWFs in mixed areas this effect is per definition not relevant
and in frontal, less stratified areas the effect is less clear as the
stratification becomes naturally interrupted by changes in the
frontal position. Changes in mixed layer depth have been
reported earlier as a consequence of offshore wind farm wakes
due to the reduced wind induced mixing8, but also due to the
upwelling and downwelling dipoles20. Since the dipole structure is
associated with both an uplift and a depression in mixed layer
depth20 and is variable in dependence of the wind direction
(Supplementary Fig. 2), we hypothesize that the annual average
response is mainly a consequence of the reduced wind mixing.
Apart from the effect on the stratification, our simulations show
that the ocean responds with a substantial decrease in the annual
mean of the vertically-averaged horizontal current velocities in the
range of 0.003 m s−1in large parts of the southern North Sea, but
which can locally reach up to 0.0087 m s−1at the OWFs at Dogger
Bank and 0.0091 m s−1in the seasonally stratified reach of the
German Bight (Fig. 1b). In both of these areas this means a
reduction of 15% of the prevailing residual current. At the same
time there are also local increases in mean current velocities in the
German Bight area and, specifically between the OWF clusters in
that area. These result locally in changes in current velocities of
about ±10% of the prevailing residual currents, which corroborates
the findings by Christiansen et al.8, who studied the impacts of
existing OWFs in the German Bight area by using an unstructured
grid model and a very simple satellite-derived wind wake
parameterization. This also shows that the large-scale circulation
of the area will be strongly altered with potential consequences for
sediment transport as shown below.
Ecosystem impacts. In the southern North Sea, areas with par-
ticularly high primary production are co-located with the frontal
belt off the coast and around Dogger Bank (Fig. 2a, insert). The
majority of future OWF installations are planned in exactly those
highly productive areas, which are known to be ecologically
highly important33. Our model results show that the systematic
modifications of stratification and currents alter the spatial pat-
tern of ecosystem productivity (Fig. 2a). Annual net primary
production (netPP) changes in response to OWF wind wake
effects in the southern North Sea show both areas with a decrease
and areas with an increase in netPP of up to 10%. Most obvious is
the decrease in the center of the large OWF clusters in the inner
German Bight and at Dogger Bank, which are both clearly situ-
ated in highly productive frontal areas, and an increase in areas
around these clusters in the shallow, near-coastal areas of the
German Bight and at Dogger Bank. The latter might be fueled by
nutrient supply from subsurface waters as a consequence of the
upwelling and downwelling dipole as suggested in earlier
studies20. Additionally, we also find changes in netPP in areas
further away from the OWF clusters, such as a decrease along the
fresh water front of the German and Danish coasts and an
increase south-east of Dogger Bank at Oyster Grounds, which is
typically seasonally stratified and shows lower productivity.
Identifying the robustness of these patterns with respect to dif-
ferent weather conditions and interannual variations requires
additional analysis and simulations. When integrated over a lar-
ger area, the estimated positive and negative changes tend to even
Fig. 1 Annual mean ocean response to atmospheric changes due to offshore windfarms. a Estimated change (OWF-REF) in mixed layer depth (MLD);
bvertically averaged current velocity for REF (arrows) and changes (OWF-REF) (color). Gray polygons indicate location of offshore wind farms. (OWF:
simulation experiment considering offshore wind farms; REF: reference simulation).
COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0 ARTICLE
COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 |www.nature.c om/commsenv 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved
out. Regional averages for the whole North Sea (model area with
longitude <9°E) as well as for the southern North Sea area (as in
Fig. 2a) and the German Bight (latitude: 53.5–55.5°N; longitude:
4–9°E) only show reductions down to −0.5%, while the average
reductions in netPP directly at the OWF locations adds up to
−1.2 %. The direct response of the ecosystem at the OWF sites
can be assigned to the changed hydrodynamic conditions. This
includes, on the one hand, the clearly defined upwelling and
downwelling patterns (Supplementary Fig. 2), which have been
hypothesized to play a major role in the changes OWFs provoke
in marine ecosystems10,20. Those patterns depend on the wind
direction and can be expected to modify the nutrient exchange at
the thermocline, as has been shown for temperature and salinity9,
at and around the OWF clusters. On the other hand, the pro-
duction changes are directly related to the changes in stratifica-
tion. A closer look at the vertical distribution of netPP change
(Fig. 2b) averaged over the areas with OWF installations (parti-
tioned spatially into OWFs at strongly stratified and less stratified
regions and temporally into spring and summer periods) shows
that OWFs in clearly seasonally stratified waters experience an
upward shift of the vertical production maximum, which occurs
typically at the mixed layer depth in summer. This is a con-
sequence of the shallower mixed layer depth, due to reduced wind
mixing. This signal is more prominent in summer than in spring.
In contrast, OWFs in less stratified and frequently mixed waters
show a decrease in production in the upper 20 m of the water
column in spring and at the depth of the thermocline in summer.
Additionally, changes in netPP might translate into changes in
trophic interactions. The changes in netPP are clearly converted
into changes in phytoplankton biomass (Supplementary Fig. 4).
However, the response in phytoplankton biomass is relatively
small; on average below 1% both inside and outside the OWF
clusters (Fig. 3), but can reach up to 10% locally (Supplementary
Fig. 4). An exception is the biomass change inside OWF clusters
positioned in stratified areas, where the average response is about
2.4% but with large variations. Interestingly these locations also
show a relatively strong increase in zooplankton biomass (12%),
which indicates that the local ecosystem is additionally structured
by top-down control through increased grazing pressure34.In
reality the increased zooplankton production at these locations
might be mitigated by additional higher trophic levels feeding on
zooplankton, which is not represented in the model used here. In
contrast, outside OWF clusters and OWF clusters in less stratified
and mixed areas the model estimates a slight average reduction in
zooplankton biomass (<0.5%). In these regions it is difficult to
conclude on the overall trophic response, since the average
fractional change in biomass is very small and shows a large
regional variation (Fig. 3).
Besides the changes in the pelagic ecosystem our model results
highlight a substantial impact on sedimentation and seabed
processes. The overall, large-scale reduction in average current
velocities (Fig. 1b) results in reduced bottom-shear stress to up to
10% locally (Fig. 4a). The reduced resuspension of organic carbon
from the sediments results in an increased amount of organic
carbon in the sediments in large parts of the southern North Sea
(Fig. 4b). This becomes specifically evident at and close to the
OWF locations in deeper areas and at the Dogger Bank. The
average increase in sediment organic carbon amounts to almost
Fig. 2 Annual mean response of net primary productions (netPP) to atmospheric changes due to offshore wind farms. a Relative change in annual
averaged net primary production for 2010 (OWF-REF). Black contour line indicates potential energy anomaly (PEA) of 85 J m−3roughly separating seasonally
stratified from mixed areas; gray polygons indicate location of considered offshore wind farms (insert: annual average of netPP simulated for 2010). bVertical
profiles of change (mean and standard deviation) in netPP inside the offshore wind farm areas; blue: less stratified and mixed areas (PEA < 85 J m−3); green:
stratified areas (PEA ≥85 J m−3) (solid lines: spring; dashed lines: summer). (OWF: simulation experiment considering offshore wind farms; REF: reference
simulation).
Fig. 3 Fractional change ((OWF-REF)/REF) in annually and vertically
averaged phytoplankton and zooplankton biomass. Mean and standard
deviation for areas inside and outside the OWF clusters separated based on
the potential energy anomaly (PEA) into stratified (PEA ≥85 J m−3) and
less stratified and mixed areas (PEA < 85 J m−3). Note, for the analysis
areas deeper than 60 m were excluded. (OWF: simulation experiment
considering offshore wind farms; REF: reference simulation).
ARTICLE COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0
4COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 | www.nature.com/commsenv
Content courtesy of Springer Nature, terms of use apply. Rights reserved
10% directly at the OWF locations and 6% in the German Bight
area. However, averaged over larger areas the effect is less
pronounced with only a 0.2% increase North Sea wide. Our
findings on reduced resuspension are consistent with findings
from van der Molen et al.28 for his case study of an OWF located
on Dogger Bank. Their model indicated an associated reduction
in light attenuation in the water column leading to a slight
increase in primary production. In large parts of the southern
North Sea light can be considered the major limiting factor for
primary production in summer26. Our results confirm changes in
light availability (Supplementary Fig. 4c) in the subsurface,
however, the pattern is strongly related to the pattern of change in
primary production, which indicates a dominant effect of
phytoplankton self-shading. In addition, our results do not show
that the reduction in resuspension is necessarily related to an
overall reduction of particulate organic matter concentration in
the water column. Considering the cause-effect chain that leads to
higher-primary production under improved light conditions, but
would in turn increase phytoplankton self-shading, the quanti-
fication of this effect on longer time scales remains to be studied
in the future.
In addition to changes in sediment-carbon distribution the
model indicates an impact of OWF on bottom water oxygen in
the southern North Sea (Fig. 4c). Oxygen is a key biogeochemical
component in marine ecosystems, and often considered as an
indicator for ecosystem health35. Even though the highly dynamic
North Sea is not known for extensive low oxygen areas, earlier
studies reported the potential for low oxygen events in the central
North Sea, more specifically at the Oyster Grounds32,36. The
Oyster Grounds denotes a bathymetric depression, which partly
limits the exchange with the surrounding water and supports the
development of summer stratification. As a consequence, organic
material tends to accumulate in bottom waters at the Oyster
Grounds, which is associated with enhanced oxygen consump-
tion. Observations in this area show that dissolved oxygen
concentrations in bottom waters can occasionally fall below
3mgl
−132, and also in our reference simulation dissolved oxygen
in bottom water was below 4 mg l−1in late summer and autumn.
According to our simulation the Oyster Grounds is an area,
which would be especially impacted by large-scale OWF
installations. Due to increased primary production on the one
hand, but also by the reduced advective currents and bottom
shear stress the dissolved oxygen concentrations in late summer
and autumn were further reduced by about 0.3 mg l−1on average
and up to 0.68 mg l−1locally in our simulations. In other areas of
the southern North Sea, the effect was estimated to be less severe,
or even showing an increase in dissolved oxygen concentration,
like e.g., along the edges of Dogger Bank.
Consequences for higher tropic levels and management.Within
this study, we estimated the so far underrated effects of the changed
atmospheric conditions by OWFs on the large-scale features of the
lower trophic levels of the marine ecosystem in the southern North
Sea. The results highlight that, considering the extensive OWF
installation plans for the area, the marine ecosystem responds very
clearly to the changes in the atmosphere leading to changes in
ocean stratification, advective processes and a systematic decrease
in bottom shear stress. These changes can be expected to progress
into higher trophic levels of the marine ecosystem. The southern
North Sea is well-known for supporting a diversity of marine
fauna37,38 and especially the near-coastal areas are nursery grounds
for many economically relevant fish stocks. The estimated changes
in the spatial distribution of primary production might impact the
survival of fish early life stages in specific areas due to e.g., varia-
tions in the match-mismatch dynamics39 with their prey or as a
consequence of low oxygen conditions. Understanding these
changes is pivotal for successful future fisheries management in the
North Sea and could influence the identification and imple-
mentation of marine protected areas. Additionally, the estimated
changes in organic sediment distribution and quantity could have
an effect on the habitat quality for benthic species such as lesser
sandeel (Ammodytes marinus) and other benthic species that live in
the sediments in the deeper areas of the southern North Sea40.
Their spatial distributions might change as it has been shown to
depend on the available food quantity and quality41 as well as the
prevailing bottom shear stress42.
The quantification of the effects on species distribution and
diversity remains a topic for future studies as the model used here
is truncated at the secondary production level and does not allow
for species-specific estimates. In addition, the high computational
demand for running both models (atmosphere and ocean) on a
high resolution currently limits our simulation to one year only,
without an additional spinup period to allow for the system to
adjust to the OWF-induced changes. The average physical
response can be, at least partly (e.g., with respect to mixed layer
depth and reduced residual currents), considered immediate and
is mainly related to the reduced energy in the wind field.
The ecosystem components, in contrast, might need a transition
phase to establish a new ecosystem state under OWF influence.
Still, the changes we see are a systematic response to the energy
Fig. 4 Annual mean response of benthic processes to atmospheric changes due to offshore wind farms. a Relative change in annually averaged bottom
shear stress; brelative change in annually averaged sediment organic carbon; cabsolute change in dissolved bottom water oxygen (average July-
September). Black contour line indicates potential energy anomaly of 85 J m−3; gray polygons indicate location of considered OWFs. (OWF: simulation
experiment considering offshore wind farms; REF: reference simulation).
COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0 ARTICLE
COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 |www.nature.c om/commsenv 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved
extraction from the atmosphere and will likely consolidate after a
few years of simulation, but with interannual variations related to
changes in the environmental conditions. A repetition of the
simulation experiments with an “end-to-end”model approach43
and multi-annual simulations are required to shed further light
on the robustness of the estimated pattern, the transfer of the
changes into the food web and its implications for ecosystem
services and management. Additionally, further research on the
combined effects of atmospheric wakes and anthropogenic
mixing induced by the pile structures17 in the ocean is necessary,
as this might counteract the stabilizing effect of the wind wakes.
Under the ambitious plans for OWF constructions in the North
Sea17 space becomes one of the major limiting resources for a
large number of partly conflicting usage interests44. Our results
can serve to support the inevitable development of co-use
management strategies under the given conditions.
Methods
ECOSMO model description and setup. ECOSMO is a well-established, fully
coupled marine ecosystem model for the North Sea and Baltic Sea area. The version
of ECOSMO II used here has been presented in detail before45 and contains a total
of 16 state variables that describes the lower trophic components (phytoplankton
and zooplankton) of the marine ecosystem as well as the major macro-nutrient
cycles (nitrogen, phosphorus, silicon) relevant for the North Sea and Baltic Sea
system. The sediment compartment is included through a simple bottom layer
which accumulates organic material. Benthic fluxes of the different nutrients are
estimated separately in a non-Redfield manner to account for oxygen-dependent
chemical processes in the sediment. On the basis of the free-surface 3D baroclinic
coupled sea-ice model HAM(burg)S(chelf)O(cean) M(odel)46, the non-linear pri-
mitive equations are solved on a staggered Arakawa-C grid with a horizontal
resolution of ~2 km and a time step of 90 s. The impacts of the OWF wind wakes
were earlier found to be related to the internal radius of deformation19, which is
about 10 km in the North Sea area47. The smallest scales resolved by the model are
twice the grid size (approx. 4 km). Hence, the internal radius of deformation is well
resolved by the model. The vertical dimension is simulated with z-level coordinates
with a maximum of 30 layers, with a higher resolution in the upper layers the
surface to represent ocean stratification, and increasing level thickness in deeper
layers (5 m for the first two layers; 4 m up to depths of 50 m; 6 m for depths
between 50 and 92 m; 100 m; 120 m; 140 m; 160 m; 180m; 200 m; 250 m; 300 m;
400 m; 500 m; 630 m). In total, this adds up to 2516251 wet grid cells. The model
uses a second-order Lax–Wendroff advection scheme that was made TVD (total
variation diminishing) by a superbee-limiter48 that has been described in detail in
an earlier study49, and which has been shown to adequately represent the frontal
structures in the southern North Sea.
The overall model setup including forcing data is comparable to the setup used
in Zhao et al.26 but with a different set of open boundary conditions for
temperature and salinity. The latter were provided by a global simulation using the
Max Planck Institute Ocean Model (MPI-OM)50 in a higher resolution setup51
forced with the NCEP/NCAR reanalysis52.
Atmospheric forcing and Windfarm scenarios. A non-hydrostatic model
COSMO-CLM with atmospheric grid resolution of ~ 2 km (1100 × 980 grid cells)
has been used to simulate the regional climate with and without OWFs in the North
Sea. It uses 62 vertical levels with 5 levels within the rotor area. To include the
impact of OWFs in COSMO-CLM a wind farm parameterization7,53 has been
implemented that represents wind-turbine effects as momentum sink and source of
turbulent kinetic energy. In this experiment, a theoretical OWF model was used
based on the theoretical National Renewable Energy Laboratory (NREL) 5 MW
reference wind turbine. It uses a wind turbine with a hub height of 90 m and rotor
diameter of 126 m54. These turbines have a cut-in wind speed of 3 ms−1, rated wind
speed of 11.4 ms−1, and a cut-out wind speed of 25 ms−1. The atmospheric model
used a wind turbine density of about 1.8 × 10−6m−2. Due to coarse atmospheric
grid resolution (~2 km), the average effect of the wind turbines within the gridbox is
estimated using the average grid box velocity. For both the experiments, with and
without wind farms, initial and boundary conditions from coastDat3 simulations55
were used. The latter were forced by the European Center for Medium-Range
Weather Forecast (ECMWF) ERA-Interim reanalysis56. A more detailed description
of the experimental configuration, wind farm parameterization and a validation of
the parameterization can be found in a previous study7.
Strategy for using the models and data analysis. ECOSMO was forced by the
COSMO-CLM simulations with and without OWF parameterization for the year
2010. The change in forcing is thereby not constrained to the change in the wind
field but comprises changes in all required forcing parameters including pressure,
short wave radiation, 2 m air temperature, humidity, and precipitation. The
simulations in 2010 were initialized using a 2-year long (2008–2009) spinup
simulation also forced by COSMO-CLM (without OWF parameterization). Con-
sidering that the characteristic time scale of the North Sea is in the order of 1–3
years a two-year spinup is sufficient for initializing the simulation, especially since
the initial fields for physical state variables were retrieved from a previously con-
ducted simulation with a similar model setup but a different atmospheric and river
forcing. The latter simulation started in 1995 with the same setup but atmospheric
forcing from the COSMO REA6 reanalysis57 and freshwater discharges provided
by the mesoscale hydrological model (mHM)58, which is a calibrated, grid-based
hydrological model for Europe59. Ecosystem state variables were initialized from
climatological values based on the World Ocean Atlas60. Since the atmospheric
simulation is computationally very demanding, only one year of the simulation is
currently available covering the full ocean model domain.
Model data output has been postprocessed based on daily mean values available
for all state variables as well as for biogeochemical fluxes and bottom-shear stress.
Potential energy anomaly61, the energy required to homogenize the water column,
provides a measure for the strength of stratification. For the definition of the mixed
layer depth we used a temperature criterion suggested by de Boyer Montégut
et al.62 where the mixed layer depth is defined as the depths at which ΔT≥0.2 °C
with respect to the surface layer temperature. Figures were compiled with matlab
using the cmocean colormap63.
Reporting summary. Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The datasets generated and/or analyzed during the current study are publicly available at
the world data center for climate (www.wdc-climate.de) under http://hdl.handle.net/21.
14106/d116dd4f38f47f150e655ab9441601b34b312583.
Code availability
Model code access for the marine ecosystem model ECOSMO can be obtained upon
request.
Received: 13 June 2022; Accepted: 11 November 2022;
References
1. WindEurope. Offshore wind in Europe - Key trends and statistics 2019.
Technical Report https://windeurope.org/wp-content/uploads/files/about-
wind/statistics/WindEurope-Annual-Offshore-Statistics-2019.pdf (2019).
2. The European Green Deal. Communication from the commission to the
European parliament, the European Council, the council., the European
economic and social committee and the committee of the regions https://eur-
lex.europa.eu/resource.html?uri=cellar:b828d165-1c22-11ea-8c1f-
01aa75ed71a1.0002.02/DOC_1&format=PDF (2019).
3. Freeman, K. et al. Our Energy Our Future - How offshore wind will help Europe
go carbon-neutral https://windeurope.org/about-wind/reports/our-energy-
our-future/ (2019).
4. WindEurope. Wind energy in Europe - 2021 Statistics and the outlook for
2022–2026 https://windeurope.org/intelligence-platform/product/wind-
energy-in-europe-2021-statistics-and-the-outlook-for-2022-2026/ (2022).
5. Díaz, H. & Guedes Soares, C. Review of the current status, technology and
future trends of offshore wind farms. Ocean Eng. 209, 107381 (2020).
6. Bergström, L. et al. Effects of offshore wind farms on marine wildlife–A
generalized impact assessment. Environ. Res. Lett. 9, 034012 (2014).
7. Akhtar, N., Geyer, B., Rockel, B., Sommer, P. S. & Schrum, C. Accelerating
deployment of offshore wind energy alter wind climate and reduce future
power generation potentials. Sci. Rep. 11,1–12 (2021).
8. Christiansen, N., Daewel, U., Djath, B. & Schrum, C. Emergence of large-scale
hydrodynamic structures due to atmospheric offshore wind farm wakes. Front.
Mar. Sci. 9,1–17 (2022).
9. Ludewig, E. Influence of Offshore Wind Farms on Atmosphere and Ocean
Dynamics (University of Hamburg, 2014).
10. Floeter, J. et al. Pelagic effects of offshore wind farm foundations in the
stratified North Sea. Prog. Oceanogr. 156, 154–173 (2017).
11. Reese, A., Voigt, N., Zimmermann, T., Irrgeher, J. & Pröfrock, D.
Characterization of alloying components in galvanic anodes as potential
environmental tracers for heavy metal emissions from offshore wind
structures. Chemosphere 257, 127182 (2020).
12. Lass, H. U., Mohrholz, V., Knoll, M. & Prandke, H. Enhanced mixing
downstream of a pile in an estuarine flow. J. Marine Syst. 74, 505–527 (2008).
ARTICLE COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0
6COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 | www.nature.com/commsenv
Content courtesy of Springer Nature, terms of use apply. Rights reserved
13. Carpenter, J. R. et al. Potential impacts of offshore wind farms on North Sea
stratification. PLoS One 11,1–28 (2016).
14. Forster, R. M. The effect of monopile-induced turbulence on local suspended
sediment pattern around UK wind farms. An IECS report to The Crown Estate
https://ore.catapult.org.uk/wp-content/uploads/2018/12/The-Effect-of-
Monopile-Induced-Turbulence-on-Local-Suspended-Sediment-Pattern-
around-UK-Wind-Farms.pdf (2018).
15. Mittendorf, W., Hoyme, H. & Zielke, K. Beeinflussung der Meeresströmung
durch Windparks. In 1.Symposium Offshore - Windenergie Bau- und
umwelttechnische Aspekte, Hannover 2001 (2001).
16. van Berkel, J. et al. The effects of offshore wind farms on hydrodynamics and
implications for fishes. Oceanography 33, 108–117 (2020).
17. Dorrell, R. M. et al. Anthropogenic mixing in seasonally stratified shelf seas by
offshore wind farm infrastructure. Front. Mar. Sci. 9,1–25 (2022).
18. Platis, A. et al. Long-range modifications of the wind field by offshore wind
parks–results of the project WIPAFF. Meteorologische Zeitschrift 29, 355–376
(2020).
19. Broström, G. On the influence of large wind farms on the upper ocean
circulation. J. Marine Syst. 74, 585–591 (2008).
20. Floeter, J., Pohlmann, T., Harmer, A. & Möllmann, C. Chasing the offshore
wind farm wind-wake-induced upwelling/downwelling dipole. Front. Marine
Sci. https://doi.org/10.3389/fmars.2022.8 (2022).
21. Degraer, S. et al. Offshore wind farm artificial reefs affect ecosystem structure
and functioning: A synthesis. Oceanography 33,48–57 (2020).
22. Hutchison, Z. L. et al. Offshore wind energy and benthic habitat changes
lessons from block island wind farm. Oceanography 33,58–69 (2020).
23. Mooney, T. A., Andersson, M. & Stanley, J. Acoustic impacts of offshore wind
energy on fishery. Oceanography 33,82–95 (2020).
24. Madsen, P. T., Wahlberg, M., Tougaard, J., Lucke, K. & Tyack, P. Wind
turbine underwater noise and marine mammals: implications of current
knowledge and data needs. Mar. Ecol. Prog. Ser. 309, 279–295 (2006).
25. Barbut, L. et al. The proportion of flatfish recruitment in the North Sea
potentially affected by offshore windfarms. ICES J. Marine Sci. 77, 1227–1237
(2020).
26. Zhao, C., Daewel, U. & Schrum, C. Tidal impacts on primary production in
the North Sea. Earth Syst. Dyn. Discuss. 10, 287–317 (2019).
27. Lozier,M.S.,Dave,A.C.,Palter,J.B.,Gerber,L.M.&Barber,R.T.On
the relationship between stratification and primary productivity in the
North Atlantic. Geophys. Res. Lett. https://doi.org/10.1029/2011GL049414
(2011).
28. van der Molen, J., Smith, H. C. M., Lepper, P., Limpenny, S. & Rees, J.
Predicting the large-scale consequences of offshore wind turbine array
development on a North Sea ecosystem. Cont. Shelf Res. 85,60–72 (2014).
29. 4cOffshore. Global Offshore Renewable Map. https://map.4coffshore.com/
offshorewind/ (2022).
30. Daewel, U. & Schrum, C. Low-frequency variability in North Sea and Baltic
Sea identified through simulations with the 3-D coupled physical-
biogeochemical model ECOSMO. Earth Syst. Dyn. 8, 801–815 (2017).
31. THE ESBJERG DECLARATION on The North Sea as a Green Power Plant of
Europe.https://www.bundesregierung.de/resource/blob/974430/2040932/
b357fa6726099a0304ee97c3a64e411c/2022-18-05-erklaerung-nordsee-gipfel-
data.pdf?download=1(2022).
32. Weston, K. et al. Sedimentary and water column processes in the Oyster
Grounds: A potentially hypoxic region of the North Sea. Mar. Environ. Res.
65, 235–249 (2008).
33. Munk, P. et al. Spawning of North Sea fishes linked to hydrographic features.
Fish Oceanogr. 18, 458–469 (2009).
34. Chust, G. et al. Biomass changes and trophic amplification of plankton in a
warmer ocean. Glob. Chang. Biol. 20, 2124–39 (2014).
35. Best, M. A., Wither, A. W. & Coates, S. Dissolved oxygen as a physico-
chemical supporting element in the Water Framework Directive. Mar. Pollut.
Bull. 55,53–64 (2007).
36. Greenwood, N. et al. Detection of low bottom water oxygen concentrations in
the North Sea; implications for monitoring and assessment of ecosystem
health. Biogeosciences 7, 1357–1373 (2010).
37. Daan, N., Bromley, P. J., Hislop, J. R. G. & Nielson, N. A. Ecology of North Sea
fish. Netherlands J. Sea Res. 26, 343–386 (1990).
38. Fransz, H. G., Colebrook, J. M., Gamble, J. C. & Krause, M. The Zooplankton
of the North Sea. J. Sea Res. 28,1–52 (1991).
39. Daewel, U., Peck, M. A. & Schrum, C. Life history strategy and impacts of
environmental variability on early life stages of two marine fishes in the North
Sea: An individual-based modelling approach. Can. J. Fisheries Aquatic Sci. 68,
426–443 (2011).
40. Rindorf, A., Wright, P. J., Jensen, H. & Maar, M. Spatial differences in growth
of lesser sandeel in the North Sea. J. Exp. Mar. Biol. Ecol. 479,9–19 (2016).
41. Reiss, H. & Krönke, I. Seasonal variability of infaunal community structures in
three areas of the North Sea under different environmental conditions. Estuar
Coast Shelf Sci. 65, 253–274 (2005).
42. Donadi, S. et al. The body-size structure of macrobenthos changes predictably
along gradients of hydrodynamic stress and organic enrichment. Mar. Biol.
162, 675–685 (2015).
43. Daewel, U., Schrum, C. & MacDonald, J. I. Towards end-to-end (E2E)
modelling in a consistent NPZD-F modelling framework (ECOSMO E2E-
v1.0): application to the North Sea and Baltic Sea. Geosci. Model Dev. 12,
1765–1789 (2019).
44. Bundesministerium der Justiz und für Verbraucherschutz. Anlage zur
Verordnung über die Raumordnung in der deutschen ausschließlichen
Wirtschaftszone in der Nordsee und in der Ostsee vom 19. August 2021. In
Bundesgesetzblatt Teil I Nr. 58 vom 26. August 2021 44 (Bundesanzeiger
Verlag GmbH, 2021).
45. Daewel, U. & Schrum, C. Simulating long-term dynamics of the coupled
North Sea and Baltic Sea ecosystem with ECOSMO II: Model description and
validation. J. Marine Syst. 119–120,30–49 (2013).
46. Schrum, C. & Backhaus, J. O. Sensitivity of atmosphere-ocean heat exchange
and heat content in the North Sea and the Baltic Sea. Tellus - Series A: Dyn.
Meteorol. Oceanogr. 51, 526–549 (1999).
47. Chelton, D. B., Deszoeke, R. A., Schlax, M. G., el Naggar, K. & Siwertz, N.
Geographical variability of the first baroclinic Rossby radius of deformation. J.
Phys. Oceanogr. 28, 433–460 (1998).
48. Harten, A. High resolution schemes for hyperbolic conservation laws. Appl.
Math. Sci. 278, 260–278 (1997).
49. Barthel, K. et al. Resolving frontal structures: On the computational costs and
pay-off using a less diffusive but computational more expensive advection
scheme. Ocean Dyn. https://doi.org/10.1007/s10236-012-0578-9 (2012).
50. Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M. & Röske, F. The Max-
Planck-Institute global ocean/sea ice model with orthogonal curvilinear
coordinates. Ocean Model 5,91–127 (2003).
51. Müller, W. A. et al. A Higher-resolution Version of the Max Planck Institute
Earth System Model (MPI-ESM1.2-HR). J. Adv Model Earth Syst. 10,
1383–1413 (2018).
52. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am.
Meteorol. Soc. 77, 437–471 (1996).
53. Fitch, A. C., Olson, J. B. & Lundquist, J. K. Parameterization of wind farms in
climate models. J. Clim. 26, 6439–6458 (2013).
54. Jonkman, J. M., Butterfield, S., Musial, W. & Scott, G. Definition of a 5MW
Reference Wind Turbine for Offshore System Development.Technical Report
NREL/TP-500-38060 February 2009 https://doi.org/10.2172/947422 (2009).
55. Geyer, B., Weisse, R., Bisling, P. & Winterfeldt, J. Climatology of North Sea
wind energy derived from a model hindcast for 1958–2012. J. Wind Eng.
Indus. Aerodyn. 147,18–29 (2015).
56. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance
of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
57. Bollmeyer, C. et al. Towards a high-resolution regional reanalysis for the
european CORDEX domain. Q. J. R. Meteorol. Soc. 141,1–15 (2015).
58. Rakovec, O. & Kumar, R. Mesoscale Hydrologic Model based historical
streamflow simulation over Europe at 1/16 degree. World Data Center for
Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/
mHMbassimEur (2022).
59. Samaniego, L., Kumar, R. & Attinger, S. Multiscale parameter regionalization
of a grid-based hydrologic model at the mesoscale. Water Resour. Res. 46,
1–25 (2010).
60. Conkright, M. E. et al. World Ocean Atlas 2001: Objective Analyses, Data
Statistics, and Figures, CD-ROM Documentation. pp. 17 (National
Oceanographic Data Center, Silver Spring, MD, 2002).
61. Simpson, J. H. The shelf-sea fronts: implications of their existence and
behaviour. Philos. Trans. R. Soc. London Ser. A, Math. Phys. Sci. 302, 531–546
(1981).
62. de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D.
Mixed layer depth over the global ocean: An examination of profile data and a
profile-based climatology. J. Geophys. Res. Oceans 109,1–20 (2004).
63. Thyng, K. M., Greene, C. A., Hetland, R. D., Zimmerle, H. M. & DiMarco, S.
F. True colors of oceanography. Oceanography 29,9–13 (2016).
Acknowledgements
The study is a contribution to the BMBF funded project CoastalFutures (03F0911E), the
Helmholtz Research Program “Changing Earth- Sustaining our Future”and the EXC
2037 ‘Climate, Climatic Change, and Society’(Project Number: 390683824 funded by the
German Research Foundation (DFG)). The authors would like to acknowledge the
German Climate Computing Center (DKRZ) for providing computational resources.
Author contributions
C.S. and U.D. conceived the study and designed the study setup. U.D. performed the
model simulation with ECOSMO, data analysis, and prepared the manuscript with
contributions from all co-authors. N.A. performed the atmospheric model simulation
COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0 ARTICLE
COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 |www.nature.c om/commsenv 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved
and prepared the atmospheric forcing data for the ecosystem model. U.D., C.S., N.A., and
N.C. contributed to data analysis and manuscript writing.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s43247-022-00625-0.
Correspondence and requests for materials should be addressed to Ute Daewel.
Peer review information Communications Earth & Environment thanks Rodney Forster,
Göran Broström and the other, anonymous, reviewer(s) for their contribution to the peer
review of this work. Primary Handling Editors: Olivier Sulpis, Clare Davis, Heike
Langenberg. Peer reviewer reports are available.
Reprints and permission information is available at http://www.nature.com/reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit http://creativecommons.org/
licenses/by/4.0/.
© The Author(s) 2022
ARTICLE COMMUNICATIONS EARTH & ENVIRONMENT | https://doi.org/10.1038/s43247-022-00625-0
8COMMUNICATIONS EARTH & ENVIRONMENT | (2022) 3:292 | https://doi.org/10.1038/s43247-022-00625-0 | www.nature.com/commsenv
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.