GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-12-363-2019Nemo-Nordic 1.0: a NEMO-based ocean model for the Baltic and North
seas – research and operational applicationsNemo-NordicHordoirRobinsonrobinson.hordoir@hi.noAxellLarsHöglundAndersDieterichChristianhttps://orcid.org/0000-0001-7086-4881FransnerFilippaGrögerMatthiashttps://orcid.org/0000-0002-9927-5164LiuYePembertonPerSchimankeSemjonhttps://orcid.org/0000-0002-7208-2207AnderssonHelenLjungemyrPatrikNygrenPetterFalahatSaeedNordAdamJönssonAnetteLakeIréneDöösKristoferhttps://orcid.org/0000-0002-1309-5921HieronymusMagnusDietzeHeinerLöptienUlrikehttps://orcid.org/0000-0002-8765-4183KuznetsovIvanWesterlundAnttihttps://orcid.org/0000-0003-2006-3079TuomiLaurahttps://orcid.org/0000-0003-2471-6815HaapalaJariInstitute of Marine Research, Bergen, NorwayBjerknes Centre for Climate Research, Bergen, NorwaySwedish Meteorological and Hydrological Institute, Norrköping, SwedenGeophysical Institute, Bergen University, Bergen, NorwayDepartment of Meteorology, Stockholm University and Bolin Centre for Climate Research, Stockholm, SwedenGEOMAR, Helmholtz Centre for Ocean Research, Kiel, GermanyInstitute of Coastal Research, Helmholtz-Zentrum, Geesthacht, GermanyFinnish Meteorological Institute, Helsinki, FinlandRobinson Hordoir (robinson.hordoir@hi.no)21January20191213633865January20185April201810December201821December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://gmd.copernicus.org/articles/12/363/2019/gmd-12-363-2019.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/12/363/2019/gmd-12-363-2019.pdf
We present Nemo-Nordic, a Baltic and
North Sea model based on the NEMO ocean engine. Surrounded by highly industrialized
countries, the Baltic and North seas and their assets associated with
shipping, fishing and tourism are vulnerable to anthropogenic pressure and
climate change. Ocean models providing reliable forecasts and enabling
climatic studies are important tools for the shipping infrastructure and to
get a better understanding of the effects of climate change on the marine
ecosystems. Nemo-Nordic is intended to be a tool for both short-term and
long-term simulations and to be used for ocean forecasting as well as process
and climatic studies. Here, the scientific and technical choices within
Nemo-Nordic are introduced, and the reasons behind the design of the model
and its domain and the inclusion of the two seas are explained. The model's
ability to represent barotropic and baroclinic dynamics, as well as the
vertical structure of the water column, is presented. Biases are shown and
discussed. The short-term capabilities of the model are presented, especially
its capabilities to represent sea level on an hourly timescale with a high
degree of accuracy. We also show that the model can represent longer
timescales, with a focus on the major Baltic inflows and the variability in
deep-water salinity in the Baltic Sea.
Introduction
The Baltic Sea is a semi-enclosed sea that is heavily influenced by
freshwater input from large continental rivers. The large freshwater input and the narrow connection with the North Sea and the Atlantic Ocean through
the Danish Straits gives the Baltic Sea its brackish characteristics with a
strongly stratified water column and a water residence time of ca. 40 years
.
Due to the long residence time and the strong stratification, the Baltic Sea
ecosystem is vulnerable to anthropogenic pressure. As a result of large
nutrient inputs the sea is today classified as eutrophied ,
and a spreading of anoxic bottom waters has been observed during the last
century . Eutrophication and anoxia can have severe
impacts on the ecosystem. The cod, for example, which is a economically
important fish species , suffers from the effect of anoxic
deep waters as its eggs are found in the Baltic Sea deep waters. Further,
although all underlying mechanisms are still not clear, increases in
cyanobacteria blooms are often related to eutrophication .
The vertical structure of the Baltic Sea and its ecosystem is
in the long term influenced by inflows of salt water masses from the North
Sea through the Danish Straits e.g.. The timescale of a
typical “major Baltic inflow” (hereafter MBI), which provides salt and
oxygen to the deepest parts of the Baltic Sea and fuels the Baltic Sea
baroclinic circulation , is on the order of 40 days
. MBIs are barotropic in nature, and the variability in
the barotropic circulation on timescales of hours to days can affect the
Baltic Sea ecosystem on timescales up to several decades or a whole century.
Fuelled by advances in computer hardware, the quest towards a more
comprehensive understanding of the crucial ventilation of the Baltic Sea has
been aided by simulations of inflows with numerical general ocean circulation
models , and it has been shown that these inflows are
closely correlated to the sea surface height (hereafter SSH) variability in
both the Baltic and the North Sea . An ocean model that
provides a consistent representation of the Baltic Sea long-term ecosystem
with its specific haline dynamics and stratification on one side and that
allows us to make an accurate forecast of SSH for the Baltic and the North Sea
basin is not incompatible: it is complementary.
Compared to the Baltic Sea, the North Sea is a dynamical region with a water
residence time of only a few years . It is characterized by
strong tidal currents and a general cyclonic large-scale circulation pattern
with major inflow along the British Isles in the western
part of northern boundary and an outflow along the Norwegian Channel in the
east.
Strong local inflow of 0.29 Sv (sverdrup) occurs via the Pentland Firth and
the straits of Fair Isle, and 1.33 Sv is obtained east of the Shetland
Islands . In addition to this, around 0.15 Sv comes through
the English Channel and around 0.016 Sv is
received from the Baltic Sea . Outflow to the North Atlantic
at the northern end of the Norwegian Channel amounts to 1.79 Sv. Unlike the
Baltic Sea, the North Sea is not permanently stratified. During winter,
enhanced wind-induced mixing in combination with convective mixing maintains
well-mixed conditions almost everywhere with the exception of the Norwegian
Channel region where fresh outflow waters from the Baltic Sea dominate
. During summer, the deeper parts of the central and
northern North Sea experience a strong thermal stratification
, whereas the shallow southern North Sea remains mostly well
mixed throughout the year though short-term stratified periods are possible
. The timing and intensity of the seasonal stratification
play an important role in biogeochemical processes as they influence primary
production, the start of the spring phytoplankton bloom and nutrient cycling
in the North Sea .
For both the North Sea and the Baltic Sea
, there are a number of
models of different complexity. Because the two seas differ so much in their
oceanographical and biogeochemical characteristics, it is plausible to set up
separate models specific to each region and to prescribe lateral boundaries
at a reasonable location in the transition zone between the two seas.
However, recent studies provide evidence that the simulation of the Skagerrak
and Kattegat hydrography are often problematic in these model set-ups
. As the dynamics in this transition zone between the
Baltic and the North Sea are essential for a realistic simulation of MBIs, a
combined Baltic–North Sea model is necessary to better understand
MBI dynamics and their impact on
the Baltic and the North Sea physics and ecosystem. Especially for climate
scenario simulations, it is more appropriate to explicitly simulate potential
changes in the Kattegat–Skagerrak region than relying on a present-day
climatological prescription. Further, ocean models that include only the
North Sea do not take into account the interaction with the Baltic Sea, and
therefore rely on a freshwater provision to the North Sea that is not
accurate in strength or in time .
Early attempts of combined North Sea–Baltic Sea modelling were limited by
a relatively coarse resolution (approx. 11 000 m, or 6 nm) and constrained
to short-term simulations of 1 year . Only recently,
attempts have been made to include the two seas in one model set-up for longer
periods and at a high resolution. used a coupled
hydrodynamic–biogeochemical model and concluded that coupled Baltic and North
Sea model set-ups can help to improve the performance in the Skagerrak area.
Moreover, the model reasonably simulated the MBIs during the hindcast
period . presented a hydrodynamic model
using adaptive vertical coordinates and with a resolution of 1852 m (or
1 nm). The authors found the mean state for the period 1997–2012 as well as
the timing and amplitude of MBIs were well represented by the model.
as well as each presented a
hydrodynamic model coupled to an atmospheric model but they focused more on
atmospheric dynamics and air–sea interactions than on oceanographic topics.
In the present article, we provide a description and validation of
Nemo-Nordic in its first mature version, Nemo-Nordic 1.0, based on NEMO 3.6.
Nemo-Nordic is divided into two resolutions of 2 nautical miles (3704 m) and
1 nautical mile (1852 m). First prototype versions were already used for
process studies in or to specifically
investigate the added value of interactive air–sea exchange of mass and
energy fluxes . The present version is the first reference
used both for research and forecast. This article provides a full validation
of both barotropic and baroclinic dynamics, it shows the qualities and biases
of the model. It is also a milestone that will be used for further
development at a time when Nemo-Nordic extends beyond SMHI (Norwegian
Institute of Marine Research, Danish Meteorological Institute, Finnish
Meteorological Institute, Deutsche Wetterdienst, German Federal Maritime and
Hydrographic Agency BSH). Further developments are to be done in the
different European institutions within the Copernicus framework, for example,
for operational applications and for research.
Nemo-Nordic is a NEMO based ocean model for the Baltic and
the North Sea that can be used for climate, oceanographic process study and
operational oceanographic applications. Nemo-Nordic has been designed to be
an advanced compromise to provide forecasts or study Baltic and/or North Sea
dynamics at various timescales (operational or climate timescales), with a
representation of processes occurring in both basins, including overflows and
sea ice, within a reasonable range of computing resources. The inclusion of
both seas makes it possible to study the exchange between the two seas
because the boundary conditions of the model are far
enough from where this exchange occurs, unlike, for example, a Baltic Sea
only ocean model . Unlike Nemo-Nordic, the model described
by is in any case limited by its linear free surface,
which produces conservation errors for the Baltic Sea in long-term
simulations, and prevents any possibility of representing ocean dynamics
properly in any region of higher sea level variability, such as the Kattegat,
the Skagerrak and the entire North Sea.
Nemo-Nordic is also not the first model to include both the Baltic and the North Sea
basins; one could cite or as
examples of models that already do. However, these models do not permit us to
represent the main driver of the Baltic Sea ecosystem: the dense overflows
that feed its very specific sill-bounded estuarine circulation; this
circulation is difficult to represent in z coordinates since such
coordinate systems do not represent very dense overflows. In addition, the
Baltic Sea halocline is tilted and has a very low-turbulence
environment. Representing the Baltic Sea halocline therefore requires a
possibility to rotate the diffusion tensor to avoid diapycnal mixing and to
limit the vertical mixing length in case of low turbulence. The NEMO ocean
engine allows us to have tools such as bottom boundary layer parameterization,
isopycnal diffusion in z coordinates and advanced vertical turbulence
schemes, which permit a better representation of such circulations. From a
more general point of view, using a community engine such as NEMO means
having the latest available developments in ocean and sea ice modelling.
Further developments are now being made regarding Nemo-Nordic, such as wind wave
coupling, which will keep this ocean modelling configuration at a
state-of-the-art level when it comes to Baltic and North Sea modelling.
Model set-up: Nemo-Nordic
Nemo-Nordic is an ocean model set-up for the Baltic and the North Sea. It is
based on the “NEMO ocean engine”, a set of ocean modelling tools supported
by a large community, and there is constant observation of the developments
in other community ocean models. More specifically, we apply the stable NEMO
3.6 version . The ocean component is coupled to the sea ice
model LIM3 . The first version of Nemo-Nordic was based on
NEMO 3.3.1, switching to NEMO 3.6, which features a new coupling between
barotropic and baroclinic modes ensuring a much better representation of the
barotropic mode, with a sea level representation of a higher quality.
Technically, NEMO 3.6 and the use of the XIOS server was a key element in a
more user-friendly version of Nemo-Nordic.
Grid and bathymetry
The model domain of Nemo-Nordic covers the English Channel, the North Sea and
the Baltic Sea (Fig. ). This domain bears similarities with
that used in the NEMO-based configuration described by .
The resolution of the configuration of is higher than
that of Nemo-Nordic but also uses the features of the NEMO ocean engine to
build regional and coastal configurations. The main interest of the two
configurations is different: that of aims at resolving
the European shelf dynamics, whereas the purpose of Nemo-Nordic is to
represent the interaction between two basins with different dynamical
features.
The model domain and bathymetry of Nemo-Nordic. The filled circles
show the locations of validation stations for salinity and temperature.
The area of Nemo-Nordic reaches from 4.15278∘ W to
30.1802∘ E and 48.4917–65.8914∘ N. The grid is
geographical, and in its 2 nautical mile version, the horizontal grid has
zonal/meridional increments of 0.05∘, which corresponds to a
horizontal resolution of approximately 2 nautical miles (3704 m). The
resolutions of approximately 1 or 2 nautical miles are given as
approximations, especially at these high latitudes where zonal-scale factors
differ between the southern and northern parts of the domain.
This resolution does not permit a proper description of the Danish Straits,
which is a critical area of this configuration. However, in order to ensure a
proper communication between the Baltic and the North Sea, we tune a proper
“impedance” of the Danish Straits in the model so that the flow between the
two areas is consistent. We never checked if this feature allows a proper
representation of the baroclinic flow within the straits, but since major
Baltic inflows are mostly barotropic events we believe this is of secondary
importance. However, a higher resolution of the Danish Straits will be
implemented in the future using the AGRIF tool.
Nemo-Nordic uses z* vertical coordinates, which is more simple than some
other Baltic Sea models such as or the
configurations developed by . Advanced hybrid
coordinates permit a better representation of dense overflows to the Baltic
Sea but have a higher computational cost. However, the salinity biases based
on the latest advances made in Nemo-Nordic show a very acceptable range, as
shown in Sect. , and the benefit of z* coordinates from a
vertical point of view allow multiple long-term simulations with realistic
computing power. Our model set-up comprises 56 vertical levels. The vertical
resolution is adapted to the physical properties of the Baltic and North
seas: the upper levels have a thickness of approximately 3 m until the
typical level of the halocline is reached at 60 m. Below 60 m the layer
thickness increases substantially: the layer thickness is 10 m at a 100 m
depth, which is the typical halocline depth of the Norwegian Coastal Current
(hereafter NCC) . Maximum values of 22 m are reached below
200 m. Note that the maximum decrease in resolution between two consecutive
vertical levels is below 11 %. With these increments, the vertical
resolution of Nemo-Nordic is adapted to Baltic Sea conditions and less
optimal for resolving the halocline of the NCC. However, the NCC is a baroclinic
Kelvin wave which has a permanent renewal of stratification coming from the
constant runoff flux of the Baltic Sea and all the river inputs located along
the western Swedish and Norwegian coasts. Thus the NCC halocline is less
sensitive to coarse vertical resolution than the halocline of the Baltic Sea,
which is not permanently renewed and is suspected of introducing spurious saltwater inflows into ocean models. Partial steps are used at bottom level to
ensure a proper fit between the input bathymetry and the vertical grid.
The cross-sectional area of the Danish Straits is critical as it ensures the
exchange between Baltic and North seas. The resolution of the model does not
permit having a proper representation of the Danish Straits. However, it is
possible to represent each cross section so that it has the same hydraulic
impedance as in reality. This was achieved by fitting the area of each
critical cross section of the model with its equivalent area in the real
world. More precisely, this means that the area of each critical “numerical
cross section” of the model is made to fit with the surface of the real
cross sections of the Danish Straits.
Physical settingsFree surface and time stepping
Nemo-Nordic uses a non-linear free surface () with a
() option to split the computation of barotropic and
baroclinic modes. In comparisons with previous ocean models such as that used
by , which used a linear free surface, these
features enable Nemo-Nordic to run in regions of high tidal amplitude like
the North Sea and the English Channel. Tuning is done in order to obtain an
accurate SSH representation. We use a variable bottom roughness
() along the cyclonic pathway of barotropic Kelvin
waves in the North Sea. This roughness is optimized to obtain the best
possible SSH amplitude. Nemo-Nordic runs with a baroclinic time step of
360 s, which proved to be the best compromise between stability and
computational speed. The barotropic time step is set to be 30 times smaller.
Mixing and dense overflows representation
Vertical and horizontal mixing (and the connections between them) is
a critical element when it comes to a Baltic and North Sea configuration. The
major difficulty is to simulate the crucial dense water overflows in the
Baltic Sea . In order to ease this process, Nemo-Nordic
uses a bottom boundary layer parametrization for tracers
() in order to reduce the biases of a z coordinate ocean
models when it comes to the representation of dense overflows. In NEMO, the
bottom boundary layer can be used with an upstream advection scheme or with a
purely diffusive scheme or with a mixture of both. For Nemo-Nordic,
sensitivity experiments have shown that the use of a purely diffusive scheme
results in more realistic deep salinity in the Baltic Sea. Using an advective
scheme results in a lower deep salinity, most likely due to a higher
entrainment rate during major Baltic inflow events. One could object rightly
that using sigma coordinates could have solved this issue without the use of
such a parameterization. But using such a coordinate system would, on the
other hand, create a strong diapycnal mixing of the Baltic Sea halocline and
most likely pressure gradient errors in the steepest places of the North Sea
such as the Norwegian trench.
However, the use of the bottom boundary layer parametrization is not enough
to achieve a correct representation of the overflows. The tuning of the
mixing was a major issue and required a tuning process. Nemo-Nordic includes
two basins which have very different dynamical features: the North Sea is
strongly influenced by tides, tidal mixing and relatively strong winds. In
the North Sea, the stratification is relatively weak, with the exception of a
summer thermocline in some regions, regions of freshwater influence
and the region of the Norwegian Coastal Current
. In contrast, the Baltic Sea has almost no tides, is less
exposed to wind forcing and has a strong permanent stratification.
Nemo-Nordic uses a two-equation turbulence closure, based on a k-ϵ
turbulence scheme (). In addition, the Galperin
parameterization is used , in order to preserve the Baltic Sea
permanent stratification. This parameterization limits the mixing length
computed by the vertical turbulence model in case of low turbulence and high
stratification, which fits perfectly with the Baltic Sea halocline
environment. This parameterization can be written following
Eq. (), in which k is turbulent kinetic energy, N is the
Brun–Väisälä frequency, and Cgalp is the Galperin
coefficient. Finally, lmax is the maximum mixing length.
lmax=Cgalp2kN
Experiments done on the configuration without this latest parameterization
yield a Baltic Sea permanent halocline that vanishes in a few years only. The
Galperin coefficient is set to 0.17, which proves to be a good compromise
between deep salinity and seasonal thermal stratification. A Galperin coefficient that is too high produces a higher deep salinity but also a very thin
and unrealistic seasonal thermocline. In addition to the Galperin
parameterization, the background levels of turbulent kinetic energy are
turned to their minimum values, the goal being always to limit as much as
possible mixing at the level of the Baltic Sea halocline.
Horizontal mixing is based on a Laplacian approach combined with the rotation
of the horizontal diffusion tensor (). However, this does
not result in a real isopycnal diffusion in the sense that Nemo-Nordic is
still based on a z coordinate system and not on isopycnal coordinates.
Close to the bottom, sensitivity experiments have demonstrated that the
rotation of the diffusion tensor did not enable us to follow dense salt
inflows: increasing horizontal diffusivity resulted in lower deep salinity or
even no penetration of dense inflows at all, showing that diapycnal mixing is
created by horizontal diffusion, even when the rotation of the diffusion
tensor is activated. The first versions of Nemo-Nordic used a Smagorinsky
approach in order to limit horizontal mixing as much as possible, but this
approach finally resulted in very weak Baltic Sea salt inflows and a
diffusivity impossible to control. Our final strategy has therefore been to
create a viscosity/diffusivity coefficient which is high where model
stability requires it the most (i.e. above the halocline), and low where it
is crucial for diffusivity to be as low as possible to avoid diapycnal
mixing. After a suite of sensitivity runs we decided on using spatially
varying values for viscosity and diffusivity. From the surface to a depth of
30 m the viscosity is set to values ranging from 30 to 50 m2 s-1
for the Baltic and the North Sea, respectively, while we set diffusivity to a
10th of this value. Below this depth the values of viscosity is reduced to
0.1 m2 s-1. The same increase was applied to the diffusivity,
which is still set to a 10th of the value for viscosity. This feature works
as long as the Baltic Sea halocline is located at this precise depth but
should be changed if the halocline depth changes. Along the open boundaries,
the viscosity is increased to 800 m2 s-1 over the entire water
column, decreasing rapidly to standard values within a few grid points inside
the domain. This value is huge but prevented the model from ever crashing at
the open boundary conditions. Several values were tried, and they proved
to have little effect on the sea level
variability inside the model domain.
A free slip option is taken for the lateral boundary conditions.
Boundary conditionsOpen boundaries
Nemo-Nordic has two open boundaries: a meridional one in the western part of
the English Channel and a zonal one set between Scotland and Norway. The
setting of the boundary conditions uses the open boundary condition module of
NEMO (), as well as the tide module ().
Several
settings can be used for boundary conditions. Tidal harmonics are taken from
and although further investigation is also
being made using the FES tidal model . Harmonical values for
SSH are interpolated on the open boundaries of Nemo-Nordic. Harmonical tidal
transports (in m2 s-1) are also interpolated in the same manner and
then divided by the local depth of the Nemo-Nordic domain. In research mode,
temperature and salinity boundary conditions may come from climatological
data or from climate simulations, and a simple storm surge model is used. The
model itself is corrected by a global ORCA0.25 configuration to take into
account seasonal variability due to temperature and salinity variations. In
operational mode, Nemo-Nordic uses ECMWF forecast data for its SSH,
temperature and salinity boundary conditions.
Atmospheric forcing
Nemo-Nordic has been so far used in forced mode using prescribed atmospheric
conditions and the CORE bulk formulation .
To cover a long period, the atmospheric forcing is based on different
sources. The driving data of the period 1961–1978 are based on downscaled
ERA40 data . The ERA40 reanalysis is downscaled with the
Rossby Centre regional atmospheric model version 4 (RCA4) with spectral
nudging . The downscaling is necessary to improve the
horizontal resolution of the global reanalysis data set. Here, we use data
from an RCA4 set-up with a horizontal resolution of 11 km. The frequency of
the driving data is hourly.
With the beginning of 1979 we change the atmospheric forcing to the SMHI
reanalysis product EURO4M which is available until the end of 2013
. The EURO4M data are available 3-hourly and
with a horizontal resolution of 22 km. EURO4M incorporates data assimilation
which ensures the best quality for our driving data. In operational mode
(Nemo-Nordic 1 nm), the simulations were forced by HIRLAM C11
(http://hirlam.org, last access: 17 January 2019).
In forecast mode, Nemo-Nordic uses a combination of hourly ECMWF LL01 (9 km)
data and Arome data (2.5 km).
Light penetration
A proper light penetration parameterization proved to be an important feature
in order to reproduce the proper thermal structure, and especially the
formation of a summer “Cold Intermediate Layer”. The Baltic Sea has turbid
waters which prevent deep light penetration, concentrating the summer heat
input close to the surface, and hence easing autumn cooling. Nemo-Nordic
uses a red–green–blue light penetration parameterization, together with a
constant chlorophyll value of 0.5 mg m-3 to represent the turbid
Baltic Seawaters. Failing to use chlorophyll concentration results in a cold intermediate layer that is too
thin and too shallow. This chlorophyll concentration
does not pretend to be realistic from a biogeochemical point of view, it
corresponds to a mean value for both basins which allows a realistic light
penetration in an area which has a water turbidity higher than that of the
global ocean.
River discharge
The Baltic Sea salinity is sensitive to the accumulated freshwater input
since the exchange with the open ocean is very limited
e.g.. Freshwater supply to the Baltic Sea in the
simulation must therefore be handled with care. Here, we are using data from
the HYdrological Predictions for the Environment (HYPE) model
. The model simulates a mean runoff to the Baltic Sea of
roughly 16 000 m3 s-1 for the period 1981–1998. However,
state that the observed runoff for the same period is
15 053 m3 s-1 with an even lower value (14 085 m3 s-1)
for the period 1902–1998. Consequently, we reduced the freshwater supply
computed by HYPE. We have chosen a general reduction of 10 % which gives
more realistic runoff for the Baltic Sea. Moreover, this improved the Baltic
Sea salinity (not shown). The freshwater input to the North Sea including the
Skagerrak and Kattegat area amounts to 11 515 m3 s-1 after the
reduction by 10 %. The river runoff is spread over 424 river mouths in the
entire model domain whereas more than 250 are located in the Baltic Sea
(excluding the Skagerrak and Kattegat area). The UNESCO equation of state for
seawater is used. The reference density is set to 1035 kg m-3, which
is most likely overestimated. Further experiments should be done on this
point.
Sea ice
Nemo-Nordic benefits from the use of the NEMO ocean engine and of its
advanced sea ice model LIM3. Sea ice is a feature specific to the Baltic Sea
that is due to the Baltic Sea's low salinity. Being able to represent sea ice
dynamics properly is compulsory when it comes to a Baltic Sea ocean model.
Models such as do not include this feature. The sea
ice in Nemo-Nordic is validated in . Comparison done
with suggest that Nemo-Nordic so far reaches the highest
accuracy of sea ice representation for the Baltic Sea.
Validation of barotropic mode and surface currentsSea level
To model and forecast SSH is one of the major aims of Nemo-Nordic. This
section provides a statistical comparison between measured and modelled SSH at
different tide gauges in the Baltic and North seas.
We have chosen tide gauges which are as representative as possible of three
respective regions: the Baltic Sea (Table ), the Danish
Straits plus the Kattegat and the Skagerrak (Table ), and the
North Sea, including the English Channel (Table ). The
comparisons are based on an 18-month period, lasting from 1 July 2011 and
ending on 31 December 2012, at an hourly frequency. The respective model
simulation was started 1 month before to allow for a spin-up time. Each area
is presented in a specific array.
SSH representation, in terms of correlation, standard deviation
(metres) and root-mean-square deviation (metres), made by Nemo-Nordic for nine
Baltic Sea stations. The comparison is based on an 18-month period, starting
on 1 July 2011, and on hourly output SSH.
SSH representation, in terms of correlation, standard deviation
(metres) and root-mean-square deviation (metres), made by Nemo-Nordic for nine
stations located in the Danish Straits, the Kattegat and the Skaggerak.
The comparison is based on an 18-month period, starting on 1 July 2011, and on hourly output SSH.
SSH representation, in terms of correlation, standard deviation
(metres) and root-mean-square deviation (metres), made by Nemo-Nordic for six
stations located in the North Sea.
The comparison is based on an 18-month period, starting on 1 July 2011, and on hourly output SSH.
There is generally a very high correlation between model and observations in
almost all regions. In the North Sea and the English Channel, correlations
are highest and mostly close to 0.99, with the one exception of Hanstholm,
where correlations are around 0.93. In the Baltic Sea, correlations are
always close to 0.95. In the narrow Danish Straits and the Kattegat, regions
with complicated topography, the correlations are lowest, but still generally exceed 0.9. Exceptionally low correlations (0.68) are obtained in
Barseback and only when Nemo-Nordic 2 nm is used. When Nemo-Nordic 1 nm is
used, the minimum depth of the ocean model is set to 3 m, compared to 9 m,
which gives a better representation of the shallow banks located in the
Öresund area and of the amplification of barotropic waves. This feature
was not implemented in Nemo-Nordic 2 nm where the main focus is the
Baltic–North Sea exchange. Using wetting and drying in the future should help
better representations of SSH in shallow areas affected by strong sea level
variability.
Biases in terms of SSH representation can be summarized as follows.
Nemo-Nordic usually has a negative bias in terms of the representation of the low
frequencies in the North Sea, the Skagerrak and the Kattegat. In the same regions,
the opposite bias may be noticed when it comes to higher frequencies: tidally
driven SSH can present overshoots in some places. In the Baltic Sea, one may notice that the tidal signal is usually a bit too high, but since its
amplitude remains very small in comparison to other frequencies, it does not
affect the quality of the SSH representation significantly. The
representation of lower frequencies does not reveal any significant bias in
the Baltic Sea.
Nemo-Nordic 1 nm represents SSH better than Nemo-Nordic 2 nm, which
is partially due to the higher resolution for most features. For example, in
the Danish Straits, the representation of shallow banks has been taken into
account in Nemo-Nordic 1 nm, and the size of the cross sections has been
adapted to represent the North Sea–Baltic Sea exchange. This allows us to
amplify incoming barotropic waves, which is essential in order to get the
right SSH variability in the Danish Straits tide gauges. In Nemo-Nordic
2 nm, the main concern has been to ensure the correct Baltic–North Sea
exchange, which is crucial for having a correct representation of Baltic Sea
salt inflows, which are one of the main drivers of the Baltic Sea ecosystem and of its long-term thermo-haline structure.
In summary, we identified the following key processes to model realistic SSH
variations.
In Nemo-Nordic, the SSH and SSH variability in the North Sea is to a first
order barotropicaly driven and is built by a combination of tidal waves entering
through the northern boundary and western boundaries, wind-driven SSH built over
the North Atlantic, and wind-driven SSH over the North Sea. To get a correct
representation of the SSH variability and the cyclonic circulation in the North
Sea, it is important to have high-frequency (hourly) boundary conditions that
take into account all these aspects.
In the Kattegat–Skagerrak region, as one moves further towards the entrance
of the Baltic Sea, the effect of tides and of high-frequency waves generated in
the North Atlantic or the North Sea becomes less important. The low frequencies,
on the other hand, generated by the storm surge model over the North Atlantic
turned out to be important for this region. The shelf break along the coast
amplifies the effect of coasts on barotropic waves arriving from the North Sea
and helps representing SSH variability and its extremes. A higher
vertical resolution in the shallow areas improves the representation of the
SSH variability, especially in the Skagerrak–Kattegat. This last effect becomes
crucial in the Danish Straits where the shallow banks need to be represented.
The SSH variability in the Baltic Sea is barely influenced by any tidal
variability but is highly influenced by low-frequency forcing coming from the North
Atlantic and the North Sea. In addition, local wind forcing over the Baltic
Sea explains higher frequencies. The only communication between the Baltic and the
North Sea being the Danish Straits, the adjustment of cross sections and of the
friction in this area are of crucial importance to chose which barotropic
frequencies can penetrate the Baltic Sea. The Danish Straits should act as a
well-tuned low-pass filter which allows low-frequency waves to penetrate the
Baltic Sea but lets little high-frequency power enter the Baltic Sea.
Surface currentsGeneral circulation
The model reproduces the general cyclonic surface circulation pattern in the
North Sea , with a southward flow in the western part of the
basin, a northeastward flow along the southern coast and a northward flow
along the Norwegian coast in the Norwegian Coastal Current
(Fig. ). The strongest modelled southward flow of Atlantic water
through the northern boundary occurs just next to the NCC. This flow forms a
current that flows south-east and enters the well-known cyclonic circulation
pattern in the Skagerrak, in good agreement with the observed surface
currents in the North Sea . A part of the southward flow along
the British Isles also deviates eastward to directly join the eastward flow
towards the Skagerrak. Another part flows further to the south and mixes with
inflow from the English Channel. The larger part of inflowing Atlantic water
from the northern boundary is restricted to north of 54∘ N and then
recirculates mainly following the topography of the Dogger Bank. The southern
North Sea is dominated by inflow waters from the English Channel.
In the Baltic Sea and its sub-basins the model reproduces the observed
general cyclonic circulation patterns , with a southward flow
in its western part and a northward flow in its eastern part. In the Gotland
basin the model reproduces the southward flow on both sides of Gotland and
the northward flow along the coast of the Baltic, giving rise to the cyclonic
structure over the Gotland Deep (Fig. ). In the Kattegat the
model simulates a general anticyclonic flow in agreement with
and resolves the northward flowing Baltic Current
along the Swedish coast, which feeds low-saline waters into the NCC.
Simulated surface (0–30 m) currents; climatology for 1979–2010.
The lines and arrows show the streamlines and directions of the current
vector field. The thickness of the line is scaled with respect to the speed
of the current. The filled contours show the current speed in m s-1.
Overturning circulation
The inflows and outflows through critical cross sections, where observational
estimates exist, have been calculated for the period 1979–2010. Inflows are
defined as volume transports directed inwards to the domain, and outflows are
defined as transports directed outwards. For the Baltic Sea a longitudinal
cross section has been taken along 12.90∘ E, and inflowing waters
are defined as transports in the positive x direction and outflows as those in the
negative direction. For the Strait of Dover a longitudinal cross section has been taken
along 50.99∘ N. Here the flow is barotropic and there is only a mean
inflow to the North Sea. For the northern boundary, taken along
58.06∘ N, the inflow is all transports in the negative
y direction and the outflow is all transports in the positive
y direction.
As shown in Table , the modelled transports agree well with
observational estimates, which is an additional indication that the general
circulation of the North Sea and the Baltic Sea is reproduced well by the model.
The modelled flows gives a water residence time of 23 and 1.8 years in the
Baltic Sea and the North Sea, respectively.
The circulation in the North Sea is mainly of a barotropic feature.
Barotropic stream functions of the horizontal overturning circulation,
showing the general cyclonic circulation of the North Sea, are displayed in
Fig. . The largest flows are found in the Norwegian trench,
where the overturning barotropic circulation amounts to 0.9 Sv, in good
agreement with the calculated fluxes in Table .
Volume flow (Sv) through cross sections; climatological mean for
1979–2010.
Model Observations Cross sectionInflowOutflowInflowOutflowReferenceStrait of Dover0.110–0.11–0.17–, and references thereinNorthern boundary0.7940.9280.93–1.731.34–1.8, and references thereinBaltic Sea0.0290.0440.0270.043
Barotropic stream function (Sv); climatology for 1979–2010.
Mean surface (0–10 m) salinity for the Baltic, North Sea and
English Channel, as simulated by Nemo-Nordic for the period 1979–2010
(a) and from observations (b) from .
Salinity and temperatureSurface temperature and salinity
A validation of the simulated mean surface salinity and temperature is made
(Figs. and ) using the
climatology. The values are computed as a mean value over the first 10 m. For the Baltic and the North Sea, the overall surface salinity is reproduced well. There is, however, a positive surface bias in the Baltic Sea. One may notice, in particular, that the penetration of the 8 PSU iso-haline
within the estuary is too high. The 7 PSU iso-haline is also located a few
nautical miles too far north. In the North Sea there is a negative bias in
freshwater-influenced areas (the NCC and in the southern German Bight). For
sea surface temperature (SST), the structure is similar to observations but
there is a positive bias of less than 1∘ over all the domain. This
bias seems to partially come from a warm bias in the atmospheric forcing (in
winter) and partially from surface overshoots during
the summer period. Sensitivity experiments have shown that the positive SST
bias during summer time is lower if the Galperin coefficient used to maintain
a stable haline stratification is lowered. In order to avoid these effects,
further development is being made to decouple the Galperin parameterization
from thermal effects.
Mean surface (0–10 m) temperature for the Baltic, North Sea and
English Channel, as simulated by Nemo-Nordic for the period
1979–2010 (a) and from observations (b) from
.
Thermohaline structure of the Baltic Sea
The thermohaline structure of the Baltic Sea exhibits two types of
variability. First, a spatial variability which leads to the Baltic Sea having strong salinity gradients from the surface to the bottom but also
presents estuarine features which result in a decreasing salinity from south
to north. Strong temperature gradients also exist as the northern
regions of the Baltic Sea have a much colder climate than those located in
its southern part. From a temporal point of view, surface salinity exhibits a
seasonal variability , and deep salinity has a lower
variability highly related to the occurrence of deep salt inflows
. Surface temperature has a strong seasonal cycle related
to summer stratification and its destruction during autumn. The model's
ability to reproduce the thermohaline structure and its variability on
different timescales will be validated below.
Haline structure of four different stations of the Baltic Sea from top to bottom: F9-A13 station in the Bothnian Bay, BY14 station in the Baltic proper, BY5 station in the Bornholm Basin and Anholt station in the Kattegat. Column (a) displays annual mean depth profiles, where the red line is
simulated salinity and black dots are observations. Columns (b) and
(c) show seasonal variations in the model and the observations,
respectively. In column (c), the transparent circles show sample
depths; the observations are based on a 1979–2010 climatology.
Thermal structure of four different stations of the Baltic Sea from top to bottom: F9-A13 station in the Bothnian Bay, BY14 station in the
Baltic proper, BY5 station in the Bornholm Basin and Anholt station in
the Kattegat. Column (a) displays annual mean depth profiles, where the red
line is simulated temperature and black dots are observations. Columns (b) and (c) show seasonal variations in the model and the observations,
respectively. In (c) the transparent circles show sample depths;
the observations are based on a 1979–2010 climatology.
Vertical structure and seasonal variability
The haline vertical structure of the Baltic Sea is in general reproduced well
by Nemo-Nordic (Fig. ), with a distinct halocline separating
the surface waters from the deeper waters. In the Bothnian Bay and the
Kattegat (F9-A13 and Anholt), the modelled depth (50 and 20 m, respectively)
and strength of the halocline correspond well to observations. There is a
small negative salinity bias over the whole water column in the Bothnian Bay,
while the overall salinity is reproduced well in the Kattegat. In the Baltic
proper and the Bornholm Basin (BY15 and BY5), the modelled halocline is weaker
and shallower than the observed one. The surface water at these stations
tends to have a positive bias, while the deeper waters tend to have a negative
bias, suggesting too strong a mixing between surface and deep waters. No
stronger seasonal cycles exist in the haline structure, except for a
freshwater pulse arriving in the surface waters during summer months, which
is captured by the model at all stations.
Time series of modelled salinity (black) and observations
(red) for different stations. The stations are, from top to bottom,
Anholt, BY2, BY5 and BY15 (see Fig. for their
location). All stations are relevant for inflowing salty water
masses from the North Sea to the Baltic Sea. The chosen levels are
all close to the bottom of the corresponding location.
The thermal vertical structure is dominated by the seasonal thermocline. In
the Kattegat, Bornholm Basin and the Baltic proper (Anholt, BY5 and BY15), it
starts forming earlier than in the Bothnian Bay (F9-A13), both in the model
and in the observations (Fig. ). The later formation of the
thermocline in the Bothnian Bay is partially due to the lower insolation at
higher latitudes but also due to the non-linearity of the equation of state
at low salinities. The temperature of maximum density is higher at lower
salinities, meaning that the water column has to completely mix before the
formation of the thermocline can start, which is reproduced well by the
model. The model's development of the seasonal thermocline and its deepening
agrees well with observations in the Kattegat, the Bornholm Basin and in the
Baltic proper. In the Bothnian Bay no conclusions can be drawn on this aspect
due to a lack of measurements between 15 and 40 m, where the thermocline is
situated. The break-up of the thermocline is also well represented in the
model. The model has, on the other hand, difficulties in representing the
colder intermediate waters in the Bornholm Basin and the Baltic proper, which
have a warm bias. This might be related to biases in the atmospheric forcing
, as these waters are formed during winter convection.
Indeed, the simulated winter surface temperatures at BY5 and BY15 tend to
have a warm bias. The modelled cold intermediate waters also descend too deep
towards the end of the year. This might be related to the weaker halocline,
and thus probably stronger mixing, in the model. The deep waters below the
halocline are warmer than the intermediate waters, which is reproduced by the
model. In the model it is, however, about 0.5∘ warmer than in the
observations at the BY15 station.
Interannual variability
From the comparison between the observations and their variability
(Fig. ), one notices that Nemo-Nordic in general reproduces the variability in the deep-water salinity close to the bottom well,
both in the Kattegat and in the Baltic Sea, despite the constant background
bias in the salinity. Especially, it is interesting to note from the BY15
salinity that the model is able to reproduce the major Baltic inflows of 1993
and 2003. The mechanisms behind the major Baltic inflows are actually mostly
barotropic, although their propagation to the bottom of the Baltic Sea
involves mixing processes. Therefore, the representation of sea level
variability is a key element in reproducing the variability in the major
Baltic inflows. A comparison of the modelled and measured sea level
differences during the recorded durations of these inflows shows the model's
accuracy to represent the underlying barotropic processes. During the
duration of the 1993 major Baltic inflow, the sea level at Landsort increases
by 1 m in observations against 1.02 m in the model, while during the 2003
major Baltic inflow, the sea level at Landsort increases by 0.58 m both in
the model and in the observations. This further suggests that a
misrepresented baroclinic process must be responsible for the negative bias
in deep-water salinity in the Baltic Sea. Even though this model uses a
bottom boundary layer parameterization , it is still a z
coordinate model, which means that the representation of dense overflows is
not as accurate as it would be in a sigma or generalized vertical coordinate
model. It is also interesting to note that Nemo-Nordic tends to overestimate
the variability in deep-water salinity at the Anholt station in the Kattegat,
while it slightly underestimates the variability at the BY2 station.
Comparison of temperature profiles from an Argo float (a)
and Nemo-Nordic (b). Observational data in (a) have been redrawn from the
data set used by Westerlund and Tuomi (2016). Panel (c) shows the
difference between the observation and model run. The model was larger than
the measurement where the difference is positive. Model results have been
taken along the buoy route in the Bothnian Sea in 2013.
Near-surface temperature in 2013 from the Argo float data and from
Nemo-Nordic. Thermocline depth estimated as the maximum of vertical
temperature gradient in panel (b).
Short-term variability – comparison with Argo floats data
As an example of Nemo-Nordic's performance in the shorter term, we compared the
model results to temperature observations from an autonomous Argo buoy. Over
100 profiles were collected during a mission in the Bothnian Sea, which
lasted from 13 June to 2 October 2013. The profiles were taken from between
61.57 and 62.47∘ N in latitude and between 19.59 and
20.42∘ E in longitude. This data set has been described in detail by
, who also provided an illustration of the buoy route.
From a comparison of observed and modelled temperature profiles we see that
the seasonal thermocline is visible throughout the summer
(Fig. ). The vertical structure of temperature
was relatively reproduced well by Nemo-Nordic near the surface. In August the
thermocline reached maximum depth and temperature. The model was able to
describe well how the mixed layer deepened during the summer. The temperature
gradient of the thermocline was also well represented. The surface layer
responded to atmospheric forcing in a similar way in observations and model.
In layers under the thermocline, model temperatures were somewhat too high.
This bias increased in late summer. Deeper, the dicothermal (old winter
water) layer was not as pronounced in the model as it was in the
observations. In late August, the model predicted larger thermocline depths than
were observed. In general, temperature profiles were smoother in the model
than in observations. Furthermore, some finer-scale features were not
completely reproduced by the model.
Observed and modelled near-surface temperatures, along with estimated
thermocline depths, are shown in
Fig. . Near-surface temperature was
taken to be the temperature of the model point at the depth of the topmost
data point in the observations, which was typically around 4 m, depending on
the profile. In most cases this is very close to the surface temperature.
The location of the thermocline was taken as the place of the maximum temperature
gradient along the z axis. Near-surface temperature in the model overall reproduced the seasonal temperature cycle, although in early September surface
temperatures were around 1∘ greater in the model than in
observations. Thermocline depths were represented quite well in the model,
except for the aforementioned time in late August.
presented a similar comparison to a different model configuration, derived
from an earlier version of Nemo-Nordic. That model used different atmospheric
forcing fields taken from an operational HIRLAM forecast from the FMI
(Finnish Meteorological Institute), climatological boundary conditions,
climatological river runoffs and initial conditions from FMI's operational
Baltic Sea forecast. Furthermore, it did not have the light penetration
parameterization present in the official Nemo-Nordic configuration described
in this paper. Compared to those results, the near-surface temperature in the
official Nemo-Nordic results differs more from the observations in autumn but shows less bias in early summer. Thermocline depths were quite similar in
both configurations.
Thermohaline structure of the North Sea
Compared to the Baltic Sea, the North Sea is more homogeneous regarding its
thermohaline structure. It exhibits mostly seasonal variations in the form of
the formation of the seasonal thermocline and seasonal variations in
freshwater forcing. Haline fronts between coastal and offshore areas are
found in the southern and eastern North Sea due to the relatively large river
runoff from continental rivers at the southern shore of the North Sea and
the Norwegian Coastal Current carrying low-saline waters from the Baltic Sea.
Haline structure of four different stations of the North Sea from top to bottom: Southern Bight station, Frisian Front station, NCC station and Fladen Ground station. Column (a) displays annual mean depth profiles,
where the red line is simulated salinity and black dots are observations. Columns (b) and (c) show seasonal variations in the model and the
observations, respectively. The observations are based on a 1979–2010
climatology.
Thermal structure of four different stations of the North Sea from top to bottom: Southern Bight station, Frisian Front station, NCC station and Fladen Ground station. Column (a) displays annual mean depth
profiles, where the red line is simulated temperature and black dots are
observations. Columns (b) and (c) show seasonal variations
in the model and the observations, respectively. The observations are based
on a 1979–2010 climatology.
Vertical structure and seasonal variability
In this section we validate the vertical structure and seasonal variability
at four stations representative of different hydrological regimes in the
North Sea. Two stations are located near the boundaries towards the Atlantic
Ocean (Fladen Ground and the Southern Bight). Validating the temperature and
salinity structure in these areas also gives a validation of the boundary
conditions and the properties of the inflowing water. The open boundary
condition fields for tracers (T and S) come from a climatology in this
case, so it is interesting to check whether their use with the model enables
it to represent measurements. The two other stations (NCC and Frisian Front) are located in areas where there are relatively large horizontal and vertical
(only NCC) salinity gradients due to the freshwater forcing from the Baltic
Sea and the continental rivers draining into the southern North Sea. The
observational data comes from the KLIWAS data set provided by the University
of Hamburg . It is composed of all available measurements
between 1970 and 2013 in the North Sea, which have been put into a
1×1 grid.
The haline vertical structure at the four stations in the North Sea is
displayed in Fig. . The only station with a distinct
permanent haline stratification is the NCC station. The vertical haline
structure at this station is reproduced well by the model, although the
surface waters are less saline than the observations by about 1 PSU. The
other stations are rather homogeneous in the vertical with respect to
salinity. In the Southern Bight and the Fladen Ground, the model captures the
overall mean salinity. At the Frisian Front the modelled salinity is about
1.5 PSU too low, which is probably related to a displacement of the front
between coastal waters with lower salinity and offshore waters. At all
stations the model simulates a seasonal cycle in the surface salinity with a
freshening during the summer months, in agreement with the observations. The
timing and the amplitude of this summer freshening is, however, subject to some
biases. Because the data set does not contain regular measurements from these
positions it can, on the other hand, give rise to biases in the observational
estimates of the seasonal cycle.
The thermal vertical and seasonal structure is, as for the Baltic Sea,
dominated by the seasonal warming and cooling of the surface waters
(Fig. ). In the Southern Bight and at the Frisian Front the
waters are well mixed from surface to bottom throughout the year, and no
seasonal thermocline develops, which is reproduced well by the model. In the
Southern Bight there is a warm bias of the order of 0.5 ∘C (annual
mean) in the model throughout the water column. At the Frisian Front there is
a warm bias of about 0.3 ∘C in the surface waters. At the two deeper
stations, the seasonal development and the depth of the thermocline are reproduced well in the model, although the start of the thermocline formation is
somewhat too early in the model. This is the case especially at the Fladen
Ground, where it starts almost 1 month too early. Also the winter SSTs are
too warm in the model, resulting in too weak a winter convection or temperatures of the convecting water that are too warm, which in its turn gives a warm bias in
the deep waters. At both stations there is a warm bias of about
0.5 ∘C (annual mean) in the surface waters.
Seasonal cycle of thermocline structure. (a) Thermocline
intensity (∘C m-1) averaged over the entire thermal stratified
area. (b) Same as in (a) but for thermocline depth (m).
Modelled thermocline dynamics
For biogeochemical processes summer thermal conditions are important when
water temperature and light intensity stimulate the growth of phytoplankton.
Contemporaneously, thermal stratification develops in the deeper basin of the
northern North Sea and the inflow of Atlantic water weakens ;
accordingly atmospheric forcing at the surface becomes more important. This
leads to substantially better reproduction of interannual variability even in
the north (Fig. b). We analyse in the following the modelled
thermocline dynamics following previous approaches for the North Sea
, which define the presence of
thermocline conditions when a certain vertical temperature gradient is
exceeded. We here chose a critical gradient of 0.25∘C m-1. The
yearly maximum extent of stratified areas is primarily governed by topography
and wind stress and varies between 100 000 and
185 000 km2 in 1990 and 2005. Stratified conditions begin to develop
in May and reach their maximal intensity during July and August. Already
during September wind stress strengthens and temperatures lower, which
increases mixing. The thermocline weakens then and is shifted downward. As a
result, nutrient-rich water reaches the euphotic zone. During this time, a
second phytoplankton bloom can be sometimes observed in the North Sea
.
Quantitative comparison of simulated and observed state variables
derived from Taylor statistics (Taylor, 2001). Rms: root mean squared;
corr: Pearson's correlation; SD: standard deviation;
N: number of observations. The statistics have been derived from all
1∘×1∘ boxes. See text for details about data set and
data handling.
State variablermscorrSD observationSD simulationNWinter Temperature0.740.780.861.186139Salinity0.560.790.540.886034Summer Temperature1.100.943.063.156808Salinity0.560.830.720.986638
(a) Left-hand panels: Nemo-Nordic multiyear (1990–2005)
DJF average SST. Middle panels: Nemo-Nordic
minus BSH SST. Right-hand panels: Correlation between Nemo-Nordic SST and BSH
SST. Note that regions where not enough observational data were present have
been coloured grey. Panel (b) is same as (a) but for JJA.
Interannual variability
Surface properties are in many cases dominated by local meteorological
forcing . Therefore, it is important to also validate
subsurface properties, which are also influenced also the by circulation
dynamics (like Atlantic inflow). For this, we use the KLIWAS observational
data set . We here follow previous approaches and subdivide
the North Sea into 1∘×1∘ boxes for which for each
month and at each box, standard level Taylor statistics are
calculated (for details, see Raddach and Moll, 2006; Gröger et al., 2013).
In order to estimate the interannual variability in seasonal signals, we
chose January and July as representative of winter and summer as in these
month observations were most abundant in the observational data set. The
results are shown in Table . In total, 25 619 observations were
used for the analysis.
Overall, good correlation with the temperature and salinity observations for
summer and winter indicates the model's skill to capture interannual
variability and thus the ability to realistically respond to low-frequency
modes of climate variations such as decadal variability or the
NAO (North Atlantic Oscillation) which likely influence the North Sea hydrography (e.g. Hjøllo et al.,
2009; Mathis et al., 2015). The root mean square values lie well within the
standard deviation of the observations (with the exception of January
salinities where rms is slightly higher; Table ). The model's
salinity variability (as indicated by standard deviation) is too high
(∼50 %), but the spatial distribution of the observations is much
coarser than the model grid, which explains this result.
Comparison with satellite data
In the following we compare the modelled SST with a widely used satellite
product provided by the Federal Maritime and Hydrographic Agency of Germany,
Hamburg (Bundesamt für Seeschifffahrt und Hydrographie; hereafter BSH). By
this we are able to investigate how the modelled SST reproduces interannual
variability, which is important for the application of the model to climate
services and the simulation of various climate scenarios.
In contrast to the warm bias relative to the in situ temperature
measurements, the simulated winter SST (Fig. ) is almost
everywhere colder than the satellite estimations of SST. Cold biases relative
to satellite measurements were also obtained in other ocean models driven by
the ERA40 reanalysis data set in and . Given that
in situ temperature measurements give a more direct measure than satellite
estimations of SSTs and that the atmospheric forcing used for these
simulations has a warm bias, especially in the North Sea during winter
, it is likely that the satellite underestimates the SST.
Despite this, the satellite SSTs can be used for validation of spatial and
temporal variability.
Figure a shows an overall good correlation of modelled
interannual SST variations with the BSH data set. Likewise the lower
correlation in the northern deeper parts could indicate problems with
convective and/or wind-induced mixing which would influence the heat transfer
from the deeper layers to the surface. In contrast, in the southern and
shallower parts, SSTs are dominated largely by the atmospheric forcing
, and thus correlation increases. Good correlation is also
seen along the Norwegian coast where the effective heat capacity is limited
due to haline stratification, which makes SSTs more sensitive to the
atmospheric forcing. The same is true for nearly the entire Baltic Sea, where
correlation drops below 0.8 almost nowhere.
Conclusions
In this article we provide a detailed description of the dynamic features of
Nemo-Nordic, a newly developed joint set-up for the Baltic Sea and the North
Sea. Its performance when it comes to sea ice is the subject of a specific
article . We have shown that Nemo-Nordic
is able to reproduce the barotropic and baroclinic dynamics, as well as the
thermohaline structure, of Baltic and North Sea basins. The key to achieve
this overall good representation of the physics in the Baltic Sea and the
North Sea has been to get as good a representation of the barotropic dynamics as possible. This ability, which is detailed in the present article, has been
validated with the most demanding procedure: Nemo-Nordic is used as the
official forecast model of SMHI for the Baltic and North seas, including for SSH, for which the model does not benefit of any data assimilation. In forecast
mode, Nemo-Nordic is used with a higher resolution (1 nm or 1852 m),
compared to longer integrations where the set-up is used with a 2 nm (or
3704 m) resolution. The ability of the model to represent barotropic
dynamics at high-frequency timescales (hours to several days) is the main
reason for its good representation of transports and water exchanges in and
between the Baltic Sea and the North Sea. It also allows for an accurate
representation of the Baltic Sea inflows and the initiation of the Baltic
Sea baroclinic dynamics. In order for Nemo-Nordic to properly represent
baroclinic dynamics in both basins, a specific tuning of mixing is done from
both horizontal and vertical points of view. For example, a limitation of the
vertical mixing length is needed in order to help the model keep the Baltic Sea
halocline at a realistic level.
Although Nemo-Nordic reproduces the overall physics of Baltic and North Sea
well, some biases may be noticed. The sea level representation is better in
forecast mode than that of the previous SMHI operational model HIROMB, but
some improvements are still needed to better represent extreme events.
Regarding this precise point, a coupling with wind waves appears to be the
next step, as it was recently shown that the contribution of wind waves has a
major impact on extreme sea levels . Ongoing
development based on the new implementations within the NEMO ocean engine
concerns the inclusion of wetting and drying processes, which can also affect
sea level in shallow areas. Further, an important margin of improvements
exists in atmospheric forcing, bottom friction and bathymetry. A margin that
should lead to an even greater accuracy in sea level representation: recent
tests done with a bathymetry computed from the GEBCO database (The GEBCO-2014
Grid, version 20150318, http://www.gebco.net, last access:
17 January 2019) show a high improvement of sea level representation in the
North Sea and along the Swedish west coast.
From a baroclinic perspective, several aspects can be improved in the model.
First, even though we could show that the barotropic variability in the Baltic and the North Sea basins allows MBIs, there is still a bias
in the deep salinity due to an over-ventilation of the intermediate layers.
So far, the only solutions which were found to solve this bias are to increase
resolution. But a major future development would be the use of a hybrid
vertical coordinate system with z* close to the surface and σ
coordinates close to the bottom. A minor development concerns the Galperin
parameterization as mentioned before, which needs to be decoupled from
thermal dynamics. Ongoing tests are being made concerning this latest point.
The geometry of the horizontal diffusivity is to be improved as well in order
to limit its effect on Baltic Sea inflows. The underlying idea is to avoid
any horizontal diffusivity close to the bottom. Other developments in
Nemo-Nordic concern its coupling with biogeochemical models. Nemo-Nordic has
been coupled with the SCOBI model , for which validation
work is ongoing. Nemo-Nordic has also been coupled with the BFM model
in a part of its domain .
Nemo-Nordic and spin-off configurations provide a tool not only for ocean
forecasting but also for a wide variety of ocean research. Nemo-Nordic can be used
for long-term simulations either for process purpose studies
e.g. or
climate-change-related studies . It can also be used for biogeochemical-ecosystem studies, using a simple passive tracer
with a decay rate or coupled with a complex
biogeochemical model . Nemo-Nordic is also the ocean
component of an RCA4–NEMO coupled model, which is the basis of a regional
climate model used in several studies
.
Finally, Nemo-Nordic has also been used as a boundary condition for
high-resolution sub-basin scale set-ups .
Nemo-Nordic builds on the standard NEMO code
(nemo_v3_6_STABLE, revision 5628) with only minor changes,
including the fast-ice parametrization and a spatially varying background
viscosity/diffusivity that could be read in from the file. The standard NEMO
code can be downloaded from the NEMO website
(http://www.nemo-ocean.eu/, last access: 17 January 2019). The
nemo_v3_6_STABLE version is available via the following link:
http://forge.ipsl.jussieu.fr/nemo/version/release-3.6 (last access:
17 January 2019). The new code blocks that are introduced (relative to the
standard NEMO code nemo_v3_6_STABLE, revision 5628) into our
Nemo-Nordic code are included as supplemental material. Nemo-Nordic is
released under the terms of the CeCill license (http://www.cecill.info,
last access: 17 January 2019), and its code is available in the zenodo
archive (10.5281/zenodo.1493117, ).
Access to the forcing data, analysis scripts and data used to produce the
figures in this study can be made available upon request to the corresponding
author.
Most of the development and design of this model was conducted by RH.
LA helped tuning several parameters especially within the
turbulence scheme. AH and CD provided expertise and help to design forcing
and boundary conditions. FF conducted several experiments and designed
diagnostics. MG, YL and PP provided ocean modelling expertise. SS provided important expertise regarding atmospheric forcing. HA provided scientific
expertise and management. PL, PN, SF, AN, AJ and IL provided expertise in the
design of the operational version of Nemo-Nordic. KD, MH, HD, UL and IK
provided expertise and advice on the representation of processes for the
Baltic and North Sea area. AW, LT and JH provided comparisons with
measurements and expertise on sea ice modelling. All the authors contributed to the paper with comments.
The authors declare that they have no conflict of
interest.
Acknowledgements
The research presented in this study is part of the project BONUS STORMWINDS
and has received funding from BONUS, the joint Baltic Sea research and
development programme (Art 185), funded jointly from the European Union's
Seventh Programme for research, technological development and demonstration
and from the Swedish research council for environment, agriculture sciences
and spatial planning (FORMAS). This work has been also supported by the
Strategic Research Council at the Academy of Finland, project SmartSea (grant
number 292 985). The Nemo-Nordic simulations were conducted on the Bi
supercomputer of the National Supercomputing Center, Linköping University,
Sweden. The corresponding author wants to express his gratitude to Sofia
Bergenbrant, lawyer at SMHI, for the energy
she put into encouraging the Nemo-Nordic technology to be exported outside
SMHI.Edited by: James R. Maddison
Reviewed by: two anonymous referees
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