For understanding and forecasting of hydrodynamics in coastal regions, numerical models have served as an important tool for many years. In order to assess the model performance, we compared simulations to observational data of water temperature and salinity. Observations were available from FerryBox transects in the southern North Sea and, additionally, from a fixed platform of the MARNET network. More detailed analyses have been made at three different stations, located off the English eastern coast, at the Oyster Ground and in the German Bight.
FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface measurements along selected tracks on a regular basis.
The results of two operational hydrodynamic models have been evaluated for
two different time periods: BSHcmod v4 (January 2009 to April 2012) and FOAM
AMM7 NEMO (April 2011 to April 2012). While they adequately simulate
temperature, both models underestimate salinity, especially near the coast in
the southern North Sea. Statistical errors differ between the two models and
between the measured parameters. The root mean square error
(RMSE) of water temperatures amounts to 0.72
The study results reveal weaknesses in both models, in terms of variability, absolute levels and limited spatial resolution. Simulation of the transition zone between the coasts and the open sea is still a demanding task for operational modelling. Thus, FerryBox data, combined with other observations with differing temporal and spatial scales, can serve as an invaluable tool not only for model evaluation, but also for model optimization by assimilation of such high-frequency observations.
The North Sea is a marginal sea that has among the highest densities of ship traffic in the world. It is an economically important region, sustaining commercial fisheries, wind farming, oil production and tourism (Kannen, 2012; OSPAR, 2010). As a major part of the north-western European continental shelf, the North Sea has a mean depth of 90 m. Bathymetry varies, and while the southern part is shallow (15–50 m), the northern part deepens to 100–200 m in the Norwegian Trench to well below 200 m. The south-eastern part of the North Sea is known as the German Bight, with the Wadden Sea at its coastal margins. Because of freshwater inflow from several rivers in the southern North Sea (e.g. Rhine, Maas, Elbe), salinity near the coasts is in the range of 15–25. In the central North Sea, salinity is approximately 35 (Janssen et al., 1999; OSPAR, 2000). Besides the freshwater inflow, the North Sea is also strongly influenced by tides and residual circulation, which is governed by bathymetry, density distribution and wind stress (Queste et al., 2013). An anti-clockwise circulation dominates the North Sea, with North Atlantic water entering at its north-western boundary near the Shetland Islands (0.4–0.5 Sv, OSPAR, 2000), travelling along the Scottish and English coast, and leaving along the Norwegian coasts (Turrell, 1992) (Fig. 2). Some of the North Atlantic water entering from the north reaches the southern North Sea, but the majority circulates north of the Dogger Bank. A much smaller portion of North Atlantic water enters through the Dover Strait (approximately 0.07–0.12 Sv, OSPAR, 2000) and travels up to the entrance of the Baltic Sea, where less saline water is entrained into the North Sea water through the Skagerrak and Kattegat. The relatively salty English Channel water (> 35) is mixed on its way along the south-eastern way of the North Sea coasts with freshwater from several rivers, passes the German Bight, and enters the Norwegian Trench region, mixing with the northern branch of the North Sea circulation. The estimated residence time of North Sea water is less than 1 year (Jickells, 1998; Lenhart and Pohlmann, 1997; Thomas et al., 2003).
Given the importance of the North Sea to the European economy and to the coastal communities, it is vital to monitor and understand its current ecological state. The FerryBox system provides regular high-frequency scientific measurements of ecologically important parameters, including temperature and salinity. It is installed on ships of opportunity (SoO) in European coastal regions, as well as on fixed onshore stations near harbours, river banks or estuaries (e.g. at Cuxhaven harbour located at the mouth of the Elbe River estuary). It is a flow-through system that continuously measures biogeochemical parameters every 10 s. FerryBoxes are a valuable platform to test and operate new developed oceanographic sensors in a sheltered environment (e.g. ship or container) without limitation of power supply.
During the FerryBox project from 2002 to 2005 (FerryBox, 2014; Petersen et al., 2005), a cooperation between several international oceanographic institutions was launched, which targeted development of new sensors and observing systems, as well as best practices in quality control, maintenance and biofouling prevention (Hydes et al., 2009; Petersen et al., 2005, 2007).
Shelf seas are complex regions governed by many processes. Along with operational monitoring using in situ and satellite observing systems, numerical simulation has long been acknowledged to be important for understanding the hydrodynamics of coastal regions. Since the 1980s, baroclinic 3-D models have been developed to predict water temperature and salinity variations in the North Sea. All countries around the North Sea have been contributing to this effort, i.e. Denmark (Vested et al., 1992), Norway (Svendsen et al., 1996), the UK (Proctor and James, 1996), Belgium (Delhez and Martin, 1992; Luyten et al., 1996), the Netherlands (de Kok, 1997) and Germany (Backhaus, 1985; Dick et al., 2001). For the present study, two different hydrodynamic models, BSHcmod and FOAM AMM7 NEMO, were used. These models provide the hydrodynamics for other studies, e.g. for ecosystem modelling (Edwards et al., 2012; Maar et al., 2011) and predicting wave–tide–current interactions (Pleskachevsky et al., 2009) in the North Sea. The German Federal Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt und Hydrographie, BSH) developed the BSHcmod hydrodynamic model for operational use in the North and Baltic seas (Dick et al., 2001). The coupled Forecasting Ocean Assimilation Model (FOAM) consists of a hydrodynamic (O'Dea et al., 2012) and an ecosystem (Edwards et al., 2012) part. The hydrodynamics are provided by the Nucleus for European Modelling of the Ocean (NEMO, Madec, 2008), while the ecosystem part is supplied by the European Regional Seas Ecosystem Model (ERSEM, Baretta et al., 1995; Blackford et al., 2004). The FOAM is a regional model, nested to the UK Met Office global ocean model (Blockley et al., 2014).
Besides the FerryBoxes, several other measurement networks are available in the North Sea, including the COSYNA coastal observing system (COSYNA, 2014; Grayek et al., 2011; Riethmuller et al., 2009; Stanev et al., 2011). Also, other observational networks like MARNET (BSH, 2014) and the SmartBuoys network (Cefas, 2014; Mills et al., 2003) measure water temperature and salinity on buoys and fixed platforms. Satellite coverage is generally limited in temporal resolution and even more restricted due to cloud coverage, e.g. when using visible parts of the spectrum (Petersen et al., 2008; Volent et al., 2011).
FerryBox data can bridge the gap between existing in situ observations typically used for data assimilation, as they provide reliable and high-resolution in situ data for transects in the North Sea (Petersen et al., 2008). However, the FerryBox data coverage is limited to grid points along a transect. To overcome this limitation, Wehde et al. (2006) and Petersen et al. (2011) applied a water transport model for comparison of FerryBox measurements with other operational observations.
The aim of the present study was to compare numerical model data with in situ measurements of different monitoring systems (FerryBox, fixed platforms). The goal of this study was to evaluate the quality of modelled water temperature and salinity data in different areas of the North Sea and to identify related weaknesses of the AMM7 and BSHcmod v4 operational models. These models are used by a variety of sectors for a range of applications. The most important applications supported by BSHcmod v4 are, however, the sea-level prediction and storm surge warning service for the German coast and different kinds of drift forecasts (e.g. for oil spill combating or search-and-rescue at sea), with sea surface heights and currents being the primary outputs required. This study gives some indication of where it could be beneficial to improve the computed mass distribution or baroclinic dynamics.
FerryBox routes and crossing points in the North Sea. The blue line
marks TorDania route Cuxhaven–Immingham and the red lines indicate Lysbris
route England–Norway–Germany. Specific analysis points of FerryBox routes
are indicated by black dots and labelled p1, p2, and p3. p1 is situated at
the English eastern coast. p2 marks the analysis point in the Oyster Ground
area. At p3, MARNET station
The first section of our study describes the data sets and the applied methods. Then, data from a complete FerryBox transect data set are compared with model results. Discrete point comparisons of model data and observations are then presented.
In general, all European FerryBox systems have a similar design. The differences are in the design of the flow-through system, the degree of automation and biofouling prevention, as well as the possibilities of supervision and remote control. The FerryBox systems used in the present study are designed and manufactured by 4H-Jena engineering GmbH and Helmholtz-Zentrum Geesthacht (HZG) and have the following specifications.
The water is pumped from a subsurface inlet (located at 5 m depth) into the
flow-through system containing multiple sensors. Due to the ship's movement
and turbulence, the water pumped into the FerryBox system originates from
the surface layer of the water column. A debubbling unit removes air
bubbles, which may enter the system during heavy seas. Coupled to the
debubbler, an internal water loop circulates the seawater with a constant
velocity of about 1 m s
More information about the FerryBox system can be found e.g. in Petersen et al. (2003, 2005).
For this study, the data sets of two different commercial ships have been
used. The data are available at the FerryBox database at Helmholtz-Zentrum
Geesthacht (HZG) (
For FerryBox water temperature and salinity measurements, the Citadel TS-NH
thermosalinograph (Teledyne Technology Company) is used. The basic salinity
instrument measures inductive conductivity, while the temperatures are
measured by a thermistor in close proximity. Accuracy for salinity is
The MARNET station network consists of several measurement sites in the
German coastal parts of the Baltic Sea and the North Sea (BSH, 2014). It is
also part of the COSYNA observing system in the North and Arctic seas. MARNET
has a long tradition of monitoring in coastal waters (on unmanned light ships
since 1984) and is operated by the BSH. This study uses data from North Sea
MARNET station
The BSHcmod is a 3-D baroclinic ocean circulation model for the North Sea and Baltic Sea (Dick et al., 2001) and version 4 (v4) has been in operational use since the beginning of 2008. Daily, it provides 3-day forecasts of water levels, currents, water temperatures, salinity and ice cover. For the German Bight, the large variability of daily surface circulation patterns can be viewed at BSH (2015), along with the pertaining statistical distribution. The monthly simulated BSH mean surface circulation for the whole North Sea is published in several reports, e.g. in Loewe (2009) and Loewe et al. (2013), showing a pronounced seasonal as well as inter-annual variability strongly related to the atmospheric circulation pattern over the North Sea. This has also been described in a detailed review of the physical oceanography for the North Sea by Otto et al. (1990).
The model is based on the Reynolds-averaged Navier–Stokes equations which are discretized on a geographical Arakawa-C grid and on adaptive vertical coordinates. A two-way nesting approach is applied with a coarse-resolution grid (5 km grid spacing) in the North and Baltic seas and a fine-resolution grid (900 m grid spacing) in the German Bight and the western part of the Baltic Sea (focus region). Internally, BSHcmod v4 makes use of adaptive layers with variable thickness (8 m in the English Channel, 1–2 m in the German Bight), depending e.g. on tidal amplitude. There are 36 layers in the coarse grid, and 25 layers in the fine-grid domain. The mixing scheme used in the model is described by Dick et al. (2001). When archived, BSHcmod data are interpolated on a coarser grid with constant vertical layer resolution. Thus, the archived data at forecast time step 0 applied here are from the surface layer, which has a thickness of 5 m and a temporal resolution of 15 min.
Meteorological forcing is provided by the German Weather Service (Deutscher
Wetterdienst, DWD) (Doms and Schättler, 1999). The 10 m wind components
are extrapolated from the lowest pressure level height data, considering also
the stability conditions in the Prandtl layer. The freshwater input into the
North Sea is estimated using the daily averaged data of 5 rivers (i.e. the
Rhine, Ems, Weser, Elbe, and Eider), obtained from river gauge observations.
For the remaining rivers in the North Sea, the constant mean annual values of
freshwater runoff are used (in total 80 rivers). The temperature of the river
water is set to equal the temperature of the grid cell where river inputs
discharged. The salinity of the inflowing river water is assumed to be zero.
The BSH model simulates tides based on 14 harmonic constituents which are
provided at the open boundaries in the northern part of the North Sea
(60
The AMM7 includes a 3-D hydrodynamic component based upon the NEMO model, which is included as part of the Met Office Forecasting Ocean Assimilation Model (FOAM) suite of forecast systems that run daily and include assimilation of in situ observations. The AMM7 system also contains the ERSEM ecosystem model (Baretta et al., 1995; Blackford et al., 2004; Siddorn et al., 2007).
The model domain encompasses the European north-western continental shelf on
a regular lat–lon grid (42–65
The NEMO model itself is a community model particularly developed in Europe (Madec, 2008). Though it has been developed for the deep ocean, it has then been modified for usability for shelf seas. Details of the model and its implementation are given in O'Dea et al. (2012). Vertical mixing is resolved using the generic length scale (GLS) model and a second-moment algebraic closure model for the two dynamical equations of turbulent kinetic energy (TKE) and TKE dissipation.
The system assimilates observations using an optimal interpolation scheme (Martin et al., 2007), with updates described in Storkey et al. (2010) and adaptations to enable it to address the particular requirements for shelf applications (O'Dea et al., 2012; Siddorn et al., 2007). The assimilation system uses a first guess at appropriate time (FGAT) scheme to calculate model–observation differences (innovations) which are converted to model increments using an iterative method. A daily analysis window is used, with the model being rerun for the same day with an incremental analysis update (IAU) scheme to update the model state using these increments. Only sea surface temperature (SST) data are assimilated. Temperature and salinity profile assimilation along with sea surface height assimilation are technically more challenging in the shelf environment and will be implemented as future developments to the system.
Data assimilated include in situ data and level-2 satellite SST data provided by the Global High-Resolution Sea Surface Temperature project (GHRSST). In situ data are obtained from a variety of sources and include measurements taken by ships, moored buoys, and drifters. Satellite observations are obtained from the Advanced Microwave Scanning Radiometer-Earth observing system (AMSRE), the Advanced Along-Track Scanning Radiometer (AATSR), and the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the NOAA and MetOp satellites. Also assimilated are data from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI). All data are quality controlled and a bias correction scheme, based on comparisons to in situ and AATSR data, is applied to the AMSRE, AVHRR, and SEVIRI observations. A full description of the satellite data types, and the scheme used to correct them, can be found in Donlon et al. (2012).
It is worth noting that although a number of SoO data were assimilated into the system, including reasonable data density in the southern North Sea, the FerryBox data used in this study were not available for assimilation and so were not included.
At the open boundaries, AMM7 is one-way nested into the Met Office
operational FOAM 1/12
The data for the flux between the Kattegat and the Baltic are derived from the Danish Hydrographic Institutes' Dynamics of Connected Seas (DYNOCS) experiment and are applied as a monthly mean climatology of vertical temperature and salinity structure. The atmospheric forcing is provided by the Met Office Numerical Weather Forecast model.
For the present study, the AMM7 data set of the analyses is provided by the MyOcean database (McLaren et al., 2015) in hourly time resolution and 7 km grid resolution. Data are taken from the surface layer, which in the shallow waters of the southern North Sea is valid for approximately the surface metre or less of the water column.
A variety of statistical measures were applied to evaluate the model performance. Since the time periods for the evaluated models are different, the statistical measures are valid for different but overlapping time periods.
If the observations are denoted as obs and model predictions as sim, the bias can
be described as the difference between the mean of simulations and the mean
of observations, i.e.
The cost function (cf) field, introduced by Berntsen and Svendsen (1999) and
later adapted by Søiland and Skogen (2000), is a measure for discrepancies
of parameter
General circulation scheme in the North Sea (from OSPAR (2000); adapted from Turrell, 1992).
The southern North Sea has different regions with different characteristics.
To take that into account, three positions for detailed investigation have
been selected for the time period of 2006–2013
(Fig. 1):
English coast point at 53.553 Oyster Ground point at 54.04 German Bight point at 54.17
Position p1 is situated near to the coast and not far from the mouth of the
Humber estuary. It is influenced both by the freshwater discharge from the
Humber estuary and by the southerly flowing cold Scottish coastal water
current (< 15
The second point (p2) is located near the Oyster Ground, a region with water depths of up to 40 m. TorDania travels along the German and Dutch coasts to England and back. In Petersen et al. (2011), this point was previously selected for analysis of low-salinity waters of fluvial origin, which have been observed by two FerryBox transects crossing at this point. The region is thermally stratified in the summer season and belongs to a transition zone between the stratified central North Sea and the well-mixed coastal zones (Fig. 2). Due to spring algae bloom, stratification in the summer leads to low oxygen concentration, which is a serious problem considering the predicted warming climate and oceans (Queste et al., 2013). Together with salty water (> 35) from the English Channel, frontal zones form in this region, as has been observed e.g. by FerryBox measurements (Petersen et al., 2011).
The German Bight area, where p3 is situated, is influenced by the Continental Coastal Water current, the input of freshwater, and the exchange processes between the Wadden Sea and the North Sea (e.g. exchange of nutrients, suspended matter, tidal flow). The German Bight also has one of the highest tidal amplitudes of the North Sea (> 4 m) (OSPAR, 2000).
Model data have been taken from the HZG model archive (BSHcmod) and from the
MyOcean database (AMM7). For BSHcmod, data from the surface box down to 5 m
depth were taken, with instantaneous grid values at 15 min resolution. AMM7
is taken from the surface box of the model and has instantaneous values for a
7 km grid box mean every hour. As the model uses an
FerryBox data with 10 s resolution have been taken from the HZG FerryBox
database. For the detailed analysis of the three positions in the North Sea,
an internal search routine of the HZG database has been applied with a search
radius of 5 km around the fixed positions p1, p2 and p3 (5 km is the
default search radius). The retrieved FerryBox time series have been
interpolated using a nearest neighbour
approach for model time steps with a time range of
For the evaluation of the complete transect between the UK and Germany,
FerryBox data from 5 m depth along the complete transect with a time
resolution of 10 s have been sampled on a longitudinal grid with intervals
of 0.05
A calibration with discrete samples was done to validate the FerryBox salinity measurements. On both ships – TorDania and Lysbris – water samples have been taken at fixed stations and analysed in the laboratory. Generally, it is not feasible to compare FerryBox water temperature measurements to water samples analysed in the laboratory, so instead a cross-check between the TorDania FerryBox and MARNET observations was done.
Comparison of FerryBox salinity measurements and water sample analyses in the laboratory for TorDania (left) and Lysbris (right).
Comparison of water temperature (left) and salinity (right) measurements in the German Bight at geographical point p3 from 2007 to 2011.
In Fig. 3, comparisons of FerryBox measurements and laboratory analyses of salinity for TorDania and Lysbris are shown. The water samples are taken regularly along the FerryBox transect from 2007 to 2011 and from 2009 to 2012, respectively. The data correspond in both cases very well, and only a few outliers were observed. Note the different scales of salinity in the graphs. In the case of Lysbris, a higher range of salinity values is covered. This is due to the included FerryBox route section in the Elbe River estuary up to the port of Hamburg. The correlation is 0.96 for TorDania and 0.99 for Lysbris, which indicates a high reliability of FerryBox salinity measurements. The RMSE for Lysbris salinity is slightly lower than for TorDania (0.68 compared to 0.79).
For the evaluation of water temperature accuracy, MARNET measurements were compared to FerryBox observations for the German Bight region. TorDania passes the MARNET station (p3) every second day on its way between Cuxhaven (GER) and Immingham (UK). Only TorDania data from 2007 until 2011, recorded less than 10 km away from MARNET, have been considered.
For both parameters, water temperature (left panel) and salinity (right
panel), a good agreement was observed in Fig. 4. TorDania water temperature
measurements are higher than corresponding MARNET observations. The bias
(FerryBox–MARNET) amounts to 0.37
Differences in water temperatures for the TorDania transect (left
side BSH–TorDania 2009–2011, right side AMM7–TorDania 2011–2012). The
eastern England coast is located on the left side, the German Bight on the
right side. Positive values indicate model overestimation. Differences are
statistically significant beyond
Standard deviation of error (stde), bias and root mean square
error (RMSE) (up) and skill variance (skvar) (down) of BSHcmod 2009–2011
Grayek et al. (2011) also compared FerryBox data to MARNET observations and the OSTIA satellite data package (Donlon et al., 2009) and found similar agreement between the temperature data sets.
At MARNET station, water temperatures are measured at several depths,
including 3 and 6 m. To get a more concise picture of variation of water
temperature in the surface layer, data at both depths have been
analysed. The mean water temperature
difference is 0.09
The time series of salinity are also in good agreement (Fig. 4). However, the figure shows a higher scattering than for water temperature, with the MARNET station observing higher values. The standard deviation of the difference amounts to 0.57; the determination coefficient amounting to 0.82 is, thus, not as high as for water temperatures. Therefore, the agreement between FerryBox and MARNET salinity observation is good; however, the 10 km distance between FerryBox measurements and MARNET data, along with the large influence of tides and river discharge on salinity, may explain the lower correlation.
All in all, this suggests that different FerryBox sensor observations are reliable, and that there is high agreement between different measurement systems (FerryBox and MARNET). The FerryBox parameters water temperature and salinity are well suited for comparison with model data, which will be described in the next section.
Together with model output of BSHcmod v4 and AMM7, the complete TorDania transect between Germany and England has been analysed regarding differences in simulated and observed water temperatures and salinity.
In Fig. 5, the water temperature differences from 2009 to 2011 for
BSHcmod (a) and from April 2011 to April 2012 for AMM7 (b) are shown. Note
the different timescales of the model comparisons in both figures. Positive
(negative) differences indicate overly high (low)
simulated temperatures. The differences have been marked in the figure
according to the double SD of the FerryBox data, which has been described
in the previous Sect. 2.7. Thus, differences beyond
At first glance, water temperature differences range around
Statistical measures for performance analysis of BSHcmod v4 and AMM7.
Close to the English coast, temperature differences are systematically
negative, dropping down to
Results for AMM7 (Fig. 5b) show general good agreement with FerryBox
observations for April 2011 to April 2012, as the bias for the whole transect
amounts to 0.19
The statistical measures for AMM7 are shown in Fig. 6b, confirming the results in Fig. 5b: stde and bias show two local extreme positions; near the English coast and in the German Bight. The skvar is around 1 or slightly higher, reflecting good model performance for water temperature variability.
Overestimation of water temperatures near the English coast in 2011 around
0.5
Differences in salinity for the TorDania transect (left side
BSH–TorDania, right side AMM7–TorDania). The eastern England coast is
located on the left side, the German Bight on the right side. Positive values
indicate model overestimation. Differences are statistically significant
beyond
Standard deviation of error (stde), bias and RMSE (up) and skill
variance (skvar) (down) of BSHcmod
As for water temperatures, the error in simulated salinity of BSHcmod and AMM7 has been calculated for the whole transect and is shown in Fig. 7a and b. Positive (negative) values show overly high (low) simulated salinity values. For both models, differences can be divided into three sectors all over the TorDania transect. Both coastal zones (English eastern coast and the German Bight) are dominated by high negative differences, whereas in the central part absolute differences are significantly lower, negative for AMM7 in most parts, and positive for BSHcmod. However, they are not significant, as they are lower than 2-fold SD of FerryBox.
For BSHcmod, positive differences occur in the western part of the transect
between 0.5 and 5
Upper panels: time series of temperature
differences and absolute FerryBox values (in green)
Upper panels: time series of temperature differences and absolute
FerryBox values (in green)
Upper panels: time series
The salinity is generally underestimated by AMM7, except for the region
between 3 and 6.5
A combination of several factors seems to be responsible for the
underestimation of salinity in the German Bight for both models. First of
all, the runoff from the Elbe River and thus the freshwater input into the
region seems to be overestimated, although in BSHcmod v4 daily averaged
runoff rates of German rivers are included. For AMM7, climatological runoff
is provided. An underestimation of vertical mixing in the BSHcmod v4
simulation possibly contributes to the underestimation of the salinity by
mixing bottom water with higher salinity into the top layer sampled by the
FerryBox. In BSHcmod the western boundary of the high-resolution grid nested
into the coarse North Sea grid is located at 6
In this section, time series of measurements and model simulations for the time period of 2009 to 2012 are presented. The observations have been recorded by the FerryBox of TorDania and Lysbris. To address the different results along the transect between the UK and Germany, described in the previous sections, three single positions in the southern North Sea have been selected.
The time series of the water temperature difference at the English coast point (p1) for 2009 to 2011 is shown in Fig. 9a. The figure contains FerryBox data of TorDania and Lysbris, as well as model data of BSHcmod v4 and AMM7. The TorDania time series from 2009 to 2012 has some data gaps in 2009. The time series of Lysbris generally has many gaps, because the vessel is at the same position only every 2 weeks.
Both models show similar behaviour, except for their bias (Fig. 9b). The bias
of AMM7 temperature amounts to 0.39
Results of comparison between salinity observations and simulations for the eastern England coast are shown in Fig. 9c, and statistical measures in Fig. 9d. In the time period of 2009–2012, observations range between 30 and 35, with a mean value of 33.03. Some low-salinity events occur below 30, mainly in winter months. These low-salinity events are not entirely reproduced by BSHcmod in 2010 and 2011, resulting in high positive differences. Generally, BSHcmod v4 salinity ranges around 33.67, with a bias of 0.64.
AMM7 starting in April 2011 gives salinity values between 30 and 34, with a
bias of
The reduced level of agreement in both models can for the most part be explained by the model forcing concerning freshwater discharges. For most rivers entering the North Sea and the Baltic Sea, BSHcmod uses either river runoff data derived from measured water levels or runoff forecasts of a hydrological model of the Swedish Meteorological Institute (SMHI). For British rivers, BSHcmod uses constant annual mean values. Therefore, at the eastern coast of England, the BSH model shows only weak seasonal fluctuations and is not able to simulate the large observed fluctuations. The AMM7 model also uses climatological runoff data for British rivers, but monthly variations are included, and this is visible in Fig. 9c.
At Oyster Ground point p2, BSHcmod and AMM7 simulations of water temperatures
match observations most of the time. The water temperatures differ mainly in
summer seasons (upper left panel), ranging between
In Fig. 10c, the time series of salinity difference for the Oyster Ground
point p2 are shown. The mean level of observed salinity (mean
value
As was already described in Petersen et al. (2011), low-salinity intrusions can be observed in that North Sea region, often originating from the Rhine/Maas River estuary. The salinity dropped in 2011 to a level of 33.5. In 2008, an even more pronounced salinity drop to 32 was observed (not shown). The drop event of 2011 has been recognized by BSHcmod v4 and AMM7; however, the amplitude has been underestimated, resulting in high differences between model and in situ data. This is also visible in Fig. 7b by positive values between April and June 2011 for AMM7. However, subsequent to the observed salinity drop, AMM7 shows a second, even more pronounced drop in summer 2011 which has not been observed by the FerryBox and by BSHcmod v4 at that position.
Therefore, both models are able to simulate riverine influence in most of the North Sea, except near river outflows. However, mixing of coastal and estuarine water is probably underestimated in the models. It is known for example that the AMM7 model underestimates the tidal amplitudes in the German Bight (MyOcean QuID, McLaren et al., 2015), which will result in reduced flushing of the freshwater input to the region. This is likely to be partially responsible for the underestimates of salinity in the region.
Moreover, long-persisting low-salinity water masses, as reported by Petersen et al. (2011), seem to cover only small scales in space and could be missed either by the model or the FerryBox travelling along the route, resulting in higher discrepancies between model and FerryBox. In this context, the different spatial characteristics of model and FerryBox should be noted. Whereas the FerryBox samples data of spots along a track, the model represents means of an area of several tens of square kilometres.
In Fig. 11a, the annual cycles of water temperatures for MARNET, FerryBox on
TorDania and both models are shown. The highest water temperature amplitude
of the analysed time period is observed in 2010, with an 18
Consequently, the bias of AMM7 water temperatures is
Figure 11c shows the time series of salinity, which features three large salinity drops below 31 in June 2010 and January and May 2011. The first one lasts more than 1 month and is represented by BSHcmod v4, albeit later than observed. The next low-salinity event in January 2011 is also seen in the BSHcmod results, although slightly underestimating the freshening. The third event is recognized by BSHcmod v4; whereas in the observations the salinity quickly returns, BSHcmod salinity remains low for the summer period. The AMM7 does not represent well the timing or variability shown in the observations.
In summer, the simulated salinity drops to below 32, while observations show
values of around 33. This holds not only for the MARNET position, but also
for the German Bight east of 6
The statistical measures are shown in Fig. 11d. The bias of salinity
simulation is negative for BSHcmod v4 (
The statistical tests indicate that AMM7 could be improved by reducing the
offset of mean temperature levels (AMM7 0.19
There is a slight misfit in BSHcmod simulations of the annual cycle of water temperatures (too low in winter, too high in summer). The bias is near zero, but the RMSE is twice as high as for AMM7. Both models reveal deficits in the prediction of variations of water temperatures near the coasts, and in particular in the cold Scottish coastal current (only for BSHcmod v4). That is probably due to weak vertical mixing or overestimation of cold water currents in BSHcmod, especially at the end of summer. This particular circumstance has to be further investigated to deepen the understanding of the underlying processes. BSH is currently transitioning to a new model code (HBM, HIROMB–BOOS model) which uses a different vertical mixing scheme. We recommend further model evaluation to analyse the expected benefits from that transition.
Comparisons of salinity show much higher differences between observations and simulations and reveal geographical dependencies of the model performance. Altogether, both models show certain limitations.
BSHcmod does not capture properly the variability or the correct salinity
range in the German Bight east of 6
Low-salinity events occurring in the southern North Sea are caught by BSHcmod v4 and AMM7 to some extent. In order to improve salinity values in the model, we recommend using validated daily freshwater input data for all main rivers entering the North Sea.
The models' representation of vertical and horizontal mixing as well as river boundary conditions should be further studied. In addition, for BSHcmod v4, the nesting process of different grid sizes also has to be further evaluated.
FerryBox measurements, routinely validated for accuracy and precision using
external checks and laboratory analyses, can serve as a reliable proxy for
the state of the surface temperature and salinity variations in the North
Sea. The operational FerryBox measurements are routinely checked against
water probes. Salinity measurements are validated against laboratory analyses
and revealed good results. The FerryBox and the MARNET measurements are also
in good agreement. There is a bias of 0.37
While FerryBox measurements are done along transects in European marginal seas, fixed stations provide longer time series at a particular site, but a lack of spatial information for the neighbouring regions. In this study, using the FerryBox and the MARNET data sets, both types of measurements were examined.
Previously, FerryBox transect data have been successfully assimilated in North Sea models, as has been demonstrated by Stanev et al. (2011) and Grayek et al. (2011). The latter have shown that FerryBox data provide reliable information with limited coverage. They could be analysed parallel to satellite-derived SST data (extracted from the OSTIA data set, Donlon et al., 2009) and to other measurements from fixed stations for increasing the information efficiency derived from the FerryBox data. For the Aegean Sea, Korres et al. (2009) also have assimilated FerryBox sea surface salinity (SSS) data together with AVHRR sea surface temperature data into a hydrodynamic model. They showed that the assimilation of satellite SST data enhanced the model performance. Additional FerryBox salinity data helped to improve model results even more, by significantly decreasing the RMSE statistics for the southern Aegean Sea.
Data assimilation of FerryBox data is performed in most cases using a Kalman filter approach to extrapolate 1-D data on 2-D fields. Since the influence of the assimilated FerryBox data is restricted to a rather shallow area around the FerryBox track, one method for data assimilation could be the use of particle tracking algorithms for (approximately) conservative parameters like temperature and salinity in combination with 2-D North Sea current fields, e.g. of operational BSHcmod. A data assimilation scheme for operational use is under development at BSH (Losa et al., 2012, 2014). It is based on the local singular evolutive interpolated Kalman (SEIK) filter algorithm which has been coded within the Parallel Data Assimilation Framework (PDAF). So far this method has been tested during the assimilation of satellite-derived SST data along with vertical temperature and salinity profiles.
The AMM7 model already assimilates SST from SoO managed under the Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology, where telecommunications have been established to transmit data via the Global Telecommunications System (GTS) in real time. FerryBox data, like the ones used in this study, could also be included relatively easily, if communications allowed it.
The operational implementation of FerryBox data is one of the next steps for completion of the scheme. An important next step is overcoming the delayed mode limitation of FerryBox measurements for assimilation into operational forecast modelling systems. This has been partly achieved already, mainly at recently installed FerryBoxes using satellite communication. For the operational assimilation, operational post-processing of FerryBox data for quality assessment is also necessary and has also been partly established. Recommendations of real-time FerryBox data processing have been formulated e.g. in the Data Management, Exchange and Quality (DATA-MEQ) EuroGOOS working group and described in Petersen (2014).
In this study, we compared the hydrodynamic model simulations of BSHcmod and
AMM7 to continuous operational FerryBox and MARNET in situ water temperature
and salinity observations along the FerryBox route from England to Germany,
as well as in detail for three positions also situated along the transect.
For water temperatures, data assimilation gives a significant benefit for
better performance for AMM7, reducing the RMSE to 0.44
For salinity, model results reveal limitations, especially near the coasts, where river input, vertical mixing and tidal fluctuations are important features for the variability and general range of salinity.
The operational implementation of FerryBox data would be an important next step as previous studies showed benefits of assimilation of FerryBox data in North Sea models. Also, the assimilation of SSS data would be beneficial for model performance of salinity simulation, as has been noted by Korres et al. (2009).
Especially near the coasts, weaknesses of the models are apparent. They could be affected by wrong mixing and stratification simulation as well as misfits in river runoff simulation. More realistic river runoff data could increase model performance of salinity simulation.
We want to thank the shipping company DFDS Tor Line and DFDS Lys Line and the crews on TorDania and Lysbris. It is much work to maintain our FerryBoxes to keep them running, so we are especially thankful to our technical and data management staff.
We are very thankful for the helpful advice from the reviewers to improve this publication.
This work was partly funded within the 7th Framework Program of the European Union under grant number 262584 in project JERICO and partly by the Coastal Observing System for Northern and Arctic Seas (COSYNA), financed by the Helmholtz-Zentrum Geesthacht Centre for Materials and Coastal Research GmbH (HZG). The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: M. Hoppema