Highlights

Long-term changes and marine weather statistics

One of the key objectives for developing coastDat was to derive a consistent and mostly homogeneous database for assessing marine weather statistics and long-term changes. Here, homogeneity refers to a data set which is free from effects caused by changes in instrumentation or measurement techniques. The latter is particularly important when long-term changes are considered. As an example, Figure 1 shows wind speed measurement obtained from two different data sources around the same place in the North Pacific. Both time series are expected to carry the same information. However, while one of the data set shows a long-term change in wind speed, the other does not. Obviously, at least one of the data sets is corrupted by changes in the way the observations have been made and is thus not homogeneous.

Figure 1: Annual mean wind speed anomalies in the North Pacific in the area of ocean weather ship P derived from the ICOADS data (light grey) and from measurements aboard ocean weather ship P (dark grey). Courtesy of Hans-Jörg Isemer, Helmholtz-Zentrum Geesthacht.

Figure 1: Annual mean wind speed anomalies in the North Pacific in the area of ocean weather ship P derived from the ICOADS data (light grey) and from measurements aboard ocean weather ship P (dark grey). Courtesy of Hans-Jörg Isemer, Helmholtz-Zentrum Geesthacht.

In coastDat techniques developed in atmospheric sciences were used to project the state of the coastal system as known from a finite set of imperfect, irregularly distributed observations onto a regular grid. Procedures are kept fixed over the periods for which the analyses are performed, making the gridded data as consistent and temporally homogeneous as possible.

Figure 2: Pressure derived storm indices for Northern Europe derived from observations (blue lines) and from the coastDat database (black lines). Courtesy of Oliver Krüger, Helmholtz-Zentrum Geesthacht.

Figure 2: Pressure derived storm indices for Northern Europe derived from observations (blue lines) and from the coastDat database (black lines). Courtesy of Oliver Krüger, Helmholtz-Zentrum Geesthacht.

Over the oceans, only a few homogeneous long-term data sets are available to demonstrate the homogeneity of the coastDat record. An example is shown in Figure 2 in which a proxy for storm activity in Northern Europe derived from pressure observations is compared with the same index derived from the coastDat database. While based on pressure observations, the index is considered to be mostly homogeneous as pressure measurements have hardly changed in the course of time and are less affected by small scale changes in the surrounding of the measurement site. Figure 2 illustrates that the qualitative features of long-term changes in storm activity are reasonably covered by the coastDat data, in particular the minimum storm activity around 1960, the subsequent increase until the mid-1990s, and the following decrease.

Homogeneous proxies are available for only a few places. When carefully validated, gridded data products such as coastDat complement the information from such proxies. Data from coastDat have thus been used to describe in detail long-term changes in the wind, wave and storm surge climate mostly for the North Sea, but at a growing extent also for other areas, for example SE Asia, northern high latitudes, or the Baltic Sea.

Figure 3: Near-surface marine wind speed at platform K13 in the southern North Sea derived from observations and from the coastDat database.

Figure 3: Near-surface marine wind speed at platform K13 in the southern North Sea derived from observations and from the coastDat database.

Particular care was taken in analysing the extent to which extreme events are represented by coastDat. As the main emphasis was on reproducing observed weather statistics, individual events may or may not be reasonably reproduced in the coastDat database. Figure 3 illustrates an example for near-surface marine wind speed. While in general there is good agreement when coastDat wind speeds are compared with observations, some extreme events may be over- or underestimated (see events around 1 March) while others are reasonably reproduced (for example between 16 February and 1 March). When extreme value statistics, such as five or 25-year return values, are studied the observed values are, however, usually reproduced within error bounds (see Figure 4). This suggests that coastDat is not necessarily a good database to investigate a given observed extreme event in more detail, but is generally more suited for studying the statistics of such events and their potential long-term changes.

Figure 4: Return levels of extreme near-surface marine wind speeds at K13 in the southern North Sea derived from observations (red) and from the coastDat database (blue).

Figure 4: Return levels of extreme near-surface marine wind speeds at K13 in the southern North Sea derived from observations (red) and from the coastDat database (blue).

Risk assessment

Data from coastDat were used for risk assessment in several ways. Return periods of extreme wind speed, surge and wave heights are used by a variety of users involved the design of for example, offshore wind farms. Planning of such farms is supported by estimating probabilities of weather windows; that is, for example the probability of an extended period with wave heights below a given threshold to enable installation.

Assessments of changing risks from oil accidents and chronic pollution represent another issue. A toolbox called PELLETS-2D including an oil chemistry model was developed to allow for offline Lagrangian transport calculations based on ocean currents derived from coastDat. The toolbox was successfully applied to both, assessing risks from accidental and chronic oil pollution along the German North Sea coast.

Chronic pollution does mostly not correlate with recorded ship accidents but is caused by illegal oil dumping such as tank washing or the disposal of bilge water, for instance. The absolute amount of oil spilled is difficult to estimate as discharges often go undetected by aerial surveillance. Time and again, either corpses of oil-contaminated birds are found along the German North Sea coast or pollution is directly observed in certain coastal areas.

To what extent pollution affects coastal areas depends on winds and currents prevailing at the time of the incident. The coastDat database provides a detailed description of these parameters during the past decades. Using PELETS-2D this information was used to reconstruct drift paths of hypothetical oil slicks assumed to be released continuously along major shipping routes in the German Bight. Performing a large number of such simulations allows for a general assessment of how the exposure of the German coast to chronic oil pollution varies in time and between different coastal areas.

Chrastansky and Callies (2009) used PELETS-2D to establish drift statistics for the period 1958-2003. Assuming a constant frequency of oil releases, a large number of simulations (hypothetical oil spills) at different locations was initialized with constant time lags (28 h) between them. Subdividing the German North Sea coast into a couple of receptor regions, results provided a proper description of both the mean risk exposure of different coastal areas and corresponding variability. Chrastansky et al. (2009) were successful in using these data for a more in depth interpretation of monitoring data.

Chrastansky and Callies (2011) summarized the results from the above study in terms of probabilistic relationships that describe spatial dependences and sensitivities between parameters addressed in the study. The mutual interactions were represented using Bayesian Network (BN) technology. The resulting BN is accessible via an interactive graphical user interface and allows for an interactive exploration of how simulated results depend on parameters like location of the spill, wind direction, wind speed, season of the year, or assumed half-life of the hypothetical pollutant.

Figure 1 gives an example of how a large number of simulations started at different times within a long period of several decades results in a sound statistical estimate of tracer particle travel times, for instance, Such ensemble simulations covering all realistic weather conditions can provide important information that supports the assessment of efficient countermeasures considered in the context of risk studies. Chronic Oil Pollution in the German Bight: Ensemble Drift Simulations

Figure 1: Distribution of simulated travel times for passive tracer particles that reach box 14 after having been released within one of the four different colour coded source regions in the left panel. Results are based on 13615 simulations initialized within the years 1958-1999.

Figure 1: Distribution of simulated travel times for passive tracer particles that reach box 14 after having been released within one of the four different colour coded source regions in the left panel. Results are based on 13615 simulations initialized within the years 1958-1999.

Coastal defences are generally designed to provide efficient protection against today’s exposure. The situation may change considerably during the coming decades as a consequence of future anthropogenic climate change which is expected to be associated with rising mean sea levels and changes in wind and storm surge climate. Data from coastDat were used by a re-insurance company to assess potential increases in coastal flood damages in Northern Europe due to future anthropogenic climate change concluding that adapting land use planning and strengthening of sea defences are the prerequisites for keeping the residual risk constant and manageable (Swiss Re Focus Report, 2009).

PELETS-2D

PELETS-2D is a toolbox written in Fortran 90 and linked to coastDat that allows for offline Lagrangian transport calculations (i.e. particle tracking) based on 2D current fields stored on at least an hourly basis. The toolbox was developed for its use in connection with model based re-analyses of past atmospheric and marine conditions. Depending on a study's objective, drift simulations may be performed either forward or backward in time. For substances drifting at the water surface an extra wind drift may be taken into account. PELETS-2D was developed for applications on unstructured triangular grids, but interfaces to a couple of hydrodynamic models on regular grids are also available (cf. Callies et al. 2011). If hydrodynamic models provide 3D current fields, PELETS may refer to either vertical mean velocities or velocities from the top layer. PELETS-2D supports dealing with large ensembles of particle cloud simulations. Simulations may be initialized, for instance, with constant time lags so as to cover different atmospheric conditions with an implicit weighting according to the frequency of their occurrence in long-term re-analyses. Alternatively, simulations may be scheduled to meet special requirements of biological studies (e.g. the selection of specific seasons), for instance. PELETS-2D provides a couple of general tools for the evaluation of such large ensembles, including the generation of composites and principal component analysis.

Marine and offshore energy

Data from coastDat have been used extensively for example for designing, planning and installation of offshore wind farms. Return periods of extreme wind speed, surge and wave heights are used by a variety of users involved the design and construction of offshore wind parks. Moreover, planning of installation and maintenance requires the estimation of probabilities of weather windows; that is, for example the probability of an extended period with wave heights below a given threshold to enable installation and/or maintenance. Data from coastDat were frequently used in such cases as observational data are too often too short to derive reliable statistics.

Figure 1: Offshore wind farms (red ballons) planned with coastDat data in the North Sea and Baltic Sea; e.g. in the exclusive economic zone (EEZ) of Germany (light blue)

Offshore wind farms need to be connected with the land based grid. Such connections can be established at a few points only with limited capacity. Data from coastDat were used by Wiese (2008) to simulate the impacts on the national grid for a scenario in which all planned offshore wind farms in the German exclusive economic zone in the North Sea are fully operational.

Figure 2: Annual supply of energy from offshore wind parks (blue from top to bottom) and a four block coal burning power plant (black and grey) into the electricity network with a planned capacity of 7,000 MW at Brunsbüttel. When blue lines intersect with the yellow area the land-based network is fully exhausted; the conventional power plant is idle and part of the offshore wind energy supplied is lost. When blue lines intersect with the black and grey blocks the net is feed with energy from offshore wind and partly from the conventional power plant operating at reduced load. Only when there is no intersection and last block is marked in orange there is idle capacity of the electricity network (after Wiese 2008).

Figure 2: Annual supply of energy from offshore wind parks (blue from top to bottom) and a four block coal burning power plant (black and grey) into the electricity network with a planned capacity of 7,000 MW at Brunsbüttel. When blue lines intersect with the yellow area the land-based network is fully exhausted; the conventional power plant is idle and part of the offshore wind energy supplied is lost. When blue lines intersect with the black and grey blocks the net is feed with energy from offshore wind and partly from the conventional power plant operating at reduced load. Only when there is no intersection and last block is marked in orange there is idle capacity of the electricity network (after Wiese 2008).

Figure 2 shows an example of this analysis in which the efficiency of newly planned coal-burning power plant at a point connecting offshore wind farms with the land based grid with a planned capacity of 7,000MW was estimated. As the German renewable energy law requires that available wind energy is introduced into the grid, the four 800 MW coal-burning power plants are expected to have noticeably less base-load hours as planned and the frequent run-ups and run-downs of the conventional power plant are expected to decrease the efficiency of the energy production.

Figure 3: Average 50-year (1958-2007) theoretical wave energy flux (kWm-1) for the south-eastern North Sea.

Figure 3: Average 50-year (1958-2007) theoretical wave energy flux (kWm-1) for the south-eastern North Sea.

Data from coastDat were also used to estimate marine energy potential (such as those from waves and currents) along the German North and Baltic Sea coasts (Marx 2010). As an example the long-term 1958-2007 average of the theoretical wave energy flux (depending on significant wave height, wave period and water depth) based on coastDat is shown in Figure 3. Generally, wave energy fluxes are largest further away from the coasts at larger depths and wave heights. Seasonal variability is substantial. Highest wave energy fluxes occur during winter while smallest fluxes are found during summer. From a global perspective, wave energy potential along the German coast line is limited.

Shipping & Design

Depending on their area of navigation vessels are subjected to different environmental conditions such as different wave heights, periods or currents. Considering these environmental conditions, their variability and change over the expected life time of the vessel represents an economic advantage and can improve design and operation of vessels. Based on coastDat an innovative concept was developed at the Flensburger Schiffbaugesellschaft (FSG) and tested for the North Sea. Data from coastDat have been incorporated into the operational design system at the FSG and are now used on a routine basis (Figure 1). A more detailed description can be found in Weisse et al. (2009).

Figure 1: Data from coastDat have been used by the Flensburger Schiffbau Gesellschaft to optimize RoRo ferry operating in the North Sea. Data have been used for instance during the design process of the ferry Jasmine. The photo shows the vessel at the shipyard shortly before launch.

Figure 1: Data from coastDat have been used by the Flensburger Schiffbau Gesellschaft to optimize RoRo ferry operating in the North Sea. Data have been used for instance during the design process of the ferry Jasmine. The photo shows the vessel at the shipyard shortly before launch.

Design and planning of marine structures require long environmental data sets that are often unavailable. Data from coastDat were used complementary in many design and planning studies such as for example, planning of the Fehmarn Belt crossing, planning and design of coastal protection structures at the Dune island of Helgoland, or for planning dredging activities.

In the coming decades coastal climate is expected to change as a consequence of rising anthropogenic greenhouse gas concentrations. In particular, rising mean sea levels and potential changes in wind, wave and storm surge climate may require adaptation to keep risks constant and manageable. Such adaptations may comprise land-use changes or engineering measures. Data from coastDat have been used in evaluating adaptation strategies showing in particular, that generic strategies such as to retreat or to accommodate are not generally useful but that individual case studies are urgently needed (Weisse et al. 2011) and that innovative multipurpose strategies are needed, in particular in heavily anthropgenically influenced areas under multiple pressures (Sothmann 2011)