Applications have been developed by the Institute of Coastal Research at HZG or developed in cooperation with early adopter of coastDat data.
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). Graph: HZG/ Hans-Jörg Isemer
In coastDat techniques developed in atmospheric sciences are 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). Graph: HZG/ Oliver Krüger
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.
Figure 3: Near-surface marine wind speed at platform K13 in the southern North Sea derived from observations and from the coastDat database. Graph: HZG/Ralf Weisse
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).
Moreover, Figure 4 displays results on extreme value statistics showing that the observed values are usually reproduced within error bounds. 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). Graph: HZG/ Ralf Weissetop
Long-term changes in marine ecosystems of the North Sea and Baltic Sea
Map: HZG/ Ute Daewel
Long-term variations and major changes in ecosystem dynamics occur throughout all trophic levels and have earlier been reported on in a number of studies for both the North Sea and Baltic Sea system. A majority of those studies have been thereby focussing on potential regime shifts (”Changes in marine system function that are relatively abrupt, persistent, occurring at a large spatial scale, observed at different trophic levels and related to climate forcing.“ deYoung et al., 2004). These changes typically coincides with changes in ecosystem biomass and species composition and are thus of economical relevance for the fisheries sector.
By using the three dimensional coupled physical-biogeochemical model ECOSMO II (Daewel and Schrum, 2013) for the North Sea and Baltic Sea (data available through coastDat) we can identify long-term changes in the physical environment and relate those changes to processes that are relevant for long-term ecosystem variability in the area (Daewel and Schrum, 2017). The analysis of a 61 years (1948-2008) long hind cast reveals a quasi-decadal variation on salinity, temperature and current fields in the North Sea in addition to singular events of major changes during restricted time frames. These changes in hydrodynamic variables where found to be associated to changes in ecosystem productivity that are temporally aligned with the timing of reported “regime shifts” in the areas. Especially in the North Sea a correlation analysis between atmospheric forcing and primary production reveals significant correlations for the North Atlantic Oscillation and wind forcing for the central part of the region (see figure), while the Atlantic Multidecadal Oscillation and air temperature are correlated to long-term changes in the southern North Sea frontal areas. Understanding those processes is a prerequisite for reliable projections of ecosystem dynamics under climate change conditions.
Long-term changes in Net Primary Production
Graph: HZG/ Ute Daewel
Graph: HZG/ Ute Daewel
Dominant mode of long term changes in simulated net primary production of the North Sea and Baltic Sea from ECOSMO II. The dominant modes were estimated using an Empirical Orthogonal Function analysis (first principal component).deYoung et al., 2004 Daewel and Schrum, 2013 Daewel and Schrum, 2017 top
Historical atmospheric reconstruction
Map: HZG/ Frederik Schenk
The historical dataset of High Resolution Atmospheric Forcing Fields (HiResAFF) allows long-term analysis and/or forcing models on longer timescales covering the period 1850–2009. The dataset consists of key meteorological variables Variables such as sea-surface pressure, u- & v-wind, relative humidity, total cloud cover, 2m-temperature and precipitation on daily scale which are typically used to drive ocean or ecosystem models. The fields are reconstructed through non-linear statistical upscaling using the Analog-Method (Schenk and Zorita, 2012). The method resamples atmospheric fields from a regional climate model (RCAO/RCA3) in time based on the closest pattern similarity in the predictor space of homogenous historical station data since 1850. The resampling of atmospheric fields ensures that the properties of different meteorological variables are kept consistent (e.g. probability distributions, variance) while the historical station data is used maintain the temporal correlation of the fields with observations back to 1850. The analog-method and reconstruction skill of HiResAFF is described in Schenk and Zorita (2012) and the extended dataset for 1850-2009 in Schenk (2015). The dataset can be downloaded from the World Data Centre for Climate (WDCC) (see Schenk 2017).
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP/2007-2013) under grant agreement no. 217246 made with the joint Baltic Sea research and development programme BONUS, and the German Federal Ministry of Education and Research (03F0492A).
North Sea storm surge and wave conditions under AR4 climate change scenarios
Figure 5: Spatial distribution of the number of projections for which the climate change signals of the 30-year mean of the annual 99th percentile storm surge (left) and
significant wave heights (right) for 2071-2100 relative to 1961-1990 have a positive sign. These spatial distributions are based on four water level and ten wave projections. Map: HZG/ Nikolaus Groll
Long-term changes of storms, storm surges and sea state and a rise of mean sea level in the North Sea as they may occur with anthropogenic climate change can endanger the safety of the low lying coastal areas and can affect the off- and onshore activities.
To assess possible impact of changed atmospheric conditions on water level and sea state a variety of future climate projections has been analyzed with respect to corresponding reference simulations. The atmospheric forcing for these four storm surge and ten sea state projections and respective reference simulations originates from regionalized future global climate projections in which different models, initial states and development scenarios (included in the Forth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC)) were incorporated.
Towards the end of the 21st century the pattern and the magnitude of the meteorologically (wind and sea surface pressure) induced changes in storm surge and wave heights vary between the four surge as well as between the ten wave projections (comparing 2071-2100 to 1961-1990). The surge projections indicate an increase of severe surge heights in the southeastern North Sea and the German Bight locally exceeding 10% whereas there are only comparably small changes in the other parts of the North Sea (Figure 5). Nine to ten wave projections show an increase of severe wave heights (99th percentile) in the southeastern and eastern North Sea and more than half of the projections show a decrease in the western and northwestern North Sea. The changes range between about ±15%.
Within the 21st century variations on multi-decadal time scales can be seen in time series of surge and wave heights for chosen areas. These multi-decadal variations are in the same order of magnitude as the changes towards the end of the century. Furthermore, frequency and intensity of extreme events show also strong multi-decadal variability and the highest events must not occur at the end of the century (Figure 6). This emphasizes the importance of the internal climate variability which is superimposed on anthropogenic climate changes
Figure 6: Thirty-year running means of the annual 99th percentile significant wave height exemplarily for a location seaward of the island of Sylt (German Bight) relative to the corresponding mean for 1961-1990 for four wave projections. Graph: HZG/ Nikolaus GrollGaslikova et al., 2013 Groll et al., 2014 Grabemann et al., 2015 top
Risk assessment (Oil)
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 7 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 7: 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. Graph: HZG/ Ulrich Callies
Lagrangian transport calculations: 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. PELETS-2Dtop
Risk assessment (Design)
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.
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)
Sothmann, J.; Schuster, D.; Kappenberg, J.; Ohle, N. 2011: Efficiency of artificial sandbanks in the mouth of the Elbe Estuary for damping the incoming tidal energy.5th International Short Conference on Applied Coastal Research, 2011, Aachen, Germany, 255-263.
Furthermore 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
Figure 8: 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 this vessel at the shipyard shortly before launch. Photo: Flensburger Schiffbau Gesellschaft
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. A more detailed description can be found in Weisse et al. (2009).
Weisse, R.; von Storch, H.; Callies, U.; Chrastansky, A.; Feser, F.; Grabemann, I. et al. 2009: Regional meteorological–marine reanalyses and climate change projections. Bull. Amer. Meteor. Soc., 90, 849–860.
Marine and offshore energy
Figure 9: Offshore wind farms (red dots) planned with coastDat data in the North Sea and Baltic Sea; e.g. in the exclusive economic zone (EEZ) of Germany (blue). Map: HZG/ Elke Meyer
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 10: Average 50-year (1958-2007) theoretical wave energy flux (kWm-1) for the south-eastern North Sea. Map: HZG/ Janina Marx
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 10. 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.
Figure 11: 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). Graph: Frauke Wiese
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 11 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.
Climatology of North Sea wind energy
Figure 12: Long-term average wind speeds (m/s) at a height of 100 m for the period of 1958–2012. The locations of the planned wind parks are labelled using the abbreviations listed in (Geyer et al., 2015; Table 2).
Map: HZG/ Beate Geyer
Model-based wind speed data derived from the coastDat-2 atmospheric data set for the North Sea were used to assess wind power potential considering both spatial and temporal variability (Geyer et al., 2015). Following conclusions were drawn by the study: a) Inter-annual to decadal variability plays an important role in wind energy; wind power estimates based on short observational time series, particularly from the late 1990s, may exhibit high biases. b) Up-scaling from wind speeds at a height of 10 m using conventional power laws may result in similar biases. c) On inter-annual to decadal time scales, synergies are not expected from the different arrays in the North Sea, i.e., a decrease in the power output of an array may not be balanced by another. Instead, the joint production by all arrays is characterized by higher volatilities compared with that from a single array. Geyer et al., 2015
Figure 13: Time series of annual power output of eight major arrays planned in the North Sea using hourly wind speeds from our coastDat-2-data, the synthetic power-velocity curve and the planned capacities given in Geyer et al., 2015; Fig. 4 and Table 2, respectively. Wake and line losses and losses due to operational unavailability are not taken into account. Graph: HZG/ Beate GeyerGeyer et al., 2015 top
Outreach of coastDat data from the North German Climate Office
Photo: HZG/ Insa Meinke
Photo: HZG/ Elke Meyer
This application invites you to experience the complexity of the North Sea hydrodynamic regime. You may release a virtual larva, an oil particle, a message in a bottle or a rubber duck at any location in the German Bight and watch its movements during a period of 20 days. Depending on both the location and the release time you choose (any hour within the years 1958-2016) the objects will behave very differently. Possibly you may prefer to release a rubber duck instead of a bottle. The duck sitting on the water will be much more affected by prevailing wind conditions. As simulations are based on realistic environmental conditions, it is possible to refer to specific events in the past like major storm surges, for instance.
Drift simulations (most suitable for a screen resolution of 1920x1080)
The research behind the game
The shifting wind, current and sea state behaviour in the North and Baltic Seas can be reconstructed for the past several decades with the help of numerical model data sets from the Institute of Coastal Research at the Helmholtz-Zentrum Geesthacht. This game is based on computer programs and data sets, which are used by scientists, government authorities and the commercial sector in real-world applications. Two different issues can essentially be investigated using these model calculations: Where do things come from? Where do things drift? Both questions play an important role in science.
Interpretation of measurement and monitoring programs (where do things come from?).
Without knowing the effects of currents, the scientists cannot decipher the interaction of different processes in the North Sea.
1. Example: Higher algae concentration was measured today than yesterday. Did the algae reproduce, or have currents replaced the clear water from yesterday with turbid water?
2. Example: Larvae of particular fish species observed in one region numbered fewer than usual. Is there a disturbance in reproduction for this species (overfishing?) or have the changes in currents due to weather conditions simply driven the larvae to other regions?
3. Example: Fewer birds have been found covered in oil on the beach than the previous year: Have, in fact, fewer birds been covered in oil or have the currents carried the dead birds out to sea so that they haven’t been observed?
In all three examples, different interpretations of observations lead to entirely different conclusions and options for taking action.
Forecasts (where do things drift?)
What forecasts can scientists make? Currents are driven by a) the astronomical constellation (ebb and flood) and b) by the effects of changing weather conditions (e.g., storm floods). While the first component is virtually always known ahead of time, reliable weather forecasts are limited to a few days.
1. Example: In an oil spill, the oil drift is predicted for the next three days. These forecasts are important for optimizing countermeasures.
2. Example: Even if forecasts are incapable of covering longer periods, probable drift routes can always be analysed by looking at the past. What often happened in past years? And what has never happened? This helps biologists reconstruct species’ typical migration routes. What would have happened if an oil spill had occurred at particular times? Such calculations form the basis of risk assessments.
3. Example: Where will the message in a bottle wind up? Unfortunately, this question can only be answered in hindsight, after the weather and the related currents are known (the basis for this game!)