The coastdat data set and its potential for coastal and offshore applications

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1 The coastdat data set and its potential for coastal and offshore applications Elke M. I. Meyer 1, Ralf Weisse 1, Janina Sothmann 1, Heinz Günther 1, Ulrich Callies 1, Hans von Storch 1, Frauke Feser 1, Katja Woth 1,2 and Iris Grabemann 1 Abstract CoastDat data-sets offer information about the past and possible future state for the North Sea and Baltic Sea. These data can be used for calculations of risk and application in coastal regions. The statistics of extreme wind, waves and storm surges are of central interest. Long and homogeneous data sets from numerical models are the backbones of coastdat. The methodology of these data-sets will be discussed and some examples for applications will be introduced, e.g. theoretical potential energy of wind and waves, respectively. Keywords: coastal application and risk, sea state, numerical modelling 1 Introduction Future coastal and offshore applications require appropriate planning and design. For most of them, statistics of extreme wind, waves and storm surges are of central importance. To obtain such statistics long and homogeneous time-series are needed. CoastDat is a data base of model results from regional numerical models for coastal regions. Numerical models can simulate the atmospheric and oceanic conditions for the recent past (hindcast) or for the future (scenarios). CoastDat offers a unique combination of consistent atmospheric (e.g. wind) and oceanic (sea state) variables at a high spatial and temporal resolution, even at places for which no measurements have been made. The backbones of coastdat are regional wind, waves and storm surge hindcasts and scenarios mainly for the North Sea and the Baltic Sea. Furthermore, hindcast simulations are available for temperature, salinity, water level and currents for the North Sea for the last 60 years. The hindcasts are validated with observations such as wind (Weisse et al. 2005, Winterfeldt and Weisse, 2009), tide surge simulations (Weisse and Pluess, 2006), wave model hindcast (Weisse and Günther, 2007) and sea temperature (Meyer et al. 2011). The validations show that multi-decadal hindcasts are perfectly suited to study statistics of events, their variability and their long-term changes, but single extreme events may or may not be reproduced (Weisse and Pluess, 2006). 2 Methodology A regional atmosphere model with focus on Europe and adjacent seas is driven by the global NCEP/NCAR re-analysis (Kalnay et al. 1996) in combination with a simple data assimilation algorithm (Feser et al. 2001) in order to obtain a better representation of near-surface marine wind fields (von Storch et al. 2000). From this regional simulation, near-surface marine windfields and other parameters are stored hourly. Subsequently, the parameters are used to drive storm surge and wind wave models for the North Sea (Figure 1). In this way a high-resolution and consistent meteo-marine hindcast for the past 60 years has been generated. Here consistency refers to the fact that the output fields from the different models (wind, waves and storm surges) are in physical agreement, a fact that is frequently ignored e.g., when waves and surges from different sources are analysed jointly. 1 Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Centre for Materials and Coastal Research GmbH, Max-Planck-Straße 1, Geesthacht, Germany, elke.meyer@hzg.de 2 Max Planck Institute for Meteorology, Bundesstraße 53, Hamburg, Germany

Figure 1: Layout of the consistent metocean hindcast 1948 2007 for the southern North Sea.

2 Figure 1: Layout of the consistent metocean hindcast for the southern North Sea. From the (middle) regional atmosphere hindcast hourly wind fields were used to force a tide surge (right) and a wave model hindcast (left). The figure shows an example of consistent metocean conditions obtained from the hindcast for 1200 UTC 21 Feb Near-surface (10-m height) marine wind fields (m s-1), and corresponding wind direction obtained from the regional atmospheric reconstruction (middle). Corresponding significant wave height fields (m) and mean wave direction from the coarse and the fine grid wave model hindcast (left). Tide surge levels (m) from the corresponding tide surge hindcast (right). After Weisse and Günther (2007). 3 Applications The coastdat data are already used for a number of coastal and offshore applications for example in optimisation studies of ship design (Weisse et al. 2009), for the assessment of oil risk spreading or chronic oil pollution (Chrastansky et al. 2009, Chrastansky and Callies, 2009) and in studies of the future development of storm surge changes (Woth et al. 2006). Another example for the application of coastdat data is the planning of offshore wind farms. The assessment of wind and sea state statistics supports the investigation of wind power availability. Also, the data are used to estimate the magnitude of rare events that may impact the fatigue life of wind turbines, such as the 50-year return value for near surface wind speed or significant wave heights. For the design process probability distributions such as any combination of wind speed, wind direction, significant wave height, wave periods and wave direction are frequently requested. The statistics of time slots for installation and maintenance (weather windows) of the wind turbines are important for an economic operation. Another application is the requirement of energy capacity calculated for the onshore electricity network. Only at special locations the energy supplies in the electricity network, which has only limited feeding-in capacity. CoastDat enhances the planning of energy requirements from coalburning power plants. Wiese (2008) calculated the realisable potential of full-load hours from wind turbine parks and coal-burning power plants (Fig. 2). Figure (2) shows the feasible potential of coal-burning power plants on the left side and that of wind parks on the right side (yaxis). The yellow area marks the excess supply of energy during the year (x-axis), due to the limited capacity of the electricity network. The white spacing displays the potential of energy supply. The German renewable energy law induces, that the available wind energy supplies in the electricity net. Wiese (2008) calculated for the four 800 MW coal-burning power plants fullload hours of 4111, 4411, 5243 and 6190 per year (8760h). Thus the coal-burning power plants have noticeable less base-load hours as planned. The frequent run-up and run-down of the coal-burning power plants decrease the efficiency of energy production and therefore cause a

3 rise in costs. Investigations shall be done to find possibilities to expand the base-load energy with different methods. Figure 2: Annual supply of energy from offshore wind parks and coal burning power plants into an electricity network with a capacity of 7000MW (supply point Brunsbüttel; after Wiese, 2008). Marx (2010) investigated the theoretical wave energy based on the coastdat data-set (Fig. 3). Wave energy depends on the significant wave height, wave period, wavelength and water depth. In Figure 3 the average 50-year ( ) wave energy flux (kwm -1 ) calculated for the south-eastern North Sea (53-56 N; 3-9 E) is shown. Close to the coast only small wave energy fluxes are calculated. Offshore, the wave energy increases due to the higher significant wave height and water depth. The inter-annual variability of the wave energy flux is quite high. Highest fluxes are found for winter (December February; <4-40kWm -1 ) whereas small fluxes are found in summer (June-August; <4-8kWm -1 ). In spring (March - May) and autumn (September - December) the energy fluxes are of similar magnitude with <4-22kWm -1 and <4-28kWm -1, respectively. Compared to other locations the annual potential of energy fluxes with 6-24kWm -1 in the German Bight is very low for commercial applications. For example west of Great Britain and west of South-India theoretical energy potentials of ~ 30-90kWm -1 are expected (Graw, 1995). Marx (2010) investigated the theoretical potential of wind and wave energy depending on the potential of available wind power. Probably both can use the existing infrastructure to feed into the electricity net, but both energy extractions are subject to large temporal variability and thus are not suited for a constant energy input. The potential wave energy depending on the technical, ecological, economical and social potential and it can become much lower than the theoretical potential. The coastdat data-set is a suitable tool to calculate the feasible potential.

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

4 Figure 3: Average 50-year ( ) theoretical wave energy flux (kwm -1 ) for the southeastern North Sea. 4 Summary and Outlook The coastdat data-set is a compilation of coastal analyses and scenarios for the future from various sources. It contains no direct measurements but results from numerical models that have been driven either by observed data in order to achieve the best possible representation of observed past conditions or by climate change scenarios for the near future. Contrary to direct measurements which are often rare and incomplete, coastdat offers a unique combination of consistent atmospheric, oceanic, sea state and other parameters at high spatial and temporal detail, even for locations and variables for which no measurements have been made so far. Currently, the coastdat data base will be enhanced with results from ensemble calculations of regional models. These results will allow an evaluation of the statistics and risks of future wind and wave characteristics in coastal regions. CoastDat can be applied amongst others as basis for decision making. 5 Acknowledgement The authors thank Frauke Wiese for assignment of Figure 2 and Beate Gardeike for preparation of the figures. 6 References Chrastansky, A.; Callies, U.; Fleet,D.M. (2009): Estimation of the impact of prevailing weather conditions on the occurrence of oil-contaminated dead birds on the German North Sea coast. In: Environmental Pollution 157, pp Chrastansky, A.; Callies, U. (2009): Model-based long-term reconstruction of weather-driven variations in chronic oil pollution along the German North sea coast. In: Marine Pollution Bulletin 58, pp Feser, F.; Weisse, R.; von Storch H. (2001): Multi-decadal atmospheric modelling for Europe yields multi-purpose data. In: Eos Transactions, 82, pp. 305, 310.

5 Graw, K.-U. (1995): Wellenenergie eine hydromechanische Analyse. In: Bericht Nr. 8 des Lehr- und Forschungsgebietes Wasserbau und Wasserwirtschaft, Bergische Universität - GH Wuppertal Kalnay et al. (1996): The NCEP/NCAR 40-year reanalysis project, In: Bulletin of the American Meteorological Society, 77, pp , Marx, J. (2010): Langzeitige Variabilität der Wellenenergiepotenziale in der Nordsee, Thesis, University of Basel Master- Meyer, E. M. I.; Pohlmann, T.; Weisse, R. (2011): Thermodynamic variability and change in the North Sea ( ) derived from a multidecadal hindcast. In: Journal of Marine Systems. Vol. 86, pp von Storch, H.; Langenberg, H,; Feser, F. (2000): A spectral nudging technique for dynamical downscaling purposes. Monthly Weather Review, 128, pp Weisse, R.; von Storch, H.; Feser, F. (2005): Northeast Atlantic and North Sea storminess as simulated by a regional climate model and comparison with observations. Journal of Climate, 18, pp Weisse, R.; Pluess, A. (2006): Storm-related sea level variations along the North Sea coast as simulated by a high-resolution model In: Ocean Dynamics. TIERGARTENSTRASSE 17, D HEIDELBERG, GERMANY, MAY, Vol. 56({1}), pp SPRINGER HEIDELBERG. Weisse, R.; Günther, H. (2007): Wave climate and long-term changes for the Southern North Sea obtained from a high-resolution hindcast In: Ocean Dynamics. TIERGARTENSTRASSE 17, D HEIDELBERG, GERMANY, JUN, Vol. 57(3), pp SPRINGER HEIDELBERG. Weisse, R.; von Storch, H.; Callies, U.; Chrastansky, A.; Feser, F.; Grabemann, I.; Günther, H.; Pluess, A.; Stoye, T.; Tellkamp, J.; Winterfeldt, J.; Woth, K. (2009): Regional Meteorological-Marine Reanalyses and Climate Change Projections: Results for Northern Europe and Potential for Coastal and Offshore Applications. In: Bulletin of the American Meteorological Society. 45 BEACON ST, BOSTON, MA USA, JUN, Vol. 90(6), pp AMER METEOROLOGICAL SOC. Wiese, Frauke (2008): Auswirkungen der Offshore-Windenergie auf den Betrieb von Kohlekraftwerken in Brunsbüttel, Diploma-Thesis, Universität und Fachhochschule Flensburg, Energie- und Umweltmanagement Winterfeldt, J.; Weisse, R. (2009): Assessment of Value Added for Surface Marine Wind Speed Obtained from Two Regional Climate Models. In: Monthly Weather Review. 45 BEACON ST, BOSTON, MA USA, SEP 29, Vol. 137(9), pp AMER METEOROLOGICAL SOC. Woth, K.; Weisse, R.; von Storch, H. (2006): Climate change and North Sea storm surge extremes: an ensemble study of storm surge extremes expected in a changed climate projected by four different regional climate models. In: Ocean Dynamics. TIERGARTENSTRASSE 17, D HEIDELBERG, GERMANY, MAY, Vol. 56(1), pp SPRINGER HEIDELBERG.

Climate change and its impact on regional seas: What do we know? Marine weather and its impact

Climate change and its impact on regional seas: What do we know? Marine weather and its impact Hans von Storch Institute for Coastal Research GKSS Research Center, Germany Berlin, PlanCoast Meeting, 21.