Elsevier

Renewable Energy

Volume 169, May 2021, Pages 512-526
Renewable Energy

Analysis of potential changes in the Black Sea wave power for the 21st century

https://doi.org/10.1016/j.renene.2021.01.042Get rights and content

Highlights

  • Wave power estimations for the 21st century under different climate scenarios in the Black Sea.

  • Downscaling of wind fields from two different climate models by using a novel two-layer RBF.

  • Spectral wave model simulations are conducted between 1979 and 2100.

  • Wave power potential for the most of the basin is expected to decrease by the end of the century.

Abstract

This study aims to project wave power variations in the Black Sea throughout the 21st century. A spectral wave modeling study is done using the open-source software SWAN. Wind fields were downscaled with a two-layered system where the first layer is a radial basis function, and the second layer is a generalized linear model. Two representative pathways and two different global circulations models were considered. Mean wave power in the basin fluctuates around 4 kW/m and associated maximum wave powers in the basin could even reach 20 kW/m, mostly accounted for the winter season and the Western part of the basin. Although both increasing and decreasing wave power areas exist in the basin for different models and representative concentration pathways scenarios, future projections showed no distinguishable change in the spatial distribution of the wave power. At the end of the century, basin-averaged differences for the historical period are ranging between +0.14 kW/m and −0.32 kW/m. Seasonal variability is shown to be high. Spring has the biggest change in the basin-averaged wave power with a decrease of up to 20% considering all models and scenarios. Inter-annual variability is greater for the Climate Model Version 3 model.

Introduction

Renewable energy sources will be the key player towards reducing greenhouse gas emissions and provide a sustainable future [1]. The major sectors in the renewable energy are wind energy, wave energy, tidal energy, geothermal energy, solar energy, and biomass. Offshore renewables, which includes both wind and waves, have a great potential and ocean energy is reaching to maturity which makes them attractive for future applications [2]. Among them, wind energy is well established in means of both commercial applications and scientific background whereas little importance is given to wave energy [57]. In 2010, the ocean energy (wave and tidal) capacity in Europe was 3.8 MW, but European Commission’s offshore renewable energy strategy ambitiously targets to reach 40 GW by 2050 [3].

Global coastal wave energy potential is estimated to be over 3.37 TW [4]. Harnessing this energy has great advantages together with engineering challenges. Wave energy is advantageous due to presenting higher energy density allowing more compact structures, providing higher potential in winter when the energy demand is the highest, being more predictable and having relatively high utilization factor which allows efficient management, and being closer to the market as the largest high-energy demanding cities are near coasts [5,6]. Disadvantages can be summarized as, the aggressive sea environment, and the difficulties in accessing remote offshore high energy areas, random waves cause the structures to operate at non steady operation conditions [5].

Wave energy converters benefit from the surrounding wave condition and the estimation of both mean and extreme waves is critical on their design [7]. Therefore, it is important to have the best possible estimate of the present and future wave conditions to harvest the highest energy from these devices.

Our understanding of global climate change improved significantly after the recent growing research interest in the subject in the scientific community. Observed climate change-related trends on the atmospheric parameters are expected to continue until the end of the 21st century. The change in the temperature will lead to a change in the wind speed and therefore in the wave climate [8]. Changing wave climate will surely affect the coastal processes, shoreline shape, and orientation. In this regard, it is a necessity to investigate the future wave climate to be able to develop coastal zone management policies, design coastal and offshore structures, and renewable energy devices. Better designed devices will not just provide energy to the community, but also help to reduce greenhouse gas emissions and mitigate the effects of climate change.

General Circulation Models (GCMs) are primary tools to understand both the historical and future conditions of climate variables [9]. In 2008, the Intergovernmental Panel on Climate Change initiated a framework involving modeling groups around the world to promote a new set of climate model experiments [10]. The framework is called Coupled Model Intercomparison Project Phase 5 (CMIP5) and it aims to evaluate how realistic the models are in simulating the recent past, provide projections of future climate change and understand some of the factors responsible for differences in model projections [11]. The CMIP5 includes climate scenarios from approximately 60 different GCMs by 28 different modeling groups [12]. Models are available for greenhouse gas emission scenarios of Representative Concentration Pathway (RCP) 2.6, 4.5, 6.0, and 8.5 which defines the severity of the emission. For example, RCP 4.5 W/m2 peaks around 2040, but RCP 8.5 W/m2 continues to increase until the end of the 21st century [13].

CMIP5 GCMs include a variety of atmospheric variables, but the coarse resolution is not suitable for regional studies. Recently, regional climate models are developed to overcome this issue [14]. For the regional models, deviations from global models have been reported by many researchers [15]. Therefore, they should be conducted to discuss the patterns on the local scale. The main obstacle behind the regional studies is the necessity of downscaling of the wind data, to improve the spatial resolution of the models. Studies that use dynamical GCM models are limited, and the uncertainty relating to the dynamical downscaling process is not well studied. On the other hand, statistical downscaling methods are more commonly used, and computational cost is lower [12]. It is shown that statistical methods are suited for the wave climate models as well as the dynamical approaches, despite some deficiencies observed in the summer season and the upper tail of the significant wave heights [16].

The future wave climate projections are discussed using either absolute or percentage change to a reference historical dataset, and according to Mohan and Bhaskaran [17] choice of a reference dataset is still an open challenge to the scientific community. Many researchers use a historical run of their wave model as a reference dataset. In means of future period, wave climate studies primarily focus on the end of the 21st century to picture the possible effects of climate change. However, projecting the wave parameters to the mid-period of the 21st century is also a necessity to fully understand the potential changes in the wave climate and allow better mid-term planning [8].

Several climate change projections along the European and global seas have been presented by different researchers. Bricheno and Wolf [18] simulated wave climate along the European seas using downscaled EC-Earth GCM. The model is validated using ERA-Interim reanalysis wind data. In general, they suggested a decrease in mean wave heights and an increase in maximum wave heights for the analysis period of 2006–2100. The changes in mean wave conditions are homogeneous with a decrease of around 0.2 m in significant wave height along the northeast Atlantic. The same pattern on the mean wave heights is also reported by Perez et al. [19] and Hemer et al. [20]. Perez et al. [19] found a decrease in the mean wave heights and periods in the North Atlantic and Mediterranean seas for the period of 2070–2099. The magnitude of the decrease seems to increase for higher concentration RCP scenarios. For the RCP 8.5 scenario, the decrease in mean energy flux reaches 0.1 kW/m in the Mediterranean and 0.2 kW/m in the Atlantic. Hemer et al. [20] find an agreed decrease in annual mean significant wave height over 26% of the global ocean in the context of a coordinated ocean-wave climate projection cooperation. The biggest decrease observed in the January–March period of the year. An increase is also observed for 7% of the global ocean. Sierra et al. [21] studied the coasts of Menorca Island located in the northwestern Mediterranean Sea. Six different regional climate models are used based on GCMs to evaluate the wave climate at nine points located offshore waters of the island. They showed a slight decrease in the annual and seasonal wave power, except in summer for the future period of 2071–2100. However, a large variability is observed between the models, for instance, energy change in the same point ranges between +5% and −10% considering different models. Also, inter-annual variability was found lower for winter and higher for autumn. Unlike the wave-spectrum based numerical approaches to predict the future wave climate, Casas-Prat et al. [22] adopted a statistical approach. Multiple linear regression is used to predict significant wave heights in the sea area around the Catalan coast. It is indicated that the method can be applied to different areas to project future changes in the wave climate.

Although wave climate projections extending towards 2100 are available world-wide (e.g. Ref. [23], and European seas, a limited number of studies have been conducted on a regional scale. Wave condition in the Black Sea for the past is well known and well-studied (e.g., Refs. [[24], [25], [26], [27], [28], [29], [30]]. The future condition is studied by Rusu [31] and Rusu [32] who evaluated the near and far-future wave energy potential using the SWAN wave model forced by downscaled the Swedish Meteorological and Hydrological Institute Regional Climate Model RCA4. The researcher found a maximum wave energy of 3.8 kW/m for the years of 2021–2050 [31]. For the far-future period of 2071–2100, the maximum values decreased to 3.5 kW/m for both scenarios [32].

In this study, a regional wave power projection of the Black Sea is developed for all near-future (2020–2040), medium-future (2040–2060; 2060–2080), and far-future (2080–2100) projection terms. Projected wind fields of RCP 4.5 and RCP 8.5 scenarios form the GCM CMIP5 models Climate Model Version 3 (CM3) and Earth System Model (ESM2M) are considered to force the spectral wave model. Wind fields are downscaled by a novel two-layer the Radial Basis Function (RBF) method. The study aims to present a comprehensive analysis of wave power in the basin for 120-year period, starting from 1979 through the end of the century, by investigating the inter- and intra-annual variability, seasonal variability and the variability between the GCMs used. Possible locations for the wave energy harnessing sites are selected to provide detailed temporal variability of the wave power.

Section snippets

Study area

The Black Sea is an inland sea located between Europe and Asia. It lies between the 27°–42° Eastern longitudes and 40°–48° Northern latitudes, with approximately 440,000 km2 surface area and 2200 m of maximum depth. It is connected to the Sea of Azov in Northeast and the Marmara Sea in Southwest (Fig. 1).

Riparian countries in the Black Sea are Turkey, Georgia, Russia, Ukraine, Romania, and Bulgaria. Diversified coastal geomorphology can be seen along the coastline. The Eastern part of the basin

Validation

Wave models created for both CM3 and ESM2M were validated against historic measured data collected at buoys at Sinop, Hopa, and Gelendzhik near points 3,5,6 in Fig. 1, respectively. Observed data consists of 2029 waves measured between November 04, 1994 and June 14, 1996 at Sinop, 12,235 waves measured between July 9, 1996 and December 6, 2003 at Gelendzhik, and 11,410 waves measured between December 27, 1994 and April 26, 1999 at Hopa [53]. Comparisons were done for the same time frames of

Conclusions

The current study aims to project the wave power climate in the Black Sea basin for the 21st century. To this aim, spectral wave simulations carried out using downscaled CM3 and ESM2M GCM wind speed data on a 0.125° resolution numerical grid. Results are discussed through investigating the mean wave power values, by considering four different future time frames of 2020–2040, 2040–2060, 2060–2080, 2080–2100. All presented data in Figs. 4–12 are available in supplementary material in NetCDF

CRediT authorship contribution statement

Burak Aydoğan: Conceptualization, Methodology, Spectral Wave Modeling, Writing - review & editingWriting – review & editing. Tahsin Görmüş: Formal analysis, Data Analysis, Visualization, Writing - original draftWriting – original draft. Berna Ayat: Supervision, Conceptualization, Methodology, Writing - review & editingWriting – review & editing. Tunay Çarpar: Downscaling of the wind data.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Authors thank to the Geophysical Fluid Dynamics Laboratory for providing the Coupled Model Intercomparison Project Phase 5 wind data, to the European Centre for Medium-Range Weather Forecasts for providing Era Interim wind data, to the General Bathymetric Chart of the Oceans for providing the bathymetric data, and to Prof. Dr. Erdal Özhan who was the Director of the NATO TU-WAVES for providing the buoy data at Gelendzhik, Hopa, and Sinop. Tahsin Görmüş is supported by both The Scientific and

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