Abstract
Current research on long-term predictions of meteorological and oceanic elements is abundant, but research on long-term prediction of wave energy is scarce, which is urgently needed in the long-term planning of resource development. The long-term projection of wave energy can be usually achieved by the following three methods: (1) Analyzing the relationship among wave energy, MJO (Madden-Julian Ocillation), nino3 and other important factors, combined with the long-term trend characteristics of wave energy, to achieve medium- and long-term prediction of wave energy; (2) with wave model driven by CMIP (Coupled Model Intercomparison Project) data, to project the future wave energy; (3) using methods of Hilbert Huang, artificial neural network (ANN), etc., combined with long-term sequence wave energy data to make long-term projection of wave energy. This chapter proposed a long-term projection scheme of wave energy, with the CMIP5 data to drive the current international advanced numerical wave model WW3, to simulate the global wave field from 2020 to 2059, and then project and analyze the wave energy of the Maritime Silk Road for the future 40 years. The projection parameters include the WPD, available rate, energy richness, stability, and monthly variation, to provide scientific evidence for long-term planning of resource development.
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References
Bao XH, Zhang FQ (2013) Evaluation of NCEP-CFSR, NCEP-NCAR, ERA-Interim, and ERA-40 reanalysis datasets against independent sounding observations over the Tibetan Plateau. J Clim 26:206–214
Chu PC, Qi YQ, Chen YC et al (2004) South China Sea wind-wave characteristics. Part I: Validation of Wavewatch-III using TOPEX/Poseidon data. J Atmos Oceanic Technol 21:1718–1733
Collins M, Knutti MR, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term climate change: Projections, commitments and irreversibility. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge and New York
Cornett AM (2008) A global wave energy resource assessment. In: Proceedings of the eighteenth international offshore and polar engineering conference, Canada, 2008
Dee DP, Uppala SM, Simmons AJ, et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597
Kirtman B, Power SB, Adedoyin JA, Boer GJ, Bojariu R, Camilloni I, Doblas-Reyes FJ, Fiore AM, Kimoto M, Meehl GA, Prather M, Sarr A, Schär C, Sutton R, van Oldenborgh GJ, Vecchi G, Wang HJ (2013) Near-term climate change: projections and predictability. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge and New York
Mirzaei A, Tangang F, Juneng L, Mustapha MA, Husain ML, Akhir MF (2013) Wave climate simulation for southern region of the South China Sea. Ocean Dyn 63(8):961–977
Mirzaei A, Tangang F, Juneng L (2014) Wave energy potential along the east coast of Peninsular Malaysia. Energy 68:722–734
Mori N, Yasuda T, Mase H, Oku Y (2010) Projection of extreme wave climate change under global warming. Hydrol Res Lett 4:15–19
Ren JL, Luo YY, Zhong YJ (2008) The implementation for the analysis system of ocean wave resources and the application of wave energy power generation. J Zhejiang Univ Technol 36(2):186–191
Ren JL, Luo YY, Chen JJ (2009) Research on wave power application by the information system for ocean wave resources evaluation. Renew Energy 27(3):93–97
Song LN, Liu ZL, Wang F (2015) Comparison of wind data from ERA-Interim and buoys in the Yellow and East China Seas. Chin J Oceanol Limnol 33(1):282–288
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. B Am Meteorol Soc 93:485–498
Wang XL, Feng Y, Swail VR (2014) Changes in global ocean wave heights as projected using multimodel CMIP5 simulations. Geophys Res Lett 41:1026–1034. https://doi.org/10.1002/2013GL058650
Zheng CW (2018) 21st century maritime silk road: wave energy evaluation and decision and proposal of the Sri Lankan waters. J Harbin Engg Univ 39(4):614–621
Zheng CW, Li CY (2017) Propagation characteristic and intraseasonal oscillation of the swell energy of the Indian Ocean. Appl Energy 197:342–353
Zheng CW, Shao LT, Shi WL et al (2014) An assessment of global ocean wave energy resources over the last 45 a. Acta Oceanol Sin 33(1):92–101
Zheng CW, Wang Q, Li CY (2017) An overview of medium- to long-term predictions of global wave energy resources. Renew Sustain Energy Rev 79:1492–1502
Zheng CW, Wu GX, Chen X, Wang Q, Gao ZS, Chen YG, Luo X (2019a) CMIP5-based wave energy projection: case studies of the South China Sea and the East China Sea. IEEE Access 7(1):82753–82763
Zheng CW, Pei SQ, Li W (2019b) Projection of wave energy resource for the future 40 years in the 21st century Maritime Silk Road. J Harbin Eng Univ, in press
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Zheng, C., Xu, J., Zhan, C., Wang, Q. (2020). Long-Term Projection of Wave Energy in the Maritime Silk Road. In: 21st Century Maritime Silk Road: Wave Energy Resource Evaluation. Springer Oceanography. Springer, Singapore. https://doi.org/10.1007/978-981-15-0917-9_6
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DOI: https://doi.org/10.1007/978-981-15-0917-9_6
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