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      Climatology of Wind-Seas and Swells in the China Seas from Wave Hindcast

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          Abstract

          The wind-sea and swell climates in the China Seas are investigated by using the 27-yr Integrated Ocean Waves for Geophysical and other Applications (IOWAGA) hindcast data. A comparison is made between the significant wave height from the IOWAGA hindcasts and that from a jointly calibrated altimetry dataset, showing the good performance of the IOWAGA hindcasts in the China Seas. A simple but practical method of diagnosing whether the sea state is wind-sea-dominant or swell-dominant is proposed based on spectral partitioning. Different from the characteristics of wind-seas and swells in the open ocean, the wave fields in the enclosed seas such as the China Seas are predominated by wind-sea events in respect of both frequencies of occurrences and energy weights, due to the island sheltering and limited fetches. The energy weights of wind-seas in a given location is usually more significant than the occurrence probability of wind-sea-dominated events, as the wave energy is higher in the wind-sea events than in the swell events on average and extreme wave heights are mostly related to wind-seas. The most energetic swells in the China Seas (and other enclosed seas) are ‘local swells’, having just propagated out of their generation areas. However, the swells coming from the West Pacific also play an important role in the wave climate of the China Seas, which can only be revealed by partitioning different swell systems in the wave spectra as the energy of them is significantly less than the ‘local swells’.

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          Author and article information

          Journal
          JOUC
          Journal of Ocean University of China
          Science Press and Springer (China )
          1672-5182
          20 December 2019
          01 February 2020
          : 19
          : 1
          : 90-100
          Affiliations
          1North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266100, China
          2Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266000, China
          3College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
          4College of Marine Science and Technology, China University of Geosciences, Wuhan 430000, China
          5College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 200000, China
          Author notes
          *Corresponding author: JIANG Haoyu, E-mail: haoyujiang@ 123456cug.edu.cn
          Article
          s11802-019-3924-4
          10.1007/s11802-019-3924-4
          Copyright © Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020.

          The copyright to this article, including any graphic elements therein (e.g. illustrations, charts, moving images), is hereby assigned for good and valuable consideration to the editorial office of Journal of Ocean University of China, Science Press and Springer effective if and when the article is accepted for publication and to the extent assignable if assignability is restricted for by applicable law or regulations (e.g. for U.S. government or crown employees).

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          Self URI (journal-page): https://www.springer.com/journal/11802

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