Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020

Jichao Wang; Peidong Sun; Zhihong Liao; Fan Bi; Guiyan Liu

doi:10.1007/s13131-021-1974-0

Volume 41 Issue 7

Jul. 2022

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Article Navigation > Acta Oceanologica Sinica > 2022 > 41(7): 97-112

Jichao Wang, Peidong Sun, Zhihong Liao, Fan Bi, Guiyan Liu. Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020[J]. Acta Oceanologica Sinica, 2022, 41(7): 97-112. doi: 10.1007/s13131-021-1974-0

Citation:

Jichao Wang, Peidong Sun, Zhihong Liao, Fan Bi, Guiyan Liu. Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020[J]. Acta Oceanologica Sinica, 2022, 41(7): 97-112. doi: 10.1007/s13131-021-1974-0

Citation:

Jichao Wang, Peidong Sun, Zhihong Liao, Fan Bi, Guiyan Liu. Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020[J]. Acta Oceanologica Sinica, 2022, 41(7): 97-112. doi: 10.1007/s13131-021-1974-0

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Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020

doi: 10.1007/s13131-021-1974-0

1.
College of Science, China University of Petroleum, Qingdao 266580, China
2.
National Meteorological Information Center, Beijing 100081, China
3.
North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China
4.
Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China

Funds: The National Natural Science Foundation of China under contract No. 42176011; the Shandong Provincial Natural Science Foundation under contract No. ZR2020MD060; the Fundamental Research Funds for the Central Universities under contract No. 19CX05003A-5.

More Information

Corresponding author: E-mail: wangjc@upc.edu.cn
Received Date: 2021-04-08
Accepted Date: 2021-09-03

Available Online: 2022-04-15

Publish Date: 2022-07-08

Abstract

Abstract

Reasonably understanding of the long-term wave characteristics is very crucial for the ocean engineering. A feedforward neural network is operated for interpolating ERA5 wave reanalysis in this study, which embodies a detailed record from 1950 onwards. The spatiotemporal variability of wave parameters in the Bohai Sea, especially the significant wave height (SWH), is presented in terms of combined wave, wind wave and swell by employing the 71 years (1950–2020) of interpolated ERA5 reanalysis. Annual mean SWH decreases at −0.12 cm/a estimated by Theil-Sen estimator and 95th percentile SWH reflecting serve sea states decreases at −0.20 cm/a. Inter-seasonal analysis shows SWH of wind wave has steeper decreasing trend with higher slopes than that of swell, especially in summer and winter, showing the major decrease may attribute to the weakening of monsoon. The inner Bohai Sea reveals a general decreasing trend while the intersection connecting with the Yellow Sea has the lower significance derived by Mann-Kendall test. Meanwhile, 95th percentile SWH decreases at a higher rate while with a lower significance in comparison with the mean state. The frequencies of mean wave directions in sub-sector are statistically calculated to find the seasonal prevailing directions. Generally, the dominant directions in summer and winter are south and north. A similar variation concerning to SWH, the trend of the mean wave period is provided, which also shows a decrease for decades.
- wave characteristic,
- wind wave,
- swell,
- long-term trend,
- Bohai Sea

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References(35)

References

[1]	Aboobacker V M, Shanas P R. 2018. The climatology of Shamals in the Arabian Sea-Part 1: surface winds. International Journal of Climatology, 38(12): 4405–4416. doi: 10.1002/joc.5711
[2]	Aydoğan B, Ayat B. 2018. Spatial variability of long-term trends of significant wave heights in the Black Sea. Applied Ocean Research, 79: 20–35. doi: 10.1016/j.apor.2018.07.001
[3]	Bacon S, Carter D J T. 1991. Wave climate changes in the North Atlantic and North Sea. International Journal of Climatology, 11(5): 545–558
[4]	Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656): 553–597. doi: 10.1002/qj.828
[5]	Ding Yihui, Wang Zunya, Sun Ying. 2008. Inter-decadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I: observed evidences. International Journal of Climatology, 28(9): 1139–1161. doi: 10.1002/joc.1615
[6]	Goovaerts P. 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1−2): 113–129. doi: 10.1016/S0022-1694(00)00144-X
[7]	Guo Weijun, Zhang Shuo, Wu Guoxiang. 2019. Quantitative oil spill risk from offshore fields in the Bohai Sea, China. Science of the Total Environment, 688: 494–504. doi: 10.1016/j.scitotenv.2019.06.226
[8]	Haxel J H, Holman R A, 2004. The sediment response of a dissipative beach to variations in wave climate. Marine Geology, 206(1–4): 73–99
[9]	Hersbach H, Bell B, Berrisford P, et al. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999–2049. doi: 10.1002/qj.3803
[10]	Hirsch R M, Slack J R, Smith R A. 1982. Techniques of trend analysis for monthly water quality data. Water Resources Research, 18: 107–121. doi: 10.1029/WR018i001p00107
[11]	Kang Shugang, Wang Xulong, Roberts H M, et al. 2018. Late Holocene anti-phase change in the East Asian summer and winter monsoons. Quaternary Science Reviews, 188: 28–36. doi: 10.1016/j.quascirev.2018.03.028
[12]	Kundzewicz Z W, Robson A. 2000. Detecting Trend and Other Changes in Hydrological Data. Geneva: World Climate Programme and Monitoring
[13]	Liang Bingchen, Liu Xin, Li Huajun, et al. 2016. Wave climate hindcasts for the Bohai Sea, Yellow Sea, and East China Sea. Journal of Coastal Research, 32(1): 172–180
[14]	Liu Min, Zhao Dongliang. 2019. Comprehensive studies on wind and wave climates in China Seas based on ERA-20C reanalysis data. Periodical of Ocean University of China, 49(7): 1–10
[15]	Lv Xiangcui, Yuan Dekui, Ma Xiaodi, et al. 2014. Wave characteristics analysis in Bohai Sea based on ECMWF wind field. Ocean Engineering, 91: 159–171. doi: 10.1016/j.oceaneng.2014.09.010
[16]	Mahmoodi K, Ghassemi H, Razminia A. 2019. Temporal and spatial characteristics of wave energy in the Persian Gulf based on the ERA5 reanalysis dataset. Energy, 187: 115991. doi: 10.1016/j.energy.2019.115991
[17]	Mann H B. 1945. Nonparametric tests against trend. Econometrica. 13(3): 245–259
[18]	Ren Huiru, Li Guosheng, Cui Linlin, et al. 2017. Simulating wave climate fluctuation in the Bohai Sea related to oscillations in the East Asian circulation over a sixty year period. Journal of Coastal Research, 33(4): 829–838. doi: 10.2112/JCOASTRES-D-15-00209.1
[19]	Semedo A, Sušelj K, Rutgersson A, et al. 2011. A global view on the wind sea and swell climate and variability from ERA-40. Journal of Climate, 24(5): 1461–1479. doi: 10.1175/2010JCLI3718.1
[20]	Semedo A, Vettor R, Breivik Ø, et al. 2015. The wind sea and swell waves climate in the Nordic seas. Ocean Dynamics, 65(2): 223–240. doi: 10.1007/s10236-014-0788-4
[21]	Sen P K. 1968. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324): 1379–1389. doi: 10.1080/01621459.1968.10480934
[22]	Sun Liancheng. 1991. Analysis of wave characteristics of the west offshore area of the Bohai Bay. Journal of Oceanography of Huanghai & Bohai Seas, 9(3): 50–58
[23]	Teng Xuechun, Wu Xiujie. 1994. A study on the characteristics of annual extreme and design waves in the south region of the Bohai Sea. Journal of Oceanography of Huanghai & Bohai Seas, 12(3): 3–12
[24]	Theil H. 1992. A rank-invariant method of linear and polynomial regression analysis. In: Raj B, Koerts J, eds. Henri Theil’s Contributions to Economics and Econometrics. Dordrecht: Springer, 345–381
[25]	Tobler W R. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(S1): 234–240
[26]	Vanem E, Bitner-Gregersen E M. 2012. Stochastic modelling of long-term trends in the wave climate and its potential impact on ship structural loads. Applied Ocean Research, 37: 235–248. doi: 10.1016/j.apor.2012.05.006
[27]	Vanem E, Walker S E. 2013. Identifying trends in the ocean wave climate by time series analyses of significant wave height data. Ocean Engineering, 61: 148–160. doi: 10.1016/j.oceaneng.2012.12.042
[28]	Wang Daolong, Hua Feng, Jiang Zhihui. 2010. Application of coastal wave model SWAN to Liaodong Bay. Advances in Marine Science, 28(3): 285–291
[29]	Wang Zhifeng, Wu Kejian, Zhou Liangming, et al. 2012. Wave characteristics and extreme parameters in the Bohai Sea. China Ocean Engineering, 26(2): 341–350. doi: 10.1007/s13344-012-0026-0
[30]	Wu Wenfan, Li Peiliang, Zhai Fangguo, et al. 2020. Evaluation of different wind resources in simulating wave height for the Bohai, Yellow, and East China Seas (BYES) with SWAN model. Continental Shelf Research, 207: 104217. doi: 10.1016/j.csr.2020.104217
[31]	Yang Xiaochen, Zhang Qinghe. 2013. Joint probability distribution of winds and waves from wave simulation of 20 years (1989−2008) in Bohai Bay. Water Science and Engineering, 6(3): 296–307
[32]	Yin Baoshu, Hou Yijun, Cheng Minghua, et al. 2001. Numerical study of the influence of waves and tide-surge interaction on tide-surges in the Bohai Sea. Chinese Journal of Oceanology and Limnology, 19(2): 97–102. doi: 10.1007/BF02863032
[33]	Young I R, Ribal A. 2019. Multiplatform evaluation of global trends in wind speed and wave height. Science, 364(6440): 548–552. doi: 10.1126/science.aav9527
[34]	Zheng Chongwei, Pan jing, Li Chongyin. 2016. Global oceanic wind speed trends. Ocean & Coastal Management, 129: 15–24
[35]	Zhu Di, Cheng Ximeng, Zhang Fan, et al. 2020. Spatial interpolation using conditional generative adversarial neural networks. International Journal of Geographical Information Science, 34(4): 735–758. doi: 10.1080/13658816.2019.1599122