[go: up one dir, main page]

Skip to main content
Log in

Long-Term Extreme Wave Characteristics in the Water Adjacent to China Based on ERA5 Reanalysis Data

  • Published:
Journal of Ocean University of China Aims and scope Submit manuscript

Abstract

Extreme waves have a profound impact on coastal infrastructure; thus, understanding the variation law of risky analysis and disaster prevention in coastal zones is necessary. This paper analyzed the spatiotemporal characteristics of extreme wave heights adjacent to China from 1979 to 2018 based on the ERA5 datasets. Nonstationary extreme value analysis is undertaken in eight representative points to investigate the trends in the values of 50- and 100-year wave heights. Results show that the mean value of extreme waves is the largest in the eastern part of Taiwan Island and the smallest in the Bohai Sea from 1979 to 2018. Only the extreme wave height in the northeastern part of Taiwan Island shows a significant increase trend in the study area. Nonstationary analysis shows remarkable variations in the values of 50- and 100-year significant wave heights in eight points. Considering the annual mean change, E1, E2, S1, and S2 present an increasing trend, while S3 shows a decreasing trend. Most points for the seasonal mean change demonstrate an increasing trend in spring and winter, while other points show a decreasing trend in summer and autumn. Notably, the E1 point growth rate is large in autumn, which is related to the change in typhoon intensity and the northward movement of the typhoon path.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Agarwal, A., Venugopal, V, and Harrison, G. P., 2013. The assessment of extreme wave analysis methods applied to potential marine energy sites using numerical model data. Renewable Sustainable Energy Reviews, 27: 244–257.

    Article  Google Scholar 

  • Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C., Goyette, S., et al., 2007. Future extreme events in European climate: An exploration of regional climate model projections. Climate Change, 81: 71–95.

    Article  Google Scholar 

  • Bernardino, M., Goncalves, M., and Guedes Soares, C., 2021. Marine climate projections towards the end of the 21st century in the North Atlantic. Journal of Offshore Mechanics and Arctic Engineering-Transactions of the ASMME, 143: 1–34.

    Google Scholar 

  • Caires, S., and Swail, V., 2004. Global wave climate trend and variability analysis, paper presented at the eighth international workshop on wave hindcasting and forecasting. U.S. Army Engineering Research and Development Center, North Shore, Hawaii. Google Scholar, 14–19.

    Google Scholar 

  • Campos, R. M., and Guedes Soares, C., 2016. Comparison of HIPOCAS and ERA wind and wave reanalysis in the North Atlantic Ocean. Ocean Engineering, 112: 320–334.

    Article  Google Scholar 

  • Cheng, L., AghaKouchak, A., Gilleland, E., and Katz, R. W., 2014. Non-stationary extreme value analysis in a changing climate. Climate Change, 127: 353–369.

    Article  Google Scholar 

  • Coles, S., 2001. An Introduction to Statistical Modeling of Extreme Value. Springer, London, 209pp.

    Book  Google Scholar 

  • Cooley, D., 2009. Extreme value analysis and the study of climate change. Climatic Change, 97: 77–83.

    Article  Google Scholar 

  • Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., 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.

    Article  Google Scholar 

  • Galiatsatou, P., Makris, C., Krestenitis, Y., and Prinos, P., 2021. Nonstationary extreme value analysis of nearshore sea-state parameters under the effects of climate change: Application to the Greek coastal zone and port structures. Journal of Marine Science and Engineering, 9(8): 817–841.

    Article  Google Scholar 

  • Gemmrich, J., Thomas, B., and Bouchard, R., 2011. Observational changes and trends in Northeast Pacific wave records. Geophysical Research Letters, 38: L22601.

    Article  Google Scholar 

  • Gower, J. F. R., 2002. Temperature, wind and wave climatologies, and trends from marine meteorological buoys in the Northeast Pacific. Journal of Climate, 15(24): 3709–3718.

    Article  Google Scholar 

  • Guedes Soares, C., and Moan, T., 1991. Model uncertainty in the long term distribution of wave induced bending moments for fatigue design of ship structures. Marine Structure, 4: 295–315.

    Article  Google Scholar 

  • Gulev, S. K., and Grigorieva, V., 2006. Variability of the winter wind waves and swell in the North Atlantic and North Pacific as revealed by the voluntary observing ship data. Journal of Climate, 19: 5667–5685.

    Article  Google Scholar 

  • Hemer, M. A., Church, J. A., and Hunter, J. R., 2010. Variability and trends in the directional wave climate of the Southern Hemisphere. International Journal of Climatology, 30(4): 475–491.

    Article  Google Scholar 

  • Katz, R. W., 2013. Statistical methods for nonstationary extremes. In: Extremes in a Changing Climate. AghaKouchak, A., et al., eds., Springer, Netherlands, 15–37.

    Chapter  Google Scholar 

  • Katz, R. W., Parlang, M. B., and Naveau, P., 2002. Statistics of extremes in hydrology. Advances in Water Resources, 25: 1287–1304.

    Article  Google Scholar 

  • Kumar, V. S., and Naseef, T. M., 2015. Performance of ERA-interim wave data in the nearshore waters around India. Journal of Atmospheric and Oceanic Technology, 32(6): 1257–1269.

    Article  Google Scholar 

  • Lee, E. Y., and Park, K. A., 2022. Application of non-stationary extreme value analysis to satellite-observed sea surface temperature data for past decades. Frontiers Marine Science, 8: 798408.

    Article  Google Scholar 

  • Lemos, G., Menéndez, M., Semedo, A., Miranda, P. M. A., and Hemer, M., 2021. On the decreases in North Atlantic significant wave heights from climate projections. Climate Dynamic, 57(9–10): 2301–2324, DOI: https://doi.org/10.1007/s00382-021-05807-8.

    Article  Google Scholar 

  • LeTraon, P. Y., 2013. From satellite altimetry to Argo and operational oceanography: Three revolutions in oceanography. Ocean Science, 9: 901–915.

    Article  Google Scholar 

  • Li, S., Jiang, H., Hou, Y., Wang, N., and Lu, J., 2020. Increasing historical tropical cyclone-induced extreme wave heights in the northern East China Sea during 1979 to 2018. Remote Sensing, 12(15): 2464.

    Article  Google Scholar 

  • Meehl, G. A., Karl, T., Easterling, D. R., Changnon, S., Pielke, R. J., Changnon, D., et al., 2000. An introduction to trends in extreme weather and climate events: Observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bulletin of the American Meteorological Society, 81: 413–416.

    Article  Google Scholar 

  • Mei, W., and Xie, S. P., 2016. Intensification of landfalling typhoons over the Northwest Pacific since the late 1970. Nature Geoscience, 9(10): 753–757.

    Article  Google Scholar 

  • Menéndez, M., Méndez, F. J., Losada, I. J., and Graham, N. E., 2008. Variability of extreme wave heights in the Northeast Pacific Ocean based on buoy measurements. Geophysical Research Letters, 35(22): L22607, DOI: https://doi.org/10.1029/2008GL035394.

    Article  Google Scholar 

  • Osinowo, A., Lin, X., Zhao, D., and Wang, Z., 2016. Long-term variability of extreme significant wave height in the South China Sea. Advances in Meteorology, 2016: 1–21.

    Article  Google Scholar 

  • Patra, A., Min, S., and Seong, M., 2020. Climate variability impacts on global extreme wave heights: Seasonal assessment using satellite data and ERA5 reanalysis. Journal of Geophysical Research: Oceans, 125: 1–12.

    Google Scholar 

  • Portilla, J., Sosa, J., and Cavaleri, L., 2013. Wave energy resources: Wave climate and exploitation. Renewable Energy, 57: 594–605.

    Article  Google Scholar 

  • Prpic-Oršic, J., Vettor, R., Guedes Soares, C., and Faltinsen, O. M., 2015. Influence of ship routes on fuel consumption and CO2 emission. In: Maritime Technology and Engineering. Guedes Soares, C., and Santos, T. A., eds., Taylor & Francis Group, London, 857–864.

    Google Scholar 

  • Shi, H. Y., Cao, X. F., Li, Q. J., Li, D. L., Sun, J. C., You, Z. J., et al., 2021. Evaluating the accuracy of ERA5 wave reanalysis in the water around China. Journal of Ocean University of China, 20(1): 1–9.

    Article  Google Scholar 

  • Stopa, J. E., and Cheung, K. F., 2014. Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Modelling, 75: 65–83.

    Article  Google Scholar 

  • Swarna, K., Chalabi, Z., and Youngman, B., 2018. Spatio-temporal distribution of historical extreme winter temperatures in England and Scotland–A non-stationary extreme value analysis. Journal of Extreme Events, 5(1): 1750005.

    Article  Google Scholar 

  • Takbash, A., and Young, I., 2020. Long-term and seasonal trends in global wave height extremes derived from ERA-5 reanalysis data. Journal of Marine Science and Engineering, 8(12): 1015–1031.

    Article  Google Scholar 

  • Timmermans, B. W., Gommenginger, C. P., Dodet, G., and Bidlot, J.-R., 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys. Geophysical Research Letters, 47: e2019GL086880.

    Article  Google Scholar 

  • Towler, E., Rajagopalan, B., Gilleland, E., Summers, R. S., Yates, D., and Katz, R. W., 2010. Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory. Water Resource Research, 46: W11504.

    Article  Google Scholar 

  • Vasiliades, L., Galiatsatou, P., and Loukas, A., 2015. Nonstationary frequency analysis of annual maximum rainfall using climate covariates. Water Resource Management, 29: 339–358.

    Article  Google Scholar 

  • Wang, X. L., Yang, F., and Swail, V. R., 2012. North atlantic wave height trends as reconstructed from the 20th century reanalysis. Geophysical Research Letters, 39: 1–6.

    Article  Google Scholar 

  • Young, I. R., Vinoth, J., Zieger, S., and Babanin, A. V., 2012. Investigation of trends in extreme value wave height and win speed. Journal of Geophysical Research, 117: C00J06.

    Article  Google Scholar 

  • Young, I. R., Ziegler, S., and Babanin, A. V., 2011. Global trends in wind speed and wave height. Science, 332: 451–455.

    Article  Google Scholar 

  • Zhang, X. G., 2020. Variation characteristics and prediction of typhoon activity frequency over the western North Pacific. PhD thesis. Nanjing University of Information Science & Technology, Nanjing.

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the support of the Natural Science Foundation of China (No. 51909114), and the Major Research Grant (Nos. U1806227, U1906231) from the National Natural Science Foundation of China (NSFC).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hongyuan Shi or Kuncheng Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, W., Zhang, X., Shi, H. et al. Long-Term Extreme Wave Characteristics in the Water Adjacent to China Based on ERA5 Reanalysis Data. J. Ocean Univ. China 23, 1–10 (2024). https://doi.org/10.1007/s11802-024-5446-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11802-024-5446-y

Key words

Navigation