目的 量化西太平洋极端海浪时空演变特征,揭示极端风场对极端波高的驱动机制,为海洋结构物设计和作业提供参考。方法 基于欧洲中期天气预报中心（ECMWF）发布的ERA5再分析数据集（1995—2024年）,系统量化西太平洋海域的极端海浪特征。首先,解析危险海浪的发生频率及其构成特性;进而采用高百分位数法,刻画极端海浪强度的时空格局与年际/季节演变趋势,并揭示极端风场对海浪的驱动机制。在此基础上,通过灾害风险指数评估区域风险水平,最终结合Gumbel分布模型预测重现期波高。结果 西太平洋危险海浪主要由危险风浪产生,但危险涌浪在开阔海域传播更远。极端海浪在台湾省周围海域及西太平洋东北海域呈现显著的增加趋势,增长幅度在0.04 m/a左右,且该变化主要受控于极端风速演变。研究揭示了西太平洋海浪风险水平的分布,指出50年和100年一遇的极端波高分别可达16.11、17.69 m。结论 本研究为西太平洋海洋工程的抗浪设计与安全运维提供了关键环境参数依据。
Abstract
Extreme waves pose a great threat to the safety of marine equipment. The work aims to quantify the temporal and spatial evolution characteristics of extreme waves in the western Pacific and reveal the driving mechanism of extreme wind field on extreme wave height, thus providing reference for the design and operation of marine structures. Based on the ERA5 reanalysis dataset (1995-2024) published by the European Centre for Medium-Range Weather Forecasts (ECMWF), the extreme wave characteristics in the western Pacific were systematically quantified. Firstly, the frequency and composition characteristics of dangerous ocean waves were analyzed. Furthermore, the high percentile method was used to describe the spatial-temporal pattern and interannual/seasonal evolution trend of extreme wave intensity, and to reveal the driving mechanism of extreme wind field on waves. On this basis, the regional risk level was evaluated by the disaster risk index, and finally the Gumbel distribution model was used to predict the return period wave height.Dangerous waves in the western Pacific were mainly generated by dangerous wind waves, but dangerous swells propagated farther in open waters. The extreme waves showed a significant increasing trend in the sea area around Taiwan Island and the northeast area of the western Pacific, with an increase of about 0.04 m/a, and the change was mainly controlled by the evolution of extreme wind speed. The study revealed the distribution of wave risk levels in the western Pacific, and pointed out that the extreme wave heights of 50-year and 100-year return periods could reach 16.11 m and 17.69 m.This study provides key environmental parameters for the wave resistance design and safe operation and maintenance of the Western Pacific Ocean project.
关键词
极端海浪 /
极端风速 /
海浪风险 /
西太平洋 /
ERA5
Key words
extreme waves /
extreme wind speed /
wave risk /
western Pacific /
ERA5
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参考文献
[1] YOUNG I R, RIBAL A.Multiplatform Evaluation of Global Trends in Wind Speed and Wave Height[J]. Science, 2019, 364(6440): 548-552.
[2] GAO J L, MA X Z, ZANG J, et al.Numerical Investigation of Harbor Oscillations Induced by Focused Transient Wave Groups[J]. Coastal Engineering, 2020, 158: 103670.
[3] MENÉNDEZ M, MÉNDEZ F J, LOSADA I J, et al. Variability of Extreme Wave Heights in the Northeast Pacific Ocean Based on Buoy Measurements[J]. Geophysical Research Letters, 2008, 35(22): L22607.
[4] SARDANA D, KUMAR P, BHASKARAN P K, et al.The Projected Changes in Extreme Wave Height Indices over the Indian Ocean Using COWCLIP2.0 Datasets[J]. Climate Dynamics, 2023, 61(1): 255-269.
[5] DU W Y, ZHANG X R, SHI H Y, et al.Long-Term Extreme Wave Characteristics in the Water Adjacent to China Based on ERA5 Reanalysis Data[J]. Journal of Ocean University of China, 2024, 23(1): 1-10.
[6] PATRA A, MIN S.Natural Climate Variability Impact on Global Extreme Wave Heightsusing Satellite Data and ERA-5 Reanalysis[J]. Korean Meteorological Society, 2019, 125(12): 228.
[7] SALCEDO-CASTRO J, DA SILVA N P, DE CAMARGO R, et al. Estimation of Extreme Wave Height Return Periods from Short-Term Interpolation of Multi-Mission Satellite Data: Application to the South Atlantic[J]. Ocean Science, 2018, 14(4): 911-921.
[8] TIMMERMANS B W, GOMMENGINGER C P, DODET G, et al. Global Wave Height Trends and Variability from New Multimission Satellite Altimeter Products, Reanalyses,Wave Buoys[J]. Geophysical Research Letters, 2020, 47(9): e2019GL086880.
[9] LIU H, LI D L, CHEN Q J, et al.The Multiscale Variability of Global Extreme Wind and Wave Events and Their Relationships with Climate Modes[J]. Ocean Engineering, 2024, 307: 118239.
[10] LI S Q, JIANG H Y, HOU Y J, et al.Increasing Historical Tropical Cyclone-Induced Extreme Wave Heights in the Northern East China Sea during 1979 to 2018[J]. Remote Sensing, 2020, 12(15): 2464.
[11] 宋轲新. 西北太平洋热带气旋快速加强长期变化的机理研究及预估[D]. 南京: 南京信息工程大学, 2024.
SONG K X.Study on the Mechanism and Prediction of the Long-Term Variation of Tropical Cyclones in the Northwest Pacific Ocean[D]. Nanjing: Nanjing University of Information Science & Technology, 2024.
[12] BEATTIE J C, ELSBERRY R L.Western North Pacific Monsoon Depressions: Transitions to Pre-Tropical Cyclone Seedlings[J]. Asia-Pacific Journal of Atmospheric Sciences, 2016, 52(5): 413-435.
[13] LI R, WU K J, ZHANG W Q, et al.Analysis of the 20-Year Variability of Ocean Wave Hazards in the Northwest Pacific[J]. Remote Sensing, 2023, 15(11): 2768.
[14] YANG Z H, NIU X J.Trends of Extreme Waves around Hainan Island during Typhoon Processes[J]. Ocean Engineering, 2024, 308: 118247.
[15] JIANG D, HUANG B G, MIAO Q S, et al.Typhoon Wind and Wave Numerical Forecasting Optimization in the South China Sea Based on Observation Data[J]. Natural Hazards, 2025, 121(8): 9653-9677.
[16] DIVINSKY B V, SAPRYKINA Y V.Extreme Wind Waves on the Northeastern Shelf of the Black Sea[J]. Doklady Earth Sciences, 2024, 517(1): 1224-1233.
[17] ZHENG C W, LI C Y.Propagation Characteristic and Intraseasonal Oscillation of the Swell Energy of the Indian Ocean[J]. Applied Energy, 2017, 197: 342-353.
[18] JIANG H Y, YANG Z.A Revisit of Global Wind-Sea and Swell Climate and Variability Using Multiplatform Altimeters[J]. Remote Sensing of Environment, 2022, 271: 112922.
[19] MUNK W, MILLER G, SNODGRASS F, et al.Directional Recording of Swell from Distant Storms[J]. Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences, 2013, 371(1989): 20130039.
[20] ZHANG Z W, LI X M.Global Ship Accidents and Ocean Swell-Related Sea States[J]. Natural Hazards and Earth System Sciences, 2017, 17(11): 2041-2051.
[21] YUAN Y P, ZHANG Y, MAO J B, et al.Diverse Changes in Shipping Emissions around the Western Pacific Ports under the Coeffect of the Epidemic and Fuel Oil Policy[J]. Science of the Total Environment, 2023, 879: 162892.
[22] LI J X, PAN S Q, CHEN Y P, et al.Numerical Estimation of Extreme Waves and Surges over the Northwest Pacific Ocean[J]. Ocean Engineering, 2018, 153: 225-241.
[23] K BHASKARAN P.Wind-Wave Climate Projections for the Indian Ocean from Satellite Observations[J]. Journal of Marine Science: Research & Development, 2014, 1(S11): 14-28.
[24] SOCI C, HERSBACH H, SIMMONS A, et al.The ERA5 Global Reanalysis from 1940 to 2022[J]. Quarterly Journal of the Royal Meteorological Society, 2024, 150(764): 4014-4048.
[25] ZHANG W W, ZHAO H J, CHEN G P, et al.Assessing the Performance of SWAN Model for Wave Simulations in the Bay of Bengal[J]. Ocean Engineering, 2023, 285: 115295.
[26] FAN Y, HUANG C M, ZHANG S D, et al. Long-Term Study of Quasi-16-Day Waves Based on ERA5 Reanalysis Data and EOS MLS Observations from2005 to 2020[J]. Journal of Geophysical Research: Space Physics, 2022, 127(4): e2021JA030030.
[27] NASEEF T M, KUMAR V S.Climatology and Trends of the Indian Ocean Surface Waves Based on 39-Year Long ERA5 Reanalysis Data[J]. International Journal of Climatology, 2020, 40(2): 979-1006.
[28] HONG H P, LI S H, MARA T G.Performance of the Generalized Least-Squares Method for the Gumbel Distribution and Its Application to Annual Maximum Wind Speeds[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2013, 119: 121-132.
[29] ANGUS J E.Extreme Value Theory in Engineering[J]. Technometrics, 1990, 32(1): 107-110.
[30] 游宇琦, 廖晓婷, 程思. 1979—2017年登陆福建的热带气旋活动特征分析[J]. 海峡科学, 2022(9): 3-8.
YOU Y Q, LIAO X T, CHENG S. Analysis on the Activity Characteristics of Tropical Cyclones Landing in Fujian from1979 to 2017[J]. Straits Science, 2022(9): 3-8.
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