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.
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.
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.
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.
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.
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 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 direction in summer and winter is 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.
Figure 1. Study area with ETOPO1 bathymetry. Regions partition by dashed lines and buoy locations used for validation are denoted by red dots.
Figure 2. ERA5 model (the black dots with square pane) and the interpolated (the blue) positions.
Figure 3. The scatterplot between the extended ERA5 C-SWH and buoy C-SWH.
Figure 4. Annual time series of mean (top) /95th percentile (bottom) C-SWH (left), SHWW and SHTS (right) with their linear trend estimations in the whole Bohai.
Figure 5. Mean trend in monthly C-SWH (a), SHWW and SHTS (b) over the period of 1950–2020 in the whole Bohai.
Figure 6. 95th percentile trend in monthly C-SWH (a), SHWW and SHTS (b) over the period of 1950–2020 in the whole Bohai.
Figure 7. Heatmaps of spatial distribution in mean and 95th percentile C-SWH (m, the left panel), SHWW (m, the middle panel) and SHTS (m, the right panel).
Figure 8. Heatmaps of inter-annual trend (cm/a, top) and confidence levels (%, bottom) in mean and 95th percentile C-SWH.
Figure 9. Heatmaps of inter-seasonal trend (cm/a) in mean C-SWH for each month.
Figure 10. Spatial distribution of the mean C-SWH inter-annual variation.
Figure 11. Heatmaps of inter-seasonal trend (cm/a) in 95th percentile C-SWH for each month.
Figure 12. Spatial distribution of the 95th percentile C-SWH inter-annual variation.
Figure 13. The box plot of mean wave period during 1950–2020.
Figure 14. Annual time series of mean (top) /95th percentile (bottom) C-MWP (left), MPWW and MPTS (right) with their linear trend estimations.
Figure 15. Heatmaps of inter-annual trend (s/a, top) and confidence levels (%, bottom) in mean and 95th percentile C-MWP.
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