11:00 〜 13:00
[ACG36-P08] Analysis of spatial and temporal variation of ocean waves and swell height in the East China Sea
キーワード：波高、うねり、東シナ海
Wave climate is important for marine disaster prevention and ocean development. The East China Sea (ECS) is an open sea to the Pacific Ocean and is expected to be strongly affected by swell. In this study, we used the ERA5 reanalysis data to investigate the interannual variability of the wave height distribution, especially the swell wave height. Wave heights by ERA5 and those measured by JMA moored buoys (1990-2000) are compared. They showed good agreement between them. We also compared the accuracy of ERA5 wave heights after the introduction of altimeters in 1991 with that before. There was no difference in the accuracy between the two, and both were highly accurate.
Monthly mean wave height shows that the highest wave height in the ECS is around the boundary with the Pacific Ocean. However, there are differences from month to month. The location of the maximum mean wave height varies from month to month.The ratio of monthly average swell wave height to wind-wave height shows that swell wave height is large in most areas of the ECS.
We examined the variability of the spatial distribution of the monthly mean wave height anomaly from 1979 to 2020. The interannual variability (standard deviation) of wave height anomalies is largest around the boundary with the Pacific Ocean for swell wave height. Interannual variability of wind-wave heights was largest in the north-south part of the ECS (near Kyushu and Taiwan). The correlation between local wind speed anomalies and wave height anomalies is high for wind-wave height, but low for swell wave height. Variations in the spatial distribution of wave height anomalies are investigated by empirical orthogonal function (EOF) analysis. The first mode is a pattern that fluctuates in the ECS near the boundary with the Pacific Ocean (around 25 N and 125E). The second mode is a pattern that fluctuates near Kyushu and Taiwan in the north and south of the ECS.
These patterns are seen in both swell and wind wave heights, but the contribution of the second mode is higher for wind-wave heights than those for swell wave heights. The correlation between the EOF time series of monthly mean wind anomaly and the EOF time series of wave height anomaly is examined.
The computational domain of the EOF analysis of wind anomalies was varied in various regions. We searched for the domain of maximum correlation of EOF time series of wave height and wind. The first mode of wave height anomaly has the highest correlation with the EOF first mode time series of wind speed anomaly in the region including the Pacific Ocean east of the ECS.
The second mode of the wave height anomaly is most highly correlated with the EOF first mode time series of wind vector anomalies over the area including the southern part of the ECS and the Pacific region east of the ECS.
Furthermore, monthly wave height anomaly maps were classified by self-organizing map analysis. The most frequent wave height anomaly maps are related with the distributions of EOF first and second modes. The most frequent pattern of local maximum or minimum values near the with the Pacific Ocean is particularly frequent in summer.
Self-organizing map analysis was also conducted for wind anomalies. We searched for wind anomaly classification regions so that the proximity of wave height anomaly classification and wind anomaly classification would be the closest.As a result, the swell wave height distribution matched best with the wind distribution in the region including the Pacific Ocean east of the ECS.
Monthly mean wave height shows that the highest wave height in the ECS is around the boundary with the Pacific Ocean. However, there are differences from month to month. The location of the maximum mean wave height varies from month to month.The ratio of monthly average swell wave height to wind-wave height shows that swell wave height is large in most areas of the ECS.
We examined the variability of the spatial distribution of the monthly mean wave height anomaly from 1979 to 2020. The interannual variability (standard deviation) of wave height anomalies is largest around the boundary with the Pacific Ocean for swell wave height. Interannual variability of wind-wave heights was largest in the north-south part of the ECS (near Kyushu and Taiwan). The correlation between local wind speed anomalies and wave height anomalies is high for wind-wave height, but low for swell wave height. Variations in the spatial distribution of wave height anomalies are investigated by empirical orthogonal function (EOF) analysis. The first mode is a pattern that fluctuates in the ECS near the boundary with the Pacific Ocean (around 25 N and 125E). The second mode is a pattern that fluctuates near Kyushu and Taiwan in the north and south of the ECS.
These patterns are seen in both swell and wind wave heights, but the contribution of the second mode is higher for wind-wave heights than those for swell wave heights. The correlation between the EOF time series of monthly mean wind anomaly and the EOF time series of wave height anomaly is examined.
The computational domain of the EOF analysis of wind anomalies was varied in various regions. We searched for the domain of maximum correlation of EOF time series of wave height and wind. The first mode of wave height anomaly has the highest correlation with the EOF first mode time series of wind speed anomaly in the region including the Pacific Ocean east of the ECS.
The second mode of the wave height anomaly is most highly correlated with the EOF first mode time series of wind vector anomalies over the area including the southern part of the ECS and the Pacific region east of the ECS.
Furthermore, monthly wave height anomaly maps were classified by self-organizing map analysis. The most frequent wave height anomaly maps are related with the distributions of EOF first and second modes. The most frequent pattern of local maximum or minimum values near the with the Pacific Ocean is particularly frequent in summer.
Self-organizing map analysis was also conducted for wind anomalies. We searched for wind anomaly classification regions so that the proximity of wave height anomaly classification and wind anomaly classification would be the closest.As a result, the swell wave height distribution matched best with the wind distribution in the region including the Pacific Ocean east of the ECS.