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This study applies the wavelet analysis to the tidal gauge records, alongshore winds, atmospheric temperature and pressure along the China coast in winter 2008. The analysis results show three events of sea level oscillations (SLOs) on the shelf induced by winter storms. The first event occurred from January 9 to 21. The SLO periods were double-peaked at 1.6–5.3 and 7.0–16.0 d with the power densities of 0.04–0.05 and 0.10–0.15 m2·d, respectively. The second event occurred from February 5 to 18. The SLO period was single-peaked at 2.3–3.5 d with power density of 0.03–0.04 m2·d. The third event occurred from February 20 to March 8. The SLO periods were doublepeaked at 1.5–4.3 and 6.1–8.2 d with the power densities of 0.08–0.11 and 0.02–0.08 m2·d, respectively. The SLOs propagated along the coast from Zhejiang in north to Guangdong in south. The phase speeds ranged about 9–29 m/s from Kanmen to Pingtan, 5–11 m/s from Xiamen to Huizhou and 11–22 m/s from Huizhou to Shuidong. The dispersion relation of the SLOs shows their nature of coastal-trapped wave.

From the analyses of the satellite altimeter Maps of Sea Level Anomaly (MSLA) data, tidal gauge sea level data and historical sea level data, this paper investigates the long-term sea level variability in the East China Sea (ECS). Based on the correlation analysis, we calculate the correlation coefficient between tidal gauge and the closest MSLA grid point, then generate the map of correlation coefficient of the entire ECS. The results show that the satellite altimeter MSLA data is effective to observe coastal sea level variability. An important finding is that from map of correlation coefficient we can identify the Kuroshio. The existence of Kuroshio decreases the correlation between coastal and the Pacific sea level. Kurishio likes a barrier or a wall, which blocks the effect of the Pacific and the global change. Moreover, coastal sea level in the ECS is mainly associated with local systems rather than global change. In order to calculate the long-term sea level variability trend, the empirical mode decomposition (EMD) method is applied to derive the trend on each MSLA grid point in the entire ECS. According to the 2-D distribution of the trend and rising rate, the sea level on the right side of the axis of Kuroshio rise faster than in its left side. This result supports the barrier effect of Kuroshio in the ECS. For the entire ECS, the average sea level rose 45.0 mm between 1993 and 2010, with a rising rate of (2.5±0.4) mm/a which is slower than global average. The relatively slower sea level rising rate further proves that sea level rise in the ECS has less response to global change due to its own local system effect.

This study aims to gain insight into the sea level response to typhoon in the northern South China Sea using typhoon records from 1978 to 1997, simultaneous tidal gauge records and satellite altimeter along-track sea level data. We find that the ocean sea level response signatures in the deep water agree well with a single sea level soliton, of which the average soliton amplitude is (34 ± 6) cm and the average characteristic half width is (115 ± 12) km. Evolution of the single soliton as propagating across a shoaling bottom topography obeys solutions of the perturbed Korteweg and de Vries equation. The solutions reveal that forced by the hyperbolic-tangent-shaped topography, the single soliton evolves to a solitary wave packet consisting of a large leading soliton and smaller following waves in an oscillatory tail as approaching the shallow water. This phenomenon is verified by observed sea level signatures of 19 storm surges. The correlation coefficient between theoretical solution and observations is as high as 0.91. Meanwhile, the soliton amplitude grows with a soliton amplitude growth ratio (SAGR), which depends on the deep water and shallow water depths. However, without consideration of viscous effects, the SAGR model overestimates the soliton amplitude growth. Thus the vorticity equation for viscous fluid is adopted to solve this issue. The results indicate that turbulent viscosities are responsible to soliton amplitude decay concurring with the growth and causes dependence of the storm surge amplitude on the typhoon incident angle.

The AREMv2.3 mesoscale numerical model is used to explore storm processes in South China during the pre-rainy season in 2006 by imposing perturbations on the initial fields of physical variables (temperature, humidity, and wind fields). Sensitivity experiments are performed to examine the impacts of initial uncertainties on precipitation, on the error growth, and on the predictability of mesoscale precipitation in South China. The primary conclusion is that inherent initial condition uncertainties can significantly limit the predictability of storm. The 24-h accumulated precipitation is most sensitive to temperature perturbations. Larger-amplitude initial uncertainties generally lead to larger perturbation energies than those with smaller amplitude, but these kinds of differences decrease with time monotonically so the mechanism for the growth of perturbation energy is nonlinear. The power spectra of precipitation differences indicate that predictability increases with accumulated time. This also indicates the difficulties faced for short-term, small-scale precipitation forecasting.