基于自动探测方法的南海北部涡致海表温度锋面的特征研究

南海北部具有丰富的温度锋面和中尺度涡，它们调节着局地的热量和能量平衡。本文利用卫星海洋高度异常和海表温度数据，并基于自动探测方法，探究了2007年至2017年南海北部中尺度涡边缘的海表温度锋面（涡致锋面）特征。反气旋/气旋边缘出现锋面的概率可达20%。气旋涡在各个方向上出现锋面的概率比较均匀，反气旋涡的东北部和西南部出现锋面的概率大于西北部和东南部。中尺度涡致锋面的数量有明显的季节变化，而涡动能未表现出明显的季节变化。中尺度涡致锋区的总涡动能是中尺度涡内动能的3倍，并且反气旋涡致锋面的总涡动能明显强于气旋涡致锋面的总涡动能。中尺度涡致锋面的数量和涡动能的年际变化与厄尔尼诺南方涛动指数没有明显的相关性。本研究也讨论了中尺度涡致锋面的可能机制，但是中尺度涡对海表温度锋的贡献需要进一步定量研究。     

南海南部上层的湍流混合特征

A turbulent microstructure experiment was undertaken at a low latitude of 10°N in the South China Sea in late August 2012. The characteristics of the eddy diffusivity above 650 m were analyzed, which is one order of magnitude larger than that in the open ocean at that low latitude. Enhanced eddy diffusivities by strong shears and sharp changes in topography were observed. The strongest eddy diffusivity occurred in the mixed layer, and it reached O(10–2 m2/s). Strong stratification in the thermocline inhibited the penetration of surface eddy diffusivities through the thermocline, where the mixing was weakest. Below the thermocline, where the background eddy diffusivity was approximately O(10–6 m2/s), the eddy diffusivity increased with depth, and its largest value was O(10–3 m2/s).     

南海北部中尺度反气旋涡的湍流混合空间分布特征

文章利用GHP细结构参数化方法和Thorpe-scale方法，分析水下滑翔机于2015年5月在南海北部采集的数据，估算了南海北部中尺度反气旋涡的湍流混合空间分布特征。结果显示该反气旋涡的混合具有明显的空间非对称性，混合率在其运动方向的后侧边缘明显增强达到O(10^-3 m²/s)量级；而在其运动方向的前侧边缘，平均混合率要小一个量级。这一混合非对称特征与中尺度的涡动能密切相关性。中尺度涡后侧边缘处存在高流速剪切，容易引起垂向剪切不稳定，可能是引起该处混合增强的主要因素。另外，中尺度涡后侧边缘发展的次中尺度过程同样导致了该处强混合。本研究结果有助于人们进一步认识南海北部的混合过程。     

海浪区湍流通量的涡度协方差观测

Turbulent eddies play a critical role in oceanic flows. Direct measurements of turbulent eddy fluxes beneath the sea surface were taken to study the direction of flux-carrying eddies as a means of supplementing our understanding of vertical fluxes exchange processes and their relationship to tides. The observations were made at 32 Hz at a water depth of ~1.5 m near the coast of Sanya, China, using an eddy covariance system, which mainly consists of an acoustic doppler velocimeter(ADV) and a fast temperature sensor. The cospectra-fit method-an established semi-empirical model of boundary layer turbulence to the measured turbulent cospectra at frequencies below those of surface gravity waves-was used in the presence of surface gravity waves to quantify the turbulent eddy fluxes(including turbulent heat flux and Reynolds stress). As much as 87% of the total turbulent stress and 88% of the total turbulent heat flux were determined as being at band frequencies below those of surface gravity waves. Both the turbulent heat flux and Reynolds stress showed a daily successive variation;the former peaked during the low tide period and the later peaked during the ebb tide period.Estimation of roll-off wavenumbers, k0, and roll-off wavelengths, λ0(where λ0=2π/k0), which were estimated as the horizontal length scales of the dominant flux-carrying turbulent eddies, indicated that the λ0 of the turbulent heat flux was approximately double that of the Reynolds stress. Wavelet analysis showed that both the turbulent heat flux and the Reynolds stress have a close relationship to the semi-diurnal and diurnal tides, and therefore indicate the energy that is transported from tides to turbulence.