环渤海沿岸海表温度资料的均一性检验与订正

本文对环渤海沿岸具有代表性且资料完整的6个海洋观测站的月均海表温度(SST)序列作均一性检验和订正。我国海洋观测站密集度低,难以选择参考序列,本文首先采用不依赖参考序列的惩罚最大F检验(PMFT)方法对SST序列检验,利用详尽的元数据对检验结果进行确认,再对不连续点订正,该方法适用于元数据详尽的海洋观测站。对于元数据不详尽的观测站,使用惩罚最大T检验(PMT)方法,选取与海洋台站距离近且相关显著的气象观测站的均一化地面气温序列来制作参考序列,对SST序列进行检验和订正。结果表明,环渤海地区SST序列都存在一定非均一性,观测站较大距离迁移和观测系统变更(从人工观测到自动化观测)是造成非均一性的重要原因。订正后的环渤海地区年平均SST增温趋势更加明显。本文使用不同方法来检验SST序列的均一性,该思路对沿海其他海区观测站SST均一性检验和订正有一定参考价值和应用前景,可为沿海气候变化研究提供科学准确的第一手资料。     

畸形波研究的进展及存在问题

畸形波是近10年海浪研究领域的热点问题.作为一种新认识的物理现象,其经历了发现—认识—确认的认知过程.畸形波生成机制的研究目前取得了较大的进展,波列演化的调制不稳定性被认为是畸形波生成最具可能的原因.畸形波的研究及认识仍存在一些根本性的问题,这些问题的解决是正确及深入探究畸形波这种海洋物理现象的基础.通过对畸形波研究中存在的一些基本问题进行阐述和分析,探讨解决这些基本问题的思路及方法,为畸形波的研究提供一些有价值的参考.     

平均海平面上升对东中国海台风风暴潮影响的数值模拟

本文采用ECOMSED模式模拟了影响东中国海的3次台风过程,经与实测资料对比验证了模型的可靠性。在此基础上设计了敏感性试验以考察海平面上升对风暴潮造成的影响。结果表明,海平面上升对风暴潮的影响在空间分布上不是一致的,且因具体台风过程而异。整体而言,海平面上升对风暴潮造成的影响有限。海平面上升0．5m,大部分站位风暴增水极值基本不变,即使海平面上升5m大部分站位的风暴增水极值相对改变量都小于10％。     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

东海沿海季节性海平面异常成因

Based on the analysis of sea level, air temperature, sea surface temperature(SST), air pressure and wind data during 1980–2013, the causes of seasonal sea level anomalies in the coastal region of the East China Sea(ECS) are investigated. The research results show:(1) sea level along the coastal region of the ECS takes on strong seasonal variation. The annual range is 30–45 cm, larger in the north than in the south. From north to south, the phase of sea level changes from 140° to 231°, with a difference of nearly 3 months.(2) Monthly mean sea level(MSL)anomalies often occur from August to next February along the coast region of the ECS. The number of sea level anomalies is at most from January to February and from August to October, showing a growing trend in recent years.(3) Anomalous wind field is an important factor to affect the sea level variation in the coastal region of the ECS. Monthly MSL anomaly is closely related to wind field anomaly and air pressure field anomaly. Wind-driven current is essentially consistent with sea surface height. In August 2012, the sea surface heights at the coastal stations driven by wind field have contributed 50%–80% of MSL anomalies.(4) The annual variations for sea level,SST and air temperature along the coastal region of the ECS are mainly caused by solar radiation with a period of12 months. But the correlation coefficients of sea level anomalies with SST anomalies and air temperature anomalies are all less than 0.1.(5) Seasonal sea level variations contain the long-term trends and all kinds of periodic changes. Sea level oscillations vary in different seasons in the coastal region of the ECS. In winter and spring, the oscillation of 4–7 a related to El Ni?o is stronger and its amplitude exceeds 2 cm. In summer and autumn, the oscillations of 2–3 a and quasi 9 a are most significant, and their amplitudes also exceed 2 cm. The height of sea level is lifted up when the different oscillations superposed. On the other hand, the height of sea level is fallen down.     

基于浮标资料的海气动量交换系数研究

大尺度模型里大气边界层和海洋表面的耦合通常由海表拖曳系数进行参数化,海表拖曳系数一般看作是风速的函数.低风速下的海表拖曳系数一般看作是风速的线性函数,但有研究显示在极低的风速值下,海表拖曳系数并不是随着风速而单调增加的,而是随着风速的增加先减小再增大.极低风速下海表拖曳系数的估计值对不同计算方法有很强的依赖性,低风速下海表拖曳系数随风速的变化规律存在争议.因此,本文对10m处风速进行4种不同平均方法的选取以探究海表拖曳系数随风速的变化规律,为进一步改进海洋模式提供了依据,提升了海洋再分析的业务化能力.     

基于观测和再分析数据的LSTM深度神经网络沿海风速预报应用研究

基于海洋气象历史观测资料和再分析数据等,利用LSTM深度神经网络方法,开展在有监督学习情况下的海面风场短时预报应用研究。以中国近海5个代表站为研究区域,通过气象台站观测数据和ERA-Interim 6 h再分析数据构建数据集。选取21个变量作为预报因子,分别构建两个LSTM深度神经网络框架(OBS_LSTM和ALL_LSTM)。经与2017年WRF模式6 h预报结果对比分析,得出如下结论:构建的两个LSTM风速预报模型可以大幅降低风速预报误差,RMSE分别降低了41.3%和38.8%,MAE平均降低了43.0%和40.0%;风速误差统计和极端大风分析发现,LSTM模型能够抓住地形、短时大风和台风等敏感信息,对于大风过程预报结果明显优于WRF模式;两种LSTM模型对比发现,ALL_LSTM模型风速预报误差最小,具有很好的稳定性和鲁棒性,OBS_LSTM模型应用范围更广泛。