基于MIKE SA模型的厦门西港海域溢油影响的数值模拟研究

基于MIKE SA溢油模块,以燃料油为油种,建立了厦门西港海域溢油模型,模拟静风、主导风向（东北东风）和不利风向（西南风）3种风场条件下,一个潮周期内涨急、高潮、落急和低潮4个时段发生10 t溢油后油膜的漂移路径和影响范围.结果显示,发生在厦门西港海域的溢油在海面的漂移过程主要受潮流和风的影响,其中潮流起着主导作用.不同风向条件下,24 h内油膜的影响范围不同,静风条件下溢油浓度超一类（或二类,≥0.05 mg/dm3）、超三类（≥0.30 mg/dm3）和超四类（≥0.50 mg/dm3）的总影响面积分别为31.33、19.63和11.74 km2;主导风向条件下溢油浓度超一类（或二类）、超三类和超四类的总影响面积分别为99.62、69.01和8.99 km2;不利风向溢油浓度超一类（或二类）、超三类和超四类的总影响面积分别为8.38、5.05和2.10 km2.该预测结果可给出溢油事故发生后的影响范围、影响程度和影响敏感目标的时间,可为溢油事故应急决策的制定及溢油损害评估提供科学决策和支持,提升厦门海域环境风险管理应急能力建设.     

红翎菜科4种海藻ITS和rbcL序列的分子系统学研究

通过PCR扩增基因片段的方法测定了红翎菜科琼枝属(Betaphycus)、卡帕藻属(Kappaphycus)和麒麟菜属(Eucheuma)4种海藻共19株个体的核糖体基因转录间隔区(ITS)(含5.8SrDNA)和核酮糖-1,5-二磷酸羧化酶/加氧酶基因大亚基(rbcL)全长基因序列。其中rbcL全长序列为首次测定,为从蛋白质水平探讨红翎菜科分子系统进化提供了可靠的保证。琼枝属、卡帕藻属、麒麟菜属ITS序列长度分别为1 024、629~669、1 001bp,GC含量在45.6%~52%之间;rbcL序列长度均为1 467bp,GC含量在37.1%~37.6%之间。结合GenBank中现有的红翎菜科海藻ITS和rbcL序列进行系统进化分析,2个片段聚类结果均明显的将所有样品分为琼枝属、卡帕藻属和麒麟菜属3大分支,表现出明显的属间差异。本研究的11株长心卡帕藻根据ITS序列差异分成明显2种类型,推测这2种类型长心卡帕藻的ITS序列差异与其地理环境、无性繁殖时间(代数)和藻体颜色无关。     

Dominant patterns of winter Arctic surface wind variability

Dominant statistical patterns of winter Arctic surface wind(WASW) variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function(CVEOF) method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns(WASWP1 and WASWP2). Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWP1 and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland—Barents—Kara seas from autumn to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns(WASWP3 and WASWP4). Their time evolutions are significantly correlated with the North Atlantic Oscillation(NAO) index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure(SLP) north of 70°N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations: Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation(or NAO), effectively affect the spatial distribution of Arctic sea ice anomalies.     

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EST-SSR标记的开发及其在南黄海浒苔研究中的应用

Ulva species can grow rapidly in nutrient-rich habitats causing green tides and marine fouling. A more complete understanding of the reasons behind these outbreaks is urgently required. Accordingly, this study attempts to use microsatellite markers based expressed sequence tag（EST） to analyze the genetic variation of several Ulva prolifera populations in the South Yellow Sea of China. Two hundred and thirty-eight SSRs were identified from 8 179 unique ESTs（6 203 newly sequenced and 1 976 downloaded from NCBI database） and 37 primer pairs were successfully designed according to the ESTs; 11 pairs were selected to detect the genetic diversity and relationship of 69 attached U. prolifera samples and 13 free-floating samples collected from coastal and off-coast areas of the South Yellow Sea. The results of cross-species transferability showed that six of the 11 EST-SSR primers could give good amplification in other five Ulva species and the average allele number was 4.67. Genetic variation analysis indicated that all 82 U. prolifera samples were clearly divided and most samples collected from the same site clustered together as a group in the dendrogram tree produced by unweighted pair-group mean analysis（UPGMA） method and the cluster results showed some consistency with the geographical origins. In addition, 13 free-floating samples（except HT-001-2） were grouped as a single clade separated from the attached samples.     

High-resolution mesoscale analysis data from the South China heavy rainfall experiment (SCHeREX): Data generation and quality evaluation

In this study, the observational data acquired in the South China Heavy Rainfall Experiment (SCHeREX) from May to July 2008 and 2009 were integrated and assimilated with the US National Oceanic and Atmospheric Administration’s (NOAA) Local Analysis and Prediction System (LAPS; information available online at ). A high-resolution mesoscale analysis dataset was then generated at a spatial resolution of 5 km and a temporal resolution of 3 h in four observational areas: South China, Central China, Jianghuai area, and Yangtze River Delta area. The quality of this dataset was evaluated as follows. First, the dataset was qualitatively compared with radar reflectivity and TBB image for specific heavy rainfall events so as to examine its capability in reproduction of mesoscale systems. The results show that the SCHeREX analysis dataset has a strong capability in capturing severe mesoscale convective systems. Second, the mean deviation and root mean square error of the SCHeREX mesoscale analysis fields were analyzed and compared with radiosonde data. The results reveal that the errors of geopotential height, temperature, relative humidity, and wind of the SCHeREX analysis were within the acceptable range of observation errors. In particular, the average error was 45 m for geopotential height between 700 and 925 hPa, 1.0–1.1°C for temperature, less than 20% for relative humidity, 1.5–2.0 m s⁻¹ for wind speed, and 20°–25° for wind direction. The above results clearly indicate that the SCHeREX mesoscale analysis dataset is of high quality and sufficient reliability, and it is applicable to refined mesoscale weather studies.     

地市级气象灾害预警发布系统

随着极端天气气候事件频发,气象灾害及其次生灾害发生的几率也越来越大,气象灾害的及时预警发布在服务过程中显得尤为重要。以地市级气象灾害预警发布系统为例,从基于卫星资料云顶亮温反演模式的灾害预警、自动站数据监测自动报警、预警信息发布方式等方面阐述了系统的建设使用情况。