黄东海浮游生物群落呼吸率对碳平衡的重要性

黄东海接受长江冲淡水和黑潮带来的大量营养盐和有机物质,其碳循环对陆架海碳源汇格局至关重要.浮游生物群落呼吸是影响碳循环的重要过程.为揭示黄东海浮游生物群落呼吸率(PCR)对碳平衡的贡献,于2011年四季使用黑白瓶培养法测定黄海南部及东海北部浮游群落呼吸率和初级生产力,并同步测定温度、盐度、营养盐、叶绿素和细菌丰度等环境...     

东海油气资源区海底沉积物的工程地质特征

根据2001年6月的航次调查及随后的沉积物样品的试验室分析资料,从物理性质和力学特征各方面对东海油气资源区海底沉积物进行了综合工程地质特征研完。发现本区沉积物结构、剪切特征、含水量和液塑限随深度的变化而变化;本区海底沉积物类型既分布有中砂、细砂和粉砂3种砂性土,也分布有扮质粘土、淤泥质粘土和粘质扮土3种粘性土,沉积物的物理力学性质与相立的土类性质相一致。     

埕岛油田海域海底沉积特征与工程地质特性

详细阐述了埕岛油田海域海底沉积和冲淤特征,介绍了沉积物的成分、类型和分布特征,并综合描述了埕岛海域海底地貌形态和海底沉积物的物理力学特性,上述研究对埕岛油田的进一步研究和开发有指导意义。     

河流入海物质通量对海、陆环境变化的响应

入海河流物质通量研究是陆—海相互作用和全球海洋通量联合研究计划的重要命题。我国是最早开展物质通量研究的国家之一。自20世纪90年代以来,国家自然科学基金项目和国家重大基础研究计划项目都开展了有关河流和边缘海物质通量的研究,即将开始的全国海岸带环境调查专项也把主要河流物质入海通量及其海洋环境效应研究作为主要内容之一。根据当前国内外河流物质通量研究的最新进展,较系统地阐述了河流入海物质通量的概念和对邻近大陆和海洋环境变化的响应。并在此基础上强调指出,河流入海物质通量是研究陆—海相互作用及其全球变化效应的重要参量。归纳了河流入海物质通量研究中需要解决的关键问题。     

强弱南海夏季风年广西的天气及预测概念模型

利用南海季风试验研究(1997～2000)的成果资料,对强弱南海夏季风年广西的天气作了对比研究,主要分析了雨季开始、年雨量分配及丰欠、热带气旋活动特点等方面的差异并归纳出一个简单的预测概念模型,可供业务工作者在制作年景预测上参考。     

The physical structures of snow and sea ice in the Arctic section of 150°-180°W during the summer of 2010

The physical structures of snow and sea ice in the Arctic section of 150°-180°W were observed on the basis of snow-pit, ice-core, and drill-hole measurements from late July to late August 2010. Almost all the investigated floes were first-year ice, except for one located north of Alaska, which was probably multi-year ice transported from north of the Canadian Arctic Archipelago during early summer. The snow covers over all the investigated floes were in the melting phase, with temperatures approaching 0℃ and densities of 295-398 kg/m3 . The snow covers can be divided into two to five layers of different textures, with most cases having a top layer of fresh snow, a round-grain layer in the middle, and slush and/or thin icing layers at the bottom. The first-year sea ice contained about 7%-17% granular ice at the top. There was no granular ice in the lower layers. The interior melting and desalination of sea ice introduced strong stratifications of temperature, salinity, density, and gas and brine volume fractions. The sea ice temperature exhibited linear cooling with depth, while the salinity and the density increased linearly with normalized depth from 0.2 to 0.9 and from 0 to 0.65, respectively. The top layer, especially the freeboard layer, had the lowest salinity and density, and consequently the largest gas content and the smallest brine content. Both the salinity and density in the ice basal layer were highly scattered due to large differences in ice porosity among the samples. The bulk average sea ice temperature, salinity, density, and gas and brine volume fractions were-0.8℃, 1.8, 837 kg/m3 , 9.3% and 10.4%, respectively. The snow cover, sea ice bottom, and sea ice interior show evidences of melting during mid-August in the investigated floe located at about 87°N, 175°W.     

二维EMD 方法在微尺度波斜率波数谱提取中的应用

进行海面微尺度波频率分析时,为消除重力波影响,引入二维 EMD(经验模态分解)方法滤除造成频率混叠的长重力波,然后进行傅立叶变换,得到海面微尺度波谱,进一步处理得到斜率波数谱。斜率波数谱提取结果表明：采用二维EMD方法可以有效滤除图像中混叠的长波信息,在此基础上进行微尺度斜率波谱计算的结果与国外已有结果基本一致。文中还将二维 EMD 方法与小波方法的处理结果进行了比对,结果表明在滤除长波的影响时,二维EMD方法具有其较明显的优势。     

Accuracy Assessment of the TOPEX/Poseidon Ionosphere Measurements

We conducted an assessment of the TOPEX dual-frequency nadir ionosphere observations in the TOPEX/Poseidon (T/P) GDR by comparing TOPEX with the Center for Orbit Determination in Europe (CODE) Global Ionosphere Map (GIM), the climatological model IRI2001, and the DORIS (onboard T/P) relative ionosphere delays. We investigated the TOPEX (TOPEX Side A and TOPEX Side B altimeters, TSA and TSB, respectively) ionosphere observations for the time period 1995–2001, covering periods of low, intermediate, and high solar activity. Here, we use absolute path delays (at Ku-band frequency of the TOPEX altimeter and with positive signs) rather than Total Electron Content (TEC). We found significant biases between GIM and TOPEX (GIM–TOPEX) nadir ionosphere path delays: ?8.1 ± 0.4 {mm} formal uncertainties and equivalent to 3.7 TECu) and ?9.0 ± 0.7 {mm} (4.1 TECu) for TSA and TSB, respectively, indicating that the TOPEX path delay is longer (or with higher TECu) than GIM. The estimated relative biases vary with latitude and with daytime or nighttime passes. The estimated biases in the path delays (DORIS–TOPEX) are: ?10.9 ± 0.4 {mm} (5.0 TECu) and ?14.8 ± 0.6 {mm} (6.7 TECu), for TSA and TSB, respectively. There is a distinct jump of the DORIS path delays (?3.9 ± 0.7 {mm}, TSA delays longer than TSB delays) at the TSB altimeter switch in February 1999, presumably due to inconsistent DORIS processing. The origin of the bias between GIM (GPS, L-band) and TOPEX (radar altimeter, Ku-band) is currently unknown and warrants further investigation. Finally, the estimated drift rates between GIM and TSA, DORIS and TSA ionosphere path delays for the 6-year study span are ?0.4 mm/yr and ?0.8 mm/yr, respectively, providing a possible error bound for the TOPEX/Poseidon sea level observations during periods of low and intermediate solar activity.     

Altimeter Signal-to-Noise for Deep Ocean Processes in Operational Systems

The ocean signal for this study is the sea surface height due to the slowly varying (greater than 5-day) ocean processes, which are predominantly the deep ocean mesoscale. These processes are the focus of present assimilation systems for monitoring and predicting ocean circulation due to ocean fronts and eddies and the associated environmental changes that impact real time activities in areas with depths greater than about 200 m. By this definition, signal-to-noise may be estimated directly from altimeter data sets through a crossover point analysis. The RMS variability in crossover differences is due to instrument noise, errors in environmental corrections to the satellite observation, and short time period oceanic variations. The signal-to-noise ratio indicates that shallow areas are typically not well observed due to the high frequency fluctuations. Many deep ocean areas also contain significant high frequency variability such as the subpolar latitudes, which have large atmospheric pressure systems moving through, and these in turn generate large errors in the inverse barometer correction. Understanding the spatial variations of signal to noise is a necessary prerequisite for correct assimilation of the data into operational systems.