登陆台风精细结构的观测、预报与影响评估

2015年立项的国家重点基础研究发展计划项目(973计划)"登陆台风精细结构的观测、预报与影响评估"将通过进行登陆台风精细结构的野外科学试验,开展登陆台风精细结构的多源资料分析理论和方法研究,探索登陆台风精细化结构演变规律及其对风雨分布的影响机理,发展高分辨率台风数值预报模式关键技术,开展台风灾害影响(预)评估方法研究等,揭示环境因子及其自身内部的多尺度系统相互作用如何影响登陆台风精细化结构的演变,以及精细结构的演变如何影响台风风雨强度和分布,力争提高登陆台风精细结构的模拟、预报和影响评估能力。     

黑潮化学物质输入东海的途径与通量

在对黑潮水文特征分析的基础上,探讨了黑潮对中国近海化学物质输入途径,归纳总结了黑潮对东海生源要素、痕量元素、同位素及其他化学物质的输入特征,提出了黑潮对中国近海化学物质输入研究应着重关注的问题。主要的结论包括:吕宋海峡、中国台湾东北部及日本九州西南海域是黑潮向中国近海输送化学物质的3个关键区域;黑潮表层水向东海陆架的扩展、次表层水和中层水的涌升以及黑潮季节性流轴摆动是黑潮向东海输送化学物质的基本途径。黑潮主体生源要素的浓度随深度的增加而增大。黑潮对东海生源要素的影响夏季最弱、春季次之、秋冬季最强,其对东海生源要素的输入通量要远高于河流、大气等其他输入源,并且各水层中以中层水的生源要素输入通量为最大。痕量元素在黑潮主体的分布、对东海的输入通量等方面均表现出了与生源要素的差异,各痕量元素的分布及输入通量随痕量元素的不同而不尽相同,其中次表层水的涌升在对邻近海域痕量元素分布的影响中起到了重要作用。同位素可指示黑潮对东海陆架的入侵程度及变化规律,并与营养盐等有一定的相关性。黑潮是东海微量温室气体N2O,CH4等其他化学物质的重要输入源,进而对东海生态环境产生影响。进一步研究应着重从系统性外海观测、痕量元素及同位素等化学物质调查、黑潮对中国近海输入化学物质的控制过程等方面深入开展。     

Interannual variability of the spring atmospheric heat source over the Tibetan Plateau forced by the North Atlantic SSTA

Climate Dynamics - Data analysis indicates that the interannual variability of the spring atmospheric heat source over the Tibetan Plateau (TP) depends largely on the intensity of the overlying...     

光强和二氧化碳浓度变化对浒苔幼苗生长及生理的影响

大气CO2浓度升高引起的海洋酸化如何在光变环境下影响大型海藻固碳量的问题,关系到未来海洋初级生产力的变化趋势。为研究大型海藻对CO2浓度升高和光强变化的响应,本文选取浒苔(Ulva prolifera)幼苗为实验材料,探讨其在不同光强下[80、260μmol/(m2·s)]和两种CO2浓度(正常CO2浓度:400μL/L和高CO2浓度:1 000μL/L)下的生理变化。研究发现,在正常CO2浓度、高光条件下,浒苔幼苗的生长最快,超氧化物特化酶(SOD)活性最高,而过氧化氢酶(CAT)活性在低光、高CO2处理下有最大值。光合色素含量和光系统Ⅱ的光化学效率在不同处理间没有显著性差异,但叶绿素a与类胡萝卜素的比值在低光正常CO2处理下有最大值。同时,高光高CO2处理下,浒苔幼苗的可溶性蛋白含量最低。     

云南盐津2006地震群的强震动应急观测研究

2006年7月22日09时10分21.6秒,云南省昭通市盐津县境内(28°01′N,104°08′E)发生Ms5.1地震,震源深度9km.极震区烈度为Ⅵ度,个别Ⅶ破坏点,等震线形状呈椭圆形,长轴走向北东向.地震造成22人死亡,13人重伤,35428户、151168人受灾.2006年8月25日13时51分41.1秒,盐津县(28°03′N,104°07′E)再次发生5.1级地震,震源深度7km,较7月22日地震略浅.微观震中由722地震位置向西北偏移约4km.由于震中位置基本相同,故地震灾害相互叠加,出现灾害加重现象和灾情范围扩大趋势,地震造成2人死亡,重伤7人,1万多户、4.5万多人需要紧急转移安置,房屋倒塌1500多间,其中170所学校的校舍不同程度受损.     

Analysis of elastoplasticity and rheology due to mining subsidence

At present, as the easily mining resources are being increasingly depleted, the exploitation of coal under buildings, water-bodies and railways is imminent for the sustainable production. Probability in-tegral method is a general method for mining subsidence in the coal system. Because of poor under-standing of mining subsidence for other sections, the authors suggest probability integral method for the study of coal mining under buildings, water-bodies and railways. Moreover, the calculation result of probability integral method should be corrected by numerical simulation method. Based on practical projects, the impact has been evaluated on the security of Xifeihe left embankment under coal mining. Combining with the results of probability integral method, we propose that the 600 m far from em-bankment is a good rationality. This article provides the basis for the rational exploitation of coal re-source which is a major practical problem under the premise of Water Infrastructure Security. Fur-thermore, it also can be served as a reference for the similar projects, such as mining Xiaolangdi res-ervoir area, mining Yuecheng reservoir and mining the major channels of Middle Route South to North Water Transfer.     

渤海导管架平台桩柱冰压力随机过程模型及其参数确定

首先分析了冰压力随机过程的特征及其产生机理,从而按冰的破坏形式将冰压力随机过程分为挤压破坏过程、屈曲破坏过程和弯曲破坏过程.从渤海海冰对平合桩柱作用大量实测冰压力曲线中选取37条典型的冰挤压破坏时程曲线,经谱分析确定了相应冰压力随机过程的谱密度及其谱参数;进一步经统计分析,建立了谱参数与冰厚的关系;在此基础上,建立了绕圆桩柱的冰压力随机场模型,并利用大量实测数据,经统计回归分析,确定了场函数的具体表达形式,由此用冰压力随机场可以容易地确定圆桩柱的总冰压力随机过程及其谱密度.     

昌宁5.1级地震前潮汐形变短临异常识别

2015年10月30日19时26分39秒昌宁发生M_L 5.1地震,收集地震前90天云县地震台潮汐形变观测资料,利用倾斜固体潮Nakai拟合检验方法行处理与分析。结果发现,在该地震发生前2—3个月,云县地震台出现趋势异常和非潮汐形变地震短临异常。     

IOD-related optimal initial errors and optimal precursors for IOD predictions from reanalysis data

This study explored the spatial patterns of winter predictability barrier (WPB)-related optimal initial errors and optimal precursors for positive Indian Ocean dipole (IOD) events, and the associated physical mechanisms for their developments were analyzed using the Simple Ocean Data Assimilation dataset. Without consideration of the effects of model errors on “predictions,” it was assumed that different “predictions” are caused by different initial conditions. The two types of WPB-related optimal initial errors are almost opposite for the start months of July (–1) and July (0), although they both present a west-east dipole pattern in the tropical Indian Ocean, with the maximum errors located at the thermocline depth. Bjerknes feedback and ocean waves play important roles in the growth of prediction errors. These two physical mechanisms compete during July–December and ocean waves dominate during January–June. The spatial patterns of optimal precursors and the physical mechanisms for their developments are similar to those of WPB-related optimal initial errors. It is worth noting that large values of WPB-related optimal initial errors and optimal precursors are concentrated within a few locations, which probably represent the sensitive areas of targeted observations for positive IOD events. The great similarities between WPB-related optimal initial errors and optimal precursors suggest that were intensive observations performed over these areas, this would not only reduce initial errors and thus, prediction errors, but it would also permit the detection of the signal of IOD events in advance, greatly improving the forecast skill of positive IOD events.     

Detection of Antarctic oscillation signals in Earth’s oblateness variations

Variations of Earth’s oblateness (J ₂) reflect a large scale mass redistribution within the Earth system. The climate effect causing J ₂ interannual variations is still not clear, though previous studies indicated it may be related to EI Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). However, we have a new discovery of the significant Antarctic oscillation (AAO) signals in J ₂ interannual variations, especially on 4–6 year scales based on cross wavelet and wavelet coherence analysis with 95% confidence test during 1979–2012. The results additionally indicate that the close phase relationship between J ₂ and AAO (AAO leading J ₂ variations by 3 ± 2 months in phase) is far superior to that between J ₂ and ENSO/PDO on 4–6 year scales. In this work, we discuss, for the first time, a possible geophysical mechanism of AAO effecting J ₂ variations. The investigations are based on the definition of AAO and its spatial–temporal behavior influencing the large-scale mass movement. Finally, an approximate quantitative estimate of the AAO imprint on J ₂ with an emphasis on the atmospheric contribution is made.