孟加拉湾季风对热带气旋生成和发展的调制机制研究

孟加拉湾与其他热带海盆不同,在季风影响下,该地区热带气旋具有双气旋季的独特结构(4—5月的春季转换期和10—11月的秋季转换期)。虽然孟加拉湾气旋频数在10—11月较多,但是4—5月超强气旋(Saffir-Simpson 4,5级)的生成率却远高于10—11月。1981—2016年,春季转换期内孟加拉湾超强气旋都与第一支北传季节内振荡(First Northward-propagating Intra-Seasonal Oscillation,FNISO)相应而生,然而并不是所有伴随FNISO发生的气旋都能发展成为超强气旋。因此本研究以气旋生成指数为基础,利用气旋最佳轨道数据以及NCEP的海气参量数据,诊断指出孟加拉湾夏季风形成的强垂直风速剪切配合低层大气旋度和气旋潜在强度抵消夏季风期间水汽对气旋生成的促进作用,造成双峰分布,而中层大气相对湿度差异双峰不对称的主因。FNISO强度的不同与深对流中心与气旋中心的相对位置的差异,使得部分气旋受季节内振荡影响更大,强深对流的超越作用导致更显著的高低层大气温差,促使气旋具有且达到更大的潜在强度。在年际尺度上大气高低层温差的不同也是引起气旋潜在强度不同的主要原因。当季节内尺度和年际尺度共同作用,使得部分气旋发展成为超强气旋。     

1961—2017年新疆积雪期时空变化特征及其与气象因子的关系

利用新疆89个地面站逐日积雪深度观测资料,研究探讨了1961—2017年新疆区域积雪期、积雪初日、积雪终日的时空变化规律,分析了北疆和天山山区积雪期的年代际和周期变化特征及其与气温、降水的关系.结果表明:新疆各地积雪期、积雪初日和终日存在明显的差异,积雪期以天山为界北多南少;从空间分布看,天山山区和新疆北部阿勒泰、塔城...     

四川盆地复合盆山体系的结构构造和演化

盆山体系研究是当前大陆动力学探索的热门。四川盆地周缘为造山带所围绕,盆地与造山带存在着耦合关系。系统地分析四川盆地和周缘造山带组成的盆山体系的结构和演化特征对深入认识四川盆地的构造和油气分布规律具有重要的意义。通过构造剖面的解释、沉积充填特征和陆源碎屑物源的分析以及同位素年代学的分析,研究了四川盆地盆山体系的结构、构造变形特征和演化历史。四川盆地与周缘造山带均以冲断褶皱带相耦合,构造变形在平面上和纵向上具有明显的分带性和层次性的特点。提出了"复合盆山体系和亚盆山体系"的概念,并认为四川盆地及周缘造山带组成了一个复合盆山体系,并由多个次一级的亚盆山体系所组成,各亚盆山体系是互相影响、互相叠加、互相干扰联合的。这在一定程度上丰富了盆山体系的认识。     

济源地区中-下三叠统碎屑锆石年代学特征及其物源分析

沉积物源组成及其演化是研究沉积盆地及盆山系统演化的关键。对济源地区中-下三叠统3个组进行了碎屑锆石U-Pb同位素分析,结果显示,下三叠统刘家沟组碎屑锆石具有6个年龄峰值:270,329,385,450,1885,2511Ma,其中晚古生代碎屑锆石应来自内蒙古隆起,早古生代碎屑锆石来自秦岭造山带,而新太古代-古元古代碎屑锆石则来自华北克拉通基底。下三叠统和尚沟组碎屑锆石年龄主峰值为420Ma,次峰值为745Ma和952Ma,均来自秦岭造山带。中三叠统二马营组碎屑锆石年龄主峰值为254Ma,可能来自华北板块南缘再旋回的沉积物,次峰值为1690Ma和2524Ma,应来自华北克拉通基底,其中1690Ma的碎屑锆石对应了华北板块南缘广泛出露的熊耳群。新元古界碎屑锆石由刘家沟组至和尚沟组增多,说明秦岭造山带基底逐渐剥露,是造山作用增强的体现。中三叠世末,秦岭造山带进入全面碰撞造山阶段,从地质演化过程来看,秦岭造山带在中三叠世也应处于持续隆升状态,然而二马营组中却没有来自秦岭造山带的碎屑锆石记录,而是以华北板块南缘盖层和基底为主,推测秦岭造山带的造山作用影响到了华北板块南缘,使华北板块南缘不断抬升,阻挡了秦岭造山带为济源地区提供沉积物。     

Transition Periods Between Sea Ice Concentration and Sea Surface Air Temperature in the Arctic Revealed by an Abnormal Running Correlation

This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration(SIC) and sea surface air temperature(SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea(BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.     

深水等深流沉积中记录的米兰科维奇特性——以陕西富平地区上奥陶统赵老峪组为例

陕西富平地区上奥陶统赵老峪组为深水盆地边缘至深水斜坡沉积环境,发育深水原地沉积、等深流沉积及重力流沉积。以野外实测剖面、岩相特征等资料为基础,结合米兰科维奇理论、天文地层学、古海洋学以及古气候学,系统研究该地区等深流强度与古气候、海平面、古盐度的关系。结果表明:Fischer图解可以客观、准确地反映深水沉积地层相对海平面变化;深水沉积及等深流沉积中不同级次旋回垂向组合关系记录了米兰科维奇特性,即小级别旋回在垂向上有序地按照1∶4或者1∶5的形式叠加构成较大级别旋回;等深流强度受米兰科维奇旋回影响,呈多周期弱→强→弱递变;等深流强化周期约为0.1Ma。     

中国作物物候对气候变化的响应与适应研究进展

以气候变暖为主要特征的气候变化对作物物候产生了重要的影响,通常气温升高会导致作物生长速度加快,生育期缩短,从而造成作物产量下降,不利于农业发展。同时,作物物候变化可以直接或间接反映气候变化情况,对于气候变化具有重要的指示意义。作物物候的研究对于农业气象灾害的预防、农业生产管理水平的进步以及农业产量提高都极为关键。随着全球地表气温的持续升高,作物物候相关研究也越来越引起科学家的关注。论文结合作物物候的主要研究方法,综述了中国近几十年来小麦、玉米、水稻以及棉花、大豆等主要农作物的生育期变化特征以及主要的驱动因子,得到以下主要结论：①在研究方法上,统计分析方法应用最为普遍,其他几种方法都需要与统计分析方法相结合使用。另外,作物机理模型模拟方法易于操作、可行性强,在物候研究中应用也比较多。遥感反演方法对作物生育期的特征规律要求较高,一般主要关注作物返青期。②整体上,小麦全生育期主要呈缩短趋势,而玉米和水稻全生育期以延长趋势为主。③作物物候变化的驱动因子主要是气候变化和农业管理措施改变,其中,气候变化是主导驱动因子,对作物物候变化起决定作用,而调整农业管理措施,在一定程度上抵消气候变化对作物生育期的不利影响。作物物候对气候变化的响应和适应研究可以为农业生产适应气候变化提供重要的理论依据和对策。     

Stochastic bivariate time series models of waves in the North Sea and their application in simulation-based design

In this paper, we present and evaluate three long-term wave models for application in simulation-based design of ships and marine structures. Designers and researchers often rely on historical weather data as a source for ocean area characteristics based on hindcast datasets or in-situ measurements. The limited access and size of historical datasets reduces repeatability of simulations and analyses, making it difficult to assess the sampling variability of performance and loads on marine vessels and structures. Markov, VAR and VARMA wave models, producing independent long-term time series of significant wave height (H_s) and spectral peak period (T_p), is presented as possible solutions to this problem. The models are tested and compared by addressing how the models affect interpretation of design concepts and the ability to replicate statistical and physical characteristics of the wave process. Our results show that the VAR and VARMA models perform sufficiently in describing design performance, but does not capture the physical process fully. The Markov model is found to perform worst of the tested models in the applied tests, especially for measures covering several consecutive sea states.     

Obtaining the distribution of quiescent periods directly from the power spectral densities of Sea waves

There is a growing practical interest in the ability to increase the sea states at which marine operations can be safely undertaken by exploiting the quiescent periods that are well known to exist under a wide range of sea conditions. While the actual prediction of quiescent periods at sea for the control of operations is a deterministic process, the long term planning of future maritime tasks that rely on these quiescent periods is a statistical process involving the anticipated quiescence properties of the forecasted sea conditions in the geographical region of interest. It is in principle possible to obtain such data in tabular form either large scale simulation or from field data. However, such simulations are computationally intensive and libraries of appropriate field data are not common. Thus, it is clearly attractive to develop techniques that exploit standard wave spectral models for describing the quiescence statistics directly from such spectra. The present study focuses upon such techniques and is a first step towards the production of a computationally low-cost quiescence prediction tool and compares its efficacy against simulations. Two significant properties emerge for a large class of wave spectral models that encompasses the ubiquitous Neumann and Pierson Moskowitz or Bretschneider forms. Firstly, the auto-correlation function of the wave profile that are required to produce the quiescence property can be obtained analytically in terms of standard special functions. This considerably reduces the computational cost making desktop computer-based planning tools a reality. Secondly, for each class of these parametric spectra, the probability of a given number of consecutive wave heights (normalised to the significant wave heights) less than some critical value is in fact independent of absolute wave height. Thus, for a broad class of practically interesting wave spectra all that is required to obtain the statistical distribution of the quiescent periods is simple rescaling.     

A 10-year wave energy resource assessment and trends of Indonesia based on satellite observations

Wave energy resource assessment and trends around Indonesian's ocean has been carried out by means of analyzing satellite observations. Wave energy flux or wave power can be approximated using parameterized sea states derived from satellite data. Unfortunately, only some surface parameters can be measured from remote sensing satellites, for example for ocean surface waves: significant wave height. Others, like peak wave period and energy period are not available, but can instead be estimated using empirical models. The results have been assessed by meteorological season. The assessment shows clearly where and when the wave power resource is promising around Indonesian's ocean. The most striking result was found from June to August, in which about 30–40 kW/m(the 90 th percentile: 40–60 kW/m, the 99th percentile: 50–70 kW/m) wave power energy on average has been found around south of the Java Island. The significant trends of wave energy at the 95% level have also been studied and it is found that the trends only occurred for the extreme cases, which is the 99th percentile(i.e.,highest 1%). Wave power energy could increase up to 150 W/m per year. The significant wave heights and wave power have been compared with the results obtained from global wave model hindcast carried out by wave model WAVEWATCH III. The comparisons indicated excellent agreements.