1961-2008年中国西南地区极端气候变化幅度的海拔效应(英文)

A total of 12 indices of temperature extremes and 11 indices of precipitation ex-tremes at 111 stations in southwestern China at altitudes of 285-4700 m were examined for the period 1961-2008. Significant correlations of temperature extremes and elevation in-cluded the trends of diurnal temperature range, frost days, ice days, cold night frequency and cold day frequency. Regional trends of growing season length, warm night frequency, coldest night and warmest night displayed a statistically significant positive correlation with altitude. These characteristics indicated the obvious warming with altitude. For precipitation extreme indices, only the trends of consecutive dry days, consecutive wet days, wet day precipitation and the number of heavy precipitation days had significant correlations with increasing alti-tude owing to the complex influence of atmospheric circulation. It also indicated the increased precipitation mainly at higher altitude areas, whereas the increase of extreme precipitation events mainly at lowers altitude. In addition, the clearly local influences are also crucial on climate extremes. The analysis revealed an enhanced sensitivity of climate extremes to ele-vation in southwestern China in the context of recent warming.     

Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.     

Recovering magnetic susceptibility from electromagnetic data over a one-dimensional earth

While the inversion of electromagnetic data to recover electrical conductivity has received much attention, the inversion of those data to recover magnetic susceptibility has not been fully studied. In this paper we invert frequency-domain electromagnetic (EM) data from a horizontal coplanar system to recover a 1-D distribution of magnetic susceptibility under the assumption that the electrical conductivity is known. The inversion is carried out by dividing the earth into layers of constant susceptibility and minimizing an objective function of the susceptibility subject to fitting the data. An adjoint Green's function solution is used in the calculation of sensitivities, and it is apparent that the sensitivity problem is driven by three sources. One of the sources is the scaled electric field in the layer of interest, and the other two, related to effective magnetic charges, are located at the upper and lower boundaries of the layer. These charges give rise to a frequency-independent term in the sensitivities. Because different frequencies penetrate to different depths in the earth, the EM data contain inherent information about the depth distribution of susceptibility. This contrasts with static field measurements, which can be reproduced by a surface layer of magnetization. We illustrate the effectiveness of the inversion algorithm on synthetic and field data and show also the importance of knowing the background conductivity. In practical circumstances, where there is no a priori information about conductivity distribution, a simultaneous inversion of EM data to recover both electrical conductivity and susceptibility will be required.     

A comprehensive analysis of phenological changes in forest vegetation of the Funiu Mountains,China

Journal of Geographical Sciences - This paper reports the phenological response of forest vegetation to climate change (changes in temperature and precipitation) based on Moderate Resolution...     

狐狸座BW星的周期分析

利用文[1]中权重5的大量高精度测光数据,用文[2]中的周期分析法,得出BWVul的主要脉动周期P_1=0.21010425d。另有变幅仅为前者十分之一的二倍频P_2=0.10052300d及四倍频P_4=0.05026096d。文[3]求出的变幅更小的P_3、P_5、P_6、P_7、P_8等倍频可信度很低。故多重周期不是引起P_1长期变化及限制脉动变幅的主因。     

The simulation of LUCC based on Logistic-CA-Markov model in Qilian Mountain area,China

The Qilian mountain area was examined for using the Logistic-CA-Markov coupling model combined with GIS spatial analyst technology to research the transformation of LUCC, driving force system and simulate future tendency of variation. Results show that:(1) Woodland area decreased by 12.55%, while grassland, cultivated land, and settlement areas increased by 0.22%, 7.92%, and 0.03%, respectively, from 1986 to 2014. During the period of 1986 to 2000, forest degradation in the middle section of the mountain area decreased by 1,501.69 km~2. Vegetation cover area improved, with a net increase of grassland area of 38.12 km~2 from 2000 to 2014.(2) For constructing the system driving force, the best simulation scale was 210m×210m. Based on logistic regression analysis, the contribution(weight) of composite driving forces to land use and cover change was obtained, and the weight value was more objectively compared with AHP and MCE method.(3) In the natural scenarios, it is predicted that land use and cover distribution maps of Qilian mountain area in 2028 and 2042, and the Lee-Sallee index test was adopted. Over the next 27 years(2015–2042), farmland, woodland, grassland, settlement areas show an increasing trend, especially settlements with an obvious change of 0.56%. The area of bare land will decrease by 0.89%. Without environmental degradation, tremendous structural change of LUCC will not occur, and typical characteristic of the vertical zone of the mountain would remain. Farmland and settlement areas will increase, but only in the vicinity of Qilian and Sunan counties.     

中国留学人员创业园分布及产业特征

以《中国留学人员创业年鉴》为基础数据,采用GIS空间分析技术,运用核密度估计法、L函数和最近邻层次聚类分析等方法,分析2006~2015年中国留学人员创业园空间分布规律以及园区产业集聚特征,结果表明：中国留学人员创业园集聚特征显著,而且随着时间的推移,其多中心趋势明显,集聚规模迅速增大,集聚强度呈“先升后降,但总体增大”趋势;主要集聚于长三角、京津冀和珠三角等地区,尤以前两者最显著,且三大集聚区的集聚强度与集聚规模差异明显,其中长三角留创园的集聚规模最大,京津冀集聚强度最高,而珠三角则比较复杂,其在很小空间尺度范围内服从集聚分布,而在较大空间尺度范围内则服从随机分布;基于不同主导产业的中国留学人员创业园依然集中于长三角、京津冀和珠三角等地区,其主导产业分布格局则差异显著,规律性不强。     

青藏高原古人类的艺术创作——基于中更新世手脚印形态分析

在第二次青藏科考过程中,作者在西藏自治区拉萨市邱桑村的古泉华沉积上新发现了由5个手印和5个脚印组成 “岩面艺术” （parietal art）。对这一“岩面艺术”进行解剖学测量、3D 模型模拟、铀系测年和手脚印形态分析后,得到以下认识：① “岩面艺术”是在泉华没有成岩之前由古人类有意压上去的;② “岩面艺术”所在岩层的U/Th年代为226~169 ka B.P.,这其中包含一个手指印岩层的表面直接年龄（207~188 ka B.P.）;③ 这一发现是迄今为止发表的古人类在青藏高原上活动的最早记录之一,也为古人类适应高寒、低氧环境提供了新的证据;④ 此岩面艺术是目前世界上已知的最古老的艺术,上面的手印也是古人类留下的最早的手印;⑤ 这一原始的岩面艺术表明,创作它的古老人种可能具有一定的认知和空间感知能力,此岩面艺术的发现也将有可能改写人类艺术史,并且加深我们对已消失的古人类认知水平的认识。     

末次间冰期127 ka时期植被反馈增强东亚夏季风降水的数值模拟研究

黄土和石笋等古气候代用资料表明在末次间冰期间,东亚夏季风增强、降水增多。本研究利用地球系统模式EC-Earth模拟了末次间冰期127 ka时期的气候,通过和工业革命前的气候模拟控制试验做比对,分析了127 ka时期由于地球轨道参数变化导致的东亚夏季风的空间变化特征。我们利用了两种EC-Earth的模式配置,即"大气-陆面-海洋-海冰"耦合模式和"大气-陆面-海洋-海冰-动态植被"耦合模式,分别估算轨道强迫和植被反馈对东亚夏季风降水变化的贡献。数值模拟结果表明,地球轨道强迫导致的海陆热力差异使得东亚夏季风系统显著增强并北移西伸,中国中部及华北地区降水增多而东部沿海地区降水减少。耦合了动态植被模式的试验结果表明,127 ka时期温暖湿润的气候致使东亚地区植被增多,植被的蒸腾作用使得地表的感热和潜热通量显著增大,从而增强了局地水循环,使降水进一步增多。植被的反馈作用在原本温暖湿润的华南地区对降水的影响并不显著,但是对相对干旱的我国中部和华北地区降水有显著影响。数值试验结果表明轨道强迫和植被反馈的共同作用能使内陆的四川盆地到华北一带夏季降水增加约40%,其中30%的增加是由于轨道强迫作用,约10%是由于植被反馈。这个研究也提醒我们,要得到更加合理的对过去或未来气候变化的模拟结果,有必要使用耦合动态植被的气候系统模式。