洱海湖滨带水生植物多样性及分布现状

湖滨带水生植物对湖泊生态系统健康的维持具有重要作用。为了解当前洱海湖滨带水生植物现状,本研究于2020—2021年间每季度对洱海湖滨带水生植物进行调查。调查结果表明:(1)洱海湖滨带现有水生植物206种,属56科156属,其中湿生植物149种,挺水植物24种,沉水植物21种,漂浮植物7种,浮叶植物5种;常见种有狗牙根(Cynodon dactylon)、菰(Zizania latifolia)、苦草(Vallisneria natans)、菱(Trapa bispinosa)等,偶见种为忍冬(Lonicera japonica)、披碱草(Elymus dahuricus)等。(2)从区系分布来看,洱海湖滨带物种主要为世界分布(83种)和热带分布(55种)两种类型,分别占总物种数比例的40.28%和26.71%。(3)在植物群落方面,洱海湖滨带共有18个主要植物群落类型,其中湿生植物群落4种,挺水植物群落3种,沉水植物群落9种,浮叶植物群落2种;以狗牙根群落、菰群落、苦草群落、菱群落为主。通过与历史文献结果的对比分析得出,近年来洱海湖滨带水生植物多样性有了显著提高,但目前存在挺水植物群落...     

１０～３０ｄ延伸期数值天气预报的策略思考———直面混沌

针对大气系统的混沌特性,提出了10~30 d延伸期数值天气预报的策略思考。可预报性问题,实质上是时空尺度问题,10~30 d的预报虽然超出了逐日天气的可预报时限,但仍存在着可预报的分量。以数值模式为基础,阐述了10~30 d延伸期可预报分量的提取方法。在此基础上,提出了针对可预报分量和混沌分量应采用不同的策略和方法。该设想可借鉴利用现有数值模式的变分同化系统,且无需构建新的模式,因此具有可行性。     

New aromatic biomarkers and possible maturity indicators found in New Albany Shale extracts

Aromatic hydrocarbons from benzene extracts of New Albany Shale were characterized. A biomarker that has a molecular weight of 546 and a structural configuration consistent with that of an alkyl-aromatic hydrocarbon (C₄₀H₆₆) was tentatively identified. It was found that the relative concentrations of the biomarker are indicative of differing levels of thermal maturity of the shale organic matter. A 40-carbon bicyclic carotenoid (C₄₀H₄₈) is proposed as the geochemical precursor of this biomarker. Thermal maturity of the shale organic matter can also be differentiated by observing differences in “fingerprints” as obtained by field-ionization mass spectrometry on the aromatic hydrocarbon fraction. Using this technique, we found that the more mature shale samples from southeastern Illinois contain more low molecular weight extractable aromatic hydrocarbons and the less mature shale samples from northwestern Illinois contain more high molecular weight extractable aromatic hydrocarbons. It was demonstrated that field-ionization and tandem mass spectrometric techniques through fingerprint and individual compound identification, are useful for shale aromatic hydrocarbon fraction characterization and for thermal maturation interpretation.     

The activity of convectively coupled equatorial waves in CMIP3 global climate models

This study evaluates the convectively coupled equatorial waves in ten coupled general circulation models (GCMs) in the twentieth century experiment from the Coupled Model Intercomparison Project phase 3 of the World Climate Research Programme. The antisymmetric bands in all GCMs are weaker than in observations, and the mixed Rossby-gravity (MRG) wave seems to be a mixture of the equatorial Rossby (ER) and tropical depression-type (TD-type) waves rather than a mixture of the ER and inertiogravity waves found in observations. The simulated TD-type wave is more organized than in observations with a quasilinear wavenumber–frequency relationship. In most GCMs, the two observed activity centers of the MRG and TD-type waves over the southern Indian Ocean and the southwestern Pacific cannot be separated; only one wave activity center is found over the Maritime Continent. The observed northwestward propagation of the TD-type wave over the western North Pacific is also not well simulated in the GCMs. The simulated active season of the MRG and TD-type waves over the northern hemisphere during the boreal summer and fall is much shorter than in observations. The models from CCSR utilizing the Pan and Randall scheme with the convection suppression simulate the realistic Kelvin wave activity with the maximum activity near the equator, while the wave activities filtered for the Kelvin wave in the other GCMs are similar to the extratropical Rossby wave with the maximum activity at higher latitudes. Likewise, only these two models produce a realistic seasonal cycle of the Kelvin wave activity.     

Projections of climate change impacts on central America tropical rainforest

Tropical rainforest plays an important role in the global carbon cycle, accounting for a large part of global net primary productivity and contributing to CO₂ sequestration. The objective of this work is to simulate potential changes in the rainforest biome in Central America subject to anthropogenic climate change under two emissions scenarios, RCP4.5 and RCP8.5. The use of a dynamic vegetation model and climate change scenarios is an approach to investigate, assess or anticipate how biomes respond to climate change. In this work, the Inland dynamic vegetation model was driven by the Eta regional climate model simulations. These simulations accept boundary conditions from HadGEM2-ES runs in the two emissions scenarios. The possible consequences of regional climate change on vegetation properties, such as biomass, net primary production and changes in forest extent and distribution, were investigated. The Inland model projections show reductions in tropical forest cover in both scenarios. The reduction of tropical forest cover is greater in RCP8.5. The Inland model projects biomass increases where tropical forest remains due to the CO₂ fertilization effect. The future distribution of predominant vegetation shows that some areas of tropical rainforest in Central America are replaced by savannah and grassland in RCP4.5. Inland projections under both RCP4.5 and RCP8.5 show a net primary productivity reduction trend due to significant tropical forest reduction, temperature increase, precipitation reduction and dry spell increments, despite the biomass increases in some areas of Costa Rica and Panama. This study may provide guidance to adaptation studies of climate change impacts on the tropical rainforests in Central America.     

Four-dimensional data assimilation method based on SVD: Theoretical aspect

Summary A new method of four-dimensional data assimilation based on Singular Value Decomposition (SVD) is proposed. In it, a set of atmospheric states is obtained by integrating a numerical weather prediction model and simulated observations are taken and calculated from the model variables. Then the SVD technique is used to create the base vectors from this coupled data set. Finally, the analysis is obtained by projecting actual observation data into a space spanned by the base vectors. Using this approach, the four-dimensional data assimilation becomes a simple linear inverse problem the linearization of the nonlinear forward model is avoided, and the developments of the adjoint and background error covariance matrix are no longer needed. Since the SVD technique is used here, the method is simply called 4DSVD.     

甘肃“5·5"黑风暴小波分析

用小波变换理论对１９９３年春季甘肃省河西走廊地区一场特大黑风暴进行了分析。结果表明，小波分析能比较客观地将时间序列中不同时间尺度的振动分离开，使我们能清楚地看到黑风暴中的中尺度扰动。     

天气预报的相似-动力方法

本文将预报对象分解为参考态和扰动态两部分,参考态根据与预报对象初始场相似的原则从历史观测资料中选定,其演变过程是已知的;扰动态的演变则用动力方法预报。用准地转正压模式作了若干模拟实验,结果表明,该方法能利用历史资料提供的信息部分弥补预报模式的缺陷,不要求参考态与预报对象有很高的相似性,其预报结果就明显优于现行的数值预报方法。     

Gallium and germanium in the metal and silicates of L- and LL-chondrites

Concentrations of Ga and Ge in the metal of 31 L-, 8 LL- and 2 H-chondrites, and in the silicates of 12 L- and LL-chondrites have been determined by spectrophotometric methods. The ranges of Ga contents in the metal of L- and LL-chondrites are 1.1–36.9 ppm and 1.0–34.1 ppm, respectively. The Ge content in the metal is positively correlated with Ga and ranges from 89.1–160 ppm, and 126–308 ppm for L- and LL-chondrites, respectively. The Ga content in the silicates of L-chondrites varies from 4.0 to 8.9 ppm.The Ga and Ge contents in the metal are clearly lower in unequilibrated than in equilibrated L- and LL-group chondrites. Unequilibrated (type 3 and 4) and equilibrated (type 5 and 6) chondrites are well separated in the plots of Ga vs Ge in the metal and the L- and LL-groups are also well resolved. The Ga and Ge in the metal are well correlated with petrologic grade. This suggests that Ga and Ge variations in the metal are related to thermal metamorphism. There is evidence of an enrichment of Ga in the metal due to shock-reheating.     

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

Texture information offers an extensive solution for image classification by providing better accuracy of image information. However, huge amounts of improper additional texture information may result in a chaotic state, and this leads to uncertainty in the classification process. Considerable portion of earlier works have been carried out through the generally acknowledged procedure of Principal Components Analysis (PCA). However, the PCA method has flaws in the area of influenced and non- influenced attributes. On the whole, whether PCA provides an effective solution to determine the value of knowledge rule in image information still remains a question. This study proposes an innovative method, called Discrete Rough Set method, as a tool for image classification. This study focuses on two crucial issues: (1) The core attributes of the target categories in image classification are systematically analyzed while eliminating surplus attributes rationally; (2) The unique point of each attribute, which influenced the target categories, is successfully found. This is a crucial aspect that is very helpful for the construction of decision rule. Finally, in this study we utilized the expert knowledge classifier and the overall accuracy of Discrete Rough Set (96.67%) exceeds that of the conventional PCA (86.00%) of paddy rice area evaluation from Quickbird image. This result shows that the appropriate classification knowledge can be presented by Discrete Rough Set, and this information can effectively improve the accuracy of image classification. An erratum to this article can be found at