Energy partitioning over a semi‐arid shrubland in northern China

During the last decade, the widely distributed shrublands in northern China have shown significant signs of recovery from desertification, the result of widespread conservation practices. However, to support the current efforts in conservation, more knowledge is needed on surface energy partitioning and its biophysical controls. Using eddy‐covariance measurements made over a semi‐arid shrubland in northwest China in 2012, we examined how surface energy‐balance components vary on diurnal and seasonal scales, and how biophysical factors control bulk surface parameters and energy exchange. Sensible heat flux (H) exceeded latent heat flux (λE) during most of the year, resulting in an annual Bowen ratio (β, i.e. H/λE) of 2.0. λE exceeded H only in mid‐summer when frequent rainfall co‐occurred with the seasonal peak in leaf area index (LAI). Evapotranspiration reached a daily maximum of 3.3 mm day^?1, and summed to 283 mm yr^?1. The evaporative fraction (EF, i.e. λE/R_n), Priestley–Taylor coefficient (α), surface conductance (g_s) and decoupling coefficient (Ω) were all positively correlated with soil water content (SWC) and LAI. The direct enhancement of λE by high vapour pressure deficit (VPD) was buffered by a concurrent suppression of g_s. The g_s played a direct role in controlling EF and α by mediating the effects of LAI, SWC and VPD. Our results highlight the importance of adaptive plant responses to water scarcity in regulating ecosystem energy partitioning, and suggest an important role for revegetation in the reversal of desertification in semi‐arid areas. Copyright © 2015 John Wiley & Sons, Ltd.     

组合粗差探测的MHSS ARAIM算法

针对目前MHSS ARAIM (multiple hypothesis solution separation advanced receiver autonomous integrity monitoring)算法存在的抗差能力弱、计算子集过多、计算量过大等不足,提出一种组合粗差探测的MHSS ARAIM算法。该算法先用粗差探测方法对原始数据进行粗差识别与剔除,而后用MHSS ARAIM算法处理经粗差探测后的数据,可弥补MHSS ARAIM算法的不足。对若干IGS和全球连续监测评估系统iGMAS(international GNSS monitoring and assessment system)监测站观测数据进行计算和分析。结果表明:在航行LPV-200阶段,该算法应用于GPS和BDS导航的性能优于MHSS ARAIM;在假设单故障情况下,该算法对GPS和BDS观测数据的有效监视门限EMT(effective monitor threshold)的精度分别提高了22.47%和9.63%,对VPL(vertical protection level)的精度分别提高了32.28%和12.98%;在假设双故障情况下,对EMT的精度分别提高了80.85%和29.88%,对VPL的精度分别提高了49.66%和18.24%。     

More than two hundred and fifty thousand spectroscopic binary or variable star candidates discovered by LAMOST

About 786.4 thousand stars were observed by LAMOST twice or more during the first stage of its spectroscopic survey. The radial velocity differences for about 256 thousand targets are larger than10 km s~(-1) and they are possible spectroscopic binary or variable candidates(SBVCs). It is shown that most SBVCs are slightly metal poorer than the Sun. There are two peaks in the temperature distribution of SBVCs around 5760 K and 4870 K, while there are three peaks in the distribution of the gravitational acceleration at 2.461, 4.171 and 4.621 cm s~(-2). The locations of SBVCs on the [Fe/H]-T, [Fe/H]-log g, log g-T and H-R diagrams are investigated. It is found that the detected SBVCs could be classified into four groups. The first group has higher log g～4.621 and lower T ～ 4870 K which are mainly cool red dwarf binaries. The second group of SBVCs has logg around 4.171 cm s~(-2) that includes binaries and pulsating stars such as δSet and γ Dor variables. The gravitational accelerations of the third group of SBVCs are higher and some of them are below the zero-age main sequence. They may be contact binaries in which the primary components are losing energy to the secondaries in the common envelopes and are at a special stellar evolutionary stage.The last group is composed of giants or supergiants with log g around 2.461 cm s~(-2) that may be evolved pulsating stars. One target(C134624.29+333921.2) is confirmed as an eclipsing binary with a period of 0.65 days. A preliminary analysis suggests that it is a detached binary with a mass ratio of 0.46. The primary fills its critical Roche lobe by about 89%, indicating that mass transfer will occur between the two components.     

依兰-伊通断裂新构造活动规律分析

作为郯庐断裂带北段主干的依兰-伊通断裂, 其新构造活动性与活动规律仍然存在不同的认识.本次工作通过详细的野外调查, 发现该断裂内活断层广泛存在, 由东、西两支北东走向的主干活断层构成, 沿着古近纪地堑边界断层发育.这些活断层主要呈破碎型结构, 多为逆右行平移活动.通过对这些活断层一系列实测擦痕反演应力场, 显示它们多是在东西向挤压中活动的, 而现今应力场转变为北东东-南西西向区域性挤压.依据本次野外观察与¹⁴ C定年, 并结合前人定年结果与近代地震分布, 表明依兰-伊通西支活断层的最新活动时代为全新世与晚更新世相间, 而东支活断层的最新活动时代主要为早-中更新世.依兰-伊通断裂内活断层显示了明显的差异性活动, 表现为西支的活动强度明显大于东支, 西支的最新活动时代皆晚于东支, 沿走向上活动性强、弱相间与最新活动时代不断变化, 以及近代地震活动不均一分布.它们沿走向上的分段性、差异性活动主要是因为被一系列北西向断层切断所致.     

Mesoscale numerical simulation study of warm fog dissipation by salt particles seeding

Based on the dynamic framework of WRF and Morrison 2-moment explicit cloud scheme, a salt-seeding scheme was developed and used to simulate the dissipation of a warm fog event during 6–7 November 2009 in the Beijing and Tianjin area. The seeding effect and its physical mechanism were studied. The results indicate that when seeding fog with salt particles sized 80 μm and at a quantity of 6 gm~(-2) at the fog top, the seeding effect near the ground surface layer is negative in the beginning period, and then a positive seeding effect begins to appear at 18 min, with the best effect appearing at 21 min after seeding operation. The positive effect can last about 35 min. The microphysical mechanism of the warm fog dissipation is because of the evaporation due to the water vapor condensation on the salt particles and coalescence with salt particles.The process of fog water coalescence with salt particles contributed mostly to this warm fog dissipation. Furthermore, two series of sensitivity experiments were performed to study the seeding effect under different seeding amounts and salt particles sizes. The results show that seeding fog with salt particles sized of 80 μm can have the best seeding effect, and the seeding effect is negative when the salt particle size is less than 10 μm. For salt particles sized 80 μm, the best seeding effect, with corresponding visibility of 380 m, can be achieved when the seeding amount is 30 g m~(-2).     

Ensemble simulation of land evapotranspiration in China based on a multi-forcing and multi-model approach

In order to reduce the uncertainty of offline land surface model(LSM) simulations of land evapotranspiration(ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS(Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs(BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982–2007. The results showed that various simulations of each member and their arithmetic mean(Ens Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates(Obs MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens Mean was closer to Obs MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs MTE and Ens Mean exhibited a significant increasing trend during 1982–98; whereas after 1998, when the last big EI Ni ?no event occurred, the Ens Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.     

Incorporation of a dynamic root distribution into CLM4.5: Evaluation of carbon and water fluxes over the Amazon

Roots are responsible for the uptake of water and nutrients by plants and have the plasticity to dynamically respond to different environmental conditions. However, most land surface models currently prescribe rooting profiles as a function only of vegetation type, with no consideration of the surroundings. In this study, a dynamic rooting scheme, which describes root growth as a compromise between water and nitrogen availability, was incorporated into CLM4.5 with carbon–nitrogen(CN) interactions(CLM4.5-CN) to investigate the effects of a dynamic root distribution on eco-hydrological modeling. Two paired numerical simulations were conducted for the Tapajos National Forest km83(BRSa3) site and the Amazon, one using CLM4.5-CN without the dynamic rooting scheme and the other including the proposed scheme. Simulations for the BRSa3 site showed that inclusion of the dynamic rooting scheme increased the amplitudes and peak values of diurnal gross primary production(GPP) and latent heat flux(LE) for the dry season, and improved the carbon(C) and water cycle modeling by reducing the RMSE of GPP by 0.4 g C m~(-2)d~(-1), net ecosystem exchange by 1.96 g C m~(-2)d~(-1), LE by 5.0 W m~(-2), and soil moisture by 0.03 m~3m~(-3), at the seasonal scale, compared with eddy flux measurements, while having little impact during the wet season. For the Amazon, regional analysis also revealed that vegetation responses(including GPP and LE) to seasonal drought and the severe drought of 2005 were better captured with the dynamic rooting scheme incorporated.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.