基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析

云南沿边地区包括8个地州,共56个县,其中有25个县市与老挝、缅甸和越南直接毗邻,具有重要的地缘位置。本研究利用土地利用数据和夜间灯光数据在实现云南沿边地区GDP空间化的基础上,对GDP的空间分布格局进行深入探讨,这对缩小区域经济差异及促进地区共同发展具有一定的指导意义。采用土地利用数据对国内生产总值（Gross Domestic Product, GDP）数据的第一产业进行空间化拟合,采用DMSP/OLS夜间灯光数据对GDP的第二、三产业进行拟合,将第一产业和第二、三产业空间化拟合的结果相加,实现云南沿边地区1992-2013年的GDP的空间化拟合。在此基础上对云南沿边地区GDP空间分布差异进行分析。结果表明：① 土地利用数据对第一产业建模的效果较好,拟合的多期数据的相对误差均低于1.12%,采用夜间灯光数据,基于“分类回归”方法对第二、三产业拟合相对误差最大仅为6.404%,最终二者之和拟合的GDP拟合精度都较好,相对误差最大仅为4.241%;② 22期GDP数据在空间分布上均呈现正的相关性,且均为显著集聚;③ GDP空间分布局部集聚的高值-高值区域集中在开远、蒙自等县域,低值-低值地区集中在绿春、西蒙等地区;④ 云南沿边地区县域之间的经济差异在1992-1996年逐渐增强,1996年之后,经济差异波动缩小,空间关联效应呈现波动式的增强和减弱;⑤ 云南沿边地区的三维插值结果均呈现出西北至东南一线的“洼地-丘陵-平地-高峰”地势变化格局,沿边地区的东南角地区即红河州的建水、个旧和开远等县市的GDP最高,“丘陵”地势主要集中在腾冲、保山市以及最南部的景洪地区,“洼地-平地”地势主要分布在沿边地区西北角的贡山和福贡等县域、西南角的西蒙和孟连等县及中部区域的绿春和江城县等地区。     

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.     

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.     

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.     

Factors controlling the interannual variations of MJO intensity

The interannual variations of intensity of the Madden-Julian Oscillation (MJO) during boreal winter are investigated by using the observed outgoing longwave radiation (OLR) and the reanalysis data of ECMWF and NCEP. The standard deviation of 20-80-day filtered OLR anomaly is used to measure the MJO intensity. The dominant spatial structure of the interannual variability is revealed by an EOF analysis of the MJO intensity field. It is found that the leading mode is associated with eastern Pacific type ENSO, whereas the second mode is related to central Pacific type ENSO. A simple atmospheric model is used to investigate the relative roles of background moisture and wind changes in affecting the overall strength of MJO. The numerical experiments indicate that the background moisture effect is dominant while the background wind change has a minor effect.