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.     

近54年阿勒泰地区夏季极端降水事件气候特征

本文采用阿勒泰地区7个国家级气象站近54a(1960～2013年)夏季(6～8月)的日有效降水量（20～20h降水量≥0.1mm）资料,用WMO推荐的百分位法计算了全地区过去54a夏季极端强降水的阈值、进一步分析了当地夏季时空分布特征及变化趋势,结果表明：阿勒泰地区夏季极端强降水阈值呈西部、南部小,北部、东部大,并且空间异常分布特征如下: 夏季以及夏季各月的极端强降水日数和强度均可总结出5种最主要模型;极端强降水量可总结出8种最主要模型;并且通过时间标准化序列分析各种模型都有对应的降水日数、量级、强度明显偏多（强）和偏少（弱）的时段。日数、量级、强度近54a来,除吉木乃略有下降以外,其余各县（市）均为增长趋势,尤其是北部、东部地区.同时上述三指标存在着显著的年代际和年纪尺度的周期变化,上世纪90年代和2010年至今为三个指标最多(强)的年代,而上世纪70年代为最少（最差）的年代.并通过周期分析（小波分析）可知,均有对应的显著变化周期。     

加密探空资料在华东区域业务模式中的应用试验

采用中国气象局2014年6月1日—30日14时加密探空资料,利用华东区域中尺度数值预报业务系统比较同化加密探空观测资料前后模式预报结果的差异。研究表明,同化加密探空资料后,对模式初始时刻不同高度的位势高度、比湿、温度、风速等变量均有一定的影响;对位势高度、温度和风场的影响在高层100—150 h Pa比较显著,而对比湿的影响主要体现在低层700—750 h Pa。同化加密探空资料后模式初始场更接近实况。批量数值试验的统计检验表明,同化加密探空观测资料后对强降水及形势场预报均有不同程度改进,24 h暴雨和大暴雨量级降水的预报技巧分别提高了2.5%和8.1%。     

无人直升机搭载大气颗粒物采样装置的前期地面评估

与搭载气溶胶观测和分析仪器测量相比,利用无人直升机搭载气囊采集高空大气颗粒物是一种经济、便捷和安全的采集方式。为了解无人直升机采样装置的采样效果,选择2014年10月至2015年3月的4次重污染天气过程,由无人直升机搭载气囊采集大气颗粒物,然后利用气溶胶采样器采集气囊内的大气颗粒物样品并计算采样时间,再利用气溶胶采样器采集相同时间段内的地面大气颗粒物(气囊外),并将两者进行对比。结果表明:在稳定的污染天气条件下,采集气囊内的大气颗粒物,与相同时间段内利用气溶胶采样器直接采集气囊外的大气颗粒物相比,两种方式采集到的大气颗粒物质量最大差值29%,最小差值11%,平均为21%;利用无人直升机的大气采样装置采集大气颗粒物对碳气溶胶组分影响很小,气囊内与气囊外OC/TC最大相差2.0%,最小相差0.5%。上述结果表明,无人直升机的大气采样装置有比较好的采集效率,利用气囊这种经济、便捷、安全的方式,搭载在无人机平台上,采集高空大气是一种获取气溶胶垂直廓线的有效方法,有望在未来的气溶胶气候和环境研究中提供宝贵数据。     

Study on the Temporal-Spatial Evolution of Prehistoric Settlements and Its Correlation with Subsistence Strategy and Climate History in the Western Liao River Area

Western Liao River area is one of the most important birthplaces of agricultural civilization in northern China. With recent works like relics survey, site excavation and environmental archaeology going further, the time sequence, cultural content and subsistence strategy history of this area are getting explicit. Questions like ancient exploiting history of natural resources, climate background and cultural impact of subsistence strategy have been paid great attention. Traditional environmental archaeology methods like plant and animal remains analysis are mostly utilized to conduct in-site investigation, current studies rarely adopt quantitative methods to examine the sites’ macro temporal-spatial distribution and aggregation pattern. Based on abundant digitized relic survey data, GIS methods like kernel density analysis are utilized to indicate the temporal-spatial distribution and aggregation pattern of prehistoric cultures in Western Liao River area. The latitudinal, longitudinal and altitudinal migrations of site distributional core are indicated by 3D perspective shift and elevation statistics. By referencing existing studies on subsistence strategy and paleoclimate history, results of spatial analysis are used to reveal the correlation among sites’ temporal-spatial distribution, subsistence strategy adopted and climate variation. This also contributes to our understanding and utilization of massive locational information of prehistoric sites in ancient man-land relationship study.     

Study on the Global Carbon Assimilation System Based on Multisource Remote Sensing Data

The Paris agreement signed in April, 2016 aims to balance global anthropogenic carbon emissions and terrestrial carbon sinks by the middle of the 21^st century. To fulfill this goal, it is necessary to calculate carbon fluxes of different regions reliably. The global carbon assimilation system is an effective technique for achieving this goal. The Ministry of Science and Technology of China supports the project entitled as study on the global carbon assimilation system based on multisource remote sensing data through the national key research and development programs for global change and adaptation during the thirteen-five period. This project will develop synergic inversion techniques for retrieving key parameters of biological and atmospheric cycles and for assimilating multisource remote sensing and ground based data. Then, the high resolution global carbon assimilation system coupled with an ecological model will be constructed. This system is able to assimilate jointly multisource observation data and to optimize key model parameters, photosynthesis and respiration carbon fluxes of global terrestrial ecosystems, and anthropogenic carbon emission fluxes of key regions. This system will be used to study quantitatively the spatial and temporal patterns of carbon fluxes of global terrestrial ecosystems and anthropogenic carbon emission fluxes of key regions and to identify the mechanisms driving the global terrestrial carbon sinks and sources. The outputs of this study will be helpful for the fulfillment of the key research and development programs for global change and adaptation and provide valuable data and technical support for the decision-making in China.     

A Method for Automatically Selecting Aids to Navigation Based on their Spatial Influence Domains

To address the limitations of manually selecting aids to navigation (AtNs) on charts, a method for automatically selecting AtNs based on their spatial influence domains (SIDs) is proposed. First, the associations between the spatial attributes of an AtN are analyzed. Second, an SID of the AtN is defined, and a model of the SID is constructed based on the associations between the spatial attributes. Third, the importance of the location of the AtN is weighted based on the SID model. Fourth, an algorithm to automatically select AtNs based on the maximum coverage of the SIDS of preselected AtNs is developed. Finally, several AtNs are selected automatically using the algorithm. The experimental results demonstrate that (1) the proposed method can automatically select AtNs and the results comply with the requirements; (2) the automatic selection can eliminate the human-induced errors or the inconsistent results of manual selections from different operators; and (3) the efficiency of the proposed method is higher than that of current manual methods.