PM_(2.5)污染的社会成本——基于74城市动态气候经济综合模型分析

中国的PM_(2.5)已经产生巨大的社会成本。借助气候经济动态综合模型(DICE-2013R),综合分析中国74个城市中与PM_(2.5)社会成本有关的福利目标、经济变量和地球物理等模型,估算这些城市PM_(2.5)的社会成本。研究结果发现:2017年各城市PM_(2.5)的单位社会成本介于16.21亿~232.15亿元之间,所有城市的单位社会成本总量达到9 863.94亿元,预计到2030年,单位社会成本总量将达到15 000亿元;若74个城市PM_(2.5)浓度都下降到35μg/m^3或10μg/m^3,社会总成本将达到14万亿或40万亿元,将产生高昂成本;治理PM_(2.5)必须走市场化碳金融之路。

甘肃临夏高寒地区春玉米螟动态气候预测模型

利用临夏地区1990-2007年的气温、降水、日照等气象资料,采用数理统计及多元回归方法,分析高寒地区干旱和阴湿两种不同气候背景下玉米螟生成的主要气候影响因子,按不同气候种植区,建立玉米螟动态气候预测模式。经检验,临夏地区玉米螟的发生与当地的气象条件有直接的关系,且玉米螟发生期早期和晚期预测准确率达70%以上,效果良好。

长白山阔叶红松林近22年的气候动态

长白山阔叶红松林作为典型的温带森林生态系统,其长期的气候动态状况对研究全球变化具有重要的意义.本文采用中国科学院长白山森林生态系统定位站气象观测场1982～2003年的地面常规气象观测资料,对长白山阔叶红松林的光能因子(包括年日照时数、年日照百分率)、热量因子(包括年平均气温、1月、7月月平均气温、年极端最高、最低气温、年积温)、水分因子(包括年总降水量、年最大雪深、相对湿度、年总蒸发量)、以及年平均风速与风向等气候因子进行了分析,从而得出这些因子22年的平均值及其动态变化趋势,进而为相关领域的研究提供基础资料.

气候变化对长江口鱼类资源密度分布的重塑作用

本研究以2012-2013年长江口鱼类资源密度分布为基础,通过动态生物气候分室模型(DBEM)预估了不同气候变化情景下(IPCC,RCP2.6、RCP6.0和RCP8.5)长江口鱼类资源密度增量分布的变化。在RCP2.6、RCP6.0和RCPS.5这3种气候变化情景下,鱼类资源密度增量、底层鱼类资源密度增量随着时间推移均呈递增趋势,且递增程度和增量重心分布范围随着温室气体排放的增加而扩大(RCP8.5>RCP6.0>RCP2.6)。鱼类资源密度增量重心主要分布在长江口崇明岛沿岸水域,长江口外侧水域资源密度增量相对较低,并且资源密度增量重心有向南迁移的趋势。

中国亚热带地区柑桔气候风险评估

综合考虑柑桔气候适宜性水平及其变率变化,构建柑桔的风险度模型,运用滑动建模技术对中国亚热带地区柑桔的气候风险性进行动态分析与评估。根据风险分布强度将中国亚热带地区柑桔温度、降水、日照和气候风险大致分为三类型:低风险型、中风险型和高风险型。温度风险度大致呈纬度地带性分布,除西部高山区外,由低纬向高纬风险度依次增高;降水风险度呈现亚热带中部低,北部和西部高;与降水风险度相反,日照风险度在亚热带中部高,北部和西部低;气候风险度受温度变化的主导,也大致呈现纬度地带性,呈现高纬和西部高海拔区高,低纬和东部沿海区低。柑桔气候风险在时间和空间变化上都存在着差异,近46年来,中国亚热带地区柑桔气候风险度有逐渐增加的趋势,尤其以20世纪80年代初以来增加的最快;由于全球气候变暖的影响,亚热带东部和南部风险较低的区域分布有逐渐减少的趋势,而北部和西部风险高的区域分布有进一步增大并向东部和南部扩展的可能。从中国亚热带地区柑桔减产率大于10%、20%、30%的气候风险度分布区域变动过程来看,柑桔各减产率的气候风险度分布具有很明显的区域性和连续性,大体上由东南向西北呈增高趋势。

夏季西太平洋副热带高压南北位置变动特征及其影响

使用ECMWF再分析数据集资料中的位势高度场、三维风场、温度场、相对湿度场、海温场以及中国气象局730站地面降水资料和日最高温度资料,对比分析研究了7月份西太平洋副高位置偏南和偏北过程(持续满足南、北位置指标6天以上定义为一次南、北部型过程)所对应的动态气候特征及其演变。分析首先揭示出了西太平洋副高的南北位置变化存在季节内振荡特征;从西太平洋副高偏南、偏北位置所对应的各层环流特征、中国东部降水分布、中国日最高温度的分布,研究了西太平洋副高南北位置对东亚及西北太平洋地区天气气候的影响。结果表明,尽管同在7月份,西太平洋副高南北位置的不同对大气环流和天气气候的影响却有显著的差异,需要从大气系统变化的角度研究和认识某时期的天气气候特征。针对两类过程的演变,分析了西太平洋副高南、北位相过程开始的前兆气候特征,过程中的变化特征以及过程结束后的后延影响,揭示出了一些以前研究所忽略的气候演变特征及其差异。

MODAS试验数据统计分析

模块化海洋数据同化系统(MODAS)通过同化卫星遥感测得的海面温度和海面高度,产生一种动态气候态,能够更接近地预报出海洋的真实状况。介绍了MODAS基本原理,并选择试验海区,对MODAS数据进行了统计和分析。

DICE/RICE模型中碳循环模块的比较

碳循环模型的正确构建是影响综合集成评估模型IAM(Integrated Assessment Model)模拟结果的重要因素之一。DICE/RICE模型中的碳循环模型主要有两个,即Nordhaus单层碳库模型和Nordhaus三层碳库模型,但这两个模型的主要缺陷是不考虑陆地生态系统在碳循环中的贡献,因此,引入了包含陆地生态系统的Svirezhev碳循环模型,并将其与Nordhaus单层碳库模型、Nordhaus三层碳库模型展开比较研究。结果表明,在基于历史数据的模型检验中,Svirezhev碳循环模型对全球二氧化碳浓度模拟的准确度优于其他两个模型。对于未来全球气候变化的模拟,3个模型模拟得到了至2100年的温度预测值分别为2.98,3.54,2.91℃,二氧化碳浓度值分别为608.04,733.04,594.70μL/L。其中,Svirezhev碳循环模型的模拟值在3个模型中最低,表明了陆地生态系统和海洋对二氧化碳的吸收作用对抑制全球升温的贡献;而分析也发现Nordhaus三层碳库模型对陆地生态系统和海洋碳库的模拟与实际观测值偏离较大。最后,通过敏感性分析,研究发现DICE/RICE模型中使用的气候响应模块在短期温度模拟中对地表温度的初值较为敏感,在长期温度模拟中敏感度显著下降。总之,从碳循环机制的模拟性能而言,Svirezhev碳循环模型优于其他两个模型,而Nordhaus单层碳库模型虽然机制较为简单却保证了模拟的准确性,但Nordhaus三层碳库模型虽然丰富了碳库的表征,实际上各碳库的模拟准确性差,降低了模型的可靠性。

松突圆蚧自然种群时间序列研究

以福清市周围松林为研究点,对松突圆蚧的种群动态进行了系统观察研究.林间种群消长有4个明显的波峰,4月中旬的波峰最突出.全年4月上旬至7月上旬为松突圆蚧的高发期.利用时间序列分析方法研究了松突圆蚧自然种群序列和气候序列之间的关系.在福清地区,各个气候主要因子的时间序列不存在自相关关系,相反松突圆蚧种群动态各主要因子之间存在自相关关系.松突圆蚧1龄若虫的死亡率与旬平均温度呈极显著相关.总的虫口死亡率与旬平均温度呈显著相关.降水量和空气相对湿度对松突圆蚧的死亡率相关性不显著.性比与旬平均相对湿度呈显著相关.性比与旬平均温度呈显著相关.林间虫口密度与降水量呈极显著负相关,降水量对初孵若虫的死亡率呈极显著相关,是影响初孵若虫种群的关键因子.

Vegetation-Climate Relationship and Its Application in the Division of Vegetation Zone in China

Distribution of vegetation is closely coupled with climate; the climate controls distribution of vegetation and the vegetation type reflects regional climates. To reveal vegetation_climate relationships is the foundation for understanding the vegetation distribution and theoretically serving vegetation regionalization. Vegetation regionalization is a theoretical integration of vegetation studies and provides a base for physiogeographical regionalization as well as agriculture and forestry regionalization. Based on a brief historical overview on studies of vegetation_climate relationships and vegetation regionalization conducted in China, we review the principles, bases and major schemes of previous vegetation regionalization and discuss on several contentious boundaries of vegetation zones in the present paper. We proposed that, under the circumstances that the primary vegetation has been destroyed in most parts of China, the division of vegetation zones/regions should be based on the distribution of primary and its secondary vegetation types and climatic indices that delimit distribution of the vegetation types. This not only reveals the closed relationship between vegetation and climate, but also is feasible practically. Although there still are divergence of views on the name and their boundaries of the several vegetation zones, it is commonly accepted that there are eight major vegetation regions in China, i.e. cold temperate needleleaf forest region, temperate needleleaf and broadleaf mixed forest region, warm temperate deciduous broadleaf forest region, subtropical evergreen broadleaf forest region, tropical monsoon forest and rain forest region, temperate steppe region, temperate desert region, and Qinghai_Xizang (Tibetan) Plateau high_cold vegetation region. Analyzing characteristics of vegetation and climate of major vegetation boundaries, we suggested that: 1) Qinling Mountain_Huaihe River line is an important arid/humid climatic, but not a thermal climatic boundary, and thus can not also be regarded as the northern limit of the subtropical vegetation zone; 2) the northern limit of subtropical vegetation zone in China is along the northern coast of the Yangtze River, from Hangzhou Bay, via Taihu Lake, Xuancheng and Tongling in Anhui Province, through by southern slope of the Dabie Mountains, to Wuhan and its west, coinciding with a warmth index ( WI ) value of 130-140 ℃·month; 3) the tropical region is limited in a very small area in southeastern Hainan Island and southern edge of Taiwan Island; and 4) considering a significant difference in climates between the southern and northern parts of the warm temperate zone, we suggested that the warm temperate zone in China is divided into two vegetation regions, deciduous broadleaf woodland region and deciduous and evergreen broadleaf mixed forest region, the Qinling Mountain_Huaihe River line being as their boundary. We also claimed that the zonal vegetation in North China is deciduous broadleaf woodland. Finally, we emphasized the importance of dynamic vegetation regionalization linked to climate changes.

南平市小麦赤霉病发生和气象条件的关系

以2003—2011年南平地区小麦3个对照品种的赤霉病发生程度为研究对象,对小麦赤霉病的病情指数、发病率进行调查,并与开花期温度、降雨量的进行相关性分析,然后在逐步回归分析的基础上建立小麦赤霉病对照品种发生程度的动态预测模式,拟合率为92.6%。

1982—2013年准噶尔盆地植被长势变化分析

利用准噶尔盆地1982—2013年的GIMMS NDVI3g数据,以及温度、降水、日照等气候因子数据,结合当地防沙治沙工程等人类活动,分析近32年来该区域的植被变化状况,结果表明:1)准噶尔盆地33.0%的区域呈现植被退化现象,约20.0%的区域呈现植被恢复现象。2)植被退化区域主要分布在干旱区域,植被覆盖稀疏,为半荒漠地带;植被显著恢复的区域主要位于植被较浓密区域。3)近32年来温度的上升使得干旱区的蒸发量增大,因降雨量变化不明显,因此干旱趋势加剧,植被的长势变弱;而准噶尔盆地南部区域,因区域供水平衡稳定,温度的上升促进了植被的生长。4)人类活动对该区域的植被长势变化影响不明显。土地沙漠化治理需要一个较长的周期,应该不断加强该区域的土地沙漠化治理及治理成效的监测。

Vegetation Dynamics and Its Relationship with Climatic Factors in the Changbai Mountain Natural Reserve

This study examined the temporal variation of the Normalized Difference Vegetation Index （NDVI） and its relationship with climatic factors in the Changbai Mountain Natural Reserve （CMNR） during 2000 - 2009. The results showed as follows. The average NDVI values increased at a rate of 0.0024 year-1. The increase rate differed with vegetation types, such as 0.0034 year-1 for forest and 0.0017 year-1 for tundra. Trend analyses revealed a consistent NDVI increase at the start and end of the growing season but little variation or decrease observed in July during the study period. The NDVI in CMNR showed a stronger correlation with temperature than with precipitation, especially in spring and autumn. A stronger correlation was observed between NDVI and temperature in the tundra zone （2,000-2,600m） than in the coniferous forest （1,100-1,700m） and Korean pine-broadleaved mixed forest （7oo-1,1oom） zones. The results indicate that vegetation at higher elevations is more sensitive to temperature change. NDVI variation had a strong correlation with temperature change （r=0.7311, p〈0.01） but less significant correlation with precipitation change. The result indicates that temperature can serve as a main indicator of vegetation sensitivity in the CMNR.

Dynamics and Responses of Vegetation to Climatic Variations in Ziya- Daqing Basins, China

Examining the direct and indirect effects of climatic factors on vegetation growth is critical to understand the complex linkage between climate change and vegetation dynamics. Based on the Moderate Resolution Imaging Spectroradiometer(MODIS) Normalized Difference Vegetation Index(NDVI) data and meteorological data(temperature and precipitation) from 2001 to 2012, the trend of vegetation dynamics were examined in the Ziya-Daqing basins, China. The path analysis was used to obtain the information on the relationships among climatic factors and their effects on vegetation growth. It was found that the trends of growing season NDVI were insignificant in most plain dry land, while the upward trends were significant in forest, grass and dry land in Taihang Mountains. According to the path analysis, in 23% of the basins the inter-annual NDVI variation was dominated by the direct effect of precipitation, in 5% by the direct effects of precipitation and temperature, and in less than 1% by the direct effect of temperature or indirect effects of these two climatic factors. It indicated that precipitation significantly affected the vegetation growth in the whole basins, and this effect was not regulated by temperature. Precipitation increase(especially in July, August and September) was favorable to greenness enhancement. Summer temperature rising showed negative effect on plant productivity enhancement, but temperature rise in April was beneficial for the vegetation growth. When April temperature increases by 1℃, the onset date of greenness for natural vegetation will be 2 days in advance. There was a lag-time effect of precipitation or temperature on monthly NDVI for all land use types except grass.

An enhanced influence of sea surface temperature in the tropical northern Atlantic on the following winter ENSO since the early 1980s

Previous studies have revealed a connection between springtime sea surface temperature （SST） in the tropical northern Atlantic （TNA） and the succeeding wintertime El Nino-Southern Oscillation （ENSO）. The present analysis demonstrates that the linkage between springtime TNA SST and the following ENSO experiences an obvious interdecadal change around the early 1980s, with the connection being weak before but significant after. After the early 1980s, springtime positive TNA SST anomalies induce an anomalous cyclone over the northeastern subtropical Pacific and an anomalous Walker circulation with a descending branch over the tropical central-eastern Pacific. This leads to anomalous cold SST in the northeastern Pacific and an anomalous anticyclone over the western-central tropical Pacific, with anomalous easterlies to the equatorward side. As such, springtime TNA SST anomalies are followed by wintertime ENSO after the early 1980s. In contrast, before the early 1980s, anomalous cold SST in the northeastern Pacific and related anomalous easterlies over the western-central tropical Pacific are weak, corresponding to springtime positive TNA SST anomalies and resulting in a weak linkage between springtimeTNA SST and the succeeding wintertime ENSO. Further investigation implies that the change in the TNA SST-ENSO relationship is probably due to a change in springtime mean precipitation over the tropical Atlantic and South America.

A Dynamic Method for Quantifying Natural Warming in Urban Areas

In the study of global warming, one of the main issues is the quantification of the urbanization effect in climate records. Previous studies have contributed much to removing the impact of urbanization from surface air temperature by carefully selecting reference stations. However, due to the insufficient number of stations free from the influence of urbanization and the different criteria used to select reference stations, there are still significant controversies about the intensity of the impact of urbanization on temperature records. This study proposes a dynamic method for quantifying natural warming using information on urbanization from every station acquired from remote sensing (RS) data instead of selecting reference stations. Two different spatial scales were applied to examine the impact of urbanization, but little difference was found, indicating the stability of this method. The results showed a significant difference in original temperature data and the homogenized data-urban warming accounted for approximately 64% in the original temperature warming but only approximately 20% in the homogenized temperature records.

A dynamic model for exploring water-resource management scenarios in an inland arid area: Shanshan County, Northwestern China

Water scarcity is a challenge in many arid and semi-arid regions; this may lead to a series of environmental problems and could be stressed even further by the effects from climate change. This study focused on the water resource management in Shanshan County, an inland arid region located in northwestern China with a long history of groundwater overexploitation. A model of the supply and demand system in the study area from 2006 to2030, including effects from global climate change,was developed using a system dynamics(SD)modeling tool. This SD model was used to 1) explore the best water-resource management options by testing system responses under various scenarios and2) identify the principal factors affecting the responses, aiming for a balance of the groundwater system and sustainable socio-economic development.Three causes were identified as primarily responsible for water issues in Shanshan: low water-use efficiency low water reuse, and increase in industrial waterdemand. To address these causes, a combined scenario was designed and simulated, which was able to keep the water deficiency under 5% by 2030. The model provided some insights into the dynamic interrelations that generate system behavior and the key factors in the system that govern water demand and supply. The model as well as the study results may be useful in water resources management in Shanshan and may be applied, with appropriate modifications, to other regions facing similar water management challenges.

Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014

Using the Moderate Resolution Imaging Spectroradiometer-normalized difference vegetation index(NDVI) dataset,we investigated the patterns of spatiotemporal variation in vegetation coverage and its associated driving forces in the Qinling-Daba(Qinba) Mountains in 2000–2014.The Sen and Mann–Kendall models and partial correlation analysis were used to analyze the data,followed by calculation of the Hurst index to analyze future trends in vegetation coverage.The results of the study showed that(1) NDVI of the study area exhibited a significant increase in 2000–2014(linear tendency,2.8%/10a).During this period,a stable increase was detected before 2010(linear tendency,4.32%/10a),followed by a sharp decline after 2010(linear tendency,–6.59%/10a).(2) Spatially,vegetation cover showed a "high in the middle and a low in the surroundings" pattern.High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province.(3) The area with improved vegetation coverage was larger than the degraded area,being 81.32% and 18.68%,respectively,during the study period.Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage in 2010–2014.(4) Reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains.About 46.89% of the entire study area is predicted to decrease in the future,while 34.44% of the total area will follow a continuously increasing trend.(5) The change of vegetation coverage was mainly attributed to the deficit in precipitation.Moreover,vegetation coverage during La Nina years was higher than that during El Nino years.(6) Human activities can induce ambiguous effects on vegetation coverage: both positive effects(through implementation of ecological restoration projects) and negative effects(through urbanization) were observed.

Dynamics of Soil Organic Carbon Under Uncertain Climate Change and Elevated Atmospheric CO_2

Climate change and elevated atmospheric CO2 should affect the dynamics of soil organic carbon （SOC）. SOC dynamics under uncertain patterns of climate warming and elevated atmospheric CO2 as well as with different soil erosion extents at Nelson Farm during 1998-100 were simulated using stochastic modelling. Results based on numerous simulations showed that SOC decreased with elevated atmospheric temperature but increased with atmospheric CO2 concentration. Therefore, there was a counteract effect on SOC dynamics between climate warming and elevated CO2. For different soil erosion extents, warming 1℃ and elevated atmospheric CO2 resulted in SOC increase at least 15%, while warming 5 ℃ and elevated CO2 resulted in SOC decrease more than 29%. SOC predictions with uncertainty assessment were conducted for different scenarios of soil erosion, climate change, and elevated CO2. Statistically, SOC decreased linearly with the probability. SOC also decreased with time and the degree of soil erosion. For example, in 2100 with a probability of 50%, SOC was 1 617, 1 167, and 892 g m^-2, respectively, for no, minimum, and maximum soil erosion. Under climate warming 5 ℃ and elevated CO2, the soil carbon pools became a carbon source to the atmosphere （P 〉 95%）. The results suggested that stochastic modelling could be a useful tool to predict future SOC dynamics under uncertain climate change and elevated CO2.

Recent greening of grasslands in northern China driven by increasing precipitation

Aims Recent warmer and wetter climate in northern China remains a hot topic in recent years,yet its effect on vegetation growth has not been fully understood.This study investigated the temporal change of vegetation cover and its correlations with climatic variables from 1982 to 2018 for grasslands in northern China.Our aim is to clarify whether the warmer and wetter climate in recent years drives the greening of the vegetation in this region.Methods We investigated the temporal dynamic of vegetation normalized difference vegetation index(NDVI)and its driving forces based on long time-series data.Piecewise regression was used to examine whether there was a turning point of the trend of NDVI and climatic variables.Pearson correlation analyses were conducted to quantify the relationship between NDVI and climatic factors.Stepwise multivariable regression was used to quantify the contributions of climate variables to the temporal variations in NDVI.Important Findings We found a turning point of NDVI trend in 2008,with GIMMS NDVI indicating a slight increase of 0.00022 yr?1 during 1982–2008 to an increase of 0.002 yr?1 for GIMMS NDVI during 2008–2015 and 0.0018 yr?1 for MODIS NDVI during 2008–2018.Precipitation was the predominant driver,and air temperature and vapor pressure deficit exerted a minor impact on the temporal dynamics of NDVI.Overall,our results suggest a turning point of NDVI trend,and that recent warmer and wetter climate has caused vegetation greening,which provides insights for better predicting the vegetation cover in this region under changing climate.