基于海洋监测数据的厄尔尼诺特征可视分析EI北大核心CSCD

厄尔尼诺不仅危害海洋生物和生态环境,还造成全球多个地区气候异常.文中同时考虑海表温度、气压和降水量3个环境要素,采用多个交互视图提升对厄尔尼诺特征的理解和探索分析的深度;使用时间序列图和像素矩阵来分析厄尔尼诺的年代际和季节性规律,并利用颜色地图来展示厄尔尼诺相关的海洋环境要素的空间分布,同时构建了海表温度、气压和降水量3个海洋环境要素的相关性地图,以揭示厄尔尼诺表征海域的海洋环境要素对全球的海洋环境要素变化的影响程度;使用平行坐标系统和散点图矩阵展示了海表温度、海表气压、降水量与厄尔尼诺事件间的相互关系,并利用数据重叠率比较了3个环境要素对厄尔尼诺事件的区分度;最后通过案例分析和用户使用反馈,展示了该方法的有效性.     

基于海洋监测数据的厄尔尼诺特征可视分析

厄尔尼诺不仅危害海洋生物和生态环境,还造成全球多个地区气候异常.文中同时考虑海表温度、气压和降水量3个环境要素,采用多个交互视图提升对厄尔尼诺特征的理解和探索分析的深度;使用时间序列图和像素矩阵来分析厄尔尼诺的年代际和季节性规律,并利用颜色地图来展示厄尔尼诺相关的海洋环境要素的空间分布,同时构建了海表温度、气压和降水量3个海洋环境要素的相关性地图,以揭示厄尔尼诺表征海域的海洋环境要素对全球的海洋环境要素变化的影响程度;使用平行坐标系统和散点图矩阵展示了海表温度、海表气压、降水量与厄尔尼诺事件间的相互关系,并利用数据重叠率比较了3个环境要素对厄尔尼诺事件的区分度;最后通过案例分析和用户使用反馈,展示了该方法的有效性.     

东北植被生长峰值特征的变化及对气候和物候的响应

全球气候变化导致植被生长的季节性节律事件（如返青期、衰落期和生长峰值期等）发生显著变化。植被返青期、衰落期和生长季长度的变化已经得到广泛报道,植被生长峰值代表植被光合作用能力和对气候变化的响应,目前关于植被生长峰值特征（时间点和最大生长幅度）的时空变化和控制机理的研究相对较少,仍需在不同区域深入探讨。以植被覆盖度较好的中国东北地区为例,首先利用长时序遥感NDVI数据（GIMMS NDVI3g）和逻辑斯蒂法提取植被关键物候参数,然后分析了植被生长峰值关键特征（日期和幅度）的变化格局及对气候因子和返青期物候的响应,最后探索了生长峰值对植被生产力变化的贡献。结果表明：东北地区整体的生长峰值时间点和返青时间点呈现延迟趋势、生长幅度呈上升趋势（与MODIS EVI趋势验证一致）,生长峰值存在一个约11 a的周期;季前气温和降水对生长幅度的影响范围和幅度较小,主要作用在草原区域,对峰值时间点的显著影响主要在北部森林区域;返青期对生长峰值的控制作用大于气温和降水因素,并且返青期在森林和草原区域主要影响生长幅度变化,在农作物区主要影响生长峰值时间点变化;植被最大生长幅度对自然植被生产力长期变化的影响显著。东北地区生长峰值及对气候、物候响应的不同时空格局反映了植被生长峰值对气候变化的适应能力的差别,并造成多样的植被固碳格局。生长峰值研究有助于深入理解植被光合作用的时空变化格局和气候变化情景下的碳循环过程,对我国东北地区的生态系统评估和管理也有参考意义。     

冷静两相宜 拒绝CPU的厄尔尼诺

厄尔尼诺原本是指太平洋赤道带大范围内海洋和大气相互作用后失去平衡而产生的一种气候现象,但你知不知道,如果机箱里的CPU也出现了类似厄尔尼诺的现象,会发生什么事?     

遥感和气象数据天山东部冰雪时空变化研究

冰雪作为全球重要的环境要素,其时空变化监测对于水资源利用与气候变化研究具有重要意义。遥感是获取大范围冰雪变化的有效手段,然而大面积的云层覆盖严重影响了冰雪的提取精度,合适的冰雪监测算法十分必要。以天山东部为研究区域,采用以NDSI为基础的冰雪检测算法对2018年MODIS产品进行冰雪覆盖范围的提取,并利用分段线性插值算法对MODIS数据上由于云层遮挡而产生的不确定像元的NDSI进行恢复。在此基础上,分析了天山东部地区冰雪的变化模式,发现冰雪年内变化显著,季节性明显。进一步分析天山东部地区冰雪平均NDSI与气温和降水的关系,发现气温是影响冰雪变化的一个重要因素,气温和降水同冰雪覆盖率均呈显著负相关,二者共同受到季节变化的影响。     

沈阳地区5—9月温度和降水变化规律的研究

对沈阳地区近50年来的温度资料和90年来的降水量数据进行分析，将每年5-9月的距平累加，再用周期函数对累加序列进行拟合，分析出温度和降水变化的规律；1947-1995年温度变化总的趋势是下降的，总下降幅度为0.2℃，其间存在周期约20年的波动，20世纪50年代中后期和70年代初为两个波谷，60年代初和80年代初为波峰；1905-1995年降水量总的趋势基本不变，但年际差异较大，20世纪30-60年代属降水偏多时期，之前和之后则属偏少时期。     

深圳地区日极值气温的降尺度研究

气象与人类日常生活的关系十分密切,气象预报一直是人类社会高度关注的问题。随着经济的发展和社会的进步,人类对天气预报的准确性提出了越来越高的要求,迫切希望实现气象要素精细化预报。获取详细准确的区域气象资料是实现气象精细化预报的首要条件,全球大气环流模式是目前预估大尺度未来全球气候变化最重要的模式,能较好地模拟出大尺度的平均特征。但是模式预报输出的空间分辨率较低,无法获取精细的区域气象资料,很难对区域天气情景变化做出详细的预测,而降尺度方法可用于弥补这方面的缺陷。文章的研究工作主要是利用统计降尺度的多元线性回归方法和BP神经网络方法对深圳地区近十年的日最低温度和最高温度进行降尺度分析研究。采用的数据是美国国家环境预报中心/美国国家大气研究中心提供的FNL全球分析资料和深圳国家基本观测站——竹子林站的实际观测数据,重点研究了基于BP神经网络方法和多元线性回归方法的统计降尺度模型的设计与实现过程,并对两种方法的结果进行了比较,为区域站点的统计降尺度应用提供了设计方法和参考。     

2008~2016年MODIS多角度大气校正气溶胶产品在中国的时空分布及变化趋势

探究全国大气气溶胶分布及变化特征,准确了解中国地区气溶胶光学特性对研究大气环境污染、应对全球气候变化是非常重要的。对2008~2016年的MODIS MAIAC气溶胶光学厚度数据在中国的适用性进行验证,并采用Mann-Kendall方法,从不同的时空尺度和气溶胶类型上分析中国地区AOD值的时空变化特征。结果表明：①验证表明C6的MAIAC反演结果在中国AERONET匹配点上表现良好,C6的MAIAC反演AOD结果适用于中国区域;②从年际尺度上看,2008~2016年AOD年均值整体呈波动下降;从季节尺度上看,AOD季节变化呈春季整体高、夏季中心高、秋冬季水平低的特点,各省AOD平均值及各省份划区AOD平均值随季节变化趋势相似。③在空间上,AOD呈东南高、西北低、高值中心聚集的特征。④中国AOD变化整体呈现出东部减少且集聚,西部增加且分散的变化特征。可进一步探究不同种类气溶胶分布和气溶胶与典型大气污染物分布关系,以期为中国环境污染治理提供更好的决策指导。     

兴趣区域高分辨率叶绿素荧光遥感数据集重建框架

太阳诱导叶绿素荧光数据是反映全球植被总初级生产力的关键指标,对于监测全球或地区性的植被生产力变化和气候变化的影响具有重大意义.然而,目前为止仍没有高分辨率和全球覆盖的可用原始数据集.虽然存在一些全球性的重建数据集,但一般存在区域特异性不够明显等问题,从而一定程度上限制了该数据在特定的兴趣区域上的可用性.为了探索重建基于兴趣区域的叶绿素荧光数据的方法,本研究以华北平原为例,综合遥感数据处理技术,机器学习方法和生态学原理,对原始轨道碳观测者二号卫星所提供的叶绿素荧光数据集和MODIS地表反照率数据建模.重建数据集基于兴趣区域内原始数据的时空特征而建,具有连续的空间覆盖和更高的空间分辨率,经过验证,该框架可以为特定区域提供有效的有针对性的的叶绿素荧光数据,可为兴趣区域的与叶绿素荧光数据有关的研究提供数据支持.     

土壤碳素固定及其稳定性对土壤生产力和气候变化的影响研究

土壤碳库是陆地生态系统中最大的碳库,在全球碳循环中起着非常重要的作用。本文论述了土壤有机碳对土壤生产力和气候变化的影响。阐述了影响土壤碳含量的各种自然因素和人为因素。对土壤碳素固定以及影响土壤碳素平衡的物理、化学及生物稳定性的各种机理进行了详细评述,阐明了土壤有机碳调控的方法和途径。为控制和调节土壤有机碳储量提供了理论依据。这对今后提高土壤生产力、利用土壤的固碳潜力来降低大气中二氧化碳的浓度和缓解气候变化有着非常重要的意义。     

A typical pattern of vegetation conditions in southern Africa during El Nino years detected from AVHRR data using three-channel numerical index

The 1997-98 El Nino is one of the strongest events of the last 50 years. Its absolute magnitude, areal extent, and rapid development have raised a serious concern among decision makers because of its possible impact on global ecosystems. In this study, El Nino consequences for land ecosystem were examined using the new AVHRR-based three-channel index (VT), widely used for monitoring drought around the world. The VT index was used to identify a typical pattern of vegetation conditions in southern Africa during the recent El Nino years. Features and trends of the 1997-98 El Nino including its intensity, extent, and impact on vegetation are also discussed.     

Microwave measurement of rain and sea surface temperature by the TRMM Microwave Imager (TMI)

The Tropical Rainfall Measuring Mission (TRMM) is important for studies of the global hydrological cycle and for testing the realism of climate models and their ability to simulate and predict climate accurately; the effect of El Nino on climate could be addressed as well. This paper investigates the microwave rain measurement using satellite data from the TRMM Microwave Imager (TMI). The physical bases of rainfall estimation algorithms, vertical structure of rain and its physical processes are explained. The algorithms for processing TMI radiance and brightness temperature data are presented. Various rain maps and sea surface temperature (SST) maps are produced using TMI microwave data. The performance, calibration, analysis of results and sources of errors in the averaged monthly surface rain rate estimation are discussed.     

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Rapid global economic development has resulted in a corresponding intensification of urbanization, which has in turn impacted the ecology of vast regions of the world. A series of problems have thus been introduced, such as changes in land-use/land-cover (LULC) and changes in local climate. The process of urbanization predominantly represents changes in land-use, and is deemed by researchers to be the chief cause of climate change and ecological change. One of the principal purposes of the research in this field is to find ways to mitigate the influence of land-use change on local or global environments. In the study presented in this article, satellite images were utilized to extract information regarding land-use in Beijing City, and to develop maps of land surface temperature (LST) during two different periods of time: 2 August 1999 and 8 August 2010. A supervised classification scheme, a support vector machine, was used to derive the land-use change map for the above periods. Maps of surface temperature are derived from the thermal band of Landsat images using the mono-window algorithm. Results from post-classification comparison indicated that an increase in impervious surface areas was found to be dramatic, while the area of farmland decreased rapidly. The changes in LULC were found to have led to a variation in surface temperature, as well as a spatial distribution pattern of the urban heat island phenomenon. This research revealed that the hotspots were mainly located in areas dominated by three kinds of material: bare soil, rooftops, and marble surfaces. Results from the local Moran's I index indicated that the use of lower surface temperature materials will help to mitigate the influence of the urban heat island phenomenon. The results of this research study provide a reference for government departments involved in the process of designing residential regions. Such a reference should enable the development of areas sympathetic to environmental changes and hence mitigate the effects of the growing intensity of urbanization.     

Precipitation dynamics in Ecuador and northern Peru during the 1991/92 El Nino: A remote sensing perspective

The formation, dynamics and spatial distribution of heavy precipitation during the 1991/92 El Nino in Ecuador and northern Peru were examined by means of Meteosat-3 imagery, NOAA-AVHRR-based multichannel sea surface temperatures (MCSST) and additional meteorological observations. The Convective and Stratiform Technique (CST) was used for rain retrieval by means of Meteosat IR data and a cross-correlation approach was applied to Meteosat image sequences to derive cloud motion winds (CMW) which are essential for the analysis of circulation patterns leading to severe precipitation. From an analysis of 45 days with severe precipitation it is proven that three mechanisms were responsible for the formation of heavy rains. Each mechanism reveals a specific localized impact. (1) The most frequent mechanism (frequency of ～61%) represents an extended land-sea breeze system. During such weather conditions, predominantly locally confined precipitation patterns occured. Areas affected by the sea wind front during the day were the coastal plains up to the 1000 m contour line on the western Andean slope. Local maxima in the frequency of cloudiness leading to precipitation could be found at isolated peaks of a lower coastal cordillera. At night the highest frequency of precipitation was found over the warm water surface of the Gulf of Guayaquil, mainly due to its coastal shape which significantly favours convergence of the nocturnal land breeze. (2) Convection, initiated in the coastal plain and on the western Andean slopes during the afternoon, was significantly intensified by an entrainment of remainders of cirrus shields from the Amazon basin. These cloud fragments spilled over the Andes with well-developed trades in the mid/upper troposphere which blew in the opposite direction to the daily sea/up-slope breeze. The spill over points were characterized by areas of deep convection on the western Andean slopes and were frequently valley axes perpendicular to the mountain chain as well as the Andean depression in southern Ecuador. (3) During the main El Nino phase (March-April), heavy and persistent precipitation was extended over wide areas of the coastal plain showing neither a distinct diurnal cycle nor preferential areas. Deep convection was frequently organized in mesoscale convective complexes (MCC) and was spatially correlated with MCSST > 27 . The extensive instability of the troposphere during these weather conditions was marked by convective cloud streets and an intensification of the meridional Hadley circulation off the coast of southern Ecuador and Peru.     

Spatiotemporal association patterns of multiple parameters in the northwestern Pacific Ocean and their relationships with ENSO

The northwestern Pacific Ocean (NWPO) is a region sensitive to global climate change and regional sea–air interactions. A number of remote-sensing images from the past three decades were used to define sensitive marine regions, which were then applied to determine the spatiotemporal association patterns of abnormal variations in marine environmental parameters using a quantitative association rule-mining method. The NWPO object 1 (NWPO-obj1) region (130°–150° E, 2°–15° N) and NWPO object 2 (NWPO-obj2) region (170°–180° E, 0°–8° N) showed more pronounced changes than elsewhere, and the monthly anomaly of sea-surface temperature (SSTA), monthly anomaly of sea-surface chlorophyll-a (chl-a), monthly anomaly of sea-level anomaly (SLAA), and El Niño Southern Oscillation (ENSO) events were closely related to one another in these two regions. In NWPO-obj1, the relation between SLAA and chl-a yields a correlation coefficient of ?0.79 and the abnormal drop in SLAA was the principal factor controlling the chl-a bloom. In NWPO-obj2, the SSTA is anti-correlated with chl-a (correlation coefficient of ?0.83), and the abnormal increase in SSTA might be one of the main factors leading to the extinction of chl-a. Comparing the two regions, abnormal increases in chl-a and decreases in SSTA in NWPO-obj2 were indicators of abnormal increases in SLAA in NWPO-obj1 (positive and negative correlation coefficients of 0.60 and ?0.61, respectively), and the abnormal decrease of SLAA in NWPO-obj1 is correlated with the abnormal decrease of SSTA in NWPO-obj2 (correlation coefficient of 0.86), although ahead by one year. In addition, the abnormal decrease of SLAA in NWPO-obj1 was the only factor influenced by El Niño, while La Niña events had an impact on the abnormal increase of SLAA in NWPO-obj1 and the abnormal decrease of SSTA in NWPO-obj2, and also dominated their interrelationships.     

Phenology and carbon fixing: a satellite-based study over Continental USA

Global climate change has led to concerns about its impact on our biosphere and vegetation. Any impact of climate on vegetation can manifest in terms of changes in plant growth characteristics, its health and timing of different vegetative phenomena, such as germination, bud burst, maturity, etc. The duration and changes in the timing of plant growth stages can in turn impact the global carbon cycle. Similarly any change in plant productivity, because of changing climate will alter the carbon flux pattern by changing the overall biological flux being added or taken away from the atmosphere. We have used satellite data to study spatiotemporal changes in the plant phenology and plant productivity over the Continental USA (CONUS) to get an overall understanding of the evolution of these metrics over the past decade. Our study reveals that the prairies situated in the heartland of CONUS have become an increasingly important player in determining any changes in vegetation induced carbon source/sink patterns. The northern Great Plains has shown increased fixation of carbon in recent years, while the southern Plains has become a carbon source. This has been largely driven by changes in recent weather patterns where the northern plains have seen an increasingly cooler and wetter growing season whereas the southern plains have at the same time seen increased aridity, especially since 2011. This is also reflected in increasing growing season greenness values over the northern Plains and the opposite over the southern Plains. The gradual changing pattern of land biological fluxes over CONUS, as documented in this paper will likely be of interest to climate modellers as they seek to better understand the interaction between global carbon balance and climate change.     

Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters

The dynamic nature of climate over Indian sub-continent is well known which influences Indian monsoon. Such dynamic variability of climate factors can also have significant implications for the vegetation and agricultural productivity of this region. Using empirical orthogonal function (EOF) and wavelet decomposition techniques, normalized difference vegetation index (NDVI) monthly data over Indian sub-continent for 18 years from 1982 to 2000 have been used to study the variability of vegetation. The present study shows that the monsoon precipitation and land surface temperature over the Indian sub-continent landmass have significant impact on the distribution of vegetation. Tropospheric aerosols exert a strong influence too, albeit secondary to monsoon precipitation and prove to be a powerful governing factor. Local climate anomaly is seen to be more effective in determining the vegetation change than any global teleconnection effects. The study documents the dominating influence of monsoon precipitation and highlights the importance of aerosols on the vegetation and necessitates the need for remedial measures. The present study and an earlier one point towards a possible global teleconnection pattern of ENSO as it is seen to affect a particular mode of vegetation worldwide.     

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of today's climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.     

Global vegetation dynamics: satellite observations over Asia

Abstract

The weekly global vegetation index (GVI) derived from the NOAA AVHRR instrument has been analysed for the 1982-1985 period over a wide range of vegetation formations of Asia. Temporal development curves of the index are presented for environments ranging from the desert of central Asia to the tropical forest of Borneo. The paper shows that, despite the coarse resolution of the GVI product, a large set of useful information on ecosystem dynamics and cropping practices can be consistently derived from time series of such data. In addition, it is shown that the impact of the 1982-1983 El Nino Southern Oscillation-related drought can be detected in the GVI data through an analysis of anomalies in the development of selected vegetation formations. The relevance of such analysis for global vegetation monitoring and change detection is then underlined.     

Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events

Convection over the tropical Indian Ocean is important to the global and regional climate. This study presents the monthly climatology of convection, inferred from the outgoing longwave radiation (OLR), over the tropical Indian Ocean. We also examine the impact of El Niño/La Niña events on the convection pattern and how variations in convection over the domain influence the spatial rainfall distribution over India. We used 35 recent years (1974–2008) of satellite-derived OLR over the area, the occurrence of El Niño/La Niña events and high resolution grid point rainfall data over India. The most prominent feature of the annual cycle of OLR over the domain is the movements of convection from south-east to north and north-west during the winter to the summer monsoon season. This feature represents the movement of the inter-tropical convergence zone (ITCZ). The climatology of OLR during the winter months (December–February) over the domain is characterized by high subsidence over central India with a decrease of OLR values towards the north and south. Moderate convection is also seen over the Himalayan Range and the south-east Indian Ocean. In contrast, during the summer (June–September) the OLR pattern indicates deep convection along the monsoon trough and over central India, with subsidence over the extreme north-west desert region. The annual march of convection over the Arabian Sea and Bay of Bengal sector shows that the Arabian Sea has a limited role, compared to the Bay of Bengal, in the annual cycle of the convection over the tropical Indian Ocean. The composite OLR anomalies for the El Niño cases during the summer monsoon season show suppressed convection over all of India and moderate convection over the central equatorial Indian Ocean and over the northern part of the Bay of Bengal. Meanwhile in La Niña events the OLR pattern is nearly opposite to the El Niño case, with deep convection over entire Indian region and adjoining seas and subsidence over the northern Bay of Bengal and extreme north-west region. The spatial variability of the 1°?×?1° summer monsoon rainfall data over India is also examined during El Niño/La Niña events. The results show that rainfall of the summer monsoon season over the southern peninsular of India and some parts of central India are badly affected during El Niño cases, while the region lying along the monsoon trough and the west coast of India have received good amounts of rainfall. This spatial seasonal summer monsoon rainfall distribution pattern seems to average out the influence of El Niño events on total summer monsoon rainfall over India. It seems that, in El Niño events, the convection pattern over the Bay of Bengal remains unaffected during summer monsoon months and thus this region plays an important role in giving good summer monsoon rainfall over the northern part of India, which dilutes the influence of El Niño on seasonal scale summer monsoon rainfall over India. These results are also confirmed by using a monthly bias-corrected OLR dataset.