非正态分布的天气气候序列极值特征诊断方法研究

推广了非正态假设下的交叉理论,且将其用于极值特征的诊断,并从理论上导出了适用性更广的基于Gamma分布和负指数分布的极值特征量诊断公式及其样本估计式.以有关降水要素的时间序列为例,说明了这种方法在天气气候诊断与气候影响研究中的应用前景.     

利用随机模拟研究极值天气的预报模型试验

将随机过程的交叉理论应用于天气气候极值分析，以长江三角洲地区逐月最高最低气温为例，说明了交叉理论在极值研究中的作用。基于该理论，对上海市近100a一月气温序列，用随机模拟的方法讨论了极端温度出现的频数、持续时间、时间间隔等参数对于气候变化的敏感性，并根据气候变化趋势，预测了未来气候极值统计特征的变化规律。     

天气和气候之间的桥梁——分数阶导数

天气和气候虽然遵从流体力学规律,但是却显示出随机性,研究天气和气候之间的关系必须引入分数阶的导数和积分,从物理上讲不外乎说明天气和气候的随机程度是不相同的。为此提出气候的q(0≤q≤1)阶微商是天气,扩展了Hasselmann的结果。实际资料分析表明,气候距平具有长程相关,比天气有更好的记忆性。气候距平的概率密度分布函数有较长的尾巴,较长的尾巴反映了极值气候所发生的频率。     

高温少雨墒情差 持续干旱灾情重：1999年6～8月天气气候特点

１９９９年夏季,我省大部分地区高温少雨,尤其是７月下旬出现了持续５～６天的高温天气,使人倍受燥热之苦。近四分之一的县（市）日极端最高气温达到或超过历史同期极值,部分县（市）突破历史极值。１１９９９年６～８月天气气天特点我省１９９９年６～８月份天气气候特点是：北、中部地区平均气温持续偏高,较常年偏高１～２℃,南部地区平均气温接近常年,特别是７月下旬出现了持续的高温酷热天气。６～８月降水量大部分地区普遍偏少,较常年偏少３～４成,部分县（市）偏少７成左右,旱情严重,墒情差。２造成严重旱灾的原因２－１雨…     

探讨灾害规律的理论基础--极端气候事件概率

分析了国内外极端气候事件研究的现状,从经典极值分布理论、极值分布模式的参数估计、广义极值分布等方面,阐述了近年来国际上常用的理论及研究方法。为了更好地佐证进行极端气候事件发生概率研究的重要性,列举了作者在该领域的一些成功研究成果,并认为极端气候事件概率问题是探讨气象灾害规律的理论基础。     

极端气候对平均气候变化的非线性响应及其敏感性试验

平均气候与极端气候两者发生的概率具有密切联系.从理论和观测事实两方面证明一地平均气候与其极端气候的对应概率存在着明显的非线性关系.假定气候变量有对称概率分布和非对称概率分布两种状况,从理论上证明,平均气候变化前后所对应的极端气候概率具有非线性变化;通过对若干个代表站及气候场资料的平均值与所对应极值概率计算结果,证明理论符合观测事实.进一步借助于数值试验考察了概率分布模式的各个参数之变化对极端气候概率的影响.根据观测事实的举例,也表明了不同概率分布条件下,基本上都有如此变化规律.     

近10年新疆气候变化特征

着重分析了近10年新疆气候的冷暖变化和干湿变化,并与前20年进行了比较,对气候突变作了检测试验,分析了气候极值出现的频次,得出了一些有意义的结论。     

近10来新疆气候变化特征

着重分析了近１０年新疆气候的冷暖变化和干湿变化,并与前２０年进行了比较,对气候空迹作了检测试验,分析了气候极值出现的频次,得出了一些有意义的结论。     

商城县茶叶气候生态适应性分析

为扩大商城县茶叶种植面积,提高茶叶产量,根据茶叶生长发育与气候条件--光、热、水的关系,利用商城县气象局50年的气候观测资料,分析了春、夏、秋季(3-11月)的气温、降水、日照特点和灾害性天气对茶叶生长的影响,结果表明,商城县的气候条件--温度、降水、光照适应茶叶生长,虽然会出现对茶叶生长不利的春季低温、夏季高温和干旱天气,但出现几率较小,影响不大,具有大力发展茶叶产业的气候优势.同时提出了茶叶高产对策.     

珠江口海域热带气旋气候特征及最大风速计算

根据1949～2011年热带气旋路径资料,统计分析了影响珠江口海域的热带气旋气候特征,包括热带气旋频数、强度及路径趋向等特征,结果显示,在近63a间共有75个热带气旋样本影响珠江口海域,有3个年份出现多达4个热带气旋影响该海域;出现12级以上强风的台风样本约占总数的3成;热带气旋的移动方向以西北和偏西方向为主,约占总数的7成.分别采用x2检验和Колмогоров法对热带气旋样本频数及最大风速序列分别进行拟合适度检验,结果表明,影响珠江口海域的热带气旋最大风速服从Poisson-Gumbul复合极值分布,计算影响珠江口海域的热带气旋中心附近的概率风速,得到的50a一遇10min平均风速为51.1m.s-1,100a一遇10min平均风速为56.8m/s.     

Quantifying Changes in Extreme Weather Events in Response to Warmer Global Temperature

Global warming is expected to affect both the frequency and severity of extreme weather events, though projections of the response of these events to climate warming remain highly uncertain. The range of changes reported in the climate modelling literature is very large, sometimes leading to contradictory results for a given extreme weather event. Much of this uncertainty stems from the incomplete understanding of the physics of extreme weather processes, the lack of representation of mesoscale processes in coarse-resolution climate models, and the effect of natural climate variability at multi-decadal time scales. However, some of the spread in results originates simply from the variety of scenarios for future climate change used to drive climate model simulations, which hampers the ability to make generalizations about predicted changes in extreme weather events. In this study, we present a meta-analysis of the literature on projected future extreme weather events in order to quantify expected changes in weather extremes as a function of a common metric of global mean temperature increases. We find that many extreme weather events are likely to be significantly affected by global warming. In particular, our analysis indicates that the overall frequency of global tropical cyclones could decrease with global warming but that the intensity of these storms, as well as the frequency of the most intense cyclones could increase, particularly in the northwestern Pacific basin. We also found increases in the intensity of South Asian monsoonal rainfall, the frequency of global heavy precipitation events, the number of North American severe thunderstorm days, North American drought conditions, and European heatwaves, with rising global mean temperatures. In addition, the periodicity of the El Niño–Southern Oscillation may decrease, which could, in itself, influence extreme weather frequency in many areas of the climate system.     

极端异常气象资料的综合性质量控制与分析

在传统的气象资料质量控制技术中,通常利用统计方法设置控制界限,对于界限以外的资料作为可疑值被标注或作为错误值而被剔除。文中以中国1971~2000年700多基准基本站中,气温极端事件———青海托托河站1985年10月~1986年3月的极端异常月平均气温为例,介绍了极端异常气象资料的综合性质量控制方法。通过综合性质量控制与分析,即参考Metadata、邻近站检验、不同观测项目间的一致性检查,均证明托托河站1985年10月~1986年3月极端异常月平均气温资料是正确的。从而说明,利用界限控制检查出的所谓错误资料,虽然在统计学意义上很难出现,但是它有可能反映了异常极端的天气气候事件,是正确的。因此,对于极端异常的气象资料,应进一步通过综合性质量控制,分析该资料正确与否,从而保留极端天气气候事件的事实。文中最后介绍了中国1971~2000年700多基准基本站极端异常月平均气温资料的综合性质量检查结果。     

空间点过程理论在极端气候事件中的应用研究

该文定义了12种极端温度事件和6种极端降水事件,将基于空间点过程理论的k阶最近邻距离丛集点提取算法应用于极端温度和极端降水事件的研究,给出了极端温度和极端降水事件区域群发性站点的检测流程;采用多年平均的疏密差异比R和有效率η两个指标,对所定义的各种极端气候事件的区域群发性进行了有效性检验,结果表明:k阶最近邻距离丛集点提取算法适用于极端气候事件的区域群发性研究,并进一步阐明了极端气候事件区域群发性的物理含义。     

Mapping the shadow of experience of extreme weather events

Climate change will increase the frequency and/or intensity of certain extreme weather events, and perceived experience with extreme weather may influence climate change beliefs, attitudes, and behaviors. However, the aspects of extreme events that influence whether or not people perceive that they have personally experienced them remain unclear. We investigate (1) the correspondence of reported experience of extreme weather events with documented events, and (2) how characteristics of different extreme events shape the geographic area within which people are likely to report they have experienced it—the event’s perceived “shadow of experience.” We overlay geocoded survey responses indicating personal experience with hurricanes, tornadoes, and drought—from a 2012 nationally representative survey (N?=?1,008) of U.S. residents—on maps of recorded event impacts. We find that reported experiences correspond well with recorded event impacts, particularly for hurricanes and tornadoes. Reported experiences were related to event type, proximity, magnitude and duration. The results suggest locations where disaster preparedness efforts and climate change education campaigns could be most effective after an extreme weather event.     

Sensitivity of Ceres-Maize Yields to Statistical Structure of Daily Weather Series

To study impacts of climate variations on cropproduction, the growth models are used to simulateyields in present vs. changed climate conditions.Met&Roll is a four-variate (precipitation amount,solar radiation, minimum and maximum temperatures) stochasticweather generator used to supply synthetic dailyweather series for the crop growth model CERES-Maize.Three groups of experiments were conducted in thisstudy: (1) Validation of Met&Roll reveals some discrepanciesin the statistical structure of synthetic weatherseries, e.g., (i) the frequency of occurrence of longdry spells, extreme values of daily precipitationamount and variability of monthly means areunderestimated by the generator; (ii) correlations andlag-1 correlations among weather characteristicsexhibit a significant annual cycle not assumed by themodel. On the whole, the best fit of the observed andsynthetic weather series is experienced in summermonths. (2) The Wilcoxon test was employed to comparedistributions of maize yields simulated with use ofobserved vs. synthetic weather series. As nostatistically significant differences were detected,it is assumed that the generator imperfections inreproducing the statistical structure of weatherseries negligibly affect the model yields. (3) Thesensitivity of model yields to selectedcharacteristics of the daily weather series wasexamined. Emphasis was placed on the characteristicsnot addressed by typical GCM-based climate changescenarios: daily amplitude of temperature, persistenceof the weather series, shape of the distribution ofdaily precipitation amount, and frequency ofoccurrence of wet days. The results indicate that someof these characteristics may significantly affect cropyields and should therefore be considered in thedevelopment of climate change scenarios.     

极值统计理论的进展及其在气候变化研究中的应用

着重论述极值统计分布在极端天气气候事件和重大工程设计中的重要意义,综述该领域国内外研究进展。例如,基于超门限峰值法（POT）的广义帕累托分布（GPD)和基于单元极大值法（BM）的广义极值分布（GEV）及其参数间的理论关系;采用极值分布模型与多状态一阶Markov链相结合构建降尺度模型模拟局地极端降水事件,推算一定重现期的极端降水量的分位数;探讨极值分布模型分位数估计误差问题,多维极值分布理论及其应用等问题。     

Blaming climate change? How Indian mainstream media covered two extreme weather events in 2015

Reporting the links (or lack of them) between human-induced climate change and individual extreme weather events poses a series of challenges for journalists. In recent years, their task has become more complicated by the increase in the number of extreme event attribution (EEA) studies which assess how climate change is affecting the intensity or likelihood of specific weather events. Such studies are complex, contain uncertainties, and can be difficult to explain to a lay audience. Previous scholarship has largely focused on media coverage of extreme events in developed countries, and on the volume of coverage of the links to climate change, without examining references to EEA studies. To help fill this gap, we take India as our case study, and the mainstream media coverage there of the Chennai rainfall event and the heat wave in Andhra Pradesh in 2015. Both events were subject to attribution studies. Amongst our findings are that journalists most commonly used generic phrases to describe the link between such events and climate change; politicians and NGOs often ‘blamed’ climate change without reference to the science; and relevant EEA studies were seldom quoted. Based on our findings, we make some preliminary recommendations for training journalists in India and elsewhere to support accurate reporting of extreme events and their possible linkages to climate change.     

Attribution matters: Revisiting the link between extreme weather experience and climate change mitigation responses

The literature suggests that extreme weather experiences have potential to increase climate change engagement by influencing the way people perceive the proximity and implications of climate change. Yet, limited attention has been directed at investigating how individual differences in the subjective interpretation of extreme weather events as indications of climate change moderate the link between extreme weather experiences and climate change attitudes. This article contends that subjective attribution of extreme weather events to climate change is a necessary condition for extreme weather experiences to be translated into climate change mitigation responses, and that subjective attribution of extreme weather to climate change is influenced by the psychological and social contexts in which individuals appraise their experiences with extreme weather. Using survey data gathered in the aftermath of severe flooding across the UK in winter 2013/2014, personal experience of this flooding event is shown to only directly predict perceived threat from climate change, and indirectly predict climate change mitigation responses, among individuals who subjectively attributed the floods to climate change. Additionally, subjective attribution of the floods to climate change is significantly predicted by pre-existing climate change belief, political affiliation and perceived normative cues. Attempts to harness extreme weather experiences as a route to engaging the public must be attentive to the heterogeneity of opinion on the attributability of extreme weather events to climate change.