诊断天气气候时间序列极值特征的一种新方法

将平稳过程的交叉理论用于天气气候极值分析,提出了一种诊断天气气候时间序列极值特征量的新方法,在正态假设下,推证出天气气候记录中,极值出现频数、持续时间和等待时间的估计公式,论证了极值出现频数与其频谱结构的对应关系及其相互推算方法.实例应用表明,其理论计算值与实测值相当一致,这种方法对于气候变化诊断与预测和天气预报具有很强的实用价值.     

气候极值推断的不确定性及其置信区间初步探讨

提出了气候极值推断的不确定性问题.并以中国156个测站为例着重探讨了广义极值(GEV)分布模式的分位数估计的标准误差对气候极值不确定性的重要影响,评估了极值分位数的置信区间及其在地域上的分布特征.数值试验表明,样本容量(n)大小是影响广义极值的分位数标准误差的最主要因素,而随着重现期加长(概率愈小)其分位数的标准误差必然增大,因此,直接影响了置信区间——即估计的可信度.     

广西三十年气候极值特征分析

本文根据“气象异常年表”规定的统计项目,对广西近三十年(1961—1990年)气候极值进行了分析,得出气温、降水、日照等要素气候极值的时空分布特征。     

南京过去100年极端日降水量模拟研究

在南京过去100年日降水资料的基础上,利用极值理论中的区组模型和阈值模型分析了极端日降水分布特征.首先通过广义极值(GEV)模型模拟了日降水的年极值序列(AMDR),用极大似然估计(MLE)方法计算了模型的参数,并借助轮廓似然函数估计出参数的精确误差区间,同时采用4种较直观的诊断图形对模型的合理性进行全面评估,结果表明Frechet是区组模型中最适合描述极端日降水分布特征的函数.其次,将日降水序列分3种情景构建极值分布的阈值模型(GPD),考察了观测数据的规模对应用该模型的限制,重点讨论了如何针对给定观测样本选择合适的阈值收集极值信息.分析结果认为,长度不小于50年的气候序列,采用24 mm的日降水量作为临界阈值均能进行GPD分析.该阈值处于年降水序列第91个百分位附近,即对目前长度为50年左右的日观测资料,第91个百分位点以上的数据基本能满足GPD研究的需要.另外,根据GEV和GPD对未来极端降水重现水平的推断情况,GPD预测值的置信区间要比GEV的窄,极值推断的不确定性相对也较小,更适合用于研究中国目前规模不大的气候资料.最后,对GPD模型的形状参数和尺度参数进行变换,分别引入描述线性变化的动态变量,分析降水序列中潜在的变异行为对极值理论应用的影响.这种变异包括降水序列中长期的均值变化及百分位变化,从模拟结果看,暂未发现资料变异行为对极值分析产生显著于扰.     

近50年中国气温、降水极值分区的时空变化特征

在全球增暖背景下,当前极端天气气候事件频发,由此引发的气象灾害及其所带来的社会经济损失日益增加.深入了解与社会生活密切相关的气温和降水极值的特征,对开展防灾减灾工作有指导意义.文中用百分位阈值求算变量极值的概率密度值,从极值概率角度用系统聚类分析法合并站点,根据方差稳定性特征和变量最概然值的均值确定划分区域个数和对区域...     

广西三十年气候极值特征分析

本文根据“气象异常年表”规定的统计项目,对广西近三十年(1961—1990年)气候极值进行了分析,得出气温、降水、日照等要素气侯极值的时空分布特征。     

三明市气候要素及极值变化特征分析

根据三明市1959～2009年51年间主要气候要素及极值的年序列(共13个),通过趋势分析,研究了三明市气候要素及极值的变化特征.结果表明:三明市4项气温要素(平均气温、平均最高气温、平均最低气温、极端最低气温)前3项表现出一致的缓慢升温趋势,而极端最高气温则表现出降温趋势;4项降水要素变化中的3项(降水量、最大日降水...     

根据米赫尔松观象台多年观测资料看活动积温的多年变化

众所周知,国际气候研究计划中有两个十分重要的中心课题:——气候及气候变迁特征的统计学分析;——就平均气候而言气候系统状态非典型变化的气候极值分析。     

辽宁近10年异常气象统计分析

以辽宁1991～2000年的异常气象年表为依据 ,归纳出了20世纪90年代气候极值的统计特征。     

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。     

中国极端天气气候研究——“地球系统与全球变化”重点专项项目简介及最新进展

全球变暖背景下,极端天气气候事件频发,并表现出群发性、持续性、复合性等特点,不可预测性增加;持续性强降水、极端低温、复合型极端高温干旱、群发性热浪和台风等极端天气气候事件对我国经济社会和可持续发展影响巨大。然而,上述极端天气气候事件的新特征、关键过程和机理尚不完全清楚,重大极端事件的预报预测水平亟待提升。文章首先简要介绍“地球系统与全球变化”重点专项项目“中国极端天气气候事件的形成机理及其预测和归因”的基本情况。项目拟在分析全球变化背景下对我国造成重大影响的极端天气气候事件新特征的基础上,深入研究多尺度海-陆-气耦合过程影响极端天气气候事件的机理,挖掘极端天气气候事件次季节-季节预测的前兆信号;发展动力与物理统计相结合的极端事件预测新方法,研制针对中国极端事件的新一代高分辨率数值预报与检测归因系统。文章重点总结了自2022年12月项目立项至今取得的最新研究成果和进展。     

Public perception of cold weather events as evidence for and against climate change

It has been argued that public doubts about climate change have been exacerbated by cold weather events seen as a form of disconfirming evidence for anticipated ‘warming’. Although a link between perceptions of climate and weather is well-established, such assumptions have not been empirically tested. Here we show, using nationally representative data, that directly following a period of severe cold weather in the UK, three times as many people saw these events as pointing towards the reality of climate change, than as disconfirming it. This we argue was a consequence of these cold winters being incorporated into a conceptualisation of extreme or ‘unnatural’ weather resulting from climate change. We also show that the way in which people interpret cold weather is associated with levels of pre-existing scepticism about climate change, which is in turn related to more general worldviews. Drawing attention to ‘extreme’ weather as a consequence of climate change can be a useful communication device, however this is problematic in the case of seasonal cold.     

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.     

Extreme weather exposure and support for climate change adaptation

Policy efforts to address climate change are increasingly focused on adaptation, understood as adjustments in human systems to moderate the harm, or exploit beneficial opportunities, related to actual or expected climate impacts. We examine individual-level determinants of support for climate adaptation policies, focusing on whether individuals’ exposure to extreme weather events is associated with their support for climate adaptation policies. Using novel public opinion data on support for a range of adaptation policies, coupled with high resolution geographic data on extreme weather events, we find that individuals experiencing recent extreme weather activity are more likely to support climate change adaptation policy in general, but that the relationship is modest, inconsistent across specific adaptation policies, and diminishes with time. The data thus suggest that experiencing more severe weather may not appreciably increase support for climate adaptation policies.     

Possible impacts of climate change on extreme weather events at local scale in south–central Canada

Synoptic weather typing and regression-based downscaling approaches have become popular in evaluating the impacts of climate change on a variety of environmental problems, particularly those involving extreme impacts. One of the reasons for the popularity of these approaches is their ability to categorize a complex set of meteorological variables into a coherent index, facilitating the projection of changes in frequency and intensity of future daily extreme weather events and/or their impacts. This paper illustrated the capability of the synoptic weather typing and regression methods to analyze climatic change impacts on a number of extreme weather events and environmental problems for south–central Canada, such as freezing rain, heavy rainfall, high-/low-streamflow events, air pollution, and human health. These statistical approaches are helpful in analyzing extreme events and projecting their impacts into the future through three major steps or analysis procedures: (1) historical simulation modeling to identify extreme weather events or their impacts, (2) statistical downscaling to provide station-scale future hourly/daily climate data, and (3) projecting changes in the frequency and intensity of future extreme weather events and their impacts under a changing climate. To realize these steps, it is first necessary to conceptualize the modeling of the meteorology, hydrology and impacts model variables of significance and to apply a number of linear/nonlinear regression techniques. Because the climate/weather validation process is critical, a formal model result verification process has been built into each of these three steps. With carefully chosen physically consistent and relevant variables, the results of the verification, based on historical observations of the outcome variables simulated by the models, show a very good agreement in all applications and extremes tested to date. Overall, the modeled results from climate change studies indicate that the frequency and intensity of future extreme weather events and their impacts are generally projected to significantly increase late this century over south–central Canada under a changing climate. The implications of these increases need be taken into consideration and integrated into policies and planning for adaptation strategies, including measures to incorporate climate change into engineering infrastructure design standards and disaster risk reduction measures. This paper briefly summarized these climate change research projects, focusing on the modeling methodologies and results, and attempted to use plain language to make the results more accessible and interesting to the broader informed audience. These research projects have been used to support decision-makers in south–central Canada when dealing with future extreme weather events under climate change.