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.     

北京奥运期间极端天气气候事件背景分析

利用北京市 1 951～ 2 0 0 1年 51年 7月下旬至 9月上旬的日最高气温、降水量、风、天气现象等观测资料 ,分别以旬为单位统计了高温、洪涝、大风、大雾、雷暴等对体育竞赛影响较大的极端天气气候事件发生的概率及分布特点 ,以期为 2 0 0 8年北京奥运会提供极端天气气候事件发生的气候背景资料。     

菏泽市气候极值的统计分布和再现期研究

科学合理地估算某地区的气候极值再现期是气象科技服务的重要内容,对当地经济建设具有重要意义。对菏泽市35种气候极值,分别都用皮尔逊Ⅲ型（Pearson Ⅲ）、对数正态（log normal）、耿贝尔（Gumbel）、韦伯（Weibull）分布函数进行拟合,用χ2检验选出最优者。使用选出的最优分布函数,用多个再现期算出再现期极值,列成表格以备气象服务中使用。通过最优分布函数的选取,对哪个类型的气候要素较多服从于哪个分布函数问题进行了研究。研究结果发现极端气温类型的极值和日数类型的极值较多服从于皮尔逊Ⅲ型分布,降水和其他类型的极值较多服从于耿贝尔分布。简要介绍了计算中使用Excel的技巧。     

石家庄极端冷暖天气气候事件及其与气候变暖

用石家庄市1955～2002年逐日气温资料，取其第95个和第5个百分位值作为确定极端高(低)温日的阈值来检测极端冷暖事件。对发生在2002年的极端冷暖事件进行检测分析证明，上述方法能比较有效地检测极端冷暖事件的发生。计算气温趋势变率表明，石家庄气候有明显的增暖趋势，随气候变暖夏季持续性强高温和破记录高温事件的出现频次增加；寒冷期趋于缩短；极端冷日趋于减少，极端暖日趋于增加。用季极端冷(暖)日数与气温求取相关的方法，分析各季极端冷暖事件与气候变暖的联系发现，气候变暖对冬季极端冷日减少的影响最甚。城市化效应使城市变暖趋势速率高于郊区，尤以最低气温表现明显；暖事件增多、冷事件减少的趋势速率也是城市高于郊区，且冷事件减少速率高于暖事件增多速率。在气候变暖背景下应特别重视冬季冷事件减少给人类带来的潜在影响。     

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。     

奥运期间天津极端天气气候事件背景分析

利用1951～2006年天津7月下旬至9月上旬的日最高气温、降水量、最大风速和8月扬沙、雾、雷暴和冰雹的观测资料,统计了对足球比赛影响较大的高温、暴雨、大风、雾和雷暴等极端天气气候事件发生的概率及分布特点,以期为2008年北京奥运会天津分赛场的足球小组赛提供气候背景服务信息。结果表明:在奥运会期间,天津易出现高温、暴雨、雷暴和雾等不利天气;而大风、扬沙、冰雹的概率相对较小。相对而言,8月中旬至9月上旬,这些不利的极端天气气候事件发生的概率小,对各项赛事的开展较为有利。     

新、旧气候态差异及对中国地区气候和极端事件评估业务的影响

利用1961—2020年中国区域2089个地面观测站资料,分析了1991—2020年和1981—2010年新、旧气候态下,平均气温、最高气温、最低气温和降水量等变量的空间变化特征,探讨对气候距平值、极端事件等评估结果的影响。结果表明：新气候态下,全国三类气温年和季节平均均一致升高,年降水增加,空间上气温偏高（低）、降水偏多（少）的特征将弱（强）化;华北东部、华东中部和北部以及青海西南部的年平均风速和日照时数距平增加;极端高温年减少,低温年增多,其中平均气温和最低气温受到的影响较最高气温更大;夏季南北方两条雨带极端强降水年的发生概率降低,冬季东北中部和南部、华北、华东北部、西北东部极端弱降水年概率显著增加;全国超过一半的站点极端日高温、低温和强降水事件的历史频次发生改变;新气候态还减弱了极端日高温事件的增速,加快了极端日低温事件的降速。     

极端天气气候事件监测与预测研究进展及其应用综述

极端天气气候事件(简称"极端事件")分为单站极端事件和区域性极端事件。本文回顾了极端事件的研究进展,首先回顾了单站极端温度、极端降水和干旱事件的观测研究及相关指数,进而对近年来不断增多的区域性极端事件研究做了简要回顾,最后还回顾了极端事件气候预测研究进展。同时,对国内外在极端事件气候监测和预测业务现状进行了初步总结,并指出:在极端事件气候监测方面中国的业务产品较丰富,并率先开展了针对区域性极端事件的监测业务,但在产品表现形式上缺乏统一组织,特别是英文产品表现力严重不足;在极端事件气候预测方面,国家气候中心发展了两种方法:一个是基于物理统计的BP-CCA和OSR的干旱预测方法,另一个基于国家气候中心月动力延伸预报模式(DERF)的高温预测方法。最后,对极端事件监测和预测业务发展及相关科学问题给出展望,指出应根据极端事件的业务需求继续加强相关研究和业务能力建设。     

新疆阿勒泰地区冬季极端低温事件特征分析

利用阿勒泰地区7个测站1961～2011年冬季(11月到翌年3月)逐日最低温度资料,采用百分位定义法给出各站发生冬季极端低温事件的阈值。采用气候趋势系数和气候倾向率、Gumbel分布函数及R/S分析等多种统计方法分析阿勒泰地区各站冬季极端低温事件的气候特征。结果表明:阿勒泰地区各站冬季极端低温事件发生阈值为-26～-36℃。阿勒泰地区冬季极端低温事件频次倾向率均呈减少趋势,强度倾向率均呈减弱趋势。50 a一遇冬季极端低温事件极值东部较低,西部较高。阿勒泰地区西部地区冬季极端低温强度<-50℃的概率较小,东部地区冬季极端低温事件强度<-50℃概率较大。根据R/S分析,阿勒泰地区冬季极端低温事件未来发生频次可能会有所增多,冬季极端低温事件未来发生强度可能会有所增强。     

Climate change and extreme weather events: can developing countries adapt?

Developing countries are vulnerable to extremes of normal climatic variability, and climate change is likely to increase the frequency and magnitude of some extreme weather events and disasters. Adaptation to climate change is dependent on current adaptive capacity and the development models that are being pursued by developing countries. Various frameworks are available for vulnerability and adaptation (V&A) assessments, and they have both advantages and limitations. Investments in developing countries are more focused on recovery from a disaster than on the creation of adaptive capacity. Extreme climatic events create a spiral of debt burden on developing countries. Increased capacity to manage extreme weather events can reduce the magnitude of economic, social and human damage and eventually, investments, in terms of borrowing money from the lending agencies. Vulnerability to extreme weather events, disaster management and adaptation must be part of long-term sustainable development planning in developing countries. Lending agencies and donors need to reform their investment policies in developing countries to focus more on capacity building instead of just investing in recovery operations and infrastructure development.     

气候变化影响下极端水文事件的多变量统计模型研究

以黄河流域太原气象站和淮河流域鲁台子水文站为研究对象,利用Copula函数构建气候要素（降水）同极端水文事件（干旱和洪水）之间的多元统计模型,分析不同降水条件下不同等级干旱和洪水的发生概率变化。结果表明,Gumbel Copula能够较好地描述太原站7月份的前期累加降水量和帕默尔干旱指数（PDSI）的相关结构。随着降水量的增加,极端干旱的发生概率逐渐减小,重旱、中旱和轻旱的发生概率则先增加后减小。Clayton Copula能够较好地描述鲁台子水文站前期累加降水量和洪峰流量之间的相关结构。当前期累加降水量大于等于某一定值时,随着年最大洪峰x的增大,发生洪峰≥x的极端洪水事件的概率逐渐减小。在同一个极端洪水发生概率下,前期累加降水量越大,洪峰流量出现大值的可能性越大。     

A Review of Climate Change Attribution Studies

This paper reviews recent progress in climate change attribution studies. The focus is on the attribution of observed long-term changes in surface temperature, precipitation, circulation, and extremes, as well as that of specific extreme weather and climate events. Based on new methods and better models and observations, the latest studies further verify the conclusions on climate change attribution in the IPCC AR5, and enrich the evidence for anthropogenic influences on weather and climate variables and extremes. The uncertainty of global temperature change attributable to anthropogenic forcings lies in the considerable uncertainty of estimated total radiative forcing due to aerosols, while the uncertainty of precipitation change attribution arises from the limitations of observation and model simulations along with influences from large internal variability. In terms of extreme weather and climate events, it is clear that attribution studies have provided important new insights into the changes in the intensity or frequency of some of these events caused by anthropogenic climate change. The framing of the research question, the methods selected, and the model and statistical methods used all have influences on the results and conclusions drawn in an event attribution study. Overall, attribution studies in China remain inadequate because of limited research focus and the complexity of the monsoon climate in East Asia. Attribution research in China has focused mainly on changes or events related to temperature, such as the attribution of changes in mean and extreme temperature and individual heat wave events. Some progress has also been made regarding the pattern of changes in precipitation and individual extreme rainfall events in China. Nonetheless, gaps remain with respect to the attribution of changes in extreme precipitation, circulation, and drought, as well as to the event attribution such as those related to drought and tropical cyclones. It can be expected that, with the continual development of climate models, ongoing improvements to data, and the introduction of new methods in the future, climate change attribution research will develop accordingly. Additionally, further improvement in climate change attribution will facilitate the development of operational attribution systems for extreme events, as well as attribution studies of climate change impacts.     

Climate anomalies and epidemics in South America at the end of the Colonial Period

Climate is one of the most of influential natural factors on society and economy. One of the consequences of climate anomalies is the emergence of diseases and epidemics, especially in agrarian societies. The current concern with long-term climate change and its measurable consequences on health and disease gives new relevance to the question of how agrarian societies fared during sharp droughts and other climatic hardships, especially those subject to the disruptive processes of colonization. Not many studies have been done in Latin America that relate climate, epidemics and mortality from a historical perspective. This paper explores the association between climatic anomalies, epidemic events, and native demographic decline in the Alto Peru region in the highlands of Bolivia, in the late eighteenth and early nineteenth century. Studies of historic climatology indicate that adverse climate events became more frequent in the southern areas of South America during these centuries. There were extreme oscillations in precipitation, especially beginning in the 1750’s which significantly impacted the largest group of people in late colonial Alto Peru: the indigenous population, whose vulnerability increased in face of local climatic anomalies and the resulting epidemiological risk. Both the quantitative and the qualitative analysis show associations between climatic and epidemic events.     

The Effects of Climatic Variability on US Irrigation Adoption

This paper contributes to the literature underscoring the importance of climatic variance by developing a framework for incorporating the means and tails of the distributions of rainfall and temperature into empirical models of agricultural production. The methodology is applied to estimate the impact of climate change on the discrete choice decision to adopt irrigation since it is an important adaptation to climate change. We develop a discrete choice model for the decision to install irrigation capacity that captures the effects of both climate means and extremes. Climatic means and frequencies of climatic events in the upper tails of the temperature and precipitation distributions are used to estimate the parameters of a normal distribution for temperature and a Weibull distribution for precipitation. Using estimates from a probit model, we examine the independent effects of changing climatic mean and variance on the probability of adopting irrigation. Increasing the mean temperature, holding variance constant, shifts the entire distribution toward warmer temperatures—increasing the frequency of extreme temperatures. For precipitation, the specification captures the separate effects of mean rainfall, frequency of rainfall, and frequency of extreme events. The results show that the tails of the temperature and precipitation distributions, not the means, are the dominant climatic determinants in irrigation adoption. The results also show that water availability, soil characteristics, farm size and operator demographics are important determinants of irrigation.     

Tropical cyclone contribution to extreme rainfall over southwest Pacific Island nations

Southwest Pacific nations are among some of the worst impacted and most vulnerable globally in terms of tropical cyclone (TC)-induced flooding and accompanying risks. This study objectively quantifies the fractional contribution of TCs to extreme rainfall (hereafter, TC contributions) in the context of climate variability and change. We show that TC contributions to extreme rainfall are substantially enhanced during active phases of the Madden–Julian Oscillation and by El Niño conditions (particularly over the eastern southwest Pacific region); this enhancement is primarily attributed to increased TC activity during these event periods. There are also indications of increasing intensities of TC-induced extreme rainfall events over the past few decades. A key part of this work involves development of sophisticated Bayesian regression models for individual island nations in order to better understand the synergistic relationships between TC-induced extreme rainfall and combinations of various climatic drivers that modulate the relationship. Such models are found to be very useful for not only assessing probabilities of TC- and non-TC induced extreme rainfall events but also evaluating probabilities of extreme rainfall for cases with different underlying climatic conditions. For example, TC-induced extreme rainfall probability over Samoa can vary from ~ 95 to ~ 75% during a La Niña period, if it coincides with an active or inactive phase of the MJO, and can be reduced to ~ 30% during a combination of El Niño period and inactive phase of the MJO. Several other such cases have been assessed for different island nations, providing information that have potentially important implications for planning and preparing for TC risks in vulnerable Pacific Island nations.

     

Application of stochastic simulation to climatic-change studies

Stochastic modelling provides a tool for exploring the full implications of the statistical behavior of historical records and can be used to predict the changing probabilities that events of various magnitudes will occur for different climatic change scenarios. Two simulation models are presented, one for daily air temperature, and the other for daily precipitation. The simulation procedures are: (1) extract salient parameter values from historical records; (2) simulate long sequences of data using the stochastic models, with or without a climatic change scenario as provided by a general circulation model; and (3) using the simulated data as inputs, derive the probability distributions of other variables based on known deterministic or probabilistic relationships between the input and the predicted variables.Given a doubling of carbon dioxide concentration in the atmosphere, the climatic models produce varying degrees of temperature and precipitation changes. Examples of application, including the derivation of snowfall and riverice data using simulated temperature and precipitation, illustrate that stochastic modelling offers a suitable approach to quantify the possible hydrologic impacts of climatic change.