<Emphasis Type="Italic">Ua ‘afa le Aso</Emphasis> Stormy weather today: traditional ecological knowledge of weather and climate. The Samoa experience

This paper examines traditional ecological knowledge of weather and climate in Samoa, a Polynesian community in the South Pacific. The research found Samoans have their own unique seasonal calendar. The Samoan seasonal calendar is predominantly based on the observations of local environmental changes, which are in turn influenced by weather and climate. Monitoring changes in plants and animal behaviour, for example, are key indicators used by the Samoans to forecast changes in weather and climate. In addition, their communal and family social activities like hunting, fishing and feasting are driven by the seasonal calendar. The Samoans knowledge of cloud formation, conditions conducive to the formation and onset of severe weather systems and seasonal changes in climate, helped them anticipate, plan and adapt to extreme weather and climate events. The ability and knowledge of the Samoans to forecast the onset of extreme weather and climate events, relying predominantly on local environmental changes are vital tools that should be incorporated in the formulation of human induced climate change adaptation strategies.     

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.     

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.     

2004年的极端天气和气候事件及其他相关事件的概要回顾

韩国3月暴雪,德国7月飞雪,我国华南近50年最严重的秋冬连旱、东北百年一遇的春旱,加勒比海地区连续异常猛烈的飓风,欧洲夏季高温,东太平洋上出现一次弱El Nino现象.2004年仍是气象灾害频繁发生的一年,创历史记录的极端天气继续出现.     

安徽省酷热天气分析及预报指标

基于安徽省1961-2017年逐日地面最高气温资料,采用Mann-Kendall法对安徽省高温天气事件进行突变分析,发现安徽省2000年后高温事件明显增加。为分析安徽省酷热天气特征和产生机理,文中挑选了35~37℃高温天气个例对比分析。结果发现:1)500 hPa西太平洋副高位置和850 hPa气温对酷热天气预报的指示性最好。2)受西太平洋副热带高压不同位置控制,安徽省增温机制不同:当为高压中心控制时,太阳辐射在增温过程中起决定性作用,安徽省易出现酷热天气;当高压中心位于海上,脊线位于安徽省附近时,安徽省高温强度较弱。通过酷热天气个例研究和合成平均分析,文中总结了安徽省酷热天气预报指标。     

甘孜州石渠县2008年特大雪灾冻害分析

2008年1月中旬~3月中旬,四川省甘孜州石渠县发生特大雪灾冻害,尽管此次灾害是在全国大范围低温雨雪冰冻灾害的背景下发生的,但在很多方面都有其特殊性,尤其是局地性特征突出。为今后更好地做好雪灾冻害预测预报及防御工作,作者对此次极端灾害性天气气候事件造成的影响和成因及防灾减灾效果进行了认真分析。结果表明,此次雪灾冻害的强度之大,持续时间之长,危害和损失之重均为当地有气象记录以来少有;2007年8月~2008年5月的拉尼娜事件造成的大气环流异常、地面冷空气活动频繁、西太平洋副热带高压异常偏北、南支槽异常活跃是石渠发生特大雪灾冻害的重要原因,当地特殊的地理位置和地形是雪灾冻害的又一个客观因素;天气气候预报预测、气象服务和相关部门同步开展的防灾救灾工作对最大限度地减轻灾害造成的损失作用明显。      

Multipurpose agroforestry as a climate change resiliency option for farmers: an example of local adaptation in Vietnam

Increasing frequency, intensity and duration of severe weather events are posing major challenges to global food security and livelihoods of rural people. Agriculture has evolved through adaptation to local circumstances for thousands of years. Local experience in responding to severe weather conditions, accumulated over generations and centuries, is valuable for developing adaptation options to current climate change. This study aimed to: (i) identify tree species that reduce vulnerability of cropping systems under climate variability; and (ii) develop a method for rapidly assessing vulnerability and exploring strategies of smallholder farmers in rural areas exposed to climate variability. Participatory Rural Appraisal methods in combination with Geographical Information Systems tools and statistical analysis of meteorological data were used to evaluate local vulnerability to climate change and to investigate local adaptation measures in two selected villages in Vietnam, one of the countries most vulnerable to climate change. The low predictability of severe weather events makes food crops, especially grain production, insecure. This study shows that while rice and rain-fed crops suffered over 40 % yield losses in years of extreme drought or flood, tree-based systems and cattle were less affected. 13 tree species performed well under the harsh local climate conditions in home and forest gardens to provide income, food, feed and other environmental benefits. Thus, this research suggests that maintenance and enhancement of locally evolved agroforestry systems, with high resilience and multiple benefits, can contribute to climate change adaptation.     

近30a台湾非台风暴雨研究回顾

从1987年开始,台湾暴雨研究进入一个新纪元,由早期定性描述分析进入定量计算与模拟,其中包括新观测设施的建设与数值天气预报系统的建立。近年来,随着全球气候变暖,台湾极端降雨事件有所增加,其中不少极端降雨事件是由非台风暴雨所致,往往给当地社会经济和人民生命财产造成严重影响,这就迫切需要不断提高强降雨定量预报业务水平。本文通过对近30 a台湾在非台风暴雨研究发展方面取得的主要进展的回顾,重点介绍了台湾气象部门为了提高强降雨定量预报业务水平所做出的努力,同时对未来台湾非台风暴雨研究规划与方向作了简要介绍。     

Sustainable urban water management and integrated development in informal settlements: The contested politics of co-production in Santo Domingo,Dominican Republic

Given the implications of global climate change, including higher likelihood of extreme weather events, and the increasing urban density coupled with reduction in permeable surfaces in the Global South, Sustainable Urban Water Management (SUWM) has emerged as a preferred paradigm for stormwater management. However, the implementation of SUWM, which is premised on using vegetation or engineered capture technologies to control runoff at its source in an effort to replicate natural hydrology, is limited by a lack of institutional integration, not merely between administrative organs with responsibility for stormwater management but also between infrastructure departments, planning institutions, communities, and civil society organizations. This is particularly true in informal settlements in the humid tropics, where excessive impermeable surfaces and a lack of adequate solid waste collection exacerbate municipal limitations in stormwater management. This article discusses an effort to integrate local communities, civil society organizations, and local and regional authorities to improve drainage services within the framework of integrated development in the informal settlement of Los Platanitos, Santo Domingo Norte, Dominican Republic. In order to address the drainage and flooding issues in Los Platanitos while also fostering economic development, representatives of community groups, NGOs, local government, and state agencies have developed a participatory planning structure known as a mesa de concertación, or “cooperating table.” The mesa, which was established in 2014, has succeeded in bringing neighborhood, civil society, and government actors to the same “table” as a mechanism for addressing the community’s drainage challenges within the broader context of integrated community development.     

天气和气候极端事件的变化及其与全球变暖的联系——纪念2002年世界气象日“减低对天气和气候极端事件的脆弱性”

2002年3月23日世界气象日的主题是“减低天气和气候极端事件的脆弱性”。针对这个主题,作者对以下四方面问题作了阐述：（1）天气与气候极端事件以及脆弱性的定义;（2）近百年来全球天气与气候极端事件的变化及其与全球气候变化的关系;（3）未来天气与气候极端事件及其影响的预测;（4）天气与气候极端事件的适应与减缓对策。由于篇幅有限,未介绍中国在这方面的研究。     

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.     

广西贵港地区极端高温日的时间变化特征及其环流背景

极端高温事件是我国南方夏季频发的天气灾害,区域性明显,持续性高温事件的环流背景及其影响机理值得深入研究。基于台站观测资料和欧洲中期天气预报中心的再分析资料,对1961—2010年广西东南部贵港地区发生的高温事件的时间变化规律及持续性高温天气过程对应的大气环流场演变进行了统计诊断分析。(1)广西东南部贵港地区的高温日主要出现在6—9月,其中7—8月是高温日频发的月份;3天以上的持续高温日数占总高温日数的6成;8—9月的高温日数具有明显的增加趋势。(2)桂东南在6—9月的持续性高温天气与一些特定的天气系统的存在有紧密联系。持续的下沉运动是造成广西东南部升温的主要原因,而导致垂直下沉运动的主要原因又与大陆高压、副热带高压或南海-菲律宾海出现的热带低压系统的北移路径偏东有关,持续时间较长的高温过程还与热带气旋的活动时间较长和强度偏强有关。(3)高压控制的晴空少云天气可导致地表接收更多的太阳辐射,使得地表温度的升高,从而导致地表向上的长波辐射及感热通量增强,加热近地面空气,这些有利于近地面升温的热力过程以及垂直下沉绝热加热过程的增强,使得高温天气过程得以维持。      

Global warming,low-frequency variability,and biennial oscillation: an attempt to understand the physical mechanisms driving major ENSO events

Three primary global modes of sea surface temperature (SST) variability during the period of 1871–2010 are identified through cyclostationary empirical orthogonal function analysis. The first mode exhibits a clear trend and represents global SST warming with an ‘El Niño-like’ SST pattern in the tropical Pacific. The second mode is characterized by considerable low-frequency variability in both the tropical Pacific and the North Pacific regions, indicating that there is a close connection between the two regions on interannual and decadal time scales. The third mode shows a seesaw pattern between El Niño and La Niña within a two-year period; this mode is derived by the oscillatory tendency of the tropical Pacific ocean–atmosphere coupled system. A SST reconstruction based on these three modes captures a significant portion of the SST variability in the raw data, which is primarily associated with El Niño-Southern Oscillation (ENSO) events in the tropical Pacific. Additionally, this study attempts to interpret the major ENSO events that have occurred since the 1970s in terms of the interplay originating from these three modes of variability. In particular, two key points are derived from this analysis: (1) the most extreme El Niño events occurred in 1982/1983 and 1997/1998 are attributed to the positive contributions of all three modes; and (2) the central Pacific (CP) El Niño events in the 1990s and 2000s have different physical mechanisms, that is, the CP El Niño events in the early 1990s originated mainly from the low-frequency mode, while those in the early 2000s derived mainly from the global warming mode.     

Examining the social consequences of extreme weather: the outcomes of the 1946/1947 winter in upland Wales, UK

Extreme forms of weather are predicted to become more frequent experiences in the future. However, the hardest event to mitigate against is the unexpected. In the UK, the occurrence of winter snowfall is difficult to predict, highly variable, both spatially and temporally and predicted to become less common in the future. This paper examines the consequences of the severe winter of 1946/1947 at the local scale through a Welsh case study of Cwm Tywi, a community of upland sheep farms. This community had shown great resilience during the snowiest winter on record in comparison with other, more urban communities, but the inhabitants eventually abandoned their homes because of the emotional distress caused by the loss of a large proportion of the livestock. In addition to the severity of the snow, perceptions of the extreme nature of this event and the community??s ability to mitigate as a result of rurality, self-sufficiency and remoteness are investigated through the analysis of interviews, oral histories, and other documentary accounts. This case study provides an insight into the complexity of understanding vulnerability, adaptation and resilience, which are temporally and spatially specific.     

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.     

Indigenous Australians’ knowledge of weather and climate

Although the last 200 years of colonisation has brought radical changes in economic and governance structures for thousands of Aboriginal and Torres Strait Islanders living in remote areas of northern Australia, many of these Indigenous people still rely upon, and live closely connected to, their natural environment. Over millennia, living ‘on country’, many of these communities have developed a sophisticated appreciation of their local ecosystems and the climatic patterns associated with the changes in them. Some of this knowledge is recorded in their oral history passed down through generations, documented in seasonal weather calendars in local languages and, to a limited degree, transcribed and translated into English. This knowledge is still highly valued by these communities today, as it is used to direct hunting, fishing and planting as well as to inform many seasonally dependant cultural events. In recent years, local observations have been recognised by non-Indigenous scientists as a vital source of environmental data where few historic records exist. Similar to the way that phenological observations in the UK and US provide baseline information on past climates, this paper suggests that Indigenous observations of seasonal change have the potential to fill gaps in climate data for tropical northern Australia, and could also serve to inform culturally appropriate adaptation strategies. One method of recording recent direct and indirect climate and weather observations for the Torres Strait Islands is documented in this paper to demonstrate the currency of local observations of climate and its variability. The paper concludes that a comprehensive, participatory programme to record Aboriginal and Torres Strait Islander knowledge of past climate patterns, and recent observations of change, would be timely and valuable for the communities themselves, as well as contributing to a greater understanding of regional climate change that would be useful for the wider Australian population.     

Personal harm and support for climate change mitigation policies: Evidence from 10 U.S. communities impacted by extreme weather

A growing body of research examines the role of extreme weather experience—as one of the most personal, visceral (and increasingly frequent and severe) impacts of climate change—in shaping views on climate change. A remaining question is whether the experience of an extreme weather event increases climate change concern via experiential learning or reinforces existing views via motivated reasoning. Building on this work, we explore the relationship between personal experience and climate change policy preferences using surveys in 10 communities that experienced extreme weather events (3 tornadoes, 3 floods, 2 wildfires, 1 hurricane and 1 landslide). We find that self-reported personal harm aligns with objective measures of event impacts and that personal harm (i.e., experience) is associated with higher levels of policy support. However, we do not find that objective measures of event impacts are related to policy support. Though political ideology (i.e., motivated reasoning) dominates our model of policy support in predictable ways, personal harm moderates this relationship suggesting that conservatives reporting higher levels of personal harm from the event are, on average, more likely to express support for climate policy than those reporting lower levels of harm. We postulate that while extreme weather events may serve as teachable moments on climate change, their lessons may only reach conservatives who feel personally harmed, even in the communities most affected.     

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。     

Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

A major component of flood alert broadcasting is the short-term prediction of extreme rainfall events, which remains a challenging task, even with the improvements of numerical weather prediction models. Such prediction is a high priority research challenge, specifically in highly urbanized areas like Mumbai, India, which is extremely prone to urban flooding. Here, we attempt to develop an algorithm based on a machine learning technique, support vector machine (SVM), to predict extreme rainfall with a lead time of 6–48 h in Mumbai, using mesoscale (20–200 km) and synoptic scale (200–2,000 km) weather patterns. The underlying hypothesis behind this algorithm is that the weather patterns before (6–48 h) extreme events are significantly different from those of normal weather days. The present algorithm attempts to identify those specific patterns for extreme events and applies SVM-based classifiers for extreme rainfall classification and prediction. Here, we develop the anomaly frequency method (AFM), where the predictors (and their patterns) for SVM are identified with the frequency of high anomaly values of weather variables at different pressure levels, which are present before extreme events, but absent for non-extreme conditions. We observe that weather patterns before the extreme rainfall events during nighttime (1800 to 0600Z) is different from those during daytime (0600 to 1800Z) and, accordingly, we develop a two-phase support vector classifier for extreme prediction. Though there are false alarms associated with this prediction method, the model predicts all the extreme events well in advance. The performance is compared with the state-of-the-art statistical technique fingerprinting approach and is observed to be better in terms of false alarm and prediction.