Politics eclipses climate extremes for climate change perceptions

Whether or not actual shifts in climate influence public perceptions of climate change remains an open question, one with important implications for societal response to climate change. We use the most comprehensive public opinion survey data on climate change available for the US to examine effects of annual and seasonal climate variation. Our results show that political orientation has the most important effect in shaping public perceptions about the timing and seriousness of climate change. Objective climatic conditions do not influence Americans’ perceptions of the timing of climate change and only have a negligible effect on perceptions about the seriousness of climate change. These results suggest that further changes in climatic conditions are unlikely to produce noticeable shifts in Americans’ climate change perceptions.     

Perceptions of climate and ocean change impacting the resources and livelihood of small-scale fishers in the South Brazil Bight

Coastal fishing communities are closely linked to the biological and ecological characteristics of exploited resources and the physical conditions associated with climate and ocean dynamics. Thus, the human populations that depend on fisheries are inherently exposed to climate variability and uncertainty. This study applied an ethno-oceanographic framework to investigate the perceptions of fishers on climate and ocean change to better understand the impacts of climate change on the coastal fishing communities of the South Brazil Bight. Seven coastal fishing communities that cover the regional diversity of the area were selected. Fishers were interviewed using a semi-structured questionnaire. The results suggest that fishers have detected climate-related changes in their environment such as reduced rainfall, increased drought events, calmer sea conditions, increases in air and ocean temperatures, changes in wind patterns and shoreline erosion. The perceptions of the fishers were compared to the available scientific data, and correlations were found with rainfall, wind speed and air and ocean temperatures. New hypotheses were raised based on the perceptions of fishers about sea level, coastal currents and sea conditions such as the hypothesis that the sea has become calmer. These perceived changes have positive and negative effects on the yields and livelihoods of fishers. The present work is the first evaluation of the perceptions of fishers on climate and ocean change and brings new understandings of climate-fishery-human interactions as well as provides inputs for future adaptation plans.     

Who remembers a hot summer or a cold winter? The asymmetric effect of beliefs about global warming on perceptions of local climate conditions in the U.S.

This paper explores the phenomenon of local climate perception and the extent to which public perceptions match climate conditions as recorded in instrumental climate data. We further examine whether perceptions of changes in local climates are influenced by prior beliefs about global warming, through the process of motivated reasoning. Using national survey data collected in the United States in 2011, we find that subjective experiences of seasonal average temperature and precipitation during the previous winter and summer were related to recorded conditions during each season. Beliefs about global warming also had significant effects on subjective experiences with above-normal temperatures, particularly among those who believed that global warming is not happening. When asked about the summer of 2010, those who believed that global warming is not happening were significantly less likely to report that they had experienced a warmer-than-normal summer, even when controlling for demographics and local climate conditions. These results suggest that the subjective experience of local climate change is dependent not only on external climate conditions, but also on individual beliefs, with perceptions apparently biased by prior beliefs about global warming.     

Farmer perceptions of climate change: Associations with observed temperature and precipitation trends,irrigation, and climate beliefs

How individuals perceive climate change is linked to whether individuals support climate policies and whether they alter their own climate-related behaviors, yet climate perceptions may be influenced by many factors beyond local shifts in weather. Infrastructure designed to control or regulate natural resources may serve as an important lens through which people experience climate, and thus may influence perceptions. Likewise, perceptions may be influenced by personal beliefs about climate change and whether it is human-induced. Here we examine farmer perceptions of historical climate change, how perceptions are related to observed trends in regional climate, how perceptions are related to the presence of irrigation infrastructure, and how perceptions are related to beliefs and concerns about climate change. We focus on the regions of Marlborough and Hawke’s Bay in New Zealand, where irrigation is utilized on the majority of cropland. Data are obtained through analysis of historical climate records from local weather stations, interviews (n = 20), and a farmer survey (n = 490). Across both regions, no significant historical trends in annual precipitation and summer temperatures since 1980 are observed, but winter warming trends are significant at around 0.2–0.3 °C per decade. A large fraction of farmers perceived increases in annual rainfall despite instrumental records indicating no significant trends, a finding that may be related to greater perceived water availability associated with irrigation growth. A greater fraction of farmers perceived rainfall increases in Marlborough, where irrigation growth has been most substantial. We find those classes of farmers more likely to have irrigation were also significantly more likely to perceive an increase in annual rainfall. Furthermore, we demonstrate that perceptions of changing climate – regardless of their accuracy – are correlated with increased belief in climate change and an increased concern for future climate impacts. Those farmers that believe climate change is occurring and is human induced are more likely to perceive temperature increases than farmers who believe climate change is not occurring and is not human induced. These results suggest that perceptions are influenced by a variety of personal and environmental factors, including infrastructure, which may in turn alter decisions about climate adaptation.     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止，大部分海气耦合气候模式（AOGCM）的空间分辨率还较低，很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先，采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型，然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景， 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下，无论1月还是7月，统计降尺度方法模拟的温度与观测的温度有很好的一致性，而且在大多数台站，统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面，无论1月还是7月，也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型，结果表明大多数的站点都存在温度的明显上升趋势，同时7月的上升趋势与1月相比偏低。     

Bridging science and community knowledge? The complicating role of natural variability in perceptions of climate change

Although the spatial and temporal scales on which climate varies is a prominent aspect of climate research in the natural sciences, its treatment in the social sciences remains relatively underdeveloped. The result is limited understanding of the public's capacity to perceive climate variability as distinct from change, and uncertainty surrounding how and when to best communicate information on variability/change. Ignoring variability in favour of change-focused analyses and language risks significant misrepresentation of public perception and knowledge, and precludes detailed synthesis of data from the social and natural sciences. An example is presented based on a regional comparison of variability-dominated climate observations and change-focused survey data, collected in western Newfoundland (Canada). This region experiences pronounced, slow-varying natural variability, which acted to obscure broader climate trends through the 1980s and 1990s; since the late 1990s, the same variability has amplified apparent change. While survey results confirm residents perceive regional climate change, it is not clear whether respondents distinguish variability from change. This presents uncertainty in the best approach to climate science communication in this region, and raises concern that subsequent variability-driven transient cooling will erode public support for climate action. Parallels are drawn between these regional concerns and similar uncertainty surrounding treatment of variability in discussion of global temperature trends, highlighting variability perception as a significant gap in human dimensions of climate change research.     

Vulnerable or Resilient? A Multi-Scale Assessment of Climate Impacts and Vulnerability in Norway

This paper explores the issue of climate vulnerability in Norway, an affluent country that is generally considered to be resilient to the impacts of climate change. In presenting a multi-scale assessment of climate change impacts and vulnerability in Norway, we show that the concept of vulnerability depends on the scale of analysis. Both exposure and the distribution of climate sensitive sectors vary greatly across scale. So do the underlying social and economic conditions that influence adaptive capacity. These findings question the common notion that climate change may be beneficial for Norway, and that the country can readily adapt to climate change. As scale differences are brought into consideration, vulnerability emerges within some regions, localities, and social groups. To cope with actual and potential changes in climate and climate variability, it will be necessary to acknowledge climate vulnerabilities at the regional and local levels, and to address them accordingly. This multi-scale assessment of impacts and vulnerability in Norway reinforces the importance of scale in global change research.     

Semiquantitative Assessment of Regional Climate Vulnerability: The North-Rhine Westphalia Study

Climate change will bring about a sea change in environmental conditions worldwide during the 21th century. In particular, most of the extreme events and natural disaster regimes prevailing today will be transformed, thus exposing innumerable natural and socio-economic systems to novel risks that will be difficult to cope with. This crucial component of vulnerability to anthropogenic interference with the climate system is analyzed using powerful pattern recognition methods from statistical physics. The analysis is of intermediate character, with respect to spatial scale and complexity level respectively, and therefore allows a rapid regional assessment for any area of interest. The approach is based on a comprehensive inventory of all those ecological and socioeconomic assets in a region that are significantly sensitive to extreme weather (and climate) events. Advanced cluster analysis techniques are then employed to derive from the inventory a set of thematic maps that succinctly summarize – and visualize – the differential vulnerabilities characteristic of the area in question. This information can prepare decision makers and the general public for the climate change hazards to be faced and facilitates a precautionary climate change risk management. The semiquantitative methodology described and applied here can be easily extended to other aspects of climate change assessment.     

Global social and environmental change drives the management and delivery of ecosystem services from urban gardens: A case study from Central Coast,California

Urban community gardens are vital green spaces threatened by global social and environmental change factors. Population growth has reduced the amount of space available in cities, and climate change challenges plant growth thresholds. Urban community gardens provide dynamic socio-ecological systems to study how such social and environmental change factors affect the management and delivery of ecosystem services. They provide spaces where urban citizens purposefully interact with nature and receive multiple benefits. In this paper, we synthesize the results of three years of research in a case study of urban community gardens across the Central Coast of California and present a framework showing how both social and environmental change factors at the regional scale affect the ecological make-up of urban community gardens, which in turn affect the ecosystem services coming from such systems. Our study reveals that global environmental change felt at the regional level (e.g., increased built environment, climate change) interact with social change and policy (e.g., population growth, urbanization, water use policy), thus affecting regulations over garden resources (e.g., water availability) and management decisions by gardeners (e.g., soil management, crop planting decisions). These management decisions at the plot-scale, determine the ecological complexity and quality of the gardens and affect the resulting ecosystem services that come from these systems, such as food provision for both humans and urban animals. A greater understanding of how environmental and social change factors drive the management processes of urban community gardens is necessary to design policy support systems that encourage the continued use and benefits arising from such green spaces. Policies that can support urban community gardens to maintain ecological complexity and increase biodiversity through active management of soil quality and plant diversity have the potential to increase social and environmental outcomes that feedback to the larger environmental and social system.     

IPCC第六次评估报告中的《图集》

决策者和公众正在越来越多地关注气候变化带来的影响,而这需要更加丰富的、区域尺度上的当前和未来气候状况的精细信息。《图集》与IPCC第六次评估报告（AR6）第一工作组（WGI）报告中其他章节相协调,评估区域气候变化的观测、归因、预估的基本信息,并建立了在线交互图集系统。《图集》包含图集章节和交互图集两部分：图集章节基于新的区域划分,评估了各区域的气候变化,重点关注地表温度和降水的观测趋势、归因以及预估的未来变化。交互图集是AR6 WGI报告的一个新组成部分,基于观测、全球（CMIP5和CMIP6）和区域（CORDEX）模式数据,以互动地图的形式提供观测和预估时间段的气候变化和归因的综合信息。     

The uses of social and environmental health indicators in monitoring the effects of climate change

This paper argues that information collected in the field of Public Health has the potential to provide a valuable contribution to the forecasting and evaluation of the impact of climate change. The well-developed sets of social and environmental health data collected to monitor the health of human populations could also offer sensitive indicators of the effects and potential effects of climate change. City collections of public health data and general demographic information are reviewed as examples of the availability of such monitoring systems.The interests of those monitoring climate change and those monitoring health march together in other ways as well. Changes in radiation, rainfall and temperature will change food and building safety levels, as well as waste disposal capacity. People who are on the poverty line or ill, or both, have little opportunity to respond to such changes, or the strategies designed to control them. Since the Canadian Lalonde Report of 1974, health services have increasingly linked their social and physical health information to environmental data. Coordinated planning using a multidisciplinary data base and responses to change which give due regard to equity are both central considerations in responding to changes in the environment as well as to risks to human health.     

Do weak AR4 models bias projections of future climate changes over Australia?

Regional climate projections using climate models commonly use an “all-model” ensemble based on data sets such as the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted models based on specific criteria. We use a criteria based on the capacity of climate models to simulate the observed probability density function calculated using daily data, model-by-model and region-by-region for each of the AR4 models over Australia. We demonstrate that by omitting those climate models with relatively weak skill in simulating the observed probability density functions of maximum and minimum temperature and precipitation, different regional projections are obtained. Differences include: larger increases in the mean maximum and mean minimum temperatures, but smaller increases in the annual maximum and minimum temperatures. There is little impact on mean precipitation but the better models simulate a larger increase in the annual rainfall event combined with a larger decrease in the number of rain days. The weaker models bias the amount of mean warming towards lower increases, bias annual maximum temperatures to excessive warming and bias precipitation such that the amount of the annual rainfall event is under-estimated. We suggest that omitting weak models from regional scale estimates of future climate change helps clarify the nature and scale of the projected impacts of global warming.     

公众为何对气候变化的科学结论信任不充分

通过系统分析世界各国气候传播领域近年来的进展,以解释为何公众对气候变化的科学结论缺乏信任这一复杂问题.文中首先概述了各国公众对气候变化的态度以及影响公众态度的社会、政治与经济因素,再分析了各国媒体对气候变化的报道及其媒体效果,并重点探究了社会心理因素如何影响人们对气候变化的认知与态度.此外国际气候传播研究对我国气候传播...     

Public microblogging on climate change: One year of Twitter worldwide

Public perceptions of climate change are traditionally measured through surveys. The exploding popularity of social networks, however, presents a new opportunity to research the spatiotemporal pattern of public discourse in relation to natural and/or socio-economic events. Among the social networks, Twitter is one of the largest microblogging services. The architecture of Twitter makes the question “what's happening?” the cornerstone of information exchange. This inspired the notion of using Twitter users as distributed sensors, which has been successfully employed in both the natural and social sciences. In 2012 and 2013, we collected 1.8 million tweets on “climate change” and “global warming” in five major languages (English, German, Russian, Portuguese, and Spanish). We discuss the geography of tweeting, weekly and daily patterns, major news events that affected tweeting on climate change, changes in the central topics of discussion over time, the most authoritative traditional media, blogging, and the most authoritative organizational sources of information on climate change referenced by Twitter users in different countries. We anticipate that social network mining will become a major source of data in the public discourse on climate change.     

Analysis of Climate Change Indicators. Part 2. Northwestern Russia

Data on modern climate and environmental changes in the northwestern region of Russia are compared with the public perception of such changes. The analysis reveals that unusual weather patterns and single extreme events have a deeper impact on the public perception than long-term periods of climate change. The majority of population consider climate and environmental changes locally, do not associate them with global drivers, and are not prepared to adaptation. The numerical climate perception index is developed to characterize the awareness of population about the climate change and preparedness to adaptation. The index can be used for improving the awareness of policymakers for regional climate adaptation.     

Social capital and the public acceptability of climate change adaptation policies: a case study in Romney Marsh,UK

There is now a growing literature emphasizing the critical importance of social variables in the formulation of coastal management policies seeking to tackle climate change impacts. This paper focuses on the role of social capital, which is increasingly identified as having a significant role in climate change adaptation and mitigation strategies. We focus on public perceptions of the social costs and benefits arising from two management options (managed retreat/realignment and hold-the-line), the resulting level of policy acceptability, and how this acceptability is mediated by social capital parameters within coastal communities. These issues are examined by means of a quantitative social survey implemented in Romney Marsh (east Sussex/Kent, UK), an area facing significant impacts from climate change. We tested two models through path analysis with latent structures. The first correlates respondents’ perceived costs and benefits with the level of public acceptability of the two policy options. In the second model, we introduce social capital variables, investigating the impacts on perceived social costs and benefits of the policy options, and the overall effect on the level of public acceptability. Our findings demonstrate: (1) perceived social costs and benefits of proposed policy options influence the level of public acceptability of these policies; (2) these social costs and benefits are connected with the level of public acceptability; and (3) specific social capital parameters (i.e. social trust, institutional trust, social networks and social reciprocity) influence perceived policy costs and benefits, and also have a significant impact on the level of public acceptability of proposed policy options.     

Interdecadal variability of regional climate change: implications for the development of regional climate change scenarios

Summary Illustrative examples are discussed of the interdecadal variability features of the regional climate change signal in 5 AOGCM transient simulations. It is shown that the regional precipitation change signal is characterized by large variability at decadal to multidecadal scales, with the structure of the variability varying markedly across regions. Conversely, the regional temperature change signal shows low interdecadal variability. Results are compared across scenarios, models and different realizations with the same model. Our analysis indicates that, at the decadal scale, linear scaling of the regional climate change signal by the global temperature change works relatively well for temperature but less so for precipitation. The nonlinear fraction of the climate change signal tends to decrease with the magnitude of the signal. The implications of interdecadal variability for the generation of regional climate change scenarios are discussed, in particular concerning the use of multi-experiment ensembles to produce such scenarios.     

Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model

Climate change caused by anthropogenic activities has generated a variety of research focusing on investigating the past climate, predicting the future climate and quantifying the change in climate extreme events by using different climate models. Climate extreme events are valuable to evaluate the potential impact of climate change on human activities, agriculture and economy and are also useful to monitor the climate change on global scale. Here, a Regional Climate Model (RCM) simulation is used to study the future variations in the temperature extreme indices, particularly change in frequency of warm and cold spells duration over Pakistan. The analyses are done on the basis of simulating two 30 years simulations with the Hadley Center’s RCM PRECIS, at a horizontal resolution of 50 km. Simulation for the period 1961–1990 represents the recent climate and simulation for the period 2071–2100 represents the future climate. These simulations are driven by lateral boundary conditions from HadAM3P GCM of Hadley centre UK. For the validation of model, observed mean, maximum and minimum temperatures for the period 1961–1990 at all the available stations in Pakistan are first averaged and are then compared with the PRECIS averaged grid-box data. Also the observed monthly gridded data set of Climate Research Unit (UK) data is used to validate the model. Temperature indices in the base period as well as in future are then calculated and the corresponding change is observed. Percentile based spatial change of temperature shows that in summer, increase in daily minimum temperature is more as compared to the increase of daily maximum temperature whereas in winter, the change in maximum temperature is high. The occurrence of annual cold spells shows significantly decreasing trend while for warm spells there is slight increasing trend over Pakistan.     

Spatial-temporal controls on Cooling Degree Hours: An energy demand parameter

Summary Present-day and 2 × CO₂ regional climate impacts and the effects of local land use patterns on Cooling Degree Hours (CDHs — an energy demand parameter based on cumulative degrees of temperature above 75 °F) are investigated for the Phoenix metropolitan area in central Arizona. Approaches include: (1) the utilization of four readily available and commonly used Global Circulation Models (GCMs) to assess possible changes in climate for doubled CO₂, (2) the analysis of hourly temperature data collected for one year over three different land type sites, and (3) analysis of locally collected hourly temperature data, for a typical summer day from a real-time weather and climate network, to evaluate the spatial variability of CDHs over the urban landscape. Results are discussed by showing effects at the global and urban scales. Differing surface types, and expected changes in land uses in the future, induce spatial differences of CDHs (and therefore potential energy demand) comparable to GCM projections of climate change for the region.With 6 Figures     

A comparison of a GCM response to historical anthropogenic land cover change and model sensitivity to uncertainty in present-day land cover representations

This study assesses the sensitivity of the fully coupled NCAR-DOE PCM to three different representations of present-day land cover, based on IPCC SRES land cover information. We conclude that there is significant model sensitivity to current land cover characterization, with an observed average global temperature range of 0.21 K between the simulations. Much larger contrasts (up to 5 K) are found on the regional scale; however, these changes are largely offsetting on the global scale. These results show that significant biases can be introduced when outside data sources are used to conduct anthropogenic land cover change experiments in GCMs that have been calibrated to their own representation of present-day land cover. We conclude that hybrid systems that combine the natural vegetation from the native GCM datasets combined with human land cover information from other sources are best for simulating such impacts. We also performed a prehuman simulation, which had a 0.39 K ~higher average global temperature and, perhaps of greater importance, temperature changes regionally of about 2 K. In this study, the larger regional changes coincide with large-scale agricultural areas. The initial cooling from energy balance changes appear to create feedbacks that intensify mid-latitude circulation features and weaken the summer monsoon circulation over Asia, leading to further cooling. From these results, we conclude that land cover change plays a significant role in anthropogenically forced climate change. Because these changes coincide with regions of the highest human population this climate impact could have a disproportionate impact on human systems. Therefore, it is important that land cover change be included in past and future climate change simulations.