The influence of national temperature fluctuations on opinions about climate change in the U.S. since 1990

Public opinion in the United States about human-caused climate change has varied over the past 20 years, despite an increasing consensus about the issue in the expert community. Attitudes about climate change have been attributed to a number of factors including personal values, political ideology, the media environment and personal experience. Recent studies have found evidence that the temperature can influence one’s opinion about climate change and willingness to change behaviour and/or support climate policy. Although there is some evidence that individual cool or warm years have influenced large-scale opinion about climate change, the extent to which temperature can explain the past variability in public opinion and public discourse about climate change at the national level is not known. Here we isolate the relationship between opinion about climate change and temperature at the national scale, using data from opinion polls, a discourse analysis of opinion articles from five major daily newspapers, and a national air temperature database. The fraction of respondents to national polls who express “belief in” or “worry about” climate change is found to be significantly correlated to U.S. mean temperature anomalies over the previous 3–12 months. In addition, the fraction of editorial and opinion articles which “agree” with the expert consensus on climate change is also found to be significantly correlated to U.S. mean temperature anomalies at seasonal and annual scales. These results suggest that a fraction of the past variance in American views about climate change could potentially be explained by climate variability.     

Sensitivity of climate change projections to uncertainties in the estimates of observed changes in deep-ocean heat content

The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed twentieth century temperature changes. The impact on climate change projections of using the smallest and largest estimates of twentieth century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference (“business as usual”) scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by twentieth century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4’s multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However, the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the twentieth century changes in surface and upper air temperatures, combined with the expert prior for climate sensitivity.     

People as sensors: Mass media and local temperature influence climate change discussion on Twitter

This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “climate change” and “global warming” as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether people immediately notice substantial local weather anomalies such as deviations from long-term mean temperatures and connect them to climate change by contributing to climate change discourse on Twitter and (2) to examine the role of mass media in this process. Over 2 million tweets were collected for a two-year period (2012–2013) and were assigned to 157 urban areas in the continental US. The rate of tweeting on climate change was regressed on the time variables, number of climate change publications in the mass media, and a number of temperature variables. The analysis was conducted at the two levels of aggregation – national and local. The high significance of the mass media and temperature variables in the majority of regression models suggests that both the weather and mass media coverage control public interest to the topic. However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse. Overall, the findings confirmed that the public recognize extreme temperature anomalies and connect these anomalies to climate change.     

Climate Comparisons and Change Projections for the Northwest Atlantic from Six CMIP5 Models

Abstract

Key physical variables for the Northwest Atlantic (NWA) are examined in the “historical” and two future Representative Concentration Pathway (RCP) simulations of six Earth System Models (ESMs) available through Phase 5 of the Climate Model Intercomparison Project (CMIP5). The variables are air temperature, sea-ice concentration, surface and subsurface ocean temperature and salinity, and ocean mixed-layer depth. Comparison of the historical simulations with observations indicates that the models provide a good qualitative and approximate quantitative representation of many of the large-scale climatological features in the NWA (e.g., annual cycles and spatial patterns). However, the models represent the detailed structure of some important NWA ocean and ice features poorly, such that caution is needed in the use of their projected future changes. Monthly “climate change” fields between the bidecades 1986–2005 and 2046–2065 are described, using ensemble statistics of the changes across the six ESMs. The results point to warmer air temperatures everywhere, warmer surface ocean temperatures in most areas, reduced sea-ice extent and, in most areas, reduced surface salinities and mixed-layer depths. However, the magnitudes of the inter-model differences in the projected changes are comparable to those of the ensemble-mean changes in many cases, such that robust quantitative projections are generally not possible for the NWA.     

Interannual variability of sea‐ice cover in Hudson bay,Baffin bay and the Labrador sea

Abstract

The spatial and temporal relationships between subarctic Canadian sea‐ice cover and atmospheric forcing are investigated by analysing sea‐ice concentration, sea‐level pressure and surface air temperature data from 1953 to 1988. The sea‐ice anomalies in Hudson Bay, Baffin Bay and the Labrador Sea are found to be related to the North Atlantic Oscillation (NAO) and the Southern Oscillation (SO). Through a spatial Student's i‐test and a Monte Carlo simulation, it is found that sea‐ice cover in both Hudson Bay and the Baffin Bay‐Labrador Sea region responds to a Low/Wet episode of the SO (defined as the period when the SO index becomes negative) mainly in summer. In this case, the sea‐ice cover has a large positive anomaly that starts in summer and continues through to autumn. The ice anomaly is attributed to the negative anomalies in the regional surface air temperature record during the summer and autumn when the Low/Wet episode is developing. During strong winter westerly wind events of the NAO, the Baffin Bay‐Labrador Sea ice cover in winter and spring has a positive anomaly due to the associated negative anomaly in surface air temperature. During the years in which strong westerly NAO and Low/Wet SO events occur simultaneously (as in 1972/73 and 1982/83), the sea ice is found to have large positive anomalies in the study region; in particular, such anomalies occurred for a major portion of one of the two years. A spectral analysis shows that sea‐ice fluctuations in the Baffin Bay‐Labrador Sea region respond to the SO and surface air temperature at about 1.7‐, 5‐ and 10‐year periods. In addition, a noticeable sea‐ice change was found (i.e. more polynyas occurred) around the time of the so‐called “climate jump” during the early 1960s. Data on ice thickness and on ice‐melt dates from Hudson Bay are also used to verify some of the above findings.     

Mid-twenty-first century warm season climate change in the Central United States. Part I: regional and global model predictions

A regional climate model (RCM) constrained by future anomalies averaged from atmosphere–ocean general circulation model (AOGCM) simulations is used to generate mid-twenty-first century climate change predictions at 30-km resolution over the central U.S. The predictions are compared with those from 15 AOGCM and 7 RCM dynamic downscaling simulations to identify common climate change signals. There is strong agreement among the multi-model ensemble in predicting wetter conditions in April and May over the northern Great Plains and drier conditions over the southern Great Plains in June through August for the mid-twenty-first century. Projected changes in extreme daily precipitation are statistically significant over only a limited portion of the central U.S. in the RCM constrained with future anomalies. Projected changes in monthly mean 2-m air temperature are generally consistent across the AOGCM ensemble average, North American Regional Climate Change Assessment Program RCM ensemble average, and RCM constrained with future anomalies, which produce a maximum increase in August of 2.4–2.9 K over the northern and southern Great Plains and Midwest. Changes in extremes in daily 2-m air temperature from the RCM downscaled with anomalies are statistically significant over nearly the entire Great Plains and Midwest and indicate a positive shift in the warm tail of the daily 2-m temperature distribution that is larger than the positive shift in the cold tail.     

Indicators of Climate Change for the Russian Federation

Observed climate changes over the Russian Federation (RF) territory are considered. Several indicators based on monthly mean temperature and precipitation station data are used to quantify regional climate changes. Some of these are the components of two aggregated indices of climate change, suggested by Karl et al. (1996): the Climate Extremes Index (CEI) and the Greenhouse Climate Response Index (GCRI). For the RF territory as a whole, and for its western part, the "Russian Permafrost Free (RPF) territory" in particular, changes in surface air temperature are investigated, together with changes in precipitation and drought indices, and also the fraction of the Russian territory experiencing climatic anomalies below and/or above certain specified percentiles. Composite indices CEI-3 and GCRI-3 based on three parameters (air temperature, precipitation and drought indices) are examined, as well as the Climate Anomaly Index (CAI), known in Russia as Bagrov's coefficient of "anomality".It is shown, that over the area of the RPF as a whole, air temperature and the occurrence of drought has increased somewhat during the 20th century, while precipitation has decreased; these changes were non-uniform in space. The linear trend accounts for only a small fraction of the total variability, but the role of climate variations on decadal scales seems more substantial. The CEI, determined as the percentage of the area experiencing extreme anomalies (with a 10% or less frequency of occurrence) of either sign, increased for mean annual temperature, decreased for total precipitation and increased slightly for the occurrence of drought conditions; the aggregated index based on all three of these quantities increased slightly. There was also an increase in the GCRI-3 index, which is indicative of an agreement between the observed climate changes and the changes owing to the greenhouse effect as predicted by climatic models.The observed climate changes are too small to enable us confidently to reject a hypothesis that they are a reflection of the natural variability of climatic parameters within the context of a stationary climate. However, there is no doubt about the reality and importance of the observed changes.     

我国地面气温参考站点遴选的依据、原则和方法

由于城市化和观测环境变化的影响,我国现有国家级气象台站网观测的地面气温资料许多已不能满足气候变化检测研究的要求。利用我国全部气象观测站网的台站信息,对地面气温资料序列质量进行了系统评价,遴选出可用于气温变化研究的参考站点。这项工作建立了地面气温参考站点遴选的原则、方法和步骤,并据此确定了138个地面气温参考站。这些台站可代表背景地面气温场,其长期气温观测资料可用作城市台站或国家级台站城市化增温评价的参考,也可直接用于我国地面气温变化的检测分析。     

How does framing affect policy support for emissions mitigation? Testing the effects of ocean acidification and other carbon emissions frames

Public support for carbon emissions mitigation is crucial to motivate action to address global issues like climate change and ocean acidification (OA). Yet in the public sphere, carbon emissions mitigation policies are typically discussed in the context of climate change and rarely in the context of OA or other global change outcomes. In this paper, we advance research on OA and climate change perceptions and communication, by (i) examining causal beliefs about ocean acidification, and (ii) measuring support for mitigation policies from individuals presented with one of five different policy frames (climate change, global warming, carbon pollution, air pollution, and ocean acidification). Knowledge about OA causes and consequences is more widespread than we anticipated, though still generally low. Somewhat surprisingly, an “air pollution” mitigation frame elicits the highest degree of policy support overall, while “carbon pollution” performs no better than “climate change” or “global warming.” Framing effects are in part contingent on prior knowledge and attitudes, and mediated by concern. Perhaps due to a lack of OA awareness, the OA frame generates the least support overall, although it seems to close the gap in support associated with political orientation: the OA frame increases support among those (few) conservatives who report having heard of OA before the survey. These findings complement previous work on climate change communication and suggest the need for further research into OA as an effective way to engage conservatives in carbon emissions mitigation policy. Potentially even more promising is the air pollution framing.     

由最高最低气温求算的平均气温对我国年平均气温序列影响

针对研究全国近百年平均气温长期变化的实际需要，利用603个测站1961—2002年气温观测资料，比较分析了最高最低平均气温距平序列和4次观测记录平均气温距平序列的差异，讨论了最高、最低气温变化趋势。结果表明:两种统计方法得到的平均气温距平序列及增温速率的差异均不明显，在一定条件下两者可以互相替换。此外，最高、最低气温变化普遍存在不对称现象，且可分为4种类型，这种不对称性对平均气温变化速率并没有明确一致的影响。     

ENSO nonlinearity in a warming climate

The El Niño Southern Oscillation (ENSO) is known as the strongest natural inter-annual climate signal, having widespread consequences on the global weather, climate, ecology and even on societies. Understanding ENSO variations in a changing climate is therefore of primordial interest to both the climate community and policy makers. In this study, we focus on the change in ENSO nonlinearity due to climate change. We first analysed high statistical moments of observed Sea Surface Temperatures (SST) timeseries of the tropical Pacific based on the measurement of the tails of their Probability Density Function (PDF). This allows defining relevant metrics for the change in nonlinearity observed over the last century. Based on these metrics, a zonal “see-saw” (oscillation) in nonlinearity patterns is highlighted that is associated with the change in El Niño characteristics observed in recent years. Taking advantage of the IPCC database and the different projection scenarios, it is showed that changes in El Niño statistics (or “flavour”) from a present-day climate to a warmer climate are associated with a significant change in nonlinearity patterns. In particular, in the twentieth century climate, the “conventional” eastern Pacific El Niño relates more to changes in nonlinearity than to changes in mean state whereas the central Pacific El Niño (or Modoki El Niño) is more sensitive to changes in mean state than to changes in nonlinearity. An opposite behaviour is found in a warmer climate, namely the decreasing nonlinearity in the eastern Pacific tends to make El Niño less frequent but more sensitive to mean state, whereas the increasing nonlinearity in the west tends to trigger Central Pacific El Niño more frequently. This suggests that the change in ENSO statistics due to climate change might result from changes in the zonal contrast of nonlinearity characteristics across the tropical Pacific.     

从理念到行动：温室气体与局地污染物减排的协同效益与协同控制研究综述

自20世纪90年代IPCC提出协同效益概念以来,大量研究充分证实了温室气体减排政策、措施能产生可观的局地生态环境质量和健康效益。相应地,既有研究也证实局地大气污染物减排政策、措施对温室气体减排同样具有协同效益。中国进入工业化成熟期不久,局地大气污染压力即达到峰值,又迎头遭遇国际应对气候变化浪潮,同时面临空气污染物与温室气体双重减排压力。因此,国内研究不仅关注“由碳及污”或“由污及碳”的单向协同效益评估,更加重视对综合减排措施的协同效益评价。21世纪初,美国国家环保局提出的温室气体与局地大气污染物协同控制概念在中国得到更为广泛的欢迎和接受,并由中国学者首先定义了协同控制的内涵,认为协同控制是实现最大化协同效益的手段和途径。这一进展将人们对协同效益的认识提升到“全球视野、局地行动”的新高度,推动人们从被动地接受“协同效益”,转而主动寻求“协同控制”温室气体和局地大气污染物,为统筹全球和国内（局地）两个减排战场,提供了从认识论、方法论到实践论的全方位支持。中国学者在国内外协同效益、协同控制研究基础上,构建协同控制效应评价和协同控制路径规划方法,并通过多个行业、城市、区域的案例研究证实了该方法体系的科学性和可行性。“协同控制”也已上升成为国家应对气候变化和持续改善大气环境质量的重要策略。在中国推进美丽中国建设、实现碳达峰目标和碳中和愿景的过程中,协同控制的理念、措施、政策将发挥愈加重要的作用。未来,协同控制研究需要将所关注的对象要素,从仅局限于大气扩展至整个生态环境系统;而对建立协同控制的治理体系的研究,将成为实现宏观层面气候变化与生态环境治理协同的关键。     

Variations in cereal crop phenology in Spain over the last twenty-six years (1986–2012)

Over recent years, the Iberian Peninsula has witnessed an increase both in temperature and in rainfall intensity, especially in the Mediterranean climate area. Plant phenology is modulated by climate, and closely governed by water availability and air temperature. Over the period 1986–2012, the effects of climate change on phenology were analyzed in five crops at 26 sites growing in Spain (southern Europe): oats, wheat, rye, barley and maize. The phenophases studied were: sowing date, emergence, flag leaf sheath swollen, flowering, seed ripening and harvest. Trends in phenological response over time were detected using linear regression. Trends in air temperature and rainfall over the period prior to each phenophase were also charted. Correlations between phenological features, biogeographical area and weather trends were examined using a Generalized Lineal Mixed Model approach. A generalized advance in most winter-cereal phenophases was observed, mainly during the spring. Trend patterns differed between species and phenophases. The most noticeable advance in spring phenology was recorded for wheat and oats, the “Flag leaf sheath swollen” and “Flowering date” phenophases being brought forward by around 3 days/year and 1 day/year, respectively. Temperature changes during the period prior to phenophase onset were identified as the cause of these phenological trends. Climate changes are clearly prompting variations in cereal crop phenology; their consequences could be even more marked if climate change persists into the next century. Changes in phenology could in turn impact crop yield; fortunately, human intervention in crop systems is likely to minimize the negative impact.     

Influence of ENSO and the South Atlantic Ocean on climate predictability over Southeastern South America

We perform a systematic study of the predictability of surface air temperature and precipitation in Southeastern South America (SESA) using ensembles of AGCM simulations, focusing on the role of the South Atlantic and its interaction with the El Niño-Southern Oscillation (ENSO). It is found that the interannual predictability of climate over SESA is strongly tied to ENSO showing high predictability during the seasons and periods when there is ENSO influence. The most robust ENSO signal during the whole period of study (1949–2006) is during spring when warm events tend to increase the precipitation over Southeastern South America. Moreover, the predictability shows large inter-decadal changes: for the period 1949–1977, the surface temperature shows high predictability during late fall and early winter. On the other hand, for the period 1978–2006, the temperature shows (low) predictability only during winter, while the precipitation shows not only high predictability in spring but also in fall. Furthermore, it is found that the Atlantic does not directly affect the climate over SESA. However, the experiments where air–sea coupling is allowed in the south Atlantic suggest that this ocean can act as a moderator of the ENSO influence. During warm ENSO events the ocean off Brazil and Uruguay tends to warm up through changes in the atmospheric heat fluxes, altering the atmospheric anomalies and the predictability of climate over SESA. The main effect of the air–sea coupling is to strengthen the surface temperature anomalies over SESA; changes in precipitation are more subtle. We further found that the thermodynamic coupling can increase or decrease the predictability. For example, the air–sea coupling significantly increases the skill of the model in simulating the surface air temperature anomalies for most seasons during period 1949–1977, but tends to decrease the skill in late fall during period 1978–2006. This decrease in skill during late fall in 1978–2006 is found to be due to a wrong simulation of the remote ENSO signal that is further intensified by the local air–sea coupling in the south Atlantic. Thus, our results suggest that climate models used for seasonal prediction should simulate correctly not only the remote ENSO signal, but also the local air–sea thermodynamic coupling.     

Double exposure: assessing the impacts of climate change within the context of economic globalization

This paper considers synergisms between the impacts of two global processes, climate change and economic globalization. Both processes entail long-term changes that will have differential impacts throughout the world. Despite widespread recognition that there will be “winners” and “losers” with both climate change and globalization, the two issues are rarely examined together. In this paper, we introduce the concept of double exposure as a framework for examining the simultaneous impacts of climate change and globalization. Double exposure refers to the fact that certain regions, sectors, ecosystems and social groups will be confronted both by the impacts of climate change, and by the consequences of globalization. By considering the joint impacts of the two processes, new sets of winners and losers emerge.     

Northern hemisphere winter atmospheric climate: modes of natural variability and climate change

Under anthropogenic climate change it is possible that the increased radiative forcing and associated changes in mean climate may affect the “dynamical equilibrium” of the climate system; leading to a change in the relative dominance of different modes of natural variability, the characteristics of their patterns or their behavior in the time domain. Here we use multi-century integrations of version three of the Hadley Centre atmosphere model coupled to a mixed layer ocean to examine potential changes in atmosphere-surface ocean modes of variability. After first evaluating the simulated modes of Northern Hemisphere winter surface temperature and geopotential height against observations, we examine their behavior under an idealized equilibrium doubling of atmospheric CO₂. We find no significant changes in the order of dominance, the spatial patterns or the associated time series of the modes. Having established that the dynamic equilibrium is preserved in the model on doubling of CO₂, we go on to examine the temperature pattern of mean climate change in terms of the modes of variability; the motivation being that the pattern of change might be explicable in terms of changes in the amount of time the system resides in a particular mode. In addition, if the two are closely related, we might be able to assess the relative credibility of different spatial patterns of climate change from different models (or model versions) by assessing their representation of variability. Significant shifts do appear to occur in the mean position of residence when examining a truncated set of the leading order modes. However, on examining the complete spectrum of modes, it is found that the mean climate change pattern is close to orthogonal to all of the modes and the large shifts are a manifestation of this orthogonality. The results suggest that care should be exercised in using a truncated set of variability EOFs to evaluate climate change signals.     

基于动态重现期的极端高温气候评估方法研究

利用全国754站逐日最高气温观测序列,在论证极端温度概率分布与非平稳性关系的基础上,构建和比较了多种非平稳广义极值模型,定义了极端高温的动态重现期和重现水平,提出了一种极端高温事件的新型评估思想和方法,并将其应用于极端气候变化研究。通过该方法可以更好地解释极端事件的真实极端性,有效地增强极端事件之间的可比性,从而保留更多历史极端气候事件的信息。动态重现期的变换运用可对当前极端事件发生的真实状态和趋势提出更准确评估。该方法的提出可有效澄清学术领域和公共舆论对于多年一遇极端事件的理解上长期混淆重现期的绝对值和概率性这一分歧和谬误。     

Spatiotemporal change in geographical distribution of global climate types in the context of climate warming

After standardizing global land climate gridded data from the Climatic Research Unit TS (time-series) 3.1 dataset for the period 1901–2009, cluster analysis is used to objectively classify world climates into 14 climate types. These climate types establish a baseline classification map and the types are named according to Köppen–Geiger climate classifications. Although the cluster analysis and Köppen classification methods are very different, the distributions of climate types obtained by the two methods are similar. Moreover, the climate types we identify also coincide well with their corresponding vegetation types. Thus, cluster analysis can be used as an effective alternative to the Köppen classification method for classifying world climate types. The spatial and temporal changes in geographical distribution of global climate types were investigated in 25-year intervals, and Cohen’s kappa coefficient is used to detect agreement between the periods. Globally, although an obvious trend in increasing global temperature is found, distribution of climate types overall show no distinct changes over the periods. However, at the regional scale, spatial change in distribution of climate types is evident in South America and Africa. In South America, larger areas of the “fully humid equatorial rainforest” (Af) and “equatorial savannah with dry winter” (Aw) climate types have changed types. In Africa, changes mainly occurred in the Af, “equatorial savannah with dry summer” (As), Aw, “steppe climate” (BS), and “desert climate” (BW) climate types. Moreover, some climate types, including Af, “equatorial monsoon” (Am), BS, BW, and “tundra climate” (ET), were susceptible to temporal climate changes, especially in the period 1976–2009.     

Quantifying the sensitivity of simulated climate change to model configuration

This study used “factor separation” to quantify the sensitivity of simulated present and future surface temperatures and precipitation to alternative regional climate model physics components. The method enables a quantitative isolation of the effects of using each physical component as well as the combined effect of two or more components. Simulation results are presented from eight versions of the Mesoscale Modeling System Version 5 (MM5), one-way nested within one version of the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM). The MM5 simulations were made at 108 km grid spacing over the continental United States for five summers in the 1990s and 2050s. Results show that the choice of cumulus convection parameterization is the most important “factor” in the simulation of contemporary surface summer temperatures and precipitation over both the western and eastern USA. The choice of boundary layer scheme and radiation package also increases the range of model simulation results. Moreover, the alternative configurations give quite different results for surface temperature and precipitation in the 2050s. For example, simulated 2050s surface temperatures by the scheme with the coolest 1990s surface temperatures are comparable to 1990s temperatures produced by other schemes. The study analyzes the spatial distribution of 1990s to 2050s projected changes in the surface temperature for the eight MM5 versions. The predicted surface temperature change at a given grid point, averaged over all eight model configurations, is generally about twice the standard deviation of the eight predicted changes, indicating relative consensus among the different model projections. Factor separation analysis indicates that the choice of cumulus parameterization is the most important modeling factor amongst the three tested contributing to the computed 1990s to 2050s surface temperature change, although enhanced warming over many areas is also attributable to synergistic effects of changing all three model components. Simulated ensemble mean precipitation changes, however, are very small and generally smaller than the inter-model standard deviations. The MM5 versions therefore offer little consensus regarding 1990s to 2050s changes in precipitation rates.