Climate change scepticism and public support for mitigation: Evidence from an Australian choice experiment

Public scepticism surrounding climate change is an obstacle for implementing climate change mitigation measures in many countries. However, very little is known about: (1) the nature and sources of climate change scepticism; and (2) its influence on preferences for climate change mitigation policies. In this paper, we investigate these two issues using evidence and analysis from an Australian public survey and choice experiment. The study has three key findings. First, the intensity of scepticism varies depending on its type; we observed little scepticism over the cause, trend and impact of climate change and widespread scepticism over the effectiveness of mitigation measures and global co-operation. Second, cause and mitigation scepticism play significant roles in determining public support for climate change abatement. Respondents who believed in human-induced climate change were significantly more supportive of mitigation. Likewise, respondents who believed that mitigation would be successful in slowing down climate change were significantly more likely to be supportive. Third, the general public tend to give the benefit of the doubt to supporting mitigation. Those who expressed higher uncertainty about climate outcomes were more supportive of mitigation than others with similar expectations but lower uncertainty.     

Evaluation of the ENSEMBLES multi-model seasonal forecasts of Indian summer monsoon variability

The performance of the new multi-model seasonal prediction system developed in the frame work of the ENSEMBLES EU project for the seasonal forecasts of India summer monsoon variability is compared with the results from the previous EU project, DEMETER. We have considered the results of six participating ocean-atmosphere coupled models with 9 ensemble members each for the common period of 1960–2005 with May initial conditions. The ENSEMBLES multi-model ensemble (MME) results show systematic biases in the representation of mean monsoon seasonal rainfall over the Indian region, which are similar to that of DEMETER. The ENSEMBLES coupled models are characterized by an excessive oceanic forcing on the atmosphere over the equatorial Indian Ocean. The skill of the seasonal forecasts of Indian summer monsoon rainfall by the ENSEMBLES MME has however improved significantly compared to the DEMETER MME. Its performance in the drought years like 1972, 1974, 1982 and the excess year of 1961 was in particular better than the DEMETER MME. The ENSEMBLES MME could not capture the recent weakening of the ENSO-Indian monsoon relationship resulting in a decrease in the prediction skill compared to the “perfect model” skill during the recent years. The ENSEMBLES MME however correctly captures the north Atlantic-Indian monsoon teleconnections, which are independent of ENSO.     

Predictability of the summer East Asian upper-tropospheric westerly jet in ENSEMBLES multi-model forecasts

The interannual variation of the East Asian upper-tropospheric westerly jet(EAJ) significantly affects East Asian climate in summer. Identifying its performance in model prediction may provide us another viewpoint,from the perspective of uppertropospheric circulation,to understand the predictability of summer climate anomalies in East Asia. This study presents a comprehensive assessment of year-to-year variability of the EAJ based on retrospective seasonal forecasts,initiated from1 May,in the five state-of-the-art coupled models from ENSEMBLES during 1960–2005. It is found that the coupled models show certain capability in describing the interannual meridional displacement of the EAJ,which reflects the models' performance in the first leading empirical orthogonal function(EOF) mode. This capability is mainly shown over the region south of the EAJ axis. Additionally,the models generally capture well the main features of atmospheric circulation and SST anomalies related to the interannual meridional displacement of the EAJ. Further analysis suggests that the predicted warm SST anomalies in the concurrent summer over the tropical eastern Pacific and northern Indian Ocean are the two main sources of the potential prediction skill of the southward shift of the EAJ. In contrast,the models are powerless in describing the variation over the region north of the EAJ axis,associated with the meridional displacement,and interannual intensity change of the EAJ,the second leading EOF mode,meaning it still remains a challenge to better predict the EAJ and,subsequently,summer climate in East Asia,using current coupled models.     

Climate change mitigation scenarios and policies and measures: the case of Kazakhstan

This article illustrates the main difficulties encountered in the preparation of GHG emission projections and climate change mitigation policies and measures (P&M) for Kazakhstan. Difficulties in representing the system with an economic model have been overcome by representing the energy system with a technical-economic growth model (MARKAL-TIMES) based on the stock of existing plants, transformation processes, and end-use devices. GHG emission scenarios depend mainly on the pace of transition in Kazakhstan from a planned economy to a market economy. Three scenarios are portrayed: an incomplete transition, a fast and successful one, and even more advanced participation in global climate change mitigation, including participation in some emission trading schemes. If the transition to a market economy is completed by 2020, P&M already adopted may reduce emissions of CO₂ from combustion by about 85 MtCO₂ by 2030 – 17% of the emissions in the baseline (WOM) scenario. One-third of these reductions are likely to be obtained from the demand sectors, and two-thirds from the supply sectors. If every tonne of CO₂ not emitted is valued up to US$10 in 2020 and $20 in 2030, additional P&M may further reduce emissions by 110 MtCO₂ by 2030.     

Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.     

Climate change mitigation: trade-offs between delay and strength of action required

Climate change mitigation via a reduction in the anthropogenic emissions of carbon dioxide (CO₂) is the principle requirement for reducing global warming, its impacts, and the degree of adaptation required. We present a simple conceptual model of anthropogenic CO₂ emissions to highlight the trade off between delay in commencing mitigation, and the strength of mitigation then required to meet specific atmospheric CO₂ stabilization targets. We calculate the effects of alternative emission profiles on atmospheric CO₂ and global temperature change over a millennial timescale using a simple coupled carbon cycle-climate model. For example, if it takes 50 years to transform the energy sector and the maximum rate at which emissions can be reduced is ?2.5% $\text{year}^{-1}$ , delaying action until 2020 would lead to stabilization at 540 ppm. A further 20 year delay would result in a stabilization level of 730 ppm, and a delay until 2060 would mean stabilising at over 1,000 ppm. If stabilization targets are met through delayed action, combined with strong rates of mitigation, the emissions profiles result in transient peaks of atmospheric CO₂ (and potentially temperature) that exceed the stabilization targets. Stabilization at 450 ppm requires maximum mitigation rates of ?3% to ?5% $\text{year}^{-1}$ , and when delay exceeds 2020, transient peaks in excess of 550 ppm occur. Consequently tipping points for certain Earth system components may be transgressed. Avoiding dangerous climate change is more easily achievable if global mitigation action commences as soon as possible. Starting mitigation earlier is also more effective than acting more aggressively once mitigation has begun.     

Climate change hotspots over South America: from CMIP3 to CMIP5 multi-model datasets

This study identifies possible hotspots of climate change in South America through an examination of the spatial pattern of the Regional Climate Change Index (RCCI) over the region by the end of the twenty-first century. The RCCI is a qualitative index that can synthesize a large number of climate model projections, and it is suitable for identifying those regions where climate change could be more pronounced in a warmer climate. The reliability and uncertainties of the results are evaluated by using numerous state-of-the-art general circulation models (GCMs) and forcing scenarios from the Coupled Model Intercomparison Project phases 3 and 5. The results show that southern Amazonia and the central-western region and western portion of Minas Gerais state in Brazil are persistent climate change hotspots through different forcing scenarios and GCM datasets. In general, as the scenarios vary from low- to high-level forcing, the area of high values of RCCI increase and the magnitude intensify from central-western and southeast Brazil to northwest South America. In general, the climatic hotspots identified in this study are characterized by an increase of mean surface air temperature, mainly in the austral winter; by an increase of interannual temperature variability, predominantly in the austral summer; and by a change in the mean and interannual variability of precipitation during the austral winter.     

Climate change adaptation implications for drought risk mitigation: a perspective for India

There is a growing evidence that the climate change do has implications for drought vulnerable India with studies projecting future possible reductions in monsoon related rainfall in the country. The existing drought risk mitigation and response mechanisms were looked into and gaps were identified by drawing lessons from previous disasters and response mechanisms. In absence of reliable climate predictions at the scales that make them useful for policy level planning, the emphasis was on identifying no-regret adaptation options those would reduce current vulnerabilities while mainstreaming the adaptation in the long run. The most notable climate change implications for the drought vulnerable India are the enhanced preparedness with due emphasis to the community based preparedness planning, reviewing the existing monsoon and drought prediction methodologies, and establishing drought monitoring and early warning systems in association with a matching preparedness at the input level.     

Climate change mitigation on tropical peatlands: A triple burden for smallholder farmers in Indonesia

The agricultural use and conversion of tropical peatlands is considered a major source of land-based greenhouse gas emissions. Thus, the protection and restoration of tropical peatlands has recently turned into an important strategy to mitigate global climate change. Little research exists that has investigated the impacts and dynamics that such climate mitigation efforts evoke in local communities living in and around peatlands. We present insights on this from Sumatra, Indonesia and use a climate justice lens to evaluate local outcomes. We show how an increasingly transnational network of state and non-state actors has become involved in developing new laws, policy programs and land-use agreements on Sumatra’s coastal peatlands, aiming at supposedly win–win low-carbon development pathways. We argue that such efforts are open to much of the same criticism that has been raised regarding previous policies and projects aimed at reducing GHGE from deforestation and forest degradation. These projects disregard local perspectives on development, fail to deliver the promised benefits and, through a reconfiguration of local land-use rights, reduce the capabilities of smallholder farmers to benefit from their land. In sum, our analysis suggests that recent policies and projects aimed at mitigating GHGE from tropical peatlands contribute to a redistribution of the global climate mitigation burden onto smallholder farmers in Indonesia. This occurs through their threefold assignment to protect forests, prevent fires and help restore degraded peatlands.     

Predictability of the western North Pacific summer climate associated with different ENSO phases by ENSEMBLES multi-model seasonal forecasts

Predictability of the western North Pacific (WNP) summer climate associated with different El Niño–Southern Oscillation (ENSO) phases is investigated in this study based on the 1-month lead retrospective forecasts of five state-of-the-art coupled models from ENSEMBLES. During the period from 1960 to 2005, the models well capture the WNP summer climate anomalies during most of years in different ENSO phases except the La Niña decaying summers. In the El Niño developing, El Niño decaying and La Niña developing summers, the prediction skills are high for the WNP summer monsoon index (WNPMI), with the prediction correlation larger than 0.7. The high prediction skills of the lower-tropospheric circulation during these phases are found mainly over the tropical western Pacific Ocean, South China Sea and subtropical WNP. These good predictions correspond well to their close teleconnection with ENSO and the high prediction skills of tropical SSTs. By contrast, for the La Niña decaying summers, the prediction skills are considerably low with the prediction correlation for the WNPMI near to zero and low prediction skills around the Philippines and subtropical WNP. These poor predictions relate to the weak summer anomalies of the WNPMI during the La Niña decaying years and no significant connections between the WNP lower-tropospheric circulation anomalies and the SSTs over the tropical central and eastern Pacific Ocean in observations. However, the models tend to predict an apparent anomalous cyclone over the WNP during the La Niña decaying years, indicating a linearity of the circulation response over WNP in the models prediction in comparison with that during the El Niño decaying years which differs from observations. In addition, the models show considerable capability in describing the WNP summer anomalies during the ENSO neutral summers. These anomalies are related to the positive feedback between the WNP lower-tropospheric circulation and the local SSTs. The models can capture this positive feedback but with some uncertainties from different ensemble members during the ENSO neutral summers.     

Assessment of the Capability of ENSEMBLES Hindcasts in Predicting Spring Climate in China

Using the hindcasts provided by the Ensemble-Based Predictions of Climate Changes and Their Impacts(ENSEMBLES) project for the period of 1980–2005, the forecast capability of spring climate in China is assessed mainly from the aspects of precipitation, 2-m air temperature, and atmospheric circulations. The ENSEMBELS can reproduce the climatology and dominant empirical orthogonal function(EOF) modes of precipitation and 2-m air temperature, with some differences arising from different initialization months. The multi-model ensemble(MME) forecast of interannual variability is of good performance in some regions such as eastern China with February initialization.The spatial patterns of the MME interannual and inter-member spreads for precipitation and 2-m air temperature are consistent with those of the observed interannual spread, indicating that internal dynamic processes have major impacts on the interannual anomaly of spring climate in China. We have identified two coupled modes between intermember anomalies of the 850-hPa vorticity in spring and sea surface temperature(SST) both in spring and at a lead of 2 months, of which the first mode shows a significant impact on the spring climate in China, with an anomalous anticyclone located over Northwest Pacific and positive precipitation and southwesterly anomalies in eastern China.Our results also suggest that the SST at a lead of two months may be a predictor for the spring climate in eastern China. A better representation of the ocean–atmosphere interaction over the tropical Pacific, Northwest Pacific, and Indian Ocean can improve the forecast skill of the spring climate in eastern China.     

Climate sensitivity and climate change under strong forcing

A version of the National Centre for Atmospheric Research (NCAR) coupled climate model is integrated under current climate conditions and in a series of experiments with climate forcings ranging from modest to very strong. The purpose of the experiments is to investigate the nature and behaviour of the climate feedback/sensitivity of the model, its evolution with time and climate state, the robustness of model parameterizations as forcing levels increase, and the possibility of a “runaway” warming under strong forcing. The model is integrated for 50 years, or to failure, after increasing the solar constant by 2.5, 10, 15, 25, 35, and 45% of its control value. The model successfully completes 50 years of integration for the 2.5, 10, 15, and 25% solar constant increases but fails for increases of 35% and 45%. The effective global climate sensitivity evolves with time and analysis indicates that a new equilibrium will be obtained for the 2.5, 10, and 15% cases but that runaway warming is underway for the 25% increase in solar constant. Feedback processes are analysed both locally and globally in terms of longwave and shortwave, clear-sky/surface, and cloud forcing components. Feedbacks in the system must be negative overall and of sufficient strength to balance the positive forcing if the system is to attain a new equilibrium. Longwave negative feedback processes strengthen in a reasonably linear fashion as temperature increases but shortwave feedback processes do not. In particular, solar cloud feedback becomes less negative and, for the 25% forcing case, eventually becomes positive, resulting in temperatures that “run away”. The conditions under which a runaway climate warming might occur have previously been investigated using simpler models. For sufficiently strong forcing, the greenhouse effect of increasing water vapour in a warmer atmosphere is expected to overwhelm the negative feedback of the longwave cooling to space as temperature increases. This is not, however, the reason for the climate instability experienced in the GCM. Instead, the model experiences a “cloud feedback” warming whereby the decrease in cloudiness that occurs when temperature increases beyond a critical value results in an increased absorption of solar radiation by the system, leading to the runaway warming.     

Modelling risk adaptation and mitigation behaviour under different climate change scenarios

The main objective of this study is to simulate household choice behavior under varying climate change scenarios using choice experiments. Economic welfare measures are derived for society’s willingness to pay (WTP) to reduce climate change induced flood risks through private insurance and willingness to accept compensation (WTAC) for controlled flooding under varying future risk exposure levels. Material flood damage and loss of life are covered in the insurance policy experiment, while the WTAC experiment also captures the economic value of immaterial flood damage such as feelings of discomfort, fear and social disruption. The results show that WTP and WTAC are substantial, suggesting a more prominent role of external social damage costs in cost-benefit analysis of climate change and flood mitigation policies.     

Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios

We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System—recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827–840, 2013)—we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region.     

Climate change and the agricultural sector in Ireland: examining farmer awareness and willingness to adopt new advisory mitigation tools

Agriculture is responsible for the bulk of Ireland’s greenhouse gas (GHG) emissions. However, the potential to mitigate some of these emissions through the adoption of more efficient farm management practices may be hampered by farmers’ awareness and attitude towards climate change and agriculture’s role in contributing to GHG emissions. This paper presents results from a survey of 746 Irish farmers in 2014, with a view to understanding farmers’ awareness of, and attitudes to, climate change and GHG emissions. Survey results show that there was a general uncertainty towards a number of questions related to agricultural GHG emissions, e.g. if tilling of land causes GHG emissions, and that farmers were reluctant to take action to reduce GHG emissions on their farm. To further explore farmers’ attitudes towards climate change, a multinomial logit model was used to examine the socio-economic factors that affect farmers’ willingness to adopt an advisory tool that would show the potential reduction in GHG emissions from the adoption of new technologies. Results show that farmers’ awareness of human-induced global climate change was positively related to the tool’s adoption.

Key policy insights

• Irish farmers are generally not sufficiently aware of the impact of their activities on climate change.

• A quarter of farmers believed that climate change will only impact on their business in the long-term; such an attitude may lead to a reluctance amongst these farmers to adopt management practices that reduce GHG emissions.

• Awareness of climate change affects positively the adoption of new tools to reduce GHG emissions on farmers’ farms.

• IT literacy affects willingness to adopt new tools to address GHG emissions.

• Reception of agri-environmental advice can have a positive influence on farmers’ willingness to adopt new GHG emission abatement tools.

• Farmers in receipt of environmental subsidies are more likely to adopt new abatement tools, either because they are more environmentally conscious or because the subsidy raised their environmentally consciousness.

• Willingness to adopt differs between different farm enterprises; operating dairy enterprise increases the willingness to adopt new advisory mitigation tools.