Climate sensitivity estimated from ensemble simulations of glacial climate

The concentration of greenhouse gases (GHGs) in the atmosphere continues to rise, hence estimating the climate system’s sensitivity to changes in GHG concentration is of vital importance. Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change. Here we present a new approach for constraining this key uncertainty by combining ensemble simulations of the last glacial maximum (LGM) with paleo-data. For this purpose we used a climate model of intermediate complexity to perform a large set of equilibrium runs for (1) pre-industrial boundary conditions, (2) doubled CO₂ concentrations, and (3) a complete set of glacial forcings (including dust and vegetation changes). Using proxy-data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity. We show that irrespective of uncertainties in model parameters and feedback strengths, in our model a close link exists between the simulated warming due to a doubling of CO₂, and the cooling obtained for the LGM. Our results agree with recent studies that annual mean data-constraints from present day climate prove to not rule out climate sensitivities above the widely assumed sensitivity range of 1.5–4.5°C (Houghton et al. 2001). Based on our inferred close relationship between past and future temperature evolution, our study suggests that paleo-climatic data can help to reduce uncertainty in future climate projections. Our inferred uncertainty range for climate sensitivity, constrained by paleo-data, is 1.2–4.3°C and thus almost identical to the IPCC estimate. When additionally accounting for potential structural uncertainties inferred from other models the upper limit increases by about 1°C.     

Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions

We present how uncertainty and learning are classically studied in economic models. Specifically, we study a standard expected utility model with two sequential decisions, and consider two particular cases of this model to illustrate how uncertainty and learning may affect climate policy. While uncertainty has generally a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as a general argument to increase or reduce emissions today without studying the specific intertemporal costs and benefits. Considering limits in applying the expected utility framework to climate change problems, we then consider a more recent framework with ambiguity-aversion which accounts for situations of imprecise or multiple probability distributions. We discuss both the impact of ambiguity-aversion on decisions and difficulties in applying such a non-expected utility framework to a dynamic context.     

Imprecise probabilities of climate change: aggregation of fuzzy scenarios and model uncertainties

Whilst the majority of the climate research community is now set upon the objective of generating probabilistic predictions of climate change, disconcerting reservations persist. Attempts to construct probability distributions over socio-economic scenarios are doggedly resisted. Variation between published probability distributions of climate sensitivity attests to incomplete knowledge of the prior distributions of critical parameters and structural uncertainties in climate models. In this paper we address these concerns by adopting an imprecise probability approach. We think of socio-economic scenarios as fuzzy linguistic constructs. Any precise emissions trajectory (which is required for climate modelling) can be thought of as having a degree of membership in a fuzzy scenario. Next, it is demonstrated how fuzzy scenarios can be propagated through a low-dimensional climate model, MAGICC. Fuzzy scenario uncertainties and imprecise probabilistic representation of climate model uncertainties are combined using random set theory to generate lower and upper cumulative probability distributions for Global Mean Temperature anomaly. Finally we illustrate how non-additive measures provide a flexible framework for aggregation of scenarios, which can represent some of the semantics of socio-economic scenarios that defy conventional probabilistic representation.     

Learning and climate change

Abstract

Learning—i.e. the acquisition of new information that leads to changes in our assessment of uncertainty—plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.     

On the coattails of climate? Opportunities and threats of a warming Earth for biodiversity conservation

The relationship between climate change and biodiversity was a central issue at the 10th Conference of the Parties (COP 10) to the Convention on Biological Diversity (CBD). In this paper we draw from participant observation data collected at COP 10, and related policy documentation, to examine how concerns about climate change are shaping the conservation policy landscape – in terms of the knowledge and rationales used as inputs, networks of actors involved, objectives sought, and actions proposed. We find that debates at the intersection of climate and biodiversity were overwhelmingly framed in relation to, or through the lens of carbon. Through a discussion of four core Climate-Motivated Responses, we illustrate how “carbon-logic”, and the initiatives that it generates, simultaneously creates threats to the objectives sought by some actors, and opportunities for the objectives sought by others. We situate our observations in the context of some of the historical dilemmas that have faced conservation, and discuss this current moment in the dynamic trajectory of conservation governance: a moment when decisions about conserving biodiversity are becoming entangled with carbon-logic and the market. In this case, while some actors seek opportunities for biodiversity ends by riding the coattails of the climate agenda, the threats of doing so may undermine the biological and social objectives of the CBD convention itself.     

Uncertainty, scepticism and attitudes towards climate change: biased assimilation and attitude polarisation

‘Scepticism’ in public attitudes towards climate change is seen as a significant barrier to public engagement. In an experimental study, we measured participants’ scepticism about climate change before and after reading two newspaper editorials that made opposing claims about the reality and seriousness of climate change (designed to generate uncertainty). A well-established social psychological finding is that people with opposing attitudes often assimilate evidence in a way that is biased towards their existing attitudinal position, which may lead to attitude polarisation. We found that people who were less sceptical about climate change evaluated the convincingness and reliability of the editorials in a markedly different way to people who were more sceptical about climate change, demonstrating biased assimilation of the information. In both groups, attitudes towards climate change became significantly more sceptical after reading the editorials, but we observed no evidence of attitude polarisation—that is, the attitudes of these two groups did not diverge. The results are the first application of the well-established assimilation and polarisation paradigm to attitudes about climate change, with important implications for anticipating how uncertainty—in the form of conflicting information—may impact on public engagement with climate change.     

Negative learning

New technical information may lead to scientific beliefs that diverge over time from the a posteriori right answer. We call this phenomenon, which is particularly problematic in the global change arena, negative learning. Negative learning may have affected policy in important cases, including stratospheric ozone depletion, dynamics of the West Antarctic ice sheet, and population and energy projections. We simulate negative learning in the context of climate change with a formal model that embeds the concept within the Bayesian framework, illustrating that it may lead to errant decisions and large welfare losses to society. Based on these cases, we suggest approaches to scientific assessment and decision making that could mitigate the problem. Application of the tools of science history to the study of learning in global change, including critical examination of the assessment process to understand how judgments are made, could provide important insights on how to improve the flow of information to policy makers.     

Learning about climate change and implications for near-term policy

Climate change is an issue of risk management. The most important causes for concern are not the median projections of future climate change, but the low-probability, high-consequence impacts. Because the policy question is one of sequential decision making under uncertainty, we need not decide today what to do in the future. We need only to decide what to do today, and future decisions can be revised as we learn more. In this study, we use a stochastic version of the DICE-99 model (Nordhaus WD, Boyer J (2000) Warming the world: economic models of global warming. MIT Press, Cambridge, MA, USA) to explore the effect of different rates of learning on the appropriate level of near-term policy. We show that the effect of learning depends strongly on whether one chooses efficiency (balancing costs and benefits) or cost-effectiveness (stabilizing at a given temperature change target) as the criterion for policy design. Then, we model endogenous learning by calculating posterior distributions of climate sensitivity from Bayesian updating, based on temperature changes that would be observed for a given true climate sensitivity and assumptions about errors, prior distributions, and the presence of additional uncertainties. We show that reducing uncertainty in climate uncertainty takes longer when there is also uncertainty in the rate of heat uptake by the ocean, unless additional observations are used, such as sea level rise.     

Learning about parameter and structural uncertainty in carbon cycle models

Uncertainty in the response of the global carbon cycle to anthropogenic emissions plays a key role in assessments of potential future climate change and response strategies. We investigate how fast this uncertainty might change as additional data on the global carbon budget becomes available over the twenty-first century. Using a simple global carbon cycle model and focusing on both parameter and structural uncertainty in the terrestrial sink, we find that additional global data leads to substantial learning (i.e., changes in uncertainty) under some conditions but not others. If the model structure is assumed known and only parameter uncertainty is considered, learning is rather limited if observational errors in the data or the magnitude of unexplained natural variability are not reduced. Learning about parameter values can be substantial, however, when errors in data or unexplained variability are reduced. We also find that, on the one hand, uncertainty in the model structure has a much bigger impact on uncertainty in projections of future atmospheric composition than does parameter uncertainty. But on the other, it is also possible to learn more about the model structure than the parameter values, even from global budget data that does not improve over time in terms of its associated errors. As an example, we illustrate how one standard model structure, if incorrect, could become inconsistent with global budget data within 40 years. The rate of learning in this analysis is affected by the choice of a relatively simple carbon cycle model, the use of observations only of global emissions and atmospheric concentration, and the assumption of perfect autocorrelation in observational errors and variability. Future work could usefully improve the approach in each of these areas.     

Rancher and farmer perceptions of climate change in Nevada, USA

Farming and ranching communities in arid lands are vulnerable to the adverse impacts of climate change. We surveyed Nevada ranchers and farmers (n?=?481) during 2009–2010 to assess climate change related knowledge, assumptions, and perceptions. The large majority of this group agreed that we are in a period of climate change; however, only 29 % of them believed that human activity is playing a significant role. Female ranchers and farmers hold more scientifically accurate knowledge about climate change than do their male counterparts, regardless of Democratic or Republican affiliation. Partisan affiliation, political ideology, and gender have strong impacts on climate change knowledge and perceptions. Republican, conservative and male rural residents view climate change as a low national priority, less important to themselves, and less harmful to their communities. Female ranchers and farmers are more concerned about the negative impacts of climate change. We found that only 4 % of our subjects (n?=?299) attribute local environment changes to climate change or global warming. The knowledge gained from this study will help researchers and natural resource managers understand how to best communicate about climate change with rural communities, and support policy makers in identifying potentially effective adaptation and mitigation policies and outreach programs.     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

Implementing a knowledge system: Lessons from the global stewardship of climate services

Knowledge systems are mechanisms that can drive climate adaptation through the pursuits of enhancing resource sharing, collaboration, and learning, while at the same time helping to develop trust and credibility among individuals and intuitions. While these goals are widely discussed, less is known about the activities and strategies that knowledge systems undertake to achieve these goals. We analyze the Global Framework of Climate Services (GFCS) as a knowledge system organized around the translation of weather and climate information for decision-making. The GFCS brings together the World Meteorological Organization, national meteorological and hydrological services, and some of the world’s largest multilateral scientific, humanitarian, and development organizations. Our analysis draws on key informant interviews, focus groups conducted in African countries, and an online survey of GFCS participants. We describe the main activities pursued by the GFCS that shaped the vision of climate services, built capacity in national climate adaptation, and created connections among diverse actors and organizations worldwide. We show how these activities generated tensions about the purpose of the GFCS and how influence among the knowledge system was distributed. Based on our results, we illustrate new ways to conceptualize the strategies of knowledge systems, which we describe as (1) theorizing the norms of practice and mechanisms of change, (2) legitimizing actors, and (3) managing knowledge. These strategies identify pathways for, and pitfalls to, a knowledge system’s pursuit of its goals, providing guidance to managers of knowledge systems and an analytical framework to evaluate their impacts.     

Knowing like a global expert organization: Comparative insights from the IPCC and IPBES

In this paper we draw on Science and Technology (STS) approaches to develop a comparative analytical account of the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services (IPBES). The establishment of both of these organizations, in 1988 and 2012 respectively, represented important ‘constitutional moments’ in the global arrangement of scientific assessment and its relationship to environmental policymaking. Global environmental assessments all share some similarities, operating at the articulation between science and policy and pursuing explicit societal goals. Although the IPCC and IPBES have different objectives, they are both intergovernmental processes geared towards the provision of knowledge to inform political debates about, respectively, climate change and biodiversity loss. In spite of these similarities, we show that there are significant differences in their knowledge practices and these differences have implications for environmental governance. We do this by comparing the IPCC and IPBES across three dimensions: conceptual frameworks, scenarios and consensus .We argue that, broadly speaking, the IPCC has produced a ‘view from nowhere’, through a reliance on mathematical modelling to produce a consensual picture of global climate change, which is then ‘downscaled’ to considerations of local impacts and responses. By contrast IPBES, through its contrasting conceptual frameworks and practices of argumentation, appears to seek a ‘view from everywhere’, inclusive of epistemic plurality, and through which a global picture emerges through an aggregation of more placed-based knowledges. We conclude that, despite these aspirations, both organizations in fact offer ‘views from somewhere’: situated sets of knowledge marked by politico-epistemic struggles and shaped by the interests, priorities and voices of certain powerful actors. Characterizing this ‘somewhere’ might be aided by the concept of institutional epistemology, a term we propose to capture how particular knowledge practices become stabilized within international expert organizations. We suggest that such a concept, by drawing attention to the institutions’ knowledge practices, helps reveal their world-making effects and, by doing so, enables more reflexive governance of both expert organizations and of global environmental change in general.     

Perceptions of climate change: Linking local and global perceptions through a cultural knowledge approach

Understanding public perceptions of climate change is fundamental to both climate science and policy because it defines local and global socio-political contexts within which policy makers and scientists operate. To date, most studies addressing climate change perceptions have been place-based. While such research is informative, comparative studies across sites are important for building generalized theory around why and how people understand and interpret climate change and associated risks. This paper presents a cross-sectional study from six different country contexts to illustrate a novel comparative approach to unraveling the complexities of local vs global perceptions around climate change. We extract and compare ‘cultural knowledge’ regarding climate change using the theory of ‘culture as consensus’. To demonstrate the value of this approach, we examine cross-national data to see if people within specific and diverse places share ideas about global climate change. Findings show that although data was collected using ethnographically derived items collected through place-based methods we still find evidence of a shared cultural model of climate change which spans the diverse sites in the six countries. Moreover, there are specific signs of climate change which appear to be recognized cross-culturally. In addition, results show that being female and having a higher education are both likely to have a positive effect on global cultural competency of individuals. We discuss these result in the context of literature on environmental perceptions and propose that people with higher education are more likely to share common perceptions about climate change across cultures and tentatively suggest that we appear to see the emergence of a ‘global’, cross-cultural mental model around climate change and its potential impacts which in itself is linked to higher education.     

Do probabilistic expert elicitations capture scientists’ uncertainty about climate change?

Expert elicitation studies have become important barometers of scientific knowledge about future climate change (Morgan and Keith, Environ Sci Technol 29(10), 1995; Reilly et al., Science 293(5529):430–433, 2001; Morgan et al., Climate Change 75(1–2):195–214, 2006; Zickfeld et al., Climatic Change 82(3–4):235–265, 2007, Proc Natl Acad Sci 2010; Kriegler et al., Proc Natl Acad Sci 106(13):5041–5046, 2009). Elicitations incorporate experts’ understanding of known flaws in climate models, thus potentially providing a more comprehensive picture of uncertainty than model-driven methods. The goal of standard elicitation procedures is to determine experts’ subjective probabilities for the values of key climate variables. These methods assume that experts’ knowledge can be captured by subjective probabilities—however, foundational work in decision theory has demonstrated this need not be the case when their information is ambiguous (Ellsberg, Q J Econ 75(4):643–669, 1961). We show that existing elicitation studies may qualitatively understate the extent of experts’ uncertainty about climate change. We designed a choice experiment that allows us to empirically determine whether experts’ knowledge about climate sensitivity (the equilibrium surface warming that results from a doubling of atmospheric CO₂ concentration) can be captured by subjective probabilities. Our results show that, even for this much studied and well understood quantity, a non-negligible proportion of climate scientists violate the choice axioms that must be satisfied for subjective probabilities to adequately describe their beliefs. Moreover, the cause of their violation of the axioms is the ambiguity in their knowledge. We expect these results to hold to a greater extent for less understood climate variables, calling into question the veracity of previous elicitations for these quantities. Our experimental design provides an instrument for detecting ambiguity, a valuable new source of information when linking climate science and climate policy which can help policy makers select decision tools appropriate to our true state of knowledge.     

Using the social cost of carbon to value earth observing systems

The goal of this study is to show how to quantify the benefits of accelerated learning about key parameters of the climatic system and use this knowledge to improve decision-making on climate policy. The US social cost of carbon (SCC) methodology is used in innovative ways to value new Earth observing systems (EOSs). The study departs from the strict US SCC methodology, and from previous work, in that net benefits are used instead of only damages to calculate the value of information of the enhanced systems. In other respects the US SCC methodology is followed closely. We compute the surfeit expected net benefits of learning the actionable information earlier, with the enhanced system, versus learning later with existing systems. The enhanced systems are designed to give reliable information about climate sensitivity on accelerated timescales relative to existing systems; therefore, the decision context stipulates that a global reduced emissions path would be deployed upon receiving suitable information on the rate of temperature rise with a suitable level of confidence. By placing the enhanced observing system in a decision context, the SCC enables valuing this system as a real option.

Policy relevance

Uncertainty in key parameters of the climatic system is often cited as a barrier for near-term reductions of carbon emissions. It is a truism among risk managers that uncertainty costs money, and its reduction has economic value. Advancing policy making under uncertainty requires valuing the reduction in uncertainty. Using CLARREO, a new proposed EOS,as an example, this article applies value of information/real option theory to value the reduction of uncertainty in the decadal rate of temperature rise. The US interagency social cost of carbon directive provides the decision context for the valuations. It is shown that the real option value of the uncertainty reduction, relative to existing observing systems, is a very large multiple of the new system's cost.     

Effects of Greenhouse Gas Emissions on World Agriculture, Food Consumption, and Economic Welfare

Because of many uncertainties, quantitative estimates of agriculturally related economic impacts of greenhouse gas emissions are often given low confidence. A major source of uncertainty is our inability to accurately project future changes in economic activity, emissions, and climate. This paper focuses on two issues. First, to what extent do variable projections of climate generate uncertainty in agriculturally related economic impacts? Second, to what extent do agriculturally related economic impacts of greenhouse gas emissions depend on economic conditions at the time of impacts? Results indicate that uncertainty due to variable projections of climate is fairly large for most of the economic effects evaluated in this analysis. Results also indicate that economic conditions at the time of impact influence the direction and size of as well as the confidence in the economic effects of identical projections of greenhouse gas impacts. The economic variable that behaves most consistently in this analysis is world crop production. Increases in mean global temperature, for example, cause world crop production to decrease on average under both 1990 and improved economic conditions and in both instances the confidence with respect to variable projections of climate is medium (e.g.,67%) or greater. In addition and as expected, CO₂ fertilization causesworld crop production to increase on average under 1990 and improved economic conditions. These results suggest that crop production may be a fairly robust indicator of the potential impacts of greenhouse gas emissions.A somewhat unexpected finding is that improved economic conditions are not necessarily a panacea to potential greenhouse-gas-induced damages, particularly at the region level. In fact, in some regions, impacts of climate change or CO₂ fertilization that are beneficial undercurrent economic conditions may be detrimental under improved economic conditions (relative to the new economic base). Australia plus New Zealand suffer from this effect in this analysis because under improved economic conditions they are assumed to obtain a relatively large share of income from agricultural exports. When the climate-change and CO₂-fertilization scenariosin this analysis are also included, agricultural exports from Australia plus New Zealand decline on average. The resultant declines in agricultural income in Australia plus New Zealand are too large to be completely offset by rising incomes in other sectors. This indicates that regions that rely on agricultural exports for relatively large shares of their income may be vulnerable not only to direct climate-induced agricultural damages, but also to positive impacts induced by greenhouse gas emissions elsewhere.     

Identifying Key Sources of Uncertainty in Climate Change Projections

What sources of uncertainty shouldbe included in climate change projections and whatgains can be made if specific sources of uncertaintyare reduced through improved research?DIALOGUE, anintegrated assessment model, has been used to answerthese questions. Central in the approach of DIALOGUEis the concept of parallel modeling, i.e., for eachstep in the chain from emissions to climate change anumber of equivalent models areimplemented. The followingconclusions are drawn:The key source of uncertainty in global temperatureprojections appears to be the uncertainty inradiative forcing models. Within this group ofmodels uncertainty within aerosol forcing models isabout equal to the total forcing of greenhouse gasmodels. In the latter group CO₂ is dominant.The least important source of uncertainty appears tobe the gas cycle models. Within this group of modelsthe role of carbon cycle models is dominant.Uncertainty in global temperature projections hasnot been treated consistently in the literature.First, uncertainty should be calculated as a productof all uncertainty sources. Second, aparticular choice of a base year for global warmingcalculations influences the ranking of uncertainty.Because of this, a comparison of ranking resultsacross different studies is hampered. We argue that`pre-Industrial' is the best choice for studies onuncertainty.There is a linear relationship between maximumuncertainty in the year 2100 and cumulativeemissions of CO₂ over the period 1990–2100:higher emissions lead to more uncertainty.     

Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.     

Does effective climate policy require well-informed citizen support?

Citizen support for climate policies is typically seen as an important criterion in climate policy making. Some studies of climate policy support assume that a significant number of citizens need to be aware of the policies in question and able to provide informed opinions. In this study, we probe this assumption using a web-based survey of residents of the Canadian province of British Columbia (n = 475) by assessing: (1) citizen awareness and knowledge of climate policies, (2) citizen support for different climate policies, (3) the relationship between citizen knowledge and policy support, and (4) the effect of information provision on policy support. Our main finding is that most survey respondents are not aware of any of British Columbia's climate policies, and have little understanding of the potential effect of these on reducing greenhouse gas emissions. Once they are made aware of different types of climate policies, respondents are more likely to express support for regulations, such as the zero-emissions electricity standard and energy efficiency regulations, and less likely to support a carbon tax. Statistical analysis indicates that citizen knowledge of policy is not associated with higher policy support. Furthermore, providing information on likely policy effectiveness to our survey respondents did not translate into higher support, suggesting that widespread knowledge and well-informed citizen support are not necessarily required for implementation of effective climate policies.