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1.
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.  相似文献   

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3.
This essay explores as to whether probabilistic climate forecasting is consistent with the prerequisites of democratic scientific policy advice. It argues that, given the boundaries of our current knowledge, it is highly problematic to assign exact, unconditional probabilities to possible values of climate sensitivity. The range of possible–instead of probable–future climate scenarios is what climate policy should be based on.  相似文献   

4.
There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.  相似文献   

5.
Socio-economic scenarios constitute an important tool for exploring the long-term consequences of anthropogenic climate change and available response options. A more consistent use of socio-economic scenarios that would allow an integrated perspective on mitigation, adaptation and residual climate impacts remains a major challenge. We assert that the identification of a set of global narratives and socio-economic pathways offering scalability to different regional contexts, a reasonable coverage of key socio-economic dimensions and relevant futures, and a sophisticated approach to separating climate policy from counter-factual “no policy” scenarios would be an important step toward meeting this challenge. To this end, we introduce the concept of “shared socio-economic (reference) pathways”. Sufficient coverage of the relevant socio-economic dimensions may be achieved by locating the pathways along the dimensions of challenges to mitigation and to adaptation. The pathways should be specified in an iterative manner and with close collaboration between integrated assessment modelers and impact, adaptation and vulnerability researchers to assure coverage of key dimensions, sufficient scalability and widespread adoption. They can be used not only as inputs to analyses, but also to collect the results of different climate change analyses in a matrix defined by two dimensions: climate exposure as characterized by a radiative forcing or temperature level and socio-economic development as classified by the pathways. For some applications, socio-economic pathways may have to be augmented by “shared climate policy assumptions” capturing global components of climate policies that some studies may require as inputs. We conclude that the development of shared socio-economic (reference) pathways, and integrated socio-economic scenarios more broadly, is a useful focal point for collaborative efforts between integrated assessment and impact, adaptation and vulnerability researchers.  相似文献   

6.
Decision-makers have confirmed the long term objective of preventing a temperature increase greater than 2 °C. This paper aims at appraising by means of a cost-benefit analysis whether decision makers’ commitment to meet the 2 °C objective is credible or not. Within the framework of a cost-benefit type integrated assessment model, we consider that the economy faces climate damages with a threshold at 2 °C. We run the model for a broad set of scenarios accounting for the diversity of “worldviews” in the climate debate. For a significant share of scenarios we observe that it is considered optimal to exceed the threshold. Among those “non-compliers” we discriminate ”involuntary non-compliers” who cannot avoid the exceedance due to physical constraint from ”deliberate compliers” for whom the exceedance results from a deliberate costs-benefit analysis. A second result is that the later mitigation efforts begin, the more difficult it becomes to prevent the exceedance. In particular, the number of ”deliberate non-compliers” dramatically increases if mitigation efforts do not start by 2020, and the influx of involuntary non-compliers become overwhelming f efforts are delayed to 2040. In light of these results we argue that the window of opportunity for reaching the 2 °C objective with a credible chance of success is rapidly closing during the present decade. Further delay in finding a climate agreement critically undermines the credibility of the objective.  相似文献   

7.
Agricultural risk management policies under climate uncertainty   总被引：1，自引：0，他引：1  
Climate change is forecasted to increase the variability of weather conditions and the frequency of extreme events. Due to potential adverse impacts on crop yields it will have implications for demand of agricultural risk management instruments and farmers’ adaptation strategies. Evidence on climate change impacts on crop yield variability and estimates of production risk from farm surveys in Australia, Canada and Spain, are used to analyse the policy choice between three different types of insurance (individual, area-yield and weather index) and ex post payments. The results are found to be subject to strong uncertainties and depend on the risk profile of different farmers and locations; the paper provides several insights on how to analyse these complexities. In general, area yield performs best more often across our countries and scenarios, in particular for the baseline and marginal climate change (without increases in extreme events). However, area yield can be very expensive if farmers have limited information on how climate change affects yields (misalignment in expectations), and particularly so under extreme climate change scenarios. In these more challenging cases, ex post payments perform well to increase low incomes when the risk is systemic like in Australia; Weather index performs well to reduce the welfare costs of risks when the correlation between yields and index is increased by the extreme events. The paper also analyses the robustness of different instruments in the face of limited knowledge of the probabilities of different climate change scenarios; highlighting that this added layer of uncertainty could be overcome to provide sound policy advice under uncertainties introduced by climate change. The role of providing information to farmers on impacts of climate change emerges as a crucial result of this paper as indicated by the significantly higher budgetary expenditures occurring across all instruments when farmers’ expectations are misaligned relative to actual impacts of climate change.  相似文献   

8.
This article traces the development of uncertainty analysis through three generations punctuated by large methodology investments in the nuclear sector. Driven by a very high perceived legitimation burden, these investments aimed at strengthening the scientific basis of uncertainty quantification. The first generation building off the Reactor Safety Study introduced structured expert judgment in uncertainty propagation and distinguished variability and uncertainty. The second generation emerged in modeling the physical processes inside the reactor containment building after breach of the reactor vessel. Operational definitions and expert judgment for uncertainty quantification were elaborated. The third generation developed in modeling the consequences of release of radioactivity and transport through the biosphere. Expert performance assessment, dependence elicitation and probabilistic inversion are among the hallmarks. Third generation methods may be profitably employed in current Integrated Assessment Models (IAMs) of climate change. Possible applications of dependence modeling and probabilistic inversion are sketched. It is unlikely that these methods will be fully adequate for quantitative uncertainty analyses of the impacts of climate change, and a penultimate section looks ahead to fourth generation methods.  相似文献   

9.
In the Arkansas River Basin in southeastern Colorado, surface irrigation provides most of the water required for agriculture. Consequently, the region’s future could be significantly affected if climate change impacts the amount of water available for irrigation. A methodology to model the expected impacts of climate change on irrigation water demand in the region is described. The Integrated Decision Support Consumptive Use model, which accounts for spatial and temporal variability in evapotranspiration and precipitation, is used in conjunction with two climate scenarios from the Vegetation-Ecosystem Modeling and Analysis Project. The two scenarios were extracted and scaled down from two general circulation models (GCMs), the HAD from the Hadley Centre for Climate Prediction and Research and the CCC from the Canadian Climate Centre. The results show significant changes in the water demands of crops due to climate change. The HAD and CCC climate change scenarios both predict an increase in water demand. However, the projections of the two GCMs concerning the water available for irrigation differ significantly, reflecting the large degree of uncertainty concerning what the future impacts of climate change might be in the study region. As new or updated predictions become available, the methodology described here can be used to estimate the impacts of climate change.  相似文献   

10.
Long-term emissions scenarios have served as the primary basis for assessing future climate change and response strategies. Therefore, it is important to regularly reassess the relevance of emissions scenarios in light of changing global circumstances and compare them with long-term developments to determine if they are still plausible, considering the newest insights. Four scenario series, SA90, IS92, SRES, and RCP/SSP, were central in the scenario-based literature informing the five Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) and the sixth assessment cycle. Here we analyze the historical trends of carbon dioxide (CO2) emissions from fossil fuel combustion and industry and emissions drivers between 1960 and 2017. We then compare the emission scenario series with historical trends for the period 1990–2017/2018. The results show that historical trends are quite consistent with medium scenarios in each series. As a result, they can be regarded as valid inputs for past and future analyses of climate change and impacts. Global CO2 emissions 1960–2018 (and 1990–2018) comprised six (and three) overall subperiods of emissions growth significantly higher and lower than average. Historically, CO2 emissions (in absolute numbers and growth rate) are tightly coupled with primary energy and indirectly with GDP. Global emissions generally followed a medium-high pathway, captured by “middle-of-the-road” scenario narratives in the earlier series, and by combinations of “global-sustainability” and “middle-of-the-road” narratives in the most recent series (SRES and SSP-baselines). Historical non-OECD trends were best captured by “rapid-growth” and “regional-competition” scenarios, while OECD trends were close to regional-sustainability and global-sustainability scenarios. Areas where the emissions scenarios captured the historical trends less well, are renewable and nuclear primary energy supply. The fact that the actual historical development is consistent with rapid-growth narratives in the non-OECD regions might have important implications for future greenhouse gas emissions and associated climatic change.  相似文献   

11.
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.  相似文献   

12.
Over the last decade, hundreds of climate change adaptation projects have been funded and implemented. Despite the importance of these first-generation adaptation projects for establishing funders and implementors’ “best practices,” very little is known about how early adaptation projects have endured, to what ends, and for whom. In this article, I propose a community-based methodology for ex-post assessment of climate change adaptation projects. This methodology contributes to recognitional justice by asking the individuals and collectives tasked with sustaining adaptation initiatives to define adaptation success and what criteria for success should be assessed. I apply this subjective assessment approach in 10 communities across Ecuador that participated in an internationally funded adaptation project that concluded in 2015. My analysis draws together participatory mapping, walking interviews with local leaders, participant observation, and surveys with former project participants. The results highlight that even adaptation projects that were deemed highly successful at their closure have uncertain futures. I find that the sustainability mechanisms that were envisioned by project implementors have not functioned, and communities are shouldering the burden of reviving failing adaptation interventions. These findings highlight that the current model of episodic funding for climate change adaptation projects and evaluation processes needs to be revisited to acknowledge the long-term challenges faced by communities. This analysis also calls attention to the importance of ex-post assessment for adaptation projects and the potential of subjective assessment approaches for building more ontological and epistemological pluralism in understandings of successful climate change adaptation.  相似文献   

13.
The urge for higher resolution climate change scenarios has been widely recognized, particularly for conducting impact assessment studies. Statistical downscaling methods have shown to be very convenient for this task, mainly because of their lower computational requirements in comparison with nested limited-area regional models or very high resolution Atmosphere–ocean General Circulation Models. Nevertheless, although some of the limitations of statistical downscaling methods are widely known and have been discussed in the literature, in this paper it is argued that the current approach for statistical downscaling does not guard against misspecified statistical models and that the occurrence of spurious results is likely if the assumptions of the underlying probabilistic model are not satisfied. In this case, the physics included in climate change scenarios obtained by general circulation models, could be replaced by spatial patterns and magnitudes produced by statistically inadequate models. Illustrative examples are provided for monthly temperature for a region encompassing Mexico and part of the United States. It is found that the assumptions of the probabilistic models do not hold for about 70 % of the gridpoints, parameter instability and temporal dependence being the most common problems. As our examples reveal, automated statistical downscaling “black-box” models are to be considered as highly prone to produce misleading results. It is shown that the Probabilistic Reduction approach can be incorporated as a complete and internally consistent framework for securing the statistical adequacy of the downscaling models and for guiding the respecification process, in a way that prevents the lack of empirical validity that affects current methods.  相似文献   

14.
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15.
Climate change scenarios generated by general circulation models have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river basin scales. This study presents a new non-parametric statistical K-nearest neighbor algorithm for downscaling climate change scenarios for the Rohini River Basin in Nepal. The study is an introduction to the methodology and discusses its strengths and limitations within the context of hindcasting basin precipitation for the period of 1976?C2006. The actual downscaled climate change projections are not presented here. In general, we find that this method is quite robust and well suited to the data-poor situations common in developing countries. The method is able to replicate historical rainfall values in most months, except for January, September, and October. As with any downscaling technique, whether numerical or statistical, data limitations significantly constrain model ability. The method was able to confirm that the dataset available for the Rohini Basin does not capture long-term climatology. Yet, we do find that the hindcasts generated with this methodology do have enough skill to warrant pursuit of downscaling climate change scenarios for this particularly poor and vulnerable region of the world.  相似文献   

16.
Climate is a major determinant of energy demand. Changes in climate may alter energy demand as well as energy demand patterns. This study investigates the implications of climate change for energy demand under the hypothesis that impacts are scale dependent due to region-specific climatic variables, infrastructure, socioeconomic, and energy use profiles. In this analysis we explore regional energy demand responses to climate change by assessing temperature-sensitive energy demand in the Commonwealth of Massachusetts. The study employs a two-step estimation and modeling procedure. The first step evaluates the historic temperature sensitivity of residential and commercial demand for electricity and heating fuels, using a degree-day methodology. We find that when controlling for socioeconomic factors, degree-day variables have significant explanatory power in describing historic changes in residential and commercial energy demands. In the second step, we assess potential future energy demand responses to scenarios of climate change. Model results are based on alternative climate scenarios that were specifically derived for the region on the basis of local climatological data, coupled with regional information from available global climate models. We find notable changes with respect to overall energy consumption by, and energy mix of the residential and commercial sectors in the region. On the basis of our findings, we identify several methodological issues relevant to the development of climate change impact assessments of energy demand.  相似文献   

17.
There is a growing need of the climate change impact modeling and adaptation community to have more localized climate change scenario information available over complex topography such as in Switzerland. A gridded dataset of expected future climate change signals for seasonal averages of daily mean temperature and precipitation in Switzerland is presented. The basic scenarios are taken from the CH2011 initiative. In CH2011, a Bayesian framework was applied to obtain probabilistic scenarios for three regions within Switzerland. Here, the results for two additional Alpine sub-regions are presented. The regional estimates have then been downscaled onto a regular latitude-longitude grid with a resolution of 0.02° or roughly 2 km. The downscaling procedure is based on the spatial structure of the climate change signals as simulated by the underlying regional climate models and relies on a Kriging with external drift using height as auxiliary predictor. The considered emission scenarios are A1B, A2 and the mitigation scenario RCP3PD. The new dataset shows an expected warming of about 1 to 6 °C until the end of the 21st century, strongly depending on the scenario and the lead time. Owing to a large vertical gradient, the warming is about 1 °C stronger in the Alps than in the Swiss lowlands. In case of precipitation, the projection uncertainty is large and in most seasons precipitation can increase or decrease. In summer a distinct decrease of precipitation can be found, again strongly depending on the emission scenario.  相似文献   

18.
Many decisions concerning long-lived investments already need to take into account climate change. But doing so is not easy for at least two reasons. First, due to the rate of climate change, new infrastructure will have to be able to cope with a large range of changing climate conditions, which will make design more difficult and construction more expensive. Second, uncertainty in future climate makes it impossible to directly use the output of a single climate model as an input for infrastructure design, and there are good reasons to think that the needed climate information will not be available soon. Instead of optimizing based on the climate conditions projected by models, therefore, future infrastructure should be made more robust to possible changes in climate conditions. This aim implies that users of climate information must also change their practices and decision-making frameworks, for instance by adapting the uncertainty-management methods they currently apply to exchange rates or R&D outcomes. Five methods are examined: (i) selecting “no-regret” strategies that yield benefits even in absence of climate change; (ii) favouring reversible and flexible options; (iii) buying “safety margins” in new investments; (iv) promoting soft adaptation strategies, including long-term prospective; and (v) reducing decision time horizons. Moreover, it is essential to consider both negative and positive side-effects and externalities of adaptation measures. Adaptation–mitigation interactions also call for integrated design and assessment of adaptation and mitigation policies, which are often developed by distinct communities.  相似文献   

19.
Mountain social-ecological systems (MtSES) are transforming rapidly due to changes in multiple environmental and socioeconomic drivers. However, the complexity and diversity of MtSES present challenges for local communities, researchers and decision makers seeking to anticipate change and promote action towards sustainable MtSES. Participatory scenario planning can reveal potential futures and their interacting dynamics, while archetype analysis aggregates insights from site-based scenarios. We combined a systematic review of the global MtSES participatory scenarios literature and archetype analysis to identify emergent MtSES archetypal configurations. An initial sample of 1983 rendered 42 articles that contained 142 scenarios within which were 852 ‘futures states’. From these future states within the scenarios, we identified 59 desirable and undesirable futures that were common across studies. These ‘common futures’ were grouped into four clusters that correlated significantly with three social-ecological factors (GDP per capita, income inequality, and mean annual temperature). Using these clusters and their associated significant factors, we derived four MtSES scenario archetypal configurations characterized by similar key adaptation strategies, assumptions, risks, and uncertainties. We called these archetypes: (1) “revitalization through effective institutions and tourism”; (2) “local innovations in smallholder farming and forestry”; (3) “upland depopulation and increased risk of hazards”; and (4) “regulated economic and ecological prosperity”. Results indicate risks to be mitigated, including biodiversity loss, ecosystem degradation, cultural heritage change, loss of connection to the land, weak leadership, market collapse, upland depopulation, increased landslides, avalanches, mudflows and rock falls, as well as climate variability and change. Transformative opportunities lie in adaptive biodiversity conservation, income diversification, adaptation to market fluxes, improving transport and irrigation infrastructure, high quality tourism and preserving traditional knowledge. Despite the uncertainties arising from global environmental changes, these archetypes support better targeting of evidence-informed actions across scales and sectors in MtSES.  相似文献   

20.
With record breaking heat waves, dryness, and floods in several parts of the world in recent years the question arises whether and to what extent the hazard of hydrometeorological extreme weather events has changed, and if changes can be attributed to specific causes. The methodology of probabilistic event attribution allows to evaluate such potential changes in the occurence probabilities of particular types of extreme events. We show that such a probabilistic assessment not only provides information on changing hazards in hydrometerological events but also permits statements about multivariate combinations of hydrometeorological variables. Hence attribution studies could be targeted specifically towards relevant impacts in particular sectors, if different suitable multivariate proxies are used. We demonstrate our methodology by using combinations of temperature, precipitation and humidity in the large ensemble of climate simulations within the weather@home project in order to derive impact-relevant quantities such as a seasonal water balance and heat stress imposed on the human body. Finally, we estimate the hazard probabilities of those events in South-East Europe, a region that has recently experienced severe summer dryness (2012) in combination with multiple heat waves.  相似文献   

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