Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

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.     

Future growth patterns of world regions – A GDP scenario approach

Global GDP projections for the 21st century are needed for the exploration of long-term global environmental problems, in particular climate change. Greenhouse gas emissions as well as climate change mitigation and adaption capacities strongly depend on growth of per capita income. However, long-term economic projections are highly uncertain. This paper provides five new long-term economic scenarios as part of the newly developed shared socio-economic pathways (SSPs) which represent a set of widely diverging narratives. A method of GDP scenario building is presented that is based on assumptions about technological progress, and human and physical capital formation as major drivers of long-term GDP per capita growth. The impact of these drivers differs significantly between different shared socio-economic pathways and is traced back to the underlying narratives and the associated population and education scenarios. In a highly fragmented world, technological and knowledge spillovers are low. Hence, the growth impact of technological progress and human capital is comparatively low, and per capita income diverges between world regions. These factors play a much larger role in globalization scenarios, leading to higher economic growth and stronger convergence between world regions. At the global average, per capita GDP is projected to grow annually in a range between 1.0% (SSP3) and 2.8% (SSP5) from 2010 to 2100. While this covers a large portion of variety in future global economic growth projections, plausible lower and higher growth projections may still be conceivable. The GDP projections are put into the context of historic patterns of economic growth (stylized facts), and their sensitivity to key assumptions is explored.     

Deep uncertainty in long-term hurricane risk: Scenario generation and implications for future climate experiments

Current projections of long-term trends in Atlantic hurricane activity due to climate change are deeply uncertain, both in magnitude and sign. This creates challenges for adaptation planning in exposed coastal communities. We present a framework to support the interpretation of current long-term tropical cyclone projections, which accommodates the nature of the uncertainty and aims to facilitate robust decision making using the information that is available today. The framework is populated with projections taken from the recent literature to develop a set of scenarios of long-term hurricane hazard. Hazard scenarios are then used to generate risk scenarios for Florida using a coupled climate–catastrophe modeling approach. The scenarios represent a broad range of plausible futures; from wind-related hurricane losses in Florida halving by the end of the century to more than a four-fold increase due to climate change alone. We suggest that it is not possible, based on current evidence, to meaningfully quantify the relative confidence of each scenario. The analyses also suggest that natural variability is likely to be the dominant driver of the level and volatility of wind-related risk over the coming decade; however, under the highest scenario, the superposition of this natural variability and anthropogenic climate change could mean notably increased levels of risk within the decade. Finally, we present a series of analyses to better understand the relative adequacy of the different models that underpin the scenarios and draw conclusions for the design of future climate science and modeling experiments to be most informative for adaptation.     

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.     

Linking regional stakeholder scenarios and shared socioeconomic pathways: Quantified West African food and climate futures in a global context

The climate change research community’s shared socioeconomic pathways (SSPs) are a set of alternative global development scenarios focused on mitigation of and adaptation to climate change. To use these scenarios as a global context that is relevant for policy guidance at regional and national levels, they have to be connected to an exploration of drivers and challenges informed by regional expertise.In this paper, we present scenarios for West Africa developed by regional stakeholders and quantified using two global economic models, GLOBIOM and IMPACT, in interaction with stakeholder-generated narratives and scenario trends and SSP assumptions. We present this process as an example of linking comparable scenarios across levels to increase coherence with global contexts, while presenting insights about the future of agriculture and food security under a range of future drivers including climate change.In these scenarios, strong economic development increases food security and agricultural development. The latter increases crop and livestock productivity leading to an expansion of agricultural area within the region while reducing the land expansion burden elsewhere. In the context of a global economy, West Africa remains a large consumer and producer of a selection of commodities. However, the growth in population coupled with rising incomes leads to increases in the region’s imports. For West Africa, climate change is projected to have negative effects on both crop yields and grassland productivity, and a lack of investment may exacerbate these effects. Linking multi-stakeholder regional scenarios to the global SSPs ensures scenarios that are regionally appropriate and useful for policy development as evidenced in the case study, while allowing for a critical link to global contexts.     

The Shared Socioeconomic Pathways and their energy,land use,and greenhouse gas emissions implications: An overview

This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO₂ emissions of the baseline scenarios range from about 25 GtCO₂ to more than 120 GtCO₂ per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m² that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).     

Projecting long-term armed conflict risk: An underappreciated field of inquiry?

Little research has been done on projecting long-term conflict risks. Such projections are currently neither included in the development of socioeconomic scenarios or climate change impact assessments nor part of global agenda-setting policy processes. In contrast, in other fields of inquiry, long-term projections and scenario studies are established and relevant for both strategical agenda-setting and applied policies. Although making projections of armed conflict risk in response to climate change is surrounded by uncertainty, there are good reasons to further develop such scenario-based projections. In this perspective article we discuss why quantifying implications of climate change for future armed conflict risk is inherently uncertain, but necessary for shaping sustainable future policy agendas. We argue that both quantitative and qualitative projections can have a purpose in future climate change impact assessments and put out the challenges this poses for future research.     

River discharge to the Baltic Sea in a future climate

This study reports on new projections of discharge to the Baltic Sea given possible realisations of future climate and uncertainties regarding these projections. A high-resolution, pan-Baltic application of the Hydrological Predictions for the Environment (HYPE) model was used to make transient simulations of discharge to the Baltic Sea for a mini-ensemble of climate projections representing two high emissions scenarios. The biases in precipitation and temperature adherent to climate models were adjusted using a Distribution Based Scaling (DBS) approach. As well as the climate projection uncertainty, this study considers uncertainties in the bias-correction and hydrological modelling. While the results indicate that the cumulative discharge to the Baltic Sea for 2071 to 2100, as compared to 1971 to 2000, is likely to increase, the uncertainties quantified from the hydrological model and the bias-correction method show that even with a state-of-the-art methodology, the combined uncertainties from the climate model, bias-correction and impact model make it difficult to draw conclusions about the magnitude of change. It is therefore urged that as well as climate model and scenario uncertainty, the uncertainties in the bias-correction methodology and the impact model are also taken into account when conducting climate change impact studies.     

Global socio-economic and climate change mitigation scenarios through the lens of structural change

This paper analyses structural change in the economy as a key but largely unexplored aspect of global socio-economic and climate change mitigation scenarios. Structural change can actually drive energy and land use as much as economic growth and influence mitigation opportunities and barriers. Conversely, stringent climate policy is bound to induce specific structural and socio-economic transformations that are still insufficiently understood. We introduce Multi-Sectoral macroeconomic Integrated Assessment Models as tools to capture the key drivers of structural change and we conduct a multi-model study to assess main structural effects – changes of the sectoral composition and intensity of trade of global and regional economies – in a baseline and 2°C policy scenario by 2050. First, the range of baseline projections across models, for which we identify the main drivers, illustrates the uncertainty on future economic pathways – in emerging economies especially – and inform on plausible alternative futures with implications for energy use and emissions. Second, in all models, climate policy in the 2°C scenario imposes only a second-order impact on the economic structure at the macro-sectoral level – agriculture, manufacturing and services - compared to changes modelled in the baseline. However, this hides more radical changes for individual industries – within the energy sector especially. The study, which adopts a top-down framing of global structural change, represents a starting point to kick-start a conversation and propose a new research agenda seeking to improve understanding of the structural change effects in socio-economic and mitigation scenarios, and better inform policy assessments.     

The Effects of Spatial Scale of Climate Scenarios on Economic Assessments: An Example from U.S. Agriculture

The appropriate level of spatial resolution for climate scenarios is a key uncertainty in climate impact studies and regional integrated assessments. To the extent that such uncertainty may affect the magnitude of economic estimates of climate change, it has implications for the public policy debates concerning the efficiency of CO₂ control options. In this article, we investigate the effects that different climate scenario resolutions have on economic estimates of the impacts of future climate changeon agriculture in the United States. These results are derived via a set of procedures and an analytical model that has been used previously in climate change assessments. The results demonstrate that the spatial scale of climate scenarios affects the estimates of both regional changes in crop yields and the economic impact on the agricultural sector as a whole. An assessment based on the finer scale climatological information consistently yielded a less favorable assessment of the implications of climate change. Regional indicators of economic activity were of opposite sign in some regions, based on the scenario scale. Such differences in economic magnitudes or signs are potentially important in examining whether past climate change assessments may misstate the economic consequences of such changes. The results reported here suggest that refinement of the spatial scale of scenarios should be carefully considered in future impacts research.     

Integrating top–down and bottom–up approaches to design global change adaptation at the river basin scale

The high uncertainty associated with the effect of global change on water resource systems calls for a better combination of conventional top–down and bottom–up approaches, in order to design robust adaptation plans at the local scale. The methodological framework presented in this article introduces “bottom–up meets top–down” integrated approach to support the selection of adaptation measures at the river basin level by comprehensively integrating the goals of economic efficiency, social acceptability, environmental sustainability and adaptation robustness. The top–down approach relies on the use of a chain of models to assess the impact of global change on water resources and its adaptive management over a range of climate projections. Future demand scenarios and locally prioritised adaptation measures are identified following a bottom–up approach through a participatory process with the relevant stakeholders and experts. The optimal combinations of adaptation measures are then selected using a hydro-economic model at basin scale for each climate projection. The resulting adaptation portfolios are, finally, climate checked to define a robust least-regret programme of measures based on trade-offs between adaptation costs and the reliability of supply for agricultural demands.This innovative approach has been applied to a Mediterranean basin, the Orb river basin (France). Mid-term climate projections, downscaled from 9 General Climate Models, are used to assess the uncertainty associated with climate projections. Demand evolution scenarios are developed to project agricultural and urban water demands on the 2030 time horizon. The results derived from the integration of the bottom–up and top–down approaches illustrate the sensitivity of the adaptation strategies to the climate projections, and provide an assessment of the trade-offs between the performance of the water resource system and the cost of the adaptation plan to inform local decision-making. The article contributes new methodological elements for the development of an integrated framework for decision-making under climate change uncertainty, advocating an interdisciplinary approach that bridges the gap between bottom–up and top–down approaches.     

An assessment of the performance of scenarios against historical global emissions for IPCC reports

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.     

Climate-related uncertainties in projections of the twenty-first century terrestrial carbon budget: off-line model experiments using IPCC greenhouse-gas scenarios and AOGCM climate projections

A terrestrial ecosystem model (Sim-CYCLE) was driven by multiple climate projections to investigate uncertainties in predicting the interactions between global environmental change and the terrestrial carbon cycle. Sim-CYCLE has a spatial resolution of 0.5°, and mechanistically evaluates photosynthetic and respiratory CO₂ exchange. Six scenarios for atmospheric-CO₂ concentrations in the twenty-first century, proposed by the Intergovernmental Panel on Climate Change, were considered. For each scenario, climate projections by a coupled atmosphere–ocean general circulation model (AOGCM) were used to assess the uncertainty due to socio-economic predictions. Under a single CO₂ scenario, climate projections with seven AOGCMs were used to investigate the uncertainty stemming from uncertainty in the climate simulations. Increases in global photosynthesis and carbon storage differed considerably among scenarios, ranging from 23 to 37% and from 24 to 81 Pg C, respectively. Among the AOGCM projections, increases ranged from 26 to 33% and from 48 to 289 Pg C, respectively. There were regional heterogeneities in both climatic change and carbon budget response, and different carbon-cycle components often responded differently to a given environmental change. Photosynthetic CO₂ fixation was more sensitive to atmospheric CO₂, whereas soil carbon storage was more sensitive to temperature. Consequently, uncertainties in the CO₂ scenarios and climatic projections may create additional uncertainties in projecting atmospheric-CO₂ concentrations and climates through the interactive feedbacks between the atmosphere and the terrestrial ecosystem.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Co-use of existing scenario sets to extend and quantify the shared socioeconomic pathways

More often than not, assessments of future climate risks are based on future climatic conditions superimposed on current socioeconomic conditions only. The new IPCC-guided alternative global development trends, the shared socioeconomic pathways (SSPs), have the potential to enhance the integration of future socioeconomic conditions—in the form of socioeconomic scenarios—within assessments of future climate risks. Being global development pathways, the SSPs lack regional and sectoral details. To increase their suitability in sectoral and/or regional studies and their relevance for local stakeholders, the SSPs have to be extended. We propose here a new method to extend the SSPs that makes use of existing scenario studies, the (re)use of which has been underestimated so far. Our approach lies in a systematic matching of multiple scenario sets that facilitates enrichment of the global SSPs with regional and sectoral information, in terms of both storylines and quantitative projections. We apply this method to develop extended SSPs of human vulnerability in Europe and to quantify them for a number of key indicators at the sub-national level up to 2050, based on the co-use of the matched scenarios’ quantitative outputs. Results show that such a method leads to internally consistent extended SSPs with detailed and highly quantified narratives that are tightly linked to global contexts. This method also provides multiple entry points where the relevance of scenarios to local stakeholders can be tested and strengthened. The extended SSPs can be readily employed to explore future populations’ vulnerability to climate hazards under varying levels of socioeconomic development.     

Towards the Construction of Climate Change Scenarios

Climate impacts assessments need regional scenarios of climate change for a wide range of projected emissions. General circulation models (GCMs) are the most promising approach to providing such information, but as yet there is considerable uncertainty in their regional projections and they are still too costly to run for a large number of emission scenarios. Simpler models have been used to estimate global-mean temperature changes under a range of scenarios. In this paper we investigate whether a fixed pattern from a GCM experiment scaled by global-mean temperature changes from a simple model provides an acceptable estimate of the regional climate change over a range of scenarios. Changes estimated using this approximate approach are evaluated by comparing them with results from ensembles of a coupled ocean-atmosphere model. Five specific emissions scenarios are considered. For increases in greenhouse gases only, the 'error' in annual mean temperature for the cases considered is smaller than the sampling error due to the model's internal variability. The method may break down for scenarios of stabilisation of concentrations, because the patterns change as the model approaches equilibrium. The inclusion of large local perturbations due to sulphate aerosols can lead to significant deviations of the temperature pattern from that obtained using greenhouse gases alone. Combining separate patterns for the responses to greenhouse gases and aerosols may improve the accuracy of approximation. Finally, the accuracy of the scaling approach is more difficult to assess for deriving changes in regional precipitation because many of the regional changes are not statistically significant in the climate change projections considered here. If precipitation changes are only marginally significant in other models, the apparent disagreement between different models may be as much due to sampling error as to genuine differences in model response.     

Response of Soybean and Sorghum to Varying Spatial Scales of Climate Change Scenarios in the Southeastern United States

This study examines how uncertainty associated with the spatial scale of climate change scenarios influences estimates of soybean and sorghum yield response in the southeastern United States. We investigated response using coarse (300-km, CSIRO) and fine (50-km, RCM) scale climate change scenarios and considering climate changes alone, climate changes with CO₂ fertilization, and climate changes with CO₂ fertilization and adaptation. Relative to yields simulatedunder a current, control climate scenario, domain-wide soybean yield decreased by 49% with the coarse-scale climate change scenario alone, and by26% with consideration for CO₂ fertilization. By contrast, thefine-scale climate change scenario generally exhibited higher temperatures and lower precipitation in the summer months resulting in greater yield decreases (69% for climate change alone and 54% with CO₂fertilization). Changing planting date and shifting cultivars mitigated impacts, but yield still decreased by 8% and 18% respectively for the coarse andfine climate change scenarios. The results were similar for sorghum. Yield decreased by 51%, 42%, and 15% in response to fine-scaleclimate change alone, CO₂ fertilization, and adaptation cases, respectively– significantly worse than with the coarse-scale (CSIRO) scenarios. Adaptation strategies tempered the impacts of moisture and temperature stress during pod-fill and grain-fill periods and also differed with respect to the scale of the climate change scenario.     

IPCC emission scenarios: How did critiques affect their quality and relevance 1990–2022?

Long-term global emission scenarios enable the analysis of future climate change, impacts, and response strategies by providing insight into possible future developments and linking these different climate research elements. Such scenarios play a crucial role in the climate change literature informing the Intergovernmental Panel on Climate Change’s (IPCC) Assessment Reports (ARs) and support policymakers. This article reviews the evolution of emission scenarios, since 1990, by focusing on scenario critiques and responses as published in the literature. We focus on the issues raised in the critiques and the possible impact on scenario development. The critique (280) focuses on four areas: 1) key scenario assumptions (40%), 2) the emissions range covered by the scenarios and missing scenarios (25%), 3) methodological issues (24%), and 4) the policy relevance and handling of uncertainty (11%). Scenario critiques have become increasingly influential since 2000. Some areas of critique have decreased or become less prominent (probability, development process, convergence assumptions, and economic metrics). Other areas have become more dominant over time (e.g., policy relevance & implications of scenarios, transparency, Negative Emissions Technologies (NETs) assumptions, missing scenarios). Several changes have been made in developing scenarios and their content that respond to the critique.     

Long-term economic growth projections in the Shared Socioeconomic Pathways

Long-term economic scenarios (up to 2100) are needed as a basis to explore possible different futures for major environmental challenges, including climate change. Given the high level of uncertainty involved, such scenarios would need to span a wide range of possible growth trajectories. The recently developed storylines of the Shared Socioeconomic Pathways (SSPs) provide a basis for making such projections. This paper describes a consistent methodology to derive (per capita) GDP trend pathways on a country basis. The methodology is based on a convergence process and places emphasis on the key drivers of economic growth in the long run: population, total factor productivity, physical capital, employment and human capital, and energy and fossil fuel resources (specifically oil and gas). The paper uses this methodology to derive country-level economic growth projections for 184 countries. The paper also investigates the influence of short-term growth rate estimates on the long-term income levels in various countries. It does so by comparing long-term projections based on short-term forecasts from 2011 with the projections based on forecasts from 2013. This highlights the effects of the recent economic crisis and uncertainty in short term developments on longer term growth trends. The projections are subject to large uncertainties, particularly for the later decades, and disregard a wide range of country-specific drivers of economic growth that are outside the narrow economic framework, such as external shocks, governance barriers and feedbacks from environmental damage. Hence, they should be interpreted with sufficient care and not be treated as predictions.     

Communicating uncertainty in the IPCC’s greenhouse gas emissions scenarios

The issue of climate change required the development of the Special Report on Emission Scenarios (SRES) by the IPCC. The complexity of the subject and the unique science-policy relation resulted in confusion and discussions appeared in popular media like The Economist. This paper reviews scenario literature and SRES, identifies the most vulnerable elements in the communication of SRES. In the communication of GHG emission scenarios through SRES, the weaknesses that have been identified by the authors of this paper are the normative character of climate change assessment, the plausibility of the scenarios, and the risk of simplification of complex messages. The causes of these communicative issues have been identified as the intrinsic difficulties of interdisciplinary science, the uniqueness of the science-policy relation, and the need for a high degree of transparency. This paper suggests improving future communication of complex messages from scientists to their audience by means of clear reasoning when communicating with non-scientists, explicitly normative emission scenarios, and increased stakeholder participation in scenario development. Based on a presentation at the “IIASA-YSSP uncertainty seminars,” 22 July 2004, and the discussions thereafter.