Safe policies in an uncertain climate: an application of FUND

Various aspects of the role of uncertainty in greenhouse gas emission reduction policy are analyzed with the integrated assessment model FUND. FUND couples simple models of economy, climate, climate impacts, and emission abatement. Probability distribution functions are assumed for all major parameters in the model. Monte Carlo analyses are used to study the effects of parametric uncertainties. Uncertainties are found to be large and grow over time. Uncertainties about climate change impacts are more serious than uncertainties about emission reduction costs, so that welfare-maximizing policies are stricter under uncertainty than under certainty. This is more pronounced without than with international cooperation. Whether or not countries cooperate with one another is more important than whether or not uncertainty is considered. Meeting exogenously defined emission targets may be more or less difficult under uncertainty than under certainty, depending on the asymmetry in the uncertainty and the central estimate of interest. The major uncertainty in meeting emissions targets in each of a range of possible future is the timing of starting (serious) reduction policies. In a scenario aiming at a stable CO₂ concentration of 550 ppm, the start date varies 20 years for Annex I countries, and much longer for non-Annex countries. Atmospheric stabilization at 550 ppm does not avoid serious risks with regard to climate change impacts. At the long term, it is possible to avoid such risks but only through very strict emission control at high economic costs.     

The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas (GHG) emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of GHG emissions in the country. We use those projections to produce probabilistic forecasts of GHG emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets.

POLICY INSIGHTS

• No minimum transparency and quality standards exist to guide the development of GHG emission scenario forecasts, not even when these forecasts are used to set national climate change mitigation targets.

• No accountability mechanisms appear to be in place at the national level to ensure that national governments rely on scientifically sound processes to develop GHG emission scenarios.

• Using probabilistic forecasts to underpin emission reduction targets represents a scientifically sound option for reflecting in the target the uncertainty to which those forecasts are subject, thus increasing the validity of the target.

• Setting up minimum transparency and quality standards, and holding governments accountable for their choice of forecasting methods could lead to more robust emission reduction targets nationally and, by extension, internationally.

     

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.     

A Demonstration of the Uncertainty in Projections of UK Climate Change Resulting from Regional Model Formulation

Regional climate models (RCMs) are now commonly used to downscale climate change projections provided by global coupled models to resolutions that can be utilised at national and finer scales. Although this extra tier of complexity adds significant value, it inevitably contributes a further source of uncertainty, due to the regional modelling uncertainties involved. Here, an initial attempt is made to estimate the uncertainty that arises from typical variations in RCM formulation, focussing on changes in UK surface air temperature (SAT) and precipitation projected for the late twenty-first century. Data are provided by a relatively large suite of RCM and global model integrations with widely varying formulations. It is found that uncertainty in the formulation of the RCM has a relatively small, but non-negligible, impact on the range of possible outcomes of future UK seasonal mean climate. This uncertainty is largest in the summer season. It is also similar in magnitude to that of large-scale internal variations of the coupled climate system, and for SAT, it is less than the uncertainty due to the emissions scenario, whereas for precipitation it is probably larger. The largest source of uncertainty, for both variables and in all seasons, is the formulation of the global coupled model. The scale-dependency of uncertainty due to RCM formulation is also explored by considering its impact on projections of the difference in climate change between the north and south of the UK. Finally, the implications for the reliability of UK seasonal mean climate change projections are discussed.     

Developed and developing world contributions to climate system change based on carbon dioxide,methane and nitrous oxide emissions

One of the key issues in international climate negotiations is the formulation of targets for emissions reduction for all countries based on the principle of "common but differentiated responsibilities". This formulation depends primarily on the quantitative attribution of the responsibilities of developed and developing countries for historical climate change. Using the Commuity Earth System Model(CESM), we estimate the responsibilities of developed countries and developing countries for climatic change from 1850 to 2005 using their carbon dioxide, methane and nitrous oxide emissions. The results indicate that developed countries contribute approximately 53%–61%, and developing countries approximately 39%–47%, to the increase in global air temperature, upper oceanic warming, sea-ice reduction in the NH, and permafrost degradation. In addition, the spatial heterogeneity of these changes from 1850 to 2005 is primarily attributed to the emissions of greenhouse gases(GHGs)in developed countries. Although uncertainties remain in the climate model and the external forcings used, GHG emissions in developed countries are the major contributor to the observed climate system changes in the 20 th century.     

Impact of climate change on Pacific Northwest hydropower

The Pacific Northwest (PNW) hydropower resource, central to the region’s electricity supply, is vulnerable to the impacts of climate change. The Northwest Power and Conservation Council (NWPCC), an interstate compact agency, has conducted long term planning for the PNW electricity supply for its 2005 Power Plan. In formulating its power portfolio recommendation, the NWPCC explored uncertainty in variables that affect the availability and cost of electricity over the next 20 years. The NWPCC conducted an initial assessment of potential impacts of climate change on the hydropower system, but these results are not incorporated in the risk model upon which the 2005 Plan recommendations are based. To assist in bringing climate information into the planning process, we present an assessment of uncertainty in future PNW hydropower generation potential based on a comprehensive set of climate models and greenhouse gas emissions pathways. We find that the prognosis for PNW hydropower supply under climate change is worse than anticipated by the NWPCC’s assessment. Differences between the predictions of individual climate models are found to contribute more to overall uncertainty than do divergent emissions pathways. Uncertainty in predictions of precipitation change appears to be more important with respect to impact on PNW hydropower than uncertainty in predictions of temperature change. We also find that a simple regression model captures nearly all of the response of a sequence of complex numerical models to large scale changes in climate. This result offers the possibility of streamlining both top-down impact assessment and bottom-up adaptation planning for PNW water and energy resources.     

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.     

Population, uncertainty, and learning in climate change decision analysis

The prospect of learning about various uncertainties relevant to analyses of the climate change issue is important because it can affect estimates of the costs of both damages and mitigation, and it can influence the optimal timing of emissions reductions. Baseline scenarios representing future emissions in the absence of mitigation are one of the major sources of uncertainty. Here we investigate how fast we might realistically expect to learn about the outlook for long-term population growth, as one determinant of future baseline emissions. That is, we estimate how long it might take to substantially revise current estimates of the likelihood of various population size outcomes over the twenty-first century. We draw on recent work showing that, because population growth is path dependent, we can learn about the long term outlook by waiting to observe how population changes in the short term. We then explore the implications of uncertainty and of this learning potential for mitigation costs and for optimal emissions. Using a simple model, we show that uncertainty in population growth translates into an uncertainty in the optimal tax rate of about $200/tC by 2050 for a range of stabilization levels. When learning is taken into account, it allows for mitigation strategies to change in response to new information, leading to a slight reduction in the expected value of mitigation costs, and a substantial reduction in the likelihood of high cost outcomes. We also find that while learning can lead to large revisions over the next few decades in anticipated population growth, this potential does not imply large changes in near-term optimal emissions reductions. Results suggest that further work on the potential for learning about other determinants of emissions could have larger effects on expected mitigation costs.     

1.5℃全球温控目标浅析

《巴黎协定》将努力控制全球温升到2100年不超过工业化前的1.5℃确定为全球温控目标之一。继2℃目标后,1.5℃也被作为应对气候变化的全球温控目标之一。目前科学界对于1.5℃目标的研究还十分有限。已有的科学研究表明,尽管区域差异很大,将全球温升控制在1.5℃范围内地球各系统要承受的气候风险可能要低于2℃。相比于2℃目标,1.5℃目标对全球减缓行动的要求更为严苛。尽管在《巴黎协定》中各缔约方承诺了各自到2030（2025）年的减排目标,但相对于实现1.5℃目标而言仍有很大的差距。多家研究机构的模拟结果表明,如完全执行当前国家自主决定贡献（NDC),到21世纪末全球温升范围为2.2～3.4℃。截至2025年,实现当前NDC的减排承诺后,2℃温升目标下全球仍有467 Gt CO₂（万亿t CO₂当量）的排放空间,1.5℃温升目标下全球仅剩17 Gt CO₂。到2030年,基于NDC的排放已经超过了1.5℃目标的排放量。按当前的路径来看,若想实现将全球温升控制在1.5℃的范围内,全球不仅需要立即行动并采取强有力的减排、脱碳和固碳措施,在2100年前,还必须实现负排放才有可能实现这一目标。尽管当前的科学研究仍存在很大的不确定性,但1.5℃目标已是全球努力应对气候变化的方向,也是开启未来世界低碳可持续发展的重要标志。     

To what extent can a long-term temperature target guide near-term climate change commitments?

The question of appropriate timing and stringency of future greenhouse gas (GHG) emission reductions remains an issue in the discussion of mitigation responses to the climate change problem. It has been argued that our near-term action should be guided by a long-term vision for the climate, possibly a long-term temperature target. In this paper, we review proposals for long-term climate targets to avoid ‘dangerous’ climate change. Using probability estimates of climate sensitivity from the literature, we then generate probabilistic emissions scenarios that satisfy temperature targets of 2.0, 2.5, and 3.0°C above pre-industrial levels with no overshoot. Our interest is in the implications of these targets on abatement requirements over the next 50 years. If we allow global industrial GHG emissions to peak in 2025 at 14 GtCeq, and wish to achieve a 2.0°C target with at least 50% certainty, we find that the low sensitivity estimate in the literature suggests our industrial emissions must fall to 9 GtCeq by 2050: equal to the level in 2000. However, the average literature sensitivity estimate suggests the level must be less than 2 GtCeq; and in the high sensitivity case, the target is simply unreachable unless we allow for overshoot. Our results suggest that in light of the uncertainty in our knowledge of the climate sensitivity, a long-term temperature target (such as the 2.0°C target proposed by the European Commission) can provide limited guidance to near-term mitigation requirements.     

基于各国NDC/INDC目标的全球减排不确定性研究

基于各国提交的165份国家自主贡献文件,以其中提出的减排目标为基准,尽可能充分地考虑了减排目标的范围不确定性、不同经济情景带来的碳强度减排目标不确定性、减排气体种类边界差异、碳排放达峰约束等因素,并通过蒙特卡洛模拟的方法对全球、各区域和主要经济体的温室气体排放总量、不确定度及其来源进行了定量分析.结果表明,到2030年...     

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.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15

Total uncertainty in greenhouse gas (GHG) emissions changes over time due to “learning” and structural changes in GHG emissions. Understanding the uncertainty in GHG emissions over time is very important to better communicate uncertainty and to improve the setting of emission targets in the future. This is a diagnostic study divided into two parts. The first part analyses the historical change in the total uncertainty of CO₂ emissions from stationary sources that the member states estimate annually in their national inventory reports. The second part presents examples of changes in total uncertainty due to structural changes in GHG emissions considering the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) emissions scenarios that are consistent with the EU’s “20-20-20” targets. The estimates of total uncertainty for the year 2020 are made under assumptions that relative uncertainties of GHG emissions by sector do not change in time, and with possible future uncertainty reductions for non-CO₂ emissions, which are characterized by high relative uncertainty. This diagnostic exercise shows that a driving factor of change in total uncertainty is increased knowledge of inventory processes in the past and prospective future. However, for individual countries and longer periods, structural changes in emissions could significantly influence the total uncertainty in relative terms.     

Projections of climate change impacts on central America tropical rainforest

Tropical rainforest plays an important role in the global carbon cycle, accounting for a large part of global net primary productivity and contributing to CO₂ sequestration. The objective of this work is to simulate potential changes in the rainforest biome in Central America subject to anthropogenic climate change under two emissions scenarios, RCP4.5 and RCP8.5. The use of a dynamic vegetation model and climate change scenarios is an approach to investigate, assess or anticipate how biomes respond to climate change. In this work, the Inland dynamic vegetation model was driven by the Eta regional climate model simulations. These simulations accept boundary conditions from HadGEM2-ES runs in the two emissions scenarios. The possible consequences of regional climate change on vegetation properties, such as biomass, net primary production and changes in forest extent and distribution, were investigated. The Inland model projections show reductions in tropical forest cover in both scenarios. The reduction of tropical forest cover is greater in RCP8.5. The Inland model projects biomass increases where tropical forest remains due to the CO₂ fertilization effect. The future distribution of predominant vegetation shows that some areas of tropical rainforest in Central America are replaced by savannah and grassland in RCP4.5. Inland projections under both RCP4.5 and RCP8.5 show a net primary productivity reduction trend due to significant tropical forest reduction, temperature increase, precipitation reduction and dry spell increments, despite the biomass increases in some areas of Costa Rica and Panama. This study may provide guidance to adaptation studies of climate change impacts on the tropical rainforests in Central America.     

Uncertainty in an emissions-constrained world

Our study focuses on uncertainty in greenhouse gas (GHG) emissions from anthropogenic sources, including land use and land-use change activities. We aim to understand the relevance of diagnostic (retrospective) and prognostic (prospective) uncertainty in an emissions-temperature setting that seeks to constrain global warming and to link uncertainty consistently across temporal scales. We discuss diagnostic and prognostic uncertainty in a systems setting that allows any country to understand its national and near-term mitigation and adaptation efforts in a globally consistent and long-term context. Cumulative emissions are not only constrained and globally binding but exhibit quantitative uncertainty; and whether or not compliance with an agreed temperature target will be achieved is also uncertain. To facilitate discussions, we focus on two countries, the USA and China. While our study addresses whether or not future increase in global temperature can be kept below 2, 3, or 4 °C targets, its primary aim is to use those targets to demonstrate the relevance of both diagnostic and prognostic uncertainty. We show how to combine diagnostic and prognostic uncertainty to take more educated (precautionary) decisions for reducing emissions toward an agreed temperature target; and how to perceive combined diagnostic and prognostic uncertainty-related risk. Diagnostic uncertainty is the uncertainty contained in inventoried emission estimates and relates to the risk that true GHG emissions are greater than inventoried emission estimates reported in a specified year; prognostic uncertainty refers to cumulative emissions between a start year and a future target year, and relates to the risk that an agreed temperature target is exceeded.     

Synergies and trade-offs between adaptation,mitigation and development

To succeed in meeting carbon emissions reduction targets to limit projected climate change impacts, it is imperative that improved synergies be developed between mitigation and adaptation strategies. This is especially important in development policy among remote indigenous communities, where demands for development have often not been accompanied by commensurate efforts to respond to future climate change impacts. Here we explore how mitigation and adaptation pathways can be combined to transform rural indigenous communities toward sustainability. Case studies from communities in Alaska and Nepal are introduced to illustrate current and potential synergies and trade-offs and how these might be harnessed to maximize beneficial outcomes. The adaptation pathways approach and a framework for transformational adaptation are proposed to unpack these issues and develop understanding of how positive transformational change can be supported.     

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.     

Transient climate changes in a perturbed parameter ensemble of emissions-driven earth system model simulations

We describe results from a 57-member ensemble of transient climate change simulations, featuring simultaneous perturbations to 54 parameters in the atmosphere, ocean, sulphur cycle and terrestrial ecosystem components of an earth system model (ESM). These emissions-driven simulations are compared against the CMIP3 multi-model ensemble of physical climate system models, used extensively to inform previous assessments of regional climate change, and also against emissions-driven simulations from ESMs contributed to the CMIP5 archive. Members of our earth system perturbed parameter ensemble (ESPPE) are competitive with CMIP3 and CMIP5 models in their simulations of historical climate. In particular, they perform reasonably well in comparison with HadGEM2-ES, a more sophisticated and expensive earth system model contributed to CMIP5. The ESPPE therefore provides a computationally cost-effective tool to explore interactions between earth system processes. In response to a non-intervention emissions scenario, the ESPPE simulates distributions of future regional temperature change characterised by wide ranges, and warm shifts, compared to those of CMIP3 models. These differences partly reflect the uncertain influence of global carbon cycle feedbacks in the ESPPE. In addition, the regional effects of interactions between different earth system feedbacks, particularly involving physical and ecosystem processes, shift and widen the ESPPE spread in normalised patterns of surface temperature and precipitation change in many regions. Significant differences from CMIP3 also arise from the use of parametric perturbations (rather than a multimodel ensemble) to represent model uncertainties, and this is also the case when ESPPE results are compared against parallel emissions-driven simulations from CMIP5 ESMs. When driven by an aggressive mitigation scenario, the ESPPE and HadGEM2-ES reveal significant but uncertain impacts in limiting temperature increases during the second half of the twenty-first century. Emissions-driven simulations create scope for development of errors in properties that were previously prescribed in coupled ocean–atmosphere models, such as historical CO₂ concentrations and vegetation distributions. In this context, historical intra-ensemble variations in the airborne fraction of CO₂ emissions, and in summer soil moisture in northern hemisphere continental regions, are shown to be potentially useful constraints, subject to uncertainties in the relevant observations. Our results suggest that future climate-related risks can be assessed more comprehensively by updating projection methodologies to support formal combination of emissions-driven perturbed parameter and multi-model earth system model simulations with suitable observational constraints. This would provide scenarios underpinned by a more complete representation of the chain of uncertainties from anthropogenic emissions to future climate outcomes.