What are robust strategies in the face of uncertain climate threshold responses?

We use an integrated assessment model of climate change to analyze how alternative decision-making criteria affect preferred investments into greenhouse gas mitigation, the distribution of outcomes, the robustness of the strategies, and the economic value of information. We define robustness as trading a small decrease in a strategy’s expected performance for a significant increase in a strategy’s performance in the worst cases. Specifically, we modify the Dynamic Integrated model of Climate and the Economy (DICE-07) to include a simple representation of a climate threshold response, parametric uncertainty, structural uncertainty, learning, and different decision-making criteria. Economic analyses of climate change strategies typically adopt the expected utility maximization (EUM) framework. We compare EUM with two decision criteria adopted from the finance literature, namely Limited Degree of Confidence (LDC) and Safety First (SF). Both criteria increase the relative weight of the performance under the worst-case scenarios compared to EUM. We show that the LDC and SF criteria provide a computationally feasible foundation for identifying greenhouse gas mitigation strategies that may prove more robust than those identified by the EUM criterion. More robust strategies show higher near-term investments in emissions abatement. Reducing uncertainty has a higher economic value of information for the LDC and SF decision criteria than for EUM.     

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.     

Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach

Although emerging technologies like carbon capture and storage and advanced nuclear are expected to play leading roles in greenhouse gas mitigation efforts, many engineering and policy-related uncertainties will influence their deployment. Capital-intensive infrastructure decisions depend on understanding the likelihoods and impacts of uncertainties such as the timing and stringency of climate policy as well as the technological availability of carbon capture systems. This paper demonstrates the utility of stochastic programming approaches to uncertainty analysis within a practical policy setting, using uncertainties in the US electric sector as motivating examples. We describe the potential utility of this framework for energy-environmental decision making and use a modeling example to reinforce these points and to stress the need for new tools to better exploit the full range of benefits the stochastic programming approach can provide. Model results illustrate how this framework can give important insights about hedging strategies to reduce risks associated with high compliance costs for tight CO₂ caps and low CCS availability. Metrics for evaluating uncertainties like the expected value of perfect information and the value of the stochastic solution quantify the importance of including uncertainties in capacity planning, of making precautionary low-carbon investments, and of conducting research and gathering information to reduce risk.     

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.     

Using Decision Analysis to Include Climate Change in Water Resources Decision Making

Is the prospect of possible climate change relevant to water resources decisions being made today? And, if so, how ought that prospect be considered? These questions can be addressed by decision analysis, which we apply to two investments in the Great Lakes region: a regulatory structure for Lake Erie, and breakwaters to protect Presque Isle State Park, PA. These two decisions have the elements that potentially make climate change relevant: long lived, "one shot" investments; benefits or costs that are affected by climate-influenced variables; and irreversibilities. The decision analyses include the option of waiting to obtain better information, using Bayesian analysis to detect whether climate change has altered water supplies. The analyses find that beliefs about climate change can indeed affect optimal decisions. Furthermore, ignoring the possibility of climate change can lead to significant opportunity losses—in the cases here, as much as 10% or more of the construction cost. Yet the consequences of climate uncertainty for Great Lakes management do not appear to be qualitatively different from those of other risks, and thus do not deserve different treatment. The methods of sensitivity analysis, scenario planning, and decision analysis, all of which are encouraged under US federal guidelines for water planning, are applicable. We recommend increased use of decision trees and Bayesian analysis to consider not only climate change risks, but also other important social and environmental uncertainties.     

Multiple adaptation types with mitigation: A framework for policy analysis

Effective climate policy will consist of mitigation and adaptation implemented simultaneously in a policy portfolio to reduce the risks of climate change. Previous studies of the tradeoffs between mitigation and adaptation have implicitly framed the problem deterministically, choosing the optimal paths for all time. Because climate change is a long-term problem with significant uncertainties and opportunities to learn and revise, critical tradeoffs between mitigation and adaptation in the near-term have not been considered. We propose a new framework for considering the portfolio of mitigation and adaptation that explicitly treats the problem as a multi-stage decision under uncertainty. In this context, there are additional benefits to near-term investments if they reduce uncertainty and lead to improved future decisions. Two particular features are fundamental to understanding the relevant tradeoffs between mitigation and adaptation: (1) strategy dynamics over time in reducing climate damages, and (2) strategy dynamics under uncertainty and potential for learning. Our framework strengthens the argument for disaggregating adaption as has been proposed by others. We present three stylized classes of adaptation investment types as a conceptual framework: short-lived “flow” spending, committed “stock” investment, and lower capacity “option” stock with the capability of future upgrading. In the context of sequential decision under uncertainty, these subtypes of adaptation have important tradeoffs among them and with mitigation. We argue that given the large policy uncertainty that we face currently, explicitly considering adaptation “option” investments is a valuable component of a near-term policy response that can balance between the flexible flow and committed stock approaches, as it allows for the delay of costly stock investments while at the same time allowing for lower-cost risk management of future damages.     

Learning about parameter and structural uncertainty in carbon cycle models

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

Negative learning

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

Identifying uncertainties in Arctic climate change projections

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

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.     

Modeling adaptation as a flow and stock decision with mitigation

An effective policy response to climate change will include, among other things, investments in lowering greenhouse gas emissions (mitigation), as well as short-term temporary (flow) and long-lived capital-intensive (stock) adaptation to climate change. A critical near-term question is how investments in reducing climate damages should be allocated across these elements of a climate policy portfolio, especially in the face of uncertainty in both future climate damages and also the effectiveness of yet-untested adaptation efforts. We build on recent efforts in DICE-based integrated assessment modeling approaches that include two types of adaptation—short-lived flow spending and long-lived depreciable adaptation stock investments—along with mitigation, and we identify and explore the uncertainties that impact the relative proportions of policies within a response portfolio. We demonstrate that the relative ratio of flow adaptation, stock adaptation, and mitigation depend critically on interactions among: 1) the relative effectiveness in the baseline of stock versus flow adaptation, 2) the degree of substitutability between stock and flow adaptation types, and 3) whether there exist physical limits on the amount of damages that can be reduced by flow-type adaptation investments. The results indicate where more empirical research on adaptation could focus to best inform near-term policy decisions, and provide a first step towards considering near-term policies that are flexible in the face of uncertainty.     

Engineering Design and Uncertainties Related to Climate Change

To explore how uncertain climate events might affect investment decisions that need to be made in the near future, this paper examines (1) the relative magnitude of the uncertainties arising from climate change on engineering design in water resources planning and (2) a restricted set of water resource planning techniques that deal with the repeated choice of investment decisions over time. The classical capacity-expansion model of operations research is exploited to show the relative impacts upon engineering design choices for variations in future demand attributable to changes in the climate or other factors and the possible shortfall of supply due to climate change. The type of engineering decisions considered in the paper are sequential, enabling adjustments to revealed uncertainty in subsequent decisions. The range of possible impacts analyzed in the paper lead to similar engineering design decisions. This result means that engineers must be on their guard with respect to under-design or over-design of systems with and without the threat of climate change, but that the sequential nature of the decision-making does not call for drastic action in the early time periods.     

Disconcerting learning on climate sensitivity and the uncertain future of uncertainty

How will our estimates of climate uncertainty evolve in the coming years, as new learning is acquired and climate research makes further progress? As a tentative contribution to this question, we argue here that the future path of climate uncertainty may itself be quite uncertain, and that our uncertainty is actually prone to increase even though we learn more about the climate system. We term disconcerting learning this somewhat counter-intuitive process in which improved knowledge generates higher uncertainty. After recalling some definitions, this concept is connected with the related concept of negative learning that was introduced earlier by Oppenheimer et al. (Clim Change 89:155–172, 2008). We illustrate disconcerting learning on several real-life examples and characterize mathematically certain general conditions for its occurrence. We show next that these conditions are met in the current state of our knowledge on climate sensitivity, and illustrate this situation based on an energy balance model of climate. We finally discuss the implications of these results on the development of adaptation and mitigation policy.     

Technical characteristics of a solar geoengineering deployment and implications for governance

ABSTRACT

Consideration of solar geoengineering as a potential response to climate change will demand complex decisions. These include not only the choice of whether to deploy solar engineering, but decisions regarding how to deploy, and ongoing decision-making throughout deployment. Research on the governance of solar geoengineering to date has primarily engaged only with the question of whether to deploy. We examine the science of solar geoengineering in order to clarify the technical dimensions of decisions about deployment – both strategic and operational – and how these might influence governance considerations, while consciously refraining from making specific recommendations. The focus here is on a hypothetical deployment rather than governance of the research itself. We first consider the complexity surrounding the design of a deployment scheme, in particular the complicated and difficult decision of what its objective(s) would be, given that different choices for how to deploy will lead to different climate outcomes. Next, we discuss the on-going decisions across multiple timescales, from the sub-annual to the multi-decadal. For example, feedback approaches might effectively manage some uncertainties, but would require frequent adjustments to the solar geoengineering deployment in response to observations. Other decisions would be tied to the inherently slow process of detection and attribution of climate effects in the presence of natural variability. Both of these present challenges to decision-making. These considerations point toward particular governance requirements, including an important role for technical experts – with all the challenges that entails.

Key policy insights

• Decisions about solar geoengineering deployment will be informed not only by political choices, but also by climate science and engineering.

• Design decisions will pertain to the spatial and temporal goals of a climate intervention and strategies for achieving those goals.

• Some uncertainty can be managed through feedback, but this would require frequent operational decisions.

• Some strategic decisions will depend on the detection and attribution of climatic effects from solar geoengineering, which may take decades.

• Governance for solar geoengineering deployment will likely need to incorporate technical expertise for making short-term adjustments to the deployment and conducting attribution analysis, while also slowing down decisions made in response to attribution analysis to avoid hasty choices.

     

Projected increased risk of water deficit over major West African river basins under future climates

Despite decades of research, large multi-model uncertainty remains about the Earth’s equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations.     

Uncertainty and learning: implications for the trade-off between short-lived and long-lived greenhouse gases

The economic benefits of a multi-gas approach to climate change mitigation are clear. However, there is still a debate on how to make the trade-off between different greenhouse gases (GHGs). The trade-off debate has mainly centered on the use of Global Warming Potentials (GWPs), governing the trade-off under the Kyoto Protocol, with results showing that the cost-effective valuation of short-lived GHGs, like methane (CH₄), should be lower than its current GWP value if the ultimate aim is to stabilize the anthropogenic temperature change. However, contrary to this, there have also been proposals that early mitigation mainly should be targeted on short-lived GHGs. In this paper we analyze the cost-effective trade-off between a short-lived GHG, CH₄, and a long-lived GHG, carbon dioxide (CO₂), when a temperature target is to be met, taking into consideration the current uncertainty of the climate sensitivity as well as the likelihood that this will be reduced in the future. The analysis is carried out using an integrated climate and economic model (MiMiC) and the results from this model are explored and explained using a simplified analytical economic model. The main finding is that the introduction of uncertainty and learning about the climate sensitivity increases the near-term cost-effective valuation of CH₄ relative to CO₂. The larger the uncertainty span, the higher the valuation of the short-lived gas. For an uncertainty span of ±1°C around an expected climate sensitivity of 3°C, CH₄ is cost-effectively valued 6.8 times as high as CO₂ in year 2005. This is almost twice as high as the valuation in a deterministic case, but still significantly lower than its GWP₁₀₀ value.     

Integrated Global System Model for Climate Policy Assessment: Feedbacks and Sensitivity Studies

Alternative policies to address global climate change are being debated in many nations and within the United Nations Framework Convention on Climate Change. To help provide objective and comprehensive analyses in support of this process, we have developed a model of the global climate system consisting of coupled sub-models of economic growth and associated emissions, natural fluxes, atmospheric chemistry, climate, and natural terrestrial ecosystems. The framework of this Integrated Global System Model is described and the results of sample runs and a sensitivity analysis are presented. This multi-component model addresses most of the major anthropogenic and natural processes involved in climate change and also is computationally efficient. As such, it can be used effectively to study parametric and structural uncertainty and to analyze the costs and impacts of many policy alternatives. Initial runs of the model have helped to define and quantify a number of feedbacks among the sub-models, and to elucidate the geographical variations in several variables that are relevant to climate science and policy. The effect of changes in climate and atmospheric carbon dioxide levels on the uptake of carbon and emissions of methane and nitrous oxide by land ecosystems is one potentially important feedback which has been identified. The sensitivity analysis has enabled preliminary assessment of the effects of uncertainty in the economic, atmospheric chemistry, and climate sub-models as they influence critical model results such as predictions of temperature, sea level, rainfall, and ecosystem productivity. We conclude that uncertainty regarding economic growth, technological change, deep oceanic circulation, aerosol radiative forcing, and cloud processes are important influences on these outputs.