by Judith Curry
This post discusses Workshop presentations on methodologies and application examples of decision analytical strategies to support robust decision making on climate adaptation.
This post is a follow-on to the two previous posts:
- UK-US Workshop on Climate Science to Support Robust Adaptation Decisions
- UK-US Workshop Part II: Perspectives from the private sector on climate adaptation
Perspective from the Grantham Institute
Simon Buckle and Emily Critchley of the Grantham Institute for Climate Change have written an overview article on the Workshop for Imperial College- London’s blog entitled Climate science to support resilience. Excerpts:
The Workshop was an important opportunity to illustrate and discuss the diversity of methods already available that could supplement the insights gleaned from climate models and help inform robust decision making in the face of climate variability and change, whether by business, government or international organisations. These additional tools included, but were not limited to, formal methods of robust decision making, scenarios that capture a wider range of drivers beyond just greenhouse gas emissions, more effective exploitation of historical empirical data about climate variability globally and in specific regions, and the smarter use of climate models of all types to identify what can be said robustly on shorter (decadal) timescales and what cannot.
Commenting on the workshop, Dr Yvan Biot, Senior Scientist at the UK Department for International Development, said that, “the future doesn’t just happen – it is for us to create. The techniques I have learnt in this Workshop will help us select those activities that are most likely to create a better and more secure future for poor people in developing countries.”
The provision of these “climate services” to inform the way that decision makers think about risk is likely to become increasingly important in coming decades. Discussion at the Workshop touched on the ethical dimensions and high professional standards required for the success of this emerging profession. The Workshop concluded that consideration should be given to developing a code of professional standards for practitioners, notably in terms of transparency, objectivity and full disclosure of data, assumptions and methods as well as an obligation to make available forecasts and projections on timescales that allowed those affected by risks to take action in time to mitigate their potential impact, which does not always happen at present.
Robust decision making for climate adaptation
Below is a summary of 4 Workshop presentations on this topic:
Robert Lempert - Rand Corporation: Information needs for developing robust adaptive strategies
Lempert laid out the challenge in this way. Climate-related decisions involve incomplete information from new, fast-moving, and sometimes irreducibly uncertain science; many different interests and values; long time scales; near certainty of surprise. How can we make plans more robust and adaptable while preserving public accountability? Supply and demand of scientific information may be mismatched. Decision support bridges supply and demand with a focus on decision processes.
Traditional approach to risk management works well well when the future isn’t changing fast, isn’t hard to predict, doesn’t generate much disagreement. Predict then act methods can backfire in deeply uncertain conditions: uncertainties are underestimated, competing analyses can contribute to gridlock, and misplaced concreteness can blind decision makers to surprise. Believing forecasts of the unpredictable can contribute to bad decisions. Deep uncertainty occurs when the parties do not know or do not agree on the likelihood of alternative futures or how actions are related to consequences.
Robust decision making manages deep uncertainty by running the analysis backwards: start with a proposed strategy, use multiple model runs to identify conditions that best distinguish futures where strategy does and does not meet its goals, identify steps that can be taken so strategy may succeed over wider ranges of futures. Stakeholders debate about how much robustness they can afford – which is more useful than debating what the future will be. Tradeoff curves help decision makers choose robust strategies. RDM creates demand for decision support methods and tools for managing and summarizing large and diverse sets of information in a decision-relevant context. See the presentation for detailed analysis of several case study applications.
Adapting to climatic change (for example, in the design of coastal infrastructure) poses nontrivial conceptual challenges. Relevant examples include (i) the deep uncertainty surrounding projections of the coupled natural and human systems, (ii) the diversity of priors and value judgments across stakeholders and decision makers, and (iii) the choice of appropriate decision-analytical frameworks.
Climate change adaptation imposes multi-objective trade-offs under dynamic and deep uncertainty. Current adaptation analyses often neglect: known unknowns – leading to overconfidence; endogenous dynamics of imperfect learning; and relevant decision criteria. Inverse decision analysis combined with mission-oriented basic science can help overcome these problems. Uncertain parameters interact in nonlinear ways. One at a time sensitivity analyses can miss important nonlinear interaction effects.
Pulwarty asks the questions: Adapting to what? What are strategies for appraising and evaluating climate adaptation plans? He emphasized the weather-to-climate continuum and the existing adaptation deficits. Climate change adaptation is so difficult because of the cumulative nature of hazards, extremes and disasters; difficulty of proactive decision making and taking advantage of learning and policy windows; and the challenges of information services to support adaptation in changing environments.
The following lessons have been identified (if not necessarily learned):
- Acknowledge the cross-scale of climate, of early warning and adaptation response
- Disciplinary challenges shape problem definitions, scenarios, and recommendations (e.g. primary consideration being climate model impacts versus vulnerability assessment)
- Communication is critical but not sufficient. Need to understand the socialization of lessons learned by particular individuals and organizations through their own, direct brian and error experiences.
- Rules for gathering, storing, communicating information are essential elements of operating procedures
Criteria for robustness need re-evaluation. Understand adaptation as being driven by crises, learning and redesign – role of “surprises” in shaping responses. Generate risk profiles and a portfolio of measures-and broader economic, social and environmental benefits. Approach climate model output far more critically than at present, especially for impact assessment and scenario development at the local level. Develop information systems for critical thresholds across climate time and space scales.
Key issues re improving the linkages between information and decision making: quality and pedigree of information available to decision makers at all levels; factors influencing whether or not such information will be used; factors influencing whether risk communications are trusted; prototyping strategies and practices for adapting the decision making systems to the different levels of decision makers.
Applications to U.S. drought and water resource adaptation are discussed.
In a decentralized model of policy innovation, experiments in climate change adaptation arise from the operational levels of a system; from cohorts of practitioners or inventive individuals. In this model, innovations spread horizontally via communities of practice, with local adaptation rather than comprehensive adoption. This creates the potential for redundancy, and along with it, flexibility in the face of anomalous conditions. Like biodiversity in the natural world, this “innovation dividend” is a reserve of creative potential that enhances resilience in the face of future shocks. Diversity rather than efficiency is a goal value in the decentralized model, and yet it has been observed, particularly in cases concerning common good resources such as water, that allocative efficiency is enhanced. This can be explained by the fact that decision-making authority is set where the participants are optimally positioned to assess costs and benefits. This presentation will use this perspective to explore the ways in which the benefits of subsidiarity are being realized in the otherwise highly centralized Murray-Darling Basin Authority, which manages the arid river system formed from Australia’s three longest rivers.
JC reflections: This series of presentations was in many ways the lynch pin of the Workshop. Robust decision making strategies are the critical link between the supply of climate information and the demand from decision makers to support climate adaptation policies.
All of the talks recognize the existence of substantial uncertainty, and the lack of utility of the traditional ‘predict then act’ model for decision making. Robust decision making provides a framework for making decisions under deep uncertainty, where the disagreement is deflected away from arguments about likely futures to the amount of resilience that can be afforded.
The need for a broader range of climate information than the global climate models was clearly identified in the first three talks, and Keller refers to the need for mission-oriented climate information. This highlights the point that I made in Part I, where ‘mitigation science’ (e.g. sensitivity, attribution) is not the same kind of climate information need to support climate adaptation. Exactly how to approach mission-oriented climate science to support adaptation decisions is discussed in some of the other presentations that will be discussed in future posts.