Improving the Contribution of Climate Model Information to Decision Making: the Value and Demands of Robust Decision Frameworks
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Advanced Review Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks Christopher P. Weaver,1∗ Robert J. Lempert,2 Casey Brown,3 John A. Hall,4 David Revell5 and Daniel Sarewitz6 In this paper, we review the need for, use of, and demands on climate modeling to support so-called ‘robust’ decision frameworks, in the context of improving the contribution of climate information to effective decision making. Such frameworks seek to identify policy vulnerabilities under deep uncertainty about the future and propose strategies for minimizing regret in the event of broken assumptions. We argue that currently there is a severe underutilization of climate models as tools for supporting decision making, and that this is slowing progress in developing informed adaptation and mitigation responses to climate change. This underutilization stems from two root causes, about which there is a growing body of literature: one, a widespread, but limiting, conception that the usefulness of climate models in planning begins and ends with regional-scale predictions of multidecadal climate change; two, the general failure so far to incorporate learning from the decision and social sciences into climate-related decision support in key sectors. We further argue that addressing these root causes will require expanding the conception of climate models; not simply as prediction machines within ‘predict-then-act’ decision frameworks, but as scenario generators, sources of insight into complex system behavior, and aids to critical thinking within robust decision frameworks. Such a shift, however, would have implications for how users perceive and use information from climate models and, ultimately, the types of information they will demand from these models—and thus for the types of simulations and numerical experiments that will have the most value for informing decision making. © 2012 John Wiley & Sons, Ltd. How to cite this article: WIREs Clim Change 2013, 4:39–60. doi: 10.1002/wcc.202 3Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA, USA This article is a U.S. Government work, and as such, is in the public domain in the United States of America. 4Strategic Environmental Research and Development Pro- ∗ gram/Environmental Security Technology Certification Program, Correspondence to: [email protected] U.S. Department of Defense, Alexandria, VA, USA 1National Center for Environmental Assessment, Office of 5Environmental Science Associates—Environmental Hydrology, Research and Development, U.S. Environmental Protection Agency, San Francisco, CA, USA Washington, DC, USA 6Consortium for Science, Policy, and Outcomes, Arizona State 2The RAND Corporation, Santa Monica, CA, USA University, Tempe, AZ, USA Volume 4, January/February 2013 © 2012 John Wiley & Sons, Ltd. 39 Advanced Review wires.wiley.com/climatechange INTRODUCTION this broader perspective on the value of climate mod- els for supporting decision making, have the potential ssessment of environmental and societal risks to simultaneously and effectively address the challenge Afrom climate change, and the design and above. evaluation of strategies to build readiness and Such a shift, however, would have implications resilience in the face of these risks, is a generational for the ways in which users perceive and use scientific challenge. How should society steer climate information from climate models and, ultimately, the research to best meet this challenge? How should types of information they will demand from these we attempt to use insights and information from models—and thus for the types of simulations and climate science to inform decision making about numerical experiments that will have the most value responses to climate change? These questions are for societal applications. In this context, we review the particularly relevant now, as national governments use of, and demands on, climate modeling to support and international scientific bodies are discussing these frameworks, in the context of improving uptake how best to organize research efforts and to of climate information into decision making. deliver knowledge, data, tools, and advice—climate services—to a potentially huge universe of clients.1–3 There has been some self-organization around LONG-TERM CLIMATE PREDICTION IS two strategic framings of the problem. The first DIFFICULT ... (dominant) paradigm assumes the need to improve our ability to predict, using physically based computer As noted by various commentators,4,5 one of models, multidecadal, regional climate change as the most oft-repeated statements in the climate a prerequisite for effective planning; the second impacts literature goes something like this: ‘Accurate, to instead improve our understanding of regional high-resolution predictions of future climate are and sectoral climate-related vulnerabilities, and the a prerequisite for developing effective responses cognitive, social, and institutional contexts within to climate change impacts at regional scales.’ A which these will be managed, in light of the deep number of recent papers and editorials have called uncertainties associated with climate change and its for increased efforts to deliver these improved possible impacts. These two conversations have to predictions.6–12 At the same time, everyone seems date largely occurred in parallel. to broadly agree that we are a long way from fulfilling The purpose of this paper is to discuss potential this need. For example: synergies between climate modeling for decision support as currently practiced, and the more bottom- The climate science community now faces a major up approaches to climate change vulnerability and new challenge of providing society with reliable impacts assessment, as a pathway to more effectively regional climate predictions. Adapting to climate informing climate-related decisions. We argue that change while pursuing sustainable development will require accurate and reliable predictions of changes in currently there is a severe underutilization of climate regional weather systems, especially extremes ...Yet, models as tools to support decision making. This current climate models have serious limitations in underutilization stems from a double challenge noted simulating regional weather variations and therefore by a growing number of scholars: a widespread, but in generating the requisite information about regional limiting, conception that the potential usefulness of changes with a level of confidence required by climate models in planning begins and ends with ever- society.12 finer regional-scale predictions of multidecadal climate 8–10,13,14 change; and the general failure so far to incorporate Andsoon(seealsoRefs and many learning from the decision and social sciences into others) (Box 1). climate-related decision support in key sectors such as As the number of legal and political mandates water resources, coastal protection, agriculture, and for incorporating climate change information into public health. decision making increase, the demands on climate In this paper, we argue that alleviating this prob- science and modeling to deliver so-called ‘actionable’ lem will require expanding the conception of climate information in direct support of planning will also models, not simply as prediction machines within continue to increase—as will scrutiny of their ability ‘predict-then-act’ decision frameworks, but as aids to do so. to exploratory modeling—for scenario generation, It is well understood that current limits on sci- insight into complex system behavior, and supports entific understanding and computing power result for critical thinking—within so-called ‘robust’ deci- in deficiencies in simulating impact-relevant cli- sion frameworks. Such frameworks, combined with mate system elements—clouds, precipitation, winds, 40 © 2012 John Wiley & Sons, Ltd. Volume 4, January/February 2013 WIREs Climate Change The value and demands of robust decision frameworks BOX 1 employ them as shorthand for a continuum of decreasingly confident statements about the NOTES ON LANGUAGE: PREDICTION, future, from prediction, through projection, to PROJECTION, AND SCENARIO scenario, using them to manage expectations created by the word prediction.16–22 In addition, AccordingtotheIPCC15: ‘A climate prediction the IPCC definition of projection above still or climate forecast is the result of an attempt implies that at least the climate portion is to produce an estimate of the actual evolution deterministic, and it is only the future emissions of the climate in the future, for example, at pathway that is non-deterministic and thus must seasonal, interannual, or long-term time scales. be captured via a set of scenarios. Since the future evolution of the climate system Rather than using projection and scenario may be highly sensitive to initial conditions, such as if they are simply a kind of less-accurate predictions are usually probabilistic in nature.’ prediction, it would seem to make more sense Here, the term ‘prediction’ refers to attempts to to adopt functional definitions that relate to answer questions posed as ‘What will happen (in how the different objects are actually used the future)?’ This definition also suggests that to support decision making. How would one various factors will prevent us from achieving envision using a climate prediction to support an 100% accuracy, and that an important part actual planning decision, and how would that of any