Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks

Abstract

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. WIREs Clim Change 2013, 4:39–60. doi: 10.1002/wcc.202 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Vulnerability and Adaptation to Climate Change > Institutions for Adaptation