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

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  • Christopher P. Weaver
  • Robert J. Lempert
  • Casey Brown
  • John A. Hall
  • David Revell
  • Daniel Sarewitz

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

Suggested Citation

  • Christopher P. Weaver & Robert J. Lempert & Casey Brown & John A. Hall & David Revell & Daniel Sarewitz, 2013. "Improving the contribution of climate model information to decision making: the value and demands of robust decision frameworks," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 4(1), pages 39-60, January.
    • Handle: RePEc:wly:wirecc:v:4:y:2013:i:1:p:39-60
    DOI: 10.1002/wcc.202
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    1. Dou, Yue & Silva, Ramon Felipe Bicudo da & Batistella, Mateus & Torres, Sara & Moran, Emilio & Liu, Jianguo, 2023. "Mapping crop producer perceptions: The role of global drivers on local agricultural land use in Brazil," Land Use Policy, Elsevier, vol. 133(C).
    2. Iain Brown & Pam Berry, 2022. "National Climate Change Risk Assessments to inform adaptation policy priorities and environmental sustainability outcomes: a knowledge systems perspective," Climatic Change, Springer, vol. 175(3), pages 1-24, December.
    3. Kevin Raaphorst & Gerben Koers & Gerald Jan Ellen & Amy Oen & Bjørn Kalsnes & Lisa van Well & Jana Koerth & Rutger van der Brugge, 2020. "Mind the Gap: Towards a Typology of Climate Service Usability Gaps," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    4. Moallemi, Enayat A. & Elsawah, Sondoss & Ryan, Michael J., 2020. "Strengthening ‘good’ modelling practices in robust decision support: A reporting guideline for combining multiple model-based methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 175(C), pages 3-24.
    5. Funk, Bryana & Amer, Saud A. & Ward, Frank A., 2023. "Sustainable aquifer management for food security," Agricultural Water Management, Elsevier, vol. 281(C).
    6. Moallemi, Enayat A. & Elsawah, Sondoss & Ryan, Michael J., 2020. "Robust decision making and Epoch–Era analysis: A comparison of two robustness frameworks for decision-making under uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    7. Zou, Guang & Faber, Michael Havbro & González, Arturo & Banisoleiman, Kian, 2021. "Computing the value of information from periodic testing in holistic decision making under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    8. Kim, Kangsoo & Cho, Hyejin & Park, Sunwook & You, Soyoung Iris, 2024. "Uncertainties in the economic analysis of Korea’s preliminary feasibility study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    9. Gitonga, Zachary M. & Visser, Martine & Mulwa, Chalmers, 2020. "Can climate information salvage livelihoods in arid and semiarid lands? An evaluation of access, use and impact in Namibia," World Development Perspectives, Elsevier, vol. 20(C).
    10. Kahagalage, Sanath Darshana & Turan, Hasan Hüseyin & Elsawah, Sondoss & Gary, Michael Shayne, 2024. "Exploratory modelling and analysis to support decision-making under deep uncertainty: A case study from defence resource planning and asset management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    11. Kangsoo Kim & Jinseog Kim & Hyejin Cho & Donghyung Yook, 2024. "Determining contract conditions in a PPP project among deep uncertainty in future outturn travel demand," Transportation, Springer, vol. 51(3), pages 937-961, June.
    12. Kai Kornhuber & Corey Lesk & Carl F. Schleussner & Jonas Jägermeyr & Peter Pfleiderer & Radley M. Horton, 2023. "Risks of synchronized low yields are underestimated in climate and crop model projections," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Theodore G. Shepherd, 2021. "Bringing physical reasoning into statistical practice in climate-change science," Climatic Change, Springer, vol. 169(1), pages 1-19, November.

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