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The near-term risk of climate uncertainty among the U.S. states

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Abstract

This article describes a study employing a risk-assessment methodology for evaluating uncertain future climatic conditions. To understand the implications of uncertainty on risk and to provide a near-term rationale for policy interventions, the study estimated the impacts from responses to climate change on U.S. state- and national-level economic activity. The study used results of the climate-model CMIP3 dataset developed for the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment Report to 1) estimate a proxy for representing climate uncertainty over the next 40 years, 2) map the simulated weather from the climate models hydrologically to the county level to determine the physical consequences on economic activity at the state level, and 3) perform a detailed, economy-wide, 70-industry analysis of economic impacts among the interdependent lower-48 states for the years 2010 through 2050. The analysis determined the interacting industry-level effects, employment impacts at the state level, interstate population migration, consequences to personal income, and ramifications for the U.S. trade balance. When compared to a baseline economic forecast, the calculations produced an average risk of damage of $1 trillion to the U.S. economy from climate change over the next 40 years, with losses in employment equivalent to nearly 7 million full-time jobs. Added uncertainty would increase the estimated risk.

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Acknowledgments

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. This study was performed under the Laboratory-Directed Research and Development (LDRD) program at Sandia National Laboratories (Project 138735), and we gratefully thank the Sandia LDRD program for its financial support of this study. We acknowledge the additional expert contributions of the following colleagues at Sandia: Tim Trucano, Vince Tidwell, Mark Ehlen, Geoffrey Klise, Verne Loose, Len Malczynski, Rhonda Reinert, Kevin Stamber, Vanessa Vargas, and Aldo Zagonel, David Robinson, Arnie Baker, Brian Adams, Elizabeth Richards, John Siirola, Mark Boslough, Mark Taylor, Ray Finely, Lillian Snyder, Dan Horschel, Andjelka Kelic, Jesse Roach, Marissa Reno, William Stubblefield, Laura Swiler, Laura Cutler, Anna Weddington, William Fogelman, Jim Strickland, John Mitchiner, Howard Hirano, and James Peery. Contributions by Dr. James P. Smith with the Computer and Computational Sciences Group at Los Alamos National Laboratory (LANL), by Dr. David Higdon with the Statistical Sciences Group at LANL, and by Dr. Joe Galewsky from the Department of Earth & Planetary Sciences at the University of New Mexico are also greatly appreciated. In addition, we are grateful to the following study-reviewers for their insightful comments and suggestions: Dr. Terry Barker, Director, Cambridge Centre for Climate Change Mitigation Research, Department of Land Economy, Cambridge University; Dr. Chris Hope, Energy and Environment Research Group, Judge Business School, Cambridge University; Dr. Robert Harriss, President and CEO of the Houston Advanced Research Center and former Director of the Institute for the Study of Society and the Environment at the National Center for Atmospheric Research; Dr. Michael Mastrandrea in the Center for Environmental Science and Policy at Stanford University; Dr. Elizabeth Stanton at the Global Development and Environment Institute at Tufts University and the Stockholm Environment Institute; and Dr. Jonathan Overpeck, Co-Director in the Institute of the Environment and professor in the Geosciences and Atmospheric Sciences departments at the University of Arizona. Lastly, we thank the anonymous publication-reviewers for their thoughtful comments and suggestions to improve the content of this article.

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Authors and Affiliations

  1. Discrete Mathematics and Complex Systems Department, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM, 87185, USA

    George A. Backus

  2. Earth Systems Department, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM, 87185, USA

    Thomas S. Lowry

  3. Strategic Studies Department, Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM, 87185, USA

    Drake E. Warren

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  1. George A. Backus
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Correspondence to George A. Backus.

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Backus, G.A., Lowry, T.S. & Warren, D.E. The near-term risk of climate uncertainty among the U.S. states. Climatic Change 116, 495–522 (2013). https://doi.org/10.1007/s10584-012-0511-8

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