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Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions

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  • Francis X. Diebold

    (University of Pennsylvania)

  • Glenn D. Rudebusch

    (Brookings Institution)

Abstract

Arctic sea ice has steadily diminished as atmospheric greenhouse gas concentrations have increased. Using observed data from 1979 to 2019, we estimate a close contemporaneous linear relationship between Arctic sea ice area and cumulative carbon dioxide emissions. For comparison, we provide analogous regression estimates using simulated data from global climate models (drawn from the CMIP5 and CMIP6 model comparison exercises). The carbon sensitivity of Arctic sea ice area is considerably stronger in the observed data than in the climate models. Thus, for a given future emissions path, an ice-free Arctic is likely to occur much earlier than the climate models project. Furthermore, little progress has been made in recent global climate modeling (from CMIP5 to CMIP6) to more accurately match the observed carbon-climate response of Arctic sea ice.

Suggested Citation

  • Francis X. Diebold & Glenn D. Rudebusch, 2023. "Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions," PIER Working Paper Archive 24-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:24-010
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Rudebusch, Glenn D., 2022. "Probability assessments of an ice-free Arctic: Comparing statistical and climate model projections," Journal of Econometrics, Elsevier, vol. 231(2), pages 520-534.
    2. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe & Rudebusch, Glenn D. & Zhang, Boyuan, 2021. "Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1509-1519.
    3. H. Damon Matthews & Nathan P. Gillett & Peter A. Stott & Kirsten Zickfeld, 2009. "The proportionality of global warming to cumulative carbon emissions," Nature, Nature, vol. 459(7248), pages 829-832, June.
    4. Maria-Vittoria Guarino & Louise C. Sime & David Schröeder & Irene Malmierca-Vallet & Erica Rosenblum & Mark Ringer & Jeff Ridley & Danny Feltham & Cecilia Bitz & Eric J. Steig & Eric Wolff & Julienne , 2020. "Sea-ice-free Arctic during the Last Interglacial supports fast future loss," Nature Climate Change, Nature, vol. 10(10), pages 928-932, October.
    5. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    6. Kiefer, Nicholas M. & Salmon, Mark, 1983. "Testing normality in econometric models," Economics Letters, Elsevier, vol. 11(1-2), pages 123-127.
    7. Julienne Stroeve & Mark Serreze & Marika Holland & Jennifer Kay & James Malanik & Andrew Barrett, 2012. "The Arctic’s rapidly shrinking sea ice cover: a research synthesis," Climatic Change, Springer, vol. 110(3), pages 1005-1027, February.
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    Cited by:

    1. Blazsek, Szabolcs Istvan & Kristof, Erzsebet & Escribano, Álvaro, 2024. "Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models," UC3M Working papers. Economics 39546, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2023. "When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, vol. 236(2).

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    More about this item

    Keywords

    Arctic sea ice area; climate change; climate prediction;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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