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When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume

Author

Listed:
  • Francis X. Diebold

    (University of Pennsylvania)

  • Glenn D. Rudebusch

    (Federal Reserve Bank of San Francisco)

  • Maximilian Gobel

    (University of Lisbon)

  • Philippe Goulet Coulombe

    (University of Quebec at Montreal)

  • Boyuan Zhang

    (University of Pennsylvania)

Abstract

Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating sea ice to cumulative carbon dioxide emissions and models relating sea ice directly to time. The resulting “carbon-trend” and “time-trend” projections are mutually consistent and predict an e?ectively ice-free summer Arctic Ocean by the mid-2030s with an 80% probability. Moreover, the carbon-trend projections show that global adoption of a lower emissions path would likely delay the arrival of a seasonally ice-free Arctic by only a few years.

Suggested Citation

  • Francis X. Diebold & Glenn D. Rudebusch & Maximilian Gobel & Philippe Goulet Coulombe & Boyuan Zhang, 2022. "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," PIER Working Paper Archive 22-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:22-011
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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. & Rudebusch, Glenn D., 2023. "Climate models underestimate the sensitivity of Arctic sea ice to carbon emissions," Energy Economics, Elsevier, vol. 126(C).
    3. A. Vaks & A. J. Mason & S. F. M. Breitenbach & A. M. Kononov & A. V. Osinzev & M. Rosensaft & A. Borshevsky & O. S. Gutareva & G. M. Henderson, 2020. "Palaeoclimate evidence of vulnerable permafrost during times of low sea ice," Nature, Nature, vol. 577(7789), pages 221-225, January.
    4. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    5. Sarah W. Cooley & Jonathan C. Ryan & Laurence C. Smith & Chris Horvat & Brodie Pearson & Brigt Dale & Amanda H. Lynch, 2020. "Coldest Canadian Arctic communities face greatest reductions in shorefast sea ice," Nature Climate Change, Nature, vol. 10(6), pages 533-538, June.
    6. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    7. William A. Brock & J. Isaac Miller, 2023. "Polar Amplification in a Moist Energy Balance Model: A Structural Econometric Approach to Estimation and Testing," Working Papers 2304, Department of Economics, University of Missouri.
    8. 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.
    9. 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. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.

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    More about this item

    Keywords

    Climate change; climate prediction; climate forecasting; carbon emissions; cryosphere;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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