IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/30732.html
   My bibliography  Save this paper

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

Author

Listed:
  • Francis X. Diebold
  • Glenn D. Rudebusch
  • Maximilian Göbel
  • Philippe Goulet Coulombe
  • Boyuan Zhang

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 atmospheric carbon dioxide concentration and models relating sea ice directly to time. The resulting "carbon-trend" and "time-trend" projections are mutually consistent and predict a nearly 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 carbon 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 Göbel & Philippe Goulet Coulombe & Boyuan Zhang, 2022. "When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume," NBER Working Papers 30732, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30732
    Note: EEE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w30732.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Blazsek, Szabolcs & Escribano, Alvaro & Kristof, Erzsebet, 2024. "Global, Arctic, and Antarctic sea ice volume predictions using score-driven threshold climate models," Energy Economics, Elsevier, vol. 134(C).
    3. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diebold, Francis X. & Rudebusch, Glenn D. & Göbel, Maximilian & Goulet Coulombe, Philippe & Zhang, Boyuan, 2024. "Reprint of: When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume," Journal of Econometrics, Elsevier, vol. 239(1).
    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. Banerjee, Anindya & Lumsdaine, Robin L & Stock, James H, 1992. "Recursive and Sequential Tests of the Unit-Root and Trend-Break Hypotheses: Theory and International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 271-287, July.
    4. Caporale, Guglielmo Maria & Hassapis, Christis & Pittis, Nikitas, 1998. "Unit roots and long-run causality: investigating the relationship between output, money and interest rates," Economic Modelling, Elsevier, vol. 15(1), pages 91-112, January.
    5. Peter C. B. Phillips & Zhijie Xiao, 1998. "A Primer on Unit Root Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 423-470, December.
    6. Bernard Salanié, 1999. "Guide pratique des séries non-stationnaires," Économie et Prévision, Programme National Persée, vol. 137(1), pages 119-141.
    7. Geweke, John, 1994. "Priors for Macroeconomic Time Series and Their Application," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 609-632, August.
    8. 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.
    9. Gahn, Santiago José, 2021. "On the adjustment of capacity utilisation to aggregate demand: Revisiting an old Sraffian critique to the Neo-Kaleckian model," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 325-360.
    10. Entorf, Horst, 1997. "Random walks with drifts: Nonsense regression and spurious fixed-effect estimation," Journal of Econometrics, Elsevier, vol. 80(2), pages 287-296, October.
    11. Quah, Danny, 1992. "The Relative Importance of Permanent and Transitory Components: Identification and Some Theoretical Bounds," Econometrica, Econometric Society, vol. 60(1), pages 107-118, January.
    12. Kahn, James A. & Rich, Robert W., 2007. "Tracking the new economy: Using growth theory to detect changes in trend productivity," Journal of Monetary Economics, Elsevier, vol. 54(6), pages 1670-1701, September.
    13. Bardsen, G. & Klovland, J.T., 1990. "Finding The Rigth Nominal Anchor: The Cointegration Of Money, Credit And Nominal Income In Norway," The Warwick Economics Research Paper Series (TWERPS) 350, University of Warwick, Department of Economics.
    14. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    15. Eryilmaz, Unal, 2021. "Enflasyonist Koşullarda Türkiye Ekonomisine İlişkin Bir Para Arzı Tahmini [Money Supply Forecast for the Turkish Economy in Inflationary Conditions]," MPRA Paper 111685, University Library of Munich, Germany.
    16. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2010. "Unit Roots and Structural Change: An Application to US House-Price Indices," Working papers 2010-04, University of Connecticut, Department of Economics, revised Dec 2010.
    17. M. Hakan Berument & Zulal Denaux & Yeliz Yalcin, 2012. "Turkish monetary policy and components of aggregate demand: a VAR analysis with sign restrictions model," Applied Economics, Taylor & Francis Journals, vol. 44(36), pages 4787-4798, December.
    18. Min, Chung-ki, 1998. "A Gibbs sampling approach to estimation and prediction of time-varying-parameter models," Computational Statistics & Data Analysis, Elsevier, vol. 27(2), pages 171-194, April.
    19. Tsangyao Chang & Tsung-Pao Wu & Rangan Gupta, 2015. "Are house prices in South Africa really nonstationary? Evidence from SPSM-based panel KSS test with a Fourier function," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 32-53, January.
    20. Alejandro Diaz-Bautista, 2004. "Tijuana's Dynamic Unemployment and Output Growth," Labor and Demography 0401001, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:30732. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.