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Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections

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  • Francis X. Diebold
  • Glenn D. Rudebusch

Abstract

The downward trend in the amount of Arctic sea ice has a wide range of environmental and economic consequences including important effects on the pace and intensity of global climate change. Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century. The best fitting statistical model indicates that overall sea ice coverage is declining at an increasing rate. By contrast, average projections from the CMIP5 global climate models foresee a gradual slowing of Arctic sea ice loss even in scenarios with high carbon emissions. Our long-range statistical projections also deliver probability assessments of the timing of an ice-free Arctic. These results indicate almost a 60 percent chance of an effectively ice-free Arctic Ocean sometime during the 2030s - much earlier than the average projection from global climate models.

Suggested Citation

  • Francis X. Diebold & Glenn D. Rudebusch, 2020. "Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections," Working Paper Series 2020-02, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:87377
    DOI: 10.24148/wp2020-02
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    Cited by:

    1. 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.
    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. 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).
    4. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    5. Diebold, Francis X. & Göbel, Maximilian, 2022. "A benchmark model for fixed-target Arctic sea ice forecasting," Economics Letters, Elsevier, vol. 215(C).
    6. Francis X. Diebold & Maximilian Goebel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Papers 2206.10721, arXiv.org, revised Jun 2023.
    7. 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).
    8. Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe, 2023. "Assessing and comparing fixed-target forecasts of Arctic sea ice: Glide charts for feature-engineered linear regression and machine learning models," Energy Economics, Elsevier, vol. 124(C).
    9. Marc Gronwald, 2023. "Explosive Temperatures," CESifo Working Paper Series 10680, CESifo.
    10. 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).
    11. Vasco J.Gabriel & Luis F. Martins & Anthoulla Phella, 2021. "Modelling Low-Frequency Covariability of Paleoclimatic Data," Working Papers 2022_17, Business School - Economics, University of Glasgow.
    12. Jennifer Castle & David Hendry, 2020. "Identifying the Causal Role of CO2 during the Ice Ages," Economics Series Working Papers 898, University of Oxford, Department of Economics.
    13. Philippe Goulet Coulombe & Maximilian Gobel, 2020. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Papers 2005.02535, arXiv.org, revised Mar 2021.
    14. Philippe Goulet Coulombe & Maximilian Gobel, 2021. "Arctic Amplification of Anthropogenic Forcing: A Vector Autoregressive Analysis," Working Papers 21-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    15. Francis X. Diebold & Maximilian Gobel & Philippe Goulet Coulombe, 2022. "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Working Papers 22-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.

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    More about this item

    Keywords

    Sea ice extent; climate models; climate change; climate trends; climate prediction; cryospheric science;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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