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Modeling the Impact of CO₂ on Arctic and Antarctic Sea-Ice Volume: A Dynamic Nonlinear Approach

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  • Escribano, Álvaro
  • Rodríguez, Juan Andrés

Abstract

The year 2024 marked a critical milestone in global warming, with average global temperatures exceeding pre-industrial levels by 1.55°C— the highest on human historyrecords. Polar ice loss, largely attributed to anthropogenic CO₂ emissions has profound social, economic and financial implications that demand rigorous analysis. This study assesses the impact of atmospheric CO₂ on Arctic and Antarctic sea-ice volume usingnonlinear dynamic econometric models. We extend prior sea-ice forecasting models to allow for regime-switching specifications—Threshold Autoregressive (TAR) and Smooth Transition Regressions (STR) models—to capture the complex, nonlinear, and state-dependent responses of the sea-ice to CO₂ concentration changes. Our main contribution is to provide a flexible, reduced-form alternative to general circulation models (GCMs) for evaluating long-run climate scenarios under various emissions trajectories, including IPCC’s Shared Socioeconomic Pathways (SSPs). Results suggest Arctic sea-ice could disappear by 2060 [2045–2078] under a business-as-usual scenario, while Antarctic loss may extend beyond 2100 [2071–2300]. Importantly, models accounting for threshold effects reveal critical recovery tipping points that simpler linear climate models may overlook. Under an intermediate emissions path like SSP2-4.5, a fast recovery of sea-ice volume remains possible if regime shifts are driven by changes in CO₂ growth rates, with estimated tipping points for reversal occurring around 2033 for the Arctic and 2037 for the Antarctic. In contrast, the outlook is less favorable if regime dynamics are determined by CO₂ concentration levels: no recovery is projected for the Arctic, and the Antarctic recovery tipping point is delayed until 2069.

Suggested Citation

  • Escribano, Álvaro & Rodríguez, Juan Andrés, 2025. "Modeling the Impact of CO₂ on Arctic and Antarctic Sea-Ice Volume: A Dynamic Nonlinear Approach," UC3M Working papers. Economics 47734, Universidad Carlos III de Madrid. Departamento de Economía.
  • Handle: RePEc:cte:werepe:47734
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    References listed on IDEAS

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    1. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    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. Bjørnar Karlsen Kivedal, 2023. "Long run non-linearity in CO2 emissions: the I(2) cointegration model and the environmental Kuznets curve," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(4), pages 899-931, November.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Peter Ditlevsen & Susanne Ditlevsen, 2023. "Warning of a forthcoming collapse of the Atlantic meridional overturning circulation," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Vogelsang, Timothy J & Perron, Pierre, 1998. "Additional Tests for a Unit Root Allowing for a Break in the Trend Function at an Unknown Time," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1073-1100, November.
    7. Fei Ji & Zhaohua Wu & Jianping Huang & Eric P. Chassignet, 2014. "Evolution of land surface air temperature trend," Nature Climate Change, Nature, vol. 4(6), pages 462-466, June.
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    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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