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February 16, 2026Oxford Bulletin of Economics and Statistics3 citationsOpen Access

Forecasting Climate Change Using a Multivariate Cointegrated System

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JCJennifer CastleJDJurgen A. DoornikDHD Hendry

Key Points

  • The aim is to forecast climate change and understand its relationships through a statistical model.
  • Developed a cointegrated vector equilibrium correction model for climate variables.
  • Conditioned on natural radiative forcings and El Niño–Southern Oscillation.
  • Estimated saturation to handle breaks in trends over 150 years.
  • Predicted equilibrium climate sensitivity of 2.6°C.
  • Forecasted sea-level changes with uncertainties into 2100.
  • Model reflects slow deep ocean adjustments to greenhouse gas concentrations.

Abstract

ABSTRACT A cointegrated vector equilibrium correction model of key climate variables including sea surface temperature, ocean heat content, Arctic sea‐ice extent and sea‐level change is built, driven by radiative forcing in which a stochastic trend arises due to anthropogenic emissions of greenhouse gases. A valid and congruent statistical model requires saturation estimation to model breaks in trends, while also conditioning on natural radiative forcings and El Niño–Southern Oscillation. The model is stable over 150 years, reflecting the slow adjustment of the deep oceans to increased greenhouse gas concentrations, and predicts an equilibrium climate sensitivity of 2.6°C. Projections out to 2100 highlight the many uncertainties over the coming decades.

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Cite This Study

Castle et al. (2026) studied this question.

synapsesocial.com/papers/699264d1eb1f82dc367a0a6chttps://doi.org/10.1111/obes.70047
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