Estimating Earth's Temperature Response with Transformed and Augmented OLS

Abstract

The long-term relationship between radiative forcing and surface temperature is central to predicting the impacts of climate change. This study employs multicointegration to characterize this relationship and provides the first application of the Transformed and Augmented Ordinary Least Squares (TAOLS) estimator to estimate the multicointegration model. The main objective is to estimate the Equilibrium Climate Sensitivity (ECS), defined as the global mean surface temperature increase following a doubling of atmospheric carbon dioxide. Diagnostic tests reveal that radiative forcing innovations are strongly non-Gaussian, providing a key motivation for applying semiparametric TAOLS rather than the parametric Gaussian maximum likelihood method. TAOLS is also robust to misspecification of the short-run dynamics. Using the three data pairings of Bruns et al. (2020), we obtain TAOLS estimates of ECS ranging from $1.81^{\circ}$C to $2.49^{\circ}$C, consistently below their main maximum likelihood estimate of $2.80^{\circ}$C. Because the model we use is linear and is based on global-mean data, it cannot capture state-dependent feedbacks or regional differences; extending TAOLS along either dimension is a natural next step.