Estimating Earth's Temperature Response with Transformed and Augmented OLS

TL;DR

This paper introduces a novel statistical method, TAOLS, to estimate Earth's climate sensitivity, providing more robust results that suggest a lower temperature increase than previous estimates, aiding climate change predictions.

Contribution

The study applies multicointegration and introduces TAOLS to improve estimation of Earth's temperature response, offering a new, accessible approach for climate sensitivity analysis.

Findings

Estimated ECS between 2.12°C and 2.49°C

Lower than previous maximum likelihood estimates of 2.8°C

TAOLS enhances robustness and accessibility in climate modeling

Abstract

The long-term relationship between radiative forcing and surface temperature is imperative for predicting the impacts of climate change. This study employs multicointegration to characterize this relationship and uses Transformed and Augmented Ordinary Least Squares (TAOLS) to estimate the model. The main goal is to estimate the Equilibrium Climate Sensitivity (ECS), defined as the global mean surface air temperature increase following a doubling of atmospheric carbon dioxide. Our results show that the ECS lies between $2.12^{\circ}$C and $2.49^{\circ}$C, which is lower than the existing maximum likelihood estimate of $2.8^{\circ}$C. TAOLS offers a more robust and accessible tool for climate research, providing novel insights for ongoing debates about Earth's warming trajectory.