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Bibliographic Details
Main Author: Sun, Justin
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.13766
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author Sun, Justin
author_facet Sun, Justin
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_13766
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Estimating Earth's Temperature Response with Transformed and Augmented OLS
Sun, Justin
Econometrics
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.
title Estimating Earth's Temperature Response with Transformed and Augmented OLS
topic Econometrics
url https://arxiv.org/abs/2603.13766