Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2019

Abstract

How sensitive is Earth's climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing question in climate science was recently analyzed by dynamic panel data methods using extensive spatio-temporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). Those methods revealed that atmospheric aerosol effects masked approximately one-third of the continental warming due to increasing GHG concentrations over this period, thereby implying greater climate sensitivity to GHGs than previously thought. The present study provides regularity conditions and asymptotic theory justifying the use of time series cointegration-based methods of estimation when there are both stochastic process and deterministic trends in the global forcing variables, such as GHGs, and station-level trend effects from such sources as local aerosol pollutants. The asymptotics validate estimation and confidence interval construction for econometric measures of Earth's transient climate sensitivity (TCS). The methods are applied to observational data and to data generated from several groups of global climate models (GCMs) that are sampled spatio-temporally and aggregated in the same way as the empirical observations for the time period 1964–2005. The findings indicate that 7 out of 9 of the GCM reported TCS values lie within the 95% empirical confidence interval computed econometrically from the GCM output. The analysis shows the potential of econometric methods to provide empirical estimates and confidence limits for TCS, to calibrate GCM simulation output against observational data in terms of the implied TCS estimates obtained via the econometric model, and to reveal the respective sensitivity parameters (GHG and non-GHG related) governing GCM temperature trends.

Keywords

Climate sensitivity, Cointegration, Common stochastic trend, Idiosyncratic trend, Spatio-temporal model, Unit root

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

214

Issue

1

First Page

6

Last Page

32

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2019.05.002

Publisher

Elsevier: 24 months

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1016/j.jeconom.2019.05.002

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