Econometric estimates of Earth's transient climate sensitivity
Econometric estimates of Earth's transient climate sensitivity
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.
Climate sensitivity, Cointegration, Common stochastic trend, Idiosyncratic trend, Spatio-temporal model, Unit root
6-32
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Leirvik, Thomas
fea55e25-e012-4115-9dc5-5c63e7de84bd
Storelvmo, Trude
967042c3-b43c-4989-9e85-ff1bc2de3aab
January 2020
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Leirvik, Thomas
fea55e25-e012-4115-9dc5-5c63e7de84bd
Storelvmo, Trude
967042c3-b43c-4989-9e85-ff1bc2de3aab
Phillips, Peter C.B., Leirvik, Thomas and Storelvmo, Trude
(2020)
Econometric estimates of Earth's transient climate sensitivity.
Journal of Econometrics, 214 (1), .
(doi:10.1016/j.jeconom.2019.05.002).
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.
Text
TCS_Model_Asymptotics_New_R4
- Accepted Manuscript
More information
Accepted/In Press date: 14 March 2019
e-pub ahead of print date: 6 June 2019
Published date: January 2020
Keywords:
Climate sensitivity, Cointegration, Common stochastic trend, Idiosyncratic trend, Spatio-temporal model, Unit root
Identifiers
Local EPrints ID: 429190
URI: http://eprints.soton.ac.uk/id/eprint/429190
ISSN: 0304-4076
PURE UUID: 25526c3c-a17b-440a-9c80-ae4b6d3f6862
Catalogue record
Date deposited: 22 Mar 2019 17:30
Last modified: 16 Mar 2024 07:40
Export record
Altmetrics
Contributors
Author:
Thomas Leirvik
Author:
Trude Storelvmo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
