Econometric Measurement 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 and fundamental question in climate science was recently analyzed by dynamic panel data methods using extensive spatiotemporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). These 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 asymptotic theory justifying the use of these methods when there are stochastic process trends in both the global forcing variables, such as GHGs, and station-level trend effects from such sources as local aerosol pollutants. These asymptotics validate con dence interval construction for econometric measures of Earth’s transient climate sensitivity. The methods are applied to observational data and to data generated from three leading global climate models (GCMs) that are sampled spatio-temporally in the same way as the empirical observations. The fi ndings indicate that estimates of transient climate sensitivity produced by these GCMs lie within empirically determined con dence limits but that the GCMs uniformly underestimate the effects of aerosol induced dimming. The analysis shows the potential of econometric methods to calibrate GCM performance against observational data and to reveal the respective sensitivity parameters (GHG and non-GHG related) governing GCM temperature trends.
|Date of creation:||Feb 2017|
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- Peter C. B. Phillips & Hyungsik R. Moon, 1999.
"Linear Regression Limit Theory for Nonstationary Panel Data,"
Econometric Society, vol. 67(5), pages 1057-1112, September.
- Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
- Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," Review of Economic Studies, Oxford University Press, vol. 57(1), pages 99-125.
- Magnus, Jan R. & Melenberg, Bertrand & Muris, Chris, 2011.
"Global Warming and Local Dimming: The Statistical Evidence,"
Journal of the American Statistical Association,
American Statistical Association, vol. 106(494), pages 452-464.
- Magnus, J.R. & Melenberg, B. & Muris, C.H.M., 2011. "Global Warming and Local Dimming : The Statistical Evidence," Discussion Paper 2011-004, Tilburg University, Center for Economic Research.
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