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Econometric Measurement of Earth's Transient Climate Sensitivity

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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.

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  • Thomas Leirvik & Peter C.B. Phillips & Trude Storelvmo, 2017. "Econometric Measurement of Earth's Transient Climate Sensitivity," Cowles Foundation Discussion Papers 2083, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2083
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    1. Phillips, Peter C B, 1995. "Fully Modified Least Squares and Vector Autoregression," Econometrica, Econometric Society, vol. 63(5), pages 1023-1078, September.
    2. Peter C. B. Phillips & Mico Loretan, 1991. "Estimating Long-run Economic Equilibria," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 407-436.
    3. Stock, James H & Watson, Mark W, 1993. "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, Econometric Society, vol. 61(4), pages 783-820, July.
    4. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(1), pages 95-131, April.
    5. Ibragimov, Rustam & Phillips, Peter C.B., 2008. "Regression Asymptotics Using Martingale Convergence Methods," Econometric Theory, Cambridge University Press, vol. 24(4), pages 888-947, August.
    6. 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.
    7. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    8. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
    9. Meinrat O. Andreae & Chris D. Jones & Peter M. Cox, 2005. "Strong present-day aerosol cooling implies a hot future," Nature, Nature, vol. 435(7046), pages 1187-1190, June.
    10. Park, Joon Y. & Phillips, Peter C.B., 1988. "Statistical Inference in Regressions with Integrated Processes: Part 1," Econometric Theory, Cambridge University Press, vol. 4(3), pages 468-497, December.
    11. Saikkonen, Pentti, 1991. "Asymptotically Efficient Estimation of Cointegration Regressions," Econometric Theory, Cambridge University Press, vol. 7(1), pages 1-21, March.
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    Cited by:

    1. Kahn, Matthew E. & Mohaddes, Kamiar & Ng, Ryan N.C. & Pesaran, M. Hashem & Raissi, Mehdi & Yang, Jui-Chung, 2021. "Long-term macroeconomic effects of climate change: A cross-country analysis," Energy Economics, Elsevier, vol. 104(C).

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    More about this item

    Keywords

    Climate sensitivity; Cointegration; Common stochastic trend; Idiosyncratic trend; Spatio-temporal model; Unit root;
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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