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Local Climate Sensitivity: A Statistical Approach for a Spatially Heterogeneous Planet

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Abstract

Climate sensitivity relates total radiative forcing from anthropogenic and other sources to global mean temperature, and it depends on both changes in net heat transports and changes in the spatial distribution of temperature anomalies. An energy balance model, an easily implemented statistical methodology, and a supplementary inferential procedure are proposed to estimate local climate sensitivity using the historical record and to assess the contribution to overall climate sensitivity. Results are roughly comparable with extant findings from simulations using more complicated models. In particular, areas over ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than those over land. Increases in the variation of predicted local temperature anomalies are estimated to be proportional to increases in forcings, and economic implications are discussed.

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  • J. Isaac Miller, 2017. "Local Climate Sensitivity: A Statistical Approach for a Spatially Heterogeneous Planet," Working Papers 1702, Department of Economics, University of Missouri.
  • Handle: RePEc:umc:wpaper:1702
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    File URL: https://economics.missouri.edu/sites/default/files/wp-files/locclimsens1.pdf
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    References listed on IDEAS

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    1. Yoosoon Chang & Robert K. Kaufmann & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2015. "Time Series Analysis of Global Temperature Distributions: Identifying and Estimating Persistent Features in Temperature Anomalies," Working Papers 1513, Department of Economics, University of Missouri, revised 25 Jul 2016.
    2. Robert Kaufmann & Heikki Kauppi & Michael Mann & James Stock, 2013. "Does temperature contain a stochastic trend: linking statistical results to physical mechanisms," Climatic Change, Springer, vol. 118(3), pages 729-743, June.
    3. repec:oxf:wpaper:750 is not listed on IDEAS
    4. Francisco Estrada & Pierre Perron, "undated". "Detection and attribution of climate change through econometric methods," Boston University - Department of Economics - Working Papers Series 2013-015, Boston University - Department of Economics.
    5. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(05), pages 664-703, October.
    6. Park, Joon Y. & Shin, Kwanho & Whang, Yoon-Jae, 2010. "A semiparametric cointegrating regression: Investigating the effects of age distributions on consumption and saving," Journal of Econometrics, Elsevier, vol. 157(1), pages 165-178, July.
    7. Francisco Estrada & Pierre Perron & Benjamin Martinez-Lopez, 2013. "Statistically-derived contributions of diverse human influences to 20th century temperature changes," Boston University - Department of Economics - Working Papers Series 2013-017, Boston University - Department of Economics.
    8. Brock, William A. & Engström, Gustav & Grass, Dieter & Xepapadeas, Anastasios, 2013. "Energy balance climate models and general equilibrium optimal mitigation policies," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2371-2396.
    9. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-247, February.
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    Keywords

    local climate sensitivity; energy balance model; historical temperature anomaly distributions; partially linear semiparametric model;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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