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The role of data and priors in estimating climate sensitivity

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  • Ikefuji, Masako
  • Magnus, Jan R.
  • Vasnev, Andrey L.

Abstract

In Bayesian theory, the data together with the prior produce a posterior. We show that it is also possible to follow the opposite route, that is, to use data and posterior information (both of which are observable) to reveal the prior (which is not observable). We then apply the theory to equilibrium climate sensitivity as reported by the Intergovernmental Panel on Climate Change in an attempt to get some insight into the prior beliefs of the IPCC scientists. It appears that the data contain much less information than one might think, due to the presence of correlation. We conclude that the prior in the fifth IPCC report was too low, and in the sixth report too high.

Suggested Citation

  • Ikefuji, Masako & Magnus, Jan R. & Vasnev, Andrey L., 2023. "The role of data and priors in estimating climate sensitivity," Working Papers BAWP-2023-02, University of Sydney Business School, Discipline of Business Analytics.
    • Handle: RePEc:syb:wpbsba:2123/31835
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    References listed on IDEAS

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    1. Magnus, Jan R. & Vasnev, Andrey L., 2023. "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1895-1908.
    2. In Hwang & Frédéric Reynès & Richard Tol, 2013. "Climate Policy Under Fat-Tailed Risk: An Application of Dice," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(3), pages 415-436, November.
    3. Magne Aldrin & Marit Holden & Peter Guttorp & Ragnhild Bieltvedt Skeie & Gunnar Myhre & Terje Koren Berntsen, 2012. "Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content," Environmetrics, John Wiley & Sons, Ltd., vol. 23(3), pages 253-271, May.
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    More about this item

    Keywords

    Revealed prior; climate sensitivity; data uncertainty; combining information; correlation; IPCC;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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