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An assessment of the foundational assumptions inhigh-resolution climate projections: the case of UKCP09

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  • Frigg, Roman
  • Smith, Leonard A.
  • Stainforth, David A.

Abstract

The United Kingdom Climate Impacts Programme’s UKCP09 project makes highresolution projections of the climate out to 2100 by post-processing the outputs of a large-scale global climate model. The aim of this paper is to describe and analyse the methodology used and then urge some caution. Given the acknowledged systematic, shared shortcomings in all current climate models, treating model outputs as decision relevant projections can be significantly misleading. In extrapolatory situations, such as projections of future climate change impacts, there is little reason to expect that postprocessing of model outputs can correct for the consequences of such errors. This casts doubt on our ability, today, to make trustworthy, high-resolution probabilistic projections out to the end of this century.

Suggested Citation

  • Frigg, Roman & Smith, Leonard A. & Stainforth, David A., 2015. "An assessment of the foundational assumptions inhigh-resolution climate projections: the case of UKCP09," LSE Research Online Documents on Economics 61635, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:61635
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    File URL: http://eprints.lse.ac.uk/61635/
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    References listed on IDEAS

    as
    1. Leonard Smith & Hailiang Du & Emma Suckling & Falk Nieh�rster, 2014. "Probabilistic skill in ensemble seasonal forecasts," GRI Working Papers 151, Grantham Research Institute on Climate Change and the Environment.
    2. D. A. Stainforth & T. Aina & C. Christensen & M. Collins & N. Faull & D. J. Frame & J. A. Kettleborough & S. Knight & A. Martin & J. M. Murphy & C. Piani & D. Sexton & L. A. Smith & R. A. Spicer & A. , 2005. "Uncertainty in predictions of the climate response to rising levels of greenhouse gases," Nature, Nature, vol. 433(7024), pages 403-406, January.
    3. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    4. Clara Deser & Reto Knutti & Susan Solomon & Adam S. Phillips, 2012. "Communication of the role of natural variability in future North American climate," Nature Climate Change, Nature, vol. 2(11), pages 775-779, November.
    5. Myles R. Allen & David A. Stainforth, 2002. "Towards objective probabalistic climate forecasting," Nature, Nature, vol. 419(6903), pages 228-228, September.
    6. Jun, Mikyoung & Knutti, Reto & Nychka, Douglas W, 2008. "Spatial Analysis to Quantify Numerical Model Bias and Dependence," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 934-947.
    7. James M. Murphy & David M. H. Sexton & David N. Barnett & Gareth S. Jones & Mark J. Webb & Matthew Collins & David A. Stainforth, 2004. "Quantification of modelling uncertainties in a large ensemble of climate change simulations," Nature, Nature, vol. 430(7001), pages 768-772, August.
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    Cited by:

    1. Thompson, Erica L. & Smith, Leonard A., 2019. "Escape from model-land," Economics Discussion Papers 2019-23, Kiel Institute for the World Economy (IfW Kiel).

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    Keywords

    climate change; prediction; projection; simulation; model; probability; reliability; emulation; systematic error; decision-making;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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