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An evaluation of decadal probability forecasts from state-of-the-art climate models

Author

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  • Suckling, Emma B.
  • Smith, Leonard A.

Abstract

While state-of-the-art models of Earth's climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a "dynamic climatology" empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today's state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model-model intercomparisons.

Suggested Citation

  • Suckling, Emma B. & Smith, Leonard A., 2013. "An evaluation of decadal probability forecasts from state-of-the-art climate models," LSE Research Online Documents on Economics 55142, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:55142
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    File URL: http://eprints.lse.ac.uk/55142/
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    Citations

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    Cited by:

    1. Leonard Smith & Emma Suckling & Erica Thompson & Trevor Maynard & Hailiang Du, 2015. "Towards improving the framework for probabilistic forecast evaluation," Climatic Change, Springer, vol. 132(1), pages 31-45, September.
    2. Joel Katzav & Wendy Parker, 2015. "The future of climate modeling," Climatic Change, Springer, vol. 132(4), pages 475-487, October.
    3. Bett, Philip E & Thornton, Hazel E. & Troccoli, Alberto & De Felice, Matteo & Suckling, Emma & Dubus, Laurent & Saint-Drenan, Yves-Marie & Brayshaw, David J., 2019. "A simplified seasonal forecasting strategy, applied to wind and solar power in Europe," Earth Arxiv kzwqx, Center for Open Science.
    4. Graziani, Carlo & Rosner, Robert & Adams, Jennifer M. & Machete, Reason L., 2021. "Probabilistic recalibration of forecasts," International Journal of Forecasting, Elsevier, vol. 37(1), pages 1-27.
    5. Beven, Keith, 2015. "What we see now: Event-persistence and the predictability of hydro-eco-geomorphological systems," Ecological Modelling, Elsevier, vol. 298(C), pages 4-15.

    More about this item

    Keywords

    ensembles; forecast verification/skill; hindcasts; probability forecasts/models/distribution; statistical forecasting; time series;
    All these keywords.

    JEL classification:

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

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