IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/55142.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/55142/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Joel Katzav & Wendy Parker, 2015. "The future of climate modeling," Climatic Change, Springer, vol. 132(4), pages 475-487, October.
    4. 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.
    5. 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.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:55142. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.