• Open Access

Reliability of decadal predictions

Authors

  • S. Corti,

    Corresponding author
    1. European Centre for Medium-Range Weather Forecasts, Reading, UK
    2. Istituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, Bologna, Italy
    • Corresponding author: S. Corti, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK. (susanna.corti@ecmwf.int)

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  • A. Weisheimer,

    1. European Centre for Medium-Range Weather Forecasts, Reading, UK
    2. National Centre for Atmospheric Science, Department of Physics, Atmospheric, Oceanic and Planetary Physics, Oxford University, Oxford, UK
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  • T. N. Palmer,

    1. European Centre for Medium-Range Weather Forecasts, Reading, UK
    2. National Centre for Atmospheric Science, Department of Physics, Atmospheric, Oceanic and Planetary Physics, Oxford University, Oxford, UK
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  • F. J. Doblas-Reyes,

    1. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
    2. Institut Català de Ciències del Clima, Barcelona, Spain
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  • L. Magnusson

    1. European Centre for Medium-Range Weather Forecasts, Reading, UK
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Abstract

[1] The reliability of multi-year predictions of climate is assessed using probabilistic Attributes Diagrams for near-surface air temperature and sea surface temperature, based on 54 member ensembles of initialised decadal hindcasts using the ECMWF coupled model. It is shown that the reliability from the ensemble system is good over global land areas, Europe and Africa and for the North Atlantic, Indian Ocean and, to a lesser extent, North Pacific basins for lead times up to 6–9 years. North Atlantic SSTs are reliably predicted even when the climate trend is removed, consistent with the known predictability for this region. By contrast, reliability in the Indian Ocean, where external forcing accounts for most of the variability, deteriorates severely after detrending. More conventional measures of forecast quality, such as the anomaly correlation coefficient (ACC) of the ensemble mean, are also considered, showing that the ensemble has significant skill in predicting multi-annual temperature averages.

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