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Long time-scale potential predictability in an ensemble of coupled climate models

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Abstract

A “diagnostic multi-model ensemble potential predictability study” of surface air temperature is performed using data from nine models participating in the Coupled Model Intercomparison Project (CMIP1). The data are considered to be a sample of results from the population of models “embodying current abilities to simulate the climate system” and represent a range of numerics, resolution and of physical parametrizations. The potential predictability of pentadal, decadal, and 25-year means is analyzed. The multi-model ensemble provides a statistically stable estimate of the potential predictability variance fraction (ppvf) with a narrow confidence interval. This is not the case for individual models with modest lengths of simulation data nor, by implication, for the instrument-based observational record. Potential predictability is found predominately over the high-latitude oceans. There is evidence also for potential predictability at tropical latitudes in the Pacific and Atlantic, but not the Indian oceans, on the shorter of the time scales. The potential predictability variance fraction decreases with increasing time scale but appreciable values exist at all of the time scales considered, especially for the Southern Ocean and for the North Atlantic. Values over land, while statistically non-zero, are small. The autocorrelation structure of the data is investigated to account for its effect on the statistical estimation of the ppvf and to indicate the extent to which the data reflect simple oceanic damping of white noise atmospheric forcing. Ensemble autocorrelation structures differ between tropical and extra-tropical latitudes (at least on the time scales considered) with more oscillatory behaviour implied in tropical regions compared to high latitudes. It appears that the results are inconsistent with simple ocean damping and that higher order autocorrelation structures of temperature cannot be neglected generally or in the determination of the potential predictability. The statistical results suggest that predictability in the extratropics is associated with long ocean time scales while in the tropics it is associated with the coupled atmosphere-ocean system. Physically based analyses are required to understand this long time scale behaviour and an “ensemble” view is also needed in order to determine the behaviour that is robust across models and the real system.

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Acknowledgements

Thanks to Steve Lambert for downloading the CMIP1 data and casting it into our standard format, to Greg Flato for comments, to Francis Zwiers for statistical advice, and to Slava Kaharin for both advice and comments.

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Authors and Affiliations

  1. Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, University of Victoria, PO Box 1700, Victoria, B.C. V8W 2Y2, Canada

    G. J. Boer

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Boer, G.J. Long time-scale potential predictability in an ensemble of coupled climate models. Climate Dynamics 23, 29–44 (2004). https://doi.org/10.1007/s00382-004-0419-8

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  • DOI: https://doi.org/10.1007/s00382-004-0419-8

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