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Decadal prediction skill in a multi-model ensemble

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Abstract

Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p ≈ 0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5 years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p ≈ 0.2). Furthermore, uninitialised simulations that include volcanic aerosols have similar skill. It is therefore still an open question whether initialisation improves predictions of Sahel rainfall. We conclude that the main source of skill in forecasting temperature is the trend forced by rising greenhouse gas concentrations. The ocean initial state contributes to skill in some regions, but variations in boundary forcings such as aerosols are as important in decadal forecasting.

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Notes

  1. Priv. Comm., C. C. Raible

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Acknowledgments

This work was supported by the EU FP7 large-scale collaborative project THOR (GA212643, 2008-2012) and the QWeCI project (ENV-FP7-2009-1-243964). We acknowledge the FP6 ENSEMBLES project (contract GOCE-CT-2003-505539) for the decadal forecasts and PCMDI for archiving and distributing the CMIP3 data. All data used are available from the ECMWF ENSEMBLES data server and/or the KNMI Climate Explorer.

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  1. Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, AE 3730, De Bilt, The Netherlands

    Geert Jan van Oldenborgh, Bert Wouters & Wilco Hazeleger

  2. Institució Catalana de Recerca i Estudis Avançats (ICREA) and Institut Català de Ciències del Clima (IC3), Doctor Trueta 203, 08005, Barcelona, Spain

    Francisco J. Doblas-Reyes

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  1. Geert Jan van Oldenborgh
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van Oldenborgh, G.J., Doblas-Reyes, F.J., Wouters, B. et al. Decadal prediction skill in a multi-model ensemble. Clim Dyn 38, 1263–1280 (2012). https://doi.org/10.1007/s00382-012-1313-4

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