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Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

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Abstract

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.

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Acknowledgments

The advice of Jagadish Shukla has been much appreciated, and also discussions with B. Kirtman, B. Wang, and J.-Y. Lee. The forecast data was generously provided by the NCEP Environmental Modeling Center (EMC), long run data was generously provided by K. Pegion (COLA), and the author is grateful for these contributions. The first author was supported by the Asian-Pacific Economic Cooperation Climate Center (APCC) International Research Project. The second author was supported by grants from the National Science Foundation (ATM-0332910), the National Oceanic and Atmospheric Administration (NA04OAR4310034) and the National Aeronautics and Space Administration (NNG04GG46G).

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  1. Department of Climate Dynamics, George Mason University, Fairfax, VA, USA

    Emilia K. Jin & James L. Kinter III

  2. Center for Ocean-Land-Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD, 20705, USA

    Emilia K. Jin & James L. Kinter III

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  1. Emilia K. Jin
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Correspondence to Emilia K. Jin.

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Jin, E.K., Kinter III, J.L. Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS. Clim Dyn 32, 675–691 (2009). https://doi.org/10.1007/s00382-008-0418-2

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