| Seasonality in Prediction Skill and Predictable Pattern of Tropical Indian Ocean SST - :15189 | National Weather Service (NWS)
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Seasonality in Prediction Skill and Predictable Pattern of Tropical Indian Ocean SST
  • Published Date:
  • Source:
    Journal of Climate, 28(20), 7962-7984.
Filetype[PDF-3.58 MB]

  • Description:
    Seasonality of sea surface temperature (SST) predictions in the tropical Indian Ocean (TIO) was investigated using hindcasts (1982-2009) made with the NCEP Climate Forecast System version 2 (CFSv2). CFSv2 produced useful predictions of the TIO SST with lead times up to several months. A substantial component of this skill was attributable to signals other than the Indian Ocean dipole (IOD). The prediction skill of the IOD index, defined as the difference between the SST anomaly (SSTA) averaged over 10 degrees S-0 degrees, 90 degrees-110 degrees E and 10 degrees S-10 degrees N, 50 degrees-70 degrees E, had strong seasonality, with high scores in the boreal autumn. In spite of skill in predicting its two poles with longer leads, CFSv2 did not have skill significantly better than persistence in predicting IOD. This was partly because the seasonal nature of IOD intrinsically limits its predictability. The seasonality of the predictable patterns of the TIO SST was further explored by applying the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) method to the predicted SSTA in March and October, respectively. The most predictable pattern in spring was the TIO basin warming, which is closely associated with El Nino. The basin mode, including its associated atmospheric anomalies, can be predicted at least 9 months ahead, even though some biases were evident. On the other hand, the most predictable pattern in fall was characterized by the IOD mode. This mode and its associated atmospheric variations can be skillfully predicted only 1-2 seasons ahead. Statistically, the predictable IOD mode coexists with El Nino; however, the 1994 event in a non-ENSO year (at least not a canonical ENSO year) can also be predicted at least 3 months ahead by CFSv2.

  • Document Type:
  • Funding:
    Grant No: NA09OAR4310058
  • Supporting Files:
    No Additional Files
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