The role of nonlinear forcing singular vector tendency error in causing the “spring predictability barrier” for ENSO
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With the Zebiak–Cane model, the present study investigates the role of model errors represented by the nonlinear forcing singular vector (NFSV) in the “spring predictability barrier” (SPB) phenomenon in ENSO prediction. The NFSV-related model errors are found to have the largest negative effect on the uncertainties of El Ni˜no prediction and they can be classified into two types: the first is featured with a zonal dipolar pattern of SST anomalies (SSTA), with the western poles centered in the equatorial central–western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite to the first type. The first type of error tends to have the worst effects on El Ni˜no growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSVrelated errors exhibits prominent seasonality, with the fastest error growth in spring and/or summer; hence, these errors result in a significant SPB related to El Ni˜no events. The linear counterpart of NFSVs, the (linear) forcing singular vector (FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate an SPB for El Ni˜no events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Ni˜no events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central–western Pacific, which likely represent those areas sensitive to El Ni˜no predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.
Key wordsspring predictability barrier model error optimal perturbation El Ni˜no event
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- Dommenget, D., and Y. S. Yu, 2016: The seasonally changing cloud feedbacks contribution to the ENSO seasonal phase-locking. Climate Dyn., doi: 10.1007/s00382-016-3034-6.Google Scholar
- Duan, W. S., X. C. Liu, K. Y. Zhu, et al., 2009: Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events. J. Geophys. Res., 114, C04022, doi: 10.1029/2008JC004925.Google Scholar
- Kirtman, B. P., J. Shukla, M. Balmaseda, et al., 2002: Current status of ENSO forecast skill: A report to the climate variability and predictability (CLIVAR) Numerical Experimentation Group (NEG). CLIVAR Working Group on seasonal to interannual prediction, 31 pp. (Available online at http://www.cliver.org/publications/wgpreports/wgsip/nin o3/report.html.)Google Scholar
- Lau, K. M., and S. Yang, 1996: The Asian monsoon and predictability of the tropical ocean–atmosphere system. Quart. J. Roy. Meteor. Soc., 122, 945–957.Google Scholar
- Lopez, H., and B. P. Kirtman, 2014: WWBs, ENSO predictability, the spring barrier and extreme events. J. Geophys. Res., 19, 10114–10138, doi: 10.1002/2014JD021908.Google Scholar
- Torrence, C., and P. J. Webster, 1998: The annual cycle of persistence in the El Niño/Southern Oscillation. Quart. J. Roy. Meteor. Soc., 124, 1985–2004.Google Scholar
- Yu, Y. S., M. Mu, W. S. Duan, et al., 2012b: Contribution of the location and spatial pattern of initial error to uncertainties in El Niño predictions. J. Geophys. Res., 117, C06018, doi: 10.1029/2011JC007758.Google Scholar
- Zhang, R. H., S. E. Zebiak, R. Kleeman, et al., 2003: A new intermediate coupled model for El Niño simulation and prediction. Geophys. Res. Lett., 30, doi: 10.1029/2003GL018010.Google Scholar