What Kind of Initial Errors Cause the Severest Prediction
Uncertainty of El Nino in Zebiak-Cane Model |
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Authors: | XU Hui and DUAN Wansuo |
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Institution: | State Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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Abstract: | With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is
explored by conditional nonlinear optimal perturbation (CNOP). The results demonstrate that CNOP-type errors
cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOP-type errors, we
find that for the normal states and the relatively weak El Nino events in the ZC model, the predictions tend
to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong El Nino events, the
ZC model largely underestimates their intensities. Also, our results suggest that the error growth of El Nino
in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring
and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the
most difficult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but
it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different
initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields
the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the
types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that
a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill. |
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Keywords: | ENSO predictability prediction error optimal perturbation |
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