| 摘 要： | Within a theoretical ENSO model, the authors investigated whether or not the
errors superimposed on model parameters could cause a significant ``spring
predictability barrier' (SPB) for El Nino events. First, sensitivity
experiments were respectively performed to the air--sea coupling parameter,
α and the thermocline effect coefficient μ. The results showed that the
uncertainties superimposed on each of the two parameters did not exhibit an
obvious season-dependent evolution; furthermore, the uncertainties caused a
very small prediction error and consequently failed to yield a significant
SPB. Subsequently, the conditional nonlinear optimal perturbation (CNOP)
approach was used to study the effect of the optimal mode (CNOP-P) of the
uncertainties of the two parameters on the SPB and to demonstrate that the
CNOP-P errors neither presented a unified season-dependent evolution for
different El Nino events nor caused a large prediction error, and
therefore did not cause a significant SPB. The parameter errors played only
a trivial role in yielding a significant SPB. To further validate this
conclusion, the authors investigated the effect of the optimal combined mode
(i.e. CNOP error) of initial and model errors on SPB. The results
illustrated that the CNOP errors tended to have a significant
season-dependent evolution, with the largest error growth rate in the
spring, and yielded a large prediction error, inducing a significant SPB.
The inference, therefore, is that initial errors, rather than model
parameter errors, may be the dominant source of uncertainties that cause a
significant SPB for El Nino events. These results indicate that the
ability to forecast ENSO could be greatly increased by improving the
initialization of the forecast model.
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