大气科学进展(英文版)
大氣科學進展(英文版)
대기과학진전(영문판)
ADVANCES IN ATMOSPHERIC SCIENCES
2010年
5期
1003-1013
,共11页
ENSO模型%厄尔尼诺%事件%参数误差%预报%不确定性%预测误差%扰动误差
ENSO模型%阨爾尼諾%事件%參數誤差%預報%不確定性%預測誤差%擾動誤差
ENSO모형%액이니낙%사건%삼수오차%예보%불학정성%예측오차%우동오차
ENSO predictability%optimal perturbation%error growth%model parameters
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 Nio 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 Nio 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 Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.