Spring predictability barrier of ENSO events from the perspective of an ensemble prediction system
Spring predictability barrier of ENSO events from the perspective of an ensemble prediction system
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集合预报系统视角下ENSO事件春季可预报性障碍
DOI：
10.1016/j.gloplacha.2010.01.021
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发表时间：
2010-06
影响因子：
3.9
通讯作者：
Zheng, Fei
中科院分区：
文献类型：
--
作者：
Zhu, Jiang;Zheng, Fei
基于ENSO（厄尔尼诺-南方涛动）集合预测系统（EPS），从确定性和概率意义上检查ENSO可预测性的季节变化。对于确定性预测技能，集合平均值的技能对开始预测的月份敏感。北半球春季期间异常相关性迅速下降，均方根误差在北半球春季之前和期间初始化值最大且增长率最快。然而，基于相对运行特征（ROC）曲线和面积验证方法的概率预测均表明，两次极端（暖和冷）ENSO事件不存在强烈的季节变化。对于近正常事件，概率技能的季节变化更加明显，春季集合预报的ROC面积明显小于其他季节集合预报的ROC面积。同时，仅考虑初始扰动的所有三个事件的 EPS 概率预测技能也明显对初始月份敏感。 ROC 区域技能下降最快的事实表明这一点是在春季后报进行时发生的。进一步的信噪比分析表明，EPS 概率技能中可预测性障碍的潜在来源是，春季是随机初始误差效应预计会严重降低预测技能的时期，而小的预测信号会进一步限制可预测性，从而使系统变得更加嘈杂。然而，在集合预报过程中合理考虑模型误差扰动，可以通过协调模拟预报不确定性的季节变化，缓解初始不确定性带来的障碍，显着提高概率预报技能，进而扰乱与SPB相关的季节可预报性。
Based on an ENSO (El Niño-Southern Oscillation) ensemble prediction system (EPS), the seasonal variations in the predictability of ENSO are examined in both a deterministic and a probabilistic sense. For the deterministic prediction skills, the skills of the ensemble-mean are sensitive to the month in which the forecast was initiated. The anomaly correlations decrease rapidly during the Northern Hemisphere (NH) spring, and the root mean square (RMS) errors have the largest values and the fastest growth rates initialized before and during the NH spring. However, the probabilistic predictions based on the verification methods of the relative operating character (ROC) curve and area both show that there are no strong seasonal variations for the two extreme (warm and cold) ENSO events. For the near-normal events, the seasonal variations of the probabilistic skills are much more obvious, and the ROC areas of the ensemble forecasts made in the spring are clearly smaller than those of the ensemble forecasts that began during other seasons. At the same time, the probabilistic prediction skills of the EPS for all three events that only consider the initial perturbations are also clearly sensitive to the initial months. This was indicated by the fact that the most rapid decrease of the ROC area skill occurs as the hindcasts proceed through the spring season. A further signal-to-noise ratio analysis reveals that potential sources of the predictability barrier in the probabilistic skills for the EPS are namely that the spring is the period when stochastic initial error effects can be expected to strongly degrade forecast skill, and that small predicted signals can render the system noisier by further limiting the predictability. However, reasonable considerations of the model-error perturbations during the ensemble forecast process can alleviate the barrier caused by initial uncertainties through coordinately simulating the seasonal variations of the forecast uncertainty in order to significantly improve the probabilistic prediction skills and then to disorder the seasonal predictability related to the SPB.
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