Measuring the potential predictability of ensemble climate predictions
Measuring the potential predictability of ensemble climate predictions
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DOI：
10.1029/2007jd008804
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发表时间：
2008-02
影响因子：
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通讯作者：
Youmin Tang;Hai Lin;A. Moore
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文献类型：
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作者：
Youmin Tang;Hai Lin;A. Moore
[1]本文分别利用两个耦合模式和两个大气环流模式，以及不同的集合方案，对厄尔尼诺南方涛动（ENSO）和北极涛动（AO）进行了集合预报。几种潜在的可预报性的措施，包括集合均方（EM 2），集合传播和信噪比进行了探讨，在他们的能力，估计先验的预测能力的ENSO和AO集合预测。重点考察了不使用观测值的可预报性度量与使用观测值的相关性和均方误差（MSE）模型预报技巧之间的关系。这里确定的关系提供了一个实用的方法来估计潜在的可预测性和个人预测的置信水平。结果表明，EM 2比信噪比更能反映ENSO和AO集合预报的实际水平。当使用基于相关性的度量时，预测技巧可能是EM 2的线性函数，即，EM 2越大，预测技能越高;而当使用基于MSE的度量时，它们之间存在“三角关系”，即，当EM 2大时，预测可能是可靠的，而当EM 2小时，预测技能是高度可变的。与集合天气预报（NWP）相比，集合传播不是一个很好的预测因子，在量化气候预测技能在本研究中使用的模式，因为强迫响应可能比噪声在气候时间尺度相比，NWP大得多。提出了一个统计框架来解释为什么EM 2是集合气候预测中实际预测技能的一个很好的指标。
[1] In this study, ensemble predictions of the El Nino Southern Oscillation (ENSO) and the Arctic Oscillation (AO) were conducted using two coupled models and two atmospheric circulation models, respectively, as well as various ensemble schemes. Several measures of potential predictability including ensemble mean square (EM2), ensemble spread and the ratio of signal-to-noise were explored in terms of their ability of estimating a priori the predictive skill of the ENSO and AO ensemble predictions. The emphasis was put on examining the relationship between the measures of predictability that do not use observations and the model prediction skill of correlation and mean square error (MSE) that make use of observations. The relationship identified here offers a practical means of estimating the potential predictability and the confidence level of an individual prediction. It was found that the EM2 is a better indicator of the actual skill of ensemble ENSO and AO prediction than the ratio of signal-to-noise. When correlation-based metrics are used, the prediction skill is likely to be a linear function of EM2, i.e., the larger the EM2 the higher skill the prediction; whereas when MSE-based metrics are used, a “triangular relationship” is suggested between them, namely, that when EM2 is large, the prediction is likely to be reliable whereas when EM2 is small the prediction skill is highly variable. In contrast with ensemble weather prediction (NWP), the ensemble spread is not a good predictor in quantifying climate prediction skill in the models used in this study because the forced response may be much larger than the noise in the climate timescales compared to the NWP. A statistical framework was proposed to explain why EM2 is a good indicator of actual prediction skill in the ensemble climate predictions.