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Empirical Orthogonal Function-analogue Correction of Extra-seasonal Dynamical Prediction of East-Asian Summer Monsoon
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摘要: 利用国家气候中心第2代季节气候预测模式BCC_CSM1.1(m) 的1991—2010年每年2月起报的历史回算资料集，考察模式对于5个夏季风指数的预测能力，并通过发展基于经验正交函数分解与相似分析的EOF-相似误差订正方法，对5个夏季风指数的模式预测进行再修正。交叉检验和独立样本检验结果表明：该模式对1991—2010年东亚夏季风指数与西北太平洋夏季风指数预测技巧较高；EOF-相似误差订正方法适用于模式预测技巧较低的指数，这些指数经订正后预测效果均有不同程度改进，而预测技巧较高的夏季风指数经订正后改进效果不明显；在交叉检验中，线性部分订正多优于非线性部分订正效果，而对于独立试报的年份，非线性部分订正多优于线性部分订正效果，显示出良好的应用前景。Abstract: In terms of the dataset of the second generation climate prediction model of Beijing Climate Center, BCC_CSM1.1(m) hindcasts in February from 1991 to 2010, the model is assessed on its performances in predicting five monsoon indices including the East Asian summer monsoon index (EASMI), the western North Pacific summer monsoon index (WNPSMI), the East Asian monsoon index (EAMI), the Indian summer monsoon index (ISMI) and the East Asian summer monsoon intensity index (EASMII). A correction method based on the empirical orthogonal function (EOF) analysis and the analogue analysis, called the empirical orthogonal function-analogue correction of errors (EOF-ACE) is used to correct the modes which are poorly predicted by the model.Through EOF analysis, the coordinated variation of the five monsoon indices can be reflect by the obtained modes. In addition, according to the principle of ensemble forecast, three similarity indices are used to select the similarity years. The correction process is divided into two parts, which are linear component correction and non-linear component correction.Assessments indicate that the EASMI and the WNPSMI are predicted well by BCC_CSM1.1(m). However, the prediction result of EAMI, ISMI and EASMII is poor. Through EOF-ACE correction, the model prediction skill of the monsoon indices originally unreasonable are improved, but it's unsatisfactory for the rest indices. The linear component correction mostly shows higher skill than non-linear component correction in cross-validation. However, for independent validation, the effect of non-linear component correction is mostly better than linear component correction, which displays application prospects.However, the EOF-ACE has barely effects on indices whose deviations are originally small, and correlation coefficients of some summer monsoon indices between observations and predictions are still not significant through correction. It is necessary to make further analysis on the inter-annual variation of those indices, and look for the relevant external force factors.
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图 1 EOF-相似误差订正方案流程图
Fig. 1 Illustration of the scheme for the empirical orthogonal function-analogue correction of errors
图 2 交叉检验中观测的夏季风指数分别与模式预测、线性部分订正与非线性部分订正的夏季风指数的距平相关系数
Fig. 2 Anomaly correlation coefficients between five monsoon indices of observations and model predictions, linear component correlation and non-linear component correlation for the cross-validation
图 4 1991—2010年EAMI的观测值、模式预测值及线性部分订正值
Fig. 4 Observations, model predictions and linear component correction of the east Asian monsoon index from 1991 to 2010
图 5 1991—2010年ISMI的观测值、模式预测值及非线性部分订正值
Fig. 5 Observations, model predictions and non-linear component correction of the Indian summer monsoon index from 1991 to 2010
表 1 5种夏季风指数的观测值与预测值之间的距平相关系数
Table 1 Anomaly correlation coefficients between five summer monsoon indices of observations and model predictions
夏季风指数 1991—2010年 1991—2001年 2002—2010年 EASMI 0.63 0.73 0.43 WNPSMI 0.61 0.69 0.46 EAMI 0.33 0.56 -0.03 ISMI -0.22 0.18 -0.55 EASMII 0.12 -0.13 0.29 
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表 2 5种夏季风指数观测场时间系数与预测场时间系数的距平相关系数
Table 2 Anomaly correlation coefficients between temporal coefficients of observations and model predictions
模态 1991—2010年 1991—2001年 2002—2010年 第1模态 0.56 0.67 0.31 第2模态 0.26 0.15 0.41 第3模态 0.55 0.68 0.25 第4模态 0.03 0.34 -0.08 第5模态 -0.06 -0.22 0.01
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