地球科学进展 ›› 2017, Vol. 32 ›› Issue (4): 420 -434. doi: 10.11867/j. issn. 1001-8166.2017.04.0420

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基于可预测模态分析技术的亚澳夏季风统计—动力季节预测模型及其回报技巧评估
孙倩 1( ), 吴波 2, *( ), 周天军 2, 3   
  1. 1.成都信息工程大学大气科学学院,四川 成都 610225
    2.中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京 100029
    3.中国科学院大学,北京 100049
  • 收稿日期:2016-11-07 修回日期:2017-01-28 出版日期:2017-04-20
  • 通讯作者: 吴波 E-mail:sunqiancuit@163.com;wubo@mail.iap.ac.cn
  • 基金资助:
    公益性行业(气象)科研专项项目“基于FGOALS-s、CMA和CESM气候系统模式的年代际集合预测系统的建立与研究”(编号:GYHY201506012);国家自然科学基金项目“以大西洋多年代际振荡作为主要预报因子的夏季北半球热带外气候年代际预测研究”(编号:41675089)资助

Construction of Statistical-Dynamic Prediction Model for the Asian-Australian Summer Monsoon Based on the Predictable Mode Analysis Method and Assessment of Its Predictive Skills

Qian Sun 1( ), Bo Wu 2, *( ), Tianjun Zhou 2, 3   

  1. 1. College of Atmospheric Science, Chengdu University of Information Technology,Chengdu 610225,China
    2.LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
    3.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2016-11-07 Revised:2017-01-28 Online:2017-04-20 Published:2017-04-20
  • Contact: Bo Wu E-mail:sunqiancuit@163.com;wubo@mail.iap.ac.cn
  • About author:

    First author:Sun Qian(1992-), female, Leshan City, Sichuan Province, Master student. Research areas include climate prediction.E-mail:sunqiancuit@163.com

    *Corresponding author:Wu Bo(1982-),male,Hefei City,Anhui Province,Associate professor. Research areas include climate dynamics and climate modeling.E-mail:wubo@mail.iap.ac.cn

  • Supported by:
    Foundation item:Project supported by the R&D Special Fund for Public Welfare Industry (Meteorology) “Development and research of ensemble decadal climate prediction system based on global climate models FGOALS-s, CAMS and CESM”(No.GYHY201506012);The National Natural Science Foundation of China“Research on the decadal climate prediction in the extratropical Northern Hemisphere summer with the Atlantic Multidecadal Oscillation as the main predictor”(No.41675089)

受模式性能的限制,当前的气候模式在直接预报亚澳季风区的夏季降水变化方面技巧较低。采用统计—动力相结合的方法预报亚澳夏季风降水,首先从观测数据中提取具有清晰物理意义的可预报模态;然后,将国际ENSEMBLES计划提供的多模式、多集合样本耦合模式季节预测试验预测的可预报模态的主成分时间序列与对应观测得到的可预报模态的空间型组合,重构降水场,建立了针对亚澳夏季风降水的统计—动力结合的季节预测系统。分析了该系统提前1个月、4个月和7个月的回报技巧。结果表明,统计—动力预测系统的预测技巧显著优于纯动力预测的技巧。另一方面,多模式集合平均的预测技巧优于单个模式,因此针对季风区降水开展多模式集合预测是非常必要的。

Due to the limitations of model performances, the predictive skills of current climate models for the Asian-Australian summer monsoon precipitation are still poor. The prediction based on the combination of statistical and dynamic approaches is an effective way to improve the predictive skills. We used such method to identify the predictable modes of the Asian-Australian summer monsoon precipitation with clear physical interpretation from the historical observational data. Then we combined the principal components time series of these modes predicted by the coupled models, which is derived from the seasonal prediction experiments in the ENSEMBLES project, and the corresponding spatial patterns derived from the above observational analysis to reconstruct the precipitation field. These formed a statistical-dynamic seasonal prediction model for the Asian-Australian summer monsoon precipitation. We analyzed the predictive skills of the model at 1-, 4-and 7-month leads. The result shows that the forecast skills of the statistical-dynamic prediction model are higher than those of the simple dynamic predictions. In addition, the predictive skills of the Multi-Model Ensemble (MME) mean are superior to those of any individual models. Therefore, it is very necessary to implement multi-model ensemble prediction for the monsoon precipitation.

中图分类号: 

图1 观测的亚澳夏季风(30°S~40°N,40°~160°E)降水距平年际变率的主导模态
(a)~(c)对观测的亚澳季风区夏季(6~8月)降水异常(mm/d)进行EOF分析,得到的前3个主导模态的空间分布;左上角括号里为各个模态的方差贡献;(d)~(f)3个主导模态分别对应的同期SST(K)和850 hPa风场异常(m/s)的回归,打点区域表示通过了显著性水平为5%的检验;(g)前3个EOF模态对应的标准化的PC时间序列
Fig.1 The leading modes of interannual variability of precipitation anomaly in the Asian-Australian Monsoon (AAM) domain (30°S~40°N, 40°~160°E) during boreal summer
(a)~(c) The first three leading EOF modes of precipitation anomaly (mm/d) in the AAM domain during boreal summer(JJA); Values in parentheses are the variance contribution rates of each mode;(d)~(f) The simultaneous regression maps of the SST (K) and 850 hPa wind anomalies (m/s) associated with the three modes; The dotted areas are the values with statistically significance at the 5% level; (g) The normalized principal component time series of the three modes
图2 PMA方法在亚澳季风区的预测技巧上限以及纯统计预测模型的预测技巧
(a)基于EOF分析得到的前3个模态重构的三维降水距平场与原始降水距平场的时间相关系数的空间分布,它代表PMA方法可能达到的预报技巧的上限;(b)基于SP得到的前3个PC时间序列,再结合观测数据中得到的EOF空间型重构的三维降水距平场,与原始降水距平场的时间相关系数的空间分布;括号内的值为相关系数在整个亚澳季风区的区域平均;打点区域表示通过了显著性水平为5%的检验
Fig.2 The upper level of the predictive skills based on the PMA method in the AAM region and the predictive skills of the statistical prediction model
(a) Spatial distributions of the temporal correlations between the reconstructed precipitation anomaly field based on the first three leading EOF modesof the observational precipitation anomaly (OBS3M) and the raw observational anomaly field, which represents the upper level of the predictive skills based on the PMA method;(b) as in (a), but the predicted field is restructured based on the three PC time series obtained through the statistical prediction and the corresponding EOF patterns derived from the observation; Values in parentheses are the averages of the temporal correlations in the entire AAM region; The dotted areas are the values with statistically significance at the 5% level
图3 主导模态对应的预测因子的标准化时间序列
(a)黑线为对观测降水距平场进行EOF分析得到的标准化的PC1时间序列,蓝色虚线为SP得到的PC1时间序列;(b),(c)同(a),但为PC2和PC3;括号内的值为观测与预测的PC时间序列的相关系数
Fig.3 The normalized PC time series of corresponding predictors of the leading modes
(a) The black line is the normalized PC1 time series derived from the observational precipitation anomaly, The blue dashed line is the PC1 time series obtained through the statistical prediction; (b), (c) as in (a), but for the PC2 and PC3; Values in parentheses are the temporal correlations of PC time series from the observational and predicted fields
图4 MME平均对亚澳夏季风降水距平的预测技巧
(a)MME平均提前1个月回报的亚澳夏季风降水距平场与原始降水距平场的时间相关系数的空间分布;(b),(c)同(a),但MME的起报时间分别提前4个月和7个月;括号内的值为相关系数在整个亚澳季风区的区域平均;打点区域表示通过了显著性水平为5%的检验
Fig.4 The predictive skills of precipitation anomaly in the AAM during boreal summer by the MME mean
(a) Spatial distributions of the temporal correlations between the precipitation anomaly field during summer (JJA) predicted by the MME mean at the 1-month lead and the raw precipitation anomaly references; (b), (c) as in (a), but for the MME predictions at the 4-and 7-month leads, respectively; Values in parentheses are the averages of the temporal correlations in the entire AAM region; The dotted areas are the values with statistically significance at the 5% level
图5 MME平均回报的3个主导模态对应的标准化时间序列
(a)黑线为对观测降水距平进行EOF分析得到的标准化的PC1时间序列,红色、蓝色和绿色虚线分别为MME平均提前1个月、4个月和7个月回报的PC1时间序列;(b),(c)同(a),但为PC2和PC3;括号内的值为观测与预测的PC时间序列的相关系数
Fig.5 The corresponding PC time series of the three leading modes predicted by the MME mean
(a) The black line is the normalized PC1 time series of EOF1 derived from the observational precipitation anomaly;The red, blue and green dashed lines are the PC1 time series predicted by the MME mean at the 1-month, 4-and 7-month leads, respectively;(b), (c) as in (a),but for the PC2 and PC3;Values in parentheses are the temporal correlations between the predicted PC time series and corresponding observational references
图6 统计—动力预测模型对亚澳夏季风降水距平的预测技巧
(a)基于MME平均提前1个月回报的前3个PC时间序列,再结合观测数据中得到的EOF空间型重构的三维降水距平场,与原始降水距平场的时间相关系数的空间分布;(b),(c)同(a),但分别为提前4个月和7个月回报结果;括号内的值为相关系数在整个亚澳季风区的区域平均;打点区域表示通过显著性水平为5%的检验
Fig.6 The predictive skills of precipitation anomaly in the Asian-Australian monsoon during boreal summer by the statistical-dynamic prediction model
(a) Spatial distributions of the temporal correlations between the predicted precipitation anomaly field, which is reconstructed based on the three PC time series predicted by the MME mean at the 1-month lead and the EOF patterns derived from the observation, and the raw anomaly references. (b), (c) as in (a), but for predictions at the 4-and 7-month leads, respectively; Values in parentheses are the averages of the temporal correlations in the entire AAM region. The dotted areas are the values with statistically significance at the 5% level
图7 SP模型、DP以及S-D模型对亚澳夏季风降水距平的空间相关系数技巧
黑线实线代表基于EOF分析得到的前3个模态重构的三维降水距平场与观测距平场的空间相关系数技巧,黑色虚线为SP模型的空间相关系数技巧,红色、蓝色和绿色虚线分别为DP提前1个月、4个月和7个月的空间相关系数技巧,红色、蓝色和绿色实线分别为S-D模型提前1个月、4个月和7个月的空间相关系数技巧,括号内的值为空间相关系数在27年内的平均
Fig.7 The Pattern Correlation Coefficient (PCC) skill precipitation anomaly in the AAM during boreal summer by the statistical prediction model, dynamic prediction and statistical-dynamic prediction model
The black line is the PCC skill between the reconstructed precipitation anomaly field based on the first three leading EOF modes of the observational precipitation anomaly (OBS3M) and the raw observational anomaly field, and the black dashed line is the PCC skill of the statistical prediction model, and the red, blue and green dashed lines are the PCC skills of the dynamic prediction model at the 1-month, 4-and 7-month leads respectively, and the red, blue and green lines are the PCC skill of the statistical-dynamic prediction model at the 1-month, 4-and 7-month leads, respectively. Values in parentheses are the averaged PCC skill over the 27 years
图8 基于单个模式及MME平均构建的S-D预测模型重构的降水距平场与原始距平场的时间相关系数和均方根误差在整个亚澳季风区的区域平均
(a)基于单个模式及MME平均分别提前1个月、4个月和7个月回报的前3个PC时间序列,再结合观测数据中得到的EOF空间型重构的三维降水距平场,与原始距平降水场的时间相关系数在整个亚澳季风区的区域平均;(b)同(a),但为均方根误差的区域平均
Fig.8 Averages of temporal correlations and Root Mean Square Error (RMSE) between the predicted precipitation anomaly fields constructed by statistical-dynamic prediction models based on individual models and the MME mean and the observational anomaly references in the entire AAM region
(a) Averages of temporal correlations between the predicted precipitation anomaly fields and the observational anomaly references in the entire AAM region. The predicted fields are constructed based on the first three EOF patterns derived from the observation and the PC time series predicted by the individual models and the MME mean at the 1-, 4-and 7-month leads, respectively;(b) as in (a), but for the averages of RMSE
图9 单个模式及MME平均对3个主导模态对应的标准化PC时间序列的预测技巧
(a)单个模式及MME平均提前1个月回报的3个主导模态对应的PC时间序列与对应观测的相关系数;(b),(c)同(a),但分别为提前4个月和7个月回报结果
Fig.9 The predictive skills of the corresponding PC time series of the three leading modes predicted by individual models and the MME mean
(a) Temporal correlations between the three PC time series predicted by the individual models and the MME mean at the 1-month lead, and the corresponding observational references; (b), (c) as in (a), but for the predictions at the 4-and 7-month leads, respectively
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