Forecasting Experiments of CAM 3.1 Model Using Statistic Forecast SST Data

Han Xue; Wei Fengying; Dong Min; Dong Wenjie

Vol.20, NO.3, 2009

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2009 > 20(3): 303-311

Han Xue, Wei Fengying, Dong Min, et al. Forecasting experiments of CAM 3.1 model using statistic forecast SST data. J Appl Meteor Sci, 2009, 20(3): 303-311.

Citation:

Han Xue, Wei Fengying, Dong Min, et al. Forecasting experiments of CAM 3.1 model using statistic forecast SST data. J Appl Meteor Sci, 2009, 20(3): 303-311.

Han Xue, Wei Fengying, Dong Min, et al. Forecasting experiments of CAM 3.1 model using statistic forecast SST data. J Appl Meteor Sci, 2009, 20(3): 303-311.

Citation:

Han Xue, Wei Fengying, Dong Min, et al. Forecasting experiments of CAM 3.1 model using statistic forecast SST data. J Appl Meteor Sci, 2009, 20(3): 303-311.

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Forecasting Experiments of CAM 3.1 Model Using Statistic Forecast SST Data

1.
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
National Climate Center, Beijing 100081
4.
State Key Laboratory for Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875

Received Date: 2008-11-07
Rev Recd Date: 2009-03-30
Publish Date: 2009-06-30

Abstract

Abstract

Based on the ＂two-step method＂ in month/season forecast, a new statistic model is designed to predict the global SST field. The global SST during 1981-2000 are predicted using the model. Comparing with the NCEP SST field, the results indicate that the forecast global SST field from the statistic forecast SST model could reflect primary patterns of the global SST, and can predict the variability of SST anomaly in Niño3.4 which represents E1 Niño/La Niña event. The correlation coefficient between forecast SST and NCEP SST in Niño3.4 is 0.596. Furthermore, the numerical experiments based on the forecast global SST boundary conditions for forecasting the monthly anomalies of 500 hPa geopotential height over east-Asia (1981-2000) are performed using the NCAR CAM3.1 model. The first six eigenvectors which represent typical spatial patterns of CAM3.1 500 hPa and NCEP 500 hPa are decomposed from the CAM3.1 500 hPa and NCEP 500 hPa fields using EOF respectively. The results show that primary patterns of 500 hPa height anomalies in east Asia can be forecasted by driving CAM3.1 with the forecast global SST. However, the forecast ability of 500 hPa geopotential height over east Asia in summer is faulty and unstable in forecast experiments. A statistic revision method is provided to revise the forecast results of CAM3.1 model. The root mean square error and correlation coefficients of 500 hPa height anomalies between CAM3.1 forecast (revised before and after) and NCEP in summer over the east Asia are compared, the root mean square error between the two fields decreases every year, and the mean correlation coefficient during 1981-2000 increases. These results indicate that the forecasting skill of 500 hPa height anomalies over east Asia in summer is improved using the statistic revision method to a certain extent. The experiments show this statistical method can be used to improve the forecasting skill of the dynamic model CAM3.1.
- statistic forecast SST model;
- CAM3.1 model;
- seasonal climate forecast

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References(29)

Figures and Tables

图 1 全球海温的典型空间特征向量

Fig. 1 The typical eigenvectors of the global SST

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图 2 Niño3.4海温预报距平值与NCEP/NCAR距平值比较

Fig. 2 Comparative analysis between the forecast SST anomalies and observed SST anomalies in Niño3.4

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图 3 NCEP/NCAR和CAM3.1预报的东亚500 hPa大尺度环流异常型

Fig. 3 The patterns of 500 hPa height anomalies for NCEP/NCAR and CAM3.1 forecast over the east-Asia

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图 4 订正前、后CAM3.1模式预报的东亚夏季500 hPa高度距平场与NCEP/NCAR 500 hPa高度距平场对比

(a) 均方根误差, (b) 相关系数

Fig. 4 Comparative analysis between 500 hPa height anomalies of CAM3.1 forecast (revised before and after) and NCEP/NCAR in summer over the east-Asia

(a) root mean square error, (b) correlation coefficients

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表 1 4个预报因子与海温场第2特征向量时间系数的滞后相关

Table 1 The correlation coefficients in lag between the four forecast indexes and the second lime coefficients of the global SST

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