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《CLIMATIC AND ENVIRONMENTAL RESEARCH》 1998-04
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An Improved Short-Term Climate Prediction System and Its Application to the Extraseasonal Prediction of Rainfall Anomaly in China for 1998

Lin Zhaohui, Li Xu, Zhao Yan, Zhou Guangqing, Wang Huijun, Yuan Chongguang, Guo Yufu and Zeng Qingcun (Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100080)  
By introducing the modified IAP AGCM with improved surface albedo parameterization, the IAP Prediction System of Short-term Climate Anomaly (IAP PSSCA) has been updated. Two sets of ensemble hindcasts of the summer rainfall anomaly over China have been performed with both the original and modified IAP PSSCA during the period 1980~1994. Comparison results show that, the predictive skill of the modified IAP PSSCA is generally higher than the original one over the North China, Northeast China and Hetao region, and this indicates the important role of the land surface processes in the improvement of the predictive skill over extratropical region. However, over the Huaihe and Yangtze River valley, the difference of the predictive skill between the modified and original IAP PSSCA are small, and this may suggest that the summer rainfall anomaly over these regions are dominated by the equatorial Pacific sea surface temperature anomaly (SSTA). Another improvement of the climate prediction system consists of the use of the predicted SSTA provided by IAP ENSO prediction system and such improved climate prediction system is the so-called S4 scheme. By using S4 scheme, the prediction of 1998 summer rainfall anomalies over China was issued in March 1998. The verification shows that the predicted pattern of rainfall anomaly agrees quite well with the observation (the Pacific SSTA was also well predicted), although the strength of the predicted positive anomalies are rather weak and the strong positive rainfall anomaly over Yangtze River valley does not extend westward enough. The prediction will be reanalyzed and re-examined by using better initialization of the land surface characteristics in order to have better understanding of the influencing factors and make further improvement of the prediction system.
【Fund】: 国家攀登项目 国家自然科学基金
【CateGory Index】: P456.1
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