Utilizing ARGO Data to Improve the Prediction of ENSO and Short-term Climate Prediction of Summer Rainfall in China

Zhang Renhe; Yin Yonghong; Li Qingquan; Liu Yimin; Niu Tao

Vol.17, NO.5, 2006

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2006 > 17(5): 538-547

Zhang Renhe, Yin Yonghong, Li Qingquan, et al. Utilizing ARGO data to improve the prediction of ENSO and short-term climate prediction of summer rainfall in China. J Appl Meteor Sci, 2006, 17(5): 538-547.

Citation:

Zhang Renhe, Yin Yonghong, Li Qingquan, et al. Utilizing ARGO data to improve the prediction of ENSO and short-term climate prediction of summer rainfall in China. J Appl Meteor Sci, 2006, 17(5): 538-547.

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Utilizing ARGO Data to Improve the Prediction of ENSO and Short-term Climate Prediction of Summer Rainfall in China

1.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
2.
National Climate Center, Beijing 100081

Received Date: 2006-07-03
Rev Recd Date: 2006-08-28
Publish Date: 2006-10-31

Abstract

Abstract

The acquirement of the global ARGO data provides unprecedented ocean data for researches on the climate prediction, a tropical dynamic ocean-statistical atmospheric coupled model is set up. In the coupled model, the atmospheric part is a statistical atmospheric model constructed according to the correlation relationship between observed windstress and sea surface temperature in the tropical Pacific. The ocean part is Zebiak-Cane oceanic model in which the subsurface temperature parameterization scheme is improved based on the ARGO data. The long-term hindcasts are done using the coupled model. In order to understand the performance of the ARGO data in the improvement of the hindcasts, the results of the coupled model are compared using the unimproved and improved ocean models respectively. It is found that the application of the ARGO data in the oceanic model greatly raises the capability of the coupled model in hindcasting the sea surface temperature (SST) in the tropical Pacific. The hindcast of 6 months in advance gives the abnormal variations of the SST in the Niño3.4 region in good agreement with the observation. The El Niño and La Niña events in the hindcasting period are successfully hindcasted. The correlation of SSTs between the hindcasts and the observations is increased substantially in the whole tropical Pacific Ocean.The improvement of the short-term climate prediction of summer rainfall in China is also investigated by using the global atmosphere-ocean coupled model of the China National Climate Center (NCC) using the ARGO data in the Global Ocean Data Assimilation System of NCC (NCC-GODAS). The results of seasonal hindcasts for the summer precipitation in China for the case of the summer of 2002 and also for the summers from 1998 to 2003 are showed respectively. It is found that when the ARGO data is included in the NCC-GODAS, the distribution of the hindcasted summer precipitation in China is closer to the observation. The area of the positive correlation between the hindcasted and the observed summer precipitations is enlarged. It demonstrates that hindcasts for the summer precipitation in China with ARGO data in the NCC-GODAS is much better than those without ARGO data.
- ARGO data;
- ENSO prediction;
- short-term climate prediction of summer rainfall in China

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Figures and Tables

图 1 原ZC模式-统计大气耦合模式 (a) 与改进后的ZC模式-统计大气耦合模式 (b) 提前3个月Niño3.4区 (5°S~5°N, 120°~170°W) 海面温度异常的回报结果

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图 2 说明同图 1, 但为提前6个月的回报结果

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图 3 原ZC模式-统计大气耦合模式 (a) 与改进后的ZC模式-统计大气耦合模式 (b) 提前3个月回报的海面温度异常与观测值的相关分布

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图 4 说明同图 3, 但为提前6个月回报的海面温度异常与观测值的相关分布

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图 5 利用NCC全球海气耦合模式回报的2002年我国夏季降水距平百分率 (单位:%) (a) 不包含ARGO观测资料的同化资料作为初始场, (b) 包含ARGO观测资料的同化资料作为初始场

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图 6 2002年我国夏季降水实况距平百分率 (单位:%)

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图 7 利用NCC全球海气耦合模式对1998—2003年回报与观测的我国夏季降水距平百分率相关分布 (a) 不包含ARGO观测资料的同化资料作为初始场, (b) 包含ARGO观测资料的同化资料作为初始场 (其中阴影区为正相关区域)

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