The impacts of ocean data assimilation on tropical precipitation bias in a climate model

Chen Hui; Yin Xunqiang; Song Zhenya; Song Yajuan; Bao Ying; Qiao Fangli

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Chen Hui, Yin Xunqiang, Song Zhenya, Song Yajuan, Bao Ying, Qiao Fangli. The impacts of ocean data assimilation on tropical precipitation bias in a climate model[J]. Haiyang Xuebao, 2015, 37(7): 41-53.

Citation:

Chen Hui, Yin Xunqiang, Song Zhenya, Song Yajuan, Bao Ying, Qiao Fangli. The impacts of ocean data assimilation on tropical precipitation bias in a climate model[J]. Haiyang Xuebao, 2015, 37(7): 41-53.

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The impacts of ocean data assimilation on tropical precipitation bias in a climate model

First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;Key Lab of Marine Science and Numerical Modeling, State Oceanic Administration, Qingdao 266061, China

Received Date: 2014-05-26
Rev Recd Date: 2015-03-20

Abstract

Abstract

Using the Ensemble Adjustment Kalman Filter (EAKF),two kinds of oceanic satellite observations,namely sea surface temperature (SST) and sea level anomaly (SLA),had been assimilated into the ocean model component of the FIO-ESM (First Institute of Oceanography Earth System Model version 1.0). We analyzed the differences of the atmospheric circulation,specific humidity,cloud fraction,and precipitation in tropical between the assimilation and no assimilation experiment,to investigate the impacts of ocean data assimilation on tropical precipitation simulation in a climate model. The results showed that ocean data assimilation can effectively improve the sea surface temperature and ocean heat content in the upper layer of the ocean,the absolute mean error of annual mean SST in the area of 30°S~30°N were reduced by 60%. Sea level pressure and atmospheric circulation such as lower winds had been significantly improved. The atmospheric vertical motion turns to be more reasonable,which provide reliable dynamic conditions for precipitation simulation. Improvements of SST and atmospheric circulation would further influence the spatial distribution of the specific humidity and cloud fraction,giving more reasonable moisture conditions for precipitation simulation. Finally,the spatial distribution and intensity of zonal mean were significantly improved,the peak value of precipitation in the south of the equator were obviously reduced,and the absolute mean error of annual mean precipitation in the oceanic area of 30°S~30°N were reduced by 35%.
- climate model,
- data assimilation,
- EAKF,
- precipitation

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