Analysis of seasonal and interannual variability of sea surface temperature for China seas based on CORA dataset

WU Yang; CHENG Guosheng; HAN Guijun; SHU Yeqiang; WANG Dongxiao

doi:10.3969/j.issn.0253-4193.2013.01.006

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WU Yang, CHENG Guosheng, HAN Guijun, SHU Yeqiang, WANG Dongxiao. Analysis of seasonal and interannual variability of sea surface temperature for China seas based on CORA dataset[J]. Haiyang Xuebao, 2013, 35(1): 44-54. doi: 10.3969/j.issn.0253-4193.2013.01.006

Citation:

WU Yang, CHENG Guosheng, HAN Guijun, SHU Yeqiang, WANG Dongxiao. Analysis of seasonal and interannual variability of sea surface temperature for China seas based on CORA dataset[J]. Haiyang Xuebao, 2013, 35(1): 44-54. doi: 10.3969/j.issn.0253-4193.2013.01.006

Citation:

WU Yang, CHENG Guosheng, HAN Guijun, SHU Yeqiang, WANG Dongxiao. Analysis of seasonal and interannual variability of sea surface temperature for China seas based on CORA dataset[J]. Haiyang Xuebao, 2013, 35(1): 44-54. doi: 10.3969/j.issn.0253-4193.2013.01.006

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Analysis of seasonal and interannual variability of sea surface temperature for China seas based on CORA dataset

doi: 10.3969/j.issn.0253-4193.2013.01.006

1.
School of Mathematics & Physics, Nanjing University of Information and Technology, Nanjing 210044, China;State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301, China
2.
School of Mathematics & Physics, Nanjing University of Information and Technology, Nanjing 210044, China
3.
National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, China
4.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301, China

Received Date: 2011-09-11
Rev Recd Date: 2012-09-15

Abstract

Abstract

The sea surface temperature (SST) of China Ocean Reanalysis (CORA) from 1986 to 2008 is validated using Simple Ocean Data Assimilation (SODA),Levitus and Advanced Very High Resolution Radiometer (AVHRR) dataset. Comparing with AVHRR,the bias and root mean square error(RMSE) of CORA are less than that of SODA. Comparing with Levitus,the RMSE of COAR is also less than that of SODA. The results of Empirical Orthogonal Function (EOF) analysis show that the first three modes of CORA have similar temporal and spatial variations with those of SODA dataset,and the main differences are that CORA can represent more subtle features than SODA. The seasonal and interannual variability of SST in the coastal China seas can be well presented by CORA data. The second mode of EOF reveals the SST response to wind-induced latent heat. The third mode presents the northeast-southwestern distribution of SST front,which is coincident to the transitionally seasonal characters between summer and winter. The interannual variability of SST is closely related to ENSO.The regional average of SSTA of CORA in the South China Sea has more coincident correlation to Nino indexes than that of SODA.
- seasonal and Interannual variability,
- SST,
- ENSO,
- EOF

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References(26)

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