Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters

Jiajia Hao; Yongli Chen; Junqiao Feng; Fan Wang

doi:10.1007/s00343-009-9201-4

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Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters

OriginalPaper | Updated：2023-05-16

- Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters
- Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters
- 海洋湖沼学报（英文） 2009年27卷第2期页码：218-228
- Affiliations：
  
  1. Lab. of Ocean Circulation and Wave Studies Institute of Oceanology Chinese Academy of Sciences,Key
  2. Graduate School of Chinese Academy of Sciences
  3. Yantai Institute of Costal Zone Research for Sustainable Development CAS
- Author bio：
- Funds：
  
  Supported by the Knowledge Innovation Program of Chinese Academy of Sciences (No.KZCX-3W-222;KZCX2-YW-Q11-02) and National Basic Research Program of China (No.2007CB411802;2006CB403601)
- DOI：10.1007/s00343-009-9201-4
  中图分类号：
- 网络首发：2009-07-12，
  
  纸质出版：2009
- Accepted：
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Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters[J]. 海洋湖沼学报（英文）, 2009,27(2):218-228.

Hao, J., Chen, Y., Feng, J. et al. Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters., Chin. J. Ocean. Limnol. 27, 218–228 (2009). https://doi.org/10.1007/s00343-009-9201-4
Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters[J]. 海洋湖沼学报（英文）, 2009,27(2):218-228. DOI： 10.1007/s00343-009-9201-4.

Hao, J., Chen, Y., Feng, J. et al. Reconstruction of vertical thermal structure from several subsurface temperatures in the China Seas and adjacent waters., Chin. J. Ocean. Limnol. 27, 218–228 (2009). https://doi.org/10.1007/s00343-009-9201-4 DOI：

摘要

Abstract

Empirical Orthogonal Function (EOF) analysis is used in this study to generate main eigenvector fields of historical temperature for the China Seas (here referring to Chinese marine territories) and adjacent waters from 1930 to 2002 (510 143 profiles). A good temperature profile is reconstructed based on several subsurface in situ temperature observations and the thermocline was estimated using the model. The results show that: 1) For the study area

the former four principal components can explain 95% of the overall variance

and the vertical distribution of temperature is most stable using the in situ temperature observations near the surface. 2) The model verifications based on the observed CTD data from the East China Sea (ECS)

South China Sea (SCS) and the areas around Taiwan Island show that the reconstructed profiles have high correlation with the observed ones with the confidence level >95%

especially to describe the characteristics of the thermocline well. The average errors between the reconstructed and observed profiles in these three areas are 0.69°C

0.52°C and 1.18°C respectively. It also shows the model RMS error is less than or close to the climatological error. The statistical model can be used to well estimate the temperature profile vertical structure. 3) Comparing the thermocline characteristics between the reconstructed and observed profiles

the results in the ECS show that the average absolute errors are 1.5m

1.4 m and 0.17°C/m

and the average relative errors are 24.7%

8.9% and 22.6% for the upper

lower thermocline boundaries and the gradient

respectively. Although the relative errors are obvious

the absolute error is small. In the SCS

the average absolute errors are 4.1 m

27.7 m and 0.007°C/m

and the average relative errors are 16.1%

16.8% and 9.5% for the upper

lower thermocline boundaries and the gradient

respectively. The average relative errors are all <20%. Although the average absolute error of the lower thermocline boundary is considerable

but contrast to the spatial scale of average depth of the lower thermocline boundary (165 m)

the average relative error is small (16.8%). Therefore the model can be used to well estimate the thermocline.

关键词

Keywords

references

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