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A general regression neural network approach to reconstruct global 1°×1° resolution sea surface pCO2
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Guorong Zhong1, 2, 3, 4, Xuegang Li1, 2, 3, 4, *, Baoxiao Qu1, 3, 4, Yanjun Wang4, Huamao Yuan1, 2, 3, 4, Jinming Song1, 2, 3
Haiyang Xuebao | 2020, 42(10) : 70 - 79
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Haiyang Xuebao | 2020, 42(10): 70-79
• Article •
A general regression neural network approach to reconstruct global 1°×1° resolution sea surface pCO2
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Guorong Zhong1, 2, 3, 4, Xuegang Li1, 2, 3, 4, *, Baoxiao Qu1, 3, 4, Yanjun Wang4, Huamao Yuan1, 2, 3, 4, Jinming Song1, 2, 3
Affiliations
  • 1 Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  • 2 University of Chinese Academy of Sciences, Beijing 100049, China
  • 3 Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
  • 4 Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Published: 2020-10-25 doi: 10.3969/j.issn.0253-4193.2020.10.007
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Sea surface partial pressure of carbon dioxide (pCO2) is a crucial parameter for estimating ocean carbon source and sink term, but its sparse and uneven in situ measurements in space and time lead to large uncertainty in the estimate of sea-air CO2 flux and characteristics of ocean carbon source and sink. To eliminate this uncertainty, a general regression neural network approach using the Surface Ocean CO2 Atlas (SOCAT) dataset, based on the non-liner regression of pCO2 and longitude, latitude, time, temperature, salinity and concentration of chlorophyll, was successfully used in the reconstruction of global 1°×1° resolution monthly sea surface pCO2 from 1998 to 2018, with a root mean square error (RMSE) of 16.93 μatm and a mean relative error (MRE) of 2.97%, lower than existing feed-forward neural network (FFNN), self-organizing neural network (SOM) and machine learning approaches. The global distribution of pCO2 obtained by this approach agrees well with existing researches.

general regression neural network  /  sea surface pCO2  /  global ocean grid data
Guorong Zhong, Xuegang Li, Baoxiao Qu, Yanjun Wang, Huamao Yuan, Jinming Song. A general regression neural network approach to reconstruct global 1°×1° resolution sea surface pCO2[J]. Haiyang Xuebao, 2020 , 42 (10) : 70 -79 . DOI: 10.3969/j.issn.0253-4193.2020.10.007
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doi: 10.3969/j.issn.0253-4193.2020.10.007
  • Receive Date：2019-12-29
  • Online Date：2026-03-27
  • Published：2020-10-25
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  • Received：2019-12-29
  • Revised：2020-03-23
Funding
Affiliations
    1 Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
    2 University of Chinese Academy of Sciences, Beijing 100049, China
    3 Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
    4 Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
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