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JOURNAL OF MECHANICAL ENGINEERING  > 2020  >  42(10): 70-79.
Zhong Guorong，Li Xuegang，Qu Baoxiao, et al. 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
Citation: Zhong Guorong，Li Xuegang，Qu Baoxiao, et al. 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
shu

A general regression neural network approach to reconstruct global 1°×1° resolution sea surface pCO2

doi: 10.3969/j.issn.0253-4193.2020.10.007
  • Received Date: 29 Dec 2019
  • Rev Recd Date: 23 Mar 2020
  • Publish Date: 07 Dec 2020
    Abstract
  • 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.

     

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