Preprints
https://doi.org/10.5194/bg-2021-224
https://doi.org/10.5194/bg-2021-224

  07 Sep 2021

07 Sep 2021

Review status: a revised version of this preprint is currently under review for the journal BG.

Reconstruction of global surface ocean pCO2 using region-specific predicators based on a stepwise FFNN regression algorithm

Guorong Zhong1,2,3,4, Xuegang Li1,2,3,4, Jinming Song1,2,3,4, Baoxiao Qu1,3,4, Fan Wang1,2,3,4, Yanjun Wang1,4, Bin Zhang1,4, Xiaoxia Sun1,2,3,4, Wuchang Zhang1,3,4, Zhenyan Wang1,3,4, Jun Ma1,3,4, Huamao Yuan1,2,3,4, and Liqin Duan1,2,3,4 Guorong Zhong et al.
  • 1Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  • 2University of Chinese Academy of Sciences, Beijing 101407, China
  • 3Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
  • 4Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China

Abstract. Various machine learning methods were attempted in the global mapping of surface ocean partial pressure of CO2 (pCO2) to reduce the uncertainty of global ocean CO2 sink estimate due to undersampling of pCO2. In previous researches the predicators of pCO2 were usually selected empirically based on theoretic drivers of surface ocean pCO2 and same combination of predictors were applied in all areas unless lack of coverage. However, the differences between the drivers of surface ocean pCO2 in different regions were not considered. In this work, we combined the stepwise regression algorithm and a Feed Forward Neural Network (FFNN) to selected predicators of pCO2 based on mean absolute error in each of the 11 biogeochemical provinces defined by Self-Organizing Map (SOM) method. Based on the predicators selected, a monthly global 1° × 1° surface ocean pCO2 product from January 1992 to August 2019 was constructed. Validation of different combination of predicators based on the SOCAT dataset version 2020 and independent observations from time series stations was carried out. The prediction of pCO2 based on region-specific predicators selected by the stepwise FFNN algorithm were more precise than that based on predicators from previous researches. Appling of a FFNN size improving algorithm in each province decreased the mean absolute error (MAE) of global estimate to 11.32 μatm and the root mean square error (RMSE) to 17.99 μatm. The script file of the stepwise FFNN algorithm and pCO2 product are distributed through the Institute of Oceanology of the Chinese Academy of Sciences Marine Science Data Center (IOCAS; http://dx.doi.org/10.12157/iocas.2021.0022, Zhong et al., 2021).

Guorong Zhong et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-224', Anna Denvil-Sommer, 22 Sep 2021
    • AC1: 'Reply on RC1', Guorong Zhong, 30 Sep 2021
  • RC2: 'Comment on bg-2021-224', Anonymous Referee #2, 22 Sep 2021
    • AC2: 'Reply on RC2', Guorong Zhong, 30 Sep 2021
  • RC3: 'Comment on bg-2021-224', Lucas Gloege, 08 Oct 2021
    • AC3: 'Reply on RC3', Guorong Zhong, 19 Oct 2021

Guorong Zhong et al.

Data sets

Global surface ocean pCO2 product based on a stepwise FFNN algorithm Guorong Zhong http://dx.doi.org/10.12157/iocas.2021.0022

Model code and software

Global surface ocean pCO2 product based on a stepwise FFNN algorithm Guorong Zhong http://dx.doi.org/10.12157/iocas.2021.0022

Guorong Zhong et al.

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Short summary
A predictor selection algorithm was constructed to decreasing the predicating error in the surface ocean partial pressure of CO2 (pCO2) mapping by finding better combinations of pCO2 predicators in different regions. Comparing with previous researches that using same combination of predictors in all regions, using different predictors selected by the algorithm in different regions can effectively decrease the pCO2 predicating errors.
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