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).
Highlights
As a net sink for atmospheric CO2, global oceans have been thought to have removed about one third of anthropogenic CO2 since the beginning of the industrial revolution (Sabine et al, 2004; Friedlingstein et al, 2019)
Indicators representing sampling position were listed as recommended predicators in some provinces, including latitude, longitude and sampling time, suggesting that relatively steady spatial or temporal variability pattern of surface ocean pressure of CO2 (pCO2) existed in these biogeochemical provinces
Latitude and the sine and cosine of longitude were listed as recommended predicators of pCO2 in most provinces, suggesting an obvious spatial distribution pattern of pCO2, which was not learned sufficiently by the Feed-forward neural networks (FFNN) model from existing indicators and the indicators related to spatial position were applied as supplementary
Summary
As a net sink for atmospheric CO2, global oceans have been thought to have removed about one third of anthropogenic CO2 since the beginning of the industrial revolution (Sabine et al, 2004; Friedlingstein et al, 2019). Greater pCO2 of surface water than that of overlying air indicating that CO2 released from oceans to the air, and absorption of CO2 by oceans happened when the pCO2 of surface water was lower than that of air. The ocean in these two scenarios is known as oceanic carbon source and oceanic carbon sink respectively. Sparse and uneven observations of surface ocean pCO2 in time and space severely limited the understanding of interannual variability of oceanic carbon sink, and researches based on different methods were carried out to break this barrier. Recent researches on artificial neural networks and other machine learning algorithms, such as feed-forward neural network (FFNN) method
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