Abstract
Abstract. Various machine learning methods were attempted in the global mapping of surface ocean partial pressure of CO2 (pCO2) to reduce the uncertainty of the global ocean CO2 sink estimate due to undersampling of pCO2. In previous research, the predictors of pCO2 were usually selected empirically based on theoretic drivers of surface ocean pCO2, and the same combination of predictors was applied in all areas except where there was a 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 select predictors of pCO2 based on the mean absolute error in each of the 11 biogeochemical provinces defined by the self-organizing map (SOM) method. Based on the predictors selected, a monthly global 1∘ × 1∘ surface ocean pCO2 product from January 1992 to August 2019 was constructed. Validation of different combinations of predictors based on the Surface Ocean CO2 Atlas (SOCAT) dataset version 2020 and independent observations from time series stations was carried out. The prediction of pCO2 based on region-specific predictors selected by the stepwise FFNN algorithm was more precise than that based on predictors from previous research. Applying the FFNN size-improving algorithm in each province decreased the mean absolute error (MAE) of the 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, https://doi.org/10.12157/iocas.2021.0022, Zhong, 2021.
Highlights
As a net sink for atmospheric CO2, global oceans have removed about one-third of anthropogenic CO2 since the beginning of the industrial revolution (Sabine et al, 2004; Friedlingstein et al, 2019)
Predictors representing sampling positions were listed as recommended predictors in some provinces, including latitude, longitude, and sampling time, suggesting that relatively steady spatial or temporal variability patterns of surface ocean pressure of the CO2 (pCO2) existed in these biogeochemical provinces
A stepwise feed-forward neural network (FFNN) algorithm was constructed to decrease the predicting error in the surface ocean pCO2 mapping by finding better combinations of pCO2 predictors in each biogeochemical province defined by the selforganization mapping (SOM) method, based on which a monthly 1◦ × 1◦ gridded global open-oceanic surface ocean pCO2 product from January 1992 to August 2019 was constructed
Summary
As a net sink for atmospheric CO2, global oceans have removed about one-third of anthropogenic CO2 since the beginning of the industrial revolution (Sabine et al, 2004; Friedlingstein et al, 2019). The global ocean sea– air CO2 flux averaged between 2001–2015 varies from −1.55 to −1.74 Pg C yr−1 with a maximum difference in individual years of nearly 0.6 Pg C yr−1, depending on the surface ocean partial pressure of the CO2 (pCO2) product. These differences largely stem from differences in pCO2 estimates across the products (Rödenbeck et al, 2014; Iida et al, 2015; Landschützer et al, 2014; Denvil-Sommer et al, 2019).
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