As a net sink for atmospheric CO2, global oceans have removed about one-third of anthropogenic CO2 since the beginning of the industrial revolution. However, there is significant uncertainty in the estimation of the global ocean sea-air CO2 flux, which depends on the surface ocean CO2 partial pressure (pCO2) products.
In previous pCO2 reconstructions, the differences between the drivers of surface ocean pCO2 in different regions were not considered, and no widely recognized methods for judging the importance of each predictor in the surface ocean pCO2 mapping are available yet.
Recently, a research team led by Prof. SONG Jinming from the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) constructed a stepwise feed-forward neural network (FFNN) algorithm to rank the importance of predictors and figure out the optimal combination of pCO2 predictors in each biogeochemical province defined by self-organizing map (SOM).
They also reconstructed a monthly global 1° ×1° resolution global surface ocean pCO2 product from January 1992 to August 2019.
The study was published in Biogeosciences.
The validation based on the Surface Ocean CO2 Atlas (SOCAT) dataset and independent observations showed that using regional-specific predictors selected by the stepwise FFNN algorithm retrieved a lower predicting error than globally similar predictors. The mean absolute error of the global estimate was 11.32 μatm and the root mean square error was 17.99 μatm.
The mean absolute error of the new pCO2 product was lower, suggesting that pCO2 predicting based on regional specific predictors selected by the stepwise FFNN algorithm was better than that based on the globally same predictors.
The script file of the stepwise FFNN algorithm and pCO2 product are distributed through the Marine Science Data Center, Chinese Academy of Sciences, which is available at http://english.casodc.com/data/metadata-special-detail?id=1418424272359075841.
Monthly global 1° ×1° surface ocean pCO2 product based on the stepwise FFNN algorithm (Image by IOCAS)
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