Employing machine learning methods for mapping surface ocean p CO 2 has reduced the uncertainty in estimating sea-air CO 2 flux. However, a general discrepancy exists between the Southern Ocean carbon sinks derived from p CO 2 products and those from biogeochemistry models. Here, by performing a boosting ensemble learning feed-forward neural networks method, we have identified an underestimation of the surface Southern Ocean p CO 2 due to notably uneven density of p CO 2 measurements between summer and winter, which resulted in about 16% overestimating of Southern Ocean carbon sink over the past three decades. In particular, the Southern Ocean carbon sink since 2010 was notably overestimated by approximately 29%. This overestimation can be mitigated by a winter correction in algorithms, with the average Southern Ocean carbon sink during 1992-2021 corrected to −0.87 PgC yr −1 from the original −1.01 PgC yr −1 . Furthermore, the most notable underestimation of surface ocean p CO 2 mainly occurred in regions south of 60°S and was hiding under ice cover. As the surface ocean p CO 2 under sea ice coverage in the winter is much higher than the atmosphere, if sea ice melts completely, there could be a further reduction of about 0.14 PgC yr −1 in the Southern Ocean carbon sink.
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Zhong et al. (2024) studied this question.
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