Employing machine learning methods for mapping surface ocean pCO2 has reduced the uncertainty in estimating sea-air CO2 flux. However, a general discrepancy exists between the Southern Ocean carbon sinks derived from pCO2 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 pCO2 due to notably uneven density of pCO2 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 pCO2 mainly occurred in regions south of 60°S and was hiding under ice cover. As the surface ocean pCO2 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.

Guorong Zhong, Xuegang Li, Jinming Song, Fan Wang, Baoxiao Qu, Yanjun Wang, Bin Zhang, Jun Ma, Huamao Yuan, Liqin Duan, Qidong Wang, Jianwei Xing, Jiajia Dai