Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach

The Southern Ocean comprises 25% of the global ocean surface area, accounts for nearly half of the total carbon sink of the global oceans, and is a place that significantly reduces the impacts of anthropogenic CO2 emissions. Due to the sparsity of observational data, the changes in Southern Ocean carbon sinks over time remain uncertain. In this study, we integrated correlation analysis and a feedforward neural network to improve the accuracy of carbon flux estimations in the Southern Ocean. Based on observation data from 1998-2018, we reconstructed the Southern Ocean's pCO(2) grid data during this period. The root-mean-square error obtained by fitting the observation data was 8.86 mu atm, indicating that the results were better than those of the two primary statistically based models in the Surface Ocean pCO(2) mapping intercomparison. The results also showed that the Southern Ocean's capacity to act as a carbon sink has gradually increased since 2000; it reduced during 2010-2013 but increased significantly after that. The Southern Ocean's seasonality is characterized by minimum carbon uptake in winter due to increased upwelling; this is followed by a rapid increase toward maximum uptake in summer, which is mainly biologically driven. There is an apparent double-ring structure in the Southern Ocean, as noted in other studies. This study confirms that the inner ring (50-70 degrees S) is a carbon source area gradually transforming into a carbon sink, while the outer ring (35-50 degrees S) continues to serve as a carbon sink.

Keyword(s)

Carbon sink, feedforward neural network (FFNN), machine learning, pCO(2), Southern Ocean

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Wang Yanjun, Li Xiaofeng, Song Jinming, Li Xuegang, Zhong Guorong, Zhang Bin (2021). Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach. Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. 14. 3495-3503. https://doi.org/10.1109/JSTARS.2021.3066552, https://archimer.ifremer.fr/doc/00700/81204/

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