Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean

• Published in: Biogeosciences
• Main Authors: Gregor, Luke, Kok, Schalk, Monteiro, Pedro M. S.
• Format: Text
• Language: English
• Published: Copernicus Gesellschaft Mbh
• Subjects: envir; geo; Indian; Pacific; Southern Ocean
• Online Access: https://doi.org/10.5194/bg-15-2361-2018; https://archimer.ifremer.fr/doc/00673/78492/80822.pdf; https://archimer.ifremer.fr/doc/00673/78492/80823.pdf; https://archimer.ifremer.fr/doc/00673/78492/80825.pdf; https://archimer.ifremer.fr/doc/00673/78492/

Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO(2) to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO(2). In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Land-schutzer et al. (2016). The interpolated estimates of Delta pCO(2) are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated Delta pCO(2) is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4-6-year timescale. We separate the analysis of the Delta pCO(2) and its drivers into summer and winter. We find that understanding the variability of Delta pCO(2) and its drivers on shorter timescales is critical to resolving the long-term variability of Delta pCO(2). Results show that Delta pCO(2) is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of Delta pCO(2) variability with mixed layer depth Summer pCO(2) variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower ...