Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean (original) (raw)

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19 Apr 2018

Research article | Highlight paper | | 19 Apr 2018

Abstract. 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 _p_CO2 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 _p_CO2. 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 Landschützer et al. (2016). The interpolated estimates of Δ_p_CO2 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 Δ_p_CO2 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 Δ_p_CO2 and its drivers into summer and winter. We find that understanding the variability of Δ_p_CO2 and its drivers on shorter timescales is critical to resolving the long-term variability of Δ_p_CO2. Results show that Δ_p_CO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of Δ_p_CO2 variability with mixed layer depth. Summer _p_CO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower _p_CO2 concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of Δ_p_CO2. In summary we propose that sub-decadal variability is explained by summer drivers, while winter variability contributes to the long-term changes associated with the SAM. This approach is a useful framework to assess the drivers of Δ_p_CO2 but would greatly benefit from improved estimates of Δ_p_CO2 and a longer time series.

Received: 25 Aug 2017

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Discussion started: 20 Sep 2017

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Revised: 09 Mar 2018

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Accepted: 14 Mar 2018

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Published: 19 Apr 2018