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

Show simple item record Gregor, Luke Kok, Schalk Monteiro, Pedro M.S. 2018-06-12T07:44:39Z 2018-06-12T07:44:39Z 2018-04-19
dc.description.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 pCO2 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 locationrestricted ship measurements of pCO2. 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 feedforward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of 1pCO2 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 1pCO2 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 1pCO2 and its drivers into summer and winter. We find that understanding the variability of 1pCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of 1pCO2. Results show that 1pCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of 1pCO2 variability with mixed layer depth. Summer pCO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO2 concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of 1pCO2. 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 1pCO2 but would greatly benefit from improved estimates of 1pCO2 and a longer time series. en_ZA
dc.description.department Mechanical and Aeronautical Engineering en_ZA
dc.description.librarian am2018 en_ZA
dc.description.sponsorship This work is part of a PhD funded by the ACCESS program. en_ZA
dc.description.uri en_ZA
dc.identifier.citation Gregor, L., Kok, S. & Monteiro, P.M.S. 2018, 'Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean', Biogeosciences, vol. 15, no. 7, pp. 2361-2378. en_ZA
dc.identifier.issn 1726-4170 (print)
dc.identifier.issn 1726-4189 (online)
dc.identifier.other 10.5194/bg-15-2361-2018
dc.language.iso en en_ZA
dc.publisher European Geosciences Union en_ZA
dc.rights © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. en_ZA
dc.subject Drivers en_ZA
dc.subject Southern Ocean en_ZA
dc.subject Ocean-climate community en_ZA
dc.subject Chlorophyll en_ZA
dc.subject Annular mode en_ZA
dc.subject Carbon sink en_ZA
dc.subject Support vector regression (SVR) en_ZA
dc.subject Variability en_ZA
dc.subject Fluxes en_ZA
dc.subject Phytoplankton en_ZA
dc.subject Trends en_ZA
dc.subject Temperature en_ZA
dc.subject Random forest regression (RFR) en_ZA
dc.subject Self-organising-map feedforward neural network (SOM-FFN) en_ZA
dc.title Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean en_ZA
dc.type Article en_ZA

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