Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean
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 varia...
Published in: | Biogeosciences |
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Language: | English |
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Copernicus Gesellschaft Mbh
2018
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Online Access: | 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://doi.org/10.5194/bg-15-2361-2018 https://archimer.ifremer.fr/doc/00673/78492/ |
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ftarchimer:oai:archimer.ifremer.fr:78492 2023-05-15T18:24:58+02:00 Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean Gregor, Luke Kok, Schalk Monteiro, Pedro M. S. 2018-04 application/pdf 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://doi.org/10.5194/bg-15-2361-2018 https://archimer.ifremer.fr/doc/00673/78492/ eng eng Copernicus Gesellschaft Mbh 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 doi:10.5194/bg-15-2361-2018 https://archimer.ifremer.fr/doc/00673/78492/ info:eu-repo/semantics/openAccess restricted use Biogeosciences (1726-4170) (Copernicus Gesellschaft Mbh), 2018-04 , Vol. 15 , N. 7 , P. 2361-2378 text Publication info:eu-repo/semantics/article 2018 ftarchimer https://doi.org/10.5194/bg-15-2361-2018 2021-09-23T20:36:48Z 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 pCO(2) concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of Delta pCO(2). 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 Delta pCO(2) but would greatly benefit from improved estimates of Delta pCO(2) and a longer time series. Article in Journal/Newspaper Southern Ocean Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) Indian Pacific Southern Ocean Biogeosciences 15 8 2361 2378 |
institution |
Open Polar |
collection |
Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) |
op_collection_id |
ftarchimer |
language |
English |
description |
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 pCO(2) concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of Delta pCO(2). 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 Delta pCO(2) but would greatly benefit from improved estimates of Delta pCO(2) and a longer time series. |
format |
Article in Journal/Newspaper |
author |
Gregor, Luke Kok, Schalk Monteiro, Pedro M. S. |
spellingShingle |
Gregor, Luke Kok, Schalk Monteiro, Pedro M. S. Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean |
author_facet |
Gregor, Luke Kok, Schalk Monteiro, Pedro M. S. |
author_sort |
Gregor, Luke |
title |
Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean |
title_short |
Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean |
title_full |
Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean |
title_fullStr |
Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean |
title_full_unstemmed |
Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean |
title_sort |
interannual drivers of the seasonal cycle of co2 in the southern ocean |
publisher |
Copernicus Gesellschaft Mbh |
publishDate |
2018 |
url |
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://doi.org/10.5194/bg-15-2361-2018 https://archimer.ifremer.fr/doc/00673/78492/ |
geographic |
Indian Pacific Southern Ocean |
geographic_facet |
Indian Pacific Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Biogeosciences (1726-4170) (Copernicus Gesellschaft Mbh), 2018-04 , Vol. 15 , N. 7 , P. 2361-2378 |
op_relation |
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 doi:10.5194/bg-15-2361-2018 https://archimer.ifremer.fr/doc/00673/78492/ |
op_rights |
info:eu-repo/semantics/openAccess restricted use |
op_doi |
https://doi.org/10.5194/bg-15-2361-2018 |
container_title |
Biogeosciences |
container_volume |
15 |
container_issue |
8 |
container_start_page |
2361 |
op_container_end_page |
2378 |
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1766206028212338688 |