Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean
Resolving and understanding the drivers of variability of CO 2 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 CO 2 to understand the role that seasonal vari...
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ftdoajarticles:oai:doaj.org/article:e7c1f958492a4e0fae118653910e33a5 2023-05-15T18:24:51+02:00 Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean L. Gregor S. Kok P. M. S. Monteiro 2018-04-01T00:00:00Z https://doi.org/10.5194/bg-15-2361-2018 https://doaj.org/article/e7c1f958492a4e0fae118653910e33a5 EN eng Copernicus Publications https://www.biogeosciences.net/15/2361/2018/bg-15-2361-2018.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-15-2361-2018 1726-4170 1726-4189 https://doaj.org/article/e7c1f958492a4e0fae118653910e33a5 Biogeosciences, Vol 15, Pp 2361-2378 (2018) Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 article 2018 ftdoajarticles https://doi.org/10.5194/bg-15-2361-2018 2022-12-31T12:07:21Z Resolving and understanding the drivers of variability of CO 2 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 CO 2 to understand the role that seasonal variability has in long-term CO 2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of p CO 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 Landschützer et al. (2016). The interpolated estimates of Δ p CO 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 Δ p CO 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 Δ p CO 2 and its drivers into summer and winter. We find that understanding the variability of Δ p CO 2 and its drivers on shorter timescales is critical to resolving the long-term variability of Δ p CO 2 . Results show that Δ p CO 2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of Δ p CO 2 variability with mixed layer depth. Summer p CO 2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower p CO 2 concentrations. In ... Article in Journal/Newspaper Southern Ocean Directory of Open Access Journals: DOAJ Articles Southern Ocean Pacific Indian Biogeosciences 15 8 2361 2378 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 |
spellingShingle |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 L. Gregor S. Kok P. M. S. Monteiro Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean |
topic_facet |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 |
description |
Resolving and understanding the drivers of variability of CO 2 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 CO 2 to understand the role that seasonal variability has in long-term CO 2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of p CO 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 Landschützer et al. (2016). The interpolated estimates of Δ p CO 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 Δ p CO 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 Δ p CO 2 and its drivers into summer and winter. We find that understanding the variability of Δ p CO 2 and its drivers on shorter timescales is critical to resolving the long-term variability of Δ p CO 2 . Results show that Δ p CO 2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of Δ p CO 2 variability with mixed layer depth. Summer p CO 2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower p CO 2 concentrations. In ... |
format |
Article in Journal/Newspaper |
author |
L. Gregor S. Kok P. M. S. Monteiro |
author_facet |
L. Gregor S. Kok P. M. S. Monteiro |
author_sort |
L. Gregor |
title |
Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean |
title_short |
Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean |
title_full |
Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean |
title_fullStr |
Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean |
title_full_unstemmed |
Interannual drivers of the seasonal cycle of CO 2 in the Southern Ocean |
title_sort |
interannual drivers of the seasonal cycle of co 2 in the southern ocean |
publisher |
Copernicus Publications |
publishDate |
2018 |
url |
https://doi.org/10.5194/bg-15-2361-2018 https://doaj.org/article/e7c1f958492a4e0fae118653910e33a5 |
geographic |
Southern Ocean Pacific Indian |
geographic_facet |
Southern Ocean Pacific Indian |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Biogeosciences, Vol 15, Pp 2361-2378 (2018) |
op_relation |
https://www.biogeosciences.net/15/2361/2018/bg-15-2361-2018.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-15-2361-2018 1726-4170 1726-4189 https://doaj.org/article/e7c1f958492a4e0fae118653910e33a5 |
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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1766205805583925248 |