A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink
The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO 2 ), but this sink has been shown to vary substantially in time. Here we use surface ocean CO 2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We be...
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ftdoajarticles:oai:doaj.org/article:38ad8a7a9465440796fa04007476c9b3 2023-05-15T17:32:59+02:00 A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink P. Landschützer N. Gruber D. C. E. Bakker U. Schuster S. Nakaoka M. R. Payne T. P. Sasse J. Zeng 2013-11-01T00:00:00Z https://doi.org/10.5194/bg-10-7793-2013 https://doaj.org/article/38ad8a7a9465440796fa04007476c9b3 EN eng Copernicus Publications http://www.biogeosciences.net/10/7793/2013/bg-10-7793-2013.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 1726-4170 1726-4189 doi:10.5194/bg-10-7793-2013 https://doaj.org/article/38ad8a7a9465440796fa04007476c9b3 Biogeosciences, Vol 10, Iss 11, Pp 7793-7815 (2013) Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 article 2013 ftdoajarticles https://doi.org/10.5194/bg-10-7793-2013 2022-12-31T04:33:20Z The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO 2 ), but this sink has been shown to vary substantially in time. Here we use surface ocean CO 2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i.e. the Surface Ocean CO 2 Atlas (SOCAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a two-step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO 2 ( p CO 2 ) at a resolution of 1° × 1°. From those, we compute the air–sea CO 2 flux maps using a standard gas exchange parameterization and high-resolution wind speeds. The neural networks fit the observed p CO 2 data with a root mean square error (RMSE) of about 10 μatm and with almost no bias. A check against independent time-series data and new data from SOCAT v2 reveals a larger RMSE of 22.8 μatm for the entire Atlantic Ocean, which decreases to 16.3 μatm for data south of 40° N. We estimate a decadal mean uptake flux of −0.45 ± 0.15 Pg C yr −1 for the Atlantic between 44° S and 79° N, representing the sum of a strong uptake north of 18° N (−0.39 ± 0.10 Pg C yr −1 ), outgassing in the tropics (18° S–18° N, 0.11 ± 0.07 Pg C yr −1 ), and uptake in the subtropical/temperate South Atlantic south of 18° S (−0.16 ± 0.06 Pg C yr −1 ), consistent with recent studies. The strongest seasonal variability of the CO 2 flux occurs in the temperature-driven subtropical North Atlantic, with uptake in winter and outgassing in summer. The seasonal cycle is antiphased in the subpolar latitudes relative to the subtropics largely as a result of the biologically driven winter-to-summer drawdown of CO 2 . Over the 10 yr analysis period (1998 through 2007), sea surface p CO 2 increased faster than that of the atmosphere in large areas poleward of 40° N, while in other regions of the North Atlantic ... Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Biogeosciences 10 11 7793 7815 |
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 P. Landschützer N. Gruber D. C. E. Bakker U. Schuster S. Nakaoka M. R. Payne T. P. Sasse J. Zeng A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink |
topic_facet |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 |
description |
The Atlantic Ocean is one of the most important sinks for atmospheric carbon dioxide (CO 2 ), but this sink has been shown to vary substantially in time. Here we use surface ocean CO 2 observations to estimate this sink and the temporal variability from 1998 through 2007 in the Atlantic Ocean. We benefit from (i) a continuous improvement of the observations, i.e. the Surface Ocean CO 2 Atlas (SOCAT) v1.5 database and (ii) a newly developed technique to interpolate the observations in space and time. In particular, we use a two-step neural network approach to reconstruct basin-wide monthly maps of the sea surface partial pressure of CO 2 ( p CO 2 ) at a resolution of 1° × 1°. From those, we compute the air–sea CO 2 flux maps using a standard gas exchange parameterization and high-resolution wind speeds. The neural networks fit the observed p CO 2 data with a root mean square error (RMSE) of about 10 μatm and with almost no bias. A check against independent time-series data and new data from SOCAT v2 reveals a larger RMSE of 22.8 μatm for the entire Atlantic Ocean, which decreases to 16.3 μatm for data south of 40° N. We estimate a decadal mean uptake flux of −0.45 ± 0.15 Pg C yr −1 for the Atlantic between 44° S and 79° N, representing the sum of a strong uptake north of 18° N (−0.39 ± 0.10 Pg C yr −1 ), outgassing in the tropics (18° S–18° N, 0.11 ± 0.07 Pg C yr −1 ), and uptake in the subtropical/temperate South Atlantic south of 18° S (−0.16 ± 0.06 Pg C yr −1 ), consistent with recent studies. The strongest seasonal variability of the CO 2 flux occurs in the temperature-driven subtropical North Atlantic, with uptake in winter and outgassing in summer. The seasonal cycle is antiphased in the subpolar latitudes relative to the subtropics largely as a result of the biologically driven winter-to-summer drawdown of CO 2 . Over the 10 yr analysis period (1998 through 2007), sea surface p CO 2 increased faster than that of the atmosphere in large areas poleward of 40° N, while in other regions of the North Atlantic ... |
format |
Article in Journal/Newspaper |
author |
P. Landschützer N. Gruber D. C. E. Bakker U. Schuster S. Nakaoka M. R. Payne T. P. Sasse J. Zeng |
author_facet |
P. Landschützer N. Gruber D. C. E. Bakker U. Schuster S. Nakaoka M. R. Payne T. P. Sasse J. Zeng |
author_sort |
P. Landschützer |
title |
A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink |
title_short |
A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink |
title_full |
A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink |
title_fullStr |
A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink |
title_full_unstemmed |
A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink |
title_sort |
neural network-based estimate of the seasonal to inter-annual variability of the atlantic ocean carbon sink |
publisher |
Copernicus Publications |
publishDate |
2013 |
url |
https://doi.org/10.5194/bg-10-7793-2013 https://doaj.org/article/38ad8a7a9465440796fa04007476c9b3 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Biogeosciences, Vol 10, Iss 11, Pp 7793-7815 (2013) |
op_relation |
http://www.biogeosciences.net/10/7793/2013/bg-10-7793-2013.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 1726-4170 1726-4189 doi:10.5194/bg-10-7793-2013 https://doaj.org/article/38ad8a7a9465440796fa04007476c9b3 |
op_doi |
https://doi.org/10.5194/bg-10-7793-2013 |
container_title |
Biogeosciences |
container_volume |
10 |
container_issue |
11 |
container_start_page |
7793 |
op_container_end_page |
7815 |
_version_ |
1766131320828723200 |