Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique
This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide ( p CO 2 sea ) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The p CO 2 sea distribution was computed using a self-organizing map (SOM) originally utilize...
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ftdoajarticles:oai:doaj.org/article:2b155ec3ebdd4627b76fc61275a3b46b 2023-05-15T17:36:39+02:00 Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique S. Nakaoka M. Telszewski Y. Nojiri S. Yasunaka C. Miyazaki H. Mukai N. Usui 2013-09-01T00:00:00Z https://doi.org/10.5194/bg-10-6093-2013 https://doaj.org/article/2b155ec3ebdd4627b76fc61275a3b46b EN eng Copernicus Publications http://www.biogeosciences.net/10/6093/2013/bg-10-6093-2013.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-10-6093-2013 1726-4170 1726-4189 https://doaj.org/article/2b155ec3ebdd4627b76fc61275a3b46b Biogeosciences, Vol 10, Iss 9, Pp 6093-6106 (2013) Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 article 2013 ftdoajarticles https://doi.org/10.5194/bg-10-6093-2013 2022-12-31T09:03:13Z This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide ( p CO 2 sea ) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The p CO 2 sea distribution was computed using a self-organizing map (SOM) originally utilized to map the p CO 2 sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) – are used during the training phase to enable the network to resolve the nonlinear relationships between the p CO 2 sea distribution and biogeochemistry of the basin. The observed p CO 2 sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed p CO 2 sea values agreed well with the p CO 2 sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the p CO 2 sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of p CO 2 sea that have tracked increases in atmospheric CO 2 . Estimated p CO 2 sea values accurately reproduced p CO 2 sea data at several time series locations in the North Pacific. The distributions of p CO 2 sea revealed by 7 yr averaged monthly p CO 2 sea maps were similar to Lamont-Doherty Earth Observatory p CO 2 sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of p CO 2 sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Pacific Biogeosciences 10 9 6093 6106 |
institution |
Open Polar |
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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 S. Nakaoka M. Telszewski Y. Nojiri S. Yasunaka C. Miyazaki H. Mukai N. Usui Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique |
topic_facet |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 |
description |
This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide ( p CO 2 sea ) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The p CO 2 sea distribution was computed using a self-organizing map (SOM) originally utilized to map the p CO 2 sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) – are used during the training phase to enable the network to resolve the nonlinear relationships between the p CO 2 sea distribution and biogeochemistry of the basin. The observed p CO 2 sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed p CO 2 sea values agreed well with the p CO 2 sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the p CO 2 sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of p CO 2 sea that have tracked increases in atmospheric CO 2 . Estimated p CO 2 sea values accurately reproduced p CO 2 sea data at several time series locations in the North Pacific. The distributions of p CO 2 sea revealed by 7 yr averaged monthly p CO 2 sea maps were similar to Lamont-Doherty Earth Observatory p CO 2 sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of p CO 2 sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing. |
format |
Article in Journal/Newspaper |
author |
S. Nakaoka M. Telszewski Y. Nojiri S. Yasunaka C. Miyazaki H. Mukai N. Usui |
author_facet |
S. Nakaoka M. Telszewski Y. Nojiri S. Yasunaka C. Miyazaki H. Mukai N. Usui |
author_sort |
S. Nakaoka |
title |
Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique |
title_short |
Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique |
title_full |
Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique |
title_fullStr |
Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique |
title_full_unstemmed |
Estimating temporal and spatial variation of ocean surface p CO 2 in the North Pacific using a self-organizing map neural network technique |
title_sort |
estimating temporal and spatial variation of ocean surface p co 2 in the north pacific using a self-organizing map neural network technique |
publisher |
Copernicus Publications |
publishDate |
2013 |
url |
https://doi.org/10.5194/bg-10-6093-2013 https://doaj.org/article/2b155ec3ebdd4627b76fc61275a3b46b |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Biogeosciences, Vol 10, Iss 9, Pp 6093-6106 (2013) |
op_relation |
http://www.biogeosciences.net/10/6093/2013/bg-10-6093-2013.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-10-6093-2013 1726-4170 1726-4189 https://doaj.org/article/2b155ec3ebdd4627b76fc61275a3b46b |
op_doi |
https://doi.org/10.5194/bg-10-6093-2013 |
container_title |
Biogeosciences |
container_volume |
10 |
container_issue |
9 |
container_start_page |
6093 |
op_container_end_page |
6106 |
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1766136200096120832 |