Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation

peer reviewed In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO2), the air–sea CO2 balance of the continental shelf seas remains poorly quantified. This is a consequence of these regions remaining strongly under-sampled in b...

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Published in:Biogeosciences
Main Authors: Laruelle, Goulven Gildas, Landschützer, Peter, Gruber, Nicolas, Tison, Jean-Louis, Delille, Bruno, Regnier, Pierre
Other Authors: FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège
Format: Article in Journal/Newspaper
Language:English
Published: European Geosciences Union 2017
Subjects:
Online Access:https://orbi.uliege.be/handle/2268/219828
https://orbi.uliege.be/bitstream/2268/219828/1/bg-14-4545-2017.pdf
https://doi.org/10.5194/bg-14-4545-2017
id ftorbi:oai:orbi.ulg.ac.be:2268/219828
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spelling ftorbi:oai:orbi.ulg.ac.be:2268/219828 2024-04-21T08:11:28+00:00 Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation Laruelle, Goulven Gildas Landschützer, Peter Gruber, Nicolas Tison, Jean-Louis Delille, Bruno Regnier, Pierre FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège 2017 https://orbi.uliege.be/handle/2268/219828 https://orbi.uliege.be/bitstream/2268/219828/1/bg-14-4545-2017.pdf https://doi.org/10.5194/bg-14-4545-2017 en eng European Geosciences Union https://www.biogeosciences.net/14/4545/2017/ urn:issn:1726-4170 urn:issn:1726-4189 https://orbi.uliege.be/handle/2268/219828 info:hdl:2268/219828 https://orbi.uliege.be/bitstream/2268/219828/1/bg-14-4545-2017.pdf doi:10.5194/bg-14-4545-2017 scopus-id:2-s2.0-85027261638 open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Biogeosciences, 14 (19), 4545-4561 (2017) Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique journal article http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article peer reviewed 2017 ftorbi https://doi.org/10.5194/bg-14-4545-2017 2024-03-27T14:54:09Z peer reviewed In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO2), the air–sea CO2 balance of the continental shelf seas remains poorly quantified. This is a consequence of these regions remaining strongly under-sampled in both time and space and of surface pCO2 exhibiting much higher temporal and spatial variability in these regions compared to the open ocean. Here, we use a modified version of a two-step artificial neural network method (SOM-FFN; Landschützer et al., 2013) to interpolate the pCO2 data along the continental margins with a spatial resolution of 0.25° and with monthly resolution from 1998 to 2015. The most important modifications compared to the original SOM-FFN method are (i) the much higher spatial resolution and (ii) the inclusion of sea ice and wind speed as predictors of pCO2. The SOM-FFN is first trained with pCO2 measurements extracted from the SOCATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated pCO2 field with independent data extracted from the LDVEO2015 database. The new coastal pCO2 product confirms a previously suggested general meridional trend of the annual mean pCO2 in all the continental shelves with high values in the tropics and dropping to values beneath those of the atmosphere at higher latitudes. The monthly resolution of our data product permits us to reveal significant differences in the seasonality of pCO2 across the ocean basins. The shelves of the western and northern Pacific, as well as the shelves in the temperate northern Atlantic, display particularly pronounced seasonal variations in pCO2, while the shelves in the southeastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized pCO2 for several latitudes in different oceanic basins confirms that the seasonality in shelf pCO2 cannot solely be explained by temperature-induced changes in solubility but are also ... Article in Journal/Newspaper Sea ice University of Liège: ORBi (Open Repository and Bibliography) Biogeosciences 14 19 4545 4561
institution Open Polar
collection University of Liège: ORBi (Open Repository and Bibliography)
op_collection_id ftorbi
language English
topic Physical
chemical
mathematical & earth Sciences
Earth sciences & physical geography
Physique
chimie
mathématiques & sciences de la terre
Sciences de la terre & géographie physique
spellingShingle Physical
chemical
mathematical & earth Sciences
Earth sciences & physical geography
Physique
chimie
mathématiques & sciences de la terre
Sciences de la terre & géographie physique
Laruelle, Goulven Gildas
Landschützer, Peter
Gruber, Nicolas
Tison, Jean-Louis
Delille, Bruno
Regnier, Pierre
Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation
topic_facet Physical
chemical
mathematical & earth Sciences
Earth sciences & physical geography
Physique
chimie
mathématiques & sciences de la terre
Sciences de la terre & géographie physique
description peer reviewed In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO2), the air–sea CO2 balance of the continental shelf seas remains poorly quantified. This is a consequence of these regions remaining strongly under-sampled in both time and space and of surface pCO2 exhibiting much higher temporal and spatial variability in these regions compared to the open ocean. Here, we use a modified version of a two-step artificial neural network method (SOM-FFN; Landschützer et al., 2013) to interpolate the pCO2 data along the continental margins with a spatial resolution of 0.25° and with monthly resolution from 1998 to 2015. The most important modifications compared to the original SOM-FFN method are (i) the much higher spatial resolution and (ii) the inclusion of sea ice and wind speed as predictors of pCO2. The SOM-FFN is first trained with pCO2 measurements extracted from the SOCATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated pCO2 field with independent data extracted from the LDVEO2015 database. The new coastal pCO2 product confirms a previously suggested general meridional trend of the annual mean pCO2 in all the continental shelves with high values in the tropics and dropping to values beneath those of the atmosphere at higher latitudes. The monthly resolution of our data product permits us to reveal significant differences in the seasonality of pCO2 across the ocean basins. The shelves of the western and northern Pacific, as well as the shelves in the temperate northern Atlantic, display particularly pronounced seasonal variations in pCO2, while the shelves in the southeastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized pCO2 for several latitudes in different oceanic basins confirms that the seasonality in shelf pCO2 cannot solely be explained by temperature-induced changes in solubility but are also ...
author2 FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège
format Article in Journal/Newspaper
author Laruelle, Goulven Gildas
Landschützer, Peter
Gruber, Nicolas
Tison, Jean-Louis
Delille, Bruno
Regnier, Pierre
author_facet Laruelle, Goulven Gildas
Landschützer, Peter
Gruber, Nicolas
Tison, Jean-Louis
Delille, Bruno
Regnier, Pierre
author_sort Laruelle, Goulven Gildas
title Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation
title_short Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation
title_full Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation
title_fullStr Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation
title_full_unstemmed Global high-resolution monthly pCO2 climatology for the coastal ocean derived from neural network interpolation
title_sort global high-resolution monthly pco2 climatology for the coastal ocean derived from neural network interpolation
publisher European Geosciences Union
publishDate 2017
url https://orbi.uliege.be/handle/2268/219828
https://orbi.uliege.be/bitstream/2268/219828/1/bg-14-4545-2017.pdf
https://doi.org/10.5194/bg-14-4545-2017
genre Sea ice
genre_facet Sea ice
op_source Biogeosciences, 14 (19), 4545-4561 (2017)
op_relation https://www.biogeosciences.net/14/4545/2017/
urn:issn:1726-4170
urn:issn:1726-4189
https://orbi.uliege.be/handle/2268/219828
info:hdl:2268/219828
https://orbi.uliege.be/bitstream/2268/219828/1/bg-14-4545-2017.pdf
doi:10.5194/bg-14-4545-2017
scopus-id:2-s2.0-85027261638
op_rights open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5194/bg-14-4545-2017
container_title Biogeosciences
container_volume 14
container_issue 19
container_start_page 4545
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