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...
Published in: | Biogeosciences |
---|---|
Main Authors: | , , , , , |
Other Authors: | |
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 |
---|---|
record_format |
openpolar |
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 |
op_container_end_page |
4561 |
_version_ |
1796953399538745344 |