Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation

In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO(2)), 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...

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Published in:Biogeosciences
Main Authors: Laruelle, G., Landschützer, P., Gruber, N., Tison, J., Delille, B., Regnier, P.
Format: Article in Journal/Newspaper
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/11858/00-001M-0000-002E-1785-4
http://hdl.handle.net/11858/00-001M-0000-002E-1787-F
http://hdl.handle.net/11858/00-001M-0000-002E-1788-D
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spelling ftpubman:oai:pure.mpg.de:item_2492367 2023-08-27T04:11:57+02:00 Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation Laruelle, G. Landschützer, P. Gruber, N. Tison, J. Delille, B. Regnier, P. 2017 application/pdf application/zip http://hdl.handle.net/11858/00-001M-0000-002E-1785-4 http://hdl.handle.net/11858/00-001M-0000-002E-1787-F http://hdl.handle.net/11858/00-001M-0000-002E-1788-D eng eng info:eu-repo/grantAgreement/EC/H2020/ 643052 744 info:eu-repo/semantics/altIdentifier/doi/10.5194/bg-14-4545-2017 http://hdl.handle.net/11858/00-001M-0000-002E-1785-4 http://hdl.handle.net/11858/00-001M-0000-002E-1787-F http://hdl.handle.net/11858/00-001M-0000-002E-1788-D info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Biogeosciences info:eu-repo/semantics/article 2017 ftpubman https://doi.org/10.5194/bg-14-4545-2017 2023-08-02T01:23:14Z In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO(2)), 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 p CO2 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; Landschutzer et al., 2013) to interpolate the p CO2 data along the continental margins with a spatial resolution of 0.25 ffi 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 p CO2. The SOM-FFN is first trained with p CO2 measurements extracted from the SO-CATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated pCO(2) field with independent data extracted from the LD-VEO2015 database. The new coastal pCO(2) product confirms a previously suggested general meridional trend of the annual mean pCO(2) 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 pCO(2) 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 pCO(2); while the shelves in the south-eastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized pCO(2) for several latitudes in different oceanic basins confirms that the seasonality in shelf pCO(2) cannot solely be explained by temperature-induced changes in solubility but ... Article in Journal/Newspaper Sea ice Max Planck Society: MPG.PuRe Pacific Biogeosciences 14 19 4545 4561
institution Open Polar
collection Max Planck Society: MPG.PuRe
op_collection_id ftpubman
language English
description In spite of the recent strong increase in the number of measurements of the partial pressure of CO2 in the surface ocean (pCO(2)), 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 p CO2 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; Landschutzer et al., 2013) to interpolate the p CO2 data along the continental margins with a spatial resolution of 0.25 ffi 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 p CO2. The SOM-FFN is first trained with p CO2 measurements extracted from the SO-CATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated pCO(2) field with independent data extracted from the LD-VEO2015 database. The new coastal pCO(2) product confirms a previously suggested general meridional trend of the annual mean pCO(2) 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 pCO(2) 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 pCO(2); while the shelves in the south-eastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized pCO(2) for several latitudes in different oceanic basins confirms that the seasonality in shelf pCO(2) cannot solely be explained by temperature-induced changes in solubility but ...
format Article in Journal/Newspaper
author Laruelle, G.
Landschützer, P.
Gruber, N.
Tison, J.
Delille, B.
Regnier, P.
spellingShingle Laruelle, G.
Landschützer, P.
Gruber, N.
Tison, J.
Delille, B.
Regnier, P.
Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation
author_facet Laruelle, G.
Landschützer, P.
Gruber, N.
Tison, J.
Delille, B.
Regnier, P.
author_sort Laruelle, G.
title Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation
title_short Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation
title_full Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation
title_fullStr Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation
title_full_unstemmed Global high-resolution monthly pCO(2) climatology for the coastal ocean derived from neural network interpolation
title_sort global high-resolution monthly pco(2) climatology for the coastal ocean derived from neural network interpolation
publishDate 2017
url http://hdl.handle.net/11858/00-001M-0000-002E-1785-4
http://hdl.handle.net/11858/00-001M-0000-002E-1787-F
http://hdl.handle.net/11858/00-001M-0000-002E-1788-D
geographic Pacific
geographic_facet Pacific
genre Sea ice
genre_facet Sea ice
op_source Biogeosciences
op_relation info:eu-repo/grantAgreement/EC/H2020/ 643052 744
info:eu-repo/semantics/altIdentifier/doi/10.5194/bg-14-4545-2017
http://hdl.handle.net/11858/00-001M-0000-002E-1785-4
http://hdl.handle.net/11858/00-001M-0000-002E-1787-F
http://hdl.handle.net/11858/00-001M-0000-002E-1788-D
op_rights info:eu-repo/semantics/openAccess
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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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