Global high-resolution monthly p CO 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 CO 2 in the surface ocean ( p CO 2 ), the air–sea CO 2 balance of the continental shelf seas remains poorly quantified. This is a consequence of these regions remaining strongly under-sampled in both time...
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ftdoajarticles:oai:doaj.org/article:bcea6ade62ba42298ed14f7a1a34f40e 2023-05-15T18:18:56+02:00 Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation G. G. Laruelle P. Landschützer N. Gruber J.-L. Tison B. Delille P. Regnier 2017-10-01T00:00:00Z https://doi.org/10.5194/bg-14-4545-2017 https://doaj.org/article/bcea6ade62ba42298ed14f7a1a34f40e EN eng Copernicus Publications https://www.biogeosciences.net/14/4545/2017/bg-14-4545-2017.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-14-4545-2017 1726-4170 1726-4189 https://doaj.org/article/bcea6ade62ba42298ed14f7a1a34f40e Biogeosciences, Vol 14, Pp 4545-4561 (2017) Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 article 2017 ftdoajarticles https://doi.org/10.5194/bg-14-4545-2017 2022-12-31T09:54:09Z In spite of the recent strong increase in the number of measurements of the partial pressure of CO 2 in the surface ocean ( p CO 2 ), the air–sea CO 2 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 CO 2 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 p CO 2 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 p CO 2 . The SOM-FFN is first trained with p CO 2 measurements extracted from the SOCATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated p CO 2 field with independent data extracted from the LDVEO2015 database. The new coastal p CO 2 product confirms a previously suggested general meridional trend of the annual mean p CO 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 p CO 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 p CO 2, while the shelves in the southeastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized p CO 2 for several latitudes in different oceanic basins confirms that the seasonality in shelf p CO 2 cannot solely be explained by temperature-induced changes in solubility ... Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Pacific Biogeosciences 14 19 4545 4561 |
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English |
topic |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 |
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Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 G. G. Laruelle P. Landschützer N. Gruber J.-L. Tison B. Delille P. Regnier Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation |
topic_facet |
Ecology QH540-549.5 Life QH501-531 Geology QE1-996.5 |
description |
In spite of the recent strong increase in the number of measurements of the partial pressure of CO 2 in the surface ocean ( p CO 2 ), the air–sea CO 2 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 CO 2 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 p CO 2 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 p CO 2 . The SOM-FFN is first trained with p CO 2 measurements extracted from the SOCATv4 database. Then, the validity of our interpolation, in both space and time, is assessed by comparing the generated p CO 2 field with independent data extracted from the LDVEO2015 database. The new coastal p CO 2 product confirms a previously suggested general meridional trend of the annual mean p CO 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 p CO 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 p CO 2, while the shelves in the southeastern Atlantic and in the southern Pacific reveal a much smaller seasonality. The calculation of temperature normalized p CO 2 for several latitudes in different oceanic basins confirms that the seasonality in shelf p CO 2 cannot solely be explained by temperature-induced changes in solubility ... |
format |
Article in Journal/Newspaper |
author |
G. G. Laruelle P. Landschützer N. Gruber J.-L. Tison B. Delille P. Regnier |
author_facet |
G. G. Laruelle P. Landschützer N. Gruber J.-L. Tison B. Delille P. Regnier |
author_sort |
G. G. Laruelle |
title |
Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation |
title_short |
Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation |
title_full |
Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation |
title_fullStr |
Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation |
title_full_unstemmed |
Global high-resolution monthly p CO 2 climatology for the coastal ocean derived from neural network interpolation |
title_sort |
global high-resolution monthly p co 2 climatology for the coastal ocean derived from neural network interpolation |
publisher |
Copernicus Publications |
publishDate |
2017 |
url |
https://doi.org/10.5194/bg-14-4545-2017 https://doaj.org/article/bcea6ade62ba42298ed14f7a1a34f40e |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Biogeosciences, Vol 14, Pp 4545-4561 (2017) |
op_relation |
https://www.biogeosciences.net/14/4545/2017/bg-14-4545-2017.pdf https://doaj.org/toc/1726-4170 https://doaj.org/toc/1726-4189 doi:10.5194/bg-14-4545-2017 1726-4170 1726-4189 https://doaj.org/article/bcea6ade62ba42298ed14f7a1a34f40e |
op_doi |
https://doi.org/10.5194/bg-14-4545-2017 |
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Biogeosciences |
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14 |
container_issue |
19 |
container_start_page |
4545 |
op_container_end_page |
4561 |
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1766195714504785920 |