A global monthly climatology of total alkalinity: a neural network approach
Global climatologies of the seawater CO 2 chemistry variables are necessary to assess the marine carbon cycle in depth. The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle. Total alkalinity ( A T ) is...
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ftdoajarticles:oai:doaj.org/article:6fd60a40b961494a9baa3b9560fcc616 2023-05-15T17:50:51+02:00 A global monthly climatology of total alkalinity: a neural network approach D. Broullón F. F. Pérez A. Velo M. Hoppema A. Olsen T. Takahashi R. M. Key T. Tanhua M. González-Dávila E. Jeansson A. Kozyr S. M. A. C. van Heuven 2019-07-01T00:00:00Z https://doi.org/10.5194/essd-11-1109-2019 https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 EN eng Copernicus Publications https://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf https://doaj.org/toc/1866-3508 https://doaj.org/toc/1866-3516 doi:10.5194/essd-11-1109-2019 1866-3508 1866-3516 https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 Earth System Science Data, Vol 11, Pp 1109-1127 (2019) Environmental sciences GE1-350 Geology QE1-996.5 article 2019 ftdoajarticles https://doi.org/10.5194/essd-11-1109-2019 2022-12-31T00:19:56Z Global climatologies of the seawater CO 2 chemistry variables are necessary to assess the marine carbon cycle in depth. The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle. Total alkalinity ( A T ) is one variable of the seawater CO 2 chemistry system involved in ocean acidification and frequently measured. We used the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2) to extract relationships among the drivers of the A T variability and A T concentration using a neural network (NNGv2) to generate a monthly climatology. The GLODAPv2 quality-controlled dataset used was modeled by the NNGv2 with a root-mean-squared error (RMSE) of 5.3 µ mol kg −1 . Validation tests with independent datasets revealed the good generalization of the network. Data from five ocean time-series stations showed an acceptable RMSE range of 3–6.2 µ mol kg −1 . Successful modeling of the monthly A T variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of A T were obtained passing through the NNGv2 the World Ocean Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen and the computed climatologies of nutrients from the previous ones with a neural network. The spatiotemporal resolution is set by WOA13: 1 ∘ × 1 ∘ in the horizontal, 102 depth levels (0–5500 m) in the vertical and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution. The product is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/8644 , Broullón et al., 2019). Article in Journal/Newspaper Ocean acidification Directory of Open Access Journals: DOAJ Articles Earth System Science Data 11 3 1109 1127 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 D. Broullón F. F. Pérez A. Velo M. Hoppema A. Olsen T. Takahashi R. M. Key T. Tanhua M. González-Dávila E. Jeansson A. Kozyr S. M. A. C. van Heuven A global monthly climatology of total alkalinity: a neural network approach |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Global climatologies of the seawater CO 2 chemistry variables are necessary to assess the marine carbon cycle in depth. The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle. Total alkalinity ( A T ) is one variable of the seawater CO 2 chemistry system involved in ocean acidification and frequently measured. We used the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2) to extract relationships among the drivers of the A T variability and A T concentration using a neural network (NNGv2) to generate a monthly climatology. The GLODAPv2 quality-controlled dataset used was modeled by the NNGv2 with a root-mean-squared error (RMSE) of 5.3 µ mol kg −1 . Validation tests with independent datasets revealed the good generalization of the network. Data from five ocean time-series stations showed an acceptable RMSE range of 3–6.2 µ mol kg −1 . Successful modeling of the monthly A T variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of A T were obtained passing through the NNGv2 the World Ocean Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen and the computed climatologies of nutrients from the previous ones with a neural network. The spatiotemporal resolution is set by WOA13: 1 ∘ × 1 ∘ in the horizontal, 102 depth levels (0–5500 m) in the vertical and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution. The product is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/8644 , Broullón et al., 2019). |
format |
Article in Journal/Newspaper |
author |
D. Broullón F. F. Pérez A. Velo M. Hoppema A. Olsen T. Takahashi R. M. Key T. Tanhua M. González-Dávila E. Jeansson A. Kozyr S. M. A. C. van Heuven |
author_facet |
D. Broullón F. F. Pérez A. Velo M. Hoppema A. Olsen T. Takahashi R. M. Key T. Tanhua M. González-Dávila E. Jeansson A. Kozyr S. M. A. C. van Heuven |
author_sort |
D. Broullón |
title |
A global monthly climatology of total alkalinity: a neural network approach |
title_short |
A global monthly climatology of total alkalinity: a neural network approach |
title_full |
A global monthly climatology of total alkalinity: a neural network approach |
title_fullStr |
A global monthly climatology of total alkalinity: a neural network approach |
title_full_unstemmed |
A global monthly climatology of total alkalinity: a neural network approach |
title_sort |
global monthly climatology of total alkalinity: a neural network approach |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/essd-11-1109-2019 https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Earth System Science Data, Vol 11, Pp 1109-1127 (2019) |
op_relation |
https://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf https://doaj.org/toc/1866-3508 https://doaj.org/toc/1866-3516 doi:10.5194/essd-11-1109-2019 1866-3508 1866-3516 https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 |
op_doi |
https://doi.org/10.5194/essd-11-1109-2019 |
container_title |
Earth System Science Data |
container_volume |
11 |
container_issue |
3 |
container_start_page |
1109 |
op_container_end_page |
1127 |
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1766157768167784448 |