A global monthly climatology of total alkalinity: a neural network approach
Global climatologies of the seawater CO2 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 (AT) is one...
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Copernicus Publications
2019
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Online Access: | https://doi.org/10.5194/essd-11-1109-2019 https://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:6fd60a40b961494a9baa3b9560fcc616 2023-05-15T17:50:44+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 2019-07-01 https://doi.org/10.5194/essd-11-1109-2019 https://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 en eng Copernicus Publications doi:10.5194/essd-11-1109-2019 1866-3508 1866-3516 https://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 undefined Earth System Science Data, Vol 11, Pp 1109-1127 (2019) envir geo Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2019 fttriple https://doi.org/10.5194/essd-11-1109-2019 2023-01-22T19:12:25Z Global climatologies of the seawater CO2 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 (AT) is one variable of the seawater CO2 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 AT variability and AT 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 µmol 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 µmol kg−1. Successful modeling of the monthly AT variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of AT 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 Unknown Earth System Science Data 11 3 1109 1127 |
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English |
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envir geo |
spellingShingle |
envir geo 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 |
envir geo |
description |
Global climatologies of the seawater CO2 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 (AT) is one variable of the seawater CO2 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 AT variability and AT 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 µmol 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 µmol kg−1. Successful modeling of the monthly AT variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of AT 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://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf 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 |
doi:10.5194/essd-11-1109-2019 1866-3508 1866-3516 https://www.earth-syst-sci-data.net/11/1109/2019/essd-11-1109-2019.pdf https://doaj.org/article/6fd60a40b961494a9baa3b9560fcc616 |
op_rights |
undefined |
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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1766157620117241856 |