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 (A(T)) is o...
Published in: | Earth System Science Data |
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Format: | Article in Journal/Newspaper |
Language: | English |
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2019
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Online Access: | https://hdl.handle.net/11370/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc https://research.rug.nl/en/publications/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc https://doi.org/10.5194/essd-11-1109-2019 https://pure.rug.nl/ws/files/118430314/essd_11_1109_2019.pdf |
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ftunigroningenpu:oai:pure.rug.nl:publications/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc 2024-06-02T08:12:34+00:00 A global monthly climatology of total alkalinity:A neural network approach Broullon, Daniel Perez, Fiz F. Velo, Anton Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzalez-Davila, Melchor Jeansson, Emil Kozyr, Alex van Heuven, Steven M. A. C. 2019-07-31 application/pdf https://hdl.handle.net/11370/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc https://research.rug.nl/en/publications/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc https://doi.org/10.5194/essd-11-1109-2019 https://pure.rug.nl/ws/files/118430314/essd_11_1109_2019.pdf eng eng https://research.rug.nl/en/publications/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc info:eu-repo/semantics/openAccess Broullon , D , Perez , F F , Velo , A , Hoppema , M , Olsen , A , Takahashi , T , Key , R M , Tanhua , T , Gonzalez-Davila , M , Jeansson , E , Kozyr , A & van Heuven , S M A C 2019 , ' A global monthly climatology of total alkalinity : A neural network approach ' , Earth System Science Data , vol. 11 , no. 3 , pp. 1109-1127 . https://doi.org/10.5194/essd-11-1109-2019 SURFACE OCEAN INORGANIC CARBON CO2 ACIDIFICATION VARIABILITY SATURATION CHEMISTRY IMPACTS SEA PH article 2019 ftunigroningenpu https://doi.org/10.5194/essd-11-1109-2019 2024-05-07T20:49:55Z 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 (A(T)) 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 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 mu 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 mu 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 degrees x 1 degrees 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, Broullon et al., 2019). Article in Journal/Newspaper Ocean acidification University of Groningen research database Earth System Science Data 11 3 1109 1127 |
institution |
Open Polar |
collection |
University of Groningen research database |
op_collection_id |
ftunigroningenpu |
language |
English |
topic |
SURFACE OCEAN INORGANIC CARBON CO2 ACIDIFICATION VARIABILITY SATURATION CHEMISTRY IMPACTS SEA PH |
spellingShingle |
SURFACE OCEAN INORGANIC CARBON CO2 ACIDIFICATION VARIABILITY SATURATION CHEMISTRY IMPACTS SEA PH Broullon, Daniel Perez, Fiz F. Velo, Anton Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzalez-Davila, Melchor Jeansson, Emil Kozyr, Alex van Heuven, Steven M. A. C. A global monthly climatology of total alkalinity:A neural network approach |
topic_facet |
SURFACE OCEAN INORGANIC CARBON CO2 ACIDIFICATION VARIABILITY SATURATION CHEMISTRY IMPACTS SEA PH |
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 (A(T)) 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 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 mu 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 mu 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 degrees x 1 degrees 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, Broullon et al., 2019). |
format |
Article in Journal/Newspaper |
author |
Broullon, Daniel Perez, Fiz F. Velo, Anton Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzalez-Davila, Melchor Jeansson, Emil Kozyr, Alex van Heuven, Steven M. A. C. |
author_facet |
Broullon, Daniel Perez, Fiz F. Velo, Anton Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzalez-Davila, Melchor Jeansson, Emil Kozyr, Alex van Heuven, Steven M. A. C. |
author_sort |
Broullon, Daniel |
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 |
publishDate |
2019 |
url |
https://hdl.handle.net/11370/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc https://research.rug.nl/en/publications/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc https://doi.org/10.5194/essd-11-1109-2019 https://pure.rug.nl/ws/files/118430314/essd_11_1109_2019.pdf |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Broullon , D , Perez , F F , Velo , A , Hoppema , M , Olsen , A , Takahashi , T , Key , R M , Tanhua , T , Gonzalez-Davila , M , Jeansson , E , Kozyr , A & van Heuven , S M A C 2019 , ' A global monthly climatology of total alkalinity : A neural network approach ' , Earth System Science Data , vol. 11 , no. 3 , pp. 1109-1127 . https://doi.org/10.5194/essd-11-1109-2019 |
op_relation |
https://research.rug.nl/en/publications/70c3571a-16a2-4c85-a3f2-fa8ae200d9bc |
op_rights |
info:eu-repo/semantics/openAccess |
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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