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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ftcopernicus:oai:publications.copernicus.org:essd71497 2023-05-15T17:50:50+02:00 A global monthly climatology of total alkalinity: a neural network approach Broullón, Daniel Pérez, Fiz F. Velo, Antón Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste González-Dávila, Melchor Jeansson, Emil Kozyr, Alex Heuven, Steven M. A. C. 2019-07-31 application/pdf https://doi.org/10.5194/essd-11-1109-2019 https://essd.copernicus.org/articles/11/1109/2019/ eng eng doi:10.5194/essd-11-1109-2019 https://essd.copernicus.org/articles/11/1109/2019/ eISSN: 1866-3516 Text 2019 ftcopernicus https://doi.org/10.5194/essd-11-1109-2019 2020-07-20T16:22:43Z 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). Text Ocean acidification Copernicus Publications: E-Journals Earth System Science Data 11 3 1109 1127 |
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language |
English |
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 |
Text |
author |
Broullón, Daniel Pérez, Fiz F. Velo, Antón Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste González-Dávila, Melchor Jeansson, Emil Kozyr, Alex Heuven, Steven M. A. C. |
spellingShingle |
Broullón, Daniel Pérez, Fiz F. Velo, Antón Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste González-Dávila, Melchor Jeansson, Emil Kozyr, Alex Heuven, Steven M. A. C. A global monthly climatology of total alkalinity: a neural network approach |
author_facet |
Broullón, Daniel Pérez, Fiz F. Velo, Antón Hoppema, Mario Olsen, Are Takahashi, Taro Key, Robert M. Tanhua, Toste González-Dávila, Melchor Jeansson, Emil Kozyr, Alex Heuven, Steven M. A. C. |
author_sort |
Broullón, 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://doi.org/10.5194/essd-11-1109-2019 https://essd.copernicus.org/articles/11/1109/2019/ |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
eISSN: 1866-3516 |
op_relation |
doi:10.5194/essd-11-1109-2019 https://essd.copernicus.org/articles/11/1109/2019/ |
op_doi |
https://doi.org/10.5194/essd-11-1109-2019 |
container_title |
Earth System Science Data |
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11 |
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3 |
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1109 |
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1127 |
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1766157746328043520 |