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...
Published in: | Earth System Science Data |
---|---|
Main Authors: | , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/1956/21838 https://doi.org/10.5194/essd-11-1109-2019 |
id |
ftunivbergen:oai:bora.uib.no:1956/21838 |
---|---|
record_format |
openpolar |
spelling |
ftunivbergen:oai:bora.uib.no:1956/21838 2023-05-15T17:50:42+02:00 A global monthly climatology of total alkalinity: A neural network approach Broullón, Daniel Perez, F. F. Velo, Antón Hoppema, Mario Olsen, Are Christian Sviggum Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzales-Davila, Melchor Jeansson, Emil Kozyr, Alexander van Heuven, Steven 2020-02-10T14:50:51Z application/pdf https://hdl.handle.net/1956/21838 https://doi.org/10.5194/essd-11-1109-2019 eng eng Copernicus Publications EC/H2020: 633211 Atlantos urn:issn:1866-3516 urn:issn:1866-3508 https://hdl.handle.net/1956/21838 https://doi.org/10.5194/essd-11-1109-2019 cristin:1772018 Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Author(s) Earth System Science Data Peer reviewed Journal article 2020 ftunivbergen https://doi.org/10.5194/essd-11-1109-2019 2023-03-14T17:39:10Z 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). publishedVersion Article in Journal/Newspaper Ocean acidification University of Bergen: Bergen Open Research Archive (BORA-UiB) Earth System Science Data 11 3 1109 1127 |
institution |
Open Polar |
collection |
University of Bergen: Bergen Open Research Archive (BORA-UiB) |
op_collection_id |
ftunivbergen |
language |
English |
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). publishedVersion |
format |
Article in Journal/Newspaper |
author |
Broullón, Daniel Perez, F. F. Velo, Antón Hoppema, Mario Olsen, Are Christian Sviggum Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzales-Davila, Melchor Jeansson, Emil Kozyr, Alexander van Heuven, Steven |
spellingShingle |
Broullón, Daniel Perez, F. F. Velo, Antón Hoppema, Mario Olsen, Are Christian Sviggum Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzales-Davila, Melchor Jeansson, Emil Kozyr, Alexander van Heuven, Steven A global monthly climatology of total alkalinity: A neural network approach |
author_facet |
Broullón, Daniel Perez, F. F. Velo, Antón Hoppema, Mario Olsen, Are Christian Sviggum Takahashi, Taro Key, Robert M. Tanhua, Toste Gonzales-Davila, Melchor Jeansson, Emil Kozyr, Alexander van Heuven, Steven |
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 |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://hdl.handle.net/1956/21838 https://doi.org/10.5194/essd-11-1109-2019 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Earth System Science Data |
op_relation |
EC/H2020: 633211 Atlantos urn:issn:1866-3516 urn:issn:1866-3508 https://hdl.handle.net/1956/21838 https://doi.org/10.5194/essd-11-1109-2019 cristin:1772018 |
op_rights |
Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Author(s) |
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 |
_version_ |
1766157580616335360 |