Climatologies of seawater CO2 chemistry variables: A neural network approach
1 poster presented at the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21 August 2017 - 25 August 2017.-- Daniel Broullón . et al. For decades, the anthropogenic modification of the carbon cycle has been widely studied. More recently, ocean acidification studies have increa...
Main Authors: | , , , |
---|---|
Other Authors: | , |
Format: | Still Image |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10261/157206 https://doi.org/10.13039/501100000780 https://doi.org/10.13039/501100003329 |
id |
ftcsic:oai:digital.csic.es:10261/157206 |
---|---|
record_format |
openpolar |
spelling |
ftcsic:oai:digital.csic.es:10261/157206 2024-02-11T10:07:37+01:00 Climatologies of seawater CO2 chemistry variables: A neural network approach Broullón, Daniel Pérez, Fiz F. Velo, A. Suzuki, Toru Ministerio de Economía y Competitividad (España) European Commission 2017 http://hdl.handle.net/10261/157206 https://doi.org/10.13039/501100000780 https://doi.org/10.13039/501100003329 en eng #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTM2016-76146-C3-1-R info:eu-repo/grantAgreement/EC/H2020/633211 Sí International Carbon Dioxide Conference (2017) http://hdl.handle.net/10261/157206 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003329 open Ocean Carbonate system Climatologies Neural network póster de congreso http://purl.org/coar/resource_type/c_6670 2017 ftcsic https://doi.org/10.13039/50110000078010.13039/501100003329 2024-01-16T10:26:39Z 1 poster presented at the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21 August 2017 - 25 August 2017.-- Daniel Broullón . et al. For decades, the anthropogenic modification of the carbon cycle has been widely studied. More recently, ocean acidification studies have increased significantly. Establishing robust climatologies of seawater CO2 chemistry variables and building models are a key point for a better understanding of the associated processes. The availability and quality of data is crucial for the evaluation of climate models and, consequently, to improve their predictions. Version 2 of the Global Ocean Data Analysis Project (GLODAPv2) is an internally consistent data product composed of data from 724 scientific cruises covering the entire global ocean. Among others, it contains seawater CO2 chemistry variables such as total alkalinity (AT), total dissolved inorganic carbon (TCO2) and pH. This sparse dataset has been subjected to extensive quality control and different interpolation techniques have been applied to extend the data coverage on a homogeneous grid (Lauvset et al. 2016). We propose a novel neural network approach to generate annual and monthly climatologies of AT, TCO2, pH and both calcite and aragonite saturation state from the GLODAPv2 dataset for the preindustrial and current periods. These climatologies are fitted to the World Ocean Atlas 2013 version 2 (WOA13v2) grid. WOA13v2 is a set of objectively analyzed (1° grid) climatological fields of different oceanographic variables (but not CO2 system) at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean. A feed-forward neural network was chosen in a multi-layer architecture, which allows linear and nonlinear variability to be assimilated by the network. The proposed configuration is able to approximate most functions arbitrarily well (Hagan et al., 2014). We have tested different neural network designs and sizes to obtain the minimum error. For that, the number of ... Still Image Ocean acidification Digital.CSIC (Spanish National Research Council) Hagan ENVELOPE(9.044,9.044,62.575,62.575) |
institution |
Open Polar |
collection |
Digital.CSIC (Spanish National Research Council) |
op_collection_id |
ftcsic |
language |
English |
topic |
Ocean Carbonate system Climatologies Neural network |
spellingShingle |
Ocean Carbonate system Climatologies Neural network Broullón, Daniel Pérez, Fiz F. Velo, A. Suzuki, Toru Climatologies of seawater CO2 chemistry variables: A neural network approach |
topic_facet |
Ocean Carbonate system Climatologies Neural network |
description |
1 poster presented at the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21 August 2017 - 25 August 2017.-- Daniel Broullón . et al. For decades, the anthropogenic modification of the carbon cycle has been widely studied. More recently, ocean acidification studies have increased significantly. Establishing robust climatologies of seawater CO2 chemistry variables and building models are a key point for a better understanding of the associated processes. The availability and quality of data is crucial for the evaluation of climate models and, consequently, to improve their predictions. Version 2 of the Global Ocean Data Analysis Project (GLODAPv2) is an internally consistent data product composed of data from 724 scientific cruises covering the entire global ocean. Among others, it contains seawater CO2 chemistry variables such as total alkalinity (AT), total dissolved inorganic carbon (TCO2) and pH. This sparse dataset has been subjected to extensive quality control and different interpolation techniques have been applied to extend the data coverage on a homogeneous grid (Lauvset et al. 2016). We propose a novel neural network approach to generate annual and monthly climatologies of AT, TCO2, pH and both calcite and aragonite saturation state from the GLODAPv2 dataset for the preindustrial and current periods. These climatologies are fitted to the World Ocean Atlas 2013 version 2 (WOA13v2) grid. WOA13v2 is a set of objectively analyzed (1° grid) climatological fields of different oceanographic variables (but not CO2 system) at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean. A feed-forward neural network was chosen in a multi-layer architecture, which allows linear and nonlinear variability to be assimilated by the network. The proposed configuration is able to approximate most functions arbitrarily well (Hagan et al., 2014). We have tested different neural network designs and sizes to obtain the minimum error. For that, the number of ... |
author2 |
Ministerio de Economía y Competitividad (España) European Commission |
format |
Still Image |
author |
Broullón, Daniel Pérez, Fiz F. Velo, A. Suzuki, Toru |
author_facet |
Broullón, Daniel Pérez, Fiz F. Velo, A. Suzuki, Toru |
author_sort |
Broullón, Daniel |
title |
Climatologies of seawater CO2 chemistry variables: A neural network approach |
title_short |
Climatologies of seawater CO2 chemistry variables: A neural network approach |
title_full |
Climatologies of seawater CO2 chemistry variables: A neural network approach |
title_fullStr |
Climatologies of seawater CO2 chemistry variables: A neural network approach |
title_full_unstemmed |
Climatologies of seawater CO2 chemistry variables: A neural network approach |
title_sort |
climatologies of seawater co2 chemistry variables: a neural network approach |
publishDate |
2017 |
url |
http://hdl.handle.net/10261/157206 https://doi.org/10.13039/501100000780 https://doi.org/10.13039/501100003329 |
long_lat |
ENVELOPE(9.044,9.044,62.575,62.575) |
geographic |
Hagan |
geographic_facet |
Hagan |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_relation |
#PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTM2016-76146-C3-1-R info:eu-repo/grantAgreement/EC/H2020/633211 Sí International Carbon Dioxide Conference (2017) http://hdl.handle.net/10261/157206 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003329 |
op_rights |
open |
op_doi |
https://doi.org/10.13039/50110000078010.13039/501100003329 |
_version_ |
1790606267480276992 |