A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]

The item is made of 6 files: 1) README.txt; 2) TCO2_NNGv2LDEO_climatology.nc contains the climatology of TCO2 centered in 1995 and computed with NNGv2LDEO in netcdf4 format; 3) pCO2_NNGv2LDEO_climatology.nc contains the climatology of pCO2 centered in 1995 and computed with NNGv2LDEO and NNGv2 of Br...

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Detalles Bibliográficos
Autores: Broullón, Daniel, Pérez, Fiz F., Velo, Antón, Hoppema, Mario, Olsen, Are, Takahashi, Taro, Key, Robert M., Tanhua, Toste, Santana-Casiano, Juana Magdalena, Kozyr, Alex
Tipo de recurso: conjunto de datos
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/200537
Acceso en línea:http://hdl.handle.net/10261/200537
Access Level:acceso abierto
Palabra clave:Total dissolved inorganic carbon
Monthly climatology
Neural networks
Ocean acidification
http://aims.fao.org/aos/agrovoc/c_49123b50
http://aims.fao.org/aos/agrovoc/c_90
inorganic carbon
acidification
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oai_identifier_str oai:digital.csic.es:10261/200537
network_acronym_str ES
network_name_str España
repository_id_str
spelling A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]Broullón, DanielPérez, Fiz F.Velo, AntónHoppema, MarioOlsen, AreTakahashi, TaroKey, Robert M.Tanhua, TosteSantana-Casiano, Juana MagdalenaKozyr, AlexTotal dissolved inorganic carbonMonthly climatologyNeural networksOcean acidificationhttp://aims.fao.org/aos/agrovoc/c_49123b50http://aims.fao.org/aos/agrovoc/c_90inorganic carbonacidificationThe item is made of 6 files: 1) README.txt; 2) TCO2_NNGv2LDEO_climatology.nc contains the climatology of TCO2 centered in 1995 and computed with NNGv2LDEO in netcdf4 format; 3) pCO2_NNGv2LDEO_climatology.nc contains the climatology of pCO2 centered in 1995 and computed with NNGv2LDEO and NNGv2 of Broullón et al. (2019) in netcdf4 format ; 4) NNGv2LDEO.mat is the neural network object used to create the climatology of TCO2; 5) TCO2NNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indian Ocean and, the variation in depth of one month (April); 6) Example.rar contains an example matrix of inputs and targets to the neural network, the NNGv2LDEO.mat and a MATLAB script to compute TCO2 with NNGv2LDEOThis research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant FPU15/06026), Ministerio de Economía y Competitividad through the ARIOS (CTM2016-76146-C3-1-R) project co-funded by the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER) and EU Horizon 2020 through the AtlantOS project (grant agreement 633211)NoDIGITAL.CSICMinisterio de Economía y Competitividad (España)European CommissionMinisterio de Educación, Cultura y Deporte (España)Broullón, Daniel [0000-0002-5552-5272]Pérez, Fiz F. [0000-0003-4836-8974]Velo, A. [0000-0002-7598-5700]Hoppema, Mario [0000-0002-2326-619X]Olsen, Are [0000-0003-1696-9142]Tanhua, Toste [0000-0002-0313-2557]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202020info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1mathttp://hdl.handle.net/10261/200537reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##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-Rinfo:eu-repo/grantAgreement/EC/H2020/633211World Ocean Atlas 2013 (WOA13) https://www.nodc.noaa.gov/OC5/woa13/Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2019/Daniel Broullón, Fiz F. Pérez, Antón Velo, Mario Hoppema, Are Olsen, Taro Takahashi, Robert M. Key, Toste Tanhua, Juana Magdalena Santana-Casiano and Alex Kozyr. 2020. A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach. https://doi.org/10.5194/essd-12-1725-2020. http://hdl.handle.net/10261/218890López-Mozos, Marta; Pérez, Fiz F.; Carracedo, L.; Gebbie, Geoffrey; Velo, A. 2025. A Novel Back‐Calculation Approach to Estimate Ocean Anthropogenic Carbon Using Carbon‐Based Data and a Total Matrix Intercomparison Method. https://doi.org/10.1029/2024MS004330. http://hdl.handle.net/10261/379266The climatology file can be easily opened with any netcdf reader. For a quick map viewing the Panoply NASA GISS software is strongly recommended (https://www.giss.nasa.gov/tools/panoply/download/)Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2005372026-05-22T06:33:51Z
dc.title.none.fl_str_mv A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
title A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
spellingShingle A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
Broullón, Daniel
Total dissolved inorganic carbon
Monthly climatology
Neural networks
Ocean acidification
http://aims.fao.org/aos/agrovoc/c_49123b50
http://aims.fao.org/aos/agrovoc/c_90
inorganic carbon
acidification
title_short A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
title_full A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
title_fullStr A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
title_full_unstemmed A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
title_sort A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]
dc.creator.none.fl_str_mv Broullón, Daniel
Pérez, Fiz F.
Velo, Antón
Hoppema, Mario
Olsen, Are
Takahashi, Taro
Key, Robert M.
Tanhua, Toste
Santana-Casiano, Juana Magdalena
Kozyr, Alex
author Broullón, Daniel
author_facet Broullón, Daniel
Pérez, Fiz F.
Velo, Antón
Hoppema, Mario
Olsen, Are
Takahashi, Taro
Key, Robert M.
Tanhua, Toste
Santana-Casiano, Juana Magdalena
Kozyr, Alex
author_role author
author2 Pérez, Fiz F.
Velo, Antón
Hoppema, Mario
Olsen, Are
Takahashi, Taro
Key, Robert M.
Tanhua, Toste
Santana-Casiano, Juana Magdalena
Kozyr, Alex
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
European Commission
Ministerio de Educación, Cultura y Deporte (España)
Broullón, Daniel [0000-0002-5552-5272]
Pérez, Fiz F. [0000-0003-4836-8974]
Velo, A. [0000-0002-7598-5700]
Hoppema, Mario [0000-0002-2326-619X]
Olsen, Are [0000-0003-1696-9142]
Tanhua, Toste [0000-0002-0313-2557]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Total dissolved inorganic carbon
Monthly climatology
Neural networks
Ocean acidification
http://aims.fao.org/aos/agrovoc/c_49123b50
http://aims.fao.org/aos/agrovoc/c_90
inorganic carbon
acidification
topic Total dissolved inorganic carbon
Monthly climatology
Neural networks
Ocean acidification
http://aims.fao.org/aos/agrovoc/c_49123b50
http://aims.fao.org/aos/agrovoc/c_90
inorganic carbon
acidification
description The item is made of 6 files: 1) README.txt; 2) TCO2_NNGv2LDEO_climatology.nc contains the climatology of TCO2 centered in 1995 and computed with NNGv2LDEO in netcdf4 format; 3) pCO2_NNGv2LDEO_climatology.nc contains the climatology of pCO2 centered in 1995 and computed with NNGv2LDEO and NNGv2 of Broullón et al. (2019) in netcdf4 format ; 4) NNGv2LDEO.mat is the neural network object used to create the climatology of TCO2; 5) TCO2NNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indian Ocean and, the variation in depth of one month (April); 6) Example.rar contains an example matrix of inputs and targets to the neural network, the NNGv2LDEO.mat and a MATLAB script to compute TCO2 with NNGv2LDEO
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
http://purl.org/coar/resource_type/c_ddb1
format dataset
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/200537
url http://hdl.handle.net/10261/200537
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#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
World Ocean Atlas 2013 (WOA13) https://www.nodc.noaa.gov/OC5/woa13/
Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2019/
Daniel Broullón, Fiz F. Pérez, Antón Velo, Mario Hoppema, Are Olsen, Taro Takahashi, Robert M. Key, Toste Tanhua, Juana Magdalena Santana-Casiano and Alex Kozyr. 2020. A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach. https://doi.org/10.5194/essd-12-1725-2020. http://hdl.handle.net/10261/218890
López-Mozos, Marta; Pérez, Fiz F.; Carracedo, L.; Gebbie, Geoffrey; Velo, A. 2025. A Novel Back‐Calculation Approach to Estimate Ocean Anthropogenic Carbon Using Carbon‐Based Data and a Total Matrix Intercomparison Method. https://doi.org/10.1029/2024MS004330. http://hdl.handle.net/10261/379266
The climatology file can be easily opened with any netcdf reader. For a quick map viewing the Panoply NASA GISS software is strongly recommended (https://www.giss.nasa.gov/tools/panoply/download/)
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv mat
dc.publisher.none.fl_str_mv DIGITAL.CSIC
publisher.none.fl_str_mv DIGITAL.CSIC
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406469931139072
score 15,812429

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