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dc.contributor.authorBroullón, Danieles_ES
dc.contributor.authorPérez, Fiz F.es_ES
dc.contributor.authorVelo, A.es_ES
dc.contributor.authorHoppema, M.es_ES
dc.contributor.authorOlsen, Arees_ES
dc.contributor.authorTakahashi, Taroes_ES
dc.contributor.authorKey, Robert M.es_ES
dc.contributor.authorGonzález-Dávila, Melchores_ES
dc.contributor.authorTanhua, T.es_ES
dc.contributor.authorJeansson, Emiles_ES
dc.contributor.authorKozyr, Alexes_ES
dc.contributor.authorVan Heuven, S.es_ES
dc.date.accessioned2019-06-20T09:05:04Z-
dc.date.available2019-06-20T09:05:04Z-
dc.date.issued2019-
dc.identifier.citationBroullón, Daniel; Pérez, Fiz F.; Velo, A.; Hoppema, M.; Olsen, Are; Takahashi, Taro; Key, Robert M.; González-Dávila, Melchor; Tanhua, T.; Jeansson, Emil; Kozyr, Alex; Van Heuven, S.; 2019; “A global monthly climatology of total alkalinity: a neural network approach (2019) [Dataset]”; Digital.CSIC; http://dx.doi.org/10.20350/digitalCSIC/8644-
dc.identifier.urihttp://hdl.handle.net/10261/184460-
dc.descriptionThe item is made of 5 files: 1) README.txt; 2) AT_NNGv2_climatology.nc contains the climatology of AT computed with NNGv2 in netcdf4 format and the climatologies of oxygen (median filtered from WOA13), phosphate, nitrate and silicate (these three derived from CANYON-B); 3) NNGv2 is the neural network object used to create the climatology; 4)ATNNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indean Ocean and, the variation in depth of one month (April); 5) Example.rar contains an example matrix of inputs to the neural network, the NNGv2.mat and a MATLAB script to compute AT with NNGv2es_ES
dc.description.sponsorshipThis 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)es_ES
dc.language.isoenges_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/633211es_ES
dc.relationMINECO/ICTI2013-2016/CTM2016-76146-C3-1-Res_ES
dc.relation.isbasedonWORLD OCEAN ATLAS 2013 (WOA13) https://www.nodc.noaa.gov/OC5/woa13/-
dc.relation.isbasedonGlobal Ocean Data Analysis Project version 2 (GLODAPv2) https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2/-
dc.relation.requiresThe 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/)es_ES
dc.rightsopenAccesses_ES
dc.subjectTotal alkalinityes_ES
dc.subjectMonthly climatologyes_ES
dc.subjectNeural networkes_ES
dc.subjectOcean acidificationes_ES
dc.titleA global monthly climatology of total alkalinity: a neural network approach (2019) [Dataset]es_ES
dc.typedatasetes_ES
dc.identifier.doihttp://dx.doi.org/10.20350/digitalCSIC/8644-
dc.description.peerreviewedNoes_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
Appears in Collections:(IIM) Conjuntos de datos
Files in This Item:
File Description SizeFormat 
README_Global_monthly_2019.txt1,62 kBTextView/Open
NNGv2.mat26,15 kBUnknownView/Open
Example.rar54,74 kBUnknownView/Open
AT_NNGv2_climatology.nc2,95 GBUnknownView/Open
ATNNWOA13.mp466,4 MBUnknownView/Open
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