Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/74646
Campo DC Valoridioma
dc.contributor.authorBroullón, Danielen_US
dc.contributor.authorPérez, Fiz F.en_US
dc.contributor.authorVelo, Antonen_US
dc.contributor.authorHoppema, Marioen_US
dc.contributor.authorOlsen, Areen_US
dc.contributor.authorTakahashi, Taroen_US
dc.contributor.authorKey, Robert M.en_US
dc.contributor.authorTanhua, Tosteen_US
dc.contributor.authorMagdalena Santana-Casiano, J.en_US
dc.contributor.authorKozyr, Alexen_US
dc.date.accessioned2020-10-05T08:32:28Z-
dc.date.available2020-10-05T08:32:28Z-
dc.date.issued2020en_US
dc.identifier.issn1866-3508en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/74646-
dc.description.abstractAnthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont-Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former’s variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 μmolkg1; LDEO: 11.4 μmolkg1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 μmolkg1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 11 in the horizontal, 102 depth levels (0-5500 m), and monthly (0-1500 m) to annual (1550-5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).en_US
dc.languageengen_US
dc.relationOptimizing and Enhancing the Integrated Atlantic Ocean Observing Systemen_US
dc.relation.ispartofEarth System Science Dataen_US
dc.sourceEarth System Science Data [ISSN 1866-3508], v. 12 (3), p. 1725-1743, (Agosto 2020)en_US
dc.subject251002 Oceanografía químicaen_US
dc.subject.otherCarbon cycleen_US
dc.titleA global monthly climatology of oceanic total dissolved inorganic carbon: A neural network approachen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.5194/essd-12-1725-2020en_US
dc.identifier.scopus85091493952-
dc.contributor.authorscopusid57211473584-
dc.contributor.authorscopusid56598611300-
dc.contributor.authorscopusid36007807200-
dc.contributor.authorscopusid35401714600-
dc.contributor.authorscopusid7202795681-
dc.contributor.authorscopusid7406455112-
dc.contributor.authorscopusid8987364700-
dc.contributor.authorscopusid16029608000-
dc.contributor.authorscopusid57219161910-
dc.contributor.authorscopusid6602937578-
dc.identifier.eissn1866-3516-
dc.description.lastpage1743en_US
dc.identifier.issue3-
dc.description.firstpage1725en_US
dc.relation.volume12en_US
dc.investigacionCienciasen_US
dc.type2Artículoen_US
dc.description.numberofpages9en_US
dc.utils.revisionen_US
dc.date.coverdateAgosto 2020en_US
dc.identifier.ulpgces
dc.description.sjr4,066
dc.description.jcr11,333
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.project.principalinvestigatorGonzález Dávila, Melchor-
crisitem.author.deptGIR IOCAG: Química Marina-
crisitem.author.deptIU de Oceanografía y Cambio Global-
crisitem.author.deptDepartamento de Química-
crisitem.author.orcid0000-0002-7930-7683-
crisitem.author.parentorgIU de Oceanografía y Cambio Global-
crisitem.author.fullNameSantana Casiano, Juana Magdalena-
Colección:Artículos
miniatura
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