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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.authorTanhua, T.es_ES
dc.contributor.authorSantana-Casiano, Magdalenaes_ES
dc.contributor.authorKozyr, Alexes_ES
dc.date.accessioned2020-08-31T10:30:27Z-
dc.date.available2020-08-31T10:30:27Z-
dc.date.issued2020-
dc.identifier.citationEarth System Science Data 12(3): 1725-1743 (2020)es_ES
dc.identifier.issn1866-3508-
dc.identifier.urihttp://hdl.handle.net/10261/218890-
dc.description19 pages, 7 tables, 8 figures.-- This work is distributed under the Creative Commons Attribution 4.0 License.es_ES
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 μmolkg-1; LDEO: 11.4 μmolkg-1) 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 μmolkg-1). 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 1ºx1º 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)es_ES
dc.description.sponsorshipThis research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant no. FPU15/06026); Ministerio de Economía y Competitividad through the ARIOS (grant no. CTM2016-76146-C3-1-R) project, cofunded by the Fondo Europeo de Desarrollo Regional 2014–2020 (FEDER); and EU Horizon 2020 through the AtlantOS project (grant agreement no. 633211). Are Olsen was supported by the Norwegian Research Council through ICOS (grant no. 245927). Mario Hoppema was partly supported by the European Union’s Horizon 2020 program under grant agreement no. 821001 (SO-CHIC).es_ES
dc.language.isoenges_ES
dc.publisherCopernicus Publicationses_ES
dc.relationMINECO/ICTI2013-2016/CTM2016-76146-C3-1-Res_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/633211es_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.titleA global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approaches_ES
dc.typeartículo de datoses_ES
dc.identifier.doihttp://dx.doi.org/10.5194/essd-12-1725-2020-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.5194/essd-12-1725-2020es_ES
dc.identifier.e-issn1866-3516-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.contributor.orcidPérez, Fiz F. [0000-0003-4836-8974]es_ES
dc.contributor.orcidVelo, A. [0000-0002-7598-5700]es_ES
dc.contributor.orcidHoppema, M. [0000-0002-2326-619X]es_ES
dc.contributor.orcidOlsen, Are [0000-0003-1696-9142]es_ES
dc.contributor.orcidSantana-Casiano, Magdalena [0000-0002-7930-7683]es_ES
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