Climatologies of seawater CO2 chemistry variables: A neural network approach

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Campo DC	Valor	Lengua/Idioma
dc.contributor.author	Broullón, Daniel	es_ES
dc.contributor.author	Pérez, Fiz F.	es_ES
dc.contributor.author	Velo, A.	es_ES
dc.contributor.author	Suzuki, Toru	es_ES
dc.date.accessioned	2017-11-10T13:17:59Z	-
dc.date.available	2017-11-10T13:17:59Z	-
dc.date.issued	2017	-
dc.identifier.citation	International Carbon Dioxide Conference (2017)	es_ES
dc.identifier.uri	http://hdl.handle.net/10261/157206	-
dc.description	1 poster presented at the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21 August 2017 - 25 August 2017.-- Daniel Broullón ... et al.	es_ES
dc.description.abstract	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 neurons in the network was varied and different training techniques were used. The input variables introduced in the network, which must be related to AT and TCO2 variability, were latitude, longitude, depth, potential temperature, phosphate, nitrate, silicate, year, month and atmospheric pCO2. First, the network was trained with GLODAPv2 data and then AT and TCO2 fields were derived from WOA13v2 data. Saturation states and pH were computed from these two variables. The monthly pre-industrial climatology will be generated by eliminating anthropogenic carbon from the ocean.	es_ES
dc.description.sponsorship	This research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant FPU15/06026), Ministerio de Economía y Competitividad through the A. RIOS (CTM2016-76146-C3-1-R) project co-funded by the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER) and EU Horizon2020 through the AtlantOS project (grant agreement 633211).	es_ES
dc.language.iso	eng	es_ES
dc.relation	info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTM2016-76146-C3-1-R	es_ES
dc.relation	info:eu-repo/grantAgreement/EC/H2020/633211	es_ES
dc.rights	openAccess	es_ES
dc.subject	Ocean	es_ES
dc.subject	Carbonate system	es_ES
dc.subject	Climatologies	es_ES
dc.subject	Neural networks	es_ES
dc.title	Climatologies of seawater CO2 chemistry variables: A neural network approach	es_ES
dc.type	póster de congreso	es_ES
dc.description.peerreviewed	Peer reviewed	es_ES
dc.contributor.funder	Ministerio de Economía y Competitividad (España)	es_ES
dc.contributor.funder	European Commission	es_ES
dc.relation.csic	Sí	es_ES
oprm.item.hasRevision	no ko 0 false	*
dc.identifier.funder	http://dx.doi.org/10.13039/501100000780	es_ES
dc.identifier.funder	http://dx.doi.org/10.13039/501100003329	es_ES
dc.subject.uri	http://metadata.un.org/sdg/14	es_ES
dc.type.coar	http://purl.org/coar/resource_type/c_6670	es_ES
dc.subject.sdg	Conserve and sustainably use the oceans, seas and marine resources for sustainable development	es_ES
item.languageiso639-1	en	-
item.fulltext	With Fulltext	-
item.openairecristype	http://purl.org/coar/resource_type/c_18cf	-
item.cerifentitytype	Publications	-
item.grantfulltext	open	-
item.openairetype	póster de congreso	-
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