English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/157206
COMPARTIR / IMPACTO:
Estadísticas
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Título

Climatologies of seawater CO2 chemistry variables: A neural network approach

AutorBroullón, Daniel; Pérez, Fiz F. ; Velo, A. ; Suzuki, Toru
Palabras claveOcean
Carbonate system
Climatologies
Neural network
Fecha de publicación2017
CitaciónInternational Carbon Dioxide Conference (2017)
ResumenFor 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.
Descripción1 poster presented at the 10th International Carbon Dioxide Conference, Interlaken, Switzerland, 21 August 2017 - 25 August 2017.-- Daniel Broullón ... et al.
URIhttp://hdl.handle.net/10261/157206
Aparece en las colecciones: (IIM) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
Climatologies_seawater_poster.pdfPoster3,09 MBAdobe PDFVista previa
Visualizar/Abrir
Climatologies_seawater_abstract.pdfAbstract98,51 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.