Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/59371
Título: A global monthly climatology of total alkalinity: a neural network approach
Autores/as: Broullon, Daniel
Perez, Fiz F.
Velo, Anton
Hoppema, Mario
Olsen, Are
Takahashi, Taro
Key, Robert M.
Tanhua, Toste
Gonzalez-Davila, Melchor 
Jeansson, Emil
Kozyr, Alex
van Heuven, Steven M. A. C.
Clasificación UNESCO: 251002 Oceanografía química
Palabras clave: Surface Ocean
Inorganic Carbon
Co2
Ocean acidification
Variability, et al.
Fecha de publicación: 2019
Publicación seriada: Earth System Science Data 
Resumen: Global climatologies of the seawater CO2 chemistry variables are necessary to assess the marine carbon cycle in depth. The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle. Total alkalinity (A(T)) is one variable of the seawater CO2 chemistry system involved in ocean acidification and frequently measured. We used the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2) to extract relationships among the drivers of the A(T) variability and A(T) concentration using a neural network (NNGv2) to generate a monthly climatology. The GLODAPv2 quality-controlled dataset used was modeled by the NNGv2 with a root-mean-squared error (RMSE) of 5.3 mu mol kg(-1). Validation tests with independent datasets revealed the good generalization of the network. Data from five ocean time-series stations showed an acceptable RMSE range of 3-6.2 mu mol kg(-1). Successful modeling of the monthly A(T) variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of A(T) were obtained passing through the NNGv2 the World Ocean Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen and the computed climatologies of nutrients from the previous ones with a neural network. The spatiotemporal resolution is set by WOA13: 1 degrees x 1 degrees in the horizontal, 102 depth levels (0-5500 m) in the vertical and monthly (0-1500 m) to annual (1550-5500 m) temporal resolution. The product is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/8644, Broullon et al., 2019).
URI: http://hdl.handle.net/10553/59371
ISSN: 1866-3508
DOI: 10.5194/essd-11-1109-2019
Fuente: Earth System Science Data [ISSN 1866-3508], v. 11 (3), p. 1109-1127
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