A global monthly climatology of total alkalinity: a neural network approach.

Broullón, Daniel, Perez, Fiz F. , Velo, Antón , Hoppema, Mario , Olsen, Are , Takahashi, Taro, Key, Robert M., Tanhua, Toste , Gonzalez-Davila, Melchor, Jeansson, Emil, Kozyr, Alex and van Heuven, Steven M. A. C. (2019) A global monthly climatology of total alkalinity: a neural network approach. Open Access Earth System Science Data, 11 (3). pp. 1109-1127. DOI 10.5194/essd-11-1109-2019.

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Abstract

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).

Document Type: Article
Funder compliance: info:eu-repo/grantAgreement/EC/H2020/633211
Research affiliation: OceanRep > GEOMAR > FB2 Marine Biogeochemistry > FB2-CH Chemical Oceanography
AWI
Refereed: Yes
Open Access Journal?: Yes
DOI etc.: 10.5194/essd-11-1109-2019
ISSN: 1866-3516
Related URLs:
Projects: AtlantOS
Date Deposited: 16 Aug 2019 13:01
Last Modified: 06 Feb 2020 09:03
URI: http://oceanrep.geomar.de/id/eprint/47514

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