A global monthly climatology of total alkalinity: a neural network approach (2019) [Dataset]

• Main Authors: Broullón, Daniel, Pérez, Fiz F., Velo, A., Hoppema, Mario, Olsen, Are, Takahashi, Taro, Key, Robert M., González-Dávila, Melchor, Tanhua, Toste, Jeansson, Emil, Kozyr, Alex, Van Heuven, S.
• Other Authors: European Commission, Ministerio de Economía y Competitividad (España)
• Format: Dataset
• Language: English
• Published: 2019
• Subjects: Total alkalinity; Monthly climatology; Neural network; Ocean acidification; Pacific
• Online Access: http://hdl.handle.net/10261/184460; https://doi.org/10.20350/digitalCSIC/8644; https://doi.org/10.13039/501100000780; https://doi.org/10.13039/501100003329

The item is made of 5 files: 1) README.txt; 2) AT_NNGv2_climatology.nc contains the climatology of AT computed with NNGv2 in netcdf4 format and the climatologies of oxygen (median filtered from WOA13), phosphate, nitrate and silicate (these three derived from CANYON-B); 3) NNGv2 is the neural network object used to create the climatology; 4)ATNNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indean Ocean and, the variation in depth of one month (April); 5) Example.rar contains an example matrix of inputs to the neural network, the NNGv2.mat and a MATLAB script to compute AT with NNGv2 This research was supported by Ministerio de Educación, Cultura y Deporte (FPU grant FPU15/06026), Ministerio de Economía y Competitividad through the ARIOS (CTM2016-76146-C3-1-R) project co-funded by the Fondo Europeo de Desarrollo Regional 2014-2020 (FEDER) and EU Horizon 2020 through the AtlantOS project (grant agreement 633211) No