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

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 networ...

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Bibliographic Details
Main Authors: Broullón, Daniel, Pérez, Fiz F., Velo, A., Hoppema, M., Olsen, Are, Takahashi, Taro, Key, Robert M., González-Dávila, Melchor, Tanhua, T., Jeansson, Emil, Kozyr, Alex, Van Heuven, S.
Format: Dataset
Language:English
Published: Digital.CSIC 2019
Subjects:
Total alkalinity
Monthly climatology
Neural network
Ocean acidification
Pacific
Online Access:https://dx.doi.org/10.20350/digitalcsic/8644
https://digital.csic.es/handle/10261/184460
Description
Summary: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

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