A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach [Dataset]

• Main Authors: Broullón, Daniel, Pérez, Fiz F., Velo, A., Hoppema, Mario, Olsen, Are, Takahashi, Taro, Key, Robert M., Tanhua, Toste, Santana-Casiano, Magdalena, Kozyr, Alex
• Other Authors: Ministerio de Economía y Competitividad (España), European Commission
• Format: Dataset
• Language: English
• Published: 2020
• Subjects: Total dissolved inorganic carbon; Monthly climatology; Neural network; Ocean acidification; http://aims.fao.org/aos/agrovoc/c_49123b50; http://aims.fao.org/aos/agrovoc/c_90; inorganic carbon; acidification; Indian; Pacific
• Online Access: http://hdl.handle.net/10261/200537; https://doi.org/10.20350/digitalCSIC/10551; https://doi.org/10.13039/501100000780; https://doi.org/10.13039/501100003329

The item is made of 6 files: 1) README.txt; 2) TCO2_NNGv2LDEO_climatology.nc contains the climatology of TCO2 centered in 1995 and computed with NNGv2LDEO in netcdf4 format; 3) pCO2_NNGv2LDEO_climatology.nc contains the climatology of pCO2 centered in 1995 and computed with NNGv2LDEO and NNGv2 of Broullón et al. (2019) in netcdf4 format 4) NNGv2LDEO.mat is the neural network object used to create the climatology of TCO2; 5) TCO2NNWOA13.mp4 is a video of the surface climatology, 3 vertical sections in the Pacific Ocean, Atlantic Ocean and Indian Ocean and, the variation in depth of one month (April); 6) Example.rar contains an example matrix of inputs and targets to the neural network, the NNGv2LDEO.mat and a MATLAB script to compute TCO2 with NNGv2LDEO 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) Peer reviewed