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

• 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, Steven M. A. C.
• Format: Dataset
• Language: English
• Published: Digital.CSIC 2018
• Subjects: Total alkalinity; Monthly climatology; Neural network; Ocean acidification; Pacific
• Online Access: https://dx.doi.org/10.20350/digitalcsic/8564; https://digital.csic.es/handle/10261/169529

The item is made of 4 files: 1) Readme_Global_monthly_dataset.txt; 2) ATNNWOA13.nc is the climatological data of total alkalinity computed with NN; 3) NN is the neural network object used to create the climatology; 4) NNw3RMSE is a neural network object used to evaluate the error of the network when it is trained without data beyond +-3RMSE; 5)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)