A global monthly climatology of oceanic total dissolved inorganic carbon (DIC): a neural network approach (NCEI Accession 0222469)

This NCEI accession contains global monthly climatology of oceanic total dissolved inorganic carbon (DIC). (DIC) monthly climatology was created from a neural network approach (Broullón et al., 2020). The neural network was trained with GLODAPv2.2019 (Olsen et al., 2019) and LDEOv2016 (Takahashi et al., 2017) data, using as predictor variables position (latitude, longitude and depth), year, temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. pCO2 from LDEOv2016 and AT from Broullón et al. (2019) were used to compute DIC surface values to increase the surface coverage in the training data. The relations extracted between the predictor variables and DIC were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broullón et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1ºx1º spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m.