Global climatologies of the seawater CO2 chemistry variables
are necessary to assess the marine carbon cycle in depth. The climatologies
should adequately capture seasonal variability to properly address ocean
acidification and similar issues related to the carbon cycle. Total
alkalinity (AT) is one variable of the seawater CO2 chemistry
system involved in ocean acidification and frequently measured. We used the
Global Ocean Data Analysis Project version 2.2019 (GLODAPv2) to extract
relationships among the drivers of the AT variability and AT
concentration using a neural network (NNGv2) to generate a monthly
climatology. The GLODAPv2 quality-controlled dataset used was modeled by the
NNGv2 with a root-mean-squared error (RMSE) of 5.3 µmol kg−1.
Validation tests with independent datasets revealed the good generalization
of the network. Data from five ocean time-series stations showed an
acceptable RMSE range of 3–6.2 µmol kg−1. Successful modeling of
the monthly AT variability in the time series suggests that the NNGv2
is a good candidate to generate a monthly climatology. The climatological
fields of AT were obtained passing through the NNGv2 the World Ocean
Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen
and the computed climatologies of nutrients from the previous ones with a
neural network. The spatiotemporal resolution is set by WOA13:
1∘ × 1∘ in the horizontal, 102 depth levels
(0–5500 m) in the vertical and monthly (0–1500 m) to annual (1550–5500 m)
temporal resolution. The product is distributed through the data repository
of the Spanish National Research Council (CSIC;
https://doi.org/10.20350/digitalCSIC/8644, Broullón et al., 2019)....

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

10.5194/essd-11-1109-2019