Anthropogenic emissions of CO2 to the atmosphere have
modified the carbon cycle for more than 2 centuries. As the ocean
stores most of the carbon on our planet, there is an important task in
unraveling the natural and anthropogenic processes that drive the
carbon cycle at different spatial and temporal scales. We contribute
to this by designing a global monthly climatology of total dissolved
inorganic carbon (TCO2), which offers a robust basis in
carbon cycle modeling but also for other studies related to this
cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured
to extract from the Global Ocean Data Analysis Project version 2.2019
(GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables
related to the former's variability. The global root mean square
error (RMSE) of mapping TCO2 is relatively low for the two
datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO:
11.4 µmol kg−1) and also for independent data,
suggesting that the network does not overfit possible errors in
data. The ability of NNGv2LDEO to capture the monthly variability of
TCO2 was testified through the good reproduction of the
seasonal cycle in 10 time series stations spread over different
regions of the ocean (RMSE: 3.6 to
13.2 µmol kg−1). The climatology was obtained by
passing through NNGv2LDEO the monthly climatological fields of
temperature, salinity, and oxygen from the World Ocean Atlas 2013 and
phosphate, nitrate, and silicate computed from a neural network fed
with the previous fields. The resolution is 1∘×1∘ in
the horizontal, 102 depth levels (0–5500 m), and monthly
(0–1500 m) to annual (1550–5500 m) temporal resolution, and it is
centered around the year 1995. The uncertainty of the climatology is low
when compared with climatological values derived from measured
TCO2 in the largest time series stations. Furthermore, a
computed climatology of partial pressure of CO2
(pCO2) from a previous climatology of total alkalinity and
the present one of TCO2 supports the robustness of this
product through the good correlation with a widely used pCO2
climatology (Landschützer et al., 2017). Our TCO2
climatology is distributed through the data repository of the Spanish
National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551,
Broullón et al., 2020)....

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

10.5194/essd-12-1725-2020