In this thesis a newly developed 2–step neural network approach is used to reconstruct
basin–wide monthly maps of the sea surface partial pressure of CO2 (pCO2) at a resolution
of 1��1� for both the Atlantic Ocean from 1998 through 2007 and the global ocean from
1998 through 2011. From those, air–sea CO2 flux maps are computed using a standard
gas exchange parameterization and high–resolution wind speeds.
Observations form the basis of the studies conducted in this thesis. The neural network
estimates benefit from a continuous improvement of the observations, i.e., the Surface
Ocean CO2 Atlas (SOCAT) database. Additionally, bottle samples were collected
along the UK–Caribbean line to investigate the variability of the sea surface pCO2 and its
drivers.
The neural network derived pCO2 estimates fit the observed pCO2 data with a root
mean square error (RMSE) of about 10 �atm in the Atlantic Ocean from 1998 through
2007 and about 12 �atm in the global ocean from 1998 through 2011, with almost no
bias in both studies. A check against independent pCO2 data reveals a larger RMSE, in
particular in regions with strong pCO2 variability and gradients.
Temporal mean contemporary flux estimates for the Atlantic Ocean (–0.45�0.15 Pg
C � y
