Abstract. To derive an optimal observation system for surface ocean
pCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean,
11 observation system simulation experiments (OSSEs) were completed.
Each OSSE is a feedforward neural network (FFNN) that is based on a
different data distribution and provides ocean surface pCO2 for the
period 2008–2010 with a 5 d time interval. Based on the geographical and
time positions from three observational platforms, volunteering observing
ships, Argo floats and OceanSITES moorings, pseudo-observations were
constructed using the outputs from an online-coupled physical–biogeochemical
global ocean model with 0.25∘ nominal resolution. The aim of
this work was to find an optimal spatial distribution of observations to
supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to
improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed
that the additional data from mooring stations and an improved coverage of
the Southern Hemisphere with biogeochemical ARGO floats corresponding to
least 25 % of the density of active floats (2008–2010) (OSSE 10) would
significantly improve the pCO2 reconstruction and reduce the bias of
derived estimates of sea–air CO2 fluxes by 74 % compared to ocean
model outputs.
Calculating surface ocean pCO2
from biogeochemical Argo floats equipped with pH: An uncertainty analysis
