Abstract
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.
Highlights
The ocean is a major sink of anthropogenic CO2 (Ciais et al, 2013; Friedlingstein et al, 2020)
observation system simulation experiments (OSSEs) 1 shows the largest uRMSDs (Fig. 4), as exemplified for biome 17 with uRMSD of 17.33 μatm, standard deviations (SD) of 21.11 μatm and bias of −11.63 μatm
The total averaged f gCO2 for OSSE 3 and 10 are −0.74 Pg yr−1 compared to −0.7 Pg yr−1 in Nucleus for European Modelling of the Ocean (NEMO)/PISCES, while for OSSE 1 it equals −0.99 Pg yr−1 (Table 8)
Summary
The ocean is a major sink of anthropogenic CO2 (Ciais et al, 2013; Friedlingstein et al, 2020). The data-based reconstructions rely on the interpolation of surface ocean pCO2 – derived from measurements of surface ocean CO2 fugacity – by a variety of methods (e.g. Watson et al, 2020; Gregor et al, 2019; Denvil-Sommer et al, 2019; Bittig et al, 2018; Landschützer et al, 2013, 2016; Rödenbeck et al, 2014, 2015; Fay et al, 2014; Zeng et al, 2014; Nakaoka et al, 2013; Schuster et al, 2013; Takahashi et al, 2002, 2009) These methods provide converging estimates of the global ocean carbon sink and its variability at seasonal and interannual timescales (Rödenbeck et al, 2015; Denvil-Sommer et al, 2019). They are, sensitive to the observation coverage in space and time, which contributes to inconsistent results over regions with sparse data (Denvil-Sommer et al, 2019; Rödenbeck et al, 2015) and to persistent uncertainties at a global scale (Gregor et al, 2019; Hauck et al, 2020)
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