Reconstruction of super-resolution ocean pCO2and air-sea fluxes of CO2 from satellite imagery in the southeastern Atlantic

Abstract

An accurate quantification of the role of the oceanas source/sink of greenhouse gases (GHGs) requires to ac-cess the high-resolution of the GHG air–sea flux at the in-terface. In this paper we present a novel method to recon-struct maps of surface ocean partial pressure of CO2(pCO2)and air–sea CO2fluxes at super resolution (4 km, i.e., 1/32◦at these latitudes) using sea surface temperature (SST) andocean color (OC) data at this resolution, and CarbonTrackerCO2fluxes data at low resolution (110 km). Inference ofsuper-resolutionpCO2and air–sea CO2fluxes is performedusing novel nonlinear signal processing methodologies thatprove efficient in the context of oceanography. The theo-retical background comes from the microcanonical multi-fractal formalism which unlocks the geometrical determina-tion of cascading properties of physical intensive variables.As a consequence, a multi-resolution analysis performed onthe signal of the so-called singularity exponents allows forthe correct and near optimal cross-scale inference of GHGfluxes, as the inference suits the geometric realization ofthe cascade. We apply such a methodology to the studyoffshore of the Benguela area. The inferred representationof oceanic partial pressure of CO2improves and enhancesthe description provided by CarbonTracker, capturing thesmall-scale variability. We examine different combinationsof ocean color and sea surface temperature products in or-der to increase the number of valid points and the quality ofthe inferredpCO2field. The methodology is validated using in situ measurements by means of statistical errors. We findthat mean absolute and relative errors in the inferred valuesofpCO2with respect to in situ measurements are smallerthan for CarbonTracker.