Reconstruction of super-resolution fields of ocean pCO2 and air--sea fluxes of CO2 from satellite imagery in the Southeastern Atlantic.

The knowledge of Green House Gases GHGs fluxes at the air--sea interface at high resolution is crucial to accurately quantify the role of the ocean in the absorption and emission of GHGs. In this paper we present a novel method to reconstruct maps of surface ocean partial pressure of CO₂, pCO₂, and air--sea CO₂ fluxes at super resolution (4 km) using Sea Surface Temperature (SST) and Ocean Colour (OC) data at this resolution, and CarbonTracker CO₂ fluxes data at low resolution (110 km). Inference of super-resolution of pCO₂, and air--sea CO₂ fluxes is performed using novel nonlinear signal processing methodologies that prove efficient in the context of oceanography. The theoretical background comes from the Microcanonical Multifractal Formalism which unlocks the geometrical determination of cascading properties of physical intensive variables. As a consequence, a multiresolution analysis performed on the signal of the so-called singularity exponents allows the correct and near optimal cross-scale inference of GHGs fluxes, as the inference suits the geometric realization of the cascade. We apply such a methodology to the study offshore of the Benguela area. The inferred representation of oceanic partial pressure of CO₂ improves and enhances the description provided by CarbonTracker, capturing the small scale variability. We examine different combinations of Ocean Colour and Sea Surface Temperature products in order to increase the number of valid points and the quality of the inferred pCO₂ field. The methodology is validated using in-situ measurements by means of statistical errors. We obtain that mean absolute and relative errors in the inferred values of pCO₂ with respect to in-situ measurements are smaller than for CarbonTracker. [ABSTRACT FROM AUTHOR]