Estimates of Global Carbon Flux Using In-Situ Optical Observations of Particulate Organic Carbon and Supervised Learning

The export of sinking particulate carbon from the surface ocean is critical for carbon sequestration and for providing energy to the deep-ocean biosphere. The magnitude and spatial patterns of this flux have been estimated in the past by using satellite-based algorithms and ocean biogeochemical models. However, these estimates remain highly uncertain. New, independent reconstructions of the global carbon flux could help reducing this uncertainty. Here, we present a novel analysis of a global compilation of in situ ocean particle size spectra from Underwater Vision Profilers (UVP5), from which we determine particulate carbon sinking fluxes. Particle size spectra can be approximated to first order by a power-law distribution with two parameters. Using machine learning approaches, we extrapolate sparse observation of particle size spectra to the global ocean from oceanographic variables which are more commonly observed. We reconstruct global maps of particle size distribution parameters for large sinking particles (60 µm to 2.6 cm), and combine them with empirical relationships to calculate climatological sinking carbon fluxes. We compare the results to other global estimates of particulate carbon flux, and discuss the advantages and limitations of our method. Our estimates provide a baseline for more accurate models of biogeochemical cycles that rely on knowledge of the size spectra of particulate matter in the ocean.