GLOBESINK: A global climatology of sinking organic carbon flux and particle size from optical backscattering measurements

Abstract. Sinking particles play a major role in the global carbon cycle, driving long-term carbon storage in the ocean and providing the primary food source for many deep ocean ecosystems. However, the global flux of sinking organic matter to the deep ocean is poorly constrained, due to the difficulty of measuring sinking particles at global scale. Here we present a global, gridded monthly climatology of size-fractionated optical backscattering by particles ranging from 1–800 µm, derived from the Biogeochemical Argo network of profiling floats. We leverage these data to estimate particulate organic carbon (POC) concentrations and sinking fluxes, as well as area-weighted mean particle diameter and the number concentrations of particles in three size classes: 200–400 µm, 400–800 µm, and >800µm. Each estimate of carbon concentration and flux is accompanied by an estimate of uncertainty due to the empirical relationships used to convert from backscatter, and each quantity derived from backscattering from one or more large (>200 µm) size class is accompanied by an estimate of precision due to random encounter with rare, large particles. For reference, climatologies of the following other parameters measured by the same floats are included: temperature, salinity, nitrate, pH, oxygen, and Chlorophyll a concentration. All climatologies share the same regular grid, with 4° latitude by 8° longitude resolution and spanning 0–2000 m depth, with vertical resolution varying from 10 m to 300 m. The raw climatology, containing gaps in the months, coordinates, and depths without data, is accompanied by a spatially smoothed and interpolated climatology that fills in these gaps, except for the Arctic and areas of near-permanent sea ice around Antarctica. The estimates of POC concentration and particle size are presented as is, without validation, but the estimates of sinking POC flux were validated against a global dataset of independent measurements, finding a mean relative bias of 1.2 and an r² of 0.57 on a logarithmic scale.