GLOBESINK monthly POC flux and particle size climatology

This dataset provides a global, gridded monthly climatology of particle optical backscattering, particulate organic carbon (POC), particle size, and sinking POC flux derived from observations collected by the Biogeochemical Argo (BGC-Argo) profiling float network.

The dataset includes size-fractionated optical backscattering from particles spanning approximately 1–800 µm, along with derived estimates of POC concentration, sinking POC flux, area-weighted mean particle diameter, and particle number concentrations in three size classes (200–400 µm, 400–800 µm, and >800 µm). Additional environmental variables measured by BGC-Argo floats, including temperature, salinity, nitrate, pH, oxygen, and chlorophyll-a, are also provided on the same grid.

All variables are provided as monthly climatologies on a regular global grid (4° latitude × 8° longitude) spanning 0–2000 m depth. Two versions of the dataset are included: (1) a raw climatology containing gaps where observations are unavailable, and (2) a spatially smoothed and interpolated climatology that fills these gaps except in regions of persistent sea ice.

Derived variables include uncertainty estimates. Precision uncertainty reflects stochastic sampling variability associated with rare, large particles and is provided as upper and lower 95% confidence bounds. Systematic uncertainty is propagated from uncertainty in empirical parameter values used in the conversion from backscattering to carbon and flux quantities.

This dataset is derived from observations collected by the international Argo and Biogeochemical Argo (BGC-Argo) programs (https://argo.ucsd.edu, https://biogeochemical-argo.org). It is a value-added research product and is not an official Argo product.

This dataset is intended for use in studies of the global ocean carbon cycle, particle dynamics, and biogeochemical processes. Users should note that derived quantities depend on empirical relationships and that interpolated fields should be used with caution in regions of sparse observations.