Assessing the Potential of Backscattering as a Proxy for Phytoplankton Carbon Biomass

Despite phytoplankton contributing roughly half of the photosynthesis on earth and fueling marine food-webs, field measurements of phytoplankton biomass remain scarce. The particulate backscattering coefficient (b(bp)) has often been used as an optical proxy to estimate phytoplankton carbon biomass (C-phyto). However, total observed b(bp) is impacted by phytoplankton size, cell composition, and non-algal particles. The lack of phytoplankton field data has prevented the quantification of uncertainties driven by these factors. Here, we first review and discuss existing b(bp) algorithms by applying them to b(bp) data from the BGC-Argo array in surface waters (<10 m). We find a b(bp) threshold where estimated C-phyto differs by more than an order of magnitude. Next, we use a global ocean circulation model (the MITgcm Biogeochemical and Optical model) that simulates plankton dynamics and associated inherent optical properties to quantify and understand uncertainties from b(bp)-based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error-estimations show that b(bp)-based algorithms overestimate/underestimate C-phyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where C-phyto and b(bp) are known precisely. This is not the case for algorithms derived from observations, where the largest source of uncertainty is the scarcity of phytoplankton biomass data and related methodological inconsistencies. If these other uncertainties are reduced, the model shows that b(bp) could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions.