Assessing the Potential of Backscattering as a Proxy for Phytoplankton Carbon Biomass
Name
Global Biogeochemical Cycles - 2023 - Serra‐Pompei - Assessing the Potential of Backscattering as a Proxy for Phytoplankton.pdf
Description
Published version
Size
14.01 MB
Format
Adobe PDF
Checksum (MD5)
08bdf076a92e7d3c87fbbf19692fb7a4
Author(s)
Serra‐Pompei, Camila
Hickman, Anna
Britten, Gregory L
Dutkiewicz, Stephanie
Date Issued
April 28, 2023
Journal
Global Biogeochemical Cycles
Publisher
American Geophysical Union
Citation
Serra-Pompei, C., Hickman, A., Britten, G. L., & Dutkiewicz, S. (2023). Assessing the potential of backscattering as a proxy for phytoplankton carbon biomass. Global Biogeochemical Cycles, 37, e2022GB007556.
Version
Final published version
Abstract
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 (bbp) has often been used as an optical proxy to estimate phytoplankton carbon biomass (Cphyto). However, total observed bbp 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 bbp algorithms by applying them to bbp data from the BGC-Argo array in surface waters (<10 m). We find a bbp threshold where estimated Cphyto 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 bbp-based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error-estimations show that bbp-based algorithms overestimate/underestimate Cphyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where Cphyto and bbp 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 bbp could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Woods Hole Oceanographic Institution
Center for Climate Change Science
Terms of Use
Creative Commons Attribution-NonCommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2022gb007556
