Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution

Abstract

Abstract. Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the “unit of accounting” in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size – picophytoplankton (0.5–2 µm in diameter), nanophytoplankton (2–20 µm) and microphytoplankton (20–50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield ∼ 0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

“…In the implementation used here, the input backscattering spectrum comes from the standard QAA products of the OC-CCI dataset, which are derived using Lee et al (2002) algorithm, as summarized above. This is different from the original implementations (Kostadinov et al, 2009(Kostadinov et al, , 2016, where the Loisel and Stramski (2000) algorithm was used to retrieve spectral b bp . The PSD parameters retrieved are the power-law slope (ξ ) and the scaling parameter (i.e., differential particle number concentration at a reference diameter of 2 µm, N o , [m −4 ]).…”

“…Algorithm E (Kostadinov et al, 2016) retrieves the absolute and fractional phytoplankton carbon biomass in three phytoplankton size classes (or, approximately equivalent − phytoplankton functional types) − picophytoplankton (0.5-2 µm in diameter), nanophytoplankton (2-20 µm) and microphytoplankton (20-50 µm). The algorithm uses retrievals of the particle size distribution (PSD) to estimate particle volume.…”

“…The PSD parameters retrieved are the power-law slope (ξ ) and the scaling parameter (i.e., differential particle number concentration at a reference diameter of 2 µm, N o , [m −4 ]). Kostadinov et al (2016) applied an empirical correction to the PSD scaling parameter N o obtained from the model LUT, to improve absolute phytoplankton carbon concentration estimates. A further allometry-based method, Algorithm F (Roy et al, 2017), uses chlorophyll concentration and the absorption coefficient of phytoplankton at 676 nm, a ph (676), to compute phytoplankton carbon.…”

“…Of these, the method proposed by Martínez-Vicente et al (2013) dealt with a fraction of the phytoplankton community (diameter < 20 µm), whereas those of Behrenfeld et al (2005), Sathyendranath et al (2009), and Marañón et al (2014) dealt with the whole phytoplankton community. The methods based on allometric structure (Kostadinov et al, 2009(Kostadinov et al, , 2016Roy et al, 2017), on the other hand, have the advantage of being able to target the whole of the phytoplankton community, and partition phytoplankton carbon among any user-defined size-intervals. Comparison of these algorithms is not straightforward, because of the differences in approaches used and the products obtained.…”

“…These include methods based on particle back-scattering coefficient (b bp ) at a single wavelength (Behrenfeld et al, 2005;Martínez-Vicente et al, 2013); empirical relationships based on chlorophyll concentration (Sathyendranath et al, 2009;Marañón et al, 2014); and methods based on allometric considerations combined with either the spectral slope of the particle back-scattering spectrum (Kostadinov et al, 2009(Kostadinov et al, , 2016 or with the phytoplankton absorption characteristics (Roy et al, 2017). Of these, the method proposed by Martínez-Vicente et al (2013) dealt with a fraction of the phytoplankton community (diameter < 20 µm), whereas those of Behrenfeld et al (2005), Sathyendranath et al (2009), and Marañón et al (2014) dealt with the whole phytoplankton community.…”