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.
Highlights
Oxygenic photosynthesis by marine phytoplankton is responsible for fixing ∼ 50 Gt C yr−1 (Field et al, 1998; Carr et al, 2006) and powers the biological pump, which is an important part of the carbon cycle (e.g., Siegel et al, 2014)
The mission climatology of total phytoplankton carbon (C) (Fig. 1a) indicates that biomass is lowest in the oligotrophic subtropical gyres, while higher values occur in more eutrophic regions, such as the equatorial and eastern-boundary currents, other upwelling regions and high-latitude oceans
We presented a novel method to retrieve phytoplankton carbon biomass from ocean color satellite data, based on combining volume determinations using backscattering-based particle size distribution (PSD) retrievals of Kostadinov et al (2009) with carbon-tovolume allometric relationships compiled by Menden-Deuer and Lessard (2000)
Summary
Oxygenic photosynthesis by marine phytoplankton is responsible for fixing ∼ 50 Gt C yr−1 (Field et al, 1998; Carr et al, 2006) and powers the biological pump, which is an important part of the carbon cycle (e.g., Siegel et al, 2014). Phytoplankton are grouped into phytoplankton functional types (PFTs) according to their differing biogeochemical roles (IOCCG, 2014). Since size is a master trait (e.g., Marañon, 2015), phytoplankton size classes (PSCs) are often used to define the PFTs (e.g., Le Quéré et al, 2005). Kostadinov et al.: Carbon-based phytoplankton size classes fluences (e.g., Falkowski and Oliver, 2007) and can be influenced by (e.g., Marinov et al, 2013; Cabré et al, 2014) climate (and shorter-term processes such as seasonality; e.g., Kostadinov et al, 2016a). Detailed characterization of the structure and function of oceanic ecosystems (i.e., descriptive and predictive understanding of the PFTs) is required as a crucial component of Earth system and climate modeling
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