Estimating the biomass of phytoplankton communities via remote sensing is a key requirement for understanding global ocean ecosystems. Of particular interest is the carbon associated with diatoms given their unequivocal ecological and biogeochemical roles. Satellite-based algorithms often rely on accessory pigment proxies to define diatom biomass, despite a lack of validation against independent diatom biomass measurements. We used imaging-in-flow cytometry to quantify diatom carbon in the western North Atlantic, and compared results to those obtained from accessory pigment-based approximations. Based on this analysis, we offer a new empirical formula to estimate diatom carbon concentrations from chlorophyll a. Additionally, we developed a neural network model in which we integrated chlorophyll a and environmental information to estimate diatom carbon distributions in the western North Atlantic. The potential for improving satellite-based diatom carbon estimates by integrating environmental information into a model, compared to models that are based solely on chlorophyll a, is discussed.

Phytoplankton play a major role on Earth, impacting the global distribution and cycles of carbon, oxygen, nitrogen, sulfur, and other elements, and structuring marine food webs. One fundamental trait of phytoplankton with direct biogeochemical implications is their size, as it governs metabolic and sinking rates as well as prey–predator interactions. Phytoplankton size spans approximately 3.5 orders of magnitude (when expressed as an equivalent spherical diameter), and thus measuring the full range in size distribution of phytoplankton is challenging and rarely attempted. Here, we constructed phytoplankton size spectra by merging state-of-the-art cytometry and imaging cytometry measurements that were collected in the western North Atlantic Ocean, along a latitudinal gradient (36°N to 55°N) and during different phases of the annual cycle of phytoplankton. The derived spectra show a seasonal pattern that parallels changes in phytoplankton biomass, and do not always follow a commonly assumed power-law model. Shifts in size spectra were more pronounced in the sub-Arctic and temperate subregions, compared to the subtropical region of the study area. We evaluated the relationships between different size groups and environmental parameters to derive ecologically meaningful size groups. Finally, to simulate Ocean Color remote-sensing algorithms of phytoplankton size, we compared temporal variations in descriptors of the size spectra (median particle size, phytoplankton size distribution exponent) with optical size proxies derived from light absorption and attenuation; good agreement was observed in the northern sections of the study area where temporal changes in community size structure were more pronounced.

High-resolution ocean biophysical models are now routinely being conducted at basin and global-scale, opening opportunities to deepen our understanding of the mechanistic coupling of physical and biological processes at the mesoscale. Prior to using these models to test scientific questions, we need to assess their skill. While progress has been made in validating the mean field, little work has been done to evaluate skill of the simulated mesoscale variability. Here we use geostatistical 2-D variograms to quantify the magnitude and spatial scale of chlorophyll a patchiness in a 1/10th-degree eddy-resolving coupled Community Earth System Model simulation. We compare results from satellite remote sensing and ship underway observations in the North Atlantic Ocean, where there is a large seasonal phytoplankton bloom. The coefficients of variation, i.e., the arithmetic standard deviation divided by the mean, from the two observational data sets are approximately invariant across a large range of mean chlorophyll a values from oligotrophic and winter to subpolar bloom conditions. This relationship between the chlorophyll a mesoscale variability and the mean field appears to reflect an emergent property of marine biophysics, and the high-resolution simulation does poorly in capturing this skill metric, with the model underestimating observed variability under low chlorophyll a conditions such as in the subtropics.