Revisiting Ocean Color algorithms for chlorophyll a and particulate organic carbon in the Southern Ocean using biogeochemical floats

The Southern Ocean (SO, oceans south of 30 oS) ecosystem plays a key role in global carbon cycles by sinking a major part (43 %) of the anthropogenic CO2, and being an important source of nutrients for primary producers. However, undersampling of SO biogeochemical properties limits our understanding of the mechanisms taking place in this remote area. The Southern Ocean Carbon and Climate Observing and Modeling project (SOCCOM) has been deploying a large number of autonomous biogeochemical floats to study the SO (as of December 2016, 74 floats out of 200 have been deployed). SOCCOM floats measurements can be used to extend remote sensing chlorophyll a (chla) and POC products under the clouds or during the polar night as well as adding the depth dimension to the satellites view of the SO. Chlorophyll a concentrations measured by fluorometers (exciting/detecting light at 470/685 nm) embedded on the floats and particulate organic carbon (POC) concentrations derived from backscattering coefficients (at 700 nm) were calibrated with samples collected during the floats’ deployment cruise. Float chla and POC were compared with products derived from observations of the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS) and the Visible Infrared Imaging Radioneter Suite (VIIRS) sensors. We find the Ocean Color Index (OCI) global algorithm to agree well with the matchups (within 9 %, on average, for VIIRS and 12 %, on average, for MODIS). SO specific algorithms estimating chla are offset by ~45 % south of the Sea Ice Extent Front (~ 60 oS). The remote sensing POC algorithm currently used by NASA agrees well with the float estimates throughout the SO

Inlinino: A modular software data logger for oceanography

Inlinino is an open-source software data logger whose main purpose is to log scientific measurements collected during extended periods at sea. Here, we present an application of this software to data collected with commercial instrumentation. Inlinino also provides real-time visualization of the recorded observations, which helps users troubleshoot instruments in the field and prevents the collection of bad data. Inlinino is written in Python and runs on regular computers for instruments that have a serial interface. For less than $57, we built a separate data acquisition module—a precision analog-to-serial converter—for interfacing instruments that output analog signals to Inlinino. Inlinino was designed for optical sensors but can be used with any environmental sensor that communicates through analog or serial ports. The code is sufficiently modular that anyone with moderate coding skills can add new sensors. To date, Inlinino has been deployed successfully on several research vessels and logged more than 650 days of operation

Plankton imagery data inform satellite-based estimates of diatom carbon

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chase, A. P., Boss, E. S., Haentjens, N., Culhane, E., Roesler, C., & Karp-Boss, L. Plankton imagery data inform satellite-based estimates of diatom carbon. Geophysical Research Letters, 49(13), (2022): e2022GL098076, https://doi.org/10.1029/2022GL098076.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.Funding for this work was provided by NASA grants #NNX15AE67G and #80NSSC20M0202. A. Chase is supported by a Washington Research Foundation Postdoctoral Fellowship

Phytoplankton Phenology in the North Atlantic: Insights From Profiling Float Measurements

There was a typographical error in Equation (1) of our article, where the first term in the denominator should be 0.022 rather than 0.22. The fully corrected equation should be: (Formula presented.). The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

Evaluation of diagnostic pigments to estimate phytoplankton size classes

Limnology and Oceanography: Methods published by Wiley Periodicals LLC. on behalf of Association for the Sciences of Limnology and Oceanography. Phytoplankton accessory pigments are commonly used to estimate phytoplankton size classes, particularly during development and validation of biogeochemical models and satellite ocean color-based algorithms. The diagnostic pigment analysis (DPA) is based on bulk measurements of pigment concentrations and relies on assumptions regarding the presence of specific pigments in different phytoplankton taxonomic groups. Three size classes are defined by the DPA: picoplankton, nanoplankton, and microplankton. Until now, the DPA has not been evaluated against an independent approach that provides phytoplankton size calculated on a per-cell basis. Automated quantitative cell imagery of microplankton and some nanoplankton, used in combination with conventional flow cytometry for enumeration of picoplankton and nanoplankton, provide a novel opportunity to perform an independent evaluation of the DPA. Here, we use a data set from the North Atlantic Ocean that encompasses all seasons and a wide range of chlorophyll concentrations (0.18–5.14 mg m−3). Results show that the DPA overestimates microplankton and picoplankton when compared to cytometry data, and subsequently underestimates the contribution of nanoplankton to total biomass. In contrast to the assumption made by the DPA that the microplankton size class is largely made up of diatoms and dinoflagellates, imaging-in-flow cytometry shows significant presence of diatoms and dinoflagellates in the nanoplankton size class. Additionally, chlorophyll b is commonly attributed solely to picoplankton by the DPA, but Chl b-containing phytoplankton are observed with imaging in both nanoplankton and microplankton size classes. We suggest revisions to the DPA equations and application of uncertainties when calculating size classes from diagnostic pigments

Factors driving the seasonal and hourly variability of sea-spray aerosol number in the North Atlantic

Four North Atlantic Aerosol and Marine Ecosystems Study (NAAMES) field campaigns from winter 2015 through spring 2018 sampled an extensive set of oceanographic and atmospheric parameters during the annual phytoplankton bloom cycle. This unique dataset provides four seasons of open-ocean observations of wind speed, sea surface temperature (SST), seawater particle attenuation at 660 nm (cp,660, a measure of ocean particulate organic carbon), bacterial production rates, and sea-spray aerosol size distributions and number concentrations (NSSA). The NAAMES measurements show moderate to strong correlations (0.56 \u3c R \u3c 0.70) between NSSA and local wind speeds in the marine boundary layer on hourly timescales, but this relationship weakens in the campaign averages that represent each season, in part because of the reduction in range of wind speed by multiday averaging. NSSA correlates weakly with seawater cp,660 (R = 0.36, P \u3c\u3c 0.01), but the correlation with cp,660, is improved (R = 0.51, P \u3c 0.05) for periods of low wind speeds. In addition, NAAMES measurements provide observational dependence of SSA mode diameter (dm) on SST, with dm increasing to larger sizes at higher SST (R = 0.60, P \u3c\u3c 0.01) on hourly timescales. These results imply that climate models using bimodal SSA parameterizations to wind speed rather than a single SSA mode that varies with SST may overestimate SSA number concentrations (hence cloud condensation nuclei) by a factor of 4 to 7 and may underestimate SSA scattering (hence direct radiative effects) by a factor of 2 to 5, in addition to overpredicting variability in SSA scattering from wind speed by a factor of 5

Deep maxima of phytoplankton biomass, primary production and bacterial production in the Mediterranean Sea

The deep chlorophyll maximum (DCM) is a ubiquitous feature of phytoplankton vertical distribution in stratified waters that is relevant to our understanding of the mechanisms that underpin the variability in photoautotroph ecophysiology across environmental gradients and has implications for remote sensing of aquatic productivity. During the PEACETIME (Process studies at the air-sea interface after dust deposition in the Mediterranean Sea) cruise, carried out from 10 May to 11 June 2017, we obtained 23 concurrent vertical profiles of phytoplankton chlorophyll a, carbon biomass and primary production, as well as heterotrophic prokaryotic production, in the western and central Mediterranean basins. Our main aims were to quantify the relative role of photoacclimation and enhanced growth as underlying mechanisms of the DCM and to assess the trophic coupling between phytoplankton and heterotrophic prokaryotic production. We found that the DCM coincided with a maximum in both the biomass and primary production but not in the growth rate of phytoplankton, which averaged 0.3 d−1 and was relatively constant across the euphotic layer. Photoacclimation explained most of the increased chlorophyll a at the DCM, as the ratio of carbon to chlorophyll a (C : Chl a) decreased from ca. 90–100 (g : g) at the surface to 20–30 at the base of the euphotic layer, while phytoplankton carbon biomass increased from ca. 6 mgCm−3 at the surface to 10–15 mgCm−3 at the DCM. As a result of photoacclimation, there was an uncoupling between chlorophyll a-specific and carbon-specific productivity across the euphotic layer. The ratio of fucoxanthin to total chlorophyll a increased markedly with depth, suggesting an increased contribution of diatoms at the DCM. The increased biomass and carbon fixation at the base of the euphotic zone was associated with enhanced rates of heterotrophic prokaryotic activity, which also showed a surface peak linked with warmer temperatures. Considering the phytoplankton biomass and turnover rates measured at the DCM, nutrient diffusive fluxes across the nutricline were able to supply only a minor fraction of the photoautotroph nitrogen and phosphorus requirements. Thus the deep maxima in biomass and primary production were not fuelled by new nutrients but likely resulted from cell sinking from the upper layers in combination with the high photosynthetic efficiency of a diatom-rich, low-light acclimated community largely sustained by regenerated nutrients. Further studies with increased temporal and spatial resolution will be required to ascertain if the peaks of deep primary production associated with the DCM persist across the western and central Mediterranean Sea throughout the stratification season

Small phytoplankton dominate western North Atlantic biomass

The North Atlantic phytoplankton spring bloom is the pinnacle in an annual cycle that is driven by physical, chemical, and biological seasonality. Despite its important contributions to the global carbon cycle, transitions in plankton community composition between the winter and spring have been scarcely examined in the North Atlantic. Phytoplankton composition in early winter was compared with latitudinal transects that captured the subsequent spring bloom climax. Amplicon sequence variants (ASVs), imaging flow cytometry, and flow-cytometry provided a synoptic view of phytoplankton diversity. Phytoplankton communities were not uniform across the sites studied, but rather mapped with apparent fidelity onto subpolar- and subtropical-influenced water masses of the North Atlantic. At most stations, cells < 20-µm diameter were the main contributors to phytoplankton biomass. Winter phytoplankton communities were dominated by cyanobacteria and pico-phytoeukaryotes. These transitioned to more diverse and dynamic spring communities in which pico- and nano-phytoeukaryotes, including many prasinophyte algae, dominated. Diatoms, which are often assumed to be the dominant phytoplankton in blooms, were contributors but not the major component of biomass. We show that diverse, small phytoplankton taxa are unexpectedly common in the western North Atlantic and that regional influences play a large role in modulating community transitions during the seasonal progression of blooms

Seasonal Mixed Layer Depth Shapes Phytoplankton Physiology, Viral Production, and Accumulation In the North Atlantic

Seasonal shifts in phytoplankton accumulation and loss largely follow changes in mixed layer depth, but the impact of mixed layer depth on cell physiology remains unexplored. Here, we investigate the physiological state of phytoplankton populations associated with distinct bloom phases and mixing regimes in the North Atlantic. Stratification and deep mixing alter community physiology and viral production, effectively shaping accumulation rates. Communities in relatively deep, early-spring mixed layers are characterized by low levels of stress and high accumulation rates, while those in the recently shallowed mixed layers in late-spring have high levels of oxidative stress. Prolonged stratification into early autumn manifests in negative accumulation rates, along with pronounced signatures of compromised membranes, death-related protease activity, virus production, nutrient drawdown, and lipid markers indicative of nutrient stress. Positive accumulation renews during mixed layer deepening with transition into winter, concomitant with enhanced nutrient supply and lessened viral pressure