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

Biophysical drivers of carbon dioxide and methane fluxes in a restored tidal freshwater wetland

Wetlands store large amounts of carbon (C) in biomass and soils, playing a crucial role in offsetting greenhouse gas (GHG) emissions; however, they also account for 30% of global yearly CH4 emissions. Anthropogenic disturbance has led to the decline of natural wetlands throughout the United States, with a corresponding increase in created and restored wetlands. Studies characterizing biogeochemical processes in restored forested wetlands, particularly those that are both tidal and freshwater, are lacking but essential for informing science- based carbon management

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

Tower-based greenhouse gas fluxes in a restored tidal freshwater wetland: A shared resource for research and teaching.

The goals of this study are: 1) to use an eddy-covariance system to continuously measure wetland-atmosphere CO2 and CH4 exchange in a restored forested wetland, 2) to quantity C sequestration in plant biomass and soils in restored (Kimages Creek watershed) and old-growth (Harris Creek watershed) forested wetlands, and 3) to establish a shared long-term, shared research and teaching platform centered on eddy-covariance tower measurements. Since the old-growth forest wetland has had longer to accumulate C, the current C stocks are likely much larger than those of the restored wetland; however, the rate of C accumulation (i.e., C sequestration or net ecosystem production) may be higher in young ecosystems (De Simon et al. 2 | Goodrich-Stuart (Stuart-Haëntjens) 2012). While natural wetlands generally offset the warming effect of CH4 emissions by also sequestering large amounts of CO2, but it has been suggested that, in the short-term, this may not hold true for restored wetlands (Petrescu et al. 2015). Very few restored wetlands have studied, however, so knowledge is lacking in this area

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

Recommendations for obtaining unbiased chlorophyll estimates from in situ chlorophyll fluorometers: A global analysis of WET Labs ECO sensors

Chlorophyll fluorometers provide the largest in situ global data set for estimating phytoplankton biomass because of their ease of use, size, power consumption, and relatively low price. While in situ chlorophyll a (Chl) fluorescence is proxy for Chl a concentration, and hence phytoplankton biomass, there exist large natural variations in the relationship between in situ fluorescence and extracted Chl a concentration. Despite this large natural variability, we present here a global validation data set for the WET Labs Environmental Characterization Optics (ECO) series chlorophyll fluorometers that suggests a factor of 2 overestimation in the factory calibrated Chl a estimates for this specific manufacturer and series of sensors. We base these results on paired High Pressure Liquid Chromatography (HPLC) and in situ fluorescence match ups for which non-photochemically quenched fluorescence observations were removed. Additionally, we examined matches between the factory-calibrated in situ fluorescence and estimates of chlorophyll concentration determined from in situ radiometry, absorption line height, NASA’s standard ocean color algorithm as well as laboratory calibrations with phytoplankton monocultures spanning diverse species that support the factor of 2 bias. We therefore recommend the factor of 2 global bias correction be applied for the WET Labs ECO sensors, at the user level, to improve the global accuracy of chlorophyll concentration estimates and products derived from them. We recommend that other fluorometer makes and models should likewise undergo global analyses to identify potential bias in factory calibration

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