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This study characterized ocean biological carbon pump metrics in the second iteration of the REgional Carbon Cycle Assessment and Processes (RECCAP2) project. The analysis here focused on comparisons of global and biome-scale regional patterns in particulate organic carbon (POC) production and sinking flux from the RECCAP2 ocean biogeochemical model ensemble against observational products derived from satellite remote sensing, sediment traps, and geochemical methods. There was generally good model-data agreement in mean large-scale spatial patterns, but with substantial spread across the model ensemble and observational products. The global-integrated, model ensemble-mean export production, taken as the sinking POC flux at 100 m (6.08 ± 1.17 Pg C yr−1), and export ratio defined as sinking flux divided by net primary production (0.154 ± 0.026) both fell at the lower end of observational estimates. Comparison with observational constraints also suggested that the model ensemble may have underestimated regional biological CO2 drawdown and air-sea CO2 flux in high productivity regions. Reasonable model-data agreement was found for global-integrated, ensemble-mean sinking POC flux into the deep ocean at 1,000 m (0.65 ± 0.24 Pg C yr−1) and the transfer efficiency defined as flux at 1,000 m divided by flux at 100 m (0.122 ± 0.041), with both variables exhibiting considerable regional variability. The RECCAP2 analysis presents standard ocean biological carbon pump metrics for assessing biogeochemical model skill, metrics that are crucial for further modeling efforts to resolve remaining uncertainties involving system-level interactions between ocean physics and biogeochemistry.

Shipboard sampling of ocean biogeochemical properties is necessarily limited by logistical and practical constraints. As a result, the majority of observations are obtained for the spring/summer period and in regions relatively accessible from a major port. This limitation may bias the conceptual understanding we have of the spatial and seasonal variability in important components of the Earth system. Here we examine the influence of sampling bias on global estimates of carbon export flux by sub-sampling a biogeochemical model to simulate real, realistic and random sampling. We find that both the sparseness and the 'clumpy' character of shipboard flux observations generate errors in estimates of globally extrapolated export flux of up to ∼ ± 20%. The use of autonomous technologies, such as the Biogeochemical-Argo network, will reduce the uncertainty in global flux estimates to ∼ ± 3% by both increasing the sample size and reducing clumpiness in the spatial distribution of observations. Nevertheless, determining the climate change-driven trend in global export flux may be hampered due to the uncertainty introduced by interannual variability in sampling patterns.

Characterizing marine phytoplankton community variability is crucial to designing sampling strategies and interpreting time series. Satellite remote sensing, microscopy sampling, and flow through imaging systems have widely different resolutions: from weekly or monthly with microscopy sampling to daily when no cloud cover or glint is present with polar-orbiting satellites, and hourly for autonomous imaging instruments. To improve our understanding of data robustness against sampling resolution at different taxonomic levels, we analyze 2 yr of data from an Imaging FlowCytobot with hourly resolution and resample it to daily, satellite-temporal, and weekly microscopy sampling resolution. We show that weekly and satellite-temporal resolutions are sufficient to resolve general community composition but that the randomness of satellite-temporal resolution can result in overrepresenting or underrepresenting certain categories. While the yearly phytoplankton biomass bloom is detected in late winter by all four resolutions, category-specific yearly blooms are generally consistent in timing but often underestimated or missed by the weekly and satellite-temporal resolutions, introducing a bias in year-to-year comparisons. A minimum of biweekly sampling, particularly during known bloom periods, would lower the bias in such categories. Similarly, sampling time should be considered as daily variations are category-specific. Overall, morning and low tide sampling tended to have higher biomass. We provide tables for categories detected by the IFCB in Narragansett Bay with their major bloom characteristics and recorded daily variability to inform future sampling designs. These results provide tools to interpret past and future time series, including possible detection of specific taxonomic groups with targeted satellite algorithms.

Harmful algal blooms (HABs) are an increasing threat to global fisheries and human health. The mitigation of HABs requires management strategies to successfully forecast the abundance and distribution of harmful algal taxa. In this study, we attempt to characterize the dynamics of 2 phytoplankton genera (Pseudo-nitzschia spp. and Dinophysis spp.) in Narragansett Bay, Rhode Island, using empirical dynamic modeling. We utilize a high-resolution Imaging FlowCytobot dataset to generate a daily-resolution time series of phytoplankton images and then characterize the sub-monthly (1–30 days) timescales of univariate and multivariate prediction skill for each taxon. Our results suggest that univariate predictability is low overall, different for each taxon and does not significantly vary over sub-monthly timescales. For all univariate predictions, models can rely on the inherent autocorrelation within each time series. When we incorporated multivariate data based on quantifiable image features, we found that predictability increased for both taxa and that this increase was apparent on timescales >7 days. Pseudo-nitzschia spp. has distinctive predictive dynamics that occur on timescales of around 16 and 25 days. Similarly, Dinophysis spp. is most predictable on timescales of 25 days. The timescales of prediction for Pseudo-nitzschia spp. and Dinophysis spp. could be tied to environmental drivers such as tidal cycles, water temperature, wind speed, community biomass, salinity, and pH in Narragansett Bay. For most drivers, there were consistent effects between the environmental variables and the phytoplankton taxon. Our analysis displays the potential of utilizing data from automated cell imagers to forecast and monitor harmful algal blooms.

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time-series station. We analyzed a 2-yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year-round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter-annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.

Time is perceived to be unidirectional and continuous in the philosophy of science. This continuity can play a crucial role in time series analysis as events are generally seen as an outcome of the past, or subject to events that occurred previously in time. In this study, we describe an ordinal approach to perceiving ecological time series – one that relies on pattern formation with both antecedent and future events. Our approach defines a limited set of structural shapes that can occur for past, present, and future time points. Such a library of all possible shapes can then be used for novel approaches to data visualization and time series analysis. We applied this method to simple ecological models and then to natural time series data for measles cases in London and the phytoplankter Pseudo-nitzschia spp. in Narragansett Bay, Rhode Island. Alternative perspectives on time series representation can strengthen our ability to identify important patterns in dynamics and effectively discriminate between similar time series. When used in conjunction with conventional line-plots, barcodes can be tailored to demonstrate the presence or absence of specific structural patterns or features. Our results show that data exploration without the assumption of time series continuity can yield important and novel insight into the behavior of ecological systems.

There is considerable debate about the benefits and trade-offs for colony formation in a major marine nitrogen fixer, Trichodesmium. To quantitatively analyze the trade-offs, we developed a metabolic model based on carbon fluxes to compare the performance of Trichodesmium colonies and free trichomes under different scenarios. Despite reported reductions in carbon fixation and nitrogen fixation rates for colonies relative to free trichomes, we found that model colonies can outperform individual cells in several cases. The formation of colonies can be advantageous when respiration rates account for a high proportion of the carbon fixation rate. Negative external influence on vital rates, such as mortality due to predation or micronutrient limitations, can also create a net benefit for colony formation relative to individual cells. In contrast, free trichomes also outcompete colonies in many scenarios, such as when respiration rates are equal for both colonies and individual cells or when there is a net positive external influence on rate processes (i.e., optimal environmental conditions regarding light and temperature or high nutrient availability). For both colonies and free trichomes, an increase in carbon fixation relative to nitrogen fixation rates would increase their relative competitiveness. These findings suggest that the formation of colonies in Trichodesmium might be linked to specific environmental and ecological circumstances. Our results provide a road map for empirical studies and models to evaluate the conditions under which colony formation in marine phytoplankton can be sustained in the natural environment.

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cael, B. B., Bisson, K., Conte, M., Duret, M. T., Follett, C. L., Henson, S. A., Honda, M. C., Iversen, M. H., Karl, D. M., Lampitt, R. S., Mouw, C. B., Muller-Karger, F., Pebody, C. A., Smith, K. L., & Talmy, D. Open ocean particle flux variability from surface to seafloor. Geophysical Research Letters, 48(9), (2021): e2021GL092895, https://doi.org/10.1029/2021GL092895., The sinking of carbon fixed via net primary production (NPP) into the ocean interior is an important part of marine biogeochemical cycles. NPP measurements follow a log-normal probability distribution, meaning NPP variations can be simply described by two parameters despite NPP's complexity. By analyzing a global database of open ocean particle fluxes, we show that this log-normal probability distribution propagates into the variations of near-seafloor fluxes of particulate organic carbon (POC), calcium carbonate, and opal. Deep-sea particle fluxes at subtropical and temperate time-series sites follow the same log-normal probability distribution, strongly suggesting the log-normal description is robust and applies on multiple scales. This log-normality implies that 29% of the highest measurements are responsible for 71% of the total near-seafloor POC flux. We discuss possible causes for the dampening of variability from NPP to deep-sea POC flux, and present an updated relationship predicting POC flux from mineral flux and depth., B. B. Cael and S. A. Henson acknowledge support from the National Environmental Research Council (NE/R015953/1) and the Horizon 2020 Framework Programme (820989, project COMFORT). The work reflects only the authors' views; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains. S. A. Henson also acknowledges support from a European Research Council Consolidator grant (GOCART, agreement number 724416). C. L. Follett acknowledges support from the Simons Foundation (grants #827829 and #553242). M. H. Iversen acknowledges support from the DFG-Research Center/Cluster of Excellence “The Ocean Floor – Earth's Uncharted Interface”: EXC-2077-390741603 and the HGF Young Investigator Group SeaPump “Seasonal and regional food web interactions with the biological pump”: VH-NG-1000. M. C. Honda acknowledges financial support from the Ministry of Education, Culture, Sports, Science, and Technology – Japan (grants #: KAKENHI JP18H04144 and JP19H05667). M. Conte acknowledges support from the US National Science Foundation, Division of Ocean Sciences for support for the Oceanic Flux Program time-series since inception, most recently by NSF OCE grant 1829885. D. M. Karl acknowledges support from the Gordon and Betty Moore Foundation (#3794) and the Simons Foundation (SCOPE #329108).

The sinking of carbon fixed via net primary production (NPP) into the ocean interior is an important part of marine biogeochemical cycles. NPP measurements follow a log‐normal probability distribution, meaning NPP variations can be simply described by two parameters despite NPP’s complexity. By analyzing a global database of open ocean particle fluxes, we show that this log‐normal probability distribution propagates into the variations of near‐seafloor fluxes of particulate organic carbon (POC), calcium carbonate, and opal. Deep‐sea particle fluxes at subtropical and temperate time‐series sites follow the same log‐normal probability distribution, strongly suggesting the log‐normal description is robust and applies on multiple scales. This log‐normality implies that 29% of the highest measurements are responsible for 71% of the total near‐seafloor POC flux. We discuss possible causes for the dampening of variability from NPP to deep‐sea POC flux, and present an updated relationship predicting POC flux from mineral flux and depth.

The sinking of carbon fixed via net primary production (NPP) into the ocean interior is an important part of marine biogeochemical cycles. NPP measurements follow a log-normal probability distribution, meaning NPP variations can be simply described by two parameters despite NPP's complexity. By analyzing a global database of open ocean particle fluxes, we show that this log-normal probability distribution propagates into the variations of near-seafloor fluxes of particulate organic carbon (POC), calcium carbonate, and opal. Deep-sea particle fluxes at subtropical and temperate time-series sites follow the same log-normal probability distribution, strongly suggesting the log-normal description is robust and applies on multiple scales. This log-normality implies that 29% of the highest measurements are responsible for 71% of the total near-seafloor POC flux. We discuss possible causes for the dampening of variability from NPP to deep-sea POC flux, and present an updated relationship predicting POC flux from mineral flux and depth.

Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries and oceans. Specific in situ measurements include coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering, as well as remote-sensing reflectance, and irradiance reflectance. The dataset can be downloaded from https://doi.pangaea.de/10.1594/PANGAEA.902230 (Casey et al., 2019).

Phytoplankton play a key role as the base of the marine food web and a crucial component in the Earth's carbon cycle. There have been a few regional studies that have utilized satellite‐estimated phytoplankton functional type products in conjunction with other environmental metrics. Here we expand to a global perspective and ask, what are the physical drivers of phytoplankton composition variability? Using a variety of satellite‐observed ocean color products and physical properties spanning 1997–2015, we characterize spatial and temporal variability in phytoplankton community size structure in relation to satellite‐based physical drivers. We consider the relationships globally and by major thermal regimes (cold and warm), dominant size distribution, and chlorophyll concentration variability. Globally, euphotic depth is the most important parameter driving phytoplankton size variability and also over the majority of the high‐latitude ocean and the central gyres. In all other regions, size variability is driven by a balance of light and mode of nutrient delivery. We investigated the relationship between size composition and chlorophyll concentration and the physical drivers through correlation analysis. Changes in size composition over time are regionally varying and explained by temporal shifts in the varying physical conditions. These changes in phytoplankton size composition and the varying underlying physical drivers will ultimately impact carbon export and food web processes in our changing ocean.

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682., The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined s, National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC00006

After a decade of program offerings, the Mentoring Physical Oceanography Women to Increase Retention (MPOWIR) program initiated a community-wide survey to (1) assess the impact MPOWIR has had on retention of women in the field of physical oceanography, and (2) gauge where needs are being met and where gaps still exist. To investigate the impact of MPOWIR, we compare MPOWIR participants with male and female cohorts that did not participate in MPOWIR but were at a similar career stage. The survey results indicate MPOWIR has had a substantial impact by aiding individuals in finding and developing mentoring relationships. MPOWIR women are far more likely to have a mentor, and they report having mentors in addition to their advisors, indicating proactive seeking of mentoring relationships. Survey results identify many unmet mentoring needs for both men and women, but MPOWIR participants appear to be receiving more from their mentoring relationships than their non-MPOWIR cohorts. The majority of survey respondents reported there were challenges to achieving career goals, but MPOWIR participants were significantly more likely to have attained their career goals, even though they had received their PhDs more recently. Eighty-eight percent of survey respondents with PhDs were employed in oceanography, irrespective of participation in MPOWIR. MPOWIR women indicate the program has had a large impact on their lives, with the greatest effect being expansion of professional networks and exposure to professional development skills. Senior participants in the program (who serve as mentors to junior scientists) also reported significant professional and personal growth from being involved. Data obtained independently of the survey show that, of the 173 women who have participated in MPOWIR, the recent PhDs are predominantly in postdoctoral positions as expected, but for participants receiving their PhDs prior to 2012, an impressive 80% are in faculty or university/government/nonprof

Satellite remote sensing of colored dissolved organic matter (CDOM) has focused on CDOM absorption (aCDOM) at a reference wavelength, as its magnitude provides insight into the underwater light field and large-scale biogeochemical processes. CDOM spectral slope, SCDOM, has been treated as a constant or semiconstant parameter in satellite retrievals of aCDOM despite significant regional and temporal variabilities. SCDOM and other optical metrics provide insights into CDOM composition, processing, food web dynamics, and carbon cycling. To date, much of this work relies on fluorescence techniques or aCDOM in spectral ranges unavailable to current and planned satellite sensors (e.g., <300 nm). In preparation for anticipated future hyperspectral satellite missions, we take the first step here of exploring global variability in SCDOM and fit deviations in the aCDOM spectra using the recently proposed Gaussian decomposition method. From this, we investigate if global variability in retrieved SCDOM and Gaussian components is significant and regionally distinct. We iteratively decreased the spectral range considered and analyzed the number, location, and magnitude of fitted Gaussian components to understand if a reduced spectral range impacts information obtained within a common spectral window. We compared the fitted slope from the Gaussian decomposition method to absorption-based indices that indicate CDOM composition to determine the ability of satellite-derived slope to inform the analysis and modeling of large-scale biogeochemical processes. Finally, we present implications of the observed variability for remote sensing of CDOM characteristics via SCDOM.

Ocean color remote sensing of chlorophyll concentration has revolutionized our understanding of the biology of the oceans. However, a comprehensive understanding of the structure and function of oceanic ecosystems requires the characterization of the spatio-temporal variability of various phytoplankton functional types (PFTs), which have differing biogeochemical roles. Thus, recent bio-optical algorithm developments have focused on retrieval of various PFTs. It is important to validate and inter-compare the existing PFT algorithms; however direct comparison of retrieved variables is non-trivial because in those algorithms PFTs are defined differently. Thus, it is more plausible and potentially more informative to focus on emergent properties of PFTs, such as phenology. Furthermore, ocean color satellite PFT data sets can play a pivotal role in informing and/or validating the biogeochemical routines of Earth System Models. Here, the phenological characteristics of 10 PFT satellite algorithms and 7 latest-generation climate models from the Coupled Model Inter-comparison Project (CMIP5) are inter-compared as part of the International Satellite PFT Algorithm Inter-comparison Project. The comparison is based on monthly satellite data (mostly SeaWiFS) for the 2003–2007 period. The phenological analysis is based on the fraction of microplankton or a similar variable for the satellite algorithms and on the carbon biomass due to diatoms for the climate models. The seasonal cycle is estimated on a per-pixel basis as a sum of sinusoidal harmonics, derived from the Discrete Fourier Transform of the variable time series. Peak analysis is then applied to the estimated seasonal signal and the following phenological parameters are quantified for each satellite algorithm and climate model: seasonal amplitude, percent seasonal variance, month of maximum, and bloom duration. Secondary/double blooms occur in many areas and are also quantified. The algorithms and the models are quantita

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions.

The shunt of photosynthetically derived particulate organic carbon (POC) from the euphotic zone and deep remineralization comprises the basic mechanism of the “biological carbon pump.” POC raining through the “twilight zone” (euphotic depth to 1 km) and “midnight zone” (1 km to 4 km) is remineralized back to inorganic form through respiration. Accurately modeling POC flux is critical for understanding the “biological pump” and its impacts on air‐sea CO2 exchange and, ultimately, long‐term ocean carbon sequestration. Yet commonly used parameterizations have not been tested quantitatively against global data sets using identical modeling frameworks. Here we use a single one‐dimensional physical‐biogeochemical modeling framework to assess three common POC flux parameterizations in capturing POC flux observations from moored sediment traps and thorium‐234 depletion. The exponential decay, Martin curve, and ballast model are compared to data from 11 biogeochemical provinces distributed across the globe. In each province, the model captures satellite‐based estimates of surface primary production within uncertainties. Goodness of fit is measured by how well the simulation captures the observations, quantified by bias and the root‐mean‐square error and displayed using “target diagrams.” Comparisons are presented separately for the twilight zone and midnight zone. We find that the ballast hypothesis shows no improvement over a globally or regionally parameterized Martin curve. For all provinces taken together, Martin's b that best fits the data is [0.70, 0.98]; this finding reduces by at least a factor of 3 previous estimates of potential impacts on atmospheric pCO2 of uncertainty in POC export to a more modest range [−16 ppm, +12 ppm].