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  • 1
    Publication Date: 2022-10-19
    Description: © 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.
    Description: 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.
    Description: Funding for this work was provided by NASA grants #NNX15AE67G and #80NSSC20M0202. A. Chase is supported by a Washington Research Foundation Postdoctoral Fellowship.
    Keywords: Diatoms ; Carbon ; Remote sensing ; Pigments ; Cell imagery
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-10-26
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creaive Commons Attribution License. The definitive version was published in Eveleth, R., Glover, D. M., Long, M. C., Lima, I. D., Chase, A. P., & Doney, S. C. . Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing. Frontiers in Marine Science, 8, (2021): 612764, https://doi.org/10.3389/fmars.2021.612764.
    Description: 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.
    Description: This work was supported in part by the National Aeronautics and Space Administration (NASA) as part of the North Atlantic Aerosol and Marine Ecosystems Study (NAAMES; NASA grant 80NSSC18K0018). The CESM project is supported by the National Science Foundation and the Office of Science (BER) of the United States Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation and other agencies. This research was enabled by CISL compute and storage resources.
    Keywords: Geostatistical analysis ; North Atlantic Ocean ; Community Earth System Model ; Model validataion ; Chlorophyll
    Repository Name: Woods Hole Open Access Server
    Type: Article
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