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Sampling uncertainties of particle size distributions and derived fluxes
  • +4
  • Kelsey M Bisson,
  • Rainer Kiko,
  • David A. Siegel,
  • Lionel Guidi,
  • Marc Picheral,
  • Emmanuel Boss,
  • B. B. Cael
Kelsey M Bisson
Oregon State University

Corresponding Author:bissonk@oregonstate.edu

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Rainer Kiko
GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
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David A. Siegel
University of California, Santa Barbara
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Lionel Guidi
French National Centre for Scientific Research (CNRS)
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Marc Picheral
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Emmanuel Boss
School of Marine Sciences, University of Maine, Orono, ME, USA
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B. B. Cael
National Oceanography Centre
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The Underwater Vision Profiler (UVP) provides abundant in situ data of the marine particle size distribution (PSD) on global scales and has been used for a diversity of applications, but the uncertainty associated with its measurements has not been quantified. Here we use a global compilation of UVP (version 5) observations of the PSD to assess the sampling uncertainty associated with the UVP’s sampling characteristics. We model UVP sampling uncertainty using Bayesian Poisson statistics and provide formulae for the uncertainty associated with a given sampling volume and observed particle count. We also model PSD observations using a power law with an exponential cutoff to better match the low concentration associated with rare large particles as seen by the UVP. We use the two shape parameters from this statistical model to describe changes in the PSD shape across latitude band, season, and depth. The UVP sampling uncertainty propagates into an uncertainty for modeled carbon flux exceeding 50%. The statistical model is used to extend the size interval used in a PSD-derived carbon flux model, revealing a high sensitivity of the PSD-derived flux model to the inclusion of small particles (80-128 microns). We close with recommendations on how to revise the carbon flux model, and we provide avenues to address additional uncertainties associated with UVP-derived carbon flux calculations.