Sampling uncertainties of particle size distributions and derived fluxes. (10th November 2022)

• Record Type: Journal Article
• Authors: Bisson, Kelsey M.; Kiko, Rainer; Siegel, David A.; Guidi, Lionel; Picheral, Marc; Boss, Emmanuel; Cael, B. B.
• Is Part Of: Limnology and oceanography, methods. Volume 20:Number 12(2022)
• Page Start: 754
• Page End: 767
• Publication Date: 2022-11-10
• Subjects: Limnology -- Methodology -- Periodicals; Oceanography -- Methodology -- Periodicals; 551.48
• DOI: 10.1002/lom3.10524
• Languages: English
• ISSNs: 1541-5856

Abstract

Abstract: In this study, we provide a method to quantify the uncertainty associated with sampling particle size distributions (PSD), using a global compilation of Underwater Vision Profiler observations (UVP, version 5). The UVP provides abundant in situ data of the marine PSD on global scales and has been used for a diversity of applications, but the uncertainty associated with its measurements has not been quantified, including how this uncertainty propagates into derived products of interest. 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 truncated power law 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 μ m). We provide avenues to address additional uncertainties associated with UVP‐derived carbon flux calculations.