Sampling uncertainties of particle size distributions and derived fluxes

Bisson, Kelsey M.; Kiko, Rainer; Siegel, David A.; Guidi, Lionel; Picheral, Marc; Boss, Emmanuel; Cael, B.B. ORCID: https://orcid.org/0000-0003-1317-5718. 2022 Sampling uncertainties of particle size distributions and derived fluxes. Limnology and Oceanography: Methods. https://doi.org/10.1002/lom3.10524

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Official URL: http://dx.doi.org/10.1002/lom3.10524

Abstract/Summary

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 μm). We provide avenues to address additional uncertainties associated with UVP-derived carbon flux calculations.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/lom3.10524
ISSN:	1541-5856
Date made live:	29 Nov 2022 17:23 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533648

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/lom3.10524

ISSN:

1541-5856

Date made live:

29 Nov 2022 17:23 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533648