Numerical simulations on effects of turbulence on the size spectrum of sinking particles in ocean surface boundary layer
Open accessAbstract
Sinking particles in the ocean play a crucial role in the climate system by transporting materials, such as carbon, deep into the ocean. The amount of this transport is influenced by the net sinking speed of the particles and the amount of material attached to them, both of which are determined by the size spectrum of the particles. The spectrum is shaped by aggregation and disaggregation processes, which are typically most active in the ocean surface boundary layer (OSBL), where intense turbulent flows can enhance both particle collision (aggregation) and particle fragmentation (disaggregation). This study aims to reveal the mechanism by which turbulence transforms the size spectrum through these competing processes and to determine whether turbulence alters the downward material transport from the OSBL. To achieve this, we performed large-eddy simulations to reproduce wind- and wave-induced turbulent flows, employing a Lagrangian particle model to track passive particles in the flow and simulate their aggregation and disaggregation. The model tracked groups of particles rather than individual ones. The results revealed that the shape of the simulated size spectrum was characterized by two length scales, the compensation radius (characterizing the particle floatability) and the Kolmogorov scale, which define the shear range where the turbulent shear shapes the spectrum, the sinking range where the gravitational sinking of particles shapes the spectrum, and the transition range between them. The findings revealed that turbulence tends to increase the terminal velocity and decrease the specific surface area of sinking particles when turbulent aggregation dominates over disaggregation, and vice versa. Although these results may be influenced by uncertain parameterizations (e.g., disaggregation parameterization), the study demonstrates the effectiveness of the numerical approach in investigating the fundamental processes governing particle sinking in turbulent flows.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Settling rates may also increase as a result of particle aggregation.
“Settling rates may also increase as a result of particle aggregation. 26,27”
From A framework for understanding efficiency losses of Ocean Alkalinity Enhancement (2026) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 56
- Akers, R. J., Rushton, A. G., and Stenhouse, J. I. T. (1987). Floc breakage: the dynamic response of the particle size distribution in a flocculated suspension to a step change in turbulent energy dissipation. Chem. Eng. Sci. 42, 787-798. doi:10.1016/0009- 2509(87)80038-6 · DOI
- Alldredge, A. L., and Gotschalk, C. (1988). In situ settling behavior of marine snow. Limnol. Oceanogr. 33, 339-351. doi:10.4319/lo.1988.33.3.0339 · DOI
- Andrejczuk, M., Grabowski, W. W., Reisner, J., and Gadian, A. (2010). Cloud-aerosol interactions for boundary layer stratocumulus in the Lagrangian Cloud Model. J. Geophys. Res. Atmos. 115. doi:10.1029/2010JD014248 · DOI
- Ayala, O., Rosa, B., and Wang, L.-P. (2008). Effects of turbulence on the geometric collision rate of sedimenting droplets. Part 2. Theory and parameterization. New J. Phys. 10, 075016. doi:10.1088/1367-2630/10/7/075016 · DOI
- Babler, M. U., Biferale, L., Brandt, L., Feudel, U., Guseva, K., Lanotte, A. S., et al. (2015). Numerical simulations of aggregate breakup in bounded and unbounded turbulent flows. J. Fluid Mech. 766, 104-128. doi:10.1017/jfm.2015. · DOI
- Braithwaite, K. M., Bowers, D. G., Nimmo Smith, W. a. M., and Graham, G. W. (2012). Controls on floc growth in an energetic tidal channel. J. Geophys. Res. Oceans 117. doi:10.1029/2011JC007094 · DOI
- Buesseler, K. O., Antia, A. N., Chen, M., Fowler, S. W., Gardner, W. D., Gustafsson, O., et al. (2007). An assessment of the use of sediment traps for estimating upper ocean particle fluxes. J. Mar. Res. 65, 345-416. doi:10.1357/ 002224007781567621 · DOI
- Burd, A. B., and Jackson, G. A. (2002). Modeling steady-state particle size spectra. Environ. Sci. and Technol. 36, 323-327. doi:10.1021/es010982n · DOI
- Burd, A. B., and Jackson, G. A. (2009). Particle aggregation. Annu. Rev. Mar. Sci. 1, 65-90. doi:10.1146/annurev.marine.010908.163904 · DOI
- Cael, B. B., and White, A. E. (2020). Sinking versus suspended particle size distributions in the north pacific subtropical gyre. Geophys. Res. Lett. 47, e2020GL087825. doi:10.1029/2020GL087825 · DOI
- Craig, P. D., and Banner, M. L. (1994). Modeling wave-enhanced turbulence in the ocean surface layer. J. Phys. Oceanogr. 24, 2546-2559. doi:10.1175/1520- 0485(1994)024⟨2546:MWETIT⟩2.0.CO;2 · DOI
- Craik, A. D. D., and Leibovich, S. (1976). A rational model for Langmuir circulations. J. Fluid Mech. 73, 401-426. doi:10.1017/S0022112076001420 · DOI
- Deardorff, J. W. (1980). Stratocumulus-capped mixed layers derived from a three-dimensional model. Boundary-Layer Meteorol. 18, 495-527. doi:10.1007/ · DOI
- BF00119502 · DOI
- Dyer, K. R. (1989). Sediment processes in estuaries: future research requirements. J. Geophys. Res. Oceans 94, 14327-14339. doi:10.1029/ · DOI
- JC094iC10p14327 · DOI
- Finkel, Z. V., Beardall, J., Flynn, K. J., Quigg, A., Rees, T. A. V., and Raven, J. A. (2010). Phytoplankton in a changing world: cell size and elemental stoichiometry. J. Plankton Res. 32, 119-137. doi:10.1093/plankt/fbp098 · DOI
- Fugate, D. C., and Friedrichs, C. T. (2003). Controls on suspended aggregate size in partially mixed estuaries. Estuar. Coast. Shelf Sci. 58, 389-404. doi:10.1016/S0272- 7714(03)00107-0 · DOI
- Fujiwara, Y., Yoshikawa, Y., and Matsumura, Y. (2018). A wave-resolving simulation of Langmuir circulations with a nonhydrostatic free-surface model: comparison with craik-leibovich theory and an alternative eulerian view of the driving mechanism. J. Phys. Oceanogr. 48, 1691-1708. doi:10.1175/JPO-D-17-0199. · DOI
- Gelbard, F., Tambour, Y., and Seinfeld, J. H. (1980). Sectional representations for simulating aerosol dynamics. J. Colloid Interface Sci. 76, 541-556. doi:10.1016/0021- 9797(80)90394-X · DOI
- Gruber, N., Landschützer, P., and Lovenduski, N. S. (2019). The variable southern ocean carbon sink. Annu. Rev. Mar. Sci. 11, 159-186. doi:10.1146/annurev-marine- 121916-063407 · DOI
- Haller, G., and Sapsis, T. (2008). Where do inertial particles go in fluid flows? Phys. D. Nonlinear Phenom. 237, 573-583. doi:10.1016/j.physd.2007.09.027 · DOI
- Jackson, G. A. (1990). A model of the formation of marine algal flocs by physical coagulation processes. Deep Sea Res. Part A. Oceanogr. Res. Pap. 37, 1197-1211. doi:10.1016/0198-0149(90)90038-W Jackson, G. A., Logan, B. E., Alldredge, A. L., and Dam, H. G. (1995). Combining particle size spectra from a mesocosm experiment measured using photographic and aperture impedance (Coulter and Elzone) techniques. Deep Sea Res. Part II Top. Stud. Oceanogr. 42, 139-157. doi:10.1016/0967-0645(95)00009-F Johnston, T. M. S., and Rudnick, D. L. (2009). Observations of the transition layer. J. Phys. Oceanogr. 39, 780-797. doi:10.1175/2008JPO3824. · DOI · Paper page
- Jouandet, M.-P., Jackson, G. A., Carlotti, F., Picheral, M., Stemmann, L., and Blain, S. (2014). Rapid formation of large aggregates during the spring bloom of Kerguelen Island: observations and model comparisons. Biogeosciences 11, 4393-4406. doi:10.5194/bg-11-4393-2014 · DOI
- Kostadinov, T. S., Siegel, D. A., and Maritorena, S. (2009). Retrieval of the particle size distribution from satellite ocean color observations. J. Geophys. Res. Oceans 114. doi:10.1029/2009JC005303 · DOI
- Lam, P. J., Bishop, J. K. B., Henning, C. C., Marcus, M. A., Waychunas, G. A., and Fung, I. Y. (2006). Wintertime phytoplankton bloom in the subarctic Pacific supported by continental margin iron. Glob. Biogeochem. Cycles 20. doi:10.1029/2005GB002557 · DOI
- Li, X.-y., Zhang, J.-j., and Lee, J. H. W. (2004). Modelling particle size distribution dynamics in marine waters. Water Res. 38, 1305-1317. doi:10.1016/j.watres.2003.11.010 · DOI
- Marchioli, C., and Soldati, A. (2015). Turbulent breakage of ductile aggregates. Phys. Rev. E 91, 053003. doi:10.1103/PhysRevE.91.053003 · DOI
- Marchioli, C., Bhatia, H., Sardina, G., Brandt, L., and Soldati, A. (2019). Role of large-scale advection and small-scale turbulence on vertical migration of gyrotactic swimmers. Phys. Rev. Fluids. 4, 124304. doi:10.1103/PhysRevFluids.4.124304 · DOI
- Mashayekhpour, M., Marchioli, C., Lovecchio, S., Lay, E. N., and Soldati, A. (2019). Wind effect on gyrotactic micro-organism surfacing in free-surface turbulence. Advan. Water. Resou. 129(0309-1708), 328-337. doi:10.1016/j.advwatres.2017.09.001 · DOI
- Matsumura, Y., and Ohshima, K. I. (2015). Lagrangian modelling of frazil ice in the ocean. Ann. Glaciol. 56, 373-382. doi:10.3189/2015AoG69A657 · DOI
- McCave, I. N. (1984). Size spectra and aggregation of suspended particles in the deep ocean. Deep Sea Res. Part A. Oceanogr. Res. Pap. 31, 329-352. doi:10.1016/0198- 0149(84)90088-8 · DOI
- Misumi, K., Nishioka, J., Obata, H., Tsumune, D., Tsubono, T., Long, M. C., et al. (2021). Slowly sinking particles underlie dissolved iron transport across the pacific ocean. Glob. Biogeochem. Cycles 35, e2020GB006823. doi:10.1029/2020GB006823 · DOI
- Monroy, P., Hernández-García, E., Rossi, V., and López, C. (2017). Modeling the dynamical sinking of biogenic particles in oceanic flow. Nonlinear Process. Geophys. 24, 293-305. doi:10.5194/npg-24-293-2017 · DOI
- Noh, K. M., Noh, Y., Brereton, A., and Kug, J.-S. (2021). The route to spring phytoplankton blooms simulated by a Lagrangian plankton model. J. Geophys. Res. Oceans 126, e2020JC016753. doi:10.1029/2020JC016753 · DOI
- Oh, D., and Noh, Y. (2022). Roles of drop size distribution and turbulence in autoconversion based on Lagrangian cloud model simulations. J. Geophys. Res. Atmos. 127, e2022JD036495. doi:10.1029/2022JD036495 · DOI
- Pierson, W. J., and Moskowitz, L. (1964). A proposed spectral form for fully developed wind seas based on the similarity theory of S. A. Kitaigorodskii. J. Geophys. Res. (1896-1977) 69, 5181-5190. doi:10.1029/JZ069i024p05181 · DOI
- Pollard, R. T., Rhines, P. B., and Thompson, R. O. R. Y. (1973). The deepening of the wind-Mixed layer. Geophys. Fluid Dyn. 4, 381-404. doi:10.1080/03091927208236105 · DOI
- Qiu, J., Marchioli, C., and Zhao, L. (2022). A review on gyrotactic swimmers in turbulent flows. Acta Mech. Sin. 38, 722323. doi:10.1007/s10409-022-22323-x · DOI
- Riechelmann, T., Noh, Y., and Raasch, S. (2012). A new method for large-eddy simulations of clouds with Lagrangian droplets including the effects of turbulent collision. New J. Phys. 14, 065008. doi:10.1088/1367-2630/14/6/065008 · DOI
- Saffman, P. G., and Turner, J. S. (1956). On the collision of drops in turbulent clouds. J. Fluid Mech. 1, 16-30. doi:10.1017/S0022112056000020 · DOI
- Sheldon, R. W., Prakash, A., and Sutcliffe, W. H. (1972). The size distribution of particles in the ocean1. Limnol. Oceanogr. 17, 327-340. doi:10.4319/lo.1972.17. · DOI
- 0327 · DOI
- Shima, S., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S. (2009). The super- droplet method for the numerical simulation of clouds and precipitation: a particle- based and probabilistic microphysics model coupled with a non-hydrostatic model. Q. J. R. Meteorological Soc. 135, 1307-1320. doi:10.1002/qj.441 · DOI
- Sieburth, J. M., Smetacek, V., and Lenz, J. (1978). Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnol. Oceanogr. 23, 1256-1263. doi:10.4319/lo. 1978.23.6.1256 · DOI
- Smoluchowski, M. (1918). Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Lösungen. Z. für Phys. Chem. 92U, 129-168. doi:10.1515/zpch-1918-9209 · DOI
- Spielman, L. A. (1985). "Hydrodynamic aspects of flocculation, " in Mathematical models and design methods in solid-liquid separation. Editor A. Rushton (Dordrecht: Springer Netherlands), 207-232. doi:10.1007/978-94-009-5091-7_9 · DOI
- Tagliabue, A., Buck, K. N., Sofen, L. E., Twining, B. S., Aumont, O., Boyd, P. W., et al. (2023). Authigenic mineral phases as a driver of the upper-ocean iron cycle. Nature 620, 104-109. doi:10.1038/s41586-023-06210-5 · DOI
- Takeuchi, M., Doubell, M. J., Jackson, G. A., Yukawa, M., Sagara, Y., and Yamazaki, H. (2019). Turbulence mediates marine aggregate formation and destruction in the upper ocean. Sci. Rep. 9, 16280. doi:10.1038/s41598-019-52470-5 · DOI · Paper page
- Tsuda, A., Takeda, S., Saito, H., Nishioka, J., Nojiri, Y., Kudo, I., et al. (2003). A mesoscale iron enrichment in the western subarctic pacific induces a large centric diatom bloom. Science 300, 958-961. doi:10.1126/science.1082000 · DOI
- Ushijima, Y., and Yoshikawa, Y. (2019). Mixed layer depth and sea surface warming under diurnally cycling surface heat flux in the heating season. J. Phys. Oceanogr. 49, 1769-1787. doi:10.1175/JPO-D-18-0230. · DOI
- Ushijima, Y., and Yoshikawa, Y. (2022). Nonlinearly interacting entrainment due to shear and convection in the surface ocean. Sci. Rep. 12, 9899. doi:10.1038/s41598-022- 14098-w · DOI
- Vidussi, F., Claustre, H., Manca, B. B., Luchetta, A., and Marty, J.-C. (2001). Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter. J. Geophys. Res. Oceans 106, 19939-19956. doi:10.1029/1999JC000308 · DOI
- Yoshikawa, Y. (2019). "Wind-driven mixing under the earth rotation, " in Encyclopedia of ocean sciences (Elsevier), 586-590. doi:10.1016/B978-0-12-409548- 9.10954-6 · DOI
- Yoshikawa, Y., Baba, Y., Mizutani, H., Kubo, T., and Shimoda, C. (2018). Observed features of Langmuir turbulence forced by misaligned wind and waves under destabilizing buoyancy flux. J. Phys. Oceanogr. 48, 2737-2759. doi:10.1175/JPO-D-18- 0038. · DOI
- Zikanov, O., Slinn, D. N., and Dhanak, M. R. (2003). Large-eddy simulations of the wind-induced turbulent Ekman layer. J. Fluid Mech. 495, 343-368. doi:10.1017/ · DOI
Cited by
Sorted by citations
A framework for understanding efficiency losses of Ocean Alkalinity Enhancement
Lunstrum, A., Keller, D., Hartmann, J., & Moras, C. (2026) · CDRxiv · doi:10.70212/cdrxiv.2026517.v1 · Cited by 1 · see citing works
- Authors
- Nishino, K., & Yoshikawa, Y.
- Affiliations
- Central Research Institute of Electric Power Industry (JP)
- Kyoto University (JP)
- Corresponding author
- Nishino, Keisuke
- Venue
- Frontiers in Earth Science
- Publisher
- Frontiers Media SA
- Published
- 2024-12-23
- Volume
- 12
- ISSN
- 2296-6463 (ISSN-L)
- OpenAlex
- W4405694053
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Earth Science (publishedVersion, cc-by)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by)
- Funders
- Japan Society for the Promotion of Science (21H05305)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine and coastal ecosystems
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Engineering › Ocean Engineering › Particle Dynamics in Fluid Flows
- Keywords
- sinking particles, particle size spectrum, ocean surface boundary layer, turbulent flow, particle aggregation, particle disaggregation, turbulent shear, terminal velocity, specific surface area, Kolmogorov scale, carbon transport, Lagrangian particle model
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review