Effect of oil plumes on upper-ocean radiative transfer — A numerical study

Xiao, Shuolin; Yang, Di

doi:10.1016/j.ocemod.2019.101522

Cited by 12 publications

(10 citation statements)

References 85 publications

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“…Smaller droplets (< 70 µm diameter) are affected by physical processes induced by atmospheric forcing (e.g. shear-induced turbulence, sea-surface waves, Langmuir circulations, Ekman transport, and thermal convection) in the euphotic zone, resulting in the continuous mixing of the top of the water column and the formation of the ocean mixed layer (Edson et al, 2007;Sullivan and McWilliams, 2010;Özgökmen et al, 2012;D'Asaro, 2014;Xiao and Yang, 2020). The droplets remain in suspension for extended periods of time (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo

Roche

King

2022

The Cryosphere

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Abstract. Sea ice albedo plays an important role in modulating the climate of Earth and is affected by low background concentrations of oil droplets within the ice matrix that absorb solar radiation. In this study, the albedo response of three different types of bare sea ice (melting, first-year, and multi-year sea ice) are calculated at increasing mass ratios (0–1000 ng g−1) of crude oil by using a coupled atmosphere–sea ice radiative-transfer model (TUV-snow; Tropospheric Ultraviolet–Visible) over the optical wavelengths 400–700 nm. The different types of quasi-infinite-thickness sea ice exhibit different albedo responses to oil pollution, with a 1000 ng g−1 mass ratio of oil causing a decrease to 70.9 % in multi-year sea ice, 47.2 % in first-year sea ice, and 22.1 % in melting sea ice relative to the unpolluted albedo at a wavelength of 400 nm. The thickness of the sea ice is also an important factor, with realistic-thickness sea ices exhibiting similar results, albeit with a weaker albedo response for multi-year sea ice to 75.3 %, first-year sea ice to 66.3 %, and melting sea ice to 35.9 %. The type of oil also significantly affects the response of sea ice albedo, with a relatively opaque and heavy crude oil (Romashkino oil) causing a significantly larger decrease in sea ice albedo than a relatively transparent light crude oil (Petrobaltic oil). The size of the oil droplets polluting the oil also plays a minor role in the albedo response, with weathered submicrometre droplets (0.05–0.5 µm radius) of Romashkino oil being the most absorbing across the optical wavelengths considered. Therefore, the work presented here demonstrates that low background concentrations of small submicrometre- to micrometre-sized oil droplets have a significant effect on the albedo of bare sea ice. All three types of sea ice are sensitive to oil pollution; however, first-year sea ice and particularly melting sea ice are very sensitive to oil pollution.

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Section: Introductionmentioning

confidence: 99%

“…The droplets remain in suspension for extended periods of time (i.e. weeks to months) in the presence of adequate mixing energy, with their buoyancy acting as a constant resistant force to the mixing (Ryerson et al, 2012;Yang et al, 2014;Zhao et al, 2016;Uttieri et al, 2019;Xiao and Yang, 2020). The following papers (e.g.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo

Roche

King

2022

The Cryosphere

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“…In particular, aspherical microplastics such as microplastic fibers [9,15] (MPFs) (See Fig. 1 b), which are very thin (O(1) µm) and long (100s µm) [9], account for a significant portion of directly discharged [16], airborne, and deposited microplastics found in both the natural and built environments [7,10,17,18].…”

Section: Ab)mentioning

confidence: 99%

Long-distance atmospheric transport of microplastic fibers depends on their shapes

Xiao

Cui

Brahney

et al. 2023

Preprint

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Recent studies have highlighted the importance of the atmosphere in the long-range transport of microplastic fibers (MPFs). However, both dry deposition processes and sources of MPFs are poorly understood due to their complexity in size and shape, which can be 100s $\mu m$ long, possessing round or flat cross-sections with dimensions of $O(1)\,\mu m$ thickness, and $O(10)\,\mu m$ width. Here, we develop a theory-based settling velocity model for MPFs in the atmosphere, predicting a much smaller aerodynamic size than a volumetrically equivalent spherical particle. Incorrect identification of flat fibers as cylindrical ones due to uncertainty in the thickness of sampled MPFs overestimates their dry deposition rate. Accounting for fiber thickness in sampled MPFs leads to a mean residence-time enhancement above $450\%$ compared to spherical-shaped particles, suggesting a much more efficient long-range transport of flat fibers than previously thought and that the ocean might be a major source of atmospheric plastics.

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“…The irradiance field may also be affected by the variations in the inherent optical properties that are associated with the turbulence‐induced fluctuations in temperature and salinity (Xu et al., 2012). Besides, phytoplankton, oil droplets, bubbles, dissolved organic material and mineral particles significantly affect the scattering and absorption of light (Chang & Twardowski, 2011; Mobley, 1994; Stramski et al., 2019; Twardowski et al., 2012; Xiao & Yang, 2020). Because of the complexity in the wave geometry and the nonlinear nature in the interaction between light and seawater (Darecki et al., 2011; Gernez et al., 2011), it is generally challenging to obtain an analytical solution for the underwater irradiance field.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning Method for Accelerated Modeling of the Downwelling Irradiance Field in the Upper Ocean

Hao

Shen

2022

Geophysical Research Letters

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The downwelling irradiance is a crucial part of the air‐sea heat flux and a major energy source for the photosynthesis processes in the upper ocean. The modeling of the irradiance field is often time‐consuming in conventional Monte Carlo (MC) simulations. We propose an accelerated model based on machine learning to significantly reduce the computational cost. By introducing a generalized beam spreading function, we transform the raw data generated with the MC simulation into training data of reduced dimensions, which is then used to develop an artificial neural network. To validate the machine learning model, we use the MC model to simulate the irradiance field under a broadband wave field. For both clear and turbid seawater, the irradiance field predicted by the machine learning model agrees well with the MC simulation result. Compared with the MC simulation that is computationally expensive, our machine learning model is O(1,000) times faster.

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Effect of oil plumes on upper-ocean radiative transfer — A numerical study

Cited by 12 publications

References 85 publications

Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo

Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo

Long-distance atmospheric transport of microplastic fibers depends on their shapes

A Novel Machine Learning Method for Accelerated Modeling of the Downwelling Irradiance Field in the Upper Ocean

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