Advances in Ocean Models: Uncertainties, Predictive Skills, and Physical-Biological-Biogeochemical Interactions

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 5467

Special Issue Editor


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Guest Editor
Department of Engineering and Environmental Sciences, The City University of New York, New York, NY 10031, USA
Interests: physical oceanography; ocean modeling; coastal and ocean transport; eddy mixing; physical–biological connectivity; carbon cycling; observational oceanography; marine technology; coastal and ocean mixing; climate change

Special Issue Information

Dear Colleagues,

The development of ocean circulation models has proceeded rapidly over the last 25 years, with progress being made in three key areas. First, the number and spatial extent of models for predicting transport at estuarine, shelf, basin, and global scales has increased. Second, the horizontal resolution of models has increased sufficiently to resolve eddy mixing. Third, the vertical resolution of models has increased to allow better understanding of vertical exchanges. Curiously, despite these advances, uncertainties in ocean models are rarely quantified, uncertainties are not often compared, and the limits of prediction skills in 4D (space × time) are not always investigated.

In this Special Issue, we would like to focus on established and new implementations of ocean models, with particular attention to the more recent ones, the gains made by increasing the resolution of the models, the predictive skill of alternative models, the mechanistic cause of differences in predictive skill, and efforts to understand physical, biological, and biogeochemical variability with ocean models.

Prof. David Lindo-Atichati
Guest Editor

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Keywords

  • New models
  • Data Assimilation
  • Reanalysis
  • Prediction Skill
  • Parameterization
  • Vertical transport
  • Physical-biological interactions
  • Physical-biogeochemical interactions

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Published Papers (2 papers)

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Research

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15 pages, 4017 KiB  
Article
Application of Three-Dimensional Interpolation in Estimating Diapycnal Diffusivity in the South China Sea
by Junting Guo, Yafei Nie, Shuang Li and Xianqing Lv
J. Mar. Sci. Eng. 2020, 8(11), 832; https://doi.org/10.3390/jmse8110832 - 22 Oct 2020
Cited by 5 | Viewed by 1915
Abstract
Diapycnal diffusivity is an important parameter to characterize oceanic turbulent mixing and vertical transport. However, due to the challenging accessibility of field observations, the observation of diapycnal diffusivity in the South China Sea (SCS) is rare. In this study, a three-dimensional field of [...] Read more.
Diapycnal diffusivity is an important parameter to characterize oceanic turbulent mixing and vertical transport. However, due to the challenging accessibility of field observations, the observation of diapycnal diffusivity in the South China Sea (SCS) is rare. In this study, a three-dimensional field of diapycnal diffusivity in the SCS with high spatial resolution is performed by interpolating the rare field observations, which aims to provide a reference for the value of diapycnal diffusivity in ocean models. Given the anisotropy of diapycnal diffusivity and its rapid change in the magnitude in the vertical direction, several typical interpolation methods are compared in this study. Results of two cross-validation methods demonstrate that the three-dimensional (3D) thin-plate spline interpolation method yields the most reasonable and accurate results among a total of five typical methods used in this study. Full article
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25 pages, 2899 KiB  
Review
Formation, Detection, and Modeling of Submerged Oil: A Review
by Chao Ji, Cynthia Juyne Beegle-Krause and James D. Englehardt
J. Mar. Sci. Eng. 2020, 8(9), 642; https://doi.org/10.3390/jmse8090642 - 21 Aug 2020
Cited by 12 | Viewed by 3126
Abstract
Submerged oil, oil in the water column (neither at the surface nor on the bottom), was found in the form of oil droplet layers in the mid depths between 900–1300 m in the Gulf of Mexico during and following the Deepwater Horizon oil [...] Read more.
Submerged oil, oil in the water column (neither at the surface nor on the bottom), was found in the form of oil droplet layers in the mid depths between 900–1300 m in the Gulf of Mexico during and following the Deepwater Horizon oil spill. The subsurface peeling layers of submerged oil droplets were released from the well blowout plume and moved along constant density layers (also known as isopycnals) in the ocean. The submerged oil layers were a challenge to locate during the oil spill response. To better understand and find submerged oil layers, we review the mechanisms of submerged oil formation, along with detection methods and modeling techniques. The principle formation mechanisms under stratified and cross-current conditions and the concepts for determining the depths of the submerged oil layers are reviewed. Real-time in situ detection methods and various sensors were used to reveal submerged oil characteristics, e.g., colored dissolved organic matter and dissolved oxygen levels. Models are used to locate and to predict the trajectories and concentrations of submerged oil. These include deterministic models based on hydrodynamical theory, and probabilistic models exploiting statistical theory. The theoretical foundations, model inputs and the applicability of these models during the Deepwater Horizon oil spill are reviewed, including the pros and cons of these two types of models. Deterministic models provide a comprehensive prediction on the concentrations of the submerged oil and may be calibrated using the field data. Probabilistic models utilize the field observations but only provide the relative concentrations of the submerged oil and potential future locations. We find that the combination of a probabilistic integration of real-time detection with trajectory model output appears to be a promising approach to support emergency response efforts in locating and tracking submerged oil in the field. Full article
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