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Computer simulations are advancing our understanding of how natural and anthropogenic materials are transported around the upper layer of the ocean.

Large-eddy simulations of an observed single-layer Arctic mixed-phase cloud are analyzed to study the value of forward modeling of profiling millimeter-wave cloud radar Doppler spectral width for model evaluation. Individual broadening terms and their uncertainties are quantified for the observed spectral width and compared to modeled broadening terms. Modeled turbulent broadening is narrower than the observed values when the turbulent kinetic energy dissipation rate from the subgrid-scale model is used in the forward model. The total dissipation rates, estimated with the subgrid-scale dissipation rates and the numerical dissipation rates, agree much better with both the retrieved dissipation rates and those inferred from the power spectra of the simulated vertical air velocity. The comparison of the microphysical broadening provides another evaluative measure of the ice properties in the simulation. To accurately retrieve dissipation rates as well as each broadening term from the observations, we suggest a few modifications to previously presented techniques. First, we show that the inertial subrange spectra filtered with the radar sampling volume is a better underlying model than the unfiltered −5/3 law for the retrieval of the dissipation rate from the power spectra of the mean Doppler velocity. Second, we demonstrate that it is important to filter out turbulence and remove the layer-mean reflectivity-weighted mean fall speed from the observed mean Doppler velocity to avoid overestimation of shear broadening. Finally, we provide a method to quantify the uncertainty in the retrieved dissipation rates, which eventually propagates to the uncertainty in the microphysical broadening.

The transport in the ocean mixed layer (OML) of oil plumes originated from deepwater blowouts is studied using large eddy simulations. In particular, we focus on the effects of swell on the modulation of turbulence in the OML and its impact on oil transport. Results show that when the misalignment between the wind and the swell propagation is small, Langmuir cells develop and significantly enhance the vertical dilution of the oil plume. Conversely, when the misalignment is large, vertical dilution is suppressed when compared to the no-swell case. Due to the strong directional shear of the mean flow within the OML, plume depth significantly impacts mean transport direction. The size of oil droplets in the plume also plays an important role in vertical dilution and mean transport direction.

Once oil plumes such as those originating from underwater blowouts reach the ocean mixed layer (OML), their near-surface dispersion is influenced heavily by wind and wave-generated Langmuir turbulence. In this study, the complex oil spill dispersion process is modeled using large-eddy simulation (LES). The mean plume dispersion is characterized by performing statistical analysis of the resulting fields from the LES data. Although the instantaneous oil concentration exhibits high intermittency with complex spatial patterns such as Langmuir-induced striations, it is found that the time-averaged oil distribution can still be described quite well by smooth Gaussian-type plumes. LES results show that the competition between droplet rise velocity and vertical turbulent diffusion due to Langmuir turbulence is crucial in determining both the dilution rate and overall direction of transport of oil plumes in the OML. The smoothness of the mean plume makes it feasible to aim at modeling the oil dispersion using Reynolds-averaged type formulations, such as the K-profile parameterization (KPP) with sufficient vertical resolution to capture vertical profiles in the OML. Using LES data, we evaluate the eddy viscosity and eddy diffusivity following the KPP framework. We assess the performance of previous KPP models for pure shear turbulence and Langmuir turbulence by comparing them with the LES data. Based on the assessment a modified KPP model is proposed, which shows improved overall agreement with the LES results for both the eddy viscosity and the eddy diffusivity of the oil dispersion under a variety of flow conditions and droplet sizes.

Oil spills from deep-water blowouts rise through and interact with the ocean mixed layer and Langmuir turbulence, leading to considerable diversity of oil slick dilution patterns observed on the ocean surface. Certain conditions can drive oil droplet plumes to organize into distinct bands called windrows, inhibiting oil dilution. Observations of blurred or even diffused plumes are also common, but conditions under which these various dilution regimes emerge are not well understood. Here we use large eddy simulations to explain and quantify the dilution patterns and their dependence on relevant physical parameters. Two mechanisms, the downwelling and dilution due to Langmuir cells and the inhibition of dilution due to buoyancy of oil droplets, compete. This competition can be characterized by the ratio of Stokes drift to droplet rise velocity—the drift-to-buoyancy parameter, Db. We find that plume appearance and quantitative measures of relative dilution depend mainly on Db.

[1] In water resources it is common to consider that two scalars have a similar behavior in the atmospheric surface layer. This is a consequence of Monin-Obukhov similarity theory whose direct implication is that all similarity functions between two scalars are equal. However, many works show that scalar similarity does not always hold under unstable conditions, a fact for which it is often difficult to establish a physical cause. In this paper, using a data set measured during winter over a tropical lake in Brazil (Furnas Lake), we found a relation between temperature–water vapor similarity and the strength of the surface forcing; we also confirmed that the classical balance between gradient production and molecular dissipation of scalar variance and covariance is key to scalar similarity. This balance can be disrupted by large values of the third-order transport terms, and possibly by nonstationary terms as well. In connection with the scalar variance and covariance budgets, we propose a new set of dimensionless scalar flux numbers which are able to make a good diagnosis of the aforementioned balance (or the lack thereof) for each budget. The fact that different Monin-Obukhov functions are not equally capable of identifying scalar similarity is also demonstrated and a new bulk indicator of scalar flux similarity is proposed whose absolute value, unlike the relative transfer efficiency, is bounded above by 1; this new indicator holds also in the spectral domain. Finally, we verify that low-frequency dissimilarity has a larger impact over scalar similarity than over scalar flux similarity.

[1] We propose a very simple and straightforward technique to reduce aliasing effects on the digital spectra of turbulent data from field experiments. A detailed analysis of the technique is done, and it is tested and compared with more traditional low-pass filtering and with oversampling. The proposed method shows satisfactory results for all tests performed.