Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session M35: Geophysical Fluid Dynamics: Oceanographic I |
Hide Abstracts |
Chair: Annalisa Bracco, Georgia Institute of Technology Room: Georgia World Congress Center B407 |
Tuesday, November 20, 2018 8:00AM - 8:13AM |
M35.00001: The influence of mesoscale and submesoscale circulations on carbon drawdown into the ocean In the northern Gulf of Mexico Annalisa Bracco, Guangpeng Liu We explore the influence of mesoscale circulations on carbon drawdown in the Gulf of Mexico using numerical simulations performed with a model at 1-km horizontal resolution. Results are compared to field samples collected in 2012 from two sediment traps located at 27°22.5 N, 90°30.7 W (GC600) and 27°31.5 N, 89°42.6 W (AT357), 81 km apart. Through inverse calculations, model results indicate that cross-shore transport of riverine input induced by mesoscale eddies, and convergence and divergence processes at the scale of a few kilometers, significantly impact the trajectory of sinking particles and carbon drawdown. Also, the majority of modeled particles reach the bottom faster than would be expected by their sinking speeds alone. This finding is associated with submesoscale-induced horizontal convergence in the mixed layer that aggregates particles preferentially in downwelling regions, accelerating their descent. Furthermore, this study confirms that the cone of influence of vertical fluxes is highly variable in both space and time in the presence of an energetic eddy field. |
Tuesday, November 20, 2018 8:13AM - 8:26AM |
M35.00002: Lagrangian Tracking in Stochastic Fields with Application to an Ensemble of Velocity Fields in the Red Sea Samah El Mohtar, Ibrahim Hoteit, Omar Knio, Leila Issa, Issam Lakkis We describe an efficient parallel algorithm for forward and backward tracking of passive particles in stochastic flow fields whose statistics are described are prescribed by an underlying ensemble. The construction is designed to address challenges arising from random resampling procedure applied following each assimilation cycle, which leads to rapid growth in the number of particles. To control this growth, the algorithm incorporates an adaptive binning procedure, which conserves the zeroth, first and second moments of probability (total probability, mean position, and variance). Implementation of the method is illustrated based on results of forward and backward tracking experiments, within a realistic high-resolution ensemble assimilation setting of the Red Sea. In particular, the results were used to analyze the effects of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport. |
Tuesday, November 20, 2018 8:26AM - 8:39AM |
M35.00003: The seasonality of submesoscale-induced mixing across the mixed layer in the Gulf of Mexico Guangpeng Liu, Annalisa Bracco Submesoscale circulations associated with vorticy structures of kilometer size are characterized by vertical velocities as large as 100 m/day. The intensity of submesoscale motions is primarily modulated by oceanic frontogenesis and mixed-layer instabilities and influences significantly the transport of biological tracers through the water column. To evaluate the role of submesoscale motions on the upper ocean mixing and its seasonality, the regional ocean model system is configured at 2 horizontal grid resolutions, 1 km and 5 km, in the northern Gulf of Mexico. About 25000 3D Lagrangian tracers are then deployed at different depths (5m, 50 m and 100 m) and in different seasons and tracked for about 30 days. Results indicate that following the seasonal cycle of submesoscale circulations and of mixed-layer depth, vertical mixing is strong in winter, and suppressed during summertime. Although model resolution has little effect on lateral transport, vertical dispersion increases significantly with increased grid resolution. Tracers under the influence of submesoscale motions are upwelled much faster. Considering the time scale at which nutrient uptake by phytoplankton takes place, nutrient supply by submesoscale circulations is likely to be a major player in the Gulf of Mexico. |
Tuesday, November 20, 2018 8:39AM - 8:52AM |
M35.00004: Settling of Diffusion-Limited-Aggregates in the Absence of Inertia Francois Blanchette, Eunji Yoo, Shilpa Khatri We study the settling of Diffusion-Limited-Aggregates as a model of marine aggregates and marine snow. The aggregates are assembled as a collection of cubic particles. The stresses on the surface and flow around the aggregates are computed in the limit of zero Reynolds number using a boundary integral method. We thus obtain an accurate representation of the flow around a fractal object. We compute the statistical distribution of the drag on the aggregate as a function of its size, and determine the corresponding effective radius. Time permitting, we will also present how a passive concentration, such as a low salt concentration, is advected by the flow, and how, within a porous aggregate, the concentration may change over time. |
Tuesday, November 20, 2018 8:52AM - 9:05AM |
M35.00005: Ice floe dispersion from remote sensing imagery Rosalinda Lopez, Michael Schodlok, Monica M Wilhelmus Sea ice transport directly affects the heat budget and freshwater flux in the Arctic. Quantifying the dispersion regime of free drifting sea ice is thus an important task to understand the Arctic climate system. In this talk, we employ a newly-developed automated sea ice floe detection and tracking algorithm to analyze the dispersion regime of free-drifting ice floes in the east-central coast of Greenland during the spring of 2017. Our ice floe tracker automatically processes Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images to identify ice floes with length scales ranging from 8 to 35 km. From this information, over 1300 Lagrangian ice floe trajectories are retrieved via feature matching. Ocean surface circulation and its variability are then described by quantifying differential kinematic parameters of the free drifting ice floes, such as single particle dispersion. The dispersion regime of these surface tracers is analyzed via computation of Lyapunov exponents and the Okubo-Weiss parameter. We discuss the feasibility of employing our tracking system to better understand small scale sea ice-ocean interactions, in particular in regards to ocean eddies that are evident in MODIS images. |
Tuesday, November 20, 2018 9:05AM - 9:18AM |
M35.00006: Deep learning of diapycnal mixing Hesam Salehipour, W Richard Peltier Current global ocean models rely on ad-hoc parameterizations of diapycnal mixing, in which the efficiency of mixing is globally assumed to be fixed at 20%, despite increasing evidence that this assumption is questionable. As an ansatz for small-scale ocean turbulence, we may focus on stratified shear flows susceptible to either Kelvin-Helmholtz (KHI) or Holmboe wave (HWI) instability. Recently, an unprecedented volume of data has been generated through direct numerical simulation (DNS) of these flows. Here, we describe the application of deep learning methods to the discovery of a generic parameterization of diapycnal mixing using the available DNS dataset. We furthermore demonstrate that the proposed model is far more universal compared to recently published parameterizations. We show that a neural network appropriately trained on KHI-induced turbulence is capable of predicting mixing efficiency associated with unseen initial conditions well beyond the range of the training data. Strikingly, the high-level patterns learned based on the KHI ansatz are “transferable” to predict HWI-induced mixing efficiency, suggesting that through the application of appropriate networks, significant universal abstractions of density stratified turbulent mixing have been recognized. |
Tuesday, November 20, 2018 9:18AM - 9:31AM |
M35.00007: A Reynolds-averaged methodology for simulating Langmuir cells in the coastal ocean Anthony Perez, Nityanand Sinha, Seyedmohammadjavad Zeidi, Andres Tejada-Martinez Langmuir turbulence in the upper ocean is driven by winds and waves and is characterized by Langmuir cells (LCs), parallel counter rotating vortices roughly aligned in the wind direction. In the coastal ocean, the largest LCs can span the full depth of the water column becoming more coherent and persistent than LCs in the upper ocean mixed layer. Traditionally, flows with LCs are computed via either (1) large-eddy simulation (LES) in which a range of the Langmuir turbulence (or cells) is resolved or with (2) Reynolds averaging in which none of the Langmuir scales are resolved and the effect of the Langmuir turbulence is accounted for through the turbulence model. A new solution strategy based on Reynolds averaging is introduced, relying on the coherency and persistence of full-depth LCs. Here these cells are treated as a secondary component to the wind and/or pressure gradient-driven primary flow. As such, the Reynolds-averaged governing flow equations and the mesh are designed to resolve both the primary flow and the full-depth LCs with the turbulence model accounting for the smaller Langmuir scales. The resolved LCs and associated statistics will be compared with their counterparts in LES. |
Tuesday, November 20, 2018 9:31AM - 9:44AM |
M35.00008: Role and variability of mesoscale and submesoscale dynamics along the west coast of Greenland Filippos Tagklis, Annalisa Bracco, Renato Castelao, Taka Ito, Hao Luo Greenland Ice Sheet (GrIS) mass losses have accelerated over the recent decades resulting in freshwater input to the adjacent seas. Freshwater fluxes set the upper stratification in the Labrador Sea (LS) and play an important role also because ecosystem processes are highly sensitive to such stratification. To investigate the role of model resolution and submesoscale circulations in exporting heat and salinity anomalies towards the center of the LS, we perform a set of regional simulations with varying horizontal resolution, with and without meltwater inflows from the Greenland fjords. Submesoscale contribution in the vorticity budget shows strong seasonal and interannual variability, with a clear maximum during summer. Intense lateral density gradients created by summer time meltwater inflow, are responsible for the summer peak. During the winter time, submesoscale activity is low but mesoscale variability is at its maximum. Despite the volumetric large and seasonal fresh water inputs, the area of enhanced summer submesoscale activity is confined along the coast by the strong coastal current system, and such confinement is greater, in the model, at the highest resolution. |
Tuesday, November 20, 2018 9:44AM - 9:57AM |
M35.00009: Squeeze dispersion: enhancement of diapycnal mixing by diapycnal strain Gregory L. Wagner, Raffaele Ferrari, Glenn R. Flierl, Gunnar Voet, Matthew H. Alford, Glenn S. Carter We describe a mechanism called “squeeze dispersion” whereby fluctuating strain enhances the diffusive transport of active and passive scalars. Squeeze dispersion implies that fluctuating strain always enhances diffusive transport when scalar diffusivity is constant, and lends outsized importance to correlations between strain and diffusivity in determining net diffusive transport when diffusivity varies, as is the case for oceanic diapycnal turbulent diffusivity. We illustrate squeeze dispersion with an example problem, and derive a formula for the effective diffusivity of scalars diffusing in the presence of fluctuating strain. We then estimate effective diffusivities from turbulence measurements in abyssal flow through Samoan Passage, finding that squeeze dispersion enhances diapycnal transport by factors of 2 to 3 across some deep isopycnals due to correlations between strain and mixing over bathymetric constrictions. |
Tuesday, November 20, 2018 9:57AM - 10:10AM |
M35.00010: Feature Identification in Timeseries Datasets Justin Shaw, Marek Stastna, Aaron Coutino We present a computationally inexpensive, flexible feature identification method which uses a comparison of timeseries to identify a rank ordered set of features. Features are identified as simultaneous local maxima of absolute deviation in each timeseries. The analyst tunes the method using their knowledge of the physical context. The method is applied to both a dataset from a moored array of instruments deployed in Monterey Bay, California, and a dataset from sensors placed within a submerged cavern network in Tulum, Quintana Roo, Mexico. The results show that the method allows automated identification of both features which were previously identified by analysts in an ad hoc manner as well as features in unstudied datasets which are worthy of further study. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700