Ashwanth Srinivasan

Organic Aerosol Formation Downwind from the Deepwater Horizon Oil Spill

Organic compounds of intermediate volatility play an important role in the formation of secondary organic aerosols. A large fraction of atmospheric aerosols are derived from organic compounds with various volatilities. A National Oceanic and Atmospheric Administration (NOAA) WP-3D research aircraft made airborne measurements of the gaseous and aerosol composition of air over the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico that occurred from April to August 2010. A narrow plume of hydrocarbons was observed downwind of DWH that is attributed to the evaporation of fresh oil on the sea surface. A much wider plume with high concentrations of organic aerosol (>25 micrograms per cubic meter) was attributed to the formation of secondary organic aerosol (SOA) from unmeasured, less volatile hydrocarbons that were emitted from a wider area around DWH. These observations provide direct and compelling evidence for the importance of formation of SOA from less volatile hydrocarbons.

Connectivity of Caribbean coral populations: complementary insights from empirical and modelled gene flow

Understanding patterns of connectivity among populations of marine organisms is essential for the development of realistic, spatially explicit models of population dynamics. Two approaches, empirical genetic patterns and oceanographic dispersal modelling, have been used to estimate levels of evolutionary connectivity among marine populations but rarely have their potentially complementary insights been combined. Here, a spatially realistic Lagrangian model of larval dispersal and a theoretical genetic model are integrated with the most extensive study of gene flow in a Caribbean marine organism. The 871 genets collected from 26 sites spread over the wider Caribbean subsampled 45.8% of the 1900 potential unique genets in the model. At a coarse scale, significant consensus between modelled estimates of genetic structure and empirical genetic data for populations of the reef‐building coral Montastraea annularis is observed. However, modelled and empirical data differ in their estimates of connectivity among northern Mesoamerican reefs indicating that processes other than dispersal may dominate here. Further, the geographic location and porosity of the previously described east–west barrier to gene flow in the Caribbean is refined. A multi‐prong approach, integrating genetic data and spatially realistic models of larval dispersal and genetic projection, provides complementary insights into the processes underpinning population connectivity in marine invertebrates on evolutionary timescales.

Evolution of the Macondo well blowout: Simulating the effects of the circulation and synthetic dispersants on the subsea oil transport

During the Deepwater Horizon incident, crude oil flowed into the Gulf of Mexico from 1522 m underwater. In an effort to prevent the oil from rising to the surface, synthetic dispersants were applied at the wellhead. However, uncertainties in the formation of oil droplets and difficulties in measuring their size in the water column, complicated further assessment of the potential effect of the dispersant on the subsea-to-surface oil partition. We adapted a coupled hydrodynamic and stochastic buoyant particle-tracking model to the transport and fate of hydrocarbon fractions and simulated the far-field transport of the oil from the intrusion depth. The evaluated model represented a baseline for numerical experiments where we varied the distributions of particle sizes and thus oil mass. The experiments allowed to quantify the relative effects of chemical dispersion, vertical currents, and inertial buoyancy motion on oil rise velocities. We present a plausible model scenario, where some oil is trapped at depth through shear emulsification due to the particular conditions of the Macondo blowout. Assuming effective mixing of the synthetic dispersants at the wellhead, the model indicates that the submerged oil mass is shifted deeper, decreasing only marginally the amount of oil surfacing. In this scenario, the oil rises slowly to the surface or stays immersed. This suggests that other mechanisms may have contributed to the rapid surfacing of oil-gas mixture observed initially. The study also reveals local topographic and hydrodynamic processes that influence the oil transport in eddies and multiple layers. This numerical approach provides novel insights on oil transport mechanisms from deep blowouts and on gauging the subsea use of synthetic dispersant in mitigating coastal damage.

Surface evolution of the deepwater horizon oil spill patch: Combined effects of circulation and wind-induced drift

Following the Deepwater Horizon blowout, major concerns were raised about the probability that the Loop Current would entrain oil at the surface of the Gulf of Mexico toward South Florida. However, such a scenario did not materialize. Results from a modeling approach suggest that the prevailing winds, through the drift they induced at the ocean surface, played a major role in pushing the oil toward the coasts along the northern Gulf, and, in synergy with the Loop Current evolution, prevented the oil from reaching the Florida Straits. This implies that both oceanic currents and surface wind-induced drift must be taken into account for the successful forecasting of the trajectories and landfall of oil particles, even in energetic environments such as the Gulf of Mexico. Consequently, the time range of these predictions is limited to the weather forecasting range, in addition to the range set up by ocean forecasting capabilities.

Global sensitivity analysis in an ocean general circulation model: a sparse spectral projection approach

Polynomial chaos (PC) expansions are used to propagate parametric uncertainties in ocean global circulation model. The computations focus on short-time, high-resolution simulations of the Gulf of Mexico, using the hybrid coordinate ocean model, with wind stresses corresponding to hurricane Ivan. A sparse quadrature approach is used to determine the PC coefficients which provides a detailed representation of the stochastic model response. The quality of the PC representation is first examined through a systematic refinement of the number of resolution levels. The PC representation of the stochastic model response is then utilized to compute distributions of quantities of interest (QoIs) and to analyze the local and global sensitivity of these QoIs to uncertain parameters. Conclusions are finally drawn regarding limitations of local perturbations and variance-based assessment and concerning potential application of the present methodology to inverse problems and to uncertainty management.

A priori testing of sparse adaptive polynomial chaos expansions using an ocean general circulation model database

This work explores the implementation of an adaptive strategy to design sparse ensembles of oceanic simulations suitable for constructing polynomial chaos surrogates. We use a recently developed pseudo-spectral algorithm that is based on a direct application of the Smolyak sparse grid formula and that allows the use of arbitrary admissible sparse grids. The adaptive algorithm is tested using an existing simulation database of the oceanic response to Hurricane Ivan in the Gulf of Mexico. The a priori tests demonstrate that sparse and adaptive pseudo-spectral constructions lead to substantial savings over isotropic sparse sampling in the present setting.

Bayesian Inference of Drag Parameters Using AXBT Data from Typhoon Fanapi

AbstractThe authors introduce a three-parameter characterization of the wind speed dependence of the drag coefficient and apply a Bayesian formalism to infer values for these parameters from airborne expendable bathythermograph (AXBT) temperature data obtained during Typhoon Fanapi. One parameter is a multiplicative factor that amplifies or attenuates the drag coefficient for all wind speeds, the second is the maximum wind speed at which drag coefficient saturation occurs, and the third is the drag coefficients rate of change with increasing wind speed after saturation. Bayesian inference provides optimal estimates of the parameters as well as a non-Gaussian probability distribution characterizing the uncertainty of these estimates. The efficiency of this approach stems from the use of adaptive polynomial expansions to build an inexpensive surrogate for the high-resolution numerical model that couples simulated winds to the oceanic temperature data, dramatically reducing the computational burden of the M...

Mantle 3He distribution and deep circulation in the Indian Ocean

[1] The World Ocean Circulation Experiment Indian Ocean helium isotope data are mapped and features of intermediate and deep circulation are inferred and discussed. The 3He added to the deep Indian Ocean originates from (1) a strong source on the mid-ocean ridge at about 19°S/65°E, (2) a source located in the Gulf of Aden in the northwestern Indian Ocean, (3) sources located in the convergent margins in the northeastern Indian Ocean, and (4) water imported from the Indonesian Seas. The main circulation features inferred from the 3 He distribution include (1) deep (2000-3000 m) eastward flow in the central Indian Ocean, which overflows into the West Australian Basin through saddles in the Ninetyeast Ridge, (2) a deep (2000-3000 m) southwestward flow in the western Indian Ocean, and (3) influx of Banda Sea Intermediate Waters associated with the deep core (1000-1500 m) of the through flow from the Pacific Ocean. The large-scale 3 He distribution is consonant with the known pathways of deep and bottom water circulation in the Indian Ocean.

Drag Parameter Estimation Using Gradients and Hessian from a Polynomial Chaos Model Surrogate

A variational inverse problem is solved using polynomial chaos expansions to infer several critical variables in the Hybrid Coordinate Ocean Model’s (HYCOM’s) wind drag parameterization. This alternative to the Bayesian inference approach in Sraj et al. avoids the complications of constructing the full posterior with Markov chain Monte Carlo sampling. It focuses instead on identifying the center and spread of the posterior distribution. The present approach leverages the polynomial chaos series to estimate, at very little extra cost, the gradients and Hessian of the cost function during minimization. The Hessian’s inverse yields an estimate of the uncertainty in the solution when the latter’s probability density is approximately Gaussian. The main computational burden is an ensemble of realizations to build the polynomial chaos expansion; no adjoint code or additional forward model runs are needed once the series is available. The ensuing optimal parameters are compared to those obtained in Sraj et al. where the full posterior distribution was constructed. The similarities and differences between the new methodology and a traditional adjoint-based calculation are discussed.

Abyssal upwelling in the Indian Ocean: Radiocarbon diagnostics

The GEOSECS Indian Ocean radiocarbon and carbonate chemistry data set are used to estimate the mean upwelling transport of bottom water in the Indian Ocean north of 30S. The study uses an ‘ ‘ adjusted radiocarbon concentration’ ’ which is corrected for the effects of addition of particulate radiocarbon to the deep ocean. The cross-basin uniformity in the vertical gradients of ‘ ‘ adjusted radiocarbon’ ’ allows quantie cation of vertical transfer processes using horizontally averaged concentration and e uxes. The estimated total upwelling e ux, north of 30S, is 8.2 6 1.5 3 10 6 m 3 s 2 1 . The mean upwelling velocity and the vertical diffusivity, in the 3000‐ 4500 m depth range, are estimated as 3 3 10 6 m s 2 1 and 2.53 10 2 4 m 2 s 2 1 , respectively. The results also suggest faster upwelling in the western Indian Ocean.