Mixing and transport near the shallow-crested Rumble III seamount and the implications for plankton distribution

Vessel-based observations of water column structure and flow near a shallow-crested seamount are used to quantify the physical disturbance induced by that seamount. The implications of this disturbance on the ‘feeding hole’ hypothesis are then examined based on data from moored thermistors and acoustic current profilers, as well as vessel-based acoustic sounding-derived biomass estimates, currents, and conductivity-temperature-depth profiles from a 55 km square grid of stations around the crest. Mean currents in the region of 0.2 m s^?1 are comparable to observations from surface drifters whereas the semidiurnal tidal flow amplitude was one third of this. Thorpe Scale-based estimates of energy dissipation rate were in the range 10^?9 to 2.10^?8 W kg^?1 and vertical diffusivities K_z were in the range 10^?4 to 10^?3 m² s^?1. Turbulence levels were higher upstream of the seamount–likely due to the influence of nearby seamounts Rumble IV and V. There was no evidence of a Taylor Cap in the Rumble III velocity field. The sounder data provide some evidence of a feeding hole and analysis based on diffusivities suggests that this might persist downstream of the seamount for as much as 7 days.     

Three-dimensional massively parallel electromagnetic inversion—I. Theory

An iterative solution to the non-linear 3-D electromagnetic inverse problem is obtained by successive linearized model updates using the method of conjugate gradients. Full wave equation modelling for controlled sources is employed to compute model sensitivities and predicted data in the frequency domain with an efficient 3-D finite-difference algorithm. Necessity dictates that the inverse be underdetermined, since realistic reconstructions require the solution for tens of thousands of parameters. In addition, large-scale 3-D forward modelling is required and this can easily involve the solution of over several million electric field unknowns per solve. A massively parallel computing platform has therefore been utilized to obtain reasonable execution times, and results are given for the 1840-node Intel Paragon. The solution is demonstrated with a synthetic example with added Gaussian noise, where the data were produced from an integral equation forward-modelling code, and is different from the finite difference code embedded in the inversion algorithm