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Oceanic tides have the potential to yield a vast amount of renewable energy. Tidal stream generators are one of the key technologies for extracting and harnessing this potential. In order to extract an economically useful amount of power, hundreds of tidal turbines must typically be deployed in an array. This naturally leads to the question of how these turbines should be configured to extract the maximum possible power: the positioning and the individual tuning of the turbines could significantly influence the extracted power, and hence is of major economic interest. However, manual optimisation is difficult due to legal site constraints, nonlinear interactions of the turbine wakes, and the cubic dependence of the power on the flow speed. The novel contribution of this paper is the formulation of this problem as an optimisation problem constrained by a physical model, which is then solved using an efficient gradient-based optimisation algorithm. In each optimisation iteration, a two-dimensional finite element shallow water model predicts the flow and the performance of the current array configuration. The gradient of the power extracted with respect to the turbine positions and their tuning parameters is then computed in a fraction of the time taken for a flow solution by solving the associated adjoint equations. These equations propagate causality backwards through the computation, from the power extracted back to the turbine positions and the tuning parameters. This yields the gradient at a cost almost independent of the number of turbines, which is crucial for any practical application. The utility of the approach is demonstrated by optimising turbine arrays in four idealised scenarios and a more realistic case with up to 256 turbines in the Inner Sound of the Pentland Firth, Scotland.

SUMMARYA new modelling framework is presented for application to a range of three-dimensional (3D) multi-scale oceanographic problems. The approach is based upon a finite element discretization on an unstructured tetrahedral mesh which is optimized to represent highly complex geometries. Throughout a simulation the mesh is dynamically adapted in 3D to optimize the representation of evolving solution structures. The adaptive algorithm makes use of anisotropic measures of solution complexity and a load-balanced parallel mesh optimization algorithm to vary resolution and allow long, thin elements to align with features such as boundary layers. The modelling framework presented is quite different from the majority of ocean models in use today, which are typically based on static-structured grids. Finite element (and volume) methods on unstructured meshes are, however, gaining popularity in the oceanographic community. The model presented here is novel in its use of unstructured meshes and anisotropic adaptivity in 3D, its ability to represent a range of coupled multi-scale solution structures and to simulate non-hydrostatic dynamics.

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Freshwater produced by the surface melting of ice sheets is commonly discharged into ocean fjords from the bottom of deep fjord-terminating glaciers. The discharge of the freshwater forms upwelling plumes in front of the glacier calving face. This study simulates the meltwater plumes emanated into an unstratified environment using a nonhydrostatic ocean model with an unstructured mesh and subgrid-scale mixing calibrated by comparison to established plume theory. The presence of an ice face reduces the entrainment of seawater into the meltwater plumes, so the plumes remain attached to the ice front, in contrast to previous simple models. Ice melting increases with height above the discharge, also in contrast to some simple models, and the authors speculate that this ''overcutting'' may contribute to the tendency of icebergs to topple inwards toward the ice face upon calving. The overall melt rate is found to increase with discharge flux only up to a critical value, which depends on the channel size. The melt rate is not a simple function of the subglacial discharge flux, as assumed by many previous studies. For a given discharge flux, the geometry of the plume source also significantly affects the melting, with higher melt rates obtained for a thinner, wider source. In a wider channel, two plumes are emanated near the source and these plumes eventually coalesce. Such merged meltwater plumes ascend faster and increase the maximum melt rate near the center of the channel. The melt rate per unit discharge decreases as the subglacial system becomes more channelized.

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a b s t r a c tThe $8.15 ka Storegga submarine slide was a large ($3000 km 3 ), tsunamigenic slide off the coast of Norway. The resulting tsunami had run-up heights of around 10-20 m on the Norwegian coast, over 12 m in Shetland, 3-6 m on the Scottish mainland coast and reached as far as Greenland. Accurate numerical simulations of Storegga require high spatial resolution near the coasts, particularly near tsunami run-up observations, and also in the slide region. However, as the computational domain must span the whole of the Norwegian-Greenland sea, employing uniformly high spatial resolution is computationally prohibitive. To overcome this problem, we present a multiscale numerical model of the Storegga slide-generated tsunami where spatial resolution varies from 500 m to 50 km across the entire NorwegianGreenland sea domain to optimally resolve the slide region, important coastlines and bathymetric changes. We compare results from our multiscale model to previous results using constant-resolution models and show that accounting for changes in bathymetry since 8.15 ka, neglected in previous numerical studies of the Storegga slide-tsunami, improves the agreement between the model and inferred runup heights in specific locations, especially in the Shetlands, where maximum run-up height increased from 8 m (modern bathymetry) to 13 m (palaeobathymetry). By tracking the Storegga tsunami as far south as the southern North sea, we also found that wave heights were high enough to inundate Doggerland, an island in the southern North Sea prior to sea level rise over the last 8 ka.