Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean-atmosphere interactions. Owing to their highly variable nature, both in space and time, they are notoriously difficult features to adequately sample using traditional in-situ techniques. In this paper we propose a new statistical modelling approach to detecting and monitoring ocean fronts from AVHRR SST satellite images that builds on the 'front following' algorithm of Shaw and Vennell (2000). Weighted local likelihood is used to provide a smooth, non-parametric description of spatial variations in the position, mean temperature, width and temperature change of an individual front within an image. Weightings are provided by a Gaussian kernel function whose width is automatically determined by likelihood crossvalidation. The statistical model fitting approach allows estimation of the uncertainty of each parameter to be quantified, a capability not possessed by other techniques. The algorithm is shown to be robust to noise and missing data in an image, problems that hamper many of the existing front detection schemes. The approach is general and could be used with other remotely sensed data sets, model output or data assimilation products.1
<p>Long-term observations (March&#8217;14 &#8722; July&#8217;15) of ocean density and velocity from the North West European shelf reveal a seasonality in internal wave energy linked to the seasonal cycle of stratification. Further, this seasonality extends to internal mixing associated with internal waves that can be effectively described by the buoyancy frequency (N<sup>2</sup>), with the strongest mixing associated with strongly stratified summer conditions. To better understand these results a model was used that employed three different, commonly used parameterisations of internal mixing. Each parameterisation produced some degree of seasonality in internal mixing. Contrary to observed results however, all three model scenarios produced a minimum in internal mixing during summer, with enhanced mixing observed during spring and autumn. This failure in each model was attributed to the lack of realistic levels of enhanced baroclinic energy and shear (S<sup>2</sup>) that is identified in observations to be attributable to internal waves. These observations reveal a close relationship between N<sup>2</sup>and S<sup>2</sup>, resulting in a near continuous state of marginal stability; where the gradient Richardson number is maintained at a near critical level. Due to the observed strong dependence of internal wave energy and internal mixing on stratification, a modified version of the MacKinnon and Gregg (2003a) turbulence scaling was employed. This modified parameterisation successfully replicated the observed seasonality in internal mixing. This important result implies that future parameterisations should aim to scale internal mixing on enhanced levels of S<sup>2</sup> from internal waves, which are shown here to be suitably predicted by the seasonal cycle of stratification (N<sup>2</sup>).</p>
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