Seasonality of submesoscale flows in the ocean surface boundary layer

A signature of submesoscale flows in the upper ocean is skewness in the distribution of relative vorticity. Expected to result for high Rossby-number flows, such skewness has implications for mixing, dissipation and stratification within the upper ocean. An array of moorings deployed in the Northeast Atlantic for one year as part of the OSMOSIS experiment reveals that relative vorticity is positively skewed during winter even though the scale of the Rossby number is less than 0.5. Furthermore, this skewness is reduced to zero during spring and autumn. There is also evidence of modest seasonal variations in the gradient Rossby number. The proposed mechanism by which relative vorticity is skewed is that the ratio of lateral to vertical buoyancy gradients, as summarized by the inverse gradient Richardson number, restricts its range during winter but less so at other times of the year. These results support recent observations and model simulations suggesting the upper ocean is host to a seasonal cycle in submesoscale turbulence.

Langmuir turbulence and surface heating in the ocean surface boundary layer

This study uses large-eddy simulation to investigate the structure of the ocean surface boundary layer (OSBL) in the presence of Langmuir turbulence and stabilizing surface heat fluxes. The OSBL consists of a weakly stratified layer, despite a surface heat flux, above a stratified thermocline. The weakly stratified (mixed) layer is maintained by a combination of a turbulent heat flux produced by the wave-driven Stokes drift and downgradient turbulent diffusion. The scaling of turbulence statistics, such as dissipation and vertical velocity variance, is only affected by the surface heat flux through changes in the mixed layer depth. Diagnostic models are proposed for the equilibrium boundary layer and mixed layer depths in the presence of surface heating. The models are a function of the initial mixed layer depth before heating is imposed and the Langmuir stability length. In the presence of radiative heating, the models are extended to account for the depth profile of the heating.

Geography, skills and careers patterns at the boundary of creativity & innovation: digital technology and creative arts graduates in the UK

In the last decades, research on knowledge economies has taken central stage. Within this broader research field, research on the role of digital technologies and the creative industries has become increasingly important for researchers, academics and policy makers with particular focus on their development, supply-chains and models of production. Furthermore, many have recognised that, despite the important role played by digital technologies and innovation in the development of the creative industries, these dynamics are hard to capture and quantify. Digital technologies are embedded in the production and market structures of the creative industries and are also partially distinct and discernible from it. They also seem to play a key role in innovation of access and delivery of creative content. This chapter tries to assess the role played by digital technologies focusing on a key element of their implementation and application: human capital. Using student micro-data collected by the Higher Education Statistical Agency (HESA) in the United Kingdom, we explore the characteristics and location patterns of graduates who entered the creative industries, specifically comparing graduates in the creative arts and graduates from digital technology subjects. We highlight patterns of geographical specialisation but also how different context are able to better integrate creativity and innovation in their workforce. The chapter deals specifically with understanding whether these skills are uniformly embedded across the creative sector or are concentrated in specific sub-sectors of the creative industries. Furthermore, it explores the role that these graduates play in different sub-sector of the creative economy, their economic rewards and their geographical determinants.