Mixing and Transformation in a Deep Western Boundary Current: A Case Study

Water-mass transformation by turbulent mixing is a key part of the deep-ocean overturning, as it drives the upwelling of dense waters formed at high latitudes. Here, we quantify this transformation and its underpinning processes in a small Southern Ocean basin: the Orkney Deep. Observations reveal a focussing of the transport in density space as a deep western boundary current (DWBC) flows through the region, associated with lightening and densification of the current’s denser and lighter layers, respectively. These transformations are driven by vigorous turbulent mixing. Comparing this transformation with measurements of the rate of turbulent kinetic energy dissipation indicates that, within the DWBC, turbulence operates with a high mixing efficiency, characterized by a dissipation ratio of 0.6 to 1 that exceeds the common value of 0.2. This result is corroborated by estimates of the dissipation ratio from microstructure observations. The causes of the transformation are unravelled through a decomposition into contributions dependent on the gradients in density space of the: dianeutral mixing rate, isoneutral area, and stratification. The transformation is found to be primarily driven by strong turbulence acting on an abrupt transition from the weakly-stratified bottom boundary layer to well-stratified off-boundary waters. The reduced boundary-layer stratification is generated by a downslope Ekman flow associated with the DWBC’s flow along sloping topography, and is further regulated by submesoscale instabilities acting to re-stratify near-boundary waters. Our results provide observational evidence endorsing the importance of near-boundary mixing processes to deep-ocean overturning, and highlight the role of DWBCs as hot spots of dianeutral upwelling.

From small whirls to the global ocean: how eddies affect the Atlantic Meridional Overturning Circulation

<p>In the North Atlantic Ocean, intense downward motions connect the upper and lower limbs of the Atlantic Meridional Overturning Circulation (AMOC). In addition, the AMOC also displays a pronounced signature in density space, with lighter waters moving northward and denser waters returning southward.</p><p>While at first glance it is appealing to associate this sinking of water masses in the North Atlantic Ocean with the occurrence of the formation of dense water masses by deep convection, this is not correct: the net vertical motion over convection areas is small. The downward flow required to connect the upper and lower branches of the AMOC thus has to occur outside the deep convection areas. Indeed, earlier studies have pointed out theoretically that strong sinking can only occur close to continental boundaries, where ageostrophic processes play a role. However, observations clearly indicate that convected water masses formed in marginals seas constitute an important component of the lower limb of the AMOC.</p><p>This apparent contradiction is explored in this presentation, by studying the overturning in the AMOC from a perspective in depth space (Eulerian downwelling) and density space (downwelling across isopycnals). Based on analyses of both a high-resolution global ocean model and dedicated process studies using idealized models we analyze the characteristics of the sinking, of diapycnal mixing, and investigate how these are linked. </p><p>It appears that eddies play a crucial role for the overturning, both in depth space and density space. They control the characteristics of the yearly cycle of convection and restratification, the magnitude of the Eulerian sinking near continental boundaries, and steer the export of dense waters formed in the interior of the marginal seas via the boundary current system.</p><p>These studies thus reveal a complex three-dimensional view on sinking, diapycnal water mass transformation and overturning in the North Atlantic Ocean, involving the boundary current, the interior and interactions with the eddy field.  This implies that it is essential to resolve these eddies to be able to properly represent the overturning in depth and density space in the North Atlantic Ocean and its response to changing conditions in a future climate.</p>

A local group differences test for subject-level multivariate density neuroimaging outcomes

Summary A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus.

Affective State Based Anomaly Detection in Crowd

To distinguish individuals with dangerous abnormal behaviours from the crowd, human characteristics (e.g., speed and direction of motion, interaction with other people), crowd characteristics (such as flow and density), space available to individuals, etc. must be considered. The paper proposes an approach that considers individual and crowd metrics to determine anomaly. An individual’s abnormal behaviour alone cannot indicate behaviour, which can be threatening toward other individuals, as this behaviour can also be triggered by positive emotions or events. To avoid individuals whose abnormal behaviour is potentially unrelated to aggression and is not environmentally dangerous, it is suggested to use emotional state of individuals. The aim of the proposed approach is to automate video surveillance systems by enabling them to automatically detect potentially dangerous situations.