The role of whitecapping in thickening the ocean surface boundary layer

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 45 (2015): 2006–2024, doi:10.1175/JPO-D-14-0234.1.The effects of wind-driven whitecapping on the evolution of the ocean surface boundary layer are examined using an idealized one-dimensional Reynolds-averaged Navier–Stokes numerical model. Whitecapping is parameterized as a flux of turbulent kinetic energy through the sea surface and through an adjustment of the turbulent length scale. Simulations begin with a two-layer configuration and use a wind that ramps to a steady stress. This study finds that the boundary layer begins to thicken sooner in simulations with whitecapping than without because whitecapping introduces energy to the base of the boundary layer sooner than shear production does. Even in the presence of whitecapping, shear production becomes important for several hours, but then inertial oscillations cause shear production and whitecapping to alternate as the dominant energy sources for mixing. Details of these results are sensitive to initial and forcing conditions, particularly to the turbulent length scale imposed by breaking waves and the transfer velocity of energy from waves to turbulence. After 1–2 days of steady wind, the boundary layer in whitecapping simulations has thickened more than the boundary layer in simulations without whitecapping by about 10%–50%, depending on the forcing and initial conditions.We thank Skidmore College for financial and infrastructure support, and Skidmore and the National Science Foundation for funding travel to meetings where early versions of this work were presented. We also thank the National Science Foundation, Oregon State University, Jonathan Nash, and Joe Jurisa for funding and hosting a workshop on River Plume Mixing in October, 2013, where ideas and context for this paper were developed.2016-02-0

Submicron Structures Technology and Research

Contains reports on ten research projects.Joint Services Electronics Program (Contract DAAG29-83-K-0003)Joint Services Electronics Program (Contract DAAL03-86-K-0002)National Science Foundation (Grant ECS82-05701)National Science Foundation (Grant ECS85-06565)Lawrence Livermore Laboratory (Subcontract 2069209)National Science Foundation (Grant ECS85-03443)U.S. Air Force - Office of Scientific Research (Grant AFOSR-85-0154)National Aeronautics and Space Administration (Grant NGL22-009-638)National Science Foundation (through KMS Fusion, Inc.)U.S. Navy - Office of Naval Research (Contract N00014-79-C-0908

Submicron Structures Technology and Research

Contains reports on fifteen research projects.Joint Services Electronics Program (Contract DAALO3-86-K-0002)National Science Foundation (Grant ECS 87-09806)Semiconductor Research Corporation (Contract 87-SP-080)National Science Foundation (Grant ECS 85-03443)U.S. Air Force - Office of Scientific Research (Grant AFOSR 85-0376)National Science Foundation (Grant ECS 85-06565)U.S. Air Force - Office of Scientific Research (Grant AFOSR 85-0154)Lawrence Livermore National Laboratory (Subcontract 2069209)National Aeronautics and Space Adminstration (Grant NGL22-009-683)Collaboration with KMS Fusion, Inc

Submicron Structures Technology and Research

Contains table of contents for Part I, table of contents for Section 1 and reports on fourteen research projects.Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAAL03-89-C-0001)National Science Foundation (Grant ECS-87-09806)Semiconductor Research Corporation (Contract 87-SP-080)Hampshire Instruments CorporationNational Science Foundation (Grant ECS-85-03443)U.S. Air Force - Office of Scientific Research (Grant AFOSR-88-0304)National Science Foundation (Grant ECS-85-06565)X-Opt., IncorporatedU.S. Air Force - Office of Scientific Research (Grant AFOSR-85-0154)National Aeronautics and Space Administration (Grant NGL22-009-683)KMS Fusion, Incorporate

Role of Basal Ganglia Circuits in Resisting Interference by Distracters: A swLORETA Study

BACKGROUND: The selection of task-relevant information requires both the focalization of attention on the task and resistance to interference from irrelevant stimuli. Both mechanisms rely on a dorsal frontoparietal network, while focalization additionally involves a ventral frontoparietal network. The role of subcortical structures in attention is less clear, despite the fact that the striatum interacts significantly with the frontal cortex via frontostriatal loops. One means of investigating the basal ganglia's contributions to attention is to examine the features of P300 components (i.e. amplitude, latency, and generators) in patients with basal ganglia damage (such as in Parkinson's disease (PD), in which attention is often impaired). Three-stimulus oddball paradigms can be used to study distracter-elicited and target-elicited P300 subcomponents. METHODOLOGY/PRINCIPAL FINDINGS: In order to compare distracter- and target-elicited P300 components, high-density (128-channel) electroencephalograms were recorded during a three-stimulus visual oddball paradigm in 15 patients with early PD and 15 matched healthy controls. For each subject, the P300 sources were localized using standardized weighted low-resolution electromagnetic tomography (swLORETA). Comparative analyses (one-sample and two-sample t-tests) were performed using SPM5® software. The swLORETA analyses showed that PD patients displayed fewer dorsolateral prefrontal (DLPF) distracter-P300 generators but no significant differences in target-elicited P300 sources; this suggests dysfunction of the DLPF cortex when the executive frontostriatal loop is disrupted by basal ganglia damage. CONCLUSIONS/SIGNIFICANCE: Our results suggest that the cortical attention frontoparietal networks (mainly the dorsal one) are modulated by the basal ganglia. Disruption of this network in PD impairs resistance to distracters, which results in attention disorders

White matter hyperintensities are associated with visual search behavior independent of generalized slowing in aging

A fundamental controversy is whether cognitive decline with advancing age can be entirely explained by decreased processing speed, or whether specific neural changes can elicit cognitive decline, independent of slowing. These hypotheses are anchored by studies of healthy older individuals where age is presumed the sole influence. Unfortunately, advancing age is also associated with asymptomatic brain white matter injury. We hypothesized that differences in white matter injury extent, manifest by MRI white matter hyperintensities (WMH), mediate differences in visual attentional control in healthy aging, beyond processing speed differences. We tested young and cognitively healthy older adults on search tasks indexing speed and attentional control. Increasing age was associated with generally slowed performance. WMH was also associated with slowed search times independent of processing speed differences. Consistent with evidence attributing reduced network connectivity to WMH, these results conclusively demonstrate that clinically silent white matter injury contributes to slower search performance indicative of compromised cognitive control, independent of generalized slowing of processing speed