Turbulent mixing in the oceanic boundary layer caused by internal wave reflection from sloping terrain

1996 ◽  
Vol 24 (1-4) ◽  
pp. 51-62 ◽  
Author(s):  
Donald N. Slinn ◽  
James J. Riley
Keyword(s):  
Boundary Layer ◽  
Internal Wave ◽  
Turbulent Mixing ◽  
Wave Reflection ◽  
Oceanic Boundary Layer ◽  
Sloping Terrain

scholarly journals Modeling microphysical effects of entrainment in clouds observed during EUCAARI-IMPACT field campaign

2013 ◽  
Vol 13 (16) ◽  
pp. 8489-8503 ◽  
Author(s):  
D. Jarecka ◽  
H. Pawlowska ◽  
W. W. Grabowski ◽  
A. A. Wyszogrodzki
Keyword(s):  
Boundary Layer ◽  
Turbulent Mixing ◽  
Marine Boundary Layer ◽  
Cloud Droplet ◽  
Droplet Radius ◽  
Subgrid Scale ◽  
Microphysics Scheme ◽  
Local Conditions ◽  
Field Campaign ◽  
Les Model

Abstract. This paper discusses aircraft observations and large-eddy simulation (LES) modeling of 15 May 2008, North Sea boundary-layer clouds from the EUCAARI-IMPACT field campaign. These clouds are advected from the northeast by the prevailing lower-tropospheric winds and featured stratocumulus-over-cumulus cloud formations. An almost-solid stratocumulus deck in the upper part of the relatively deep, weakly decoupled marine boundary layer overlays a field of small cumuli. The two cloud formations have distinct microphysical characteristics that are in general agreement with numerous past observations of strongly diluted shallow cumuli on one hand and solid marine stratocumulus on the other. Based on the available observations, a LES model setup is developed and applied in simulations using a novel LES model. The model features a double-moment warm-rain bulk microphysics scheme combined with a sophisticated subgrid-scale scheme allowing local prediction of the homogeneity of the subgrid-scale turbulent mixing. The homogeneity depends on the characteristic time scales for the droplet evaporation and for the turbulent homogenization. In the model, these scales are derived locally based on the subgrid-scale turbulent kinetic energy, spatial scale of cloudy filaments, mean cloud droplet radius, and humidity of the cloud-free air entrained into a cloud, all predicted by the LES model. The model reproduces contrasting macrophysical and microphysical characteristics of the cumulus and stratocumulus cloud layers. Simulated subgrid-scale turbulent mixing within the cumulus layer and near the stratocumulus top is on average quite inhomogeneous, but varies significantly depending on the local conditions.

Frontogenesis and frontal arrest of a dense filament in the oceanic surface boundary layer

2017 ◽  
Vol 837 ◽  
pp. 341-380 ◽  
Author(s):  
Peter P. Sullivan ◽  
James C. McWilliams
Keyword(s):  
Boundary Layer ◽  
Turbulent Mixing ◽  
Shear Instability ◽  
Upper Ocean ◽  
Surface Boundary ◽  
Horizontal Shear ◽  
Boundary Layer Turbulence ◽  
Filament Axis ◽  
Small Width ◽  
The Cross

The evolution of upper ocean currents involves a set of complex, poorly understood interactions between submesoscale turbulence (e.g. density fronts and filaments and coherent vortices) and smaller-scale boundary-layer turbulence. Here we simulate the lifecycle of a cold (dense) filament undergoing frontogenesis in the presence of turbulence generated by surface stress and/or buoyancy loss. This phenomenon is examined in large-eddy simulations with resolved turbulent motions in large horizontal domains using${\sim}10^{10}$grid points. Steady winds are oriented in directions perpendicular or parallel to the filament axis. Due to turbulent vertical momentum mixing, cold filaments generate a potent two-celled secondary circulation in the cross-filament plane that is frontogenetic, sharpens the cross-filament buoyancy and horizontal velocity gradients and blocks Ekman buoyancy flux across the cold filament core towards the warm filament edge. Within less than a day, the frontogenesis is arrested at a small width,${\approx}100~\text{m}$, primarily by an enhancement of the turbulence through a small submesoscale, horizontal shear instability of the sharpened filament, followed by a subsequent slow decay of the filament by further turbulent mixing. The boundary-layer turbulence is inhomogeneous and non-stationary in relation to the evolving submesoscale currents and density stratification. The occurrence of frontogenesis and arrest are qualitatively similar with varying stress direction or with convective cooling, but the detailed evolution and flow structure differ among the cases. Thus submesoscale filament frontogenesis caused by boundary-layer turbulence, frontal arrest by frontal instability and frontal decay by forward energy cascade, and turbulent mixing are generic processes in the upper ocean.

LESbrary: A library of large eddy simulation data for the calibration and uncertainty quantification of ocean surface boundary layer turbulence parameterizations

2021 ◽  
Author(s):  
Gregory Wagner ◽  
Andre Souza ◽  
Adeline Hillier ◽  
Ali Ramadhan ◽  
Raffaele Ferrari
Keyword(s):  
Boundary Layer ◽  
Turbulent Mixing ◽  
Large Scale ◽  
Ocean Surface ◽  
Limited Area ◽  
Surface Boundary ◽  
Model Errors ◽  
Surface Boundary Layer ◽  
Large Eddy ◽  
Free Parameters

<p>Parameterizations of turbulent mixing in the ocean surface boundary layer (OSBL) are key Earth System Model (ESM) components that modulate the communication of heat and carbon between the atmosphere and ocean interior. OSBL turbulence parameterizations are formulated in terms of unknown free parameters estimated from observational or synthetic data. In this work we describe the development and use of a synthetic dataset called the “LESbrary” generated by a large number of idealized, high-fidelity, limited-area large eddy simulations (LES) of OSBL turbulent mixing. We describe how the LESbrary design leverages a detailed understanding of OSBL conditions derived from observations and large scale models to span the range of realistically diverse physical scenarios. The result is a diverse library of well-characterized “synthetic observations” that can be readily assimilated for the calibration of realistic OSBL parameterizations in isolation from other ESM model components. We apply LESbrary data to calibrate free parameters, develop prior estimates of parameter uncertainty, and evaluate model errors in two OSBL parameterizations for use in predictive ESMs.</p>

scholarly journals Climate Process Team on Internal Wave–Driven Ocean Mixing

2017 ◽  
Vol 98 (11) ◽  
pp. 2429-2454 ◽  
Author(s):  
Jennifer A. MacKinnon ◽  
Zhongxiang Zhao ◽  
Caitlin B. Whalen ◽  
Amy F. Waterhouse ◽  
David S. Trossman ◽  
...  
Keyword(s):  
Internal Wave ◽  
Turbulent Mixing ◽  
Low Frequency ◽  
Internal Tide ◽  
Deep Ocean ◽  
Global Ocean ◽  
Primary Role ◽  
Inverse Models ◽  
Lee Waves ◽  
Mixing Rates

Abstract Diapycnal mixing plays a primary role in the thermodynamic balance of the ocean and, consequently, in oceanic heat and carbon uptake and storage. Though observed mixing rates are on average consistent with values required by inverse models, recent attention has focused on the dramatic spatial variability, spanning several orders of magnitude, of mixing rates in both the upper and deep ocean. Away from ocean boundaries, the spatiotemporal patterns of mixing are largely driven by the geography of generation, propagation, and dissipation of internal waves, which supply much of the power for turbulent mixing. Over the last 5 years and under the auspices of U.S. Climate Variability and Predictability Program (CLIVAR), a National Science Foundation (NSF)- and National Oceanic and Atmospheric Administration (NOAA)-supported Climate Process Team has been engaged in developing, implementing, and testing dynamics-based parameterizations for internal wave–driven turbulent mixing in global ocean models. The work has primarily focused on turbulence 1) near sites of internal tide generation, 2) in the upper ocean related to wind-generated near inertial motions, 3) due to internal lee waves generated by low-frequency mesoscale flows over topography, and 4) at ocean margins. Here, we review recent progress, describe the tools developed, and discuss future directions.

Shock-wave reflection from a turbulent boundary layer with mass bleed.

1968 ◽  
Vol 5 (5) ◽  
pp. 461-467 ◽  
Author(s):  
WILLIAM R. SEEBAUGH ◽  
GERALD C. PAYNTER ◽  
MORRIS E. CHILDS
Keyword(s):  
Boundary Layer ◽  
Shock Wave ◽  
Turbulent Boundary Layer ◽  
Wave Reflection ◽  
Shock Wave Reflection

scholarly journals Observation of Turbulent Mixing Characteristics in the Typical Daytime Cloud-Topped Boundary Layer over Hong Kong in 2019

2020 ◽  
Vol 12 (9) ◽  
pp. 1533 ◽  
Author(s):  
Tao Huang ◽  
Steve Hung-lam Yim ◽  
Yuanjian Yang ◽  
Olivia Shuk-ming Lee ◽  
David Hok-yin Lam ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Hong Kong ◽  
Vertical Velocity ◽  
Turbulent Mixing ◽  
Wind Shear ◽  
Vertical Wind Shear ◽  
Vertical Wind ◽  
Surface Heating ◽  
Cloud Base ◽  
Radiative Cooling

Turbulent mixing is critical in affecting urban climate and air pollution. Nevertheless, our understanding of it, especially in a cloud-topped boundary layer (CTBL), remains limited. High-temporal resolution observations provide sufficient information of vertical velocity profiles, which is essential for turbulence studies in the atmospheric boundary layer (ABL). We conducted Doppler Light Detection and Ranging (LiDAR) measurements in 2019 using the 3-Dimensional Real-time Atmospheric Monitoring System (3DREAMS) to reveal the characteristics of typical daytime turbulent mixing processes in CTBL over Hong Kong. We assessed the contribution of cloud radiative cooling on turbulent mixing and determined the altitudinal dependence of the contribution of surface heating and vertical wind shear to turbulent mixing. Our results show that more downdrafts and updrafts in spring and autumn were observed and positively associated with seasonal cloud fraction. These results reveal that cloud radiative cooling was the main source of downdraft, which was also confirmed by our detailed case study of vertical velocity. Compared to winter and autumn, cloud base heights were lower in spring and summer. Cloud radiative cooling contributed ~32% to turbulent mixing even near the surface, although the contribution was relatively weaker compared to surface heating and vertical wind shear. Surface heating and vertical wind shear together contributed to ~45% of turbulent mixing near the surface, but wind shear can affect up to ~1100 m while surface heating can only reach ~450 m. Despite the fact that more research is still needed to further understand the processes, our findings provide useful references for local weather forecast and air quality studies.

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