This content will become publicly available on January 1, 2024
- NSF-PAR ID:
- Date Published:
- Journal Name:
- Atmospheric Measurement Techniques
- Page Range / eLocation ID:
- 2297 to 2317
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
Abstract Daytime atmospheric boundary layer (ABL) dynamics—including potential temperature budgets, water vapour budgets, and entrainment rates—are presented from in situ flight data taken on six afternoons near Fresno in the San Joaquin Valley (SJV) of California during July/August 2016. The flights took place as a part of the California Baseline Ozone Transport Study aimed at investigating transport pathways of air entering the Central Valley from offshore and mixing down to the surface. Midday entrainment velocity estimates ranged from 0.8 to 5.4 cm s −1 and were derived from a combination of continuously determined ABL heights during each flight and model-derived subsidence rates, which averaged -2.0 cm s −1 in the flight region. A strong correlation was found between entrainment velocity (normalized by the convective velocity scale) and an inverse bulk ABL Richardson number, suggesting that wind shear at the ABL top plays a significant role in driving entrainment. Similarly, we found a strong correlation between the entrainment efficiency (the ratio of entrainment to surface heat fluxes with an average of 0.23 ± 0.15) and the wind speed at the ABL top. We explore the synoptic conditions that generate higher winds near the ABL top and propose that warm anomalies in the southern Sierra Nevada mountains promote increased entrainment. Additionally, a method is outlined to estimate turbulence kinetic energy, convective velocity scale ( w * ), and the surface sensible heat flux in the ABL from a slow, airborne wind measurement system using mixed-layer similarity theory.more » « less
This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.
Ocean turbulent mixing is a key process affecting the uptake and redistribution of heat, carbon, nutrients, oxygen and other dissolved gasses. Vertical turbulent diffusivity sets the rates of water mass transformations and ocean mixing, and is intrinsically an average quantity over process time scales. Estimates based on microstructure profiling, however, are typically obtained as averages over individual profiles. How representative such averaged diffusivities are, remains unexplored in the quiescent Arctic Ocean. Here, we compare upper ocean vertical diffusivities in winter, derived from the7Be tracer‐based approach to those estimated from direct turbulence measurements during the year‐long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, 2019–2020. We found that diffusivity estimates from both methods agree within their respective measurement uncertainties. Diffusivity estimates obtained from dissipation rate profiles are sensitive to the averaging method applied, and the processing and analysis of similar data sets must take this sensitivity into account. Our findings indicate low characteristic diffusivities around 10−6 m2 s−1and correspondingly low vertical heat fluxes.
Abstract. The important roles that the atmospheric boundary layer (ABL) plays in the central Arctic climate system have been recognized, but the atmosphericboundary layer height (ABLH), defined as the layer of continuous turbulence adjacent to the surface, has rarely been investigated. Using ayear-round radiosonde dataset during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we improve aRichardson-number-based algorithm that takes cloud effects into consideration and subsequently analyze the characteristics and variability of the ABLH over theArctic Ocean. The results reveal that the annual cycle is clearly characterized by a distinct peak in May and two respective minima in January and July. Thisannual variation in the ABLH is primarily controlled by the evolution of the ABL thermal structure. Temperature inversions in the winter and summer areintensified by seasonal radiative cooling and warm-air advection with the surface temperature constrained by melting, respectively, leading to the lowABLH at these times. Meteorological and turbulence variables also play a significant role in ABLH variation, including the near-surface potentialtemperature gradient, friction velocity, and turbulent kinetic energy (TKE) dissipation rate. In addition, the MOSAiC ABLH is more suppressed than the ABLH during the SurfaceHeat Budget of the Arctic Ocean (SHEBA) experiment in the summer, which indicates that there is large variability in the Arctic ABL structure during thesummer melting season.
Turbulence is a major source of momentum, heat, moisture, and aerosol transport in the atmosphere. Hence, it is crucial to understand and accurately characterize turbulence mechanisms in atmospheric flows. Many complex factors in the atmosphere influence the turbulence structures including stratification and background shear. However, our understanding of the interacting effects of these factors on coherent turbulence structure evolutions is still limited. In this talk, we aim to bridge this knowledge gap by using mode decomposition techniques and a wide range of large-eddy simulation (LES) data. By developing a data-driven technique, we will characterize unique features of atmospheric boundary layer (ABL) turbulence under different forcing scenarios. We will present 3D LES wind speed snapshots of different ABL flows that will be used as dynamic mode decomposition (DMD) input data. Then, the obtained modes and eigenvalues will be employed to gain insights into coherent turbulence structures in ABLs. We will explain the physical meaning of dominant modes and how each mode relates to the physical cause of turbulence structures. The dominant modes, which are selected based on the mode amplitude, contain the most important spatial and temporal characteristics of the flow. We will evaluate the accuracy of the performance of this method by reconstructing the flow field with only a small number of modes, and then calculate the mean average error between the real flow and the reconstructed flow fields. We will present different data frequencies, wind speeds, and surface heat fluxes. This allows us to elucidate the modes and determine the conditions in which the mode decomposition provides more accurate results for the ABL flows. Our findings can be used to identify the major causes of turbulence in real atmospheric flows and could provide a deeper insight into the dynamics of turbulence in ABLs. Our results will also be useful for developing reduced-order models that can rapidly predict the turbulent ABL flow fields.more » « less