Diurnal evolution of negative atmospheric ions above the boreal forest: From ground level to the free troposphere

At SMEAR II research station in Hyytiälä, located in the Finnish boreal forest, the process of new particle formation and the role of ions has been investigated for almost 20 years near the ground and at canopy level. However, above SMEAR II, the vertical distribution and diurnal variation of these different atmospheric ions are poorly characterized. In this study, we assess the atmospheric ion composition in the stable boundary layer, residual layer, mixing layer and free troposphere, and the 5 evolution of these atmospheric ions due to photochemistry and turbulent mixing through the day. To measure the vertical profile of atmospheric ions, we developed a tailored setup for online mass spectrometric measurements, capable of being deployed in a Cessna 172 with minimal modifications. Simultaneously, instruments dedicated to aerosol properties measured in a second Cessna. We conducted a total of 16 measurement flights in May 2017, during the spring, which is the most active new particle formation season. A flight day typically consisted of three distinct flights through the day (dawn, morning and afternoon) to 10 observe the diurnal variation and at different altitudes (from 100 m to 3200 m above ground), and to capture the boundary layer development from stable boundary layer, residual layer to mixing layer, and the free troposphere. Our observations showed that the ion composition is distinctly different in each layer and depends on the air mass origin and time of the day. Before sunrise, the layers are separated from each other and have their own ion chemistry. We observed that the ions present within the stable layer are of the same composition as the ions measured at the canopy level. During daytime when the mixing layer evolved and the compounds are vertically mixed, we observed that highly oxidised organic molecules are distributed to the top of the boundary layer. The ion composition in the residual layer varies with each day, showing similarities with either the stable boundary layer or the free troposphere. Finally, within the free troposphere, we detected a variety of carboxylic acids and ions that are likely containing halogens, originating from the Arctic Sea.

Flux–Profile Relationships in the Stable Boundary Layer—A Critical Discussion

Flux–profile relationships are crucial for parametrizing surface fluxes of momentum and heat, that are of central relevance for applications such as climate modelling and weather forecast. Nevertheless, their functional forms are still under discussion, and a generally accepted formulation does not exist yet. We reviewed the four main formulations proposed in the literature so far and assessed how they affect the theoretical behaviour of the kinematic heat flux (H0) and the temperature scale (T*) in the stable boundary layer, as well as their consequences on the existence of critical values for both the gradient and the flux Richardson numbers. None of them turned out to be fully consistent with the literature published so far, with two of them leading to very unreliable expressions for both H0 and T*. All considered, a convincing description of flux–profile relationships still needs to be found and seems to represents a considerable challenge.

Evaluation of a Method for Calculating the Height of the Stable Boundary Layer based on Wind Profile Lidar and Turbulent Fluxes

The height of the stable boundary layer (SBL), known as the nocturnal boundary layer height, is controlled by numerous factors of different natures. The SBL height defines the state of atmospheric turbulence and describes the diffusion capacity of the atmosphere. Therefore, it is unsurprising that many alternative (sometimes contradictory) formulations for the SBL height have been proposed to date, and no consensus has been achieved. In our study, we propose an iterative algorithm to determine the SBL height h based on the flux–profile relationship using wind profiles and turbulent fluxes. This iterative algorithm can obtain temporally continuous, accurate estimates of h and is widely applicable. The predicted h presents relatively good agreement with four observation-derived SBL heights, hJ, h1, hi, and hθ (hJ: maximum wind speed height, h1: zero wind shear height, hi: temperature inversion height, and hθ: height at which 0.8 times the inversion strength appears for the first time), especially with hθ, which shows the best fit. In addition, h exhibits a low absolute difference and relative difference with hJ, which presents the second-best result. The agreement with hi and h1 may be satisfactory, but small differences are observed, and the one standard deviation of the mean relative difference is large. In addition, the predicted h is compared with other SBL height estimation methods, including diagnostic, λ1, λ2 and λ3 (three typical dimensional scale height parameters) and prognostic equation-based methods, λ(h) (an equation for the growth of h developed). The diagnostic formulas are found to be appropriate, especially under extremely stable conditions. Additionally, the equation of λ3 presents the best result of all the dimensional scale height parameters. However, the prognostic equation λ(h) in our study is very unsatisfactory.

An Investigation of the Grid Sensitivity in Large-Eddy Simulations of the Stable Boundary Layer

We revisit the longstanding problem of grid sensitivity, i.e., the lack of grid convergence in large-eddy simulations (LES) of the stable boundary layer. We use a comprehensive set of LES of the well-known Global Energy and Water Cycle Experiment Atmospheric Boundary Layer Study 1 (GABLS1) case with varying grid spacings between 12.5 m and 1 m to investigate several physical processes and numerical features that are possible causes of grid sensitivity. Our results demonstrate that there are two resolution regimes in which grid sensitivity manifests differently. We find that changing the numerical advection schemes and the subgrid-scale models alters the simulation results, but the options tested do not fully address the grid-sensitivity issue. Moreover, sensitivity runs suggest that the surface boundary condition and the interaction of the surface with the near-surface flow, as well as the mixing with the free atmosphere, are unlikely to be the causes of the observed grid sensitivity. One interesting finding is that the grid sensitivity in the fine grid-spacing regime (grid spacings $$\le 2\,\mathrm{m}$$ ≤ 2 m ) is closely related to the reduction in the energy content of large-scale turbulence, leading to less turbulence kinetic energy and hence lower boundary-layer heights. The present work demonstrates that there is still an urgent need to address this grid-sensitivity issue in order to perform reliable LES of the stable boundary layer.

Energy cascade for highly anisotropic turbulence in the Stable Boundary Layer

<p>The broad variety of phenomena occurring on multiple scales under stably stratified conditions and their complex interactions make it difficult to get a full description of the Stable Boundary Layer (SBL). Near-surface turbulence may be intermittent and highly anisotropic even at small scales. By studying the invariants of the anisotropy Reynolds stress tensor, it is possible to analyse the eddy kinetic energy distribution over the three components of the flow. Recent analyses of SBL turbulence data highlighted a prevalence of one-component limiting state of anisotropy. The causes of this particular limiting state are not fully understood, but there is evidence that submeso activity influences turbulence topology.<span> </span></p><p>This open question motivated the present work, that addresses the issue from the point of view of space dimensionality. In large-scale atmospheric and oceanic dynamics it is well known that turbulent motions may transfer energy both to the large and to the small scales, according to density stratification and rotation. These two properties act as constraints on the flow, giving it a 2D structure, and leading turbulence to be more complex than the homogeneous and isotropic case. For a SBL in low-wind speed conditions, atmospheric stratification might be very strong and we investigate if some of the peculiar characteristics of this regime might be related to a quasi-2D dynamics, with the occurrence of an inverse energy cascade, typical of 2D-like turbulence.</p><p>Energy exchanges across larger and smaller scales are studied by analysing the direction of the momentum flux with different methods, including a coarse-graining approach based on Large Eddy Simulation (LES) theory. The SnoHATS dataset was used to this purpose, where two vertically-separated horizontal arrays of sonic anemometers over the Plaine Morte Glacier (Switzerland) allowed the computation of the full three-dimensional velocity gradient. In order to fully characterize the energy exchanges according to different states of turbulence anisotropy, energy conversion processes between eddy kinetic and potential energy have also been considered and analysed at different heights. To this purpose, the dataset FLOSSII was used, providing turbulence measurements up to 30 m above a flat grass surface, often covered by snow.<span> </span></p><p>Results seem to suggest that turbulent kinetic energy in the SBL is distributed mainly in one component more as a consequence of wave-turbulence interactions than of development of 2D-like turbulence. This gives insights on mechanisms driving turbulence anisotropy that might be used to improve turbulence parameterizations in the SBL.</p>

Thermal Submeso Motions in the Nocturnal Stable Boundary Layer. Part 2: Generating Mechanisms and Implications

In the stable boundary layer, thermal submesofronts (TSFs) are detected during the Shallow Cold Pool experiment in the Colorado plains, Colorado, USA in 2012. The topography induces TSFs by forming two different air layers converging on the valley-side wall while being stacked vertically above the valley bottom. The warm-air layer is mechanically generated by lee turbulence that consistently elevates near-surface temperatures, while the cold-air layer is thermodynamically driven by radiative cooling and the corresponding cold-air drainage decreases near-surface temperatures. The semi-stationary TSFs can only be detected, tracked, and investigated in detail when using fibre-optic distributed sensing (FODS), as point observations miss TSFs most of the time. Neither the occurrence of TSFs nor the characteristics of each air layer are connected to a specific wind or thermal regime. However, each air layer is characterized by a specific relationship between the wind speed and the friction velocity. Accordingly, a single threshold separating different flow regimes within the boundary layer is an oversimplification, especially during the occurrence of TSFs. No local forcings or their combination could predict the occurrence of TSFs except that they are less likely to occur during stronger near-surface or synoptic-scale flow. While classical conceptualizations and techniques of the boundary layer fail in describing the formation of TSFs, the use of spatially continuous data obtained from FODS provide new insights. Future studies need to incorporate spatially continuous data in the horizontal and vertical planes, in addition to classic sensor networks of sonic anemometry and thermohygrometers to fully characterize and describe boundary-layer phenomena.

Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics

Submesoscale motions within the stable boundary layer were detected during the Shallow Cold Pool Experiment conducted in the Colorado plains, Colorado, U.S.A. in 2012. The submesoscale motion consisted of two air layers creating a well-defined front with a sharp temperature gradient, and further-on referred to as a thermal submesofront (TSF). The semi-stationary TSFs and their advective velocities are detected and determined by the fibre-optic distributed-sensing (FODS) technique. An objective detection algorithm utilizing FODS measurements is able to detect the TSF boundary, which enables a detailed investigation of its spatio–temporal statistics. The novel approach in data processing is to conditionally average any parameter depending on the distance between a TSF boundary and the measurement location. By doing this, a spatially-distributed feature like TSFs can be characterized by point observations and processes at the TSF boundary can be investigated. At the TSF boundary, the air layers converge, creating an updraft, strong static stability, and vigorous mixing. Further, the TSF advective velocity of TSFs is an order of magnitude lower than the mean wind speed. Despite being gentle, the topography plays an important role in TSF formation. Details on generating mechanisms and implications of TSFs on the stable boundary layer are discussed in Part 2.