Intermittent Turbulence Research Articles

A conditional deep learning model for super-resolution reconstruction of small-scale turbulent structures in particle-Laden flows

Super-resolution reconstruction of turbulent flows using deep learning has gained significant attention, yet challenges remain in accurately capturing physical small-scale structures. This study introduces the Conditional Enhanced Super-Resolution Generative Adversarial Network (CESRGAN) for reconstructing high-resolution turbulent velocity fields from low-resolution inputs. CESRGAN consists of a conditional discriminator and a conditional generator, the latter being called CoGEN. CoGEN incorporates subgrid-scale (SGS) turbulence kinetic energy as conditional information, improving the recovery of small-scale turbulent structures with the desired level of energy. By being aware of SGS turbulence kinetic energy, CoGEN is relatively insensitive to the degree of detail in the input. As shown in the paper, its advantages become more pronounced when the model is applied to heavily filtered input. We evaluate the model using direct numerical simulation (DNS) data of forced homogeneous isotropic turbulence. The analysis of Q-criterion isosurfaces, energy spectra, and probability density functions shows that the proposed CoGEN reconstructs fine-scale vortical structures more precisely and captures turbulent intermittency better compared to the traditional generator. Particle-pair dispersion simulations validate the physical fidelity of CoGEN-reconstructed fields, closely matching DNS results across various Stokes numbers and filtering levels. This paper demonstrates how incorporating available physical information enhances super-resolution models for turbulent flows.

Exploring the intermittency of magnetohydrodynamic turbulence by synchrotron polarization radiation

Context. Magnetohydrodynamic (MHD) turbulence plays a critical role in many key astrophysical processes, such as star formation, acceleration of cosmic rays, and heat conduction. However, its properties are still poorly understood. Aims. In this work, we explore how to extract the intermittency of compressible MHD turbulence from synthetic and real observations. Methods. We used three statistical methods, namely the probability distribution function, kurtosis, and scaling exponent of the multi-order structure function, to reveal the intermittency of MHD turbulence. Results. Our numerical results demonstrate that: (1) the synchrotron polarization intensity statistics can be used to probe the intermittency of magnetic turbulence, by which we can distinguish different turbulence regimes; (2) the intermittency of MHD turbulence is dominated by the slow mode in the sub-Alfvénic turbulence regime; and (3) the Galactic interstellar medium (ISM) in the low latitude region corresponds to the sub-Alfvénic and supersonic turbulence regime. Conclusions.We have successfully measured the intermittency of the Galactic ISM from synthetic and realistic observations.

Characterizing firebrands and their kinematics during lofting

Spot fires pose a major risk and add to the already complex physics, which makes fire spread so hard to predict, especially in the wildland urban interface. Firebrands can not only cross fuel breaks and thwart other suppression efforts but also directly damage infrastructure and block evacuation routes. Transport models and computational fluid dynamics tools often make simplifications when predicting spot fire risk, but there is a relative lack of experimental data to validate such parameterizations. To this end, we present a field experiment performed at the University of California Berkeley Blodgett Research Forest in California where we recorded the flame and firebrands emanating from a nighttime hand-drawn pile fire using high-frequency imaging. We used image-processing to characterize the fire intensity and turbulence as well as particle tracking velocimetry to measure ejected firebrand kinematics as they are lofted by the plume. We further collected embers that settled around the fire at varying distances and measured their size, shape, density, and settling distributions. We also examine existing physics-based time-averaged models of firebrand lofting and note discrepancies between such models, often used due to their speed and simplicity, and our experimental observations. Finally, we discuss some implications our observations could have on future modeling efforts by considering the time-dependent fire dynamics, intermittency in the plume turbulence, and in the firebrand generation rate. To the best of our knowledge, these are the first in situ observations of firebrand generation and lofting from representative fuels, addressing a major source of data gap and uncertainty in the wildland fire literature.

GPU-enabled extreme-scale turbulence simulations: Fourier pseudo-spectral algorithms at the exascale using OpenMP offloading

Fourier pseudo-spectral methods for nonlinear partial differential equations are of wide interest in many areas of advanced computational science, including direct numerical simulation of three-dimensional (3-D) turbulence governed by the Navier-Stokes equations in fluid dynamics. This paper presents a new capability for simulating turbulence at a new record resolution up to 35 trillion grid points, on the world's first exascale computer, Frontier, comprising AMD MI250x GPUs with HPE's Slingshot interconnect and operated by the US Department of Energy's Oak Ridge Leadership Computing Facility (OLCF). Key programming strategies designed to take maximum advantage of the machine architecture involve performing almost all computations on the GPU which has the same memory capacity as the CPU, performing all-to-all communication among sets of parallel processes directly on the GPU, and targeting GPUs efficiently using OpenMP offloading for intensive number-crunching including 1-D Fast Fourier Transforms (FFT) performed using AMD ROCm library calls. With 99% of computing power on Frontier being on the GPU, leaving the CPU idle leads to a net performance gain via avoiding the overhead of data movement between host and device except when needed for some I/O purposes. Memory footprint including the size of communication buffers for MPI_ALLTOALL is managed carefully to maximize the largest problem size possible for a given node count.Detailed performance data including separate contributions from different categories of operations to the elapsed wall time per step are reported for five grid resolutions, from 20483 on a single node to 327683 on 4096 or 8192 nodes out of 9408 on the system. Both 1D and 2D domain decompositions which divide a 3D periodic domain into slabs and pencils respectively are implemented. The present code suite (labeled by the acronym GESTS, GPUs for Extreme Scale Turbulence Simulations) achieves a figure of merit (in grid points per second) exceeding goals set in the Center for Accelerated Application Readiness (CAAR) program for Frontier. The performance attained is highly favorable in both weak scaling and strong scaling, with notable departures only for 20483 where communication is entirely intra-node, and for 327683, where a challenge due to small message sizes does arise. Communication performance is addressed further using a lightweight test code that performs all-to-all communication in a manner matching the full turbulence simulation code. Performance at large problem sizes is affected by both small message size due to high node counts as well as dragonfly network topology features on the machine, but is consistent with official expectations of sustained performance on Frontier. Overall, although not perfect, the scalability achieved at the extreme problem size of 327683 (and up to 8192 nodes — which corresponds to hardware rated at just under 1 exaflop/sec of theoretical peak computational performance) is arguably better than the scalability observed using prior state-of-the-art algorithms on Frontier's predecessor machine (Summit) at OLCF. New science results for the study of intermittency in turbulence enabled by this code and its extensions are to be reported separately in the near future.

A preliminary observational study on the characteristics of surface turbulent fluxes over the South China Sea Islands

AbstractIn recent years, there has been a rise in human activities in oceanic areas, making the land–atmosphere interactions over islands a major scientific concern on a global scale. Examining the observation data from offshore areas enables a more comprehensive understanding of the turbulent fluxes in offshore atmospheric environments, patterns of momentum, energy and material exchange between the atmosphere and underlying surface in an oceanic boundary layer, and development of a heterogeneous atmospheric boundary layer. The related findings will assist in developing theoretical models and parameterization schemes to simulate the influence of heterogeneous surfaces on land–atmosphere interactions on the South China Sea Islands. Existing studies on the turbulent fluxes over the South China Sea Islands were mainly conducted on the Nansha Islands, whereas studies on the waters of the South China Sea are scarce. In this study, we used 10 Hz high‐frequency turbulence measurements to calculate the latent and sensible heat fluxes over the South China Sea Islands using the eddy correlation method. These findings were then compared with data from the Dunhuang Gobi, Ordos desert, and Xilingol grassland regions in inland China, along with the observed net radiation and surface heat fluxes. The findings indicate that the energy fluxes over the South China Sea in summer exhibit prominent diurnal variations. The magnitude of either latent or soil heat flux is low, and the net radiation is predominantly transformed into sensible heat flux, which warms the atmosphere. Furthermore, the daily variation curves of sensible and latent heat fluxes are influenced by intermittent turbulence on the islands and reefs, resulting in a less smooth pattern compared with soil heat flux. Although the South China Sea Islands have small land areas and are surrounded by the sea, the land–atmosphere interactions over the underlying surface of this region are similar to those over the underlying surface of grasslands in inland China during summer. The daily mean sensible heat flux on the islands is higher than that in an inland area, and the time lag in its response to sunrise is longer than that in inland areas by approximately 1 h. The overall energy balance ratio is approximately 0.75, c which is in line with the average level, but an energy balance residual of approximately 25% still exists. Furthermore, extreme weather conditions, such as typhoons, can disrupt the diurnal variations of sensible and latent heat fluxes, and the cyclical patterns are subsequently restored.

Study of turbulence intermittency in unstable atmospheric surface layer and its effect on saltation sand motion based on wavelet transform

Turbulence in the atmospheric surface layer, especially in deserts and semi-arid regions, significantly affects sand movement. In unstable stratification, turbulence exhibits complex intermittency, complicating its impact on saltation. This study uses wavelet transform analysis to examine the effects of turbulence intermittency in unstable stratification on saltation. Our analysis reveals that in unstable stratification, the energy distribution of turbulence is more dispersed, the intermittent characteristics are more significant, and the intermittent burst duration of streamwise turbulence is longer, while the vertical intermittent burst duration is shorter. The fitting formulas of the energy ratio and stratification stability of the streamwise wind speed, vertical wind speed, and temperature at different frequencies are given. In addition, there is a complex nonlinear relationship between stratification stability and friction velocity on saltation. In unstable stratification, the critical wind speed required for saltation is higher than that of near-neutral, and the jumping speed and horizontal transport are weakened. Moreover, the coherence between wind speed and saltation flux increases significantly at low frequency with the increase in instability, indicating that large-scale motion plays a key role in saltation under these conditions. The more unstable the stratification is, the more obvious the phase difference fluctuation of the low frequency part is, and the more unfavorable the formation of stable saltation sand conditions. This study reveals the turbulence intermittently and its complex effects on sand particle movement in unstable stratification, which is of great significance for predicting and controlling dust storms, land desertification, and soil erosion.

Environmental controls on the interannual variability in chlorophyll and phytoplankton community structure within the seasonal sub surface chlorophyll maximum in the western English channel

The subsurface chlorophyll maximum (SCM) is increasingly recognised as an important but understudied locus of primary production particularly in shelf seas. Here we report the results of a 4 year, repeat station, summer sampling programme (2013–2016) of a seasonally recurrent SCM in the Western English Channel. Interannual variability in phytoplankton community structure and chlorophyll distribution and intensity was strongly related to water column stability at the depth interval of the SCM and also to water temperature. The phytoplankton community was statistically distinct in each year. High stability, as evidenced by large Richardson numbers and a well-developed strong thermocline appeared to favour the growth of larger dinoflagellates (autotrophs or mixotrophs) and diatoms. Such conditions led to development of the most intense SCMs and these were sometimes dominated by a single or a few key species most prominently in 2015 with near monospecific concentrations of the dinoflagellate Tripos fusus with average peak SCM chlorophyll concentrations of 7.3 ± 4.4 μg l−1. By contrast, in years with low water column stability and intermittent turbulence at the thermocline (2014, 2016) there was greater chlorophyll dispersal and less intense SCM. In these low stability conditions, red fluorescent nano-phytoplankton, such as naked dinoflagellates, chlorophytes and prymnesiophytes, made a greater contribution to the community, possibly as a result of the advantages that motility and enhanced light utilisation efficiency confer within an SCM exposed to turbulence. It is also likely that turbulence disrupted the stability required by the larger dinoflagellates and diatoms. Several of the key SCM taxa were absent from surface waters including the dinoflagellates Tripos fusus, Tripos lineatus, and most of the Rhizosolenia/Proboscia diatoms, consistent with adaptations more suited to survival at depth in stratified waters. These traits include luxury nutrient uptake and storage and survival in low light (both groups) and mixotrophy (dinoflagellates). On the other hand, in 2013, diatoms including Pseudo-nitzschia spp. were abundant in both surface, SCM and bottom waters. The relatively cooler waters (11.6–12.1 °C on average in 2013 and 2016) were characterised by smaller diatoms (Chaetoceros spp. and Pseudo-nitzschia spp.) whereas the warmer waters (13.1 °C on average in 2014) contained larger diatoms (large Rhizosolenia spp., Lauderia annulata and Leptocylindrus danicus). There did not appear to be continuity of key species between years, other than for the dinoflagellate Tripos lineatus, which was significant in both 2013 and 2014 and present in 2015. In any given year, there was no correspondence between the key spring bloom phytoplankton species as monitored in the nearby Western Channel Observatory L4 station and the key SCM taxa.

A multiscale and multicriteria generative adversarial network to synthesize 1-dimensional turbulent fields

This article introduces a new neural network stochastic model to generate a 1-dimensional stochastic field with turbulent velocity statistics. Both the model architecture and training procedure ground on the Kolmogorov and Obukhov statistical theories of fully developed turbulence, so guaranteeing descriptions of (1) energy distribution, (2) energy cascade and (3) intermittency across scales in agreement with experimental observations. The model is a generative adversarial network (GAN) with multiple multiscale optimization criteria. First, we use three physics-based criteria: the variance, skewness and flatness of the increments of the generated field, that retrieve respectively the turbulent energy distribution, energy cascade and intermittency across scales. Second, the GAN criterion, based on reproducing statistical distributions, is used on segments of different length of the generated field. Furthermore, to mimic multiscale decompositions frequently used in turbulence’s studies, the model architecture is fully convolutional with kernel sizes varying along the multiple layers of the model. To train our model, we use turbulent velocity signals from grid turbulence at Modane wind tunnel.

Turbulence Intermittency Effects on the Initiation Threshold of Sediment Motion in Natural Waters

AbstractThe initiation threshold of sediment motion, a key component in quantifying sediment transport, has potential link to intermittent turbulence bursts. Here, we elaborated in situ experiments on coastal sea bottom covered with cohesive sediments, to obtain intermittency parameters. The variable “waiting time” between turbulence bursts was utilized to capture the occurrence of sediment initiation events in natural waters. A relationship between waiting time and shear stress reveals the different intermittency features of sediment flux time series before and after reaching the threshold, which can be used to determine the initiation threshold of sediment motion. Multi‐site results demonstrate the limitations of traditional empirical formulae for fine‐grained sediments, where cohesiveness becomes more pronounced as grain size decreases and the deviation can reach 600%. The empirical formula was modified using grain size, and the modified calculations were in good agreement with observed values, which will greatly assist in sediment transport and geomorphology model predictions.

Data-driven classification of sheared stratified turbulence from experimental shadowgraphs

We propose a dimensionality reduction and unsupervised clustering method for the automatic classification and reduced-order modeling of density-stratified turbulence in laboratory experiments. We apply this method to 113 long shadowgraph movies collected in a “stratified inclined duct” experiment, where turbulence is generated by instabilities arising from a sheared buoyancy-driven counterflow at Reynolds numbers Re≈300–5000, tilt angles θ=1∘–6∘, and Prandtl number Pr≈700. The method automatically detects edges representative of discrete density interfaces, extracts a low-dimensional vector of statistics representative of their morphology, projects these statistics onto a two-dimensional phase space of principal coordinates, and applies a clustering algorithm. Five clusters are detected and interpreted physically based on their typical interface morphology and an examination of representative frames, revealing distinct types of turbulence and mixing: laminarizing, braided, overturning, granular, and unstructured, as well as some intermediate types. The ratio of time spent in each cluster varies gradually across the (Re,θ) space. At intermediate values of Reθ, intermittent turbulence cycles between clusters in phase space and reveals at least two distinct routes to stratified turbulence. These insights demonstrate the potential of this method to reveal the underlying physics of complex turbulent systems from large experimental datasets. Published by the American Physical Society 2024

Verifying turbulence models in the atmospheric boundary layer by acoustical means

The impacts of turbulence on sound propagation in the atmospheric boundary layer (ABL) are important in many practical applications such as acoustic source localization, sonic boom propagation, and auralization of flying aircraft. However, turbulence impacts can vary dramatically in response to changing meteorological conditions. To address this issue, a turbulence spectral model has been introduced that captures the complexities of turbulence generation by wind shear, buoyancy instabilities, and ground blocking in the ABL. An experiment on vertical and slanted sound propagation through the ABL was conducted to verify the model. Various statistical characteristics of sound signals, including variances of the log-amplitude and phase fluctuations, coherences, and signal probability distributions were measured and compared to theoretical predictions. The dependence of the statistics on meteorological conditions was found to be accurately predicted in conditions of well-developed turbulence, as occurs in windy conditions and the daytime. Predictions in conditions of weak and intermittent turbulence, as often occur at night and around sunrise and sunset, remain challenging.

Exploring the Equivalence between Two-Dimensional Classical and Quantum Turbulence through Velocity Circulation Statistics.

We study the statistics of velocity circulation in two-dimensional classical and quantum turbulence. We perform numerical simulations of the incompressible Navier-Stokes and the Gross-Pitaevskii (GP) equations for the direct and inverse cascades. Our GP simulations display clear energy spectra compatible with the double cascade theory of two-dimensional classical turbulence. In the inverse cascade, we found that circulation intermittency in quantum turbulence is the same as in classical turbulence. We compare GP data to Navier-Stokes simulations and experimental data from Zhu etal. [Phys. Rev. Lett. 130, 214001 (2023)PRLTAO0031-900710.1103/PhysRevLett.130.214001]. In the direct cascade, for nearly incompressible GP flows, classical and quantum turbulence circulation displays the same self-similar scaling. When compressibility becomes important, quasishocks generate quantum vortices and the equivalence of quantum and classical turbulence only holds for low-order moments. Our results establish the boundaries of the equivalence between two-dimensional classical and quantum turbulence.

The Intermittency of Turbulence in Magneto-Hydrodynamical Simulations and in the Cosmos

Turbulent dissipation is a central issue in the star and galaxy formation process. Its fundamental property of space–time intermittency, well characterised in incompressible laboratory experiments, remains elusive in cosmic turbulence. Progress requires the combination of state-of-the-art modelling, numerical simulations and observations. The power of such a combination is illustrated here, where the statistical method intended to locate the extrema of velocity shears in a turbulent field, which are the signposts of intense dissipation extrema, is applied to numerical simulations of compressible magneto-hydrodynamical (MHD) turbulence dedicated to dissipation scales and to observations of a turbulent molecular cloud. We demonstrate that increments of several observables computed at the smallest lags can detect coherent structures of intense dissipation. We apply this statistical method to the observations of a turbulent molecular cloud close to the Sun in our galaxy and disclose a remarkable structure of extremely large velocity shear. At the location of the largest velocity shear, this structure is found to foster 10× more carbon monoxide molecules than standard diffuse molecular gas, an enrichment supported by models of non-equilibrium warm chemistry triggered by turbulent dissipation. In our simulations, we also compute structure functions of various synthetic observables and show that they verify Extended Self-Similarity. This allows us to compute their intermittency exponents, and we show how they constrain some properties of the underlying three-dimensional turbulence. The power of the combination of modelling and observations is also illustrated by the observations of the CH+ cation that provide unique quantitative information on the kinetic energy trail in the massive, multi-phase and turbulent circum-galactic medium of a galaxy group at redshift z=2.8.

Water Properties and Diffusive Convection in the Canada Basin

The aim of this study is to better understand diffusive convection (DC) and its role in the upper ocean dynamic environment and sea ice melting in the Canada Basin. Based on a moored dataset with 6737 profiles collected from August 2003 to August 2011 in the upper layer of the Canada Basin, DC between the warm and salty Atlantic Water (AW) and the colder and less salty Lower Halocline Water (LHW) were investigated. The moorings were designated at four stations: A, B, C, and D, located at the southwestern, southeastern, northeastern, and northwestern parts of the basin, respectively. During the observation period, the temperature, salinity, and depth of the AW and LHW exhibited unique temporal variations. The temperature and salinity of the AW varied among stations, with a decreasing trend from northwest to southeast, consistent with the propagation path of the AW in the Canada Basin. The temperature and salinity of the LHW were similar at all stations. The AW and LHW cores were located between depths of 320–500 m and 160–300 m, respectively, and both gradually deepened over time. Distinct DC staircase structures were observed between the AW and LHW, more pronounced at stations C and D than at stations A and B, which is speculated to be related to eddies at stations A and B during the observation period. The vertical heat fluxes through the DC staircase layer at stations C and D (FHc_C and FHc_D) were estimated using an empirical formula. FHc_C ranged from 0.05 to 0.94 W/m2, and FHc_D ranged from 0.05 to 0.6 W/m2, with the maximum probability value for both at approximately 0.2 W/m2. The effective diffusivities at these two stations (KT_C and KT_D) are similar, ranging from 2 × 10−6 to 3 × 10−5 m2/s, with the highest probability occurring at 6 × 10−6 m2/s. Both the probability density function of the heat flux and the effective diffusivity skewed towards larger values and obey a lognormal distribution, indicating turbulence intermittency of the DC staircase in the Canada Basin. These finding offers new insights into the heat transport and turbulence in the DC staircase, and then bring a deeper understanding of sea ice melting in the Canada Basin.

Characteristics of Turbulence Intermittency, Fine Structure, and Flux Correction in the Taklimakan Desert

Abstract The Taklimakan Desert is one of key climate regions in East Asia, both highly influencing and highly sensitive to local/regional climate change. Based on a comprehensive observation experiment from 1 to 31 May 2022 in the hinterland of the Taklimakan Desert, the characteristics and mechanisms of turbulence intermittency are investigated in this study, with the purpose to correct turbulent fluxes. Using an improved algorithm to decompose turbulence and submeso motions, two intermittency regimes are recognized in the Taklimakan Desert, namely, D and T intermittency and onD intermittency. The former occurs under strongly stable conditions, characterized by the coexistence of dynamic and thermodynamic turbulence intermittency. The latter occurs under strongly unstable conditions and represents only dynamic turbulence intermittency. Physically, the D and T intermittency regime is related to submeso waves, whereas the onD regime is caused by the horizontal convergence/divergence of convective circulations. With the influence of intermittency and submeso motions, the observed turbulent statistics deviate from reality, which would mask the similarity relationships. To overcome the problem, turbulent statistics are corrected by removing submeso components from original fluctuations. The effectiveness of this method is demonstrated based on the flux–gradient relationships. It is also suggested that, for a big dataset, the impact of onD intermittency can be simply corrected by a correction factor while that of D and T intermittency cannot. The results of this study are helpful to develop the parameterization of turbulent exchange processes in the Taklimakan Desert, which is significant to improve the accuracy of weather forecasting and climate prediction. Significance Statement The Taklimakan Desert plays an important role in the evolution of weather and climate in East Asia. With strong surface thermal forcing, turbulence often shows distinctive intermittency, which largely constrains the evaluation of land–atmosphere exchange in this key climate region. This study aims to understand the characteristics of turbulence intermittency and its physical mechanisms, and further to correct the influence of turbulence intermittency on turbulent fluxes in the Taklimakan Desert. This is significant because the results are helpful to improve the parameterization of subgrid processes in the key climate region for atmospheric models, which points the way toward enhancing the accuracy of weather forecasting and climate prediction.

Low-dimensional representation of intermittent geophysical turbulence with high-order statistics-informed neural networks (H-SiNN)

We present a novel machine learning approach to reduce the dimensionality of state variables in stratified turbulent flows governed by the Navier–Stokes equations in the Boussinesq approximation. The aim of the new method is to perform an accurate reconstruction of the temperature and the three-dimensional velocity of geophysical turbulent flows developing non-homogeneities, starting from a low-dimensional representation in latent space, yet conserving important information about non-Gaussian structures captured by high-order moments of distributions. To achieve this goal, we modify the standard convolutional autoencoder (CAE) by implementing a customized loss function that enforces the accuracy of the reconstructed high-order statistical moments. We present results for compression coefficients up to 16, demonstrating how the proposed method is more efficient than a standard CAE in performing dimensionality reduction of simulations of stratified geophysical flows characterized by intermittent phenomena, as observed in the atmosphere and the oceans.

Intermittent plasma turbulence in the Martian plasma environment

Intermittent plasma turbulence in the Martian plasma environment

Sensitivity of the polar boundary layer to transient phenomena

Abstract. Numerical weather prediction and climate models encounter challenges in accurately representing flow regimes in the stably stratified atmospheric boundary layer and the transitions between them, leading to an inadequate depiction of regime occupation statistics. As a consequence, existing models exhibit significant biases in near-surface temperatures at high latitudes. To explore inherent uncertainties in modeling regime transitions, the response of the near-surface temperature inversion to transient small-scale phenomena is analyzed based on a stochastic modeling approach. A sensitivity analysis is conducted by augmenting a conceptual model for near-surface temperature inversions with randomizations that account for different types of model uncertainty. The stochastic conceptual model serves as a tool to systematically investigate which types of unsteady flow features may trigger abrupt transitions in the mean boundary layer state. The findings show that the incorporation of enhanced mixing, a common practice in numerical weather prediction models, blurs the two regime characteristic of the stably stratified atmospheric boundary layer. Simulating intermittent turbulence is shown to provide a potential workaround for this issue. Including key uncertainty in models could lead to a better statistical representation of the regimes in long-term climate simulation. This would help to improve our understanding and the forecasting of climate change in high-latitude regions.

A Study of Intermittent Turbulence in Stable Arctic Boundary Layers

Polar boundary layers are difficult to model due to the existence of intermittent turbulence within stable layers. Here we present a case study evaluation of coherent structures in a stable boundary layer observed during a series of flights with an uncrewed aircraft system (DataHawk2) on 19 October 2016 at Oliktok Point, Alaska as part of the ERASMUS (Evaluation of Routine Atmospheric Sounding Measurements using Unmanned Systems) field campaign. During a sequence of five flights over a nine-hour period, 57 profiles of atmospheric properties (0–400 m a.g.l) were collected. Turbulence was identified using derived Richardson Number, temperature structure function parameter, and turbulence kinetic energy dissipation. Throughout all flights on this strongly stable day, intermittent turbulence was observed. These turbulent layers showed well-mixed potential temperature profiles embedded within otherwise stable potential temperature profiles; often resulting in a characteristic staircase pattern. Turbulent layers ranged from 1 to 30 m deep, with most individual layers being 1–2 m deep. Vertical propagation velocities of layers in the lower atmosphere were on the order of a few cm s−1, typical of non-convective environments. In different regions of the profile, turbulence was driven by a different balance of buoyancy and shear forces, with turbulence in the near surface environment driven by strong shear forces overcoming strong resistance to buoyancy, while turbulence in elevated layers characterized by weaker shear forces overcoming weaker resistance to buoyancy. We discuss the potential of such datasets for improving subgrid parameterizations of small-scale turbulence embedded within stable boundary layers.

Stratified inclined duct: two-layer hydraulics and instabilities

The stratified inclined duct (SID) sustains an exchange flow in a long, gently sloping duct as a model for continuously forced density-stratified flows such as those found in estuaries. Experiments have shown that the emergence of interfacial waves and their transition to turbulence as the tilt angle is increased appears to be linked to a threshold in the exchange flow rate given by inviscid two-layer hydraulics. We uncover these hydraulic mechanisms by (i) using recent direct numerical simulations (DNS) providing full flow data in the key flow regimes (Zhu et al., J. Fluid Mech., vol. 969, 2023, A20), (ii) averaging these DNS into two layers, and (iii) using an inviscid two-layer shallow-water and instability theory to diagnose interfacial wave behaviour and provide physical insight. The laminar flow is subcritical and stable throughout the duct and hydraulically controlled at the ends of the duct. As the tilt is increased, the flow becomes supercritical everywhere and unstable to long waves. An internal jump featuring stationary waves first appears near the centre of the duct, then leads to larger-amplitude travelling waves, and to stronger jumps, wave breaking and intermittent turbulence at the largest tilt angle. Long waves described by the (nonlinear) shallow-water equation are interpreted locally as linear waves on a two-layer parallel base flow described by the Taylor–Goldstein equation. This link helps us to interpret long-wave instability and contrast it with short-wave (e.g. Kelvin–Helmholtz) instability. Our results suggest a transition to turbulence in SID through long-wave instability relying on vertical confinement by the top and bottom walls.