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Eddy diffusivity models are a common method to parameterize turbulent fluxes in the atmospheric sciences community. However, their inability to handle convective boundary layers leads to the addition of a nondiffusive flux component (usually called nonlocal) alongside the original diffusive term (usually called local). Both components are often modeled for convective conditions based on the shape of the eddy diffusivity profile for neutral conditions. This assumption of shape is traditionally employed due to the difficulty of estimating both components based on numerically simulated turbulent fluxes without any a priori assumptions. In this manuscript we propose a novel method to avoid this issue and estimate both components from numerical simulations without having to assume any a priori shape or scaling for either. Our approach is based on optimizing results from a modeling perspective and taking as much advantage as possible from the diffusive term, thus maximizing the eddy diffusivity. We use our method to diagnostically investigate four different large-eddy simulations spanning different stability regimes, which reveal that nondiffusive fluxes are important even when trying to minimize them. Furthermore, the calculated profiles for both diffusive and nondiffusive fluxes suggest that their shapes change with stability, which is an effect that is not included in most models currently in use. Finally, we use our results to discuss modeling approaches and identify opportunities for improving current models.

Observational data from two field campaigns in the Amazon forest were used to study the vertical structure of turbulence above the forest. The analysis was performed using the reduced turbulent kinetic energy (TKE) budget and its associated two-dimensional phase space. Results revealed the existence of two regions within the roughness sublayer in which the TKE budget cannot be explained by the canonical flat-terrain TKE budgets in the canopy roughness sublayer or in the lower portion of the convective ABL. Data analysis also suggested that deviations from horizontal homogeneity have a large contribution to the TKE budget. Results from LES of a model canopy over idealized topography presented similar features, leading to the conclusion that flow distortions caused by topography are responsible for the observed features in the TKE budget. These results support the conclusion that the boundary layer above the Amazon forest is strongly impacted by the gentle topography underneath.

The low-wavenumber regime of the spectrum of turbulence commensurate with Townsend’s “attached” eddies is investigated here for the near-neutral atmospheric surface layer (ASL) and the roughness sublayer (RSL) above vegetation canopies. The central thesis corroborates the significance of the imbalance between local production and dissipation of turbulence kinetic energy (TKE) and canopy shear in challenging the classical distance-from-the-wall scaling of canonical turbulent boundary layers. Using five experimental datasets (two vegetation canopy RSL flows, two ASL flows, and one open-channel experiment), this paper explores (i) the existence of a low-wavenumber k−1 scaling law in the (wind) velocity spectra or, equivalently, a logarithmic scaling ln(r) in the velocity structure functions; (ii) phenomenological aspects of these anisotropic scales as a departure from homogeneous and isotropic scales; and (iii) the collapse of experimental data when plotted with different similarity coordinates. The results show that the extent of the k−1 and/or ln(r) scaling for the longitudinal velocity is shorter in the RSL above canopies than in the ASL because of smaller scale separation in the former. Conversely, these scaling laws are absent in the vertical velocity spectra except at large distances from the wall. The analysis reveals that the statistics of the velocity differences Δu and Δw approach a Gaussian-like behavior at large scales and that these eddies are responsible for momentum/energy production corroborated by large positive (negative) excursions in Δu accompanied by negative (positive) ones in Δw. A length scale based on TKE dissipation collapses the velocity structure functions at different heights better than the inertial length scale.

Scaling laws for the longitudinal structure function in the atmospheric surface layer (ASL) are studied using dimensional analysis and matched asymptotics. Theoretical predictions show that the logarithmic scaling for the scales larger than those of the inertial subrange recently proposed for neutral wall-bounded flows also holds for the shear-dominated ASL composed of weakly unstable, neutral, and all stable conditions (as long as continuous turbulence exists). A 2/3 power law is obtained for buoyancy-dominated ASLs. Data from the Advection Horizontal Array Turbulence Study (AHATS) field experiment confirm these scalings, and they also show that the length scale formed by the friction velocity and the turbulent kinetic energy dissipation rate consistently outperforms the distance from the ground z as the relevant scale in all cases regardless of stability. With this new length scale, the production range of the longitudinal structure function collapses for all measurement heights and stability conditions. A new variable to characterize atmospheric stability emerges from the theory: namely, the ratio between the buoyancy flux and the TKE dissipation rate.

A numerical study of the effect of entrainment fluxes at the top of the atmospheric boundary layer (ABL) on dissimilarity between scalars within the mixed and surface layers is conducted. Simulation results clearly show that entrainment fluxes of opposite sign cause decorrelation between the scalars throughout the entire ABL. In the upper part of the mixed layer, this decorrelation is caused by changes in the covariance between the scalars and the scalar variance as well, and is distributed over the entire range of scales resolved in the simulation. Near the surface, the reduction in correlation coefficient originates from an increasing scalar variance, which is present exclusively in the large scales. These effects are noticeable on time scales of about 24 min or longer, and could be interpreted as nonstationarity for the typical 30-min periods used in surface-layer data processing. In addition, it is shown that, for the conditions studied here, the scalar correlation coefficient within the surface layer scales with the measurement height normalized by the ABL depth and not by the Obukhov length.

An error propagation analysis is conducted to estimate random errors in the friction velocity u* and the Monin–Obukhov similarity theory (MOST) stability variable z/L from estimated random errors in the turbulent fluxes. Errors in the dimensionless mean wind shear φm and mean temperature gradient φh are also estimated. To the authors’ knowledge, this is the first time that errors in calculated values of z/L, φm, and φh have been systematically analyzed. Random errors in z/L are found to be large for unstable conditions, reaching values of 40% or greater. It is shown through statistical hypothesis tests that random errors cannot explain departures of calculated values of φm and φh from theory. The deviation of calculated values of φm from empirical curves is found to have a strong diurnal variation that increases with height; deviations of φh from theory are not found to have clear diurnal variation. These results support the findings of previous studies, which have suggested that additional dimensionless parameters representing additional physical processes need to be included in the set of governing parameters for surface layer similarity. Implications for atmospheric surface layer turbulence are also discussed.

Intermittent transitions between turbulent and nonturbulent states are ubiquitous in the stable atmospheric surface layer (ASL). Data from two field experiments in Utqiaġvik, Alaska, and from direct numerical simulations are used to probe these state transitions so as to (i) identify statistical metrics for the detection of intermittency, (ii) probe the physical origin of turbulent bursts, and (iii) quantify intermittency effects on overall fluxes and their representation in closure models. The analyses reveal three turbulence regimes, two of which correspond to weakly turbulent periods accompanied by intermittent behavior (regime 1: intermittent; regime 2: transitional), while the third is associated with a fully turbulent flow. Based on time series of the turbulence kinetic energy (TKE), two nondimensional parameters are proposed to diagnostically categorize the ASL state into these regimes; the first characterizes the weakest turbulence state, while the second describes the range of turbulence variability. The origins of intermittent turbulence activity are then investigated based on the TKE budget over the identified bursts. While the quantitative results depend on the height, the analyses indicate that these bursts are predominantly advected by the mean flow, produced locally by mechanical shear, or lofted from lower levels by turbulent ejections. Finally, a new flux model is proposed using the vertical velocity variance in combination with different mixing length scales. The model provides improved representation (correlation coefficients with observations of 0.61 for sensible heat and 0.94 for momentum) compared to Monin–Obukhov similarity (correlation coefficients of 0.0047 for sensible heat and 0.49 for momentum), thus opening new pathways for improved parameterizations in coarse atmospheric models. Significance Statement Airflow in the lowest layer of the atmosphere is often modulated by a strong gradient of temperature when the surface is much cooler than the air. Such a regime results in weak turbulence and mixing, and is ubiquitous during nighttime and in polar regions. Understanding and modeling atmospheric flow and turbulence under such conditions are further complicated by “turbulence intermittency,” which manifests as periods of strong turbulent activity interspersed in a more quiescent airflow. The turbulent periods dominate the air–surface exchanges even when they occur over a small fraction of the time. This paper develops approaches to detect and classify such intermittent regimes, examines how the turbulent bursts are generated and advected, and offers guidance on representing such regimes in geophysical models. The findings have the potential to advance weather forecasting and climate modeling, particularly in the all-important polar regions.