Are atmospheric surface layer f lows ergodic?

The transposition of atmospheric turbulence statistics from the time domain, as conventionally sampled in field experiments, is explained by the so-called ergodic hypothesis. In micrometeorology, this hypothesis assumes that the time average of a measured flow variable represents an ensemble of independent realizations from similar meteorological states and boundary conditions. That is, the averaging duration must be sufficiently long to include a large number of independent realizations of the sampled flow variable so as to represent the ensemble. While the validity of the ergodic hypothesis for turbulence has been confirmed in laboratory experiments, and numerical simulations for idealized conditions, evidence for its validity in the atmospheric surface layer (ASL), especially for nonideal conditions, continues to defy experimental efforts. There is some urgency to make progress on this problem given the proliferation of tall tower scalar concentration networks aimed at constraining climate models yet are impacted by nonideal conditions at the land surface. Recent advancements in water vapor concentration lidar measurements that simultaneously sample spatial and temporal series in the ASL are used to investigate the validity of the ergodic hypothesis for the first time. It is shown that ergodicity is valid in a strict sense above uniform surfaces away from abrupt surface transitions. Surprisingly, ergodicity may be used to infer the ensemble concentration statistics of a composite grass-lake system using only water vapor concentration measurements collected above the sharp transition delineating the lake from the grass surface. Citation: Higgins, C. W., G. G. Katul, M. Froidevaux, V. Simeonov, and M. B. Parlange (2013), Are atmospheric surface layer flows ergodic?, Geophys. Res. Lett., 40, 3342–3346, doi:10.1002/grl.50642

Flume experiments on intermittency and zero-crossing properties of canopy turbulence

How the presence of a canopy alters the clustering and the fine scale intermittency exponents and any possible connections between them remains a vexing research problem in canopy turbulence. To begin progress on this problem, detailed flume experiments in which the longitudinal and vertical velocity time series were acquired using laser Doppler anemometry within and above a uniform canopy composed of densely arrayed rods. The time series analysis made use of the telegraphic approximation (TA) and phase-randomization (PR) methods. The TA preserved the so-called zero-crossing properties in the original turbulent velocity time series but eliminated amplitude variations, while the PR generated surrogate data that preserved the spectral scaling laws in the velocity series but randomized the acceleration statistics. Based on these experiments, it was shown that the variations in the dissipation intermittency exponents were well described by the Taylor microscale Reynolds number (Reλ) within and above the canopy. In terms of clustering, quantified here using the variance in zero-crossing density across scales, two scaling regimes emerged. For spatial scales much larger than the canopy height hc, representing the canonical scale of the vortices dominating the flow, no significant clustering was detected. For spatial scales much smaller than hc, significant clustering was discernable and follows an extensive scaling law inside the canopy. Moreover, the canopy signatures on the clustering scaling laws were weak. When repeating these clustering measures on the PR data, the results were indistinguishable from the original series. Hence, clustering exponents derived from variances in zero-crossing density across scales primarily depended on the velocity correlation function and not on the distributional properties of the acceleration. In terms of the connection between dissipation intermittency and clustering exponents, there was no significant relationship. While the former varied significantly with Reλ, the latter showed only minor variations within and above the canopy sublayer

Roughness effects on fine-scale anisotropy and anomalous scaling in atmospheric flows

The effects of surface roughness on various measures of fine-scale intermittency within the inertial subrange were analyzed using two data sets that span the roughness "extremes" encountered in atmospheric flows, an ice sheet and a tall rough forest, and supplemented by a large number of existing literature data. Three inter-related problems pertaining to surface roughness effects on (i) anomalous scaling in higher-order structure functions, (ii) generalized dimensions and singularity spectra of the componentwise turbulent kinetic energy, and (iii) scalewise measures such local flatness factors and stretching exponents were addressed. It was demonstrated that surface roughness effects do not impact the fine-scale intermittency in u (the longitudinal velocity component), consistent with previous laboratory experiments. However, fine-scale intermittency in w (the vertical velocity component) increased with decreasing roughness. The consequence of this external intermittency (i.e., surface roughness induced) is that the singularity spectra of the scaling exponents are much broader for w when compared u in the context of the multifractal formalism for the local kinetic energy (instead of the usual conservative cascade studied for the dissipation rate). The scalewise evolution of the flatness factors and stretching exponents collapse when normalized using a global Reynolds number Rt = σLI/ν, where σ is the velocity standard deviation, LI is the integral length scale, and ν is the fluid viscosit

Mechanistic analytical models for long-distance seed dispersal by wind

We introduce an analytical model, the Wald analytical long-distance dispersal (WALD) model, for estimating dispersal kernels of wind-dispersed seeds and their escape probability from the canopy. The model is based on simplifications to well-established three-dimensional Lagrangian stochastic approaches for turbulent scalar transport resulting in a two-parameterWald (or inverse Gaussian) distribution. Unlike commonly used phenomenological models, WALD's parameters can be estimated from the key factors affecting wind dispersal - wind statistics, seed release height, and seed terminal velocity - determined independently of dispersal data. WALD's asymptotic power-law tail has an exponent of -3/2, a limiting value verified by a meta-analysis for a wide variety of measured dispersal kernels and larger than the exponent of the bivariate Student t-test (2Dt). We tested WALD using three dispersal data sets on forest trees, heathland shrubs, and grassland forbs and compared WALD's performance with that of other analytical mechanistic models (revised versions of the tilted Gaussian Plume model and the advection-diffusion equation), revealing fairest agreement between WALD predictions and measurements. Analytical mechanistic models, such as WALD, combine the advantages of simplicity and mechanistic understanding and are valuable tools for modeling large-scale, long-term plant population dynamics

A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes

Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO2 (FCO2) represent the "true" flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include "tall tower" instrumentation, one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions. We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO2, for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with Rn (H and LE) and PPFD (FCO2). For FCO2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.Engineering and Applied Science

Linking meteorology, turbulence, and air chemistry in the amazon rain forest

A field campaign reveals that the Amazon rain forest produces enough chemical species to undergo oxidation and generate aerosols, which can activate into cloud condensation nuclei and potentially influence cloud formation. © 2016 American Meteorological Society

Seasonality Of Ecosystem Respiration And Gross Primary Production As Derived From Fluxnet Measurements

Differences in the seasonal pattern of assimilatory and respiratory processes are responsible for divergences in seasonal net carbon exchange among ecosystems. Using FLUXNET data (http://www.eosdis.ornl.gov/FLUXNET) we have analyzed seasonal patterns of gross primary productivity (FGPP), and ecosystem respiration (FRE) of boreal and temperate, deciduous and coniferous forests, Mediterranean evergreen systems, a rainforest, temperate grasslands, and C3 and C4 crops. Based on generalized seasonal patterns classifications of ecosystems into vegetation functional types can be evaluated for use in global productivity and climate change models. The results of this study contribute to our understanding of respiratory costs of assimilated carbon in various ecosystems. Seasonal variability of FGPP and FRE of the investigated sites increased in the order tropical \u3c Mediterranean \u3c temperate coniferous \u3c temperate deciduous \u3c boreal forests. Together with the boreal forest sites, the managed grasslands and crops show the largest seasonal variability. In the temperate coniferous forests, seasonal patterns of FGPP and FRE are in phase, in the temperate deciduous and boreal coniferous forests FRE was delayed compared to FGPP, resulting in the greatest imbalance between respiratory and assimilatory fluxes early in the growing season. FGPP adjusted for the length of the carbon uptake period decreased at the sampling sites across functional types in the order C4 crops, temperate and boreal deciduous forests (7.5–8.3 g Cm−2 per day) \u3e temperate conifers, C3 grassland and crops (5.7–6.9g Cm−2 per day) \u3e boreal conifers (4.6 g Cm−2 per day). Annual FGPP and net ecosystem productivity (FNEP) decreased across climate zones in the order tropical \u3e temperate \u3e boreal. However, the decrease in FNEP with latitude was greater than the decrease in FGPP, indicating a larger contribution of respiratory (especially heterotrophic) processes in boreal systems