Departure from Flux-Gradient Relation in the Planetary Boundary Layer

It is well known that when eddies are small, the eddy fluxes can be directly related to the mean vertical gradients, the so-called flux-gradient relation, but such a relation becomes weaker the larger the coherent structures. Here, we show that this relation does not hold at heights relevant for wind energy applications. The flux–gradient relation assumes that the angle (β) between the vector of vertical flux of horizontal momentum and the vector of the mean vertical gradient of horizontal velocity is zero, i.e., these vectors are aligned. Our observations do not support this assumption, either onshore or offshore. Here, we present analyses of a misalignment between these vectors from a Doppler wind lidar observations and large-eddy simulations. We also use a real-time mesoscale model output for inter-comparison with the lidar-observed vertical profiles of wind speed, wind direction, momentum fluxes, and the angle between the horizontal velocity vector and the momentum flux vector up to 500 m, both offshore and onshore. The observations show this within the height range 100–500 m, β=−18∘ offshore and β=−12∘ onshore, on average. However, the large-eddy simulations show β≈0∘ both offshore and onshore. We show that observed and mesoscale-simulated vertical profiles of mean wind speed and momentum fluxes agree well; however, the mesoscale results significantly deviate from the wind-turning observations.

A data-driven stochastic parametrisation of intermittent turbulence in the stably stratified atmospheric boundary layer

<p>We present results on the modelling of intermittent turbulence in the nocturnal boundary layer using a data-driven approach. In conditions of high stratification and weak wind, the bulk shear can be too weak to sustain continuous turbulence, and the sporadic submeso motions play an important role for the turbulence production. We show a way to stochastically parametrise the effect of the unresolved submeso scales and include it into a 1.5-order turbulence closure scheme. This is achieved by introducing a stochastic equation, which describes the evolution of the non-dimensional flux-gradient stability correction for momentum ($\phi_m$). The unperturbed equilibrium solution of the equation follows the functional form of the universal similarity function. The stochastic perturbations reflect the instantaneous excursions from its equilibrium state, and the distribution of values covers the scatter found in observations at high stability.</p><p>The non-stationary parameters of this equations are estimated from a time-series data of the FLOSS2 experiment using a model-based clustering approach. The clustering analysis of the parameters shows a scaling relationship with the local gradient Ri number, leading to a suggested closed-form model for the stochastic flux-gradient stability correction. The spatial correlation in height of the perturbations is included in the model as well. The resulting equation captures the transition of the stability correction across and beyond the critical Ri up to a value of 10. The out-of-sample prediction shows a valid transient dynamics into and within the regime of strongly-stable stratification.</p>

Legacy effects of multiyear summer drought on soil CO2 production, transport and efflux in a sub-alpine grassland

<p>Climate change is expected to lead to an increase in frequency and severity of extreme climatic events like summer drought. Drought and rewetting have strong impacts on soil respiration, which constitutes the largest flux of CO<sub>2</sub> from terrestrial ecosystems to the atmosphere. However, little is known about the role of biotic and abiotic factors in driving CO<sub>2</sub> production and transport across the soil profile and how these processes are affected by repeated drought events. Soil CO<sub>2</sub> transport can be assessed using the flux-gradient approach, a method which assumes that diffusion is the only transport mechanism for CO<sub>2</sub> through soil, with diffusion rates primarily dependent on air-filled pore space. It is therefore generally assumed that the calculated soil CO<sub>2</sub> concentration gradient translates directly into soil CO<sub>2</sub> efflux, however, a discrepancy between measured soil CO<sub>2</sub> efflux and modeled soil CO<sub>2</sub> concentration gradients can indicate presence of non-diffusive transport mechanisms.</p><p>In a multiyear drought and rewetting experiment at a mountain meadow in the Austrian Alps, we compared soil CO<sub>2</sub> production, transport and efflux for plots which were exposed to two and twelve subsequent years of experimental summer drought, respectively, versus plots with ambient precipitation and soil moisture. We measured soil respiration using automated chambers and assessed the production and transport of CO<sub>2</sub> using the flux-gradient approach on data obtained with solid-state sensors in three soil depths through the soil profile. We tested the hypothesis that drought-driven reduction in soil respiration will be more intense for the 12-year drought treatment, but the CO<sub>2</sub> pulse induced by rewetting will be higher. We furthermore expected that non-diffusive transport mechanisms would play a crucial role during drought and would be more pronounced in the 12-year drought treatment compared to the 2-year drought treatment. Data analysis is currently in progress, the findings will be presented at the conference.</p>

Wind Speed Parameterization and Turbulence Intermittency in the Roughness Sublayers over Urban Areas: a Wind Tunnel Study

<p>The flow in inertial sublayer (ISL) is horizontally homogeneous where the Monin–Obukhov similarity theory (MOST) well describes the flux-gradient relationship.  In contrast, roughness sublayer (RSL) flow is highly inhomogeneous. Its dynamics is influenced by the length scale of individual roughness elements. This study presents an analytical solution to the mean wind profile for both ISL and RSL by adding a new function in the flux-gradient relationship to handle the RSL dynamics. The mean wind speeds measured in the wind tunnel experiments over a range of idealized and real urban geometries are well predicted by the new analytical solution. The root-mean-square errors (RMSE) are reduced over an order of magnitude compared with the conventional logarithmic law of the wall (log-law). Its key parameter, the RSL constant converges asymptotically to μ = 1.7 for urban setting which is different from that (μ = 2.6) for vegetation canopy. The RSL turbulence intermittency is revealed by higher-order moments of velocities, probability density function (PDF), quadrant analysis, and conditional sampling. Ejection Q2 (-u’’, +w”) and sweep Q4 (+u’’, -w”) dominate in both RSL and ISL but with different share. Unlike the ISL, Q2 occurs more frequently (but contributes less to momentum flux) than Q4 in the RSL. It is thus suggested that RSL turbulent transport is driven by occasional, fast motions of accelerating downward flow (Q4) and bulk, slow decelerating upward flow (Q2).</p>

Calculating canopy stomatal conductance from eddy covariance measurements, in light of the energy budget closure problem

Canopy stomatal conductance is commonly estimated from eddy covariance measurements of the latent heat flux (LE) by inverting the Penman–Monteith equation. That method ignores eddy covariance measurements of the sensible heat flux (H) and instead calculates H implicitly as the residual of all other terms in the site energy budget. Here we show that canopy stomatal conductance is more accurately calculated from eddy covariance (EC) measurements of both H and LE using the flux–gradient equations that define conductance and underlie the Penman–Monteith equation, especially when the site energy budget fails to close due to pervasive biases in the eddy fluxes and/or the available energy. The flux–gradient formulation dispenses with unnecessary assumptions, is conceptually simpler, and is as or more accurate in all plausible scenarios. The inverted Penman–Monteith equation, on the other hand, contributes substantial biases and erroneous spatial and temporal patterns to canopy stomatal conductance, skewing its relationships with drivers such as light and vapor pressure deficit.