Classification and properties of coastal wind profiles with negative gradients – an observational study

Wind profiles with a negative gradient are frequently occurring over the Baltic Sea and are important to take into consideration for offshore wind power as they affect not only the power production, but also the loads on the structure and the behavior of the wake behind the turbine. In this study, we classified non-normal profiles as wind profiles having negative shear in at least one part of the profile between 28 and 300 m: low-level jets (with a local wind maximum in the profile), profiles with a local minimum, and negative profiles. Using observations spanning over 3 years, we show that the non-normal wind profiles are common over the Baltic Sea in late spring and summer, with a peak of 40 % relative occurrence in May. Negative profiles (in the 28–300 m layer) were mostly occurring during unstable conditions, in contrast to low-level jets that primarily occurred in stable stratification. There were indications that the the zone with strong shear during low-level jets could cause a relative suppression of the variance for large turbulent eddies compared to the peak of the velocity spectra, in the layer below the jet core. Swell conditions were found to be favourable for the occurrence of negative profiles and profiles with a local minimum, as the waves fed energy into the surface layer, resulting in an increase of the wind speed from below.

Variable energy flux in turbulence

In three-dimensional hydrodynamic turbulence forced at large length scales, a constant energy flux $ \Pi_u $ flows from large scales to intermediate scales, and then to small scales. It is well known that for multiscale energy injection and dissipation, the energy flux $\Pi_u$ varies with scales. In this review we describe this principle and show how this general framework is useful for describing a variety of turbulent phenomena. Compared to Kolmogorov's spectrum, the energy spectrum steepens in turbulence involving quasi-static magnetofluid, Ekman friction, stable stratification, magnetohydrodynamics, and solution with dilute polymer. However, in turbulent thermal convection, in unstably stratified turbulence such as Rayleigh-Taylor turbulence, and in shear turbulence, the energy spectrum has an opposite behaviour due to an increase of energy flux with wavenumber. In addition, we briefly describe the role of variable energy flux in quantum turbulence, in binary-fluid turbulence including time-dependent Landau-Ginzburg and Cahn-Hillianrd equations, and in Euler turbulence. We also discuss energy transfers in anisotropic turbulence.

Investigation of Air-Sea Turbulent Momentum Flux over the Aegean Sea with a Wind-Wave Coupling Model

Near surface turbulent momentum flux estimates are performed over the Aegean Sea, using two different approaches regarding the drag coefficient formulation, a wave boundary layer model (referred here as KCM) and the most commonly used Coupled Ocean–Atmosphere Response Experiment (COARE) algorithm. The KCM model incorporates modifications in the energy-containing wave spectrum to account for the wave conditions of the Aegean Sea, and surface similarity to account for the stratification effects. Airborne turbulence data during an Etesian outbreak over Aegean Sea, Greece are processed to evaluate the simulations. KCM estimates found up to 10% higher than COARE ones, indicating that the wave-induced momentum flux may be insufficiently parameterized in COARE. Turbulent fluxes measured at about 150 m, and reduced to their surface values accounting for the vertical flux divergence, are consistently lower than the estimates. Under unstable atmospheric stratification and low to moderate wind conditions, the residuals between estimates and measurements are less than 40%. On the other hand, under stable stratification and strong winds, the majority of the residuals are more than 40%. This discrepancy is associated with the relatively high measurement level, shallow boundary layer, and the presence of a low level jet.

Process drivers, inter-model spread, and the path forward: A review of amplified Arctic warming

Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow-ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This manuscript aims to build such understanding by synthesizing current knowledge of AA and to produce a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions, specifically the water vapor triple effect, within the context of the annual cycle to constrain projected AA. We recommend raising the priority of Arctic climate sensitivity research, improving the accuracy of Arctic surface energy budget observations, rethinking climate feedback definitions, coordinating new model experiments and intercomparisons, and pursuing the role of episodic variability in AA as a research focus area.

Nondimensionalization of the Atmospheric Boundary-Layer System: Obukhov Length and Monin–Obukhov Similarity Theory

The Obukhov length, although often adopted as a characteristic scale of the atmospheric boundary layer, has been introduced purely based on a dimensional argument without a deductive derivation from the governing equations. Here, its derivation is pursued by the nondimensionalization method in the same manner as for the Rossby deformation radius and the Ekman-layer depth. Physical implications of the Obukhov length are inferred by nondimensionalizing the turbulence-kinetic-energy equation for the horizontally homogeneous boundary layer. A nondimensionalization length scale for a full set of equations for boundary-layer flow formally reduces to the Obukhov length by dividing this scale by a rescaling factor. This rescaling factor increases with increasing stable stratification of the boundary layer, in which flows tend to be more horizontal and gentler; thus the Obukhov length increasingly loses its relevance. A heuristic, but deductive, derivation of Monin–Obukhov similarity theory is also outlined based on the obtained nondimensionalization results.

Numerical Analysis of a Hybrid Stochastic Turbulence Model for Stable Stratification

<p>We present results on the modeling of intermittent turbulence in the nocturnal boundary<br>layer using a data-driven approach. In high stratification and weak wind conditions, the<br>bulk shear can be too weak to sustain continuous turbulence and the sporadic submesoscale<br>motions trigger the turbulence production.<br>The main idea is to extend a TKE-based, 1.5 order turbulence closure model by in-<br>troducing a stochastic differential equation (SDE) for the nondimensional correction of the<br>mixing length. Such a nonstationary SDE model is built upon the traditional surface-layer<br>scaling functions, which model the effect of the static stability on the surface-layer profiles<br>using scaling with the Richardson (Ri) number . The nonstationary parameters of the SDE<br>equation are determined from data with a model-based clustering approach. Furthermore, it<br>is found that parameters scale with the local gradient Ri number, resulting in a closed-form<br>nonstationary stability correction depending only on this local gradient Ri number. Benefi-<br>cial is the interpretation of the noise term of the SDE. This term is interpreted as an effect<br>of the submesoscale motions on turbulent mixing. Furthermore, the SDE model provides<br>a conceptual view on intermittent turbulence, whereby in the noise-free limit, the steady-<br>state solution converges to the traditional functional scaling. Per construction, the SDE<br>is readily incorporated in a turbulence closure by modifying the definition of the stability<br>correction. Details will be provided.<br>We will present a numerical analysis of such a hybrid model for quasi-steady-state so-<br>lutions with different model settings. Furthermore, we investigate the regime transitions<br>between weakly and strongly stable flows under intermittent mixing based on the temper-<br>ature inversion characteristics.</p>