External controls on the transition between stable boundary‐layer turbulence regimes

Abstract

The mean wind speed at which the stable boundary layer (SBL) experiences a turbulence regime transition (Vr) depends on other flow characteristics, such as its thermal stability. Here, Vr variability is examined both at a single site and across sites using three multiple‐level datasets: Santa Maria, Cooperative Atmosphere–Surface Exchange Study–1999 (CASES‐99), and Fluxes over Snow Surfaces (FLOSS II). A method to determine Vr is introduced and validated. It is taken as the mean wind speed at which the vertical gradient of the turbulence velocity scale switches sign. Emphasis is given to the control exerted on Vr by quantities that are external to the SBL, such as radiation, roughness, and soil properties. In each of the experiments, Vr increases with net radiative loss at the surface (Rn) at a rate that is site‐dependent. It also increases for smaller roughness lengths, as indicated by its wind direction dependence. No conclusive relationship has been found between Vr and downward longwave radiation observed at the surface. The across‐site comparison indicates that soil heat capacity influences the rate at which Vr increases with Rn.

Simulating the Unsteady Stable Boundary Layer With a Stochastic Stability Equation

“…The net radiation of −30 W m −2 is selected following Acevedo et al. (2021) and considered as the FLOSS2 data set average value. This setup may describe a typical cloud‐free night in springtime.…”

“…In this experiment, the focus lies on computing the transitions between weakly and strongly SBL, as in Acevedo et al. (2021) and Maroneze et al. (2019).…”

“…The stratification is imposed with two mechanisms, the first being the difference between the restoring (soil) temperature of 290 K and the potential temperature of the air 300 K, and the second being a radiative cooling enhancing the stratification. The net radiation of 30 W m 2 is selected following Acevedo et al (2021) and considered as the FLOSS2 data set average value. This setup may describe a typical cloud-free night in springtime.…”

“…The initial conditions are given in Section 3, and the simulation period is set to 30 hr, which is longer than the average nighttime. In this experiment, the focus lies on computing the transitions between weakly and strongly SBL, as in Acevedo et al (2021) and Maroneze et al (2019). The novelty of this study is that random mixing events are included in the model, representing unresolved features of the flow.…”

Sources of anisotropy in the Reynolds stress tensor in the stable boundary layer

“…In particular, a classification of the SBL in two dynamic regimes depending on the wind speed was proposed by Sun et al . (2012) and then verified by many experimental (Mahrt et al ., 2015; Vignon et al ., 2017; Acevedo et al ., 2021) and numerical studies (Baas et al ., 2019; Maroneze et al ., 2019a; Maroneze et al ., 2019b). A site‐ and height‐dependent wind‐speed threshold was found to discriminate between an SBL “decoupled regime” (where turbulence is weak, intermittent, mainly controlled by the local shear, and does not directly interact with the ground – low wind‐speed cases) and a “coupled regime” (where turbulence is strong, continuous, and controlled by the bulk shear – high wind‐speed cases).…”