Large Eddies Regulate Turbulent Flux Gradients in Coupled Stable Boundary Layers

Abstract

Due to strong mean wind shear, the stable boundary layer (SBL) becomes vertically coupled. In a coupled SBL, large turbulent eddies enhance cross‐layer mixing and vertically mix turbulent kinetic energy. However, large gradients in momentum and heat fluxes are frequently observed, degrading the performance of Monin‐Obukhov similarity theory. It is shown that increased vertical gradients of mean variables (i.e., wind speed and potential temperature) can cause flux gradients. This process is operated upon by downward penetrating large eddies with altered phase difference, contributing unevenly to fluxes and thus causing flux gradients. In the coupled SBL with small gradients in mean variables, large eddies are vertically synchronized, contributing evenly to fluxes across layers and thus causing small flux gradients. As turbulent production becomes weak and the cross‐layer difference in turbulent flux transport increases, large eddies become less vertically synchronized, contributing unevenly to fluxes across layers and thus causing large flux gradients.

Enlarged Nonclosure of Surface Energy Balance With Increasing Atmospheric Instabilities Linked to Changes in Coherent Structures

“…Ensemble empirical mode decomposition (EEMD) is an adaptive method without demanding a predetermined base function and thus applicable for analyzing nonlinear and nonstationary turbulence data (Huang et al, 1998;Wu & Huang, 2009). This method has been recently employed in analyzing turbulence structures in both unstable and stable ASL (Gao et al, 2019;Gao, Liu, et al, 2017;Lan et al, 2019). Using a sifting process, it decomposes a time series x(t) into a finite number (n) of oscillatory components (C k,x (t)) as well as an overall residual r x (t) given as…”

“…where f is the natural frequency and S k, x (f) is the Fourier transform of C k, x (t) (Huang et al, 1998). Thus, the sum of a certain number of consecutive C k, x (t) can be interpreted as turbulent motions with specific scales to be considered (Gao et al, 2019;Lan et al, 2019).…”

Decreased dissimilarity of turbulent transport attributed to large eddies

“…A post‐field EC data program was used to process the 10‐Hz time‐series data in order to obtain 30‐min time‐averaged statistics (Lan et al ., 2018, 2019). Briefly, the data processing procedures include the following: (1) removal of spikes/noise from the raw 10‐Hz time series when the Automatic Gain Control (AGC) value of the IRGA exceeds a threshold primarily due to raindrops, dusts, and condensed water drops on the window of the IRGA; (2) replacing data points with magnitude exceeding fivefold the mean standard deviations by linear interpolation; (3) double rotation is employed to coordinate rotation of wind components due to the mild topographical changes of the observation site; (4) calculating averages, variances, and covariances using the 30‐min unweighted block average method; (5) performing sonic temperature correction and air density correction for sensible heat, latent heat, and CO 2 fluxes (Webb et al ., 1980; Schotanus et al ., 1983; Liu et al ., 2001); and (6) performing quality checks (Foken et al ., 2005).…”

Non‐Closure of Surface Energy Balance Linked to Asymmetric Turbulent Transport of Scalars by Large Eddies

“…In addition, net radiation (Rn) was measured by a two‐component net radiometer (CNR2, Kipp & Zonen) at 30 m. Ground heat flux was calculated based on a variety of measurements in the soil (see the supporting information). Additional site information and other instrumentations are documented elsewhere (Finn et al., 2016; Gao et al., 2016, 2017, 2018, 2019; Lan et al., 2018, 2019). Post‐field data processing, data selection, and quadrant analysis are provided in the supporting information.…”