Large eddies modulating flux convergence and divergence in a disturbed unstable atmospheric surface layer

Abstract

The effects of large eddies on turbulence structures and flux transport were studied using data collected over a flat cotton field during the Energy Balance Experiment 2000 in the San Joaquin Valley of California in August 2000. Flux convergence (FC; larger fluxes at 8.7 m than 2.7 m) and divergence (FD) in latent heat flux (LE) were observed in a disturbed, unstable atmospheric surface layer, and their magnitudes largely departed from the prediction of Monin‐Obukhov similarity theory. From our wavelet analysis, it was identified that large eddies affected turbulence structures, scalar distribution, and flux transport differently at 8.7 m and 2.7 m under the FC and FD conditions. Using the ensemble empirical mode decomposition, time series data were decomposed into large eddies and small‐scale background turbulence, the time‐domain characteristics of large eddies were examined, and the flux contribution by large eddies was also determined quantitatively. The results suggest that large eddies over the frequency range of 0.002 Hz < f < 0.02 Hz (predominantly 300–400 m) enhanced the vertical velocity spectra more significantly at 8.7 m than 2.7 m, leading to an increased magnitude of the cospectra and thus LE at 8.7 m. In the FD case, however, these large eddies were not present and even suppressed in the vertical velocity spectra at 8.7 m. Consequently, the cospectra divergence over the low‐frequency ranges primarily caused the LE divergence. This work implies that large eddies may either improve or degrade the surface energy balance closure by increasing or decreasing turbulent fluxes, respectively.

Mechanistic links between underestimated CO 2 fluxes and non-closure of the surface energy balance in a semi-arid sagebrush ecosystem

“…measuring longitudinal, lateral, and vertical wind velocity components (u, v, and w), T, q, and c. These data were logged with a datalogger (Model CR5000, Campbell Scientific Inc.) at a sampling frequency of 10 Hz, and were then processed to obtain half-hourly turbulent fluxes. The procedures of data processing, including despiking, double rotation for the sonic anemometer velocities, and corrections of sonic temperature and density applied to the fluxes, are detailed elsewhere (Liu et al 2009, Gao et al 2016, 2017a. Here, each 30 min averaging interval is referred to as a run.…”

“…Numerous mechanisms have been proposed to explain the non-closure of the energy balance, including violations in the assumptions of steady state and horizontally homogeneous landscapes for the EC measurements, errors in measurements of each component of the energy balance, mismatch in instrument footprints, advection, and inadequate sampling of large-scale turbulent eddies (hereafter large eddies) (Wilson et al 2002, Mauder et al 2007, Foken 2008, Leuning et al 2012, Wohlfahrt and Widmoser 2013, Eder et al 2015, Gao et al 2016, 2017a, Zhou et al 2018. Of these factors, large eddies are one of the primary contributors to underestimated H and LE (Kanda et al 2004, Foken et al 2011, Gao et al 2016, 2017a, Zhou et al 2018.…”

A novel approach to evaluate soil heat flux calculation: An analytical review of nine methods

“… G 0 can account for about 20% of the available energy (i.e., R n – G 0 ) over grasslands and agricultural lands [ Kustas et al , ; Wilson et al , ; Oncley et al , ; Foken , ; G. Wang et al , ]. Thus, accurate determination of G 0 is essential for evaluating the other components of the surface energy balance (e.g., H and LE ) and examining potential reasons for the widely reported nonclosure of the surface energy balance [ Oncley et al , ; Foken et al , ; Leuning et al , ; Stoy et al , ; Sun et al , ; Gao et al , ; Purdy et al , ; Gao et al , ; Liang et al , ]. However, directly measuring G 0 is challenging since SHF plates have to be buried deep enough in the soil to avoid potential effects of soil‐atmosphere processes such as liquid water and vapor flow [ Mayocchi and Bristow , ; Liebethal et al , ; Ochsner et al , , ; Heitman et al , ; Gentine et al , ; Leuning et al , ; Peng et al , ].…”

Large Eddies Regulate Turbulent Flux Gradients in Coupled Stable Boundary Layers

“…Detailed post field data processing procedures have been provided elsewhere (Lan et al, 2018;Liu et al, 2016). Briefly, these procedures include (1) removing spikes/noise from the raw 10-Hz time data and gap filling the time series with linear interpolation (Gao et al, 2016); (2) double rotation for the three-dimensional wind components (Kaimal & Finnigan, 1994); (3) calculation of averages, variances, and covariances using the unweighted block average method; (4) sonic temperature correction (Liu et al, 2001;Schotanus et al, 1983) and density correction (Webb et al, 1980); and (5) data quality check (Foken et al, 2005). To reduce "contamination" from submeso motions on turbulent fluxes, coordinate rotation and calculation of turbulent statistics are performed over unweighted 5-min intervals.…”