Scalar turbulent behavior in the roughness sublayer of an Amazonian forest

Abstract

Abstract. An important current problem in micrometeorology is the characterization of turbulence in the roughness sublayer (RSL), where most of the measurements above tall forests are made. There, scalar turbulent fluctuations display significant departures from the predictions of MoninObukhov similarity theory (MOST). In this work, we analyze turbulence data of virtual temperature, carbon dioxide, and water vapor in the RSL above an Amazonian forest (with a canopy height of 40 m), measured at 39.4 and 81.6 m above the ground under unstable conditions. We found that dimensionless statistics related to the rate of dissipation of turbulence kinetic energy (TKE) and the scalar variance display significant departures from MOST as expected, whereas the vertical velocity variance follows MOST much more closely. Much better agreement between the dimensionless statistics with the Obukhov similarity variable, however, was found for the subset of measurements made at a low zenith angle Z, in the range 0 • < |Z| < 20 • . We conjecture that this improvement is due to the relationship between sunlight incidence and the "activation-deactivation" of scalar sinks and sources vertically distributed in the forest. Finally, we evaluated the relaxation coefficient of relaxed eddy accumulation: it is also affected by zenith angle, with considerable improvement in the range 0 • < |Z| < 20 • , and its values fall within the range reported in the literature for the unstable surface layer. In general, our results indicate the possibility of better stability-derived flux estimates for low zenith angle ranges.

Is There a Classical Inertial Sublayer Over the Amazon Forest?

“…There is low scatter in σ w / u * and good agreement with the standard function for the ISL reported in the literature (equation (11), Chor et al, ) for the levels 40 m, 55 m, and slightly less so for 81 m (Figures a, c, and e). These results confirm the findings of Zahn et al () and Chor et al () for the same site. At the 150‐ and 325‐m levels, the scatter is considerably larger than that observed at the lower levels (Figures g–i).…”

“…The failure of MOST for scalars in the RSL, on the other hand, is evident. A large scatter and deviation from MOST is observed in the values of (Figure , right side), as also noted by Dias et al (), and by Zahn et al () and Chor et al () for the same site. Moreover, the scatter increases continuously with height, with no improvement at an intermediate level that could indicate the presence of an ISL.…”

“…side), as also noted byDias et al (2009), and byZahn et al (2016) andChor et al (2017) for the same site. Moreover, the scatter increases continuously with height, with no improvement at an intermediate level that could indicate the presence of an ISL.…”

“…Before that, we note that the plots in Figure 1 may suffer from self-correlation (Hicks, 1981), and further testing of this effect is often required (Andreas & Hicks, 2002;Cava et al, 2008). Because this has already been done at the same site (Chor et al, 2017;Zahn et al, 2016), we are confident that our results are not contaminated by self-correlation. Still, an analysis that is both immune to self-correlation and able to compare SL and ML scaling is the dimensional plots of Figure 2, which shows w versus u * and w * .…”

Land–atmosphere interactions in the tropics – a review

“…While these early studies typically estimated recycling using bulk formulas derived under simplifying assumptions, more sophisticated approaches for estimating recycling have emerged (van der Ent et al, 2010), including comprehensive moisture tracking operating on subdaily inputs on models and reanalysis, e.g., the dynamic recycling model (Dominguez et al, 2006), the water accounting model (van der Ent et al, 2010), and Lagrangian approaches using parcel dispersion models such as FLEX-PART (Gimeno et al, 2012). Contemporary estimates of recycling ratios for the Amazon Basin range from 25 % to 35 % (Zemp et al, 2014). These more sophisticated approaches have also enabled identification and quantification of upstream sources of moisture that lead to downstream rainfall over tropical land regions (Dirmeyer and Brubaker, 2007;Drumond et al, 2014;Hoyos et al, 2018;Stohl and James, 2005).…”