Abstract
Abstract. Estimating representative surface fluxes using eddy covariance leads invariably to questions concerning inclusion or exclusion of low-frequency flux contributions. For studies where fluxes are linked to local physical parameters and up-scaled through numerical modelling efforts, low-frequency contributions interfere with our ability to isolate local biogeochemical processes of interest, as represented by turbulent fluxes. No method currently exists to disentangle low-frequency contributions on flux estimates. Here, we present a novel comprehensive numerical scheme to identify and separate out low-frequency contributions to vertical turbulent surface fluxes. For high flux rates (|Sensible heat flux| > 40 Wm−2, |latent heat flux|> 20 Wm−2 and |CO2 flux|> 100 mmol m−2 d−1 we found that the average relative difference between fluxes estimated by ogive optimization and the conventional method was low (5–20%) suggesting negligible low-frequency influence and that both methods capture the turbulent fluxes equally well. For flux rates below these thresholds, however, the average relative difference between flux estimates was found to be very high (23–98%) suggesting non-negligible low-frequency influence and that the conventional method fails in separating low-frequency influences from the turbulent fluxes. Hence, the ogive optimization method is an appropriate method of flux analysis, particularly in low-flux environments.
Highlights
The eddy covariance (EC) technique allows for direct, continuous and non-invasive tower-based ecosystem-scale estimation of surface–atmosphere scalar fluxes by simultaneous sampling of atmospheric fluctuations of wind and scalars (e.g., Baldocchi, 2008)
FCO2 = cdsw rc, (1c) where QSENS is the sensible heat flux, cp is the specific heat of dry air, ρd is the mass density of dry air, e.g., w = w + w is the Reynolds decomposition of vertical wind speed into its average (w) and turbulent w components, θv is potential virtual temperature, QLAT is the latent heat flux, Lh is the latent heat of vaporization, cds is the molar concentration of dry air moldry m−3, FCO2 is the CO2 flux and rq = molqmol−dr1y and rc = molcmol−dr1y are the dry mixing ratio of humidity and CO2 concentration scalars, respectively
Disregarding the high-frequency component associated with extrapolation of model results, seen here to contribute FHF ≈ −5 Wm−2 to the overall modelled sensible heat flux (Eq 4), the standard 30 min linear detrending approach will suffice to provide the turbulent flux estimate
Summary
The eddy covariance (EC) technique allows for direct, continuous and non-invasive tower-based ecosystem-scale estimation of surface–atmosphere scalar fluxes by simultaneous sampling of atmospheric fluctuations of wind and scalars (e.g., Baldocchi, 2008). These characteristics, along with ease of operation, have promoted the widespread application of the technique in both short-term experiments and longterm monitoring network operations (e.g., FLUXNET, CarboEurope, EuroFlux, and AmeriFlux). The presence of a spectral gap (Stull, 1988) is assumed to exist between these contributions, allowing investigators to disentangle contributions by separating continuous observations into quasi-stationary intervals each yielding one flux estimate. The existence of a distinct spectral gap is unclear (Lee et al, 2004) and a growing body of work suggests that
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