Surface-atmosphere exchange in a box: Space-time resolved storage and net vertical fluxes from tower-based eddy covariance
Using the eddy-covariance (EC) method to determine net surface-atmosphere exchange relies on extensive simplifications of the mass balance concept. Among others, it is assumed that the 3-D flux field within a control volume is divergence-free, which is shown to be violated e.g. from large-eddy simulations. To practically evaluate the severity of these assumptions, case studies have monitored the surrounding of an EC tower, so the control volume can be represented more explicitly. Alternatively, diagnostic tests during data processing can be used to subset the EC data for periods that more likely fulfill the underlying assumptions. However, these existing methods are constrained either by their degree of realism, resource demand, temporal coverage, varying spatial representativeness, or combinations thereof.
It is hypothesized that these deficiencies can be overcome by using the environmental response function (ERF) technique: Relating flux observations at very high spatio-temporal resolution to meteorological forcings and surface properties, and utilizing the extracted relationships to map a single, regular and stationary control volume explicitly in 3-D space and time. Here, the novel ERF virtual control volume (VCV) concept and its implications are derived, and Xu et al. (this issue) are presenting its first practical application.
Initial results show that even from a single EC tower, ERF-VCV reduces advective errors by at least one order of magnitude, and incorporates net low-frequency flux contributions. In the same process tower location bias is treated through attaining a fixed-frame, thus equitable and time-invariant representation of the net surface-atmosphere exchange across a target domain. With regard to the frequently observed non-closure of the surface energy balance, this offers the potential for reconciling “spatial heterogeneity” and “storage term” theories. In extension, ERF promises a rectifying observational operator for unbiased model-data comparison, assimilation, and process representation at model grid scale.