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 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 the control volume explicitly in 3-D space and time. Here, the novel ERF virtual control volume concept and its implications are derived, and the companion paper by Xu, Metzger and Desai is presenting its first practical application.
Initial results show that even from a single EC tower, ERF reduces the reliance on divergence assumptions by ≥2/3, and in the same process treats tower location bias effectively. Moreover, it bears the potential to reconcile spatial heterogeneity and storage term theories with regard to a frequently observed non-closure of the surface energy balance. In extension, ERF promises a rectifying observational operator for unbiased model-data comparison, assimilation, and process representation at model grid scale.