Surface-atmosphere exchange in a box: Space-time resolved storage and net vertical fluxes from tower-based eddy covariance

Systematic bias in eddy-covariance flux measurements are pervasive. These arise both from unmeasured terms such as advection, and sampling bias in representativeness of the footprint for both turbulent and storage fluxes. As a result, the majority of eddy-covariance towers suffer from unaccounted bias when comparing to gridded earth system models and fail to close the surface energy balance. We hypothesize that one cause for these two problems is a mismatch between mass and energy fluxes measured within a time-varying source area and the actual storage and net vertical flux over a presumed “control volume”, a novel concept derived theoretically in Metzger (this issue). Here, we practically implement this theory to estimate the true net surface-atmosphere exchange (NSAE) over such control volume, thus resolving “storage flux” and “vertical advection” issues by applying the environmental response function (ERF) technique to a virtual control volume (VCV). In this method, flux observations are related at high spatio-temporal resolution to meteorological forcings and surface properties within the estimated flux footprint, and these relationships are utilized to map the control volume explicitly in 3-D over space and time. Volume integration then allows, for the first time, retrieval of the NSAE. When ERF was applied to eddy covariance and profile observations in July and August 2014 from the AmeriFlux Park Falls WLEF tower in Wisconsin, USA, heat emission integrated over the target domain increased substantially over the tower observations by +18.2 Wm−2 (+20.6%). Storage flux contributes up to 30% of NSAE at hourly timescale. The systematic uncertainty of ERF-VCV method applied for vertical flux and storage flux is within 15% and 20%, respectively. This systematic uncertainty is effectively corrected in projections. Volume-controlled NSAE provides improvements for mapping unbiased surface-atmosphere exchange for model-data comparison, assimilation and model building at model grid scale. These advances also present a promising direction for reconciling energy balance non-closure.