Influence of Mesoscale Eddies on Energy Balance Closure over a Heterogeneous Ecosystem

Eddy covariance tower measurements typically show an imbalance in the Earth's energy budget. While there are multiple processes that can lead to this imbalance, it has been hypothesized that quasi-stationary mesoscale eddies are a major contributor in heterogenous ecosystems. Here we investigate the influence of mesoscale eddies on energy balance over a heterogeneous forest ecosystem - determining how energy balance closure changes between periods where the atmospheric boundary layer is strongly influenced by mesoscale eddies compared to periods without mesoscale contributions. We show how this influence compares to other processes typically implicated in non-closure: flux footprint heterogeneity, vegetation type, and strength of turbulence.

For this investigation we use data from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field campaign. CHEESEHEAD19 was an observational experiment designed to examine how the atmospheric boundary layer responds to scales of spatial heterogeneity in land-atmosphere energy fluxes. The campaign was conducted from June - October 2019, measuring surface energy fluxes over a heterogeneous forest ecosystem as fluxes transitioned from latent heat-dominated summer through sensible heat-dominated fall. Observations were made by ground, airborne, and satellite platforms within a 10 x 10 km study region. The spatial distribution of energy fluxes was observed by a network of 20 above-canopy eddy covariance towers and a low-flying aircraft. Mesoscale atmospheric properties were measured by a suite of lidar and sounding instruments, measuring winds, water vapor, temperature, and boundary layer development.

Here we use continuous Doppler wind lidars to identify periods of high/low mesoscale contribution and the EC tower network to measure domain-wide energy balance. The high density of the towers allows for fluxes to be calculated using both temporal and spatial eddy covariance techniques and enables a thorough characterization of the spatial representativeness of single tower eddy covariance measurements.