Upscaling tower-observed turbulent exchange at fine spatio-temporal resolution using environmental response functions

• Environmental Response Function (ERF) maps surface-atmosphere exchanges.
• ERF is capable of upscaling tower eddy covariance observations to regional scale.
• The regional flux maps de-bias the footprint variation in tower observations.
• ERF provides unprecedented spatio-temporal attribution and resolution.

Eddy-covariance measurements are widely used to develop and test parameterizations of land-atmosphere interactions in earth system models. However, a fundamental challenge for model-data comparisons lies in the scale mismatch between the eddy-covariance observations with small (10−1–101 km2) and temporally varying flux footprint, and the continuous regional-scale (102–104 km2) gridded predictions made in simulations. Here, a new approach was developed to project turbulent flux maps at regional scale and hourly temporal resolution using environmental response functions (ERFs). This is based on an approach employed in airborne flux observations, and relates turbulent flux observations to meteorological forcings and surface properties across the flux footprint. In this study, the fluxes of sensible heat, latent heat and CO2 integrated over a 20×2020×20 km2 target domain differed substantially from the tower observations in their expected value (+27%, −9%, and −17%) and spatio-temporal variation (−22%, −21%, and −3%, repsectively) ERF systematic uncertainties are bound within −11%, −1.5% and +16%, respectively, indicating that tower location bias might be even more pronounced for heat and CO2 fluxes than currently detectable. The ERF-projected fluxes showed general agreement with independent observations at a nearby tower location. Lastly, advantages and limitations of ERF compared to other scaling approaches are discussed, and pathways for improving model-data synthesis utilizing the ERF scaling method are pointed out.