Abstract
Eddy-covariance fluxes serve as an essential benchmark for Earth system models and remote sensing data. However, two challenges prevent model-data intercomparisons from fully utilizing eddy-covariance fluxes. The first challenge stems from the differing and variable spatial representativeness of the eddy-covariance measurements, or footprint bias and transience. The second originates from the phenomenon of a non-closed energy balance using eddy-covariance measurements, hypothesized to result from unaccounted mesoscale flows or under-sampling of hot spots by flux towers, among others. Previous studies have suggested that these two problems can be mitigated by either building multiple towers or by applying space–time rectification approaches, such as the environmental response function (ERF) approach. Here we ask: (1) How many eddy-flux towers do we need to sufficiently rectify location bias, close the energy budget, and sample the regional domain? (2) Can an advanced space–time rectification approach reduce the tower density, while still adequately sampling the regional flux domain? Furthermore, (3) How accurately can the ERF approach retrieve the surface-flux variation? To answer these questions, we used data from a large-eddy simulation of atmospheric flows above a heterogeneous surface as captured by an ensemble of virtual tower measurements. We calculated eddy-covariance fluxes by spatial and spatio-temporal methods. The spatial eddy-covariance method captured 89% of the prescribed total surface energy flux with about one tower per 15 km2, while the spatio-temporal method required only one tower per 40 km2 to capture 95% of surface energy. To capture 97% of energy, applying the ERF approach further reduced the required tower density to one tower per 200 km2, as a result of space–time rectification and incorporating mesoscale flows. This approach also enabled retrieving the surface spatial variation of the sensible heat flux. The results provide a reference for informing and designing future observation systems based on flux tower clusters, and scale-aware data products.
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Acknowledgements
K. Xu and A.R. Desai acknowledge support from Contract #3010-0401-000 from Battelle Ecology, Inc. to UW-Madison, DOE Ameriflux Network Management Project support to the ChEAS Core site cluster, and National Science Foundation AGS-1822420. The National Ecological Observatory Network is a project sponsored by the National Science Foundation and managed under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation (Grant DBI-0752017). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. M. Sühring is funded by the German Federal Ministry of Education and Research (BMBF) (Grant 01LP1601A) within the framework of Research for Sustainable Development (FONA; www.fona.de). All simulations were performed on the Cray XC40 at The North-German Supercomputing Alliance (HLRN), Hannover/Berlin. All the input and output data are available in this link: http://co2.aos.wisc.edu/data/kxu/ERF-LES/. The permission of usage of these data is not required, but we do kindly ask you contact us of your intentions, most likely because we can provide help on data analysis. Our routines were developed in GNU R version 3.1 (R Development Core Team 2012), and code and examples are being developed for a public repository. Corresponding Docker compute images, R-packages and workflow examples are being developed for a public repository, and are available upon request in the meantime.
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Xu, K., Sühring, M., Metzger, S. et al. Can Data Mining Help Eddy Covariance See the Landscape? A Large-Eddy Simulation Study. Boundary-Layer Meteorol 176, 85–103 (2020). https://doi.org/10.1007/s10546-020-00513-0
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DOI: https://doi.org/10.1007/s10546-020-00513-0