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CAPSULE SUMMARY A regional-scale observational experiment designed to address how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes.

The National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.

Abstract. Continuous measurements of net ecosystem CO 2 exchange (NEE) using the eddy-covariance method were made over an agricultural ecosystem in the southeastern US. During optimum environmental conditions, photosynthetically active radiation (PAR) was the primary driver controlling daytime NEE, accounting for as much as 67 to 89% of the variation in NEE. However, soil water content became the dominant factor limiting the NEE-PAR response during the peak growth stage. NEE was significantly depressed when high PAR values coincided with very low soil water content. The presence of a counter-clockwise hysteresis of daytime NEE with PAR was observed during periods of water stress. This is a result of the stomatal closure control of photosynthesis at high vapor pressure deficit and enhanced respiration at high temperature. This result is significant since this hysteresis effect limits the range of applicability of the Michaelis-Menten equation and other related expressions in the determination of daytime NEE as a function of PAR. The systematic presence of hysteresis in the response of NEE to PAR suggests that the gap-filling technique based on a nonlinear regression approach should take into account the presence of water-limited field conditions. Including this step is therefore likely to improve current evaluation of ecosystem response to increased precipitation variability arising from climatic changes.

Abstract. Large differences in instrumentation, site setup, data format, and operating system stymie the adoption of a universal computational environment for processing and analyzing eddy-covariance (EC) data. This results in limited software applicability and extensibility in addition to often substantial inconsistencies in flux estimates. Addressing these concerns, this paper presents the systematic development of portable, reproducible, and extensible EC software achieved by adopting a development and systems operation (DevOps) approach. This software development model is used for the creation of the eddy4R family of EC code packages in the open-source R language for statistical computing. These packages are community developed, iterated via the Git distributed version control system, and wrapped into a portable and reproducible Docker filesystem that is independent of the underlying host operating system. The HDF5 hierarchical data format then provides a streamlined mechanism for highly compressed and fully self-documented data ingest and output.The usefulness of the DevOps approach was evaluated for three test applications. First, the resultant EC processing software was used to analyze standard flux tower data from the first EC instruments installed at a National Ecological Observatory (NEON) field site. Second, through an aircraft test application, we demonstrate the modular extensibility of eddy4R to analyze EC data from other platforms. Third, an intercomparison with commercial-grade software showed excellent agreement (R 2 = 1.0 for CO 2 flux). In conjunction with this study, a Docker image containing the first two eddy4R packages and an executable example workflow, as well as first NEON EC data products are released publicly. We conclude by describing the work remaining to arrive at the automated generation of science-grade EC fluxes and benefits to the science community at large. This software development model is applicable beyond EC and more generally builds the capacity to deploy complex algorithms developed by scientists in an efficient and scalable manner. In addition, modularity permits meeting project milestones while retaining extensibility with time.

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Abstract. The interpretation of flux measurements in nocturnal conditions is typically fraught with challenges. This paper reports on how the presence of wave-like disturbances in a time series, can lead to an overestimation of turbulence statistics, errors when calculating the stability parameter, erroneous estimation of the friction velocity u * used to screen flux data, and errors in turbulent flux calculations. Using time series of the pressure signal from a microbarograph, wavelike disturbances at an AmeriFlux site are identified. The wave-like disturbances are removed during the calculation of turbulence statistics and turbulent fluxes. Our findings suggest that filtering eddy-covariance data in the presence of wave-like events prevents both an overestimation of turbulence statistics and errors in turbulent flux calculations. Results show that large-amplitude wave-like events, events surpassing three standard deviations, occurred on 18 % of the nights considered in the present study. Remarkably, on flux towers located in a very stably stratified boundary-layer regime, the presence of a gravity wave can enhance turbulence statistics more than 50 %. In addition, the presence of the disturbance modulates the calculated turbulent fluxes of CO 2 resulting in erroneous turbulent flux calculations of the order of 10 % depending on averaging time and pressure perturbation threshold criteria. Furthermore, the friction velocity u * was affected by the presence of the wave, and in at least one case, a 10 % increase caused u * to exceed the arbitrary 0.25 m s −1 threshold used in many studies. This results in an unintended bias in the data selected for analysis in the flux calculations. The impact of different averaging periods was also examined and found to be variable specific. These early case study results provide an insight into errors introduced when calculating "purely" turbulent fluxes. These results could contribute to improving modeling efforts by providing more accurate inputs of both turbulent kinetic energy, and isolating the turbulent component of u * for flux selection in the stable nocturnal boundary layer.