Global networks of eddy-covariance flux towers play a pivotal role in enhancing our predictive understanding of carbon and water cycling of biological systems in response to regional and global environmental change. Despite their broad application in numerous studies, the spatial aspects of the flux measurements have often been ignored or treated ambiguously, thereby remaining a primary source of uncertainty. The area contributing to the flux-referred to as the flux footprint-varies over time depending on wind direction, atmospheric turbulence, effective measurement heights, surface characteristics, and mesoscale forcings. The footprint dynamics, along with underlying source-sink heterogeneity, lead to spatial and temporal variability in the sensed fluxes, complicating the interpretation of flux data and their integration with a range of observations and models. This article addresses this critical link by reviewing the most up-to-date research, identifying knowledge gaps and challenges, and pointing out future research needs and opportunities. We begin with an overview of the current state of footprint modeling and its applications, from single-site studies to large-scale syntheses, summarizing how flux footprints have been used to interpret spatial flux variability and to integrate flux data with models, remote sensing, and other observations. We highlight how this critical spatial aspect could complicate the processing of eddy-covariance fluxes, the definition of mass and energy continuity, and the interpretation of flux response functions and parameters, all of which have significant implications for numerous applications and research. We then point out potential opportunities and future research needs to bridge this knowledge gap, highlighting both readily available and prominent emerging ones. We conclude by urging the scientific community to (re)consider the spatiotemporal dynamics of eddy-covariance flux measurements, and to investigate and evaluate potential approaches across scales, applications, and disciplines.

The lack of energy balance closure in Eddy-Covariance (EC) measurements is a well-known, still unresolved challenge in micrometeorology, with energy balance closure (EBC) rates typically ranging between 60% and 80%. While numerous hypotheses have been proposed to explain this imbalance, the relative contributions of neglected energy storage terms, data quality and flux processing options remain insufficiently disentangled. Using standardized ICOS and NEON datasets, we show that a significant portion of the observed energy imbalance can be attributed to overlooked or inconsistently handled energy components and turbulent flux quality control. Using data drawn from 84 sites, we show that comprehensive energy accounting-including soil heat flux, storage terms (soil, air, biomass), photosynthetic energy demand, and strict quality filtering of turbulent fluxes-improved EBC by 16% on average, with site-specific gains up to 40%. However, we also identify a persistent residual imbalance that is unlikely to be resolved through methodological refinements or additional measurements alone, pointing to fundamental physical processes that are not accounted for in the standard measurement and processing. We argue that this unresolved imbalance should be explicitly acknowledged and bounded, rather than implicitly absorbed into correction schemes, and we outline practical guidance for diagnosing and interpreting EBC in standardized flux networks. This perspective evaluates methodological advances and residual uncertainties, providing an actionable framework for the appropriate use of EC energy fluxes in carbon, water, and climate research.

Widely used in studies ranging from ecophysiology dynamics to global estimates using models and remote sensing data, FLUXNET datasets have become key to scientific research and applications. More frequently updated and high-quality FLUXNET data collections are ever more pressing, serving opportunities with new technologies and new real-world applications including nature-based and technological climate solutions, carbon credit verification, support to agriculture decision systems, and ecological forecasting. The three major FLUXNET releases (FLUXNET2015, LaThuile in 2007, and Marconi 2000) have been widely used by the scientific community, academia and industry. Nonetheless, release cycles of 7-10 years have become a major limiting factor, given the demand for continuously updated collections for anchoring remote sensing (calibration and validation), models (from hindcasts to forecasting), and real-world applications requiring near-real-time data. Regional flux networks have sought to expand the datasets by increasing the number of sites, variables and metadata shared, by improving data quality, and by moving toward open data principles via the recent adoption of the CC-BY data license. New network-level data products are being released, either regularly (e.g., AmeriFlux, ICOS, NEON, TERN datasets), or in response to specific demands (e.g. Drought2018 and WarmWinter2020 in ICOS). These data are processed using the shared and jointly maintained ONEFlux pipeline, making data products fully compatible and interoperable. However, mechanisms for global access to regional network data at a global scale are still challenging for users. The continuous development of the FAIRness and related data discovery tools further supports new strategies to create, maintain, and continuously update FLUXNET datasets. At the same time, inequity in data use, credit, recognition, and contribution is still a significant challenge that must be highlighted and solved. Here, we present a roadmap for how future FLUXNET synthesis datasets can be constructed and shared. We demonstrate a data discovery and access tool, look into benefits for data providers and users, and highlight data usage and availability. Open discussion of these challenges and solutions is encouraged. https://shuttle-demo.fluxnet.org/

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15-70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

Single point eddy covariance measurements of the Earth’s surface energy budget frequently identify an imbalance between available energy and turbulent heat fluxes. While this imbalance lacks a definitive explanation, it is nevertheless a persistent finding from single-site measurements; one with implications for atmospheric and ecosystem models. This has led to a push for intensive field campaigns with temporally and spatially distributed sensors to help identify the causes of energy balance non-closure. Here we present results from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19)—an observational experiment designed to investigate how the Earth’s surface energy budget responds to scales of surface spatial heterogeneity over a forest ecosystem in northern Wisconsin. The campaign was conducted from June–October 2019, measuring eddy covariance (EC) surface energy fluxes using an array of 20 towers and a low-flying aircraft. Across the domain, energy balance residuals were found to be highest during the afternoon, coinciding with the period of surface heterogeneity-driven mesoscale motions. The magnitude of the residual varied across different sites in relation to the vegetation characteristics of each site. Both vegetation height and height variability showed positive relationships with the residual magnitude. During the seasonal transition from latent heat-dominated summer to sensible heat-dominated fall the magnitude of the energy balance residual steadily decreased, but the energy balance ratio remained constant at 0.8. This was due to the different components of the energy balance equation shifting proportionally, suggesting a common cause of non-closure across the two seasons. Additionally, we tested the effectiveness of measuring energy balance using spatial EC. Spatial EC, whereby the covariance is calculated based on deviations from spatial means, has been proposed as a potential way to reduce energy balance residuals by incorporating contributions from mesoscale motions better than single-site, temporal EC. Here we tested several variations of spatial EC with the CHEESEHEAD19 dataset but found little to no improvement to energy balance closure, which we attribute in part to the challenging measurement requirements of spatial EC.

Long-term tall-tower eddy-covariance (EC) measurements have been recently established in three European pilot cities as part of the ICOS-Cities project. We conducted a comparison of EC software to ensure a reliable generation of interoperable flux estimates, which is the prerequisite for avoiding methodological biases and improving the comparability of the results. We analyzed datasets covering 5 months collected from EC tall-tower installations located in urbanized areas of Munich, Zurich, and Paris. Fluxes of sensible heat, latent heat, and CO2 were calculated using three software packages (i.e., TK3, EddyPro, and eddy4R) to assess the uncertainty of flux estimations attributed to differences in implemented postprocessing schemes. A very good agreement on the mean values and standard deviations was found across all three sites, which can probably be attributed to a uniform instrumentation, data acquisition, and preprocessing. The overall comparison of final flux time series products showed a good but not yet perfect agreement among the three software packages. TK3 and EddyPro both calculated fluxes with low-frequency spectral correction, resulting in better agreement than between TK3 and the eddy4R workflow with disabled low-frequency spectral treatment. These observed flux discrepancies indicate the crucial role of treating low-frequency spectral loss in flux estimation for tall-tower EC systems.

Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR-NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR-NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR-NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change.

LI-COR Biosciences & Water for Food Global Institute NEON Program, Battelle University of Nebraska CarbonSpace University of Wisconsin-Madison Agriculture and Agri-food Canada Black Swift Technologies Stanford Woods Institute for the Environment West Virginia University Nigerian Army University Biu Agrology Copernicus Institute of Sustainable Development, Utrecht University Max Planck Institute for Biogeochemistry Sitlab Technology ZEACHO University of Arkansas Susanne Schödel GmbH Environment Data Annamalai University Agoro Carbon Alliance Centre for Water Resources Development and Management

The highly interactive and variable nature of scales of space and time featured in components of the Earth system imparts enormous complexity to land‐atmosphere interactions. Here, we introduce an open special collection on Advances in Scaling and Modeling of Land ‐ Atmosphere Interactions that features articles in JGR : Biogeosciences , JGR : Atmospheres , Journal of Advances in the Modeling of Earth Systems , and Earth & Space Science . Collectively, these articles identify interactions across multiple processes, in field experiments, long‐term observations, and numerical simulations, which are then used to advance theories of scale interaction to improve predictive models.

During March–June 2017 emissions of nitrogen oxides were measured via eddy covariance at the British Telecom Tower in central London, UK. Through the use of a footprint model the expected emissions were simulated from the spatially resolved National Atmospheric Emissions Inventory for 2017 and compared with the measured emissions. These simulated emissions were shown to underestimate measured emissions during the daytime by a factor of 1.48, but they agreed well overnight. Furthermore, underestimations were spatially mapped, and the areas around the measurement site responsible for differences in measured and simulated emissions were inferred. It was observed that areas of higher traffic, such as major roads near national rail stations, showed the greatest underestimation by the simulated emissions. These discrepancies are partially attributed to a combination of the inventory not fully capturing traffic conditions in central London and both the spatial and temporal resolution of the inventory not fully describing the high heterogeneity of the urban centre. Understanding of this underestimation may be further improved with longer measurement time series to better understand temporal variation and improved temporal scaling factors to better simulate sub-annual emissions.

A quality management system is critical for ensuring that the data and services provided by an organization meet the needs of its mission. With a mission to collect long‐term open‐access ecological data to better understand how US ecosystems are changing, the National Ecological Observatory Network (NEON) is a highly standardized measurement network distributed across the United States and Puerto Rico collecting data on the biosphere and its interfaces with the pedosphere, hydrosphere and atmosphere. In order to achieve high‐quality, comparable data across the network, a quality management system was developed by applying the seven ISO 9001:2015 principles of quality management: customer focus, leadership, engagement of people, process approach, improvement, evidence‐based decision making and relationship management. The resultant system is integrated throughout NEON's organizational structure with an approach that connects people and operational processes throughout the data life cycle (process approach). We describe the system with respect to sensor data (automated measurements), demonstrating its effectiveness through examples, lessons learned and a continuous history of improvement towards quality goals, including a doubling of data quality in NEON's meteorological and soil datasets since 2015 and substantial gains in other sensor datasets. Owing to a focus on quality management principles and particularly the interconnectedness of human and information systems, NEON's quality management system can serve as a model for networks with a variety of organizational structures and sizes.

The Earth's surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale‐dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower‐based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso‐β, meso‐γ) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid‐latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface‐atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller‐scale turbulent and larger‐scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single‐point tower measurements through traditional time‐domain half‐hourly Reynolds decomposition. We report scale‐resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface‐atmospheric interactions over unstructured heterogeneities and can help inform multi‐scale model‐data integration of weather and climate models at a sub‐grid scale.

The arboreal ecosystem is vitally important to global and local biogeochemical processes, the maintenance of biodiversity in natural systems, and human health in urban environments. The ability to collect samples, observations, and data to conduct meaningful scientific research is similarly vital. The primary methods and modes of access remain limited and difficult. In an online survey, canopy researchers ( n = 219) reported a range of challenges in obtaining adequate samples, including ∼10% who found it impossible to procure what they needed. Currently, these samples are collected using a combination of four primary methods: (1) sampling from the ground; (2) tree climbing; (3) constructing fixed infrastructure; and (4) using mobile aerial platforms, primarily rotorcraft drones. An important distinction between instantaneous and continuous sampling was identified, allowing more targeted engineering and development strategies. The combination of methods for sampling the arboreal ecosystem provides a range of possibilities and opportunities, particularly in the context of the rapid development of robotics and other engineering advances. In this study, we aim to identify the strategies that would provide the benefits to a broad range of scientists, arborists, and professional climbers and facilitate basic discovery and applied management. Priorities for advancing these efforts are (1) to expand participation, both geographically and professionally; (2) to define 2–3 common needs across the community; (3) to form and motivate focal teams of biologists, tree professionals, and engineers in the development of solutions to these needs; and (4) to establish multidisciplinary communication platforms to share information about innovations and opportunities for studying arboreal ecosystems.

Improvements in numerical weather and climate modeling depend on accurate accounting of land-atmosphere interactions and the biological processes that mediate them at multiple spatial and temporal scales.Unfortunately, there is a problematic, persistent mismatch between the scales of observations and models.The substantial heterogeneity of the land surface means that observations do not always accurately reflect the entire model grid cell.Therefore, spatial and temporal scaling of surface fluxes is fundamental to how we evaluate theories on what happens within the subgrid of atmospheric models and how it feeds back onto larger-scale dynamics.This scaling is thus fundamental to assessing the parameterizations that represent land-atmosphere interactions in atmospheric models.

The observing system design of multidisciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increases in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel numerical simulation–environmental response function (NS–ERF) approach to observing system simulation experiments that aids surface–atmosphere synthesis at the interface of mesoscale and microscale meteorology. In a case study we demonstrate application of the NS–ERF approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered the placement of 20 eddy covariance flux towers, operations for 72 h of low-altitude flux aircraft measurements, and integration of various remote sensing data products. A 2 h high-resolution large eddy simulation created a cloud-free virtual atmosphere for surface and meteorological conditions characteristic of the field campaign domain and period. To explore two specific design hypotheses we super-sampled this virtual atmosphere as observed by 13 different yet simultaneous observing system designs consisting of virtual ground, airborne, and satellite observations. We then analyzed these virtual observations through ERFs to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19's potential to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its modular extensibility, NS–ERF lends itself to optimizing observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection, and multi-species applications, among other use cases.

Abstract. The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19).During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals.We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.

Flux measurements of nitrogen oxides (NOx) were made over London using airborne eddy covariance from a low-flying aircraft. Seven low-altitude flights were conducted over Greater London, performing multiple overpasses across the city during eight days in July 2014. NOx fluxes across the Greater London region (GLR) exhibited high heterogeneity and strong diurnal variability, with central areas responsible for the highest emission rates (20–30 mg m−2 h−1). Other high-emission areas included the M25 orbital motorway. The complexity of London's emission characteristics makes it challenging to pinpoint single emissions sources definitively using airborne measurements. Multiple sources, including road transport and residential, commercial and industrial combustion sources, are all likely to contribute to measured fluxes. Measured flux estimates were compared to scaled National Atmospheric Emissions Inventory (NAEI) estimates, accounting for monthly, daily and hourly variability. Significant differences were found between the flux-driven emissions and the NAEI estimates across Greater London, with measured values up to 2 times higher in Central London than those predicted by the inventory. To overcome the limitations of using the national inventory to contextualise measured fluxes, we used physics-guided flux data fusion to train environmental response functions (ERFs) between measured flux and environmental drivers (meteorological and surface). The aim was to generate time-of-day emission surfaces using calculated ERF relationships for the entire GLR; 98 % spatial coverage was achieved across the GLR at 400 m2 spatial resolution. All flight leg projections showed substantial heterogeneity across the domain, with high emissions emanating from Central London and major road infrastructure. The diurnal emission structure of the GLR was also investigated, through ERF, with the morning rush hour distinguished from lower emissions during the early afternoon. Overall, the integration of airborne fluxes with an ERF-driven strategy enabled the first independent generation of surface NOx emissions, at high resolution using an eddy-covariance approach, for an entire city region.