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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.

 

The lake breeze circulation along Lake Michigan is associated with high tropospheric ozone concentrations at shoreline locations. The 2021 Wisconsin's Dynamic Influence of Shoreline Circulation on Ozone (WiscoDISCO-21) campaign involved atmospheric measurements over Chiwaukee Prairie State Natural Area in Southeastern Wisconsin from May 21–26, 2021. Three different platforms, two uncrewed aerial systems (UAS) and a Doppler lidar instrument, were used to collect data on this campaign, supplemented by a ground-based Wisconsin DNR maintained regulatory monitor at the site. A Purdue University M210 multirotor copter, and the University of Colorado RAAVEN fixed-wing aircraft were flown in coordination. Using data from the ground station, RAAVEN and onsite lidar, lake breezes were detected on several days of the campaign. The longest sustained lake breezes during the campaign were detected on May 22, 2021, from 17:00–21:38 UTC and on May 24, 2021, from 14:24–22:51 UTC. The presence of the lake breezes correlated with detected temperature inversions measured from the RAAVEN and high ozone events measured from the M210. Lake breezes were investigated with their relationship to vertical profiles measured on the UAS, ozone concentrations, and marine boundary layer height observed with Doppler lidar to demonstrate a multi-layered lower atmosphere. A buoyant internal boundary layer was observed over land from 40–100 m AGL below highest ozone concentrations. Marine layer extent was investigated through minimum buoyancy and Richardson number analysis, showing limited vertical mixing at altitudes up to 200 m AGL, below easterly lake breeze circulation patterns extending upward to 400 m AGL in the late day. 

Climate change is intensifying the hydrologic cycle and altering ecosystem function, including water flux to the atmosphere through evapotranspiration (ET). ET is made up of evaporation (E) via non‐stomatal surfaces, and transpiration (T) through plant stomata which are impacted by global changes in different ways. E and T are difficult to measure independently at the ecosystem scale, especially across multiple sites that represent different land use and land management strategies. To address this gap in understanding, we applied flux variance similarity (FVS) to quantify how E and T differ across 13 different ecosystems measured using eddy covariance in a 10 × 10 km area from the CHEESEHEAD19 experiment in northern Wisconsin, USA. The study sites included eight forests with a large deciduous broadleaf component, three evergreen needleleaf forests, and two wetlands. Average T/ET for the study period averaged nearly 52% in forested sites and 45% in wetlands, with larger values after excluding periods following rain events when evaporation from canopy interception may be expected. A dominance analysis revealed that environmental variables explained on average 69% of the variance of half‐hourly T, which decreased from summer to autumn. Deciduous and evergreen forests showed similar E trajectories over time despite differences in vegetation phenology, and vapor pressure deficit explained some 13% of the variance E in wetlands but only 5% or less in forests. Retrieval of E and T within a dense network of flux towers lends confidence that FVS is a promising approach for comparing ecosystem hydrology across multiple sites to improve our process‐based understanding of ecosystem water fluxes.

 

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

 

Abstract. The observing system design of multidisciplinary fieldmeasurements involves a variety of considerations on logistics, safety, andscience objectives. Typically, this is done based on investigator intuitionand designs of prior field measurements. However, there is potential forconsiderable increases in efficiency, safety, and scientific success byintegrating numerical simulations in the design process. Here, we present anovel numerical simulation–environmental response function (NS–ERF)approach to observing system simulation experiments that aidssurface–atmosphere synthesis at the interface of mesoscale and microscalemeteorology. In a case study we demonstrate application of the NS–ERFapproach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balanceStudy Enabled by a High-density Extensive Array of Detectors 2019(CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered theplacement of 20 eddy covariance flux towers, operations for 72 h oflow-altitude flux aircraft measurements, and integration of various remotesensing data products. A 2 h high-resolution large eddy simulationcreated a cloud-free virtual atmosphere for surface and meteorologicalconditions characteristic of the field campaign domain and period. Toexplore two specific design hypotheses we super-sampled this virtualatmosphere as observed by 13 different yet simultaneous observing systemdesigns consisting of virtual ground, airborne, and satellite observations.We then analyzed these virtual observations through ERFs to yield an optimalaircraft flight strategy for augmenting a stratified random flux towernetwork in combination with satellite retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19'spotential to explore energy balance closure and spatial patterning scienceobjectives while substantially simplifying logistics. Owing to its modularextensibility, NS–ERF lends itself to optimizing observing system designs alsofor natural climate solutions, emission inventory validation, urban airquality, industry leak detection, and multi-species applications, among otheruse cases. 

Structurally complex forests optimize resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)‐derived canopy structural complexity (CSC) metrics across spatial scales serves as a powerful indicator of ecosystem‐scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes from nine forested sites within the 10 × 10 km CHEESEHEAD19 study domain in Northern Wisconsin, USA with drone LiDAR measurements of CSC to establish which CSC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at five resolutions (0.25, 2, 10, 25, and 50 m) to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. CSC metrics included in the structure‐function relationship as well as driver strength was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how CSC impacts light and water‐sensitive processes, and ultimately GPP. Improved models enhance our capacity to accurately simulate forest responses to management, furthering our ability to assess climate mitigation strategies.

 

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 onAdvances in Scaling and Modeling of Land‐Atmosphere Interactionsthat features articles inJGR:Biogeosciences,JGR:Atmospheres,Journal of Advances in the Modeling of Earth Systems, andEarth&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.