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Abstract Understanding the role of snow sublimation in the alpine water balance is critical to predicting future water resource availability. During winter 2022–23, the Sublimation of Snow campaign in Colorado’s East River watershed used 12 eddy covariance (EC) instruments (2–20-m height) to measure sublimation and micrometeorology on the valley floor. ECs measured 33–42 mm of snow water equivalent sublimated (8%–10% of seasonal peak snow accumulation). Midwinter sublimation was driven by blowing snow and springtime sublimation by positive net radiation. During blowing snow, EC water vapor fluxes increased with height between 3 and 10 m, on average by 26% and by up to 200% during individual events (positive vertical turbulent flux divergence). During nonblowing snow conditions, fluxes decreased with height between 3 and 20 m, on average by 36% (negative vertical turbulent flux divergence). Estimates of transport terms in a water vapor conservation equation suggest that positive divergence arose from blowing snow sublimation and negative divergence arose from vertical water vapor advection, although horizontal advection remains unquantified, limiting our conclusions. We found that keeping one instrument functional over the entire winter is more important than having instruments at multiple heights. Seasonal uncertainty in measured total sublimation due to instrument height is estimated at ±12% due to blowing snow sublimation and water vapor advection; however, for shorter deployments, this uncertainty may be larger. The optimal instrument height for estimating total sublimation, 10 m at our site, is likely to vary by location, and further work is needed to understand the role of advection. Significance StatementMountain snowpacks act as water reservoirs for populations worldwide, and snow sublimation, which is rarely measured, removes water from those reservoirs. A recent measurement campaign offered the unique opportunity to compare sublimation measurements from 12 instruments, which reveal that sublimation estimates vary both across a winter season and with instrument height. Sublimation rates are higher during times with blowing snow compared to times without. During blowing snow, higher instruments (above 5 m) measure 26% more sublimation than lower instruments. Otherwise, higher instruments (above 10 m) measure 36% less sublimation. This work concludes that when measuring alpine sublimation, instrument height can introduce uncertainty, particularly if instruments are deployed for short periods of time. To best estimate total sublimation, instruments should be deployed over an entire winter season. 

Abstract In the last decades the energy-balance-closure problem has been thoroughly investigated from different angles, resulting in approaches to reduce but not completely close the surface energy balance gap. Energy transport through secondary circulations has been identified as a major cause of the remaining energy imbalance, as it is not captured by eddy covariance measurements and can only be measured additionally with great effort. Several models have already been developed to close the energy balance gap that account for factors affecting the magnitude of the energy transport by secondary circulations. However, to our knowledge, there is currently no model that accounts for thermal surface heterogeneity and that can predict the transport of both sensible and latent energy. Using a machine-learning approach, we developed a new model of energy transport by secondary circulations based on a large data set of idealized large-eddy simulations covering a wide range of unstable atmospheric conditions and surface-heterogeneity scales. In this paper, we present the development of the model and show first results of the application on more realistic LES data and field measurements from the CHEESEHEAD19 project to get an impression of the performance of the model and how the application can be implemented on field measurements. A strength of the model is that it can be applied without additional measurements and, thus, can retroactively be applied to other eddy covariance measurements to model energy transport through secondary circulations. Our work provides a promising mechanistic energy balance closure approach to 30-min flux measurements. 

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. 

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. 

Abstract. The Arctic marine environment plays an important role inthe global carbon cycle. However, there remain large uncertainties in howsea ice affects air–sea fluxes of carbon dioxide (CO₂), partially dueto disagreement between the two main methods (enclosure and eddy covariance)for measuring CO₂ flux ( $F_{\mathrm{CO}{}_2}$). The enclosure method has appearedto produce more credible $F_{\mathrm{CO}{}_2}$ than eddy covariance (EC), but is notsuited for collecting long-term, ecosystem-scale flux datasets in suchremote regions. Here we describe the design and performance of an EC systemto measure $F_{\mathrm{CO}{}_2}$ over landfast sea ice that addresses the shortcomingsof previous EC systems. The system was installed on a 10m tower onQikirtaarjuk Island – a small rock outcrop in Dease Strait located roughly35km west of Cambridge Bay, Nunavut, in the Canadian Arctic Archipelago. Thesystem incorporates recent developments in the field of air–sea gasexchange by measuring atmospheric CO₂ using a closed-path infrared gasanalyzer (IRGA) with a dried sample airstream, thus avoiding the known watervapor issues associated with using open-path IRGAs in low-flux environments.A description of the methods and the results from 4 months of continuousflux measurements from May through August 2017 are presented, highlightingthe winter to summer transition from ice cover to open water. We show thatthe dried, closed-path EC system greatly reduces the magnitude of measured $F_{\mathrm{CO}{}_2}$ compared to simultaneous open-path EC measurements, and for thefirst time reconciles EC and enclosure flux measurements over sea ice. Thisnovel EC installation is capable of operating year-round on solar and windpower, and therefore promises to deliver new insights into the magnitude ofCO₂ fluxes and their driving processes through the annual sea icecycle.