The spatiotemporal variability of latent heat flux (LE) and water vapor mixing ratio (rv) variability are not well understood due to the scale-dependent and nonlinear atmospheric energy balance responses to land surface heterogeneity. Airborne in situ and profiling Raman lidar measurements with the wavelet technique are utilized to investigate scale-dependent relationships among LE, vertical velocity (w) variance (s2w), and rv variance (s2wv) over a heterogeneous surface in the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, s2w, and s2wv at 100 m height, with a majority scale range of 120m-4km in LE, 32m-2km in s2w, and 200 m – 8 km in s2wv. The scales are classified into three scale ranges, the turbulent scale (8m–200m), large-eddy scale (200m–2km), and mesoscale (2 km–8km) to evaluate scale-resolved LE contributed by s2w and s2wv. In the large-eddy scale in Planetary Boundary Layer (PBL), 69-75% of total LE comes from 31-51% of the total sw and 39-59% of the total s2wv. Variations exist in LE, s2w, and s2wv, with a range of 1.7-11.1% of total values in monthly-mean variation, and 0.6–7.8% of total values in flight legs from July to September. These results confirm the dominant role of the large-eddy scale in the PBL in the vertical moisture transport from the surface to the PBL. This analysis complements published scale-dependent LE variations, which lack detailed scale-dependent vertical velocity and moisture information.

The drag coefficient (CDN), Stanton number (CHN) and Dalton number (CEN) are of particular importance for the bulk estimation of the surface turbulent fluxes of momentum, heat and water vapor at water surfaces. Although these bulk transfer coefficients have been extensively studied over the past several decades mainly in marine and large-lake environments, there are no studies focusing on their synthesis for many lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy-covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that generally CDN, CHN, CEN (adjusted to neutral atmospheric stability) were within the range reported in previous studies for large lakes and oceans. CHN was found to be on average a factor of 1.4 higher than CEN for all wind speeds, therefore, likely affecting the Bowen ratio method used for lake evaporation measurements. All bulk transfer coefficients exhibit substantial increase at low wind speeds (< 3 m s-1), which could not be explained by any of the existing physical approaches. However, the wind gustiness could partially explain this increase. At high wind speeds CDN, CHN, CEN remained relatively constant at values of 2 10-3, 1.5 10-3, 1.1 10 -3, respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area or wind fetch. The empirical formula C=b1[1+b2exp(b3 U10)] described the dependence of CDN, CHN, CEN on wind speed well and it could be beneficial for modeling when coupling atmosphere and lakes.

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.

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 non-linear. 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-{$\beta$}, $\gamma$) 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 (CHEESEHEAD19) 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 H and LE fluxes through summer to autumn which wouldn’t 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 Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June-October 2019. The purpose of the study is to examine how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model-data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10×10 km domain of a heterogeneous forest ecosystem in the Chequamegon-Nicolet National Forest in northern Wisconsin USA, centered on the existing Park Falls 447-m tower that anchors an Ameriflux/NOAA supersite (US-PFa / WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft, maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology, and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large eddy simulation and scaling experiments to better understand sub-mesoscale processes and improve formulations of sub-grid scale processes in numerical weather and climate models.

Groundwater depletion in Central Wisconsin, due in part to agricultural high-capacity wells, has sparked an interest in precision irrigation to reduce groundwater pumping without a significant reduction in yield. A key challenge for bridging precision irrigation research and application is how best to monitor water stress in real-time. Aerial and satellite imagery are potential solutions. Drawbacks of these methods include cost, spatiotemporal resolution, and cloud interference, especially in humid regions. Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. We partnered with the Wisconsin Potato and Vegetable Grower Association to generate high-resolution maps of crop water stress using remotely sensed thermal and multi-spectral RPA imagery. Data were collected at a commercially irrigated potato field in the Central Sands region of Wisconsin from June to August 2019. Missions were flown weekly using a quadcopter RPA system instrumented with a newly released, combined multispectral/thermal camera developed for agricultural applications. Each mission included flights at 30, 60, and 90 m above ground level to assess tradeoffs between resolution, area, and flight time. We used biophysical data from an eddy covariance system installed within the flight domain to validate crop water stress maps generated from the remotely sensed RPA data. Ground measurements of surface temperature and soil moisture were collected throughout the domain within fifteen minutes of each mission. Ongoing results will be used to develop best practices for integrating RPAs into precision irrigation programs.

The surface is the interface through which the bedrock interacts with the boundary-layer. Here, living organisms of various shapes, sizes, and function intermingle with the mineral soil, organic residue, and canopy air space that reside there. Together, they breath, absorb momentum, exchange water and gases, and bask in the heat from the sun and clouds above and thermal reservoirs below. While many of these functions are well understood, we knew less about how those functions operate and behave at different spatial and time scales. More intriguing, surface variance with scale influences scales of motion in the atmosphere. Here, I present a generalized look at how land and atmosphere scales interact, focusing on the lens of the surface energy budget. These processes are investigated through intensive measurements and high-resolution models conducted at the CHEESEHEAD19 field experiment in Wisconsin. A combination of airborne and tower eddy covariance networks, drone and airborne canopy imaging, and turbulence resolving simulations reveal persistent mesoscale contributions in the atmosphere enabled by surface heterogeneity.

Lakes emit globally significant amounts of carbon dioxide (CO) to the atmosphere, but quantifying these rates for individual lakes is extremely challenging. The exchange of CO across the air-water interface is driven by physical, chemical, and biological processes in both the lake and the atmosphere that vary at multiple spatial and temporal scales. None of the methods we use to estimate CO flux fully capture this heterogeneous process. Here, we compared concurrent CO flux estimates from a single lake based on commonly used methods. These include floating chambers (FC), eddy covariance (EC), and two concentration gradient based methods labelled fixed (F-CO₂) and spatial (S-CO₂). At the end of summer, cumulative carbon fluxes were similar between EC, F-CO₂ and S-CO₂ methods (-4, -4 and -9.5 gC), while methods diverged in directionality of fluxes during the fall turnover period (-50, 43 and 38 gC). Collectively these results highlight the discrepancies among methods and the need to acknowledge the uncertainty when using any of them to approximate this heterogeneous process.

Extratropical cyclones are major contributors to consequential weather in the mid-latitudes and tend to develop in regions of enhanced cyclogenesis and progress along climatological storm tracks. Numerous studies have noted the influence that terrestrial snow cover exerts on atmospheric baroclinicity which is critical to the formation and trajectories of such cyclones. Fewer studies have examined the explicit role which continental snow cover extent has in determining cyclones intensities, trajectories, and precipitation characteristics. While several examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century, none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th , 50th , and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 Coupled Model Intercomparison Project Phase Five (CMIP5) models is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Cyclone trajectories and their central sea level pressure did not change substantially, but followed consistent spatial trends. Near-surface wind speed generally increased, as did precipitation with preferred phase change from solid to liquid state. Cyclone-associated precipitation often shifted poleward as snow was removed. Variable responses were dependent on the month in which cyclones occurred, with stronger responses in the midwinter than the shoulder months.

Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify the magnitude and dynamics at multiple temporal scales. We found pronounced diel and sub-monthly oscillatory variations in CO2 flux at all sites. Diel variation converted sites to daily net sinks of CO2 in only 11% of site-months. Upscaled annual emissions had an average of 25% (range 3-58%) interannual variation. Given temporal variation remains under-represented in inventories of CO2 emissions from lakes and reservoirs, revisions in CO2 flux are needed using a better representation of sub-daily to interannual variability. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.

Drought is a recurring and extreme hydroclimatic hazard with serious impacts on agriculture and overall society. Delineation and forecasting of agricultural and meteorological drought are essential for water resource management and sustainable crop production. Agricultural drought assessment is defined as the deficit of root-zone soil moisture (RZSM) during active crop growing season, whereas meteorological drought is defined as subnormal precipitation over months to years. Several indices have been used to characterize droughts, however, there is a lack of study focusing on comprehensive comparison among different agricultural and meteorological drought indices for their ability to delineate and forecast drought across major climate regimes and land cover types. This study evaluates the role of RZSM from Soil Moisture Active Passive (SMAP) mission along with two other soil moisture (SM) based indices (e.g., Palmer Z and SWDI) for agricultural and meteorological drought monitoring in comparison with two popular meteorological drought indices (e.g., SPEI and SPI) and a hybrid (Comprehensive Drought Index, CDI) drought index. Results demonstrate that SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought events better than meteorological (e.g., SPI, SPEI) and hybrid (CDI) drought indices, whereas the latter three performed better in delineating meteorological drought across the contiguous USA during 2015–2019. SM-based indices showed skills for forecasting agricultural drought (represented by end-of-growing season gross primary productivity) in the early growing seasons. The results further confirm the key role of SM on ecosystem dryness and corroborate the SM-memory in land-atmosphere coupling.

Surface-atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub-grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub-kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES-16 and ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio-temporal variation compared to geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.