The eddy covariance (EC) method, used to measure the exchange of energy and mass between the land surface and atmosphere, on-balance measures greater incoming than outgoing energy fluxes. Studies have suggested that secondary circulations may be partially responsible for this energy imbalance. The missing portion of the energy budget may be captured by quantifying the advective energy fluxes with dense tower networks. In this study, we investigated whether, and under what conditions, advective fluxes of sensible and latent heat may be estimated from a high-density network of tower measurements such as those taken during the CHEESHEAD19 experiment. We applied horizontal and vertical interpolation methods to measurements of temperature and humidity in order to calculate horizontal and vertical gradients across each EC measurement site within the CHEESEHEAD19 domain. These gradients were used to quantify horizontal and vertical advective energy fluxes. Inclusion of the advective fluxes improved energy budget closure at 6 out of 15 sites while the remaining sites show an increased energy imbalance (5) or an overcorrection (4). Results indicate that a greater spatial density of measurements and an alternative vertical velocity measurement method may allow for improved advection estimates.

The planetary boundary layer (PBL) is the atmospheric layer closest to Earth’s surface that is directly influenced by surface processes, where exchanges of momentum, heat, mass, and radiation regulate environmental conditions with direct societal relevance. Because the thermodynamic structure and dynamics of the PBL are tightly coupled to surface–atmosphere interactions, accurate characterization of these processes is essential for advancing understanding of Earth system feedbacks. However, despite their importance, significant observational gaps remain in capturing the coupled surface–PBL system across the spatial and temporal scales required for both scientific and operational applications. This perspective paper articulates the central role of surface–atmosphere interactions in PBL science and their importance for advancing multiplatform observing systems. We assess the key surface variables and their required spatiotemporal resolutions needed for accurate PBL characterization, evaluate the capabilities and limitations of current global observing systems and the Program of Record—including space-based, airborne, and ground-based assets—and review emerging technological and scientific efforts aimed at addressing these gaps. Building on this assessment, we argue that advancing toward a comprehensive, surfaceinformed PBL observing system is both a scientific and societal imperative. Such a system would overcome current observational limitations and unlock substantial benefits across a wide range of applications that depend critically on accurate PBL representation, yet remain underrecognized. By synthesizing current knowledge and defining clear observational priorities, this work aims to guide the design of future PBL global observing systems and its integration, as well as to mobilize the scientific community toward coordinated, multi-scale observations of surface–atmosphere interactions, ultimately advancing PBL science and its applications on a global scale. SIGNIFICANCE STATEMENT: The planetary boundary layer (PBL) is the lowest part of the 86

We present a novel approach to reducing the surface energy imbalance over heterogeneous terrain by explicitly quantifying dispersive flux contributions using a dense, multi–tower eddy covariance (EC) network. Data were collected during the CHEESEHEAD19 campaign across a 10 km x 10 km mixed‐landscape domain in northern Wisconsin, where 17 EC towers measured momentum, sensible and latent heat fluxes, net radiation, and soil and atmospheric storage from June to October 2019. Two averaging Protocols for computing dispersive fluxes were evaluated: the conventional method (Protocol 1), in which spatial averaging is applied directly to 30-minute tower means, and a proposed ensemble‐averaging method (Protocol 2), which temporally aggregates spatial covariances at each time of day across multiple days before spatial averaging. Coordinate rotations using local and global planar‐fit methods were also compared. The results show that local planar fit minimizes contamination of vertical velocity signals. Contrary to Protocol 1, the ensemble averaging in Protocol 2 resulted in reducing the energy imbalance. Dispersive sensible and latent heat fluxes accounted for 8\% and 38\% of their respective total nighttime fluxes, with daytime contributions being smaller but still non-negligible (namely, 1.4\% and 16.2%, respectively). Incorporating dispersive fluxes reduced the energy imbalance by 19% on average. A probability density distribution showed that higher energy balance closure percentages occur during nighttime (up to 40% - 90%), while daytime dispersive fluxes closed less than 30% of the imbalance. The results support the proposed hypothesis that heterogeneity-induced spatial structures can be persistently recurring rather than persistently existent, hence ensemble averaging the covariance of spatial fluctuations over several days can capture these structures more effectively than extending the averaging window or ensemble averaging individual quantities that assume the structure is always existent. While the results demonstrate the ability of the proposed averaging Protocol to capture dispersive fluxes in EC sites, the convective boundary layer results highlight the need for including other heterogeneity-driven terms in the energy balance equation, especially advection.

India is a large country characterised by diverse bioclimatic regions and semi-natural and managed ecosystems, with some of the largest areas of arable land and mangroves, globally. Eddy covariance represents the state-orthe-art for directly quantifying the exchange of mass and energy between land surface and atmosphere. Here, we collate eddy covariance flux observations from several sites across India, covering major land use and vegetation types and spanning twenty-seven site-years. The pattern of maximum and minimum CO2 exchange differ widely among the sites and ecosystems. Croplands exhibit maximum CO2 uptake during the monsoon in response to rainfall. Some forests, croplands, and mangroves behave as well-watered ecosystems, whereas others oscillate between well-watered and water-stressed states, due to temperature and moisture dynamics. Respiration changes commensurately with photosynthetic CO2 uptake, primarily comprising growth respiration. Grasslands have a higher carbon retention capacity, followed by croplands, forests, and mangroves. CO2, water, and sensible and latent heat fluxes peaked during different times of the day across ecosystems, imprinting phase-lags that vary by site and season. Water-limited ecosystems register the highest ecosystem water use efficiency (WUE), whereas the irrigated croplands have the lowest WUE. Forests have intermediate WUE of these two; however, Indian forests (predominantly tropical and subtropical) have lower WUE than their temperate and boreal counterparts. Canopy-atmosphere coupling is tightest during the dry periods, with their physiological controls regulating the properties of the surface atmosphere. This is reversed during the monsoon when environmental control dominates physiological control. This information is essential for the long-term monitoring of these ecosystems and climate studies and will be useful to different communities, including scientists, economists, resource managers, and policymakers.

Multi-scale heterogeneities arising from terrain-induced and land surface variabilities present in complex ecosystems require accurate measurements and models of surface-atmospheric exchanges. The spatial and temporal variability of energy and water budgets over complex terrain can influence hydrological and biogeochemical interactions that can strongly influence local and regional biosphere-atmosphere transport. Scaling and accurate model representations of these interactions across complex terrains based on single-point or local measurements present a non-trivial scientific challenge. Observations were made of surface energy balance components at NOAA surface flux and mobile SURFRAD radiation stations separated by 5 kilometers along the East River, Colorado, watershed. We present the gap-filled diurnal and seasonal evolution of surface energy balance components as the study domain transitions from low to high snow cover. The spatial variability of the measured fluxes along the valley axis is discussed with the possible influences of local advection and thermally driven circulations. The measured fluxes are compared with a new bulk Richardson number parameterization for sensible heat fluxes over heterogeneous land surfaces, introduced in Lee et al. 2021, and are here extended for complex topography. Our findings provide credence for implementing such a parameterization for surface fluxes and turbulence statistics into land-surface models for representing surface-atmospheric exchanges, although caution should be used during the early-evening transition when there are significant and persistent countergradient fluxes.

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.

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.

More frequent and more severe droughts and heat waves in the context of climate change are increasingly affecting the ability of ecosystems to meet their water demand. Quantifying the water demand of different ecosystems is therefore a fundamental step in developing management strategies to maintain intact ecosystems that function as CO2 sinks. The evapotranspiration of an ecosystem can be determined as latent heat flux using the eddy covariance (EC) method. However, a commonly found gap in the energy balance indicates that the atmospheric transport of sensible and latent heat is underestimated by single-tower EC measurements. One reason for this systematic error is transport through mesoscale secondary circulations, which inherently cannot be captured by such measurements. This transport fraction, so-called dispersive fluxes, is particularly large over heterogeneous surfaces. We present a novel model that is able to predict this dispersive flux of latent heat, thereby taking into account the effect of thermal surface heterogeneity. This model has been developed by combining a machine-learning approach with a large set of idealized large-eddy simulations covering different surface-heterogeneity scales and stability regimes. We further evaluate how the model can be applied to 30-minute EC measurements without additional instrumentation at the example of the CHEESEHEAD19 field campaign. An initial application to these real-world measurements together with realistic concurrent large-eddy simulations indicate a good agreement.

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.

We explore the evolution and impacts of surface heterogeneity induced secondary circulations on the atmospheric boundary layer through coupled diurnal large eddy simulations (LES) of the CHEESEHEAD19 field campaign. The heterogeneity induced circulations were diagnosed using time and ensemble averaging. Quasi-stationary and persistent circulations were set up in the daytime ABL that span the entire mixed layer height (zi). Their variation in time and space are presented. The simulated near surface dispersive flux contribution for both the heat and moisture fluxes were 10-15% of the total daytime surface fluxes for the convective boundary layer (CBL) simulations. In the wind shear driven, near neutral ABL, the near surface contribution of dispersive heat fluxes remained at 10-15% of total surface fluxes while the dispersive moisture flux contributions were lower than 5%. These magnitudes are the same order of magnitude as the expected surface energy budget residuals. In CBL, wavelengths ~ the effective surface heterogeneity length scales at ~ 3zi contribute the most to the heterogeneity induced transport. This scale analysis supports prior work over the study domain on scaling tower measured fluxes by including low frequency contributions.This study underscores the role of ABL static stability, the ABL height and surface heterogeneity length scales in modulating the contributions from secondary circulations and supports ongoing parameterisation efforts to include their contribution in tower measured surface energy imbalance. This conceptual framework can also be extended to include the effects of sub-grid land surface heterogeneity in numerical weather prediction and climate models and further exploring scale-aware scaling methodologies.

Abstract. Observational studies and large eddy simulations (LES) have reported secondary circulations in the turbulent atmospheric boundary layer (ABL). These circulations form as coherent turbulent structures or mesoscale circulations induced by gradients of land surface properties. However, simulations have been limited in their ability to represent these events and their diurnal evolution over realistic and heterogeneous land surfaces. In this study, we present a LES framework combining the high-resolution observational data collected as part of the CHEESEHEAD19 field campaign to overcome this gap and test how heterogeneity influences the ABL response. We simulated diurnal cycles for four days chosen from late summer to early autumn over a large (49 x 52 km) heterogeneous domain. To investigate surface atmospheric feedbacks such as self-reinforcement of mesoscale circulations over the heterogeneous domain, the simulations were forced with an interactive land surface model with coupled soil, radiative transfer and plant canopy model. The lateral and model top boundary conditions were prepared from National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) meteorological analysis fields. Comparing the simulated profile and near surface data with field measured radiosonde and eddy covariance station data showed a realistic evolution of the near-surface meteorological fields, heat and moisture fluxes and the ABL. The LES had limitations in simulating the night-time cooling in the nocturnal boundary layer. The simulated fields were strongly modulated by the imposed HRRR derived mesoscale boundary conditions, resulting in a slightly warmer and drier ABL. The simulations were run without clouds which resulted in higher daytime sensible heat fluxes for some scenarios. Our findings demonstrate the capability of the PALM model system to realistically represent the daytime evolution of the ABL response over unstructured heterogeneity and the limitations involved therein with respect to the role of boundary conditions and the representation of the nocturnal boundary layer. The simulation setup and dataset described in this manuscript build the baseline to tackle specific research questions associated with the CHEESEHEAD19 campaign, particularly to address questions about the role of heterogeneous ecosystems in modulating surface-atmosphere fluxes and near surface meteorological fields as well as highlight the needed improvements in model representations of land-atmosphere feedbacks over vegetated environments.

Processes in the atmospheric boundary layer (ABL) occur at varying scales in both time and space. This chapter introduces the fundamental concepts of scale and explains in detail the two broad areas within boundary layer meteorology where this becomes important: “scale-invariance” and “scale-dependency.” “Scale-invariance” involves coming up with ways to describe common features of different boundary layers, using simple relationships or scaling factors, otherwise known as similarity theory. This will allow us to understand and define useful relationships that work at every scale. For example, to define a common vertical wind profile in the boundary layer as a function of scales of turbulence shear strength, surface roughness, and stability. Or to define the universal nature or power-law scaling exponent of energy transfer from large to small eddies. “Scale-dependency” is used when discussing how characteristic spatial scales or boundaries of features of the surface below and the atmosphere above the ABL modify profiles and structures of temperature, humidity, winds, and clouds in the boundary layer. Understanding which spatial scales matter and how, and how to incorporate those into numerical models of the boundary layer, is a core goal for this area.

<p>A combination of technological, nature-based and demand-side solutions are envisioned to avert the most drastic consequences of climate change, connected via a greenhouse gas (GHG) economy and government policies (e.g., net-zero incentives, compensations etc.). Measurement, Reporting and Verification (MRV) of GHGs reduced or removed from the atmosphere are central to ensuring that revenue streams develop in proportion to true climate benefits with equitable rewards for small and large originators.</p> <p>However, current MRV limitations (e.g., cost, robustness, interoperability, scalability, multi-year latency, etc.) curtail our ability to approach climate solutions in a well-informed and consistent manner. This challenge can be addressed by creating an MRV benchmark that is directly and frequently measured, uniformly derived, universally applicable to the technological and nature-based solutions, and traceable in near-real time and space. In order to narrow the knowledge-action gap the social and natural sciences both recognize this need for continuous information on local GHG emission and sequestration akin to weather intelligence.</p> <p>Technology transfer of the latest, most direct GHG quantification methods from academic climate science to the climate solution marketplace provides a promising avenue for creating such a benchmark: Next-generation information reconstruction (https://tinyurl.com/flux-tower-mapping) applied to existing local-to-global networks of direct GHG flux measurements can achieve unmatched statistical power, interpretability and process insight. This integration will generate an orders-of-magnitude improved stream of directly-measured emission and sequestration rates for robustly anchoring project-scale GHG mitigation and wall-to-wall remote sensing and models. The resulting benchmark directly represents a financial commodity: the physical emission and sequestration of GHGs. Thus, they can be used to manage GHGs in day-to-day practices and to assess the value of financial derivatives such as GHG certificates based on discipline-specific protocols, while accounting for reliability, storage duration and other factors.</p> <p>This approach will result in decameter-resolution maps of GHG emission and sequestration per unit of time, locked in a secure vessel such as a blockchain to prevent tampering, deleting, or modifying. Access via mobile Apps and APIs will enable public awareness and confidence, climate solution research, GHG certificate intercomparisons, development of regulatory and financial products, tools, climate-smart technologies, practices and commercial services, and national as well as local policies. Paths to monetization include licensing to credit originators, offset buyers and marketplaces, through connecting pixel-scale GHG exchange to regulatory practice for a range of GHG certificate protocols, industries, stakeholders and management practices. With this conceptual outline, we invite all types of stakeholders to join Carbon Dew: the Community of Practice that aims to anchor equitable climate solutions worldwide in direct measurements of GHG sequestration and emission (https://tinyurl.com/join-carbon-dew).</p>

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.

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.

Secondary circulations are one of the main causes of the energy balance gap that arises from the underestimation of sensible and latent heat fluxes by eddy covariance measurements because they cannot capture the energy transported by the mean wind, i.e. the so-called dispersive flux. The magnitude of the missed sensible and latent dispersive fluxes depends significantly on atmospheric stability and surface thermal heterogeneity, but there is currently no correction method that accounts for both of these relationships. Using an idealized large-eddy simulation study, we have further developed an existing approach that models the energy balance gap as a function of atmospheric stability by additionally including thermal surface heterogeneity in the parametrization. This new model has already been tested on eddy covariance measurements that were carried out at 17 stations over the course of three months during the CHEESEHEAD19 (Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors) measurement campaign and it provides promising results.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space. Plain Language Summary The terrestrial biosphere features the largest global sources and sinks of atmospheric carbon. Changes in growing season length, disturbance frequency, human management, increasing atmospheric carbon dioxide (CO2) concentrations, amount and timing of precipitation, and warmer air temperature all influence the carbon cycle. Observations from the global eddy covariance flux tower network have been key for diagnosing these changes. However, data from most sites are limited in length. Here, we explore how multi-decadal carbon flux measurements from a cluster of flux towers in forests and wetlands in the upper Midwest USA respond to environmental change. Despite the proximity of the sites, year-to-year variation in carbon fluxes was rarely similar between sites. Surprisingly, warmer winters promoting earlier snowmelt led to later spring green-up because soil temperature was colder. Impacts of higher CO2 and warmer temperature on annual carbon fluxes were limited but did influence factors linking carbon flux sensitivity to climate. Differences in flux magnitudes from a very tall tower flux to the network show that the whole does not seem to be simply a sum of its measured parts. More elaborate approaches may be needed to understand the processes that control carbon fluxes across large landscapes.

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 continuously 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 ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r2 = 0.79, RMSE = 3.5 K) and airborne (r2 = 0.75, RMSE = 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 a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST.

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.