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

Whiplash events, defined by abrupt transitions between dry and wet conditions, have severe environmental and societal impacts, disrupting agriculture, water resources, and infrastructure. This study examines the role of land-atmosphere (L-A) interactions in driving these events by utilizing the convective triggering potential-humidity index (CTP-HI) framework to analyze dry-to-wet (DW) and wet-to-dry (WD) transitions across different regions and seasons. The results highlight that moisture availability in the lower atmosphere (HI) is a critical driver of whiplash intensity. At the same time, convective potential (CTP) is more responsive to seasonal and thermal variability, particularly during warmer periods. A global assessment identifies key high-risk hotspots in North America and Europe and localized areas across southern Africa, southern Asia, and South America, where transitions are both frequent and intense. These high-risk regions exhibit dual vulnerability: rapid moisture loss during WD events due to positive feedback and limited recovery during DW events, driven by suppressed evaporation. In contrast, low-risk regions demonstrate stronger recovery capacity and more moderated transitions. By revealing distinct L-A feedback mechanisms and transition patterns, this study improves our understanding of whiplash dynamics and highlights the global regions most susceptible to extreme shifts. The insights gained offer a valuable basis for enhancing predictive models and guiding mitigation strategies in areas facing the most tremendous hydrometeorological stress.

A global planetary boundary layer (PBL) observing system is urgently needed to address fundamental PBL science questions and societal applications related to climate, weather, and air quality. Such a PBL observing system should optimally combine emerging yet technically viable space-based observations of the PBL thermodynamic structure with complementary surface-based and suborbital assets, while taking advantage of, and helping improve, climate and weather models as well as data assimilation systems. The Earth science community has expressed great interest in improving the characterization of the atmospheric PBL in the recent National Academies of Sciences, Engineering, and Medicine (NASEM) 2017-27 decadal survey for Earth Science and Applications from Space (ESAS). Specifically, higher spatial and temporal resolution observations of PBL temperature and water vapor profiles, and of PBL height, were selected as priorities by the decadal survey, which recommended a PBL mission in its incubation class. In response, NASA established the Decadal Survey Incubation program and a PBL Study Team focused on prioritizing PBL science and technology that would require advancement and development prior to implementation. In this paper, we summarize the key findings of the NASA PBL Study Team report. SIGNIFICANCE STATEMENT: The planetary boundary layer (PBL) is the atmospheric turbulent layer adjacent to Earth's surface. Humans live in the PBL, and the weather and climate that we experience have a tremendous impact on our health, safety, and economy. The science commu-nity has manifested an urgent need for a global PBL observing system focused on PBL profiles of temperature and water vapor, as well as PBL height, to address fundamental PBL science ques-tions and societal applications. The measurement requirements of such a system will likely not be satisfied using a single measurement technology, and this global PBL observing system should optimally combine a diverse set of space-based observations with suborbital and surface-based measurements.

Extreme weather events cause significant societal impacts, in particular, when they behave unexpectedly. The "brown ocean (BO)" effect, describing the ability of the land surface via soil moisture to support tropical cyclone (TC) maintenance and intensification (TCMI) after landfall, remains poorly understood. Building upon our previous modeling framework utilizing the NASA Unified WRF (NU-WRF) system, this follow-on study explores the contributions of the dynamics of the soil moisture and advected water vapor to the TCMI of Tropical Storm (TS) Bill (2015) over the U.S. southern Great Plains (SGP). Impacts of various soil moisture conditions and surface enthalpy flux conditions on Bill's inland intensification were investigated by comparing their land-atmosphere interaction components of energy fluxes along with a backward trajectory analysis and three-dimensional visualization of low-level atmospheric moisture. Results demonstrate that the high antecedent soil moisture across the central United States (Great Plains and Mississippi Valley) from prior rainfall was crucial for the TCMI of TS Bill over the SGP. Without ample latent heat flux over land, even a moisture- laden low-level jet from the ocean rapidly dried over land, preventing intensification and causing storm dissipation in our simulations. Backward trajectory analysis suggests that high soil moisture content can enhance humidity within the storm's inflow, including the advection from the ocean, far inland, thus supporting the TCMIs. Ultimately, the inflow feeding the inland TC core is influenced by active land-air interactions within the boundary layer, where soil moisture and lower boundary conditions directly impact lower-tropospheric humidity, enabling or hindering the BO effect and subsequent TC intensification.

Commercial radio occultation (RO) satellites that utilize the Global Navigation Satellite System (GNSS) signals are emerging as key tools for observing the polar regions, which are not covered by the second-generation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) mission. This study evaluates the value of commercial RO measurements, specifically Spire and GeoOptics, for planetary boundary layer (PBL) investigations in the Arctic, a region where favorable lower-atmospheric penetration of GNSS RO is vital for observing the persistently shallow PBL. The lower tropospheric penetration capability of both Spire and GeoOptics over the Arctic Ocean, with nearly 80 % observations reaching an altitude of 500 m above mean sea level, is comparable to other RO missions such as the current Meteorological Operational satellite programme (MetOp) and the discontinued COSMIC-1 missions. A seasonal cycle in RO penetration probability, with the minimum occurring during the Arctic warm season, was observed in most RO datasets, except NASA-purchased Spire data. Monthly mean Arctic PBL height (PBLH) derived from Spire and GeoOptics compares well with MetOp observations and the reanalysis from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). A minimum penetration threshold of 500 m generally suffices for determining Arctic PBLH, although a 300 m threshold improves performance of NASA-purchased Spire data. Arctic PBLH representation is influenced less by the number of observations or instrument type and more by the algorithms used for bending angle and refractivity retrievals. These findings underscore the importance of processing algorithms in achieving accurate lower-atmospheric soundings and Arctic PBLH representation.

The Diurnal Land–Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land‐surface and atmospheric boundary‐layer modelling groups. A simple three‐stage methodology is designed to assess land–atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand‐alone models to variations in driving data is explored. For this initial study, a 3‐day clear‐sky period in the mid‐west United States over, an assumed simple, predominantly grass surface was simulated using data from the CASES‐99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within land‐surface models needs attention; (2) the height of atmospheric forcing for land‐surface models is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land–atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity, and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture, and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary‐layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the Global Land–Atmosphere Coupling Experiment.

The Diurnal Land–Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land-surface and atmospheric boundary-layer modelling groups. A simple three-stage methodology is designed to assess land–atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand-alone models to variations in driving data is explored. For this initial study, a 3-day clear-sky period in the mid-west United States over, an assumed simple, predominantly grass surface was simulated using data from the CASES-99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within land-surface models needs attention; (2) the height of atmospheric forcing for land-surface models is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land–atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity, and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture, and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary-layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the Global Land–Atmosphere Coupling Experiment.

Land–atmosphere (L–A) coupling can play a crucial role for subseasonal-to-seasonal (S2S) predictability and prediction. When coupling is strong, L–A processes and feedback are expected to enhance the system's memory, thereby increasing the predictability and prediction skill. This study evaluates the subseasonal prediction of ambient surface air temperature under conditions of strong versus weak L–A coupling in forecasts produced with NASA's state-of-the-art Goddard Earth Observing System (GEOS) S2S forecast system. By applying three L–A coupling metrics that collectively capture the connection between the soil and the free troposphere, we observe improved prediction skill for surface air temperature during weeks 3–4 of boreal summer forecasts across the Midwest and northern Great Plains, particularly when all three indices indicate strong L–A coupling at this lead time. The prediction skill indeed increases as more indices show strong coupling. The forecasts with strong L–A coupling in these regions tend to exhibit sustained warm and dry anomalies, signals that are well simulated in the model. Overall, this study highlights how better identifying and capturing relevant L–A coupling processes can potentially enhance prediction on S2S timescales.

This study investigates the assimilation of planetary boundary layer height (PBLH) data into a convective-permitting Weather Research and Forecasting (WRF) Model using the ensemble-based Data Assimilation Research Testbed (DART). We conduct experiments to (i) identify effective approaches for assimilating Doppler lidar-retrieved PBLH and (ii) assess its impact on analysis and forecast performance during four intensive observation periods (IOPs) of low-level jets (LLJs) from the Plains Elevated Convection at Night (PECAN) field campaign (June-July 2015). Using a multiphysics ensemble with a rank histogram filter and Boxcar-Ramp covariance localization (1 km vertical and 50 km horizontal half-width) and observation error of 20% of observed PBLH, our results show that PBLH assimilation improves the analysis and 6-h forecast of potential temperature (u), water vapor mixing ratio (q), and especially wind (u, y) profiles, including LLJs, relative to no assimilation. These findings demonstrate the value of PBLH assimilation despite its lack of direct constraints on thermodynamic profiles. We suggest that assimilating PBLH within the standard 12-hourly sounding intervals could serve as a complement to radiosonde assimilation, which provides direct thermodynamic constraints. Further examining PBLH sensitivity to thermodynamic variables, optimizing covariance localization, and considering surface-atmosphere interactions could strengthen observational constraints, retain information, and enhance forecast performance. SIGNIFICANCE STATEMENT: The study of data assimilation for planetary boundary layer datasets into numerical weather prediction models is still in its early stages of development. Our study tested the aforementioned procedure with various parameters and strategies. The results indicate that the data assimilation of planetary boundary layer height can improve model performance in both the analysis and forecast stages for temperature, water vapor, and wind profiles. We suggest that the data assimilation of planetary boundary layer-related measurements should also include thermodynamic measurements to optimize performance.

Abstract. Land-atmosphere (L-A) coupling can play a crucial role for subseasonal-to-seasonal (S2S) predictability and prediction. When coupling is strong, L-A processes and feedback are expected to enhance the system’s memory, thereby increasing the predictability and prediction skill. This study evaluates subseasonal prediction of ambient surface air temperature under conditions of strong versus weak L-A coupling in forecasts produced with NASA’s state-of-the-art Goddard Earth Observing System (GEOS) S2S forecast system. By applying three L-A coupling metrics that collectively capture the connection between the soil and the free troposphere, we observe improved prediction skill for surface air temperature during weeks 3–4 of boreal summer forecasts across the Midwest and northern Great Plains, particularly when all three indices indicate strong L-A coupling at this lead time. The prediction skill indeed increases as more indices show strong coupling. The forecasts with strong L-A coupling in these regions tend to exhibit sustained warm and dry anomalies, signals that are well simulated in the model. Overall, this study highlights how better identifying and capturing relevant L-A coupling processes can potentially enhance prediction on S2S timescales.

Land-use land-cover change affects weather and climate. This paper quantifies fi es land-atmosphere interactions over irrigated and nonirrigated land uses during the Great Plains Irrigation Experiment (GRAINEX). Three coupling metrics were used to quantify land-atmosphere interactions as they relate to convection. They include the convective triggering potential (CTP), the low-level humidity index (HIlow), low ), and the lifting condensation level (LCL) deficit. fi cit. These metrics were calculated from the rawinsonde data obtained from the Integrated Sounding Systems (ISSs) for Rogers Farm and York Airport along with soundings launched from the three Doppler on Wheels (DOW) sites. Each metric was categorized by intensive observation period (IOP), cloud cover, and time of day. Results show that with higher CTP, lower HIlow, low , and lower LCL deficit, fi cit, conditions were more favorable for convective development over irrigated land use. When metrics were grouped and analyzed by IOP, compared to nonirrigated land use, HI low was found to be lower for irrigated land use, suggesting favorable conditions for convective development. Furthermore, when metrics were grouped and analyzed by clear and nonclear days, CTP values were higher over irrigated cropland than nonirrigated land use. In addition, compared to nonirrigated land use, the LCL deficit fi cit during the peak growing season was lower over irrigated land use, suggesting a favorable condition for convection. It is found that with the transition from the early summer to the mid/peak summer and increased irrigation, the environment became more favorable for convective development over irrigated land use. Finally, it was found that regardless of background atmospheric conditions, irrigated land use provided a favorable environment for convective development.

Abstract. Commercial GNSS RO products are being touted for their coverage in polar regions where COSMIC-2 observations don’t reach. This study seeks to explore their value for Arctic PBL investigations where sufficient lower atmospheric penetration of GNSS RO is vital for representing the persistently shallow PBL. Both NASA purchased commercial RO products, Spire and GeoOptics, have improved lower tropospheric penetration probability over the Arctic Ocean compared to MetOp observations, with Spire having greater volume of observations (nearly two orders of magnitude) compared to GeoOptics. A seasonal cycle is evident in the RO penetration probability (except for Spire) that is found to be related to the water vapor pressure. For winter months, at the 500 m level, which is the standard cut-off threshold used for GNSS RO PBL studies, both products yield a penetration probability of ~80 % of total observations over the Arctic Ocean and up to ~100 % over the frozen sea ice region. As a result, both products are able to sufficiently represent the shallow Arctic PBLH (less than 300 m depth) which is comparable to the PBLH from MERRA-2 reanalysis, unlike MetOp observations which fails to capture PBL heights below 400 m.

​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP.

Land-atmosphere (L-A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L-A coupling (LoCo) metrics capture relevant L-A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L-A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land-atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics.Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L-A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L-A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

In recent years, there has been a growing recognition of the significance of land–atmosphere (L–A) interactions and feedback mechanisms in understanding and predicting Earth's water and energy cycles. Soil moisture plays a critical role in mediating the strength of L–A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L–A coupling strength within the convective triggering potential (CTP) and humidity index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using triple collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L–A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over 2 decades (2003–2022) where L–A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP–HI underscores the importance of soil moisture data in estimating coupling strength within the CTP–HI framework. These findings lay the groundwork for understanding the role of L–A interactions and drought evolution due to soil moisture variations by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Currently, the planetary boundary layer (PBL) is challenging to assess in PBL remote sensing retrievals from space. To address this, we have developed a new 3D deep neural network (DNN) which enhances detail and reduces noise in 3D granules of temperature and humidity retrieved from hyperspectral infrared and microwave sounders. We show that this approach improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.

Coastal areas are some of the most densely populated and economically important regions in the world. As such, protecting the health of the human population and ecosystems at the coastal interface and understanding the impacts of environmental stressors such as air pollutants provides wide-ranging benefits. Air quality (AQ) processes within coastal regions have been studied using ground and space-based platforms, with intensive field campaigns focused on addressing key science questions that are typically partitioned into either direct atmospheric effects (e.g., anthropogenic emissions creating air pollution) or indirect processes and feedback loops (e.g., terrestrial/marine biogenic processes modifying atmospheric properties). The atmospheric planetary boundary layer (PBL) and its depth (or height) connect land, air, and the water surface via many pathways, especially with transport and exchange processes tied to the complexities of the coastal interface. We still cannot accurately characterize-through field, aircraft, or space-based observations-the spatial and temporal PBL variability and processes within the PBL that couple together coastal dynamics and air quality. Several upcoming geostationary and polar-orbiting satellite missions are likely to make significant progress in characterizing these air/land/water interactions over the next decade. Here, we present a framework of the current understanding of the PBL's role in coastal regions, primarily regarding air quality and atmospheric deposition, to motivate future concerted efforts from ground- and space-based platforms to achieve a holistic understanding of the coastal interface.

A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT's "around-the-clock" measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth's radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distribu-tions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth's climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world's population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near-real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

The transport of water, heat, and momentum from the surface to the atmosphere is dependent, in part, on the characteristics of the land surface. Along with the model physics, parameterization schemes, and parameters employed, land datasets determine the spatial variability in land surface states (i.e., soil moisture and temperature) and fluxes. Despite the importance of these datasets, they are often chosen out of convenience or owing to regional limitations, without due assessment of their impacts on model results. Irrigation is an anthropogenic form of land heterogeneity that has been shown to alter the land surface energy balance, ambient weather, and local circulations. As such, irrigation schemes are becoming more prevalent in weather and climate models, with rapid developments in dataset availability and parameterization scheme complexity. Thus, to address pragmatic issues related to modeling irrigation, this study uses a high-resolution, regional coupled modeling system to investigate the impacts of irrigation dataset selection on land-atmosphere (L-A) coupling using a case study from the Great Plains Irrigation Experiment (GRAINEX) field campaign. The simulations are assessed in the context of irrigated vs. nonirrigated regions, subregions across the irrigation gradient, and sub-grid-scale process representation in coarser-scale models. The results show that L-A coupling is sensitive to the choice of irrigation dataset and resolution and that the irrigation impact on surface fluxes and near-surface meteorology can be dominant, conditioned on the details of the irrigation map (e.g., boundaries and heterogeneity), or minimal. A consistent finding across several analyses was that even a low percentage of irrigation fraction (i.e., 4 %-16 %) can have significant local and downstream atmospheric impacts (e.g., lower planetary boundary layer, PBL, height), suggesting that the representation of boundaries and heterogeneous areas within irrigated regions is particularly important for the modeling of irrigation impacts on the atmosphere in this model. When viewing the simulations presented here as a proxy for "ideal" tiling in an Earth-system-model-scale grid box, the results show that some "tiles" will reach critical nonlinear moisture and PBL thresholds that could be important for clouds and convection, implying that heterogeneity resulting from irrigation should be taken into consideration in new sub-grid L-A exchange parameterizations.