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Abstract
Diurnal variation in soil heat flux is a key constraint on the amount of energy available for sensible and latent heating of the lower troposphere. Many studies have demonstrated that soil heat flux G is strongly correlated with net radiation R n . However, methods to parameterize G based on this relationship typically do not account for the dependency of G on soil properties and ignore asymmetry in the diurnal variation of G relative to R n . In this paper, the diurnal behavior of G as a function of R n is examined for sparse cover and bare soil conditions, focusing on patterns of diurnal variation as well as on the effects of soil moisture and soil type. To this end, information from field data is combined with simulations from a multilayer, diffusion-based soil model over a range of soil conditions and vegetation densities. The results show that a relatively simple function can be used to capture the first-order diurnal covariation between G and R n . Within this framework, soil moisture exerts an important control on this relationship. When soils make the transition from stage-1 (atmosphere limited) to stage-2 (soil limited) evaporation, the ratio of G to R n tends to increase. Further, soils in stage-2 evaporation exhibit positive G later in the day relative to moist soils. Data from several field experiments show that the amplitude of diurnal surface temperature can be used to predict the magnitude and behavior of G/R n by integrating the effects of soil type and moisture. Based on these results, a method to estimate G/R n is proposed that provides a robust representation of G/R n on hourly timescales for varying soil conditions. This method provides improvement over previous semiempirical treatments for G for which diurnal energy balance closure is required.
Abstract
Diurnal variation in soil heat flux is a key constraint on the amount of energy available for sensible and latent heating of the lower troposphere. Many studies have demonstrated that soil heat flux G is strongly correlated with net radiation R n . However, methods to parameterize G based on this relationship typically do not account for the dependency of G on soil properties and ignore asymmetry in the diurnal variation of G relative to R n . In this paper, the diurnal behavior of G as a function of R n is examined for sparse cover and bare soil conditions, focusing on patterns of diurnal variation as well as on the effects of soil moisture and soil type. To this end, information from field data is combined with simulations from a multilayer, diffusion-based soil model over a range of soil conditions and vegetation densities. The results show that a relatively simple function can be used to capture the first-order diurnal covariation between G and R n . Within this framework, soil moisture exerts an important control on this relationship. When soils make the transition from stage-1 (atmosphere limited) to stage-2 (soil limited) evaporation, the ratio of G to R n tends to increase. Further, soils in stage-2 evaporation exhibit positive G later in the day relative to moist soils. Data from several field experiments show that the amplitude of diurnal surface temperature can be used to predict the magnitude and behavior of G/R n by integrating the effects of soil type and moisture. Based on these results, a method to estimate G/R n is proposed that provides a robust representation of G/R n on hourly timescales for varying soil conditions. This method provides improvement over previous semiempirical treatments for G for which diurnal energy balance closure is required.
Abstract
Rapid soil-surface drying, which is called “decoupling,” accompanied by an increase in near-surface air temperature and sensible heat flux, is typically confined to the top 1–2 cm of the soil, while the deeper layers remain relatively moist. Because decoupling depends also on a precise knowledge of fractional vegetation cover, soil properties, and soil water content, an accurate knowledge of these parameters is essential for making good predictions of temperature and humidity. Accordingly, some simulations centered on the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed Southern Great Plains site in Kansas and Oklahoma using a high-resolution substrate layer (Simulator for Hydrology and Energy Exchange at the Land Surface), the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, and derived and default values for soil water content and fractional vegetation cover are presented. In so doing, the following points are made: 1) decoupling occurs only within certain threshold ranges of soil water content that are closely related to the soil type and 2) a knowledge of fractional vegetation cover derived from concurrent observations is necessary for capturing the spatial variation in rapid soil drying in forecast models.
Abstract
Rapid soil-surface drying, which is called “decoupling,” accompanied by an increase in near-surface air temperature and sensible heat flux, is typically confined to the top 1–2 cm of the soil, while the deeper layers remain relatively moist. Because decoupling depends also on a precise knowledge of fractional vegetation cover, soil properties, and soil water content, an accurate knowledge of these parameters is essential for making good predictions of temperature and humidity. Accordingly, some simulations centered on the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed Southern Great Plains site in Kansas and Oklahoma using a high-resolution substrate layer (Simulator for Hydrology and Energy Exchange at the Land Surface), the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, and derived and default values for soil water content and fractional vegetation cover are presented. In so doing, the following points are made: 1) decoupling occurs only within certain threshold ranges of soil water content that are closely related to the soil type and 2) a knowledge of fractional vegetation cover derived from concurrent observations is necessary for capturing the spatial variation in rapid soil drying in forecast models.
Abstract
The convective planetary boundary layer (PBL) integrates surface fluxes and conditions over regional and diurnal scales. As a result, the structure and evolution of the PBL contains information directly related to land surface states. To examine the nature and magnitude of land–atmosphere coupling and the interactions and feedbacks controlling PBL development, the authors used a large sample of radiosonde observations collected at the southern Atmospheric Research Measurement Program–Great Plains Cloud and Radiation Testbed (ARM-CART) site in association with simulations of mixed-layer growth from a single-column PBL/land surface model. The model accurately predicts PBL evolution and realistically simulates thermodynamics associated with two key controls on PBL growth: atmospheric stability and soil moisture. The information content of these variables and their influence on PBL height and screen-level temperature can be characterized using statistical methods to describe PBL–land surface coupling over a wide range of conditions. Results also show that the first-order effects of land–atmosphere coupling are manifested in the control of soil moisture and stability on atmospheric demand for evapotranspiration and on the surface energy balance. Two principal land–atmosphere feedback regimes observed during soil moisture drydown periods are identified that complicate direct relationships between PBL and land surface properties, and, as a result, limit the accuracy of uncoupled land surface and traditional PBL growth models. In particular, treatments for entrainment and the role of the residual mixed layer are critical to quantifying diurnal land–atmosphere interactions.
Abstract
The convective planetary boundary layer (PBL) integrates surface fluxes and conditions over regional and diurnal scales. As a result, the structure and evolution of the PBL contains information directly related to land surface states. To examine the nature and magnitude of land–atmosphere coupling and the interactions and feedbacks controlling PBL development, the authors used a large sample of radiosonde observations collected at the southern Atmospheric Research Measurement Program–Great Plains Cloud and Radiation Testbed (ARM-CART) site in association with simulations of mixed-layer growth from a single-column PBL/land surface model. The model accurately predicts PBL evolution and realistically simulates thermodynamics associated with two key controls on PBL growth: atmospheric stability and soil moisture. The information content of these variables and their influence on PBL height and screen-level temperature can be characterized using statistical methods to describe PBL–land surface coupling over a wide range of conditions. Results also show that the first-order effects of land–atmosphere coupling are manifested in the control of soil moisture and stability on atmospheric demand for evapotranspiration and on the surface energy balance. Two principal land–atmosphere feedback regimes observed during soil moisture drydown periods are identified that complicate direct relationships between PBL and land surface properties, and, as a result, limit the accuracy of uncoupled land surface and traditional PBL growth models. In particular, treatments for entrainment and the role of the residual mixed layer are critical to quantifying diurnal land–atmosphere interactions.
Abstract
The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.
Abstract
The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.
Abstract
Relationships among convective planetary boundary layer (PBL) evolution and land surface properties are explored using data from the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed in the southern Great Plains. Previous attempts to infer surface fluxes from observations of the PBL have been constrained by difficulties in accurately estimating and parameterizing the conservation equation and have been limited to multiday averages or small samples of daily case studies. Using radiosonde and surface flux data for June, July, and August of 1997, 1999, and 2001, a conservation approach was applied to 132 sets of daily observations. Results highlight the limitations of using this method on daily time scales caused by the diurnal variability and complexity of entrainment. A statistical investigation of the relationship among PBL and both land surface and near-surface properties that are not explicitly included in conservation methods indicates that atmospheric stability in the layer of PBL growth is the most influential variable controlling PBL development. Significant relationships between PBL height and soil moisture, 2-m potential temperature, and 2-m specific humidity are also identified through this analysis, and it is found that 76% of the variance in PBL height can be explained by observations of stability and soil water content. Using this approach, it is also possible to use limited observations of the PBL to estimate soil moisture on daily time scales without the need for detailed land surface parameterizations. In the future, the general framework that is presented may provide a means for robust estimation of near-surface soil moisture and land surface energy balance over regional scales.
Abstract
Relationships among convective planetary boundary layer (PBL) evolution and land surface properties are explored using data from the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed in the southern Great Plains. Previous attempts to infer surface fluxes from observations of the PBL have been constrained by difficulties in accurately estimating and parameterizing the conservation equation and have been limited to multiday averages or small samples of daily case studies. Using radiosonde and surface flux data for June, July, and August of 1997, 1999, and 2001, a conservation approach was applied to 132 sets of daily observations. Results highlight the limitations of using this method on daily time scales caused by the diurnal variability and complexity of entrainment. A statistical investigation of the relationship among PBL and both land surface and near-surface properties that are not explicitly included in conservation methods indicates that atmospheric stability in the layer of PBL growth is the most influential variable controlling PBL development. Significant relationships between PBL height and soil moisture, 2-m potential temperature, and 2-m specific humidity are also identified through this analysis, and it is found that 76% of the variance in PBL height can be explained by observations of stability and soil water content. Using this approach, it is also possible to use limited observations of the PBL to estimate soil moisture on daily time scales without the need for detailed land surface parameterizations. In the future, the general framework that is presented may provide a means for robust estimation of near-surface soil moisture and land surface energy balance over regional scales.
Abstract
This study extends the heated condensation framework (HCF) presented in Tawfik and Dirmeyer to include variables for describing the convective background state of the atmosphere used to quantify the contribution of the atmosphere to convective initiation within the context of land–atmosphere coupling. In particular, the ability for the full suite of HCF variables to 1) quantify the amount of latent and sensible heat energy necessary for convective initiation, 2) identify the transition from moistening advantage to boundary layer growth advantage, 3) identify locally originating convection, and 4) compare models and observations, directly highlighting biases in the convective state, is demonstrated. These capabilities are illustrated for a clear-sky and convectively active day over the Atmospheric Radiation Measurement Program Southern Great Plains central station using observations, the Rapid Update Cycle (RUC) operational model, and the North American Regional Reanalysis (NARR). The clear-sky day had a higher and unattainable convective threshold, making convective initiation unlikely. The convectively active day had a lower threshold that was attained by midafternoon, reflecting local convective triggering. Compared to observations, RUC tended to have the most difficulty representing the convective state and captured the threshold for the clear-sky case only because of compensating biases in the moisture and temperature profiles. Despite capturing the observed moisture profile very well, a stronger surface inversion in NARR returned overestimates in the convective threshold. The companion paper applies the HCF variables introduced here across the continental United States to examine the climatological behavior of convective initiation and local land–atmosphere coupling.
Abstract
This study extends the heated condensation framework (HCF) presented in Tawfik and Dirmeyer to include variables for describing the convective background state of the atmosphere used to quantify the contribution of the atmosphere to convective initiation within the context of land–atmosphere coupling. In particular, the ability for the full suite of HCF variables to 1) quantify the amount of latent and sensible heat energy necessary for convective initiation, 2) identify the transition from moistening advantage to boundary layer growth advantage, 3) identify locally originating convection, and 4) compare models and observations, directly highlighting biases in the convective state, is demonstrated. These capabilities are illustrated for a clear-sky and convectively active day over the Atmospheric Radiation Measurement Program Southern Great Plains central station using observations, the Rapid Update Cycle (RUC) operational model, and the North American Regional Reanalysis (NARR). The clear-sky day had a higher and unattainable convective threshold, making convective initiation unlikely. The convectively active day had a lower threshold that was attained by midafternoon, reflecting local convective triggering. Compared to observations, RUC tended to have the most difficulty representing the convective state and captured the threshold for the clear-sky case only because of compensating biases in the moisture and temperature profiles. Despite capturing the observed moisture profile very well, a stronger surface inversion in NARR returned overestimates in the convective threshold. The companion paper applies the HCF variables introduced here across the continental United States to examine the climatological behavior of convective initiation and local land–atmosphere coupling.
Abstract
This is Part II of a two-part study introducing the heated condensation framework (HCF), which quantifies the potential convective state of the atmosphere in terms of land–atmosphere interactions. Part I introduced the full suite of HCF variables and applied them to case studies with observations and models over a single location in the southern Great Plains. It was shown in Part I that the HCF was capable of identifying locally initiated convection and quantifying energetically favorable pathways for initiation. Here, the HCF is applied to the entire conterminous United States and the climatology of convective initiation (CI) in relation to local land–atmosphere coupling (LoCo) is explored for 34 summers (June–August) using the North American Regional Reanalysis (NARR) and observations. NARR is found to be capable of capturing the convective threshold (buoyant mixing potential temperature θ BM) and energy advantage transition (energy advantage potential temperature θ adv) for most of the United States. However, there are compensating biases in the components of moisture q mix and temperature q*, resulting in low θ BM biases for the wrong reason. The HCF has been used to show that local CI occurred over the Rocky Mountains and the southern Great Plains 35%–65% of the time. Finally, the LoCo process chain has been recast in light of the HCF. Both positive and negative soil moisture–convective feedbacks are possible, with negative feedbacks producing a stronger response in CI likelihood under weak convective inhibition. Positive feedbacks are present but weaker.
Abstract
This is Part II of a two-part study introducing the heated condensation framework (HCF), which quantifies the potential convective state of the atmosphere in terms of land–atmosphere interactions. Part I introduced the full suite of HCF variables and applied them to case studies with observations and models over a single location in the southern Great Plains. It was shown in Part I that the HCF was capable of identifying locally initiated convection and quantifying energetically favorable pathways for initiation. Here, the HCF is applied to the entire conterminous United States and the climatology of convective initiation (CI) in relation to local land–atmosphere coupling (LoCo) is explored for 34 summers (June–August) using the North American Regional Reanalysis (NARR) and observations. NARR is found to be capable of capturing the convective threshold (buoyant mixing potential temperature θ BM) and energy advantage transition (energy advantage potential temperature θ adv) for most of the United States. However, there are compensating biases in the components of moisture q mix and temperature q*, resulting in low θ BM biases for the wrong reason. The HCF has been used to show that local CI occurred over the Rocky Mountains and the southern Great Plains 35%–65% of the time. Finally, the LoCo process chain has been recast in light of the HCF. Both positive and negative soil moisture–convective feedbacks are possible, with negative feedbacks producing a stronger response in CI likelihood under weak convective inhibition. Positive feedbacks are present but weaker.
Abstract
The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.
Abstract
The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.
Abstract
Irrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.
Abstract
Irrigation has the potential to modify local weather and regional climate through a repartitioning of water among the surface, soil, and atmosphere with the potential to drastically change the terrestrial energy budget in agricultural areas. This study uses local observations, satellite remote sensing, and numerical modeling to 1) explore whether irrigation has historically impacted summer maximum temperatures in the Columbia Plateau, 2) characterize the current extent of irrigation impacts to soil moisture (SM) and land surface temperature (LST), and 3) better understand the downstream extent of irrigation’s influence on near-surface temperature, humidity, and boundary layer development. Analysis of historical daily maximum temperature (TMAX) observations showed that the three Global Historical Climate Network (GHCN) sites downwind of Columbia Basin Project (CBP) irrigation experienced statistically significant cooling of the mean summer TMAX by 0.8°–1.6°C in the post-CBP (1968–98) as compared to pre-CBP expansion (1908–38) period, opposite the background climate signal. Remote sensing observations of soil moisture and land surface temperatures in more recent years show wetter soil (~18%–25%) and cooler land surface temperatures over the irrigated areas. Simulations using NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model support the historical analysis, confirming that under the most common summer wind flow regime, irrigation cooling can extend as far downwind as the locations of these stations. Taken together, these results suggest that irrigation expansion may have contributed to a reduction in summertime temperatures and heat extremes within and downwind of the CBP area. This supports a regional impact of irrigation across the study area.
Abstract
The inherent coupled nature of earth’s energy and water cycles places significant importance on the proper representation and diagnosis of land–atmosphere (LA) interactions in hydrometeorological prediction models. However, the precise nature of the soil moisture–precipitation relationship at the local scale is largely determined by a series of nonlinear processes and feedbacks that are difficult to quantify. To quantify the strength of the local LA coupling (LoCo), this process chain must be considered both in full and as individual components through their relationships and sensitivities. To address this, recent modeling and diagnostic studies have been extended to 1) quantify the processes governing LoCo utilizing the thermodynamic properties of mixing diagrams, and 2) diagnose the sensitivity of coupled systems, including clouds and moist processes, to perturbations in soil moisture. This work employs NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) mesoscale model and simulations performed over the U.S. Southern Great Plains. The behavior of different planetary boundary layers (PBL) and land surface scheme couplings in LIS–WRF are examined in the context of the evolution of thermodynamic quantities that link the surface soil moisture condition to the PBL regime, clouds, and precipitation. Specifically, the tendency toward saturation in the PBL is quantified by the lifting condensation level (LCL) deficit and addressed as a function of time and space. The sensitivity of the LCL deficit to the soil moisture condition is indicative of the strength of LoCo, where both positive and negative feedbacks can be identified. Overall, this methodology can be applied to any model or observations and is a crucial step toward improved evaluation and quantification of LoCo within models, particularly given the advent of next-generation satellite measurements of PBL and land surface properties along with advances in data assimilation schemes.
Abstract
The inherent coupled nature of earth’s energy and water cycles places significant importance on the proper representation and diagnosis of land–atmosphere (LA) interactions in hydrometeorological prediction models. However, the precise nature of the soil moisture–precipitation relationship at the local scale is largely determined by a series of nonlinear processes and feedbacks that are difficult to quantify. To quantify the strength of the local LA coupling (LoCo), this process chain must be considered both in full and as individual components through their relationships and sensitivities. To address this, recent modeling and diagnostic studies have been extended to 1) quantify the processes governing LoCo utilizing the thermodynamic properties of mixing diagrams, and 2) diagnose the sensitivity of coupled systems, including clouds and moist processes, to perturbations in soil moisture. This work employs NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) mesoscale model and simulations performed over the U.S. Southern Great Plains. The behavior of different planetary boundary layers (PBL) and land surface scheme couplings in LIS–WRF are examined in the context of the evolution of thermodynamic quantities that link the surface soil moisture condition to the PBL regime, clouds, and precipitation. Specifically, the tendency toward saturation in the PBL is quantified by the lifting condensation level (LCL) deficit and addressed as a function of time and space. The sensitivity of the LCL deficit to the soil moisture condition is indicative of the strength of LoCo, where both positive and negative feedbacks can be identified. Overall, this methodology can be applied to any model or observations and is a crucial step toward improved evaluation and quantification of LoCo within models, particularly given the advent of next-generation satellite measurements of PBL and land surface properties along with advances in data assimilation schemes.
