Search

Land–atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Changes in soil moisture and vegetation alter the partitioning of surface water and energy fluxes, influencing diurnal evolution of the planetary boundary layer (PBL). The mixing-diagram framework has proven useful in understanding the evolution of the heat and moisture budget within the convective boundary layer (CBL). We demonstrate that observations from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site provide all of the needed inputs needed for the mixing-diagram framework, allowing us to quantify the impact from the surface fluxes, advection, radiative heating, encroachment, and entrainment on the evolution of the CBL. Profiles of temperature and humidity retrieved from the ground-based infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] are a critical component in this analysis. Large-eddy simulation results demonstrate that mean mixed-layer values derived are shown to be critical to close the energy and moisture budgets. A novel approach demonstrated here is the use of network of AERIs and Doppler lidars to quantify the advective fluxes of heat and moisture. The framework enables the estimation of the entrainment fluxes as a residual, providing a way to observe the entrainment fluxes without using multiple lidar systems. The high temporal resolution of the AERI observations enables the morning, midday, and afternoon evolution of the CBL to be quantified. This work provides a new way to use observations in this framework to evaluate weather and climate models. Significance Statement: The energy and moisture budget of the planetary boundary layer (PBL) is influenced by multiple sources, and accurately representing this evolution in numerical models is critical for weather forecasts and climate predictions. The mixing-diagram approach, driven by profiling observations as illustrated here, provides a powerful way to quantify the contributions from each of these sources. In particular, the energy and moisture mixed into the PBL from above the PBL can be determined accurately from ground-based remote sensors using this approach. [ABSTRACT FROM AUTHOR]

In previous work, the similarity relationship for the water vapor variance in the interfacial layer (IL) at the top of the convective boundary layer (CBL) was proposed to be proportional to the convective velocity scale and the gradients of the water vapor mixing ratio and the Brunt‐Vaisala frequency in the entrainment zone. In the presence of wind shear in the IL, the similarity relationship was hypothesized to also include a dependence on the gradient Richardson number. Simultaneous measurements of the surface buoyancy flux, wind‐shear profiles from a radar wind profiler, water vapor mixing ratio and temperature measurements and their gradients from a Raman lidar provide a unique opportunity to thoroughly examine the function used in defining the variance and validate it. These observations were made over the Atmospheric Radiation Measurement Southern Great Plains site. We identified 19 cases from 2016 during which the CBL was quasi‐stationary and well mixed for at least 2 hr in the afternoon. Furthermore, we simulated the CBL using a large‐eddy simulation (LES) model for these cases and derived the water vapor variance and other profiles to test the similarity function. Utilizing this unique combination of observations and LES, we demonstrate that the water vapor variance in the IL has little‐to‐no dependence on wind shear. Furthermore, we demonstrate that the predicted variance using the original similarity function matches the observed and LES‐modeled variance very well, with linear correlations between the two variances of 0.82 and 0.95, respectively. Plain Language Summary: Numerical weather prediction and global circulation models need to be able to predict the variance in water vapor in the atmosphere, as this is an important signature of turbulent mixing. However, the variance is something that is not directly resolved by the model and must be approximated using variables that the models actually resolve such as gradients in water vapor, temperature, and wind. This study evaluates a commonly used approximation approach, illustrates its shortcomings, and suggests how the approximation can be improved. Key Points: A similarity relationship is used to predict water vapor variance in the interfacial layer from other variablesWind shear is shown to be unimportant for the prediction of water vapor variance in the interfacial layerThere is very high correlation between the true and predicted variance in both the observations and LES data [ABSTRACT FROM AUTHOR]

In this study, the spatial structure of cumulus cloud populations is investigated using three-dimensional snapshots from large-domain LES experiments. The aim is to understand and quantify the internal variability in cloud size distributions due to subsampling effects and spatial organization. A set of idealized shallow cumulus cases is selected with varying degrees of spatial organization, including a slowly organizing marine precipitating case and five more quickly organizing diurnal cases over land. A subdomain analysis is applied, yielding cloud number distributions at sample sizes ranging from severely undersampled to nearly complete. A strong power-law scaling is found in the relation between cloud number variability and subdomain size, reflecting an inverse linear relation. Scaling subdomain size by cloud size yields a data collapse across time points and cases, highlighting the role played by cloud spacing in controlling the stochastic variability. Spatial organization acts on top of this baseline model by increasing the maximum cloud size and by enhancing the variability in the number of smallest clouds. This reflects that the smaller clouds start to live on top of larger-scale thermodynamic structures, such as cold pools, which favor or inhibit their formation. Compositing all continental cumulus cases suggests the existence of a prototype diurnal time dependence in the spatial organization. A simple stochastic expression for cloud number variability is proposed that is formulated in terms of two dimensionless groups, which allows objective estimation of the degree of spatial organization in simulated and observed cumulus cloud populations. [ABSTRACT FROM AUTHOR]

Abstract: This study explores water vapor turbulence in the convective boundary layer (CBL) using the Raman lidar observations from the Atmospheric Radiation Measurement site located at Darwin, Australia. An autocovariance technique was used to separate out the random instrument error from the atmospheric variability during time periods when the CBL is cloud‐free, quasi‐stationary, and well mixed. We identified 45 cases, comprising of 8 wet and 37 dry seasons events, over the 5‐year data record period. The dry season in Darwin is known by warm and dry sunny days, while the wet season is characterized by high humidity and monsoonal rains. The inherent variability of the latter resulted in a more limited number of cases during the wet season. Profiles of the integral scale, variance, coefficient of the structure function, and skewness were analyzed and compared with similar observations from the Raman lidar at the Atmospheric Radiation Measurement Southern Great Plains (SGP) site. The wet season shows larger median variance profiles than the dry season, while the median profile of the variance from the dry season and the SGP site are found to be more comparable particularly between 0.4 and 0.75 zi. The variance and coefficient of the structure function show qualitatively the same vertical pattern. Furthermore, deeper CBL, larger gradient of water vapor mixing ratio at zi, and the strong correlation with the water vapor variance at zi are seen during the dry season. The median value in the skewness is mostly positive below 0.6 zi unlike the SGP site. [ABSTRACT FROM AUTHOR]

Current climate models often predict fractional cloud cover on the basis of a diagnostic probability density function (PDF) describing the subgrid-scale variability of the total water specific humidity, qt, favouring schemes with limited complexity. Standard shapes are uniform or triangular PDFs, the widths of which are assumed to scale with the grid-box mean qt or the grid-box mean saturation specific humidity, qs. In this study, the qt variability is analysed from large-eddy simulations for two stratocumulus, two shallow cumulus, and one deep convective cases. We find that, in most cases, triangles are a better approximation to the simulated PDFs than uniform distributions. In 2 of the 24 slices examined, the actual distributions were so strongly skewed that the simple symmetric shapes could not capture the PDF at all. The distribution width for either shape scales acceptably well with both the mean values of qt and qs, the former being a slightly better choice. The qt variance is underestimated by the fitted PDFs, but overestimated by the existing parametrizations. While the cloud fraction is in general relatively well diagnosed from fitted or parametrized uniform or triangular PDFs, it fails to capture cases with small partial cloudiness, and in 10-30% of the cases misdiagnoses clouds in clear skies or vice versa. The results suggest choosing a parametrization with a triangular shape, where the distribution width would scale with the grid-box mean qt using a scaling factor of 0.076. However, this is subject to the caveat that the reference simulations examined here were partly for rather small domains and driven by idealised boundary conditions. [ABSTRACT FROM AUTHOR]

We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy simulation (LES) model and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds. Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterisation is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds. Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive -- the smaller the grid size, the broader the distribution. [ABSTRACT FROM AUTHOR]

This paper presents a method for feature tracking of fields of shallow cumulus convection in large eddy simulations (LES) by connecting the projected cloud cover in space and time, and by accounting for splitting and merging of cloud objects. Existing methods tend to be either imprecise or, when using the full three-dimensional (3-D) spatial field, prohibitively expensive for large data sets. Compared to those 3-D methods, the current method reduces the memory footprint by up to a factor 100, while retaining most of the precision by correcting for splitting and merging events between different clouds. The precision of the algorithm is further enhanced by taking the vertical extent of the cloud into account. Furthermore, rain and subcloud thermals are also tracked, and links between clouds, their rain, and their subcloud thermals are made. The method compares well with results from the literature. Resolution and domain dependencies are also discussed. For the current simulations, the cloud size distribution converges for clouds larger than an effective resolution of 6 times the horizontal grid spacing, and smaller than about 20% of the horizontal domain size. [ABSTRACT FROM AUTHOR]

Trade wind cumulus clouds often organize in along-wind cloud streets and across-wind mesoscale arcs. We present a benchmark large-eddy simulation which resolves the individual clouds as well as the mesoscale organization on scales of O(10 km). Different methods to quantify organization of cloud fields are applied and discussed. Using perturbed physics large-eddy simulation experiments, the processes leading to the formation of cloud clusters and the mesoscale arcs are revealed. We find that both cold pools as well as the sub-cloud layer moisture field are crucial to understand the organization of precipitating shallow convection. Further sensitivity studies show that microphysical assumptions can have a pronounced impact on the onset of cloud organization. [ABSTRACT FROM AUTHOR]

The article describes the physical and numerical formulation of Dutch Atmospheric Large-Eddy Simulation (DALES) 3.2 version and offers insights on its applications and accomplishments. It claims that DALES is a large-eddy simulation code for studies of the physics of the atmospheric boundary layer (ABL) and of more specific cases such as dispersion and of turbulent reacting flows in the ABL. It adds an outlook on future studies that are planned to be done with DALES and on future improvements.

The article discusses the extensive description of the physical and numerical formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES). The model can be applied to studies including atmospheric boundary layer, flow over sloping or heterogeneous terrain, and dispersion of inert and chemically active species. The resolution of turbulent scales larger than a certain filter width and the parameterization of smaller, less energetic scales are aimed in Large-Eddy Simulations (LES).

Compared to dry boundary layers, dispersion in cloud-topped boundary layers has received less attention. In this LES based numerical study we investigate the dispersion of a passive tracer in the form of Lagrangian particles for four kinds of atmospheric boundary layers: 1) a dry convective boundary layer (for reference), 2) a "smoke" cloud boundary layer in which the turbulence is driven by radiative cooling, 3) a stratocumulus topped boundary layer and 4) a shallow cumulus topped boundary layer. We show that the dispersion characteristics of the smoke cloud boundary layer as well as the stratocumulus situation can be well understood by borrowing concepts from previous studies of dispersion in the dry convective boundary layer. A general result is that the presence of clouds enhances mixing and dispersion - a notion that is not always reflected well in traditional parameterization models, in which clouds usually suppress dispersion by diminishing solar irradiance. The dispersion characteristics of a cumulus cloud layer turn out to be markedly different from the other three cases and the results can not be explained by only considering the well-known top-hat velocity distribution. To understand the surprising characteristics in the shallow cumulus layer, this case has been examined in more detail by 1) determining the velocity distribution conditioned on the distance to the nearest cloud and 2) accounting for the wavelike behaviour associated with the stratified dry environment. [ABSTRACT FROM AUTHOR]