Large-Eddy Simulation of Organized Precipitating Trade Wind Cumulus Clouds

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

Automated Tracking of Shallow Cumulus Clouds in Large Domain, Long Duration Large Eddy Simulations

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

Subsiding Shells Around Shallow Cumulus Clouds

In this study large-eddy simulations (LES) are used to gain more knowledge on the shell of subsiding air that is frequently observed around cumulus clouds. First, a detailed comparison between observational and numerical results is presented to better validate LES as a tool for studies of microscale phenomena. It is found that horizontal cloud profiles of vertical velocity, humidity, and temperature are in good agreement with observations. They show features similar to the observations, including the presence of the shell of descending air around the cloud. Second, the availability of the complete 3D dataset in LES has been exploited to examine the role of lateral mixing in the exchange of cloud and environmental air. The origin of the subsiding shell is examined by analyzing the individual terms of the vertical momentum equation. Buoyancy is found to be the driving force for this shell, and it is counteracted by the pressure-gradient force. This shows that evaporative cooling at the cloud edge, induced by lateral mixing of cloudy and environmental air, is the responsible mechanism behind the descending shell. For all clouds, and especially the smaller ones, the negative mass flux generated by the subsiding shell is significant. This suggests an important role for lateral mixing throughout the entire cloud layer. The role of the shell in these processes is further explored and described in a conceptual three-layer model of the cloud

Fluctuations in A Quasi-Stationary Shallow Cumulus Cloud Ensemble

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

Effects of Surface and Top Wind Shear on the Spatial Organization of Marine Stratocumulus-Topped Boundary Layers

The convective nature of Stratocumulus topped boundary layers (STBL) involves the motion of updrafts and downdrafts, driven by surface fluxes and radiative cooling, respectively. The balance between shear and buoyant forcings at the surface can determine the organization of updrafts between cellular and roll structures. We investigate the effect of varying shear at the surface and top of the STBL using Large Eddy Simulations, taking DYCOMS II RF01 as a base case. We focus on spatial identification of the following features: coherent updrafts and downdrafts, and observe how they are affected by varying shear. Stronger surface shear organizes the updrafts in rolls, causes less well-mixed thermodynamic profiles, and decreases cloud fraction and liquid water path (LWP). Stronger top shear also decreases cloud fraction and LWP more than surface shear, by thinning the cloud from the top. Features with stronger top than surface shear are associated with a net downward momentum transport and show early signs of decoupling. Classifying updrafts and downdrafts based on their vertical span and horizontal size confirms the dominance of tall objects spanning the whole STBL. Tall objects occupy 30% of the volume in the STBL, while short ones occupy less than 1%. For updraft and downdraft fluxes, these tall objects explain 65% of the vertical velocity variance and 83% of the buoyancy flux, on average. Stronger top shear also weakens the contribution of downdrafts to the turbulent fluxes and tilts the otherwise vertical development of updrafts

Overlap Statistics of Cumuliform Boundary-Layer Cloud Fields in Large-Eddy Simulations

Overlap statistics of cumuliform boundary-layer clouds are studied using large-eddy simulations at high resolutions. The cloud overlap is found to be highly inefficient, due to the typical irregularity of cumuliform clouds over a wide range of scales. The detection of such inefficient overlap is enabled in this study by i) applying fine enough discretizations and ii) by limiting the analysis to exclusively cumuliform boundary-layer cloud fields. It is argued that these two factors explain the differences with some previous studies on cloud overlap. In contrast, good agreement exists with previously reported observations of cloud overlap as derived from lidar measurements of liquid water clouds at small cloud covers. Various candidate functional forms are fitted to the results, suggesting that an inverse linear function is most successful in reproducing the observed behavior. The sensitivity of cloud overlap to various aspects is assessed, reporting a minimal or non-systematic dependence on discretization and vertical wind-shear, as opposed to a strong case-dependence, the latter probably reflecting differences in the cloud size distribution. Finally, calculations with an offline radiation scheme suggest that accounting for the inefficient overlap in cumuliform cloud fields in a general circulation model can change the top-of-atmosphere short-wave cloud radiative forcing by −20 to −40 W m−2, depending on vertical discretization. This corresponds to about 50 to 100% of the typical values in areas of persistent shallow cumulus, respectively

Continuous Single-Column Model Evaluation at a Permanent Meteorological Supersite

Uncertainties in numerical predictions of weather and climate are often linked to the representation of unresolved processes that act relatively quickly compared to the resolved general circulation. These processes include turbulence, convection, clouds, and radiation. Single-column model (SCM) simulation of idealized cases and the subsequent evaluation against large-eddy simulation (LES) results has become an often used and relied on method to obtain insight at process level into the behavior of such parameterization schemes; benefits of SCM simulation are the enhanced model transparency and the high computational efficiency. Although this approach has achieved demonstrable success, some shortcomings have been identified; among these, i) the statistical significance and relevance of single idealized case studies might be questioned and ii) the use of observational datasets has been relatively limited. A recently initiated project named the Royal Netherlands Meteorological Institute (KNMI) Parameterization Testbed (KPT) is part of a general move toward a more statistically significant process-level evaluation, with the purpose of optimizing the identification of problems in general circulation models that are related to parameterization schemes. The main strategy of KPT is to apply continuous long-term SCM simulation and LES at various permanent meteorological sites, in combination with comprehensive evaluation against observations at multiple time scales. We argue that this strategy enables the reproduction of typical long-term mean behavior of fast physics in large-scale models, but it still preserves the benefits of single-case studies (such as model transparency). This facilitates the tracing and understanding of errors in parameterization schemes, which should eventually lead to a reduction of related uncertainties in numerical predictions of weather and climate

Power-Law Scaling in the Internal Variability of Cumulus Cloud Size Distributions due to Subsampling and Spatial Organization

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

Factors Controlling Stratocumulus Cloud Lifetime Over Coastal Land

The breakup of stratocumulus clouds over coastal land areas is studied using a combination of large-eddy simulations (LESs) and mixed-layer models (MLMs) with a focus on mechanisms regulating the timing of the breakup. In contrast with stratocumulus over ocean, strong sensible heat flux over land prevents the cloud layer from decoupling during day. As the cloud thins during day, turbulence generated by surface flux becomes larger than turbulence generated by longwave cooling across the cloud layer. To capture this shift in turbulence generation in the MLM, an existing entrainment parameterization is extended. The MLM is able to mimic cloud evolution for a variety of Bowen ratios, but only after this modification of the entrainment parameterization. Cloud lifetime depends on a combination of the cloud-top entrainment flux, the Bowen ratio of the surface, and the strength of advection of cool ocean air by the sea breeze. For dry land surface conditions, the authors’ MLM suggests a breakup time a few hours after sunrise. For relatively wet land surface conditions, the cloud layer briefly breaks into partly cloudy conditions during midday, and the stratocumulus cloud reforms in the evening