Cellular Convection Research Articles

A Detailed, Multi‐Scale Assessment of an Atmospheric River Event and Its Impact on Extreme Glacier Melt in the Southern Alps of New Zealand

AbstractNorth‐westerly airflow and associated atmospheric rivers (ARs) have been found to profoundly influence New Zealand’s west coasts, by causing flooding, landslides and extreme ablation and accumulation on glaciers in the Southern Alps. However, the response of local glacier mass balance to synoptic‐scale circulation, including events with ARs, has typically not been investigated by considering mesoscale processes explicitly. In this study, high‐resolution atmospheric simulations from the Weather Research and Forecasting model are used to investigate the mesoscale drivers of an extreme ablation event on Brewster Glacier (Southern Alps), which occurred on February 6, 2011 during the landfall of an AR on the South Island. The following processes were found to be crucial for transferring the high temperature and water vapor contained in the AR into energy available for melt on Brewster Glacier: First, the moist‐neutral character of the air mass enabled the flow to pass over the ridge, leading to the development of orographic clouds and precipitation on the windward side of the orography, and foehn winds on the leeside. These processes fueled melt through longwave radiation and strong turbulent and rain heat fluxes within the high‐condensation environment of the orographic cloud. Second, orographic enhancement occurred due to both cellular convection within the cloud and the combined effect of multiple precipitating systems by the seeder‐feeder‐mechanism. These results indicate the potential importance of AR dynamics for New Zealand’s glaciers. They also illustrate the benefit of mesoscale atmospheric modeling for advancing process understanding of the glacier‐climate relationship in New Zealand.

Environmental Conditions Associated with Horizontal Convective Rolls, Cellular Convection, and No Organized Circulations

AbstractTypical environmental conditions associated with horizontal convective rolls (HCRs) and cellular convection have been known for over 50 years. Yet our ability to predict whether HCRs, cellular convection, or no discernable organized (null) circulation will occur within a well-mixed convective boundary layer based upon easily observed environmental variables has been limited. Herein, a large database of 50 cases each of HCR, cellular convection, and null events is created that includes observations of mean boundary layer wind and wind shear, boundary layer depth; surface observations of wind, temperature, and relative humidity; and estimates of surface sensible heat flux. Results from a multiclass linear discriminant analysis applied to these data indicate that environmental conditions can be useful in predicting whether HCRs, cellular convection, or no circulation occurs, with the analysis identifying the correct circulation type on 72% of the case days. This result is slightly better than using a mean convective boundary layer (CBL) wind speed of 6 m s−1 to discriminate between HCRs and cells. However, the mean CBL wind speed has no ability to further separate out cases with no CBL circulation. The key environmental variables suggested by the discriminant analysis are mean sensible heat flux, friction velocity, and the Obukhov length.

Shallow Convection and Precipitation Over the Southern Ocean: A Case Study During the CAPRICORN 2016 Field Campaign

AbstractMarine boundary layer clouds and precipitation observed in a sustained period of open mesoscale cellular convection (MCC) over the Southern Ocean (SO) are investigated using Clouds, Aerosols, Precipitation, Radiation, and atmospherIc Composition Over the southeRn oceaN 2016 observations, Himawari‐8 products, and numerical simulations. The shallow convection was characterized by the presence of supercooled liquid water and mixed‐phase clouds in the sub‐freezing temperature range, consistent with earlier in‐situ observations where ice multiplication is found to be active in producing large quantities of ice in open MCC clouds. Ice‐phase precipitation was observed to melt below cloud base with evidence of cold pools produced in a decoupled boundary layer. Convection‐permitting simulations using the weather research and forecasting model were able to reproduce many of the surface meteorological features and their evolution. However, the evolution of the boundary layer height and the degree of decoupling were poorly simulated, along with the absence of cold pools. The observed cloud morphology and microphysical characteristics were also not well reproduced in the control simulation with the Thompson microphysics scheme, where too much supercooled water was simulated in a too homogenous cloud field. Sensitivity experiments with modified microphysical parameters led to a higher production of glaciated clouds and precipitation. Sensitivity experiments with different boundary layer schemes and vertical resolution, however, showed a smaller impact. A bias of ∼4°C in the initial boundary conditions of the sea surface temperature is discussed. This study highlights the challenge of representing the complex physical processes that underpin the cloud, precipitation, and boundary layer characteristics of the open MCC over the SO.

Description of mesoscale pattern formation in shallow convective cloud fields by using time-dependent Ginzburg-Landau and Swift-Hohenberg stochastic equations.

The time-dependent Ginzburg-Landau (or Allen-Cahn) equation and the Swift-Hohenberg equation, both added with a stochastic term, are proposed to describe cloud pattern formation and cloud regime phase transitions of shallow convective clouds organized in mesoscale systems. The starting point is the Hottovy-Stechmann linear spatiotemporal stochastic model for tropical precipitation, used to describe the dynamics of water vapor and tropical convection. By taking into account that shallow stratiform clouds are close to a self-organized criticality and that water vapor content is the order parameter, it is observed that sources must have nonlinear terms in the equation to include the dynamical feedback due to precipitation and evaporation. The nonlinear terms are derived by using the known mean field of the Ising model, as the Hottovy-Stechmann linear model presents the same probability distribution. The inclusion of this nonlinearity leads to a kind of time-dependent Ginzburg-Landau stochastic equation, originally used to describe superconductivity phases. By performing numerical simulations, pattern formation is observed. These patterns are better compared with real satellite observations than the pure linear model. This is done by comparing the spatial Fourier transform of real and numerical cloud fields. However, for highly ordered cellular convective phases, considered as a form of Rayleigh-Bénard convection in moist atmospheric air, the Ginzburg-Landau model does not allow us to reproduce such patterns. Therefore, a change in the form of the small-scale flux convergence term in the equation for moist atmospheric air is proposed. This allows us to derive a Swift-Hohenberg equation. In the case of closed cellular and roll convection, the resulting patterns are much more organized than the ones obtained from the Ginzburg-Landau equation and better reproduce satellite observations as, for example, horizontal convective fields.

Centrifugal convection in a two-layer system of reacting miscible fluids

The authors study the effect of uniform rotation on the system of two reacting miscible liquids placed in a cylindrical Hele-Shaw cell. The cell performs a rotation with a constant velocity around the axis of symmetry resulting in a radially directed inertial field. The initial configuration of the system is statically stable and consists of two concentric layers of aqueous solutions of acid and base, which are spatially separated. When liquids are brought into contact, a neutralization reaction begins, which is accompanied by the release of salt. In this work, we obtain a system of governing equations and present the results of numerical simulation. We found that reaction-diffusion processes lead to the formation of a non-monotonic density profile with a potential well. If the rotation rate gradually increases, then a cellular convection pattern can develop in the potential well. We found that with further growth of the control parameter, the periodicity of the pattern is violated due to the influence of another convective instability, which independently develops in the domain close to the axis of rotation. The action of the inertial field results in the ejection of some convective vortices from the potential well.

Machine learning method for the supplement, correction, and prediction of the nonlinear dynamics in pattern formation

The pattern formation and spatial–temporal chaos are interesting issues in nonlinear dynamics. A novel model based on machine learning methods is designed to learn and imitate the pattern evolution in Bénard–Marangoni convection (BM convection). There is a supercritical process, which is an inevitable and unique experimental phenomenon, on the way to chaos in BM convection. A single layer of fluid uniformly heated at the bottom is used as the experimental system. During the experiment, the temperature difference between top and bottom of the liquid layer is increased first to make the system enter the supercritical convection state and then decreased after a while; surface temperature distribution of the liquid layer is measured in real time with an infrared thermal imager, which visualized the formation and re-organization of cellular convection during the supercritical state. The temperature data are used as the material that meets the conditions of machine learning and then the machine learning method in charge of predicting the picture of temperature distribution that it never has seen before in two steps. The experimental data are used to train an auto-encoder model based on convolutional neural networks and an RNN–CNN joint model, in which the former is used for extracting low-dimensional features of the temperature field, and the latter is used for predicting evolution results of the low-dimensional features and recovering them back to the temperature field. The models have finally achieved the objectives of supplementing the missing experimental data and correcting actual experimental data by comparing the actual experimental results with the prediction results of the machine learning approach and theoretical analysis results. On the other hand, active exploration has been undertaken in predicting physical experimental results that have never happened before.

Investigation of Spatial and Temporal Wind-Speed Variability During Open Cellular Convection with the Model for Prediction Across Scales in Comparison with Measurements

Open cellular convection (OCC) over, for example, the North Sea is often observed in connection with cold-air outbreaks. It is accompanied by large temporal and spatial variability in wind speed, which affects offshore wind energy in the area. This study uses the global Model for Prediction Across Scales (MPAS), with regional mesh refinement down to convection-permitting scales of 2 km, to simulate an OCC episode in the North Sea, with a focus on wind-speed variability. Modelled data are combined with wind speeds retrieved from satellite data and in situ measurements to investigate the spatial and temporal variability of offshore wind speeds under OCC conditions from a synoptic to mesoscale perspective, and to examine the model’s ability to represent the OCC structures and wind-speed variability. The model can simulate realistic OCC structures and mesoscale wind-speed variability within the limits set by the effective model resolution. Under OCC conditions, significant differences from climatological conditions are found in the spatial wind-speed power spectrum and in 10-min wind-speed step changes. The very high horizontal mesh-cell spacing in the refinement region of 2 km, and the focus on OCC wind-speed variability, makes this the first investigation of this kind using the MPAS modelling framework with mesh refinement.

Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology

Abstract. Marine low clouds display rich mesoscale morphological types and distinct spatial patterns of cloud fields. Being able to differentiate low-cloud morphology offers a tool for the research community to go one step beyond bulk cloud statistics such as cloud fraction and advance the understanding of low clouds. Here we report the progress of our project that aims to create an observational record of low-cloud mesoscale morphology at a near-global (60∘ S–60∘ N) scale. First, a training set is created by our team members manually labeling thousands of mesoscale (128×128) MODIS scenes into six different categories: stratus, closed cellular convection, disorganized convection, open cellular convection, clustered cumulus convection, and suppressed cumulus convection. Then we train a deep convolutional neural network model using this training set to classify individual MODIS scenes at 128×128 resolution and test it on a test set. The trained model achieves a cross-type average precision of about 93 %. We apply the trained model to 16 years of data over the southeastern Pacific. The resulting climatological distribution of low-cloud morphology types shows both expected and unexpected features and suggests promising potential for low-cloud studies as a data product.

Identifying meteorological influences on marine low cloud mesoscale morphology using deep learning classifications

Abstract. Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events

AbstractThis research quantifies the spatiotemporal statistics of composite radar reflectivity in the vicinity of severe thunderstorm reports. By using over 20 years (1996–2017) of data and 500,000 severe thunderstorm reports, this study presents the most comprehensive analysis of the mesoscale presentation of radar reflectivity composites during severe weather events to date. We first present probability matched mean composites of approximately 5,000 radar images centred on tornado reports that contain one of three types of manually‐labelled convective storm modes—namely, (a) quasi‐linear convective system (QLCS); (b) cellular; or (c) tropical system. Next, we generate composites for tornado report data stratified by EF‐scale and for four temporal periods during which notable severe weather events took place. The data are then stratified by hazard, region, season, and time of day. The results show marked spatiotemporal and intra‐hazard variability in radar presentation. In general, cellular convection is favoured in the Great Plains of the United States, whereas QLCS convection is favoured in the Southeast United States. Night and cool‐season subsets showed a preference for QLCS convection, whereas day and warm‐season subsets showed a preference for cellular convection. These results agree well with the existing literature and suggest that the data extraction and organization approach is sound. Because of this, these data will be useful for future image classification studies in climate and atmospheric sciences—particularly those involving storm mode classification.

The effects of Open Cellular Convection on Wind Farm Operation and Wakes

Majority of the severe variability in power production of an offshore wind farm occurs when open cellular convection (OCC) is observed. With a diameter of 10-80 km, the open cells are essentially the main drivers of hour-scale wind fluctuations passing through the wind farm. Here we aim to quantify the impact of the OCC on Horns Rev-I offshore wind farm located in the North Sea, in terms of variance in the power production and turbulence intensity. Using mesoscale simulations, met-mast measurements and high frequency (1 Hz) SCADA data from all the operating turbines, the behaviour of power deficit and added turbulence intensity is explored comparatively with and without presence of open cells. The investigation is a case study performed on a ‘day-to-day’ basis with an in depth analysis of the in-farm effects, such as the wake behaviour and smaller scale atmospheric structures. For the investigated event, the study shows striking difference in wind farm operation under the open cell structures and underlines the importance of taking local mesoscale phenomena into account for wind farm operation monitoring and control, short-term wake estimation, forecasting and market participation.

On the Factors That Determine Boundary Layer Albedo

AbstractThis study investigates the factors that control marine boundary layer cloud albedo measured by the Multiangle Imaging SpectroRadiometer (MISR) over domains of (200 km)2. We use three key metrics to investigate domain albedo: cloud fraction, cloud heterogeneity, and cloud morphology. Cloud heterogeneity is quantified at the domain level with a unified heterogeneity index. Cloud morphology is determined from a cloud classification algorithm using an Artificial Neural Network (ANN) to classify each domain into one of four categories: (i) closed‐cell Mesoscale Cellular Convection (MCC); (ii) open‐cell MCC; (iii) disorganized MCC; and (iv) No MCC. These different types of MCC are usefully defined as low clouds of different morphologies. Classifications from the ANN are also combined with the satellite observations of MISR to develop relationships between cloud morphology, domain albedo, cloud fraction, and cloud heterogeneity. Cloud morphology is found to play an essential role in modulating these relationships. The cloud fraction‐albedo relationships are found to be directly a function of cloud morphology. Relationships between domain albedo and cloud heterogeneity are also found to be a function of MCC type. Our results indicate that the albedo has a strong dependence on cloud morphology and cloud heterogeneity. Understanding both the physical properties and the meteorological controls on MCC has important implications for understanding low cloud behavior and improving their representation in General Circulation Models.

Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

Abstract Humans excel at detecting interesting patterns in images, for example, those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowdsourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth’s radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish, and Gravel. On cloud-labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowdsourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.

Open cells exhibit weaker entrainment of free-tropospheric biomass burning aerosol into the south-east Atlantic boundary layer

Abstract. This work presents synergistic satellite, airborne and surface-based observations of a pocket of open cells (POC) in the remote south-east Atlantic. The observations were obtained over and upwind of Ascension Island during the CLouds and Aerosol Radiative Impacts and Forcing (CLARIFY) and the Layered Smoke Interacting with Clouds (LASIC) field experiments. A novel aspect of this case study is that an extensive free-tropospheric biomass burning aerosol plume that had been transported from the African continent was observed to be in contact with the boundary layer inversion over the POC and the surrounding closed cellular cloud regime. The in situ measurements show marked contrasts in the boundary layer thermodynamic structure, cloud properties, precipitation and aerosol conditions between the open cells and surrounding overcast cloud field. The data demonstrate that the overlying biomass burning aerosol was mixing down into the boundary layer in the stratocumulus cloud downwind of the POC, with elevated carbon monoxide, black carbon mass loadings and accumulation-mode aerosol concentrations measured beneath the trade-wind inversion. The stratocumulus cloud in this region was moderately polluted and exhibited very little precipitation falling below cloud base. A rapid transition to actively precipitating cumulus clouds and detrained stratiform remnants in the form of thin quiescent veil clouds was observed across the boundary into and deep within the POC. The subcloud layer in the POC was much cleaner than that in the stratocumulus region. The clouds in the POC formed within an ultra-clean layer (accumulation-mode aerosol concentrations of approximately a few cm−3) in the upper region of the boundary layer, which was likely to have been formed via efficient collision–coalescence and sedimentation processes. Enhanced Aitken-mode aerosol concentrations were also observed intermittently in this ultra-clean layer, suggesting that new particle formation was taking place. Across the boundary layer inversion and immediately above the ultra-clean layer, accumulation-mode aerosol concentrations were ∼ 1000 cm−3. Importantly, the air mass in the POC showed no evidence of elevated carbon monoxide over and above typical background conditions at this location and time of year. As carbon monoxide is a good tracer for biomass burning aerosol that is not readily removed by cloud processing and precipitation, it demonstrates that the open cellular convection in the POC is not able to entrain large quantities of the free-tropospheric aerosol that was sitting directly on top of the boundary layer inversion. This suggests that the structure of the mesoscale cellular convection may play an important role in regulating the transport of aerosol from the free troposphere down into the marine boundary layer. We then develop a climatology of open cellular cloud conditions in the south-east Atlantic from 19 years of September Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery. This shows that the maxima in open cell frequency (> 0.25) occurs far offshore and in a region where subsiding biomass burning aerosol plumes may often come into contact with the underlying boundary layer cloud. If the results from the observational case study applied more broadly, then the apparent low susceptibility of open cells to free-tropospheric intrusions of additional cloud condensation nuclei could have some important consequences for aerosol–cloud interactions in the region.

Simulating Real Atmospheric Boundary Layers at Gray-Zone Resolutions: How Do Currently Available Turbulence Parameterizations Perform?

Recent computational and modeling advances have led a diverse modeling community to experiment with atmospheric boundary layer (ABL) simulations at subkilometer horizontal scales. Accurately parameterizing turbulence at these scales is a complex problem. The modeling solutions proposed to date are still in the development phase and remain largely unvalidated. This work assesses the performance of methods currently available in the Weather Research and Forecasting (WRF) model to represent ABL turbulence at a gray-zone grid spacing of 333 m. We consider three one-dimensional boundary layer parameterizations (MYNN, YSU and Shin-Hong) and coarse large-eddy simulations (LES). The reference dataset consists of five real-case simulations performed with WRF-LES nested down to 25 m. Results reveal that users should refrain from coarse LES and favor the scale-aware, Shin-Hong parameterization over traditional one-dimensional schemes. Overall, the spread in model performance is large for the cellular convection regime corresponding to the majority of our cases, with coarse LES overestimating turbulent energy across scales and YSU underestimating it and failing to reproduce its horizontal structure. Despite yielding the best results, the Shin-Hong scheme overestimates the effect of grid dependence on turbulent transport, highlighting the outstanding need for improved solutions to seamlessly parameterize turbulence across scales.

Chemoconvective structures in a rotating system of reactive fluids

Chemoconvective structures in a system of two reacting miscible liquids placed in a cylindrical Hele-Shaw cell that uniformly rotates around the axis of symmetry are studied. Previously, the behavior of similar system has been studied by the authors experimentally and theoretically under the static gravity field. A radially directed inertial field created by the centrifugal force varies in space (along the radius) and can be tuned by the rotation frequency, which gives the system new degrees of freedom. The initial configuration of the system consists of two concentric layers of aqueous solutions, initially separated in space by an infinitely thin diffusion zone. Solutions of acid and base are located respectively closer to the axis of rotation and at the periphery of the cell. The concentrations of reactants are selected in such a way as to guarantee the initial stability of the system with respect to Rayleigh-Taylor disturbances. After bringing fluids into contact, a neutralization reaction begins, which is accompanied by the production of salt. An important role is played by a concentration-dependent diffusion effect, which results in a nonlinear form of the corresponding transfer equations already for the base state characterized by the reaction-diffusion processes. As in the case of static gravity, there exists a density potential well near the reaction front, which determines the nonlinear dynamics of the system. A system of nonlinear equations describing the fluid motion is obtained. The results of numerical simulation of a complete nonlinear problem are presented. We show that cellular convection develops in the potential well at a certain ratio of the initial concentrations and the values of the centrifugal Rayleigh numbers. With an increase in the rotation speed, the periodicity of the structure is violated more and more due to the influence of the DLC instability, which arises near the axis of rotation, and the action of the inertial field, which ejects some cells from the potential well.

A 10-Year Warm-Season Climatology of Horizontal Convective Rolls and Cellular Convection in Central Oklahoma

Abstract Horizontal convective rolls (HCRs) and cellular convection (cells) are frequently observed within the planetary boundary layer. Yet understanding of the evolution, seasonal variation, and characteristics of such boundary layer phenomena is limited as previous studies used observations from field experiments or satellites. As a result, little is known about the mean climatology and monthly variation of HCRs and cells. Polarimetric WSR-88D radar observations are used to develop a 10-yr April–September climatology in central Oklahoma including HCR and cell occurrence, duration, and aspect ratios as well as HCR orientation angles and wavelengths. Results indicate that HCRs or cells occur on over 92% of days without precipitation during the warm season. HCRs or cells typically form in midmorning and may persist throughout the day or transition between modes before dissipating around sunset. HCRs generally persist for 1–6 h with typical wavelengths of 2–10 km and most aspect ratios between 1 and 7. Rolls are often oriented within 10° of the mean boundary layer wind but can be as much as 30° off this direction. Mean HCR aspect ratios in this study remain constant during the afternoon, but decrease early in the day and increase late in the day, diverging from previous overland HCR studies. Cells generally persist for 2–6 h with aspect ratios of 1–6. These results should facilitate future studies on convection initiation, formation mechanisms of boundary layer organization, and model parameterization.

Characteristics of roll and cellular convection in a deep and wide semiarid valley: A large-eddy simulation study

The nature of organized motions within the convective boundary layer (CBL) depends on the surface heating and vertical wind shear. Moderate surface heating and strong shear favor horizontal convective rolls, while strong surface heating and weak shear favor open cellular convection. The transition between rolls and cells over land, particularly complex terrain, is still poorly understood. Due to lack of adequate measurements, variability of structural properties of rolls and cells (such as aspect ratio, wavelength and orientation relative to the mean surface winds) over complex terrain remains unclear. At present resolutions used in operational numerical weather prediction, rolls and cells are now becoming partly resolved, but the reliability of their forecasting in complex terrain remains elusive. The main objective of this research is the investigation of the characteristics of rolls and cells in a mountain valley compared to flat terrain.To address this objective, we use the Weather Research and Forecasting Model (WRF) in LES mode. The domains cover the Owens Valley, California, the site of the Terrain-Induced Rotor Experiment (T-REX). We show that LES adequately simulated the complex valley-slope flow observed from flux towers and a wind profiler. Additionally, the vertical profiles of higher-order moments agree with those of textbook CBL behaviour. We found the transitional regime between rolls and cells to be broader compared to flat terrain when considering the bulk CBL stability parameter −zi/L. The width of the transitional regime depends on the temporal trend of −zi/L and the along-valley flow direction. Structural parameters of rolls and cells were different relative to their flat terrain counterparts, and a valley environment tends to introduce additional linearity into convection organization. Our results do not indicate a relationship between different convection modes and upslope flow strength.

Simulation of Mesoscale Cellular Convection in Marine Stratocumulus: 2. Nondrizzling Conditions

AbstractThis is the second of two large‐eddy simulation studies on the mechanisms of mesoscale cellular organization in drizzling (open cells) and nondrizzling marine stratocumulus (closed cells). This study uses a hard nudging approach which maintains fixed horizontal‐mean temperature and humidity profiles for a well‐mixed boundary layer with a constant boundary layer depth. For the case studied, closed cells develop and broaden by 32 hr to an aspect ratio of 25. Simulations show that the closed‐cell mesoscale cellular convection is driven by positive feedback from cloud‐induced mesoscale perturbations of longwave radiative cooling. A conceptual model for closed‐cell stratocumulus as a mesoscale wavelength hydrodynamic instability in which mesoscale moist and dry anomalies spontaneously grow is presented. In simulations in which long‐wavelength sinusoidal moisture anomalies are initially imposed, these anomalies evolve into amplifying closed cells. The cell structure is visualized with a compositing approach based on sorting grid columns by their mesoscale‐smoothed total water path. A thermally direct mesoscale circulation pattern develops in the interior of the boundary layer with buoyant mesoscale updrafts, thicker cloud, and a slightly higher capping inversion in the moister columns. There is a mesoscale flow of above‐inversion air down the slightly sloping capping inversion from the moist to the dry regions, reinforced by cloud top radiative cooling. This strengthens the mesoscale anomalies by preferentially cooling and drying the already dry regions. The sloping inversion flow is not driven as efficiently if the radiative cooling is artificially horizontally homogenized, partly disrupting this positive feedback and the resulting closed‐cell development.

In situ measurements of cloud microphysical and aerosol properties during the break-up of stratocumulus cloud layers in cold air outbreaks over the North Atlantic

Abstract. A key challenge for numerical weather prediction models is representing boundary layer clouds in cold air outbreaks (CAOs). One important aspect is the evolution of microphysical properties as stratocumulus transitions to open cellular convection. Abel et al. (2017) have shown, for the first time from in situ field observations, that the break-up in CAOs over the eastern Atlantic may be controlled by the development of precipitation in the cloud system while the boundary layer becomes decoupled. This paper describes that case and examines in situ measurements from three more CAOs. Flights were conducted using the UK Facility for Airborne Atmospheric Measurements (FAAM) British Aerospace-146 (BAe-146) aircraft in the North Atlantic region around the UK, making detailed microphysical measurements in the stratiform boundary layer. As the cloudy boundary layer evolves prior to break-up, increasing liquid water paths (LWPs) and drop sizes and the formation of liquid precipitation are observed. Small numbers of ice particles, typically a few per litre, are also observed. Eventually LWPs reduce significantly due to loss of water from the stratocumulus cloud (SC) layer. In three of the cases, aerosols are removed from the boundary layer across the transition. This process appears to be similar to those observed in warm clouds and pockets of open cells (POCs) in the subtropics. After break-up, deeper convective clouds form with bases warm enough for secondary ice production (SIP), leading to rapid glaciation. It is concluded that the precipitation is strongly associated with the break-up, with both weakening of the capping inversion and boundary layer decoupling also observed.