scholarly journals A Unified View of Convective Transports by Stratocumulus Clouds, Shallow Cumulus Clouds, and Deep Convection

1989 ◽  
Author(s):  
David A. Randall
Keyword(s):  
Deep Convection ◽  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Stratocumulus Clouds ◽  
Unified View

scholarly journals Precipitation susceptibility in marine stratocumulus and shallow cumulus from airborne measurements

2016 ◽  
Vol 16 (17) ◽  
pp. 11395-11413 ◽  
Author(s):  
Eunsil Jung ◽  
Bruce A. Albrecht ◽  
Armin Sorooshian ◽  
Paquita Zuidema ◽  
Haflidi H. Jonsson
Keyword(s):  
Marine Boundary Layer ◽  
Qualitative Behavior ◽  
Liquid Water Path ◽  
Cumulus Clouds ◽  
Marine Stratocumulus ◽  
Airborne Measurements ◽  
Boundary Layer Clouds ◽  
Shallow Cumulus ◽  
Stratocumulus Clouds ◽  
Field Campaigns

Abstract. Precipitation tends to decrease as aerosol concentration increases in warm marine boundary layer clouds at fixed liquid water path (LWP). The quantitative nature of this relationship is captured using the precipitation susceptibility (So) metric. Previously published works disagree on the qualitative behavior of So in marine low clouds: So decreases monotonically with increasing LWP or cloud depth (H) in stratocumulus clouds (Sc), while it increases and then decreases in shallow cumulus clouds (Cu). This study uses airborne measurements from four field campaigns on Cu and Sc with similar instrument packages and flight maneuvers to examine if and why So behavior varies as a function of cloud type. The findings show that So increases with H and then decreases in both Sc and Cu. Possible reasons for why these results differ from those in previous studies of Sc are discussed.

EVALUATION OF CLOUDS, RADIATION, AND PRECIPITATION IN CMIP6 MODELS USING GLOBAL WEATHER STATES DERIVED FROM ISCCP-H CLOUD PROPERTY DATA

2021 ◽  
pp. 1-42
Author(s):  
George Tselioudis ◽  
William. B. Rossow ◽  
Christian Jakob ◽  
Jasmine Remillard ◽  
Derek Tropf ◽  
...  
Keyword(s):  
Cumulus Clouds ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Shallow Cumulus ◽  
Property Data ◽  
Ensemble Mean ◽  
Stratocumulus Clouds ◽  
Model Cloud ◽  
Adequate Representation ◽  
Cloud Top Pressure

AbstractA clustering methodology is applied to cloud optical depth cloud top pressure (TAU-PC) histograms from the new, 1-degree resolution, ISCCP-H dataset, to derive an updated global Weather State (WS) dataset. Then, PC-TAU histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties, to evaluate model cloud, radiation, and precipitation properties in the context of the Weather States. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H Weather States, shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

scholarly journals Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

2014 ◽  
Vol 1 (2) ◽  
pp. 1223-1282 ◽  
Author(s):  
M. Sakradzija ◽  
A. Seifert ◽  
T. Heus
Keyword(s):  
Weibull Distribution ◽  
Mass Flux ◽  
Flux Distribution ◽  
Deep Convection ◽  
Convective Cloud ◽  
Cloud Base ◽  
Cumulus Clouds ◽  
Large Eddy Simulation Model ◽  
Shallow Cumulus ◽  
Distribution Mode

Abstract. We propose an approach to stochastic parameterization of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect the information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a Large-Eddy Simulation model (LES) 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. Each distribution mode can be approximated with a Weibull distribution, explaining the deviation from a single-parameter exponential shape through the diversity in cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterization 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.

scholarly journals Boundary Layer and Shallow Cumulus Clouds in a Medium-Range Forecast of a Large-Scale Weather System

2005 ◽  
Vol 133 (7) ◽  
pp. 1938-1960 ◽  
Author(s):  
Stéphane Bélair ◽  
Jocelyn Mailhot ◽  
Claude Girard ◽  
Paul Vaillancourt
Keyword(s):  
Boundary Layer ◽  
Large Scale ◽  
Deep Convection ◽  
Cumulus Clouds ◽  
Weather System ◽  
Low Level ◽  
Boundary Layer Clouds ◽  
Shallow Cumulus ◽  
Medium Range Forecast ◽  
Medium Range

Abstract The role and impact that boundary layer and shallow cumulus clouds have on the medium-range forecast of a large-scale weather system is discussed in this study. A mesoscale version of the Global Environmental Multiscale (GEM) atmospheric model is used to produce a 5-day numerical forecast of a midlatitude large-scale weather system that occurred over the Pacific Ocean during February 2003. In this version of GEM, four different schemes are used to represent (i) boundary layer clouds (including stratus, stratocumulus, and small cumulus clouds), (ii) shallow cumulus clouds (overshooting cumulus), (iii) deep convection, and (iv) nonconvective clouds. Two of these schemes, that is, the so-called MoisTKE and Kuo Transient schemes for boundary layer and overshooting cumulus clouds, respectively, have been recently introduced in GEM and are described in more detail. The results show that GEM, with this new cloud package, is able to represent the wide variety of clouds observed in association with the large-scale weather system. In particular, it is found that the Kuo Transient scheme is mostly responsible for the shallow/intermediate cumulus clouds in the rear portion of the large-scale system, whereas MoisTKE produces the low-level stratocumulus clouds ahead of the system. Several diagnostics for the rear portion of the system reveal that the role of MoisTKE is mainly to increase the vertical transport (diffusion) associated with the boundary layer clouds, while Kuo Transient is acting in a manner more consistent with convective stabilization. As a consequence, MoisTKE is not able to remove the low-level shallow cloud layer that is incorrectly produced by the GEM nonconvective condensation scheme. Kuo Transient, in contrast, led to a significant reduction of these nonconvective clouds, in better agreement with satellite observations. This improved representation of stratocumulus and cumulus clouds does not have a large impact on the overall sea level pressure patterns of the large-scale weather system. Precipitation in the rear portion of the system, however, is found to be smoother when MoisTKE is used, and significantly less when the Kuo Transient scheme is switched on.

scholarly journals Precipitation Susceptibility in Marine Stratocumulus and Shallow Cumulus from Airborne Measurements

2016 ◽  
Author(s):  
Eunsil Jung ◽  
Bruce A. Albrecht ◽  
Armin Sorooshian ◽  
Paquita Zuidema ◽  
Haflidi H. Jonsson
Keyword(s):  
Marine Boundary Layer ◽  
Qualitative Behavior ◽  
Liquid Water Path ◽  
Cumulus Clouds ◽  
Marine Stratocumulus ◽  
Airborne Measurements ◽  
Boundary Layer Clouds ◽  
Shallow Cumulus ◽  
Stratocumulus Clouds ◽  
Field Campaigns

Abstract. Precipitation tends to decrease as aerosol concentration increases in warm marine boundary layer clouds at fixed liquid water path (LWP). The quantitative nature of this relationship is captured using the precipitation susceptibility (So) metric. Previously published works disagree on the qualitative behavior of So in marine low clouds: So decreases monotonically with increasing LWP or cloud depth (H) in stratocumulus clouds (Sc), while it increases and then decreases in shallow cumulus clouds (Cu). This study uses airborne measurements from four field campaigns on Cu and Sc with similar instrument packages and flight maneuvers to examine if and why So behavior varies as a function of cloud type. The findings show that So increases with H and then decreases in both Sc and Cu. Possible reasons for why these results differ from those in previous studies of Sc are discussed.

scholarly journals Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

2015 ◽  
Vol 22 (1) ◽  
pp. 65-85 ◽  
Author(s):  
M. Sakradzija ◽  
A. Seifert ◽  
T. Heus
Keyword(s):  
Weibull Distribution ◽  
Exponential Distribution ◽  
Mass Flux ◽  
Flux Distribution ◽  
Deep Convection ◽  
Convective Cloud ◽  
Cloud Base ◽  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Distribution Shape

Abstract. 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.

scholarly journals A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations

2018 ◽  
Vol 75 (11) ◽  
pp. 4031-4047 ◽  
Author(s):  
Yign Noh ◽  
Donggun Oh ◽  
Fabian Hoffmann ◽  
Siegfried Raasch
Keyword(s):  
Cloud Model ◽  
Cloud Microphysics ◽  
Heaviside Function ◽  
Mixing Ratio ◽  
Cumulus Clouds ◽  
Cloud Droplets ◽  
Shallow Cumulus ◽  
Initial Stage ◽  
Accretion Rates ◽  
The Moment

Abstract Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.

scholarly journals Mystery of ice multiplication in warm-based precipitating shallow cumulus clouds

2010 ◽  
Vol 37 (10) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jiming Sun ◽  
Parisa A. Ariya ◽  
Henry G. Leighton ◽  
M. K. Yau
Keyword(s):  
Cumulus Clouds ◽  
Shallow Cumulus ◽  
Ice Multiplication

scholarly journals Investigating the Scale Adaptivity of a Size-Filtered Mass Flux Parameterization in the Gray Zone of Shallow Cumulus Convection

2018 ◽  
Vol 75 (4) ◽  
pp. 1195-1214 ◽  
Author(s):  
Maren Brast ◽  
Vera Schemann ◽  
Roel A. J. Neggers
Keyword(s):  
Mass Flux ◽  
Turbulent Transport ◽  
Eddy Diffusivity ◽  
Coarse Grained ◽  
Parameterization Scheme ◽  
Quasi Equilibrium ◽  
Gray Zone ◽  
Cumulus Clouds ◽  
Eddy Simulation ◽  
Shallow Cumulus

Abstract In this study, the scale adaptivity of a new parameterization scheme for shallow cumulus clouds in the gray zone is investigated. The eddy diffusivity/multiple mass flux [ED(MF)n] scheme is a bin-macrophysics scheme in which subgrid transport is formulated in terms of discretized size densities. While scale adaptivity in the ED component is achieved using a pragmatic blending approach, the MF component is filtered such that only the transport by plumes smaller than the grid size is maintained. For testing, ED(MF)n is implemented into a large-eddy simulation (LES) model, replacing the original subgrid scheme for turbulent transport. LES thus plays the role of a nonhydrostatic testing ground, which can be run at different resolutions to study the behavior of the parameterization scheme in the boundary layer gray zone. In this range, convective cumulus clouds are partially resolved. The authors find that for quasi-equilibrium marine subtropical conditions at high resolutions, the clouds and the turbulent transport are predominantly resolved by the LES. This partitioning changes toward coarser resolutions, with the representation of shallow cumulus clouds gradually becoming completely carried by the ED(MF)n. The way the partitioning changes with grid spacing matches the behavior diagnosed in coarse-grained LES fields, suggesting that some scale adaptivity is captured. Sensitivity studies show that the scale adaptivity of the ED closure is important and that the location of the gray zone is found to be moderately sensitive to some model constants.

scholarly journals Automated tracking of shallow cumulus clouds in large domain, long duration Large Eddy Simulations

2013 ◽  
Vol 6 (2) ◽  
pp. 2287-2323 ◽  
Author(s):  
T. Heus ◽  
A. Seifert
Keyword(s):  
Large Data ◽  
Current Method ◽  
Large Eddy Simulations ◽  
Large Data Sets ◽  
Data Sets ◽  
Cumulus Clouds ◽  
3 Dimensional ◽  
Shallow Cumulus ◽  
Long Duration ◽  
Large Eddy

Abstract. 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 3 dimensional 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 6Δx, and smaller than about 20% of the horizontal domains size.

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