Convective and turbulent motions in non-precipitating Cu. Part II: LES simulated cloud represented by a starting plume

The dynamic structure of a small trade-wind Cu is analyzed using a novel approach. Cu developing in a shear-free environment was simulated by 10 m-resolution LES model with spectral bin microphysics. The aim is to clarify the dynamical nature of cloud updraft zone (CUZ) including entrainment and mixing in growing Cu. The validity of concept stating that a cloud at developing state can be represented by a parcel or a jet is tested. To investigate dynamical entrainment in CUZ performed by motions with scales larger than the turbulence scales, the modeled fields of air velocity were filtered by wavelet filter which separated convective motions from turbulent ones. Two types of objects in developing cloud were investigated: small volume ascending at maximal velocity (point parcel) and CUZ. It was found that the point parcel representing the upper part of cloud core is adiabatic. The motion of the air in this parcel ascending from cloud base determines cloud top height. The top hat (i.e., averaged) values of updraft velocity and adiabatic fraction in CUZ are substantially lower than those in the point parcel. Evaluation of the terms in the dynamical equation typically used in 1D cloud parcel models show that this equation can be applied for calculation of vertical velocities at the developing stage of small Cu, at least up to the heights of the inversion layer. Dynamically, the CUZ of developing cloud resembles the starting plume with the tail of non-stationary jet. Both the top hat vertical velocity and buoyancy acceleration linearly increase with the height, at least up to the inversion layer. An important finding is that lateral entrainment of convective (non-turbulent) nature has a little effect on the top hat CUZ velocity and cannot explain the vertical changes of conservative variables qt and θl. In contrast, entrained air lifting inside CUZ substantially decreases top hat liquid water content and its adiabatic fraction. Possible reasons of these effects are discussed.

Shallow cumulus properties as captured by adiabatic fraction in high-resolution LES simulations

Shallow convective clouds are important players in Earth’s energy budget and hydrological cycle, and are abundant in the tropical and subtropical belts. They greatly contribute to the uncertainty in climate predictions, due to their unresolved, complex processes that include coupling between the dynamics and microphysics. Analysis of cloud structure can be simplified by considering cloud motions as a combination of moist adiabatic motions like adiabatic updrafts and turbulent motions leading to deviation from adiabaticity. In this work, we study the sizes and occurrence of adiabatic regions in shallow cumulus clouds during their growth and mature stages, and use the adiabatic fraction (AF) as a continuous metric to describe cloud processes and properties from the core to the edge. To do so, we simulate isolated trade wind cumulus clouds of different sizes using the System of Atmospheric Modeling (SAM) model in high-resolution (10 m) with the Hebrew University spectral bin microphysics (SBM). The fine features in the cloud’s dynamics and microphysics, including small near-adiabatic volumes and a thin transition zone at the edge of the cloud (∼20-40 m in width) are captured. The AF is shown to be an efficient measure for analyzing cloud properties and key processes determining the droplets-size-distribution formation and shape during the cloud evolution. Physical processes governing the properties of droplets size distributions at different cloud regions (e.g. core, edge) are analyzed in relation to AF.

Revisiting adiabatic fraction estimations in cumulus clouds: high-resolution simulations with a passive tracer

The process of mixing in warm convective clouds and its effects on microphysics are crucial for an accurate description of cloud fields, weather, and climate. Still, they remain open questions in the field of cloud physics. Adiabatic regions in the cloud could be considered non-mixed areas and therefore serve as an important reference to mixing. For this reason, the adiabatic fraction (AF) is an important parameter that estimates the mixing level in the cloud in a simple way. Here, we test different methods of AF calculations using high-resolution (10 m) simulations of isolated warm cumulus clouds. The calculated AFs are compared with a normalized concentration of a passive tracer, which is a measure of dilution by mixing. This comparison enables the examination of how well the AF parameter can determine mixing effects and the estimation of the accuracy of different approaches used to calculate it. Comparison of three different methods to derive AF, with the passive tracer, shows that one method is much more robust than the others. Moreover, this method's equation structure also allows for the isolation of different assumptions that are often practiced when calculating AF such as vertical profiles, cloud-base height, and the linearity of AF with height. The use of a detailed spectral bin microphysics scheme allows an accurate description of the supersaturation field and demonstrates that the accuracy of the saturation adjustment assumption depends on aerosol concentration, leading to an underestimation of AF in pristine environments.

Impact of Giant Sea Salt Aerosol Particles on Precipitation in Marine Cumuli and Stratocumuli: Lagrangian Cloud Model Simulations

The impact of giant sea salt aerosols released from breaking waves on rain formation in marine boundary layer clouds is studied using large eddy simulations (LES). We perform simulations of marine cumuli and stratocumuli for various concentrations of cloud condensation nuclei (CCN) and giant CCN (GCCN). Cloud microphysics are modeled with a Lagrangian method that provides key improvements in comparison to previous LES of GCCN that used Eulerian bin microphysics. We find that GCCN significantly increase precipitation in stratocumuli. This effect is strongest for low and moderate CCN concentrations. GCCN are found to have a smaller impact on precipitation formation in cumuli. These conclusions are in agreement with field measurements. We develop a simple parameterization of the effect of GCCN on precipitation, accretion, and autoconversion rates in marine stratocumuli.

New Empirical Formulation for the Sublimational Breakup of Graupel and Dendritic Snow

Ice fragments are generated by sublimation of ice particles in subsaturated conditions in natural clouds. Conceivably, such sublimational breakup would be expected to cause ice multiplication in natural clouds. Any fragment that survives will grow to become ice precipitation that may sublimate and fragment further.As a first step towards assessing this overlooked process, a formulation is proposed for the number of ice fragments from sublimation of ice particles for an atmospheric model. This is done by amalgamating laboratory observations from previously published studies. The concept of a ‘sublimated mass activity spectrum’ for the breakup is applied to the dataset. The number of ice fragments is determined by the relative humidity over ice and the initial size of the parent ice particles. The new formulation applies to dendritic crystals and heavily rimed particles only.Finally, a thought experiment is performed for an idealized scenario of subsaturation with in-cloud descent. Scaling analysis yields an estimate of an ice enhancement ratio of about 5 (50) within a weak deep convective downdraft of about 2 m s-1, for an initial monodisperse population of dendritic snow (graupel) particles of 3 L-1 and 2 mm . During descent, there is a dynamic equilibrium between continual emission of fragments and their depletion by sublimation. A simplified bin microphysics parcel model exhibits this dynamical quasi-equilibrium, consistent with the thought experiment. The fragments have average lifetimes of around 90 and 240 seconds for dendrites and graupel respectively. Sublimational breakup is predicted to cause significant secondary ice production.

A Fast Linear Semi-Lagrangian Advection Scheme Coupled with Spectral (bin) Microphysics to Simulate an Idealized Super Cell Storm in WRF

A new, computationally efficient Semi-Lagrangian advection (SLA) scheme was used to simulate an idealized supercell storm using WRF coupled with Spectral (bin) Microphysics (SBM). SLA was developed to make complicated microphysical schemes more computationally accessible to cloud resolving models. The SLA is a linear combination of Semi-Lagrangian schemes of the first and the second order. It has relatively low numerical diffusion, a high level of mass conservation accuracy, and preserves the sum of multiple advected variables. In addition to idealized tests, comparisons were made with standard WRF higher-order, non-linear advection schemes. Tests of the SLA were performed using different weighting coefficients of γ for the combination of the first and second order components. The results of SLA on grids of 1 km, 500 m, and 250 m agree well with those of the standard WRF advection schemes, with results most similar to simulations with 250 m grid spacing. At the same time, the advection CPU time required by the SLA was 2.2 to 3 times shorter than the WRF advection schemes. The speed-up occurred in part because of the utilization of the same advection matrix for the advection of all hydrometeor mass bins. The findings of this work support the hypothesis that cloud microphysical simulation is more sensitive to the choice of microphysics than to the choice of advection schemes, thereby justifying the use of computationally efficient lower order linear schemes.

What is Adiabatic Fraction in Cumulus Clouds: High-Resolution Simulations with Passive Tracer

The process of mixing in warm convective clouds and its effects on microphysics, is crucial for an accurate description of cloud fields, weather, and climate. Still, it remains an open question in the field of cloud physics. Adiabatic regions in the cloud could be considered as non-mixed areas and therefore serve as an important reference to mixing. Therefore, the adiabatic fraction (AF) is an important parameter that estimates the mixing level in the cloud in a simple way. Here, we test different methods of AF calculations using high-resolution (10 m) simulations of isolated warm Cumulus clouds. The calculated AFs are compared with a normalized concentration of a passive tracer, which is a measure of dilution by mixing. This comparison enables us to examine how well the AF parameter can determine mixing effects, and to estimate the accuracy of different approaches used to calculate it. The sensitivity of the calculated AF to the choice of different equations, vertical profiles, cloud base height, and its linearity with height are all tested. Moreover, the use of a detailed spectral bin microphysics scheme demonstrates that the accuracy of the saturation adjustment assumption depends on aerosol concentration, and leads to an underestimation of AF in pristine environments.

Microphysical and Polarimetric Radar Modeling of Hydrometeor Refreezing

A unique polarimetric radar signature indicative of hydrometeor refreezing during ice pellet events has been documented in several recent studies, yet the underlying microphysical causes remain unknown. The signature is characterized by enhancements in differential reflectivity (ZDR), specific differential phase (KDP), and linear depolarization ratio (LDR), and a reduction in co-polar correlation coefficient (ρhv) within a layer of decreasing radar reflectivity factor at horizontal polarization (ZH). In previous studies, the leading hypothesis for the observed radar signature is the preferential refreezing of small drops. Here, a simplified, one-dimensional, explicit bin microphysics model is developed to simulate the refreezing of fully melted hydrometeors, and coupled with a polarimetric radar forward operator to quantify the impact of preferential refreezing on simulated radar signatures. The modeling results demonstrate that preferential refreezing is insufficient by itself to produce the observed signatures. In contrast, simulations considering an ice shell growing asymmetrically around a freezing particle (i.e., emulating a thicker ice shell on the bottom of a falling particle) produce realistic ZDR enhancements, and also closely replicate observed features in ZH, KDP, LDR, and ρhv. Simulations that assume no increase in particle wobbling with freezing produce an even greater ZDR enhancement, but this comes at the expense of reducing the LDR enhancement. It is suggested that the polarimetric refreezing signature is instead strongly related to both the distribution of the unfrozen liquid portion within a freezing particle, and the orientation of this liquid with respect to the horizontal.

Cloud droplet diffusional growth in homogeneous isotropic turbulence: bin microphysics versus Lagrangian super-droplet simulations

The increase in the spectral width of an initially monodisperse population of cloud droplets in homogeneous isotropic turbulence is investigated by applying a finite-difference fluid flow model combined with either Eulerian bin microphysics or a Lagrangian particle-based scheme. The turbulence is forced applying a variant of the so-called linear forcing method that maintains the mean turbulent kinetic energy (TKE) and the TKE partitioning between velocity components. The latter is important for maintaining the quasi-steady forcing of the supersaturation fluctuations that drive the increase in the spectral width. We apply a large computational domain (643 m3), one of the domains considered in Thomas et al. (2020). The simulations apply 1 m grid length and are in the spirit of the implicit large eddy simulation (ILES), that is, with small-scale dissipation provided by the model numerics. This is in contrast to the scaled-up direct numerical simulation (DNS) applied in Thomas et al. (2020). Two TKE intensities and three different droplet concentrations are considered. Analytic solutions derived in Sardina et al. (2015), valid for the case when the turbulence integral timescale is much larger than the droplet phase relaxation timescale, are used to guide the comparison between the two microphysics simulation techniques. The Lagrangian approach reproduces the scalings relatively well. Representing the spectral width increase in time is more challenging for the bin microphysics because appropriately high resolution in the bin space is needed. The bin width of 0.5 µm is only sufficient for the lowest droplet concentration (26 cm−3). For the highest droplet concentration (650 cm−3), an order of magnitude smaller bin size is barely sufficient. The scalings are not expected to be valid for the lowest droplet concentration and the high-TKE case, and the two microphysics schemes represent similar departures. Finally, because the fluid flow is the same for all simulations featuring either low or high TKE, one can compare point-by-point simulation results. Such a comparison shows very close temperature and water vapor point-by-point values across the computational domain and larger differences between simulated mean droplet radii and spectral width. The latter are explained by fundamental differences in the two simulation methodologies, numerical diffusion in the Eulerian bin approach and a relatively small number of Lagrangian particles that are used in the particle-based microphysics.

The adiabaticity of warm cumulus clouds simulated in high-resolution

<p>The strong coupling between dynamic, thermodynamic, and microphysical processes and the numerous environmental parameters on which they depend makes clouds a highly complex system. Adiabatic regions (i.e., undiluted core) in the cloud allow to approximate in a simple way thermodynamic and microphysical profiles and provide local boundary conditions (i.e. core is a source of adiabatic values in each level). Mixing of the cloud with its environment affects both the cloud and the environmental properties. While environmental humidity, temperature and aerosol loading affect the clouds’ buoyancy and droplets size distribution (DSD), clouds simultaneously affect their surrounding via detrainment of droplets, humid air, and processed aerosols. Mixing occurs within a large spectrum of scales and leads to deviation of parts of the cloud from adiabaticity. The level of adiabaticity can be represented continuously by the adiabatic fraction (AF; defined as the ratio of the liquid water content to the theoretical adiabatic value). In this work we used the System of Atmosphere Modeling (SAM) with the Hebrew University Spectral Bin Microphysics to simulate a few isolated non-precipitating trade cumulus clouds (in different sizes and aerosol loading) in high resolution (10m). Passive tracer was added to all the simulations. We found cloudy volumes that contain both high tracer concentration and high AF (up to the clouds’ top), compared these two measures of mixing, and discuss their differences. The accuracy of AF calculations, based on different known methods is tested. For example, we show that the saturation adjustment assumption that is often used in AF calculations can lead to an underestimation of AF in pristine environments. This will mask microphysical effects and cause biases when comparing the adiabaticity of clouds under different aerosols loading. We show that the space spanned by the AF versus height in the cloud is a good measure for describing changes in cloud’s key variables in space and time (like temperature, updraft, and DSD properties). This space of AF vs height demonstrates how certain processes (e.g. in-cloud nucleation, mixing, evaporation, etc.) dominate different regions in the cloud (core, edge), and cause different dependence of the DSD on AF under different aerosols loading.</p>