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Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

A study of the vertical structure of postfrontal shallow clouds in the marine boundary layer over the Southern Ocean is presented. The central question of this two-part study regards cloud phase (liquid/ice) of precipitation, and the associated growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-h case with continuous sea surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are mostly precipitating and shallow (tops mostly around 2 km above sea level), with weak up- and downdrafts, and with cloud-top temperatures generally around −18° to −10°C. The case study is extended to three other periods of postfrontal shallow clouds in MARCUS. While abundant supercooled liquid water is commonly present, an experimental cloud-phase algorithm classifies nearly two-thirds of clouds in the 0° to −5°C layer as containing ice (cloud ice, snow, or mixed phase), implying that much of the precipitation grows through cold-cloud processes. The best predictors of ice presence are cloud-top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment. The water-phase distribution in this cloud regime is explored through numerical simulations in Part II. Significance Statement Climate models generally predict a lower albedo than observed over the Southern Ocean, and this is largely attributed to a lack of cloudiness, especially in the postfrontal cold sector of midlatitude cyclones. This in turn may be due to an excess of ice in these simulated clouds, resulting in rapid precipitation fallout and an overly brief cloud lifespan. The objective of this study is to examine whether shallow postfrontal clouds over the Southern Ocean are dominated by supercooled drops, or by snow and ice, using data collected by a U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility deployed aboard an Australian Antarctic supply vessel. We find that these clouds contain much supercooled liquid, even though cloud-top temperatures generally are around −18° to −8°C, and that about two-thirds of the clouds just above the freezing level contain ice. Much of the precipitation appears to grow through cold-cloud processes above the freezing level, rather than drizzle/rain. Updrafts and/or turbulence in convection or in cloud-top generating cells do not initiate much ice, compared to observations elsewhere in a similar temperature range. This may be attributable to the extremely low concentration of ice nucleating particles in this environment. Ultimately, the deepest clouds with the coldest cloud tops are most likely to be ice dominated.

The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. In the absence of sedimentation and collision–coalescence, the drop size distributions (DSDs) from the LCMs exhibit similar evolution with expected physical behaviors and good interscheme agreement, with the volume mean diameter (Dvol) from the LCMs within 1%–5% of each other. In contrast, the bin schemes exhibit nonphysical broadening with condensational growth. These results further strengthen the case that LCMs are an appropriate numerical benchmark for DSD evolution under condensational growth. When precipitation processes are included, however, the simulated liquid water path, precipitation rates, and response to modified cloud drop/aerosol number concentrations from the LCMs vary substantially, while the bin and bulk schemes are relatively more consistent with each other. The lack of consistency in the LCM results stems from both the collision–coalescence process and the sedimentation process, limiting their application as a numerical benchmark for precipitation processes. Reassuringly, however, precipitation from bulk schemes, which are the basis for cloud microphysics in weather and climate prediction, is within the spread of precipitation from the detailed schemes (LCMs and bin). Overall, this intercomparison identifies the need for focused effort on the comparison of collision–coalescence methods and sedimentation in detailed microphysics schemes, especially LCMs.

Bulk ice-microphysical models parameterize the dynamic evolution of ice particles from advection, collection, and sedimentation through a cloud layer to the surface. Frozen hydrometeors can grow to acquire a multitude of shapes and sizes, which influence the distribution of mass within cloud systems. Aggregates, defined herein as the collection of ice particles, have a variety of formations based on initial ice particle size, shape, falling orientation, and the number of particles that collect. This work focuses on using the Ice Particle and Aggregate Simulator (IPAS) as a statistical tool to repetitively collect ice crystals of identical properties to derive bulk aggregate characteristics. A database of 9 744 000 aggregates is generated with resulting properties analyzed. After 150 single ice crystals (monomers) collect, the most extreme aggregate aspect ratio calculations asymptote toward and ϕca ≈ 0.50 for aggregates composed of quasi-horizontally oriented and randomly oriented monomers, respectively. The results presented are largely consistent with both a previous theoretical study and estimates derived from ground-based observations from two different geographic locations. Particle falling orientation highly influences newly formed aggregate aspect ratios from the collection of particles with extreme aspect ratios; quasi-horizontally oriented particles can produce aggregate aspect ratios an order of magnitude more extreme than randomly oriented particles but can also produce near-spherical aggregates as the number of monomers comprising the aggregate reach approximately 100. Finally, a majority of collections result in aggregates that are closer to prolate than oblate spheroids.

The Ice Particle and Aggregate Simulator (IPAS) is used to theoretically represent the aggregation process of ice crystals. Aggregates have a variety of formations based on initial ice particle size, shape, and falling orientation, all of which influence water phase partitioning. Aggregate dimensional properties and density changes are calculated for monomer–monomer (MON–MON), monomer–aggregate (MON–AGG), and aggregate–aggregate (AGG–AGG) collection to be used by ice-microphysical models for improvement in aggregation parameterizations. Aggregates are chosen from a database of 9 744 000 preformed combinations to be further collected (see Part II). AGG–AGG collection results in more extreme and a smaller range of aggregate aspect ratios than MON–AGG collection. A majority of aggregates are closer to prolate than oblate spheroids, regardless of collection type, except for quasi-horizontally oriented particles that have extreme aspect ratios to begin with. MON–AGG collection frequently results in an increase in density upon collection, whereas MON–MON and AGG–AGG collection almost always result in particle density decreases, with extreme reductions near 99% for MON–MON collection. MON–MON collection results in the greatest decreases in density but then quickly becomes unaffected by the addition of more monomers due to inherent size differences between monomers and aggregates. Finally, a holistic analysis to in situ observations of cloud particle images is presented. IPAS 2D aspect ratios surround a median value of 0.6 and closely follow that of previous studies while varying by no more than ≈12% on average from observed aggregates.

A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

A novel methodology for modeling ice–ice aggregation is presented. This methodology combines a modified hydrodynamic collection algorithm with bulk aggregate characteristic information from an offline simulator that collects ice particles, namely, the Ice Particle and Aggregate Simulator, and has been implemented into the Adaptive Habit Microphysics scheme in the Weather Research and Forecasting Model. Aggregates, or snow, are formed via collection of cloud ice particles, where initial ice characteristics and the resulting geometry determine aggregate characteristics. Upon implementation, idealized squall-line simulations are performed to examine the new methodology in comparison with commonly used bulk microphysics schemes. It is found that the adaptive habit aggregation parameterization develops snow and reduces ice mass and number concentrations compared to other schemes. The development of aggregates through the new methodology cascades into other interesting effects, including enhancements in ice and snow growth, as well as homogeneous freezing. Further microphysical analyses reveal varying sensitivities, where snow processes are most sensitive to the new parameterization, followed by ice, then cloud, rain, and graupel processes. Further, the new scheme results in enhancements in surface precipitation due to the persistence of snow at lower altitudes. This persistence is a result of shape-dependent melting and sublimation, increasing the residence time. Moreover, these low-level enhancements are reflected in increases in radar reflectivity at the surface and its spatial distribution. Finally, the ability to predict snow shape and density allows for the simulation of polarimetric radar quantities, resulting in signature enhancements compared to schemes that do not consider spatial and temporal variations in snow shape and density.

In this study, processes that broaden drop size distributions (DSDs) in Eulerian models with two-moment bin microphysics are analyzed. Numerous tests are performed to isolate the effects of different physical mechanisms that broaden DSDs in two- and three-dimensional Weather Research and Forecasting Model simulations of an idealized ice-free cumulus cloud. Sensitivity of these effects to modifying horizontal and vertical model grid spacings is also examined. As expected, collision–coalescence is a key process broadening the modeled DSDs. In-cloud droplet activation also contributes substantially to DSD broadening, whereas evaporation has only a minor effect and sedimentation has little effect. Cloud dilution (mixing of cloud-free and cloudy air) also broadens the DSDs considerably, whether or not it is accompanied by evaporation. This mechanism involves the reduction of droplet concentration from dilution along the cloud’s lateral edges, leading to locally high supersaturation and enhanced drop growth when this air is subsequently lifted in the updraft. DSD broadening ensues when the DSDs are mixed with those from the cloud core. Decreasing the horizontal and vertical model grid spacings from 100 to 30 m has limited impact on the DSDs. However, when these physical broadening mechanisms (in-cloud activation, collision–coalescence, dilution, etc.) are turned off, there is a reduction of DSD width by up to ~20%–50% when the vertical grid spacing is decreased from 100 to 30 m, consistent with effects of artificial broadening from vertical numerical diffusion. Nonetheless, this artificial numerical broadening appears to be relatively unimportant overall for DSD broadening when physically based broadening mechanisms in the model are included for this cumulus case.

The North Pacific High (NPH) is a fundamental meteorological feature present during the boreal warm season. Marine boundary layer (MBL) clouds, which are persistent in this oceanic region, are influenced directly by the NPH. In this study, we combine 11 years of reanalysis and an unsupervised machine learning technique to examine the gamut of 850 hPa synoptic-scale circulation patterns. This approach reveals two distinguishable regimes – a dominant NPH setup and a land-falling cyclone – and in between a spectrum of large-scale patterns. We then use satellite retrievals to elucidate for the first time the explicit dependence of MBL cloud properties (namely cloud droplet number concentration, liquid water path, and shortwave cloud radiative effect – CRESW) on 850 hPa circulation patterns over the northeast Pacific Ocean. We find that CRESW spans from −146.8 to −115.5 W m−2, indicating that the range of observed MBL cloud properties must be accounted for in global and regional climate models. Our results demonstrate the value of combining reanalysis and satellite retrievals to help clarify the relationship between synoptic-scale dynamics and cloud physics.

The dynamics of convective systems are inherently linked to microphysical processes through phase changes that result in warming or cooling. This is especially true of near-surface cooling via evaporation and melting of falling hydrometeors. In most numerical simulations, the melting of frozen hydrometeors (e.g., hail, graupel, snow) is computed within parameterized bulk microphysics schemes, many of which lack the ability to accurately represent mixed-phase hydrometeors (i.e., partially melted ice), which can affect hydrometeor sedimentation, melting, and evaporation of shed drops. To better understand the microphysical and dynamical effects of melting in convective storms, a bin microphysics scheme was used in the Weather Research and Forecasting Model for two idealized cases: a supercell storm and a squall line. Physically based predicted liquid fraction, instantaneous melting, and instantaneous shedding schemes were used to examine the role and importance of melting hydrometeors for these two storm modes. The results suggest that the amount of precipitation is dependent on the representation of melting. Moreover, the dynamic and thermodynamic characteristics of the simulated storms are found to differ substantially between the melting scenarios, resulting in varied storm system evolution; these differences are found to be dependent on the ambient aerosol concentration, although the differences induced by changing the representation of melting generally outweigh those of changing the aerosol loading. The results highlight the large role of melting in convective storm characteristics and suggest that further model improvements are needed in the near future.

Modeling marine low clouds and fog in coastal environments remains an outstanding challenge due to the inherently complex ocean–land–atmosphere system. This is especially important in the context of global circulation models due to the profound radiative impact of these clouds. This study utilizes aircraft and satellite measurements, in addition to numerical simulations using the Weather Research and Forecasting (WRF) Model, to examine three well-observed coastally trapped disturbance (CTD) events from June 2006, July 2011, and July 2015. Cloud water-soluble ionic and elemental composition analyses conducted for two of the CTD cases indicate that anthropogenic aerosol sources may impact CTD cloud decks due to synoptic-scale patterns associated with CTD initiation. In general, the dynamics and thermodynamics of the CTD systems are well represented and are relatively insensitive to the choice of physics parameterizations; however, a set of WRF simulations suggests that the treatment of model physics strongly influences CTD cloud field evolution. Specifically, cloud liquid water path (LWP) is highly sensitive to the choice of the planetary boundary layer (PBL) scheme; in many instances, the PBL scheme affects cloud extent and LWP values as much as or more than the microphysics scheme. Results suggest that differences in the treatment of entrainment and vertical mixing in the Yonsei University (nonlocal) and Mellor–Yamada–Janjić (local) PBL schemes may play a significant role. The impact of using different driving models—namely, the North American Mesoscale Forecast System (NAM) 12-km analysis and the NCEP North American Regional Reanalysis (NARR) 32-km products—is also investigated.

The terminal velocity (Vt) of ice hydrometeors is of high importance to atmospheric modeling. Vt is governed by the physical characteristics of a hydrometeor, including mass and projected area, as well as environmental conditions. When liquid hydrometeors coalesce to form larger hydrometeors, the resulting hydrometeor can readily be characterized by its spherical or near-spherical shape. For ice hydrometeors, it is more complicated because of the variability of ice shapes possible in the atmosphere as well as the inherent randomness in the aggregation process, which leads to highly variable characteristics. The abundance of atmospheric processes affecting ice particle dimensional characteristics creates potential for highly variable Vt for ice particles that are predicted or measured to be of the “same size.” In this article we explore the variability of ice hydrometeor Vt both theoretically and through the use of experimental observations. Theoretically, the variability in Vt is investigated by analyzing the microphysical characteristics of randomly aggregated hexagonal shapes. The modeled dimensional characteristics are then compared to aircraft probe measurements to constrain the variability in atmospheric ice hydrometeor Vt. Results show that the spread in Vt can be represented with Gaussian distributions relative to a mean. Variability expressed as the full width at half maximum of the normalized Gaussian probability distribution function is around 20%, with somewhat higher values associated with larger particle sizes and warmer temperatures. Field campaigns where mostly convective clouds were sampled displayed low variability, while Arctic and midlatitude winter campaigns showed broader Vt spectra.

Aggregation, the process by which two or more ice particles attach to each other, is typically observed in clouds that span a range of temperatures and is influenced by the crystal shape (habit). In this study, the resulting characteristics of ice–ice two-monomer aggregation is investigated, which is expected to improve microphysical parameterizations through more precise aggregate characteristics and in turn better predict the rate of aggregation and snow development. A systematic way to determine the aspect ratio of the aggregate was developed, which takes into account the expected falling orientations, overlap of each monomer, and any contact angle that may form through so-called constrained randomization. Distributions were used to obtain the most frequent aspect ratio, major axis, and minor axis of aggregated particles with respect to the monomer aspect ratio. Simulations were completed using the Ice Particle and Aggregate Simulator (IPAS), a model that uses predefined three-dimensional geometries, (e.g., hexagonal prisms) to simulate ice crystal aggregation and allows for variation in crystal size, shape, number, and falling orientation. In this study, after collection in a theoretical grid space, detailed information is extracted from the particles to determine the properties of aggregates. It was found that almost all monomer aspect ratios aggregate to less extreme aggregate aspect ratios at nearly the same rate. Newly formed aggregate properties are amenable to implementation into more sophisticated bulk microphysical models designed to predict and evolve particle properties, which is crucial in realistically evolving cloud ice mass distribution and for representing the collection process.

The ability of global climate models to simulate accurately marine stratiform clouds continues to challenge the atmospheric science community. These cloud types, which account for a large uncertainty in Earth’s radiation budget, are generally difficult to characterize due to their shallowness and spatial inhomogeneity. Previous work investigating marine boundary layer (MBL) clouds off the California coast has focused on clouds that form under the typical northerly flow regime during the boreal warm season. From about June through September, however, these northerly winds may reverse and become southerly as part of a coastally trapped disturbance (CTD). As the flow surges northward, it is accompanied by a broad cloud deck. Because these events are difficult to forecast, in situ observations of CTDs are few and far between, and little is known about their cloud physical properties. A climatological perspective of 23 CTD events—spanning the years from 2004 to 2016—is presented using several data products, including model reanalyses, buoys, and satellites. For the first time, satellite retrievals suggest that CTD cloud decks may play a unique role in the radiation budget due to a combination of aerosol sources that enhance cloud droplet number concentration and reduce cloud droplet effective radius. This particular type of cloud regime should therefore be treated differently than that which is more commonly found in the summertime months over the northeast Pacific Ocean. The potential influence of a coherent wind stress cycle on sea surface temperatures and sea salt aerosol is also explored.

Clouds are critical for weather and climate prediction. The multiple scales of cloud processes make simulation difficult. Often models and measurements are used to develop empirical relationships for large‐scale models to be computationally efficient. Machine learning provides another potential tool to improve our empirical parameterizations of clouds. To explore these opportunities, we replace the warm rain formation process in a General Circulation Model (GCM) with a detailed treatment from a bin microphysical model that causes a 400% slowdown in the GCM. We analyze the changes in climate that result from the use of the bin microphysical calculation and find improvements in the rain onset and frequency of light rain compared to high resolution process models and observations. We also find a resulting change in the cloud feedback response of the model to warming, which will significantly impact the climate sensitivity. We then replace the bin microphysical model with several neural networks designed to emulate the autoconversion and accretion rates produced by the bin microphysical model. The neural networks are organized into two stages: the first stage identifies where tendencies will be nonzero (and the sign of the tendency), and the second stage predicts the magnitude of the autoconversion and accretion rates. We describe the risks of overfitting, extrapolation, and linearization by using perfect model experiments with and without the emulator. We can recover the solutions with the emulators in almost all respects, and get simulations that perform as the detailed model, but with the computational cost of the control simulation.

This study investigates droplet size distribution (DSD) characteristics from condensational growth and transport in Eulerian dynamical models with bin microphysics. A hierarchy of modeling frameworks is utilized, including parcel, one-dimensional (1D), and three-dimensional large-eddy simulation (LES). The bin DSDs from the 1D model, which includes only vertical advection and condensational growth, are nearly as broad as those from the LES and in line with observed DSD widths for stratocumulus clouds. These DSDs are much broader than those from Lagrangian microphysical calculations within a parcel framework that serve as a numerical benchmark for the 1D tests. In contrast, the bin-modeled DSDs are similar to the Lagrangian microphysical benchmark for a rising parcel in which Eulerian transport is not considered. These results indicate that numerical diffusion associated with vertical advection is a key contributor to broadening DSDs in the 1D model and LES. This DSD broadening from vertical numerical diffusion is unphysical, in contrast to the physical mixing processes that previous studies have indicated broaden DSDs in real clouds. It is proposed that artificial DSD broadening from vertical numerical diffusion compensates for underrepresented horizontal variability and mixing of different droplet populations in typical LES configurations with bin microphysics, or the neglect of other mechanisms that broaden DSDs such as growth of giant cloud condensation nuclei. These results call into question the ability of Eulerian dynamical models with bin microphysics to investigate the physical mechanisms for DSD broadening, even though they may reasonably simulate overall DSD characteristics.

The effects of aerosol perturbations on deep convective clouds have received considerable attention in the recent literature, especially from a modeling perspective. The published responses in precipitation amount and updraft strength vary in both sign and magnitude and may be the result of different models and parameterizations. Here, a simple numerical framework is employed to determine the potential effects of warming both below the freezing level (warm invigoration) and above the freezing level (mixed-phase invigoration) due to increased aerosol loading. The role of updraft width and slope in the same framework is also examined, highlighting the relative importance of each factor on the resulting updraft strength. The results show that the potential effects of warm invigoration are 2–3 times larger than for mixed-phase invigoration. However, a relatively small response in updraft velocity to warming is found, especially in comparison with the predicted changes in updraft velocity due to small differences in system slope and width, with 87.7% and 96.4% of the subadiabatic and adiabatic realizations, respectively, showing changes in updraft velocity of less than 15% for warmings of no more than 2°C. This result suggests that observations of the aerosol effect will be largely muddled by the natural variability of convective updraft width and slope (which are related to environmental wind shear).

The squall-line event on 20 May 2011, during the Midlatitude Continental Convective Clouds (MC3E) field campaign has been simulated by three bin (spectral) microphysics schemes coupled into the Weather Research and Forecasting (WRF) Model. Semi-idealized three-dimensional simulations driven by temperature and moisture profiles acquired by a radiosonde released in the preconvection environment at 1200 UTC in Morris, Oklahoma, show that each scheme produced a squall line with features broadly consistent with the observed storm characteristics. However, substantial differences in the details of the simulated dynamic and thermodynamic structure are evident. These differences are attributed to different algorithms and numerical representations of microphysical processes, assumptions of the hydrometeor processes and properties, especially ice particle mass, density, and terminal velocity relationships with size, and the resulting interactions between the microphysics, cold pool, and dynamics. This study shows that different bin microphysics schemes, designed to be conceptually more realistic and thus arguably more accurate than bulk microphysics schemes, still simulate a wide spread of microphysical, thermodynamic, and dynamic characteristics of a squall line, qualitatively similar to the spread of squall-line characteristics using various bulk schemes. Future work may focus on improving the representation of ice particle properties in bin schemes to reduce this uncertainty and using the similar assumptions for all schemes to isolate the impact of physics from numerics.

The sensitivity of an idealized squall line to horizontal and vertical grid spacing is investigated using a new approach. Simulations are first performed at a horizontal grid spacing of 1 km until the storm reaches its mature stage. The model output is then interpolated to smaller (and larger) grid spacings, and the model is restarted using the interpolated state plus small thermodynamic perturbations to spin up small-scale motions. This framework allows an investigation of the sensitivity of the storm to changes in without complications from differences in storm initiation and early evolution. The restarted simulations reach a quasi steady state within approximately 1 h. Results demonstrate that there are two -dependent regimes with the transition between regimes occurring for between 250 and 500 m. Some storm characteristics, such as the mean convective core area, change substantially for 250 m but show limited sensitivity as is decreased below 250 m, despite better resolving smaller-scale turbulent motions. This transition is found to be independent of the chosen . Mixing in the context of varying and is also investigated via passive tracers that are initialized 1 h after restarting the simulations (i.e., after the spin up of small-scale motions). The tracer field at the end of the simulations reveals that entrainment and detrainment are suppressed in the simulations with 500 m. For decreasing , entrainment and detrainment are substantially more important, limiting the flux of low-level tracer to the upper troposphere, which has important implications for modeling studies of convective transport from the boundary layer through the troposphere.

The spatial variability of rain rate R is evaluated by using both radar observations and cloud-resolving model output, focusing on the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) period. In general, the model-predicted rain-rate probability distributions agree well with those estimated from the radar data across a wide range of spatial scales. The spatial variability in R, which is defined according to the standard deviation of R (for R greater than a predefined threshold Rmin) σ(R), is found to vary according to both the average of R over a given footprint μ(R) and the footprint size or averaging scale Δ. There is good agreement between area-averaged model output and radar data at a height of 2.5 km. The model output at the surface is used to construct a scale-dependent parameterization of σ(R) as a function of μ(R) and Δ that can be readily implemented into large-scale numerical models. The variability in both the rainwater mixing ratio qr and R as a function of height is also explored. From the statistical analysis, a scale- and height-dependent formulation for the spatial variability of both qr and R is provided for the analyzed tropical scenario. Last, it is shown how this parameterization can be used to assist in constraining parameters that are often used to describe the surface rain-rate distribution.

Deep convective storms produce raindrops through three mechanisms: condensation and coalescence growth of cloud liquid droplets (i.e., warm processes), melting of ice hydrometeors, and shedding from wet hailstones. To investigate the relative importance of these mechanisms and their contributions to exotic drop size distributions (DSDs) observed near the surface in supercell storms, an idealized simulation of a supercell is performed using a modified version of the Morrison two-moment microphysics scheme. The modified scheme includes separate categories for warm, shed, and melted rain. Rain originating from melting ice dominates the rain mass at low levels, especially along the right forward-flank precipitation shield, whereas shed-rain drops dominate a region within the left forward flank. Warm rain is only dominant in the upshear portion of the rear flank of the storm at low levels, though it dominates the total rain mass within the main updraft aloft. The warm-rain mass at low levels is associated with strong low-level downdrafts, consistent with previously published hypotheses based on polarimetric radar observations. Raindrops produced via warm processes are smaller on average than those produced by shedding and melting; drops in the latter class tend to be the largest. Overall, the simulations fail to reproduce the diverse nature of observed supercell DSDs, although the modified microphysics scheme does increase the variability of surface DSDs compared to the Control run. This implies that more sophisticated treatment of rain microphysics is needed to capture the natural variability of supercell DSDs, including the ability to evolve the DSD spectral shape through sedimentation and collisional processes.

Changes in the aerosol number concentration are reflected by changes in raindrop size and number concentration that ultimately affect the strength of cold pools via evaporation. Therefore, aerosol perturbations can potentially alter the balance between cold pool–induced and low-level wind shear–induced circulations. In the present work, simulations with increased aerosol loadings below approximately 3 km, between approximately 3 and 10 km, and at all vertical levels are performed to specifically address both the overall sensitivity of a squall line to the vertical distribution of aerosols and the extent to which low-level aerosols can affect the convective strength of the system. The results suggest that low-level aerosol perturbations have a negligible effect on the overall storm strength even though they act to enhance low-level latent heating rates. A tracer analysis shows that the low-level aerosols are either predominantly detrained at or below the freezing level or are rapidly lifted to the top of the troposphere or the lower stratosphere within the strongest convective cores. Moreover, it is shown that midlevel aerosol perturbations have nearly the same effect as perturbing the entire domain, increasing the convective updraft mass flux by more than 10%. These changes in strength are driven by a complex chain of events caused by smaller supercooled droplets, larger graupel, and larger raindrops. Combined, these changes tend to reduce the low-level bulk evaporation rate, thus weakening the cold pool and enhancing updraft strength. The results presented herein suggest that midlevel aerosol perturbations may exhibit a much larger effect on squall lines, at least in the context of this idealized framework.

The dynamical effects of increased aerosol loading on the strength and structure of numerically simulated squall lines are explored. Results are explained in the context of Rotunno–Klemp–Weisman (RKW) theory. Changes in aerosol loading lead to changes in raindrop size and number that ultimately affect the strength of the cold pool via changes in evaporation. Thus, the balance between cold pool and low-level wind shear–induced vorticities can be changed by an aerosol perturbation. Simulations covering a wide range of low-level wind shears are performed to study the sensitivity to aerosols in different environments and provide more general conclusions. Simulations with relatively weak low-level environmental wind shear (0.0024 s−1) have a relatively strong cold pool circulation compared to the environmental shear. An increase in aerosol loading leads to a weakening of the cold pool and, hence, a more optimal balance between the cold pool– and environmental shear–induced circulations according to RKW theory. Consequently, there is an increase in the convective mass flux of nearly 20% in polluted conditions relative to pristine. This strengthening coincides with more upright convective updrafts and a significant increase (nearly 20%) in cumulative precipitation. An increase in aerosol loading in a strong wind shear environment (0.0064 s−1) leads to less optimal storms and a suppression of the convective mass flux and precipitation. This occurs because the cold pool circulation is weak relative to the environmental shear when the shear is strong, and further weakening of the cold pool with high aerosol loading leads to an even less optimal storm structure (i.e., convective updrafts begin to tilt downshear).

A novel two-moment bulk aerosol parameterization is derived from a state-of-the-art 2D bin microphysics model using power-law relationships and a semi-analytical technique for activation. The activation scheme predicts both number and mass of a lognormal aerosol distribution and permits the evolution of the modal mass with time. The newly developed bulk aerosol scheme is formulated for use in traditional two-moment bulk microphysics models. The new explicit scheme is compared with the 2D bin scheme and a simple scaling aerosol parameterization, in which all the aerosol processes are scaled to the respective cloud process rates, in a kinematic model with a specified flow field. Hybrid simulations in which the explicit activation formulation is coupled to the scaling parameterization are also performed. Model results demonstrate the significance of including a physically realistic representation of aerosols contained in haze, cloud droplets, and rain. It is shown that the explicit aerosol parameterization and scaling method predict similar bulk aerosol quantities and match the results of the 2D bin model only if an explicit treatment of aerosol activation—that is, both aerosol number and mass transfer because of activation—is included in the microphysics model.

Three configurations of a bulk microphysics scheme in conjunction with a detailed bin scheme are implemented in the Weather Research and Forecasting (WRF) model to specifically address the role of the saturation adjustment assumption (i.e., condensing/evaporating the surplus/deficit water vapor relative to saturation in one time step) on aerosol-induced invigoration of deep convective clouds. The bulk model configurations are designed to treat cloud droplet condensation/evaporation using either saturation adjustment, as employed in most bulk models, or an explicit representation of supersaturation over a time step, as used in bin models. Results demonstrate that the use of saturation adjustment artificially enhances condensation and latent heating at low levels and limits the potential for an increase in aerosol concentration to increase buoyancy at mid to upper levels. This leads to a small weakening of the time- and domain-averaged convective mass flux (~ -3%) in polluted compared to clean conditions. In contrast, the bin model and bulk scheme with explicit prediction of supersaturation simulate an increase in latent heating aloft and the convective updraft mass flux is weakly invigorated (~5%). The bin model also produces a large increase in domain-mean cumulative surface precipitation in polluted conditions (~18%), while all of the bulk model configurations simulate little change in precipitation. Finally, it is shown that the cold pool weakens substantially with increased aerosol loading when saturation adjustment is applied, which acts to reduce the low-level convergence and weaken the convective dynamics. With an explicit treatment of supersaturation in the bulk and bin models there is little change in cold pool strength, so that the convective response to polluted conditions is influenced more by changes in latent heating aloft. It is concluded that the use of saturation adjustment can explain differences in the response of cold pool evolution and convective dynamics with aerosol loading simulated by the bulk and bin models, but cannot explain large differences in the response of surface precipitation between these models.

A two-dimensional (2-D) continuous spectral aerosol-droplet microphysics model is presented and implemented into the Weather Research and Forecasting (WRF) model for large-eddy simulations (LES) of warm clouds. Activation and regeneration of aerosols are treated explicitly in the calculation of condensation/evaporation. The model includes a 2-D spectrum that encompasses wet aerosol particles (i.e., haze droplets), cloud droplets, and drizzle droplets in a continuous and consistent manner and allows for the explicit tracking of aerosol size within cloud droplets due to collision-coalescence. The system of differential equations describing condensation/evaporation (i.e., mass conservation and energy conservation) is solved simultaneously within each grid cell. The model is demonstrated by simulating a marine stratocumulus deck for two different aerosol loadings (100 and 500 cm−3), and comparison with the more traditional microphysics modeling approaches (both 1-D bin and bulk schemes) is evaluated. The simulations suggest that in a 1-D bin microphysics scheme, without regeneration, too few particles are produced and hence the mode of the droplet size spectrum occurs at a larger size relative to the 2-D bin model results. Moreover, with regeneration, the 1-D scheme produces too many small droplets and thus shifts the mode toward smaller sizes. These large shifts in the droplet size distribution can potentially have significant effects on the efficiency of the collision-coalescence process, fall speeds, and ultimately precipitation.

Three-dimensional large-eddy simulations (LES) with detailed bin-resolved microphysics are performed to explore the diurnal variation of marine stratocumulus (MSc) clouds under clean and polluted conditions. The sensitivity of the aerosol-cloud-precipitation interactions to variation of sea surface temperature, free tropospheric humidity, large-scale divergence rate, and wind speed is assessed. The comprehensive set of simulations corroborates previous studies that (1) with moderate/heavy drizzle, an increase in aerosol leads to an increase in cloud thickness; and (2) with non/light drizzle, an increase in aerosol results in a thinner cloud, due to the pronounced effect on entrainment. It is shown that for higher SST, stronger large-scale divergence, drier free troposphere, or lower wind speed, the cloud thins and precipitation decreases. The sign and magnitude of the Twomey effect, droplet dispersion effect, cloud thickness effect, and cloud optical depth susceptibility to aerosol perturbations (i.e., change in cloud optical depth to change in aerosol number concentration) are evaluated by LES experiments and compared with analytical formulations. The Twomey effect emerges as dominant in total cloud optical depth susceptibility to aerosol perturbations. The dispersion effect, that of aerosol perturbations on the cloud droplet size spectrum, is positive (i.e., increase in aerosol leads to spectral narrowing) and accounts for 3% to 10% of the total cloud optical depth susceptibility at nighttime, with greater influence in heavier drizzling clouds. The cloud thickness effect is negative (i.e., increase in aerosol leads to thinner cloud) for non/light drizzling cloud and positive for a moderate/heavy drizzling clouds; the cloud thickness effect contributes 5% to 22% of the nighttime total cloud susceptibility. Overall, the total cloud optical depth susceptibility ranges from ~0.28 to 0.53 at night; an increase in aerosol concentration enhances cloud optical depth, especially with heavier precipitation and in a more pristine environment. During the daytime, the range of magnitude for each effect is more variable owing to cloud thinning and decoupling. The good agreement between LES experiments and analytical formulations suggests that the latter may be useful in evaluations of the total cloud susceptibility. The ratio of the magnitude of the cloud thickness effect to that of the Twomey effect depends on cloud base height and cloud thickness in unperturbed (clean) clouds.

The potential effects of increased aerosol loading on the development of deep convective clouds and resulting precipitation amounts are studied by employing the Weather Research and Forecasting (WRF) model as a detailed high-resolution cloud resolving model (CRM) with both detailed bulk and bin microphysics schemes. The bulk microphysics scheme incorporates a physically based parameterization of cloud droplet activation as well as homogeneous and heterogeneous freezing in order to explicitly resolve the possible aerosol-induced effects on the cloud microphysics. These parameterizations allow one to segregate the effects of an increase in the aerosol number concentration into enhanced cloud condensation nuclei (CCN) and/or ice nuclei (IN) concentrations using bulk microphysics. The bin microphysics scheme, with its explicit calculations of cloud particle collisions, is shown to better predict cumulative precipitation. Increases in the CCN number concentration may not have a monotonic influence on the cumulative precipitation resulting from deep convective clouds. We demonstrate that the aerosol-induced effect is controlled by the balance between latent heating and the increase in condensed water aloft, each having opposing effects on buoyancy. It is also shown that under polluted conditions and in relatively dry environments, increases in the CCN number concentration reduce the cumulative precipitation due to the competition between the sedimentation and evaporation/sublimation timescales. The effect of an increase in the IN number concentration on the dynamics of deep convective clouds is small, but may act to suppress precipitation. A comparison of the predictions using the bin and bulk microphysics schemes demonstrate a significant difference between the predicted precipitation and the influence of aerosol perturbations on updraft velocity within the convective core. The bulk microphysics scheme is shown to be unable to capture the changes in latent heating that occur as a result of changes in the CCN number concentration, while the bin microphysics scheme demonstrates significant increases in the latent heating aloft with increasing CCN number concentration. This suggests that a detailed two-bulk microphysics scheme, which is more computationally efficient than bin microphysics schemes, may not be sufficient, even when coupled to a detailed activation scheme, to predict small changes that result from perturbations in aerosol loading.

Following up on an almost thirty year long history of International Cloud Modeling Workshops, that started out with a meeting in Irsee, Germany in 1985, the 8th International Cloud Modeling Workshop was held in July 2012 in Warsaw, Poland. The workshop, hosted by the Institute of Geophysics at the University of Warsaw, was organized by Szymon Malinowski and his local team of students and co-chaired by Wojciech Grabowski (NCAR/MMM) and Andreas Muhlbauer (University of Washington). International Cloud Modeling Workshops have been held traditionally every four years typically during the week before the International Conference on Clouds and Precipitation (ICCP) . Rooted in the World Meteorological Organization’s (WMO) weather modification program, the core objectives of the Cloud Modeling Workshop have been centered at the numerical modeling of clouds, cloud microphysics, and the interactions between cloud microphysics and cloud dynamics. In particular, the goal of the workshop is to provide insight into the pertinent problems of today’s state-of-the-art of cloud modeling and to identify key deficiencies in the microphysical representation of clouds in numerical models and cloud parameterizations. In recent years, the workshop has increasingly shifted the focus toward modeling the interactions between aerosols and clouds and provided case studies to investigatemore » both the effects of aerosols on clouds and precipitation as well as the impact of cloud and precipitation processes on aerosols. This time, about 60 (?) scientists from about 10 (?) different countries participated in the workshop and contributed with discussions, oral and poster presentations to the workshop’s plenary and breakout sessions. Several case leaders contributed to the workshop by setting up five observationally-based case studies covering a wide range of cloud types, namely, marine stratocumulus, mid-latitude squall lines, mid-latitude cirrus clouds, Arctic stratus and winter-time orographic clouds and precipitation. Interested readers are encouraged to visit the workshop website at http://www.atmos.washington.edu/~andreasm/workshop2012/ and browse through the list of case studies. The web page also provides a detailed list of participants and the workshop agenda. Aside from contributed oral and poster presentations during the workshop’s plenary sessions, parallel breakout sessions focused on presentations and discussions of the individual cases. A short summary and science highlights from each of the cases is presented below.« less