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Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics-turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision-coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particlebased microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei ("giant CCN") do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN. [ABSTRACT FROM AUTHOR]

Variability of ice microphysical properties like crystal size and density in cirrus clouds is important for climate through its impact on radiative forcing, but challenging to represent in models. For the first time, recent laboratory experiments of particle growth (tied to crystal morphology via deposition density) are combined with a state‐of‐the‐art Lagrangian particle‐based microphysics model in large‐eddy simulations to examine sources of microphysical variability in cirrus. Simulated particle size distributions compare well against balloon‐borne observations. Overall, microphysical variability is dominated by variability in the particles' thermodynamic histories. However, diversity in crystal morphology notably increases spatial variability of mean particle size and density, especially at mid‐levels in the cloud. Little correlation between instantaneous crystal properties and supersaturation occurs even though the modeled particle morphology is directly tied to supersaturation based on laboratory measurements. Thus, the individual thermodynamic paths of each particle, not the instantaneous conditions, control the evolution of particle properties. Plain Language Summary: Thin, high‐altitude ice clouds—cirrus—are critical to climate through their interactions with incoming solar radiation and outgoing longwave radiation. With a zoomed‐in view, observations show that cirrus comprise ice crystals of diverse sizes and shapes. These crystal properties determine the bulk properties of the clouds, like their ice water content. What controls this diversity of crystal properties? To address this question, we performed numerical simulations using a state‐of‐the‐art model that tracks the growth of individual particles, informed by laboratory crystal growth experiments, in a realistic turbulent cloud flow. We show that variability in the local environmental conditions (e.g., relative humidity) that particles experience in a turbulent environment is a primary driver of crystal diversity in cirrus. Diversity is also driven by different shapes of newly formed crystals, leading to different growth characteristics. This diversity drives spatial variability of properties like mean crystal density across the cloud layer. Climate models that represent cirrus using simple relationships based on observations that are essentially snapshots of average crystal properties cannot capture this variability. Overall, our analysis indicates that the history of environmental conditions along particle trajectories, not just instantaneous conditions, are required to understand crystal growth and variability in cirrus. Key Points: Variability in the thermodynamic histories of ice crystals are a primary driver of particle variability in cirrusDiversity in crystal morphology also drives spatial variability of mean particle size and density, particularly at mid‐levels in the cloudThermodynamic histories along particle trajectories, not just local conditions, are required to understand crystal growth in cirrus [ABSTRACT FROM AUTHOR]

This study uses theory and numerical simulations to analyze the nondimensional spreading rate α (change in radius with height) of buoyant thermals as they rise and entrain surrounding environmental fluid. A focus is on how α varies with initial thermal aspect ratio Ar, defined as height divided by width of the initial buoyancy perturbation. An analytic equation for thermal ascent rate wt that depends on α is derived from the thermal-volume-averaged momentum budget equation. The thermal top height when wt is maximum, defining a critical height zc, is inversely proportional to α. The height zc also corresponds to the thermal top height when buoyant fluid along the thermal's vertical axis is fully replaced by entrained nonbuoyant environmental fluid rising from below the thermal. The time scale for this process is controlled by the vertical velocity of parcels rising upward through the thermal's core. This parcel vertical velocity is approximated from Hill's analytic spherical vortex, yielding an analytic inverse relation between α and Ar. Physically, this α–Ar relation is connected to changes in circulation as Ar is modified. Numerical simulations of thermals with Ar varied from 0.5 to 2 give α values close to the analytic theoretical relation, with a factor of ∼3 decrease in α as Ar is increased from 0.5 to 2. The theory also explains why α of initially spherical thermals from past laboratory and modeling studies is about 0.15. Overall, this study provides a theoretical underpinning for understanding the entrainment behavior of thermals, relevant to buoyantly driven atmospheric flows. Significance Statement: Thermals, which are coherent, quasi-spherical regions of upward-moving buoyant fluid, are a key feature of many convective atmospheric flows. The purpose of this study is to characterize how thermals entrain surrounding fluid and spread out as they rise. We use theory and numerical modeling to explain why entrainment rate decreases with an increase in the initial thermal aspect ratio—the ratio of height to width. This work also explains why the nondimensional spreading rate (change in thermal radius with height) of initially spherical thermals from past laboratory and numerical modeling studies is about 0.15. Overall, this work provides a framework for conceptualizing the entrainment behavior of thermals and thus improved understanding of vertical transport in convective atmospheric flows. [ABSTRACT FROM AUTHOR]

Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct numerical simulations (DNS) of turbulent Rayleigh-Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation time scale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the nonadiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed. [ABSTRACT FROM AUTHOR]

Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs. [ABSTRACT FROM AUTHOR]

Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, and the ability to run the scheme on Graphics Processing Units (GPUs). The main science changes include removing an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on (A) mean state climate, (B) cloud feedback response to warming and (C) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization without a limit on ice number. We also find that the revised scheme produces less liquid and ice, but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks, but some significant reductions in the magnitude of Aerosol Cloud Interactions (ACI). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACI. [ABSTRACT FROM AUTHOR]

This study examines dynamic pressure drag on rising dry buoyant thermals. A theoretical expression for drag coefficient Cd as a function of several other nondimensional parameters governing thermal dynamics is derived based on combining the thermal momentum budget with the similarity theory of Scorer. Using values for these nondimensional parameters from previous studies, the theory suggests drag on thermals is small relative to that on solid spheres in laminar or turbulent flow. Two sets of numerical simulations of thermals in an unstratified, neutrally stable environment using an LES configuration of the Cloud Model 1 (CM1) are analyzed. One set has a relatively low effective Reynolds number Re and the other has an order-of-magnitude-higher Re; these produce laminar and turbulent resolved-scale flows, respectively. Consistent with the theoretical Cd, the magnitude of drag is small in all simulations. However, whereas the laminar thermals have Cd ≈ 0.01, the turbulent thermals have weakly negative drag (Cd ≈ −0.1). This difference is explained by the laminar thermals having near vertical symmetry but the turbulent thermals exhibiting considerable vertical asymmetry of their azimuthally averaged flows. In the laminar thermals, buoyancy rapidly becomes concentrated around the main centers of rotation located along the horizontal central axis, leading to expansion of thermals via baroclinic vorticity generation but doing little to break vertical symmetry of the flow. Vertical asymmetry of the azimuthally averaged flow of turbulent thermals is attributed mainly to small-scale resolved eddies that are concentrated in the upper part of the thermals. [ABSTRACT FROM AUTHOR]

This study investigates the preservation of tracer interrelationships during advection in large-eddy simulations of an idealized deep convective cloud, which is particularly relevant to chemistry, aerosol, and cloud microphysics models. Employing the Cloud Model 1, advection is represented using third-, fifth-, and seventh-order weighted essentially non-oscillatory schemes. As a simplified analogy for cloud hydrometeors and aerosols, several inert passive tracers following linear and nonlinear relationships are initialized after the cloud reaches ∼6-km depth. Numerical mixing in the simulated turbulent convective clouds leads to significant deviations from the initial nonlinear relationships between tracers. In these simulations, a considerable fraction of the grid points where the tracers' nonlinear relationships are altered from advection are classified as unrealistic (e.g., ∼13% for the environmental tracers on average), including errors from range-preserving unmixing and overshooting. Errors in the sum of three tracers are also relatively large, ranging between ∼1% and 16% for 5% of the grid points in and near the cloud. The magnitude of unrealistic mixing and errors in the sum of three tracers generally increase with the order of accuracy of the advection scheme. These results are consistent across model grid spacings ranging from 50 to 200 m, and across three different flow realizations for each combination of grid spacing and advection scheme tested. Tests employing a previously proposed scalar normalization procedure show substantially reduced errors in the sum of three tracers with a relatively small negative impact on other tracer relationships. This analysis, therefore, suggests efficacy of the normalization procedure when applied to turbulent three-dimensional cloud simulations. Significance Statement: In nature, transporting several quantities through bulk motions of a fluid does not affect preexisting relationships between them. However, this is not always accomplished in numerical models of the atmosphere, because of intrinsic limitations in the transport algorithms employed. We aim to investigate how these errors behave in 3D realistic simulations of a cumulus cloud, where the turbulent flow constitutes a particular challenge. We show that relationships between quantities are significantly and frequently perturbed during bulk transport in the model. Moreover, our results suggest that increasing complexity of the bulk-transport algorithms (in a way that is conventionally employed for improving the representation of individual quantities) tends to worsen the representation of relationships between two or three quantities. [ABSTRACT FROM AUTHOR]

A state‐of‐the‐art Lagrangian microphysics scheme is used in a large‐eddy simulation to investigate the stratocumulus transition from closed to open cell structure. Processes controlling precipitation development, which is a key to the transition, are analyzed by leveraging unique benefits of Lagrangian microphysics, particularly the ability to track computational drops in the flow. Sufficient time is needed for coalescence growth of cloud drops to drizzle within the updraft‐downdraft cycle of large eddies. This favors broad drop size distributions (DSDs) and drizzle growth in downdrafts, where drops are typically much older than in updrafts. During the closed cell stage, mean cloud drop radius is too small, and the DSDs are too narrow, so that the timescale for coalescence is much longer than the large eddy turnover time and drizzle growth is limited. The closed‐to‐open cell transition occurs when these timescales become comparable and the precipitation flux increases sharply. Plain Language Summary: Low‐level stratocumulus clouds critically impact Earth's energy budget by reflecting incoming solar radiation. Accurate representation of these shallow clouds in climate models requires information on fine‐scale cloud properties and processes (e.g., cloud and drizzle drop sizes and their growth) due to significant variability driven by turbulent flow. Sufficient drizzle formation in these clouds changes their flow structure, shifting clouds from a uniform layer to a layer with an open cell structure. Using a state‐of‐the‐art particle‐based cloud model, this study showed that cloud droplet sizes and their coalescence growth change during this process in a way that sharply enhances precipitation. Once the clouds change to an open structure, cloud and drizzle drop sizes and growth differ in upward and downward moving branches of large turbulent eddies, with more drizzle formation and variation in drop sizes in downward flow regions. Information on the drop size variability and growth presented here could lead to better representation of clouds in climate models and measurements using ground and satellite‐based instruments. Key Points: The first Lagrangian microphysics simulation of the stratocumulus transition from closed to open cell structure is documentedDuring the transition, the rain rate increases sharply as the coalescence timescale decreases relative to the large eddy turnoverDrop size distributions in open cell stratocumulus are broader in downdrafts than updrafts from coalescence, evaporation and drop mixing [ABSTRACT FROM AUTHOR]

Advanced microphysics schemes (such as Eulerian bin and Lagrangian superdroplet) are becoming standard tools for cloud physics research and parameterization development. This study compares a double-moment bin scheme and a Lagrangian superdroplet scheme via large-eddy simulations of nonprecipitating and precipitating cumulus congestus clouds. Cloud water mixing ratio in the bin simulations is reduced compared to the Lagrangian simulations in the upper part of the cloud, likely from numerical diffusion, which is absent in the Lagrangian approach. Greater diffusion in the bin simulations is compensated by more secondary droplet activation (activation above cloud base), leading to similar or somewhat higher droplet number concentrations and smaller mean droplet radius than the Lagrangian simulations for the nonprecipitating case. The bin scheme also produces a significantly larger standard deviation of droplet radius than the superdroplet method, likely due to diffusion associated with the vertical advection of bin variables. However, the spectral width in the bin simulations is insensitive to the grid spacing between 50 and 100 m, suggesting other mechanisms may be compensating for diffusion as the grid spacing is modified. For the precipitating case, larger spectral width in the bin simulations initiates rain earlier and enhances rain development in a positive feedback loop. However, with time, rain formation in the superdroplet simulations catches up to the bin simulations. Offline calculations using the same drop size distributions in both schemes show that the different numerical methods for treating collision–coalescence also contribute to differences in rain formation. The stochastic collision–coalescence in the superdroplet method introduces more variability in drop growth for a given rain mixing ratio. [ABSTRACT FROM AUTHOR]

Esme Cleall observes that for nineteenth-century British missionary families ill-health was constructed as being 'reflexive of and contributory to a specifically missionary identity'. This article argues that while this was a persistent theme, a new and significantly different discourse emerged emphasizing missionary families' health. Children were central to this discursive shift. The article focuses on missionary children's health, using selected Anglo-American cases. There was an uneasy overlap of religiously motivated rhetoric that still expected illness and death to be part of missionary childhood experience, and a professionalized discourse that redefined missionary families as sites of health and well-being. This culminated in medical and academic literature within religious and missionary circles that constructed missionaries' children as a new category. Thus churches responded both to the development of the medical profession and to the development of modern child-centred thinking and practices, in the process developing a new missiological or theological response to childhood. [ABSTRACT FROM AUTHOR]

This study evaluates a hypothesis for the role of vertical wind shear in deep convection initiation (DCI) that was introduced in Part I by examining behavior of a series of numerical simulations. The hypothesis states, "Initial moist updrafts that exceed a width and shear threshold will 'root' within a progressively deeper steering current with time, increase their low-level cloud-relative flow and inflow, widen, and subsequently reduce their susceptibility to entrainment-driven dilution, evolving toward a quasi-steady self-sustaining state." A theoretical model that embodied key elements of the hypothesis was developed in Part I, and the behavior of this model was explored within a multidimensional environmental parameter space. Remarkably similar behavior is evident in the simulations studied here to that of the theoretical model, both in terms of the temporal evolution of DCI and in the sensitivity of DCI to environmental parameters. Notably, both the simulations and theoretical model experience a bifurcation in outcomes, whereby nascent clouds that are narrower than a given initial radius R0 threshold quickly decay and those above the R0 threshold undergo DCI. An important assumption in the theoretical model, which states that the cloud-relative flow of the background environment VCR determines cloud radius R, is scrutinized in the simulations. It is shown that storm-induced inflow is small relative to VCR beyond a few kilometers from the updraft edge, and VCR therefore plays a predominant role in transporting conditionally unstable air to the updraft. Thus, the critical role of VCR in determining R is validated. [ABSTRACT FROM AUTHOR]

This article introduces a novel hypothesis for the role of vertical wind shear ("shear") in deep convection initiation (DCI). In this hypothesis, initial moist updrafts that exceed a width and shear threshold will "root" within a progressively deeper steering current with time, increase their low-level cloud-relative flow and inflow, widen, and subsequently reduce their susceptibility to entrainment-driven dilution, evolving toward a quasi-steady self-sustaining state. In contrast, initial updrafts that do not exceed the aforementioned thresholds experience suppressed growth by shear-induced downward pressure gradient accelerations, will not root in a deep-enough steering current to increase their inflow, will narrow with time, and will succumb to entrainment-driven dilution. In the latter case, an externally driven lifting mechanism is required to sustain deep convection, and deep convection will not persist in the absence of such lifting mechanism. A theoretical model is developed from the equations of motion to further explore this hypothesis. The model indicates that shear generally suppresses DCI, raising the initial subcloud updraft width that is necessary for it to occur. However, there is a pronounced bifurcation in updraft growth in the model after the onset of convection. Sufficiently wide initial updrafts grow and eventually achieve a steady state. In contrast, insufficiently wide initial updrafts shrink with time and eventually decay completely without external support. A sharp initial updraft radius threshold discriminates between these two outcomes. Thus, consistent with our hypothesis and observations, shear inhibits DCI in some situations, but facilitates it in others. [ABSTRACT FROM AUTHOR]

Supersaturation fluctuations in the atmosphere are critical for cloud processes. A nonlinear dependence on two scalars—water vapor and temperature—leads to different behavior than single scalars in turbulent convection. For modeling such multiscalar processes at subgrid scales (SGS) in large-eddy simulations (LES) or convection-permitting models, a new SGS scheme is implemented in CM1 that solves equations for SGS water vapor and temperature fluctuations and their covariance. The SGS model is evaluated using benchmark direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection with water vapor as in the Michigan Tech Pi Cloud Chamber. This idealized setup allows thorough evaluation of the SGS model without complications from other atmospheric processes. DNS results compare favorably with measurements from the chamber. Results from LES using the new SGS model compare well with DNS, including profiles of water vapor and temperature variances, their covariance, and supersaturation variance. SGS supersaturation fluctuations scale appropriately with changes to the LES grid spacing, with the magnitude of SGS fluctuations decreasing relative to those at the resolved scale as the grid spacing is decreased. Sensitivities of covariance and supersaturation statistics to changes in water vapor flux relative to thermal flux are also investigated by modifying the sidewall conditions. Relative changes in water vapor flux substantially decrease the covariance and increase supersaturation fluctuations even away from boundaries. [ABSTRACT FROM AUTHOR]

This study examines two factors impacting initiation of moist deep convection: free-tropospheric environmental relative humidity (ϕE) and horizontal scale of subcloud ascent (Rsub), the latter exerting a dominant control on cumulus cloud width. A simple theoretical model is used to formulate a "scale selection" hypothesis: that a minimum Rsub is required for moist convection to go deep, and that this minimum scale decreases with increasing ϕE. Specifically, the ratio of R sub 2 to saturation deficit (1 − ϕE) must exceed a certain threshold value that depends on cloud-layer environmental lapse rate. Idealized, large-eddy simulations of moist convection forced by horizontally varying surface fluxes show strong sensitivity of maximum cumulus height to both ϕE and Rsub consistent with the hypothesis. Increasing Rsub by only 300–400 m can lead to a large increase (>5 km) in cloud height. A passive tracer analysis shows that the bulk fractional entrainment rate decreases rapidly with Rsub but depends little on ϕE. However, buoyancy dilution increases as either Rsub or ϕE decreases; buoyancy above the level of free convection is rapidly depleted in dry environments when Rsub is small. While deep convective initiation occurs with an increase in relative humidity of the near environment from moistening by earlier convection, the importance of this moisture preconditioning is inconclusive as it is accompanied by an increase in Rsub. Overall, it is concluded that small changes to Rsub driven by external forcing or by convection itself could be a dominant regulator of deep convective initiation. [ABSTRACT FROM AUTHOR]

Coarse-gridded atmospheric models often account for subgrid-scale variability by specifying probability distribution functions (PDFs) of process rate inputs such as cloud and rainwater mixing ratios (qc and qr, respectively). PDF parameters can be obtained from numerous sources: in situ observations, ground- or space-based remote sensing, or fine-scale modeling such as large-eddy simulation (LES). LES is appealing to constrain PDFs because it generates large sample sizes, can simulate a variety of cloud regimes/case studies, and is not subject to the ambiguities of observations. However, despite the appeal of using model output for parameterization development, it has not been demonstrated that LES satisfactorily reproduces the observed spatial structure of microphysical fields. In this study, the structure of observed and modeled microphysical fields are compared by applying bifractal analysis, an approach that quantifies variability across spatial scales, to simulations of a drizzling stratocumulus field that span a range of domain sizes, drop concentrations (a proxy for mesoscale organization), and microphysics schemes (bulk and bin). Simulated qc closely matches observed estimates of bifractal parameters that measure smoothness and intermittency. There are major discrepancies between observed and simulated qr properties, though, with bulk simulated qr consistently displaying the bifractal properties of observed clouds (smooth, minimally intermittent) rather than rain while bin simulations produce qr that is appropriately intermittent but too smooth. These results suggest fundamental limitations of bulk and bin schemes to realistically represent higher-order statistics of the observed rain structure. [ABSTRACT FROM AUTHOR]

The spreading rates of convective thermals are linked to their net entrainment, and previous literature has suggested differences in spreading rates between moist and dry thermals. In this study, growth rates of idealized numerically simulated axisymmetric dry and moist convective thermals are directly compared. In an environment with dry-neutral stratification, the increase of thermal radius with height dR/dz is a larger by a factor of 1.7 for dry thermals relative to moist thermals. The fractional change in thermal volume « is also greater for dry thermals within a distance of; 4 radii from the initial thermal height. Values of dR/dz are nearly constant with height for bothmoist and dry thermals consistent with classical theory based on dimensional analysis. The simulations are also consistent with theory relating impulse, circulation, and spreading rate for dry thermals proposed in previous papers and extended here to moist thermals, predicting they will spread less than dry thermals. Tests adding heating to dry thermals, either spread uniformly across the thermal volume or concentrated in the inner core, indicate that dR/dz and « are smaller for moist thermals because latent heating is confined mostly to their cores. Additional axisymmetric moist simulations using modified lapse rates and large-eddy simulations support this analysis. Overall, these results indicate that slow spreading rates are a fundamental feature of moist thermals caused by latent heating that alters the spatial distribution of buoyancy within them relative to dry thermals. [ABSTRACT FROM AUTHOR]

Tudivasum Rosenberg & Petit, 1987 is a morphologically distinct gastropod genus of low diversity. All but one species are known from Australia and they occur from the intertidal zone down to hundreds of metres on the continental shelf. These carnivorous gastropods are thought to have intracapsular development. The six currently recognized extant species are reviewed here and their geographical ranges clarified. Two new species, Tudivasum chaneyi n. sp. and T. ashmorense n. sp. are described from Ashmore Reef, Western Australia, and are characterized by differences in protoconch colour and shell sculpture. The third new species, T. westrale n. sp. is described from the mid-west coast of Western Australia, where it has long been misidentified as T. spinosum (H. Adams & A. Adams, 1864). We generated a molecular phylogeny based on mitochondrial DNA sequence data to test morphological species concepts and reconstruct relationships among four of the described species. High levels of divergence within one of the new species could indicate an additional cryptic species. [ABSTRACT FROM AUTHOR]

In the original Predicted Particle Properties (P3) bulk microphysics scheme, all ice-phase hydrometeors are represented by one or more "free" ice categories, where the physical properties evolve smoothly through changes to the four prognostic variables (per category), and with a two-moment representation of the particle size distribution. As such, the spectral dispersion cannot evolve independently, which thus results in limitations in representation of ice—in particular, hail—due to necessary constraints in the scheme to prevent excessive gravitational size sorting. To overcome this, P3 has been modified to include a three-moment representation of the size distribution of each ice category through the addition of a fifth prognostic variable, the sixth moment of the size distribution. The details of the three-moment ice parameterization in P3 are provided. The behavior of the modified scheme, with the single-ice-category configuration, is illustrated through simulations in a simple 1D kinematic model framework as well as with near large-eddy-resolving (250-m grid spacing) 3D simulations of a hail-producing supercell. It is shown that the three-moment ice configuration controls size sorting in a physically based way and leads to an improved capacity to simulate large, heavily rimed ice (hail), including mean and maximum sizes and reflectivity, and thus an overall improvement in the representation of ice-phase particles in the P3 scheme. [ABSTRACT FROM AUTHOR]

This is a rebuttal of Fan and Khain's comments (hereafter FK21) on a 2020 paper by Grabowski and Morrison (hereafter GM20) that questions the impact of ultrafine cloud condensation nuclei (CCN) on deep convection. GM20 argues that "cold invigoration," an increase of the updraft speed from lofting and freezing of additional cloud water in polluted environments, is unlikely because the latent heating from freezing of this cloud water approximately recovers the negative impact on the buoyancy from the weight of this water. FK21 suggest a variety of processes that could invalidate our claim. We maintain that our argument is valid and invite the authors to compare their microphysics scheme with ours in the same simplified modeling framework. However, pollution does affect the partitioning of latent heating within the column and likely leads to convection changes beyond a single diurnal cycle through larger-scale circulation changes. This argument explains impacts seen in our idealized mesoscale simulations and in convective–radiative equilibrium simulations by others. We agree with FK21 on the existence of a "warm invigoration" mechanism but question its interpretation. Consistent with the simulations in GM20, we argue that changes in the buoyancy can be explained by the response of the supersaturation to droplet microphysical changes induced by pollution. The buoyancy change is determined by supersaturation differences between pristine and polluted conditions, while condensation rate responds to these supersaturation changes. Finally, we agree with FK21 that the piggybacking modeling technique cannot prove or disprove invigoration; rather, it is a diagnostic technique that can be used to understand mechanisms driving simulation differences. [ABSTRACT FROM AUTHOR]

This study investigates impacts of altering subgrid-scale mixing in "convection-permitting" kilometer-scale horizontal-grid-spacing (Δh) simulations by applying either constant or stochastic multiplicative factors to the horizontal mixing coefficients within the Weather Research and Forecasting Model. In quasi-idealized 1-km Δh simulations of two observationally based squall-line cases, constant enhanced mixing produces larger updraft cores that are more dilute at upper levels, weakens the cold pool, rear-inflow jet, and front-to-rear flow of the squall line, and degrades the model's effective resolution. Reducing mixing by a constant multiplicative factor has the opposite effect on all metrics. Completely turning off parameterized horizontal mixing produces bulk updraft statistics and squall-line mesoscale structure closest to an LES "benchmark" among all 1-km simulations, although the updraft cores are too undilute. The stochastic mixing scheme, which applies a multiplicative factor to the mixing coefficients that varies stochastically in time and space, is employed at 0.5-, 1-, and 2-km Δh. It generally reduces midlevel vertical velocities and enhances upper-level vertical velocities compared to simulations using the standard mixing scheme, with more substantial impacts at 1- and 2-km Δh compared to 0.5-km Δh. The stochastic scheme also increases updraft dilution to better agree with the LES for one case, but has less impact on the other case. Stochastic mixing acts to weaken the cold pool but without a significant impact on squall-line propagation. It also does not affect the model's overall effective resolution unlike applying constant multiplicative factors to the mixing coefficients. [ABSTRACT FROM AUTHOR]

In supercell environments, previous authors have shown strong connections between the vertical wind shear magnitude, updraft width, and entrainment. Based on these results, it is hypothesized that the influences of entrainmentdriven dilution on buoyancy and maximum updraft vertical velocity w in supercell environments are a predictable function of the vertical wind shear profile. It is also hypothesized that the influences of pressure perturbation forces on maximum updraft w are small because of a nearly complete offset between upward dynamic pressure forces and downward buoyant pressure forces. To address these hypotheses, we derive a formula for the maximum updraft w that incorporates the effects of entrainment-driven dilution on buoyancy but neglects pressure gradient forces. Solutions to this formula are compared with output from previous numerical simulations. This formula substantially improves predictions of maximum updraft w over past CAPE-derived formulas for maximum updraft w, which supports the first hypothesis. Furthermore, integrated vertical accelerations along trajectories show substantial offsets between dynamic and buoyant pressure forces, supporting the second hypothesis. It is argued that the new formula should be used in addition to CAPE-derived measures for w in forecast and research applications when accurate diagnosis of updraft speed is required. [ABSTRACT FROM AUTHOR]

Recent studies have shown that cumulus updrafts often consist of a succession of discrete rising thermals with spherical vortex-like circulations. In this paper, a theory is developed for why this ''thermal chain'' structure occurs. Theoretical expressions are obtained for a passive tracer, buoyancy, and vertical velocity in axisymmetric moist updrafts. Analysis of these expressions suggests that the thermal chain structure arises from enhanced lateral mixing associated with intrusions of dry environmental air below an updraft's vertical velocity maximum. This dry-air entrainment reduces buoyancy locally. Consequently, the updraft flow above levels of locally reduced buoyancy separates from below, leading to a breakdown of the updraft into successive discrete thermals. The range of conditions in which thermal chains exist is also analyzed from the theoretical expressions. Atransition in updraft structure from isolated rising thermal, to thermal chain, to starting plume occurs with increases in updraft width, environmental relative humidity, and/or convective available potential energy. Corresponding expressions for the bulk fractional entrainment rate «are also obtained. These expressions indicate rather complicated entrainment behavior of ascending updrafts, with local enhancement of «up to a factor of ;2 associated with the aforementioned environmental-air intrusions, consistent with recent large-eddy simulation (LES) studies. These locally large entrainment rates contribute significantly to overall updraft dilution in thermal chain-like updrafts, while other regions within the updraft can remain relatively undilute. Part II of this study compares results from the theoretical expressions to idealized numerical simulations and LES. [ABSTRACT FROM AUTHOR]

Research has suggested that the structure of deep convection often consists of a series of rising thermals, or ''thermal chain,'' which contrasts with existing conceptual models that are used to construct cumulus parameterizations. Simplified theoretical expressions for updraft properties obtained in Part I of this study are used to develop a hypothesis explaining why this structure occurs. In this hypothesis, cumulus updraft structure is strongly influenced by organized entrainment below the updraft's vertical velocity maximum. In a dry environment, this enhanced entrainment can locally reduce condensation rates and increase evaporation, thus eroding buoyancy. For moderate-to-large initial cloud radius R, this breaks up the updraft into a succession of discrete pulses of rising motion (i.e., a thermal chain). For small R, this leads to the structure of a single, isolated rising thermal. In contrast, moist environments are hypothesized to favor plume-like updrafts for moderate-to-large R. In a series of axisymmetric numerical cloud simulations, R and environmental relative humidity (RH) are systematically varied to test this hypothesis. Vertical profiles of fractional entrainment rate, passive tracer concentration, buoyancy, and vertical velocity from these runs agree well with vertical profiles calculated from the theoretical expressions in Part I. Analysis of the simulations supports the hypothesized dependency of updraft structure on R and RH, that is, whether it consists of an isolated thermal, a thermal chain, or a plume, and the role of organized entrainment in driving this dependency. Additional three-dimensional (3D) turbulent cloud simulations are analyzed, and the behavior of these 3D runs is qualitatively consistent with the theoretical expressions and axisymmetric simulations. [ABSTRACT FROM AUTHOR]

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 30mhas 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. [ABSTRACT FROM AUTHOR]

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods. Plain Language Summary: In the atmosphere, microphysics—the small‐scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting—is a critical part of Earth's weather and climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a "particle‐based" approach that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics is represented in cloud, weather, and climate models. Key Points: Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertainTwo critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physicsA possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes [ABSTRACT FROM AUTHOR]

Numerical simulations of the impact of ultrafine cloud condensation nuclei (CCN) on deep convection are analyzed to investigate the idea proposed by Fan et al. that addition of ultrafine CCN to an otherwise pristine environment leads to convective invigoration. The piggybacking methodology is applied, allowing rigorous separation of the impact of aerosols from different flow realizations that typically occur when even a small element of the model physics or modeling setup is changed. The setup follows the case of daytime convective development over land based on observations during the Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia. Overall, the simulated impacts of ultrafine CCN are similar to the previous study by the authors on the impact of pollution on deep convection. There is no convective invigoration above the freezing level, but there is a small invigoration (increase in vertical velocities) below due to the supersaturation and buoyancy differences in conditions with additional ultrafine CCN compared to unperturbed pristine conditions. As in the previous study, the most significant impact is on the upper-tropospheric convective anvils that feature higher cloud fractions in conditions with ultrafine CCN. The increase comes from purely microphysical considerations as the increased cloud droplet concentrations from ultrafine CCN lead to increased ice crystal concentrations and, consequently, smaller sizes and fall velocities, and longer residence times. Mesoscale organization due to low-level shear has a small effect on the simulated ultrafine CCN impacts. Finally, an alternative explanation of increased lightning above oceanic shipping lines seen in satellite observations and argued to result from convective invigoration is provided. [ABSTRACT FROM AUTHOR]

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme's development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates. [ABSTRACT FROM AUTHOR]

A new framework is proposed for the bulk parameterization of rain microphysics: the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS). It is designed to facilitate direct constraint by observations using Bayesian inference. BOSS combines existing process-level microphysical knowledge with flexible process rate formulations and parameters constrained by observations within a Bayesian framework. Using a raindrop size distribution (DSD) normalization method that relates DSD moments to one another via generalized power series, generalized multivariate power expressions are derived for the microphysical process rates as functions of a set of prognostic DSD moments. The scheme is flexible and can utilize any number and combination of prognostic moments and any number of terms in the process rate formulations. This means that both uncertainty in parameter values and structural uncertainty associated with the process rate formulations can be investigated systematically, which is not possible using traditional schemes. In this paper, BOSS is compared to two- and three-moment versions of a traditional bulk rain microphysics scheme (denoted as MORR). It is shown that some process formulations in MORR are analytically equivalent to the generalized power expressions in BOSS using one or two terms, while others are not. BOSS is able to replicate the behavior of MORR in idealized one-dimensional rainshaft tests, but with a much more flexible and systematic design. Part II of this study describes the application of BOSS to derive rain microphysical process rates and posterior parameter distributions in Bayesian experiments using Markov chain Monte Carlo sampling constrained by synthetic observations. [ABSTRACT FROM AUTHOR]

Simulations of a squall line observed on 20 May 2011 during the Midlatitude Continental Convective Clouds Experiment (MC3E) using 750- and 250-m horizontal grid spacing are performed. The higherresolution simulation has less upshear-tilted deep convection and a more elevated rear inflow jet than the coarser-resolution simulation in better agreement with radar observations. A stronger cold pool eventually develops in the 250-m run; however, the more elevated rear inflow counteracts the cold pool circulation to produce more upright convective cores relative to the 750-m run. The differing structure in the 750-m run produces excessive midlevel front-to-rear detrainment, reinforcing excessive latent cooling and rear inflow descent at the rear of the stratiform region in a positive feedback. The contrasting mesoscale circulations are connected to early stage deep convective draft differences in the two simulations. Convective downdraft condensate mass, latent cooling, and downward motion all increase with downdraft area similarly in both simulations. However, the 750-m run has a relatively greater number of wide and fewer narrow downdrafts than the 250-m run averaged to the same 750-m grid, a consequence of downdrafts being under-resolved in the 750-m run. Under-resolved downdrafts in the 750-m run are associated with under-resolved updrafts and transport mid--upper-level zonal momentum downward to low levels too efficiently in the early stage deep convection. These results imply that under-resolved convective drafts in simulations may vertically transport air too efficiently and too far vertically, potentially biasing buoyancy and momentum distributions that impact mesoscale convective system evolution. [ABSTRACT FROM AUTHOR]

Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so‐called "natural variability" impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass‐size (m‐D) relationship (m=aDb) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m‐D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m‐D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ‐IWP relationship variability is nonexistent using the deterministic m‐D ensemble. Additional sensitivity tests are performed in which the fallspeed‐size relationship (V=cDd) is stochastically varied, resulting in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small‐amplitude noise added to the potential temperature field. Plain Language Summary: Representing snowflakes, hail, and other ice crystal types in weather and climate models is a challenging task, but properly doing so is often important in order to produce accurate forecasts. In reality, ice crystals in clouds take on many different shapes and sizes, but current models do not fully account for this variability. Thus, we implement a new method in a weather model that accounts for some of this variability in ice crystal shape and size, guided by observations obtained from aircraft flying through clouds. Our results show that accounting for variable ice crystal size and shape can alter how much sunlight reaches the surface during storms that produce expansive cloud cover. In addition, we find that altering the speed at which ice crystals fall to the ground changes the amount of precipitation accumulated during a precipitation event. These results thus provide guidance on how representing the wide range of different ice crystal shapes and sizes in weather models can impact forecasts of weather and predictions of climate. Key Points: An observationally based stochastic framework was implemented into the P3 microphysics scheme to allow for variable mass‐size relationshipThe scheme shows little precipitation structure variability relative to perturbed initial and boundary conditions in convection simulationsStochastic microphysics produces variability in cloud optical depth (and thus cloud radiative forcing) for a given ice water path [ABSTRACT FROM AUTHOR]

Microphysics parameterizations in large-scale models often account for subgrid variability in the calculation of process rates by integrating over assumed subgrid distributions of the input variables. The variances and covariances that define distribution width may be specified or diagnosed. The correlation ρ of cloud and rain mass mixing ratio/liquid water content (LWC) is a key input for accurate prediction of the accretion rate and a constant value is typically assumed. In this study, high-frequency aircraft measurements with a spatial resolution of ≈22 cm are used to evaluate the scaling behavior of cloud and rain LWC (qc and qr, respectively) and to demonstrate how and why covariability varies with length scale ℓ. It is shown that power spectral densities of both qc and qr exhibit scale invariance across a wide range of scales (2.04–142 m for qc; 33–1.45 × 104 m for qr). Because the cloud–rain cospectrum is also scale invariant, ρ is therefore expected to vary with ℓ. Direct calculation of ρ shows that it generally increases with ℓ , but there is significant variability in the ρ– ℓ relationship that primarily depends on cloud drop number concentration N and cloud cellular organization, suggesting that ρ may also vary with cloud regime. A parameterization of ρ as a function of ℓ and N is developed from aircraft observations and implications for diagnosis of ρ from limited-area model output are also discussed. [ABSTRACT FROM AUTHOR]

Weather forecasts and climate projections of precipitation phase and type in winter storms are challenging due to the complicated underlying microphysical and dynamical processes. In the Canadian numerical weather prediction model, explicit freezing rain (FR) at the surface is often overestimated during the winter season for situations in which snow is observed. For a case study simulated using this model with the Predicted Particle Properties (P3) microphysics scheme, the secondary ice production (SIP) process has a major impact on the surface precipitation type. Parameterized SIP substantially reduces FR due to increased collection of supercooled drops with ice particles formed by rime splintering. Hindcast simulations of 40 winter cases show that these results are systematic, and the decreased frequency of FR leads to improved forecast skill relative to observations. Thus, accounting for SIP in the model is critical for accurately simulating precipitation types. Plain Language Summary: Several types of winter precipitation, including snow, freezing rain (FR) and ice pellets (IP), are associated with hazards such as injuries from people falling, disruption of electrical supply, and breakdown of transportation networks due to the accumulation of ice on surfaces. Forecasts of precipitation type using weather prediction models, as well as projections for a warmer climate, are challenging because of the complicated physical processes involved. In this article, it is shown that the component of the numerical model that is used to represent clouds and precipitation in the Canadian high‐resolution weather forecast system overestimates FR at the expense of snow. This problem is mitigated when the additional process of secondary ice production (here, the generation of new ice particles from collisions of existing ice and supercooled drops) is included in the model. The presence of numerous small ice particles formed by this process decreases the amount of FR and improves forecast skill scores for 40 historic winter cases. Thus, accounting for this process in weather and climate models is important for accurately simulating FR, IP and snow. Key Points: Secondary ice production (SIP) substantially impacts precipitation phase and type in winter stormsInclusion of SIP reduces excessive freezing rain (FR) in numerical simulationsForecast metrics for FR in 40 winter cases are improved when SIP is included in a weather model [ABSTRACT FROM AUTHOR]

Representation of cloud microphysics is a key aspect of simulating clouds. From the early days of cloud modeling, numerical models have relied on an Eulerian approach for all cloud and thermodynamic and microphysics variables. Over time the sophistication of microphysics schemes has steadily increased, from simple representations of bulk masses of cloud and rain in each grid cell, to including different ice particle types and bulk hydrometeor concentrations, to complex schemes referred to as bin or spectral schemes that explicitly evolve the hydrometeor size distributions within each model grid cell. As computational resources grow, there is a clear trend toward wider use of bin schemes, including their use as benchmarks to develop and test simplified bulk schemes. We argue that continuing on this path brings fundamental challenges difficult to overcome. The Lagrangian particle-based probabilistic approach is a practical alternative in which the myriad of cloud and precipitation particles present in a natural cloud is represented by a judiciously selected ensemble of point particles called superdroplets or superparticles. The advantages of the Lagrangian particle-based approach when compared to the Eulerian bin methodology are explained, and the prospects of applying the method to more comprehensive cloud simulations—for instance, targeting deep convection or frontal cloud systems—are discussed. [ABSTRACT FROM AUTHOR]

Bulk microphysics parameterizations that are used to represent clouds and precipitation usually allow only solid and liquid hydrometeors. Predicting the bulk liquid fraction on ice allows an explicit representation of mixed-phase particles and various precipitation types, such as wet snow and ice pellets. In this paper, an approach for the representation of the bulk liquid fraction into the predicted particle properties (P3) microphysics scheme is proposed and described. Solid-phase microphysical processes, such as melting and sublimation, have been modified to account for the liquid component. New processes, such as refreezing and condensation of the liquid portion of mixed-phase particles, have been added to the parameterization. Idealized simulations using a one-dimensional framework illustrate the overall behavior of the modified scheme. The proposed approach compares well to a Lagrangian benchmark model. Temperatures required for populations of ice crystals to melt completely also agree well with previous studies. The new processes of refreezing and condensation impact both the surface precipitation type and feedback between the temperature and the phase changes. Overall, prediction of the bulk liquid fraction allows an explicit description of new precipitation types, such as wet snow and ice pellets, and improves the representation of hydrometeor properties when the temperature is near 0°C. [ABSTRACT FROM AUTHOR]

A general drop size distribution (DSD) normalization method is formulated in terms of generalized power series relating any DSD moment to any number and combination of reference moments. This provides a consistent framework for comparing the variability of normalized DSD moments using different sets of reference moments, with no explicit assumptions about the DSD functional form (e.g., gamma). It also provides a method to derive any unknown moment plus an estimate of its uncertainty from one or more known moments, which is relevant to remote sensing retrievals and bulk microphysics schemes in weather and climate models. The approach is applied to a large dataset of disdrometer-observed and bin microphysics-modeled DSDs. As expected, the spread of normalized moments decreases as the number of reference moments is increased, quantified by the logarithmic standard deviation of the normalized moments, σ. Averaging σ for all combinations of reference moments and normalized moments of integer order 0–10, 42.9%, 81.3%, 93.7%, and 96.9% of spread are accounted for applying one-, two-, three-, and four-moment normalizations, respectively. Thus, DSDs can be well characterized overall using three reference moments, whereas adding a fourth reference moment contributes little independent information. The spread of disdrometer-observed DSD moments from uncertainty associated with drop count statistics generally lies between values of σ using two- and three-moment normalizations. However, this uncertainty has little impact on the derived DSD scaling relationships or σ when considered. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δh) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δh = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here. Plain Language Summary: Regional weather prediction and climate models commonly have horizontal grid lengths of 2–4 km that cannot resolve mixing of air in cumulonimbus clouds with surrounding cooler, drier environmental air, a key process that modulates cloud and storm properties. This study evaluates a method to represent such mixing in models that induces random variability in the magnitude of mixing for a tropical oceanic deep convection case. This approach is found to alter both the intensity and areal coverage of precipitation. As space and time scales of the variability are increased, changes to precipitation coverage and intensity become greater and less systematic relative to a control simulation. Altering mixing also changes the sizes of convective clouds and the degree to which they cluster around one another. Ultimately, the new mixing approach induces variable responses in simulated convective clouds but generally makes them more intense with wider cloud cores, which does not improve upon the control simulation relative to radar observations and a higher resolution simulation. Key Points: A stochastic subgrid‐scale mixing scheme was evaluated in a tropical oceanic deep convection case using 3‐km horizontal grid spacingStochastic mixing modulates the area and intensity of convection, producing more organization and less dilute updrafts relative to a controlMore systematic impacts on convection properties occur for relatively short spatial and time scale variability of stochastic mixing [ABSTRACT FROM AUTHOR]

This study explores the sensitivity of clouds and precipitation to microphysical parameter perturbations using idealized simulations of moist, nearly neutral flow over a bell-shaped mountain. Numerous parameters are perturbed within the Morrison microphysics scheme. The parameters that most affect cloud and precipitation characteristics are the snow fall speed coefficient As, snow particle density ρs, rain accretion (WRA), and ice–cloud water collection efficiency (ECI). Surface precipitation rates are affected by As and ρs through changes to the precipitation efficiency caused by direct and indirect impacts on snow fall speed, respectively. WRA and ECI both affect the amount of cloud water removed, but the precipitation sensitivity differs. Large WRA results in increased precipitation efficiency and cloud water removal below the freezing level, indirectly decreasing cloud condensation rates; the net result is little precipitation sensitivity. Large ECI removes cloud water above the freezing level but with little influence on overall condensation rates. Two environmental experiments are performed to test the robustness of the results: 1) reduction of the wind speed profile by 30% (LowU) and 2) decreasing the surface potential temperature to induce a freezing level below the mountain top (LowFL). Parameter perturbations within LowU result in similar mechanisms acting on precipitation, but a much weaker sensitivity compared to the control. The LowFL case shows ρs is no longer a dominant parameter and As now induces changes to cloud condensation, since more of the cloud depth is present above the freezing level. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations. [ABSTRACT FROM AUTHOR]

An approximate analytic expression is derived for the ratio λ of the ascent rate of moist deep convective thermals and the maximum vertical velocity within them; λ is characterized as a function of two nondimensional buoyancy-dependent parameters y and h and is used to express the thermal ascent rate as a function of the buoyancy field. The parameter y characterizes the vertical distribution of buoyancy within the thermal, and h is the ratio of the vertically integrated buoyancy from the surface to the thermal top and the vertical integral of buoyancy within the thermal. Theoretical λ values are calculated using values of y and h obtained from idealized numerical simulations of ascending moist updrafts and compared to λ computed directly from the simulations. The theoretical values of 0.4–0.8 are in reasonable agreement with the simulated λ (correlation coefficient of 0.86). These values are notably larger than the from Hill’s (nonbuoyant) analytic spherical vortex, which has been used previously as a framework for understanding the dynamics of moist convective thermals. The relatively large values of λ are a result of net positive buoyancy within the upper part of thermals that opposes the downward-directed dynamic pressure gradient force below the thermal top. These results suggest that nonzero buoyancy within moist convective thermals, relative to their environment, fundamentally alters the relationship between the maximum vertical velocity and the thermal-top ascent rate compared to nonbuoyant vortices. Implications for convection parameterizations and interpretation of the forces contributing to thermal drag are discussed. [ABSTRACT FROM AUTHOR]

This essay focuses on early twentieth-century missionary British world missionary children and their families to provide a point of comparison with an existing body of work on nineteenth-century missionary children. Through a case study approach, focusing on two Presbyterian missionary families (Scottish and New Zealand) and using both written and oral sources, it asks how we might usefully historicize their lives. The case studies indicate that early twentieth-century children’s historical lives were primarily framed within the religiously defined narratives of the missionary family—albeit narratives that varied depending on geographical, cultural, theological, and temporal contexts or with respect to points of family origin—and that these narratives were articulated differently from children’s and parents’ perspectives. While family is central to the analysis, the article makes a case for attending to the voices of both children and parents, within broader historiographical and historical contexts. [ABSTRACT FROM AUTHOR]

Cloud-scale models apply two drastically different methods to represent condensation of water vapor to form and grow cloud droplets. Maintenance of water saturation inside liquid clouds is assumed in the computationally efficient saturation adjustment approach used in most bulk microphysics schemes. When super- or subsaturations are allowed, condensation/evaporation can be calculated using the predicted saturation ratio and (either predicted or prescribed) mean droplet radius and concentration. The study investigates differences between simulations of deep unorganized convection applying a saturation adjustment condensation scheme (SADJ) and a scheme with supersaturation prediction (SPRE). A double-moment microphysics scheme with CCN activation parameterized as a function of the local vertical velocity is applied to compare cloud fields simulated applying SPRE and SADJ. Clean CCN conditions are assumed to demonstrate upper limits of the SPRE and SADJ difference. Microphysical piggybacking is used to extract the impacts with confidence. Results show a significant impact on deep convection dynamics, with SADJ featuring more cloud buoyancy and thus stronger updrafts. This leads to around a 3% increase of the surface rain accumulation in SADJ. Upper-tropospheric anvil cloud fractions are much larger in SPRE than in SADJ because of the higher ice concentrations and thus longer residence times of anvil particles in SPRE, as demonstrated by sensitivity tests. Higher ice concentrations in SPRE come from significantly larger ice supersaturations in strong convective updrafts that feature water supersaturations of several percent. [ABSTRACT FROM AUTHOR]

New theoretical analytic expressions are derived for the evolution of a passive scalar, buoyancy, and vertical velocity in growing, entraining moist deep convective updrafts. These expressions are a function of updraft radius, height, convective available potential energy (CAPE), and environmental relative humidity RH. They are quantitatively consistent with idealized three-dimensional moist updraft simulations with varying updraft sizes and in environments with differing RH. In particular, the analytic expressions capture the rapid decrease of buoyancy with height due to entrainment for narrow updrafts in a dry environment despite large CAPE. In contrast to the standard entraining-plume model, the theoretical expressions also describe the effects of engulfment of environmental air between the level of free convection (LFC) and height of maximum buoyancy (HMB) required by mass continuity to balance upward acceleration of updraft air (i.e., dynamic entrainment). This organized inflow sharpens horizontal gradients, thereby enhancing smaller-scale lateral turbulent mixing below the HMB. For narrow updrafts in a dry environment, this enhanced mixing leads to a negatively buoyant region between the LFC and HMB, effectively cutting off the region of positive buoyancy at theHMBfrom below so that the updraft structure resembles a rising thermal rather than a plume. Thus, it is proposed that a transition from plume-like to thermal-like structure is driven by dynamic entrainment and depends on updraft width (relative to height) and environmental RH. These results help to bridge the entraining-plume and rising-thermal conceptual models of moist convection. [ABSTRACT FROM AUTHOR]

This paper describes a new approach for representing ice microphysics in climate models. In contrast with most previous schemes, this approach does not include separate categories for cloud and precipitating ice and instead uses a single two-moment category to represent all solid hydrometeors. Thus, there is no need for an ice "autoconversion" size threshold parameter, which has a critical impact on simulated climate in the Community Atmosphere Model (CAM5) yet is poorly constrained by theory or observations. Further, in the new treatment, all ice microphysical processes and parameters, including ice effective radius and mean fall speed, are formulated self-consistently and flexibly based on empirical ice particle mass-size and projected area-size relationships. This means that the scheme can represent the physical coupling between bulk particle density, mean fall speed, and effective radius, which is not possible in current schemes. Two different methods for specifying these relationships based on observations are proposed. The new scheme is tested in global simulations using CAM5. Differences in simulations using the two methods for specifying the mass- and projected area-size relationships, particularly the cloud radiative forcing, are attributable mainly to the effects on mean ice particle fall speed, impacting sedimentation and ice water path. With some tuning of parameters involved in calculating homogeneous freezing it produces a similar climate compared to the simulations using the original CAM5 microphysics. Thus, it can produce a comparable climate while improving the physical basis and self-consistency of ice particle properties and parameters. [ABSTRACT FROM AUTHOR]

The suggested impact of pollution on deep convection dynamics, referred to as the convective invigoration, is investigated in simulations applying microphysical piggybacking and a comprehensive double-moment bulk microphysics scheme. The setup follows the case of daytime convective development over land based on observations during the Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia. In contrast to previous simulations with single-moment microphysics schemes and in agreement with results from bin microphysics simulations by others, the impact of pollution simulated by the double-moment scheme is large for the upper-tropospheric convective anvils that feature higher cloud fractions in polluted conditions. The increase comes from purely microphysical considerations: namely, the increased cloud droplet concentrations in polluted conditions leading to the increased ice crystal concentrations and, consequently, smaller fall velocities and longer residence times. There is no impact on convective dynamics above the freezing level and thus no convective invigoration. Polluted deep convective clouds precipitate about 10% more than their pristine counterparts. The small enhancement comes from smaller supersaturations below the freezing level and higher buoyancies inside polluted convective updrafts with velocities between 5 and 10 m s−1. The simulated supersaturations are large, up to several percent in both pristine and polluted conditions, and they call into question results from deep convection simulations applying microphysical schemes with saturation adjustment. Sensitivity simulations show that the maximum supersaturations and the upper-tropospheric anvil cloud fractions strongly depend on the details of small cloud condensation nuclei (CCN) that can be activated in strong updrafts above the cloud base. [ABSTRACT FROM AUTHOR]

This paper discusses the advection of coupled hydrometeor quantities by air motion in atmospheric models. It is shown that any bulk property derived from a set of advected microphysical variables must meet certain conditions in order to be preserved during transport using linear or semilinear advection schemes when the property is initially uniform, with implications for physical consistency of the property. A new, efficient flux-based method for calculating hydrometeor advection, similar to vector transport applied previously in aerosol modeling, is also presented. In this method, called scaled flux vector transport (SFVT), lead scalars (the mass mixing ratios) are advected using the host model's unmodified advection scheme and secondary scalars (e.g., number mixing ratios) are advected by appropriately scaling the lead scalar fluxes. By design, SFVT retains linear relationships between the advected scalars. Analytic tests reveal that mean errors using SFVT are similar to those incurred using the traditional approach of separately advecting each variable. SFVT is applied to the multimoment predicted particle properties bulk microphysics scheme in idealized two-dimensional squall-line simulations using the Weather Research and Forecasting Model. The computational cost in total wall clock run time is reduced by 10%-15% while producing solutions similar to the traditional approach. Thus, SFVT can reduce the overall cost of using multimoment bulk microphysics schemes, making them competitive with simpler schemes having fewer prognostic variables. [ABSTRACT FROM AUTHOR]

This study investigates relationships between vertical velocity, perturbation pressure, updraft size, and dimensionality for cumulus convection. Generalized theoretical expressions are derived from approximate analytic solutions of the governing momentum and mass continuity equations for both two-dimensional (2D) and axisymmetric quasi-three-dimensional (3D) steady-state updrafts. These expressions relate perturbation pressure and vertical velocity to updraft radius R, height H, and thermal buoyancy. They suggest that the vertical velocity at the level of neutral buoyancy is reduced from perturbation pressure effects by factors of and in 2D and 3D, respectively, where is a nondimensional length, with somewhat different scalings lower in the updraft ( α is a parameter equal to the ratio of vertical velocity horizontally averaged across the updraft to that at the updraft center). They also indicate that updrafts are weaker in 2D than 3D, all else being equal, with a difference of up to a factor of 2 in vertical velocity for as a direct result of differences in mass continuity between 2D and axisymmetric 3D flow. Differences between these expressions and other analytic solutions, including those derived from single normal mode Fourier/Fourier-Bessel expansion of the buoyant perturbation pressure Poisson equation, are discussed. Part II of this study compares the theoretical expressions with numerical solutions of the buoyant perturbation pressure Poisson equation for a wide range of thermal buoyancy profiles representing shallow-to-deep moist convection and also with fully dynamical 2D and 3D updraft simulations. [ABSTRACT FROM AUTHOR]

This paper compares simple theoretical expressions relating vertical velocity, perturbation pressure, updraft size, and dimensionality for cumulus convection, derived in Part I, with numerical solutions of the anelastic buoyant perturbation pressure Poisson equation and vertical velocity w. A range of thermal buoyancy profiles representing shallow to deep moist convection are tested for both two-dimensional (2D) and three-dimensional (3D) updrafts. The theoretical expressions give similar results for w and perturbation pressure difference from updraft top to base Δ p compared to the numerical solutions over a wide range of updraft radius R. The theoretical expressions are also consistent with 2D and 3D fully dynamical updraft simulations initiated by warm bubbles of varying width. Implications for nonhydrostatic modeling in the 'gray zone,' with a horizontal grid spacing Δ x of O(1-10) km where convection is generally underresolved, are discussed. The theoretical and numerical solutions give a scaling of updraft velocity with R (~Δ x) consistent with fully dynamical 2D and 3D simulations in the gray zone, with a rapid decrease of maximum w at relatively small R and a slower decrease at large R. These results suggest that an incorrect representation of perturbation pressure may be an important contributor to biases in convective strength at these resolutions. The theoretical solutions also provide a concise physical interpretation of the 'virtual mass' coefficient in convection parameterizations and can be easily incorporated into these schemes to provide a consistent scaling of perturbation pressure effects with R, updraft height, and the buoyancy profile. [ABSTRACT FROM AUTHOR]