Double-moment Microphysics Scheme Research Articles

Modeling Condensation in Deep Convection

Abstract 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.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

Controls on phase composition and ice water content in a convection-permitting model simulation of a tropical mesoscale convective system

Abstract. Simulations of tropical convection from an operational numerical weather prediction model are evaluated with the focus on the model's ability to simulate the observed high ice water contents associated with the outflow of deep convection and to investigate the modelled processes that control the phase composition of tropical convective clouds. The 1 km horizontal grid length model that uses a single-moment microphysics scheme simulates the intensification and decay of convective strength across the mesoscale convective system. However, deep convection is produced too early, the OLR (outgoing longwave radiation) is underestimated and the areas with reflectivities > 30 dBZ are overestimated due to too much rain above the freezing level, stronger updraughts and larger particle sizes in the model. The inclusion of a heterogeneous rain-freezing parameterisation and the use of different ice size distributions show better agreement with the observed reflectivity distributions; however, this simulation still produces a broader profile with many high-reflectivity outliers demonstrating the greater occurrence of convective cells in the simulations. Examining the phase composition shows that the amount of liquid and ice in the modelled convective updraughts is controlled by the following: the size of the ice particles, with larger particles growing more efficiently through riming and producing larger IWC (ice water content); the efficiency of the warm rain process, with greater cloud water contents being available to support larger ice growth rates; and exclusion or limitation of graupel growth, with more mass contained in slower falling snow particles resulting in an increase of in-cloud residence times and more efficient removal of LWC (liquid water content). In this simulated case using a 1 km grid length model, horizontal mass divergence in the mixed-phase regions of convective updraughts is most sensitive to the turbulence formulation. Greater mixing of environmental air into cloudy updraughts in the region of −30 to 0 °C produces more mass divergence indicative of greater entrainment, which generates a larger stratiform rain area. Above these levels in the purely ice region of the simulated updraughts, the convective updraught buoyancy is controlled by the ice particle sizes, demonstrating the importance of the microphysical processes on the convective dynamics in this simulated case study using a single-moment microphysics scheme. The single-moment microphysics scheme in the model is unable to simulate the observed reduction of mean mass-weighted ice diameter as the ice water content increases. The inability of the model to represent the observed variability of the ice size distribution would be improved with the use of a double-moment microphysics scheme.

Evaluation of Subgrid-Scale Hydrometeor Transport Schemes Using a High-Resolution Cloud-Resolving Model

Abstract Potential ways of parameterizing vertical turbulent fluxes of hydrometeors are examined using a high-resolution simulation of continental deep convection. The cloud-resolving model uses a double-moment microphysics scheme that contains prognostic variables for four hydrometeor types: rain, graupel, cloud ice, and snow. The benchmark simulation with a horizontal grid spacing of 250 m is analyzed to evaluate three different ways of parameterizing the turbulent vertical fluxes of hydrometeors: an eddy-diffusion approximation, a quadrant-based decomposition, and a scaling method that accounts for within-quadrant (subplume) correlations. Results show that the downgradient nature of the eddy-diffusion approximation enforces transport of mass away from concentrated regions, whereas the benchmark simulation indicates that the vertical transport often moves mass from below the level of maximum concentration to aloft. Unlike the eddy-diffusion approach, the quadrimodal decomposition is able to capture the signs of the flux gradient but underestimates the magnitudes. The scaling approach, which accounts empirically for within-quadrant correlations, improves the representation of the vertical fluxes for all hydrometeors except snow. A sensitivity study is performed to illustrate how vertical transport effects on the vertical distribution of hydrometeors are compounded by accompanying changes in microphysical process rates. Results from the sensitivity tests show that suppressing rain or graupel transport drastically alters vertical profiles of cloud ice and snow through changes in the distribution of cloud water, which in turn governs the production of cloud ice and snow aloft. Last, a viable subgrid-scale hydrometeor transport scheme in an assumed probability density function parameterization is discussed.

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

Abstract In this study, the ability of several cloud microphysical and planetary boundary layer parameterization schemes to accurately simulate cloud characteristics within 4-km grid-spacing ensemble forecasts over the contiguous United States was evaluated through comparison of synthetic Geostationary Operational Environmental Satellite (GOES) infrared brightness temperatures with observations. Four double-moment microphysics schemes and five planetary boundary layer (PBL) schemes were evaluated. Large differences were found in the simulated cloud cover, especially in the upper troposphere, when using different microphysics schemes. Overall, the results revealed that the Milbrandt–Yau and Morrison microphysics schemes tended to produce too much upper-level cloud cover, whereas the Thompson and the Weather Research and Forecasting Model (WRF) double-moment 6-class (WDM6) microphysics schemes did not contain enough high clouds. Smaller differences occurred in the cloud fields when using different PBL schemes, with the greatest spread in the ensemble statistics occurring during and after daily peak heating hours. Results varied somewhat depending upon the verification method employed, which indicates the importance of using a suite of verification tools when evaluating high-resolution model performance. Finally, large differences between the various microphysics and PBL schemes indicate that large uncertainties remain in how these schemes represent subgrid-scale processes.

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

AbstractDoppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

The Ensemble Kalman Filter Analyses and Forecasts of the 8 May 2003 Oklahoma City Tornadic Supercell Storm Using Single- and Double-Moment Microphysics Schemes

Abstract A combined mesoscale and storm-scale ensemble data-assimilation and prediction system is developed using the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW) and the ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed (DART) software package for a short-range ensemble forecast of an 8 May 2003 Oklahoma City, Oklahoma, tornadic supercell storm. Traditional atmospheric observations are assimilated into a 45-member mesoscale ensemble over a continental U.S. domain starting 3 days prior to the event. A one-way-nested 45-member storm-scale ensemble is initialized centered on the tornadic event at 2100 UTC on the day of the event. Three radar observation assimilation and forecast experiments are conducted at storm scale using a single-moment, a semi-double-moment, and a full double-moment bulk microphysics scheme. Results indicate that the EAKF initializes the supercell storm into the model with good accuracy after a 1-h-long radar observation assimilation window. The ensemble forecasts capture the movement of the main supercell storm that matches reasonably well with radar observations. The reflectivity structure of the supercell storm using a double-moment microphysics scheme appears to compare better to the observations than that using a single-moment scheme. In addition, the ensemble system predicts the probability of a strong low-level vorticity track of the tornadic supercell that correlates well with the observed rotation track. The rapid 3-min update cycle of the storm-scale ensemble from the radar observations seems to enhance the skill of the ensemble and the confidence of an imminent tornado threat. The encouraging results obtained from this study show promise for a short-range probabilistic storm-scale forecast of supercell thunderstorms, which is the main goal of NOAA's Warn-on-Forecast initiative.

Homogeneity of the Subgrid-Scale Turbulent Mixing in Large-Eddy Simulation of Shallow Convection

Abstract This paper presents an approach to locally predict homogeneity of the subgrid-scale turbulent mixing in large-eddy simulation of shallow clouds applying double-moment warm-rain microphysics. The homogeneity of subgrid-scale mixing refers to the partitioning of the cloud water evaporation due to parameterized entrainment between changes of the mean droplet radius and changes of the mean droplet concentration. Homogeneous and extremely inhomogeneous mixing represent two limits of possible scenarios, where the droplet concentration and the mean droplet radius remains unchanged during the microphysical adjustment, respectively. To predict the subgrid-scale mixing scenario, the double-moment microphysics scheme is merged with the approach to delay droplet evaporation resulting from entrainment. Details of the new scheme and its application in the Barbados Oceanographic and Meteorological Experiment (BOMEX) shallow convection case are discussed. The simulated homogeneity of mixing varies significantly inside small convective clouds, from close to homogeneous to close to extremely inhomogeneous. The mean mixing characteristics become more homogeneous with height, reflecting increases of the mean droplet size and the mean turbulence intensity, both favoring homogeneous mixing. Model results are consistent with microphysical effects of entrainment and mixing deduced from field observations. Mixing close to homogeneous is predicted in volumes with the highest liquid water content (LWC) and strongest updraft at a given height, whereas mixing in strongly diluted volumes is typically close to extremely inhomogeneous. The simulated homogeneity of mixing has a small impact on mean microphysical characteristics. This result agrees with the previous study applying prescribed mixing scenarios and can be explained by the high humidity of the clear air involved in the subgrid-scale mixing.

Comparisons of Single- and Double-Moment Microphysics Schemes in the Simulation of a Synoptic-Scale Snowfall Event

Abstract The Canadian CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Validation Project (C3VP) provided aircraft, surface, and remotely sensed observations of cloud and precipitation characteristics to support improved simulation of cold-season precipitation within weather forecast models and new developments in satellite and radar precipitation retrievals. On 22 January 2007, the C3VP campaign executed an intensive observation period to sample widespread snowfall that occurred as a midlatitude cyclone tracked along the U.S.–Canadian border. Surface air temperature and precipitation measurements, combined with aircraft measurement of hydrometeor content and size distribution, are used to examine various assumptions and parameterizations included within four bulk water microphysics schemes available within the Weather Research and Forecasting Model (WRF). In a simulation of the 22 January 2007 event, WRF forecasts reproduced the overall precipitation pattern observed during aircraft sampling, allowing for a comparison between C3VP measurements and microphysics scheme assumptions. Single-moment schemes that provide flexibility in size distribution parameters as functions of temperature can represent much of the vertical variability observed in aircraft data, but variability is reduced in an environment where the simulated temperature profile is nearly isothermal. Double-moment prediction of total number concentration may improve the representation of ice crystal aggregation. Inclusion of both temperature dependence on distribution parameters and variability in mass–diameter or diameter–fall speed relationships suggest a means of improving upon single-moment predictions.

Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem

Abstract. This study assesses the ability of the recent chemistry version (v3.3) of the Weather Research and Forecasting (WRF-Chem) model to simulate boundary layer structure, aerosols, stratocumulus clouds, and energy fluxes over the Southeast Pacific Ocean. Measurements from the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) and satellite retrievals (i.e., products from the MODerate resolution Imaging Spectroradiometer (MODIS), Clouds and Earth's Radiant Energy System (CERES), and GOES-10) are used for this assessment. The Morrison double-moment microphysics scheme is newly coupled with interactive aerosols in the model. The 31-day (15 October–16 November 2008) WRF-Chem simulation with aerosol-cloud interactions (AERO hereafter) is also compared to a simulation (MET hereafter) with fixed cloud droplet number concentrations in the microphysics scheme and simplified cloud and aerosol treatments in the radiation scheme. The well-simulated aerosol quantities (aerosol number, mass composition and optical properties), and the inclusion of full aerosol-cloud couplings lead to significant improvements in many features of the simulated stratocumulus clouds: cloud optical properties and microphysical properties such as cloud top effective radius, cloud water path, and cloud optical thickness. In addition to accounting for the aerosol direct and semi-direct effects, these improvements feed back to the simulation of boundary-layer characteristics and energy budgets. Particularly, inclusion of interactive aerosols in AERO strengthens the temperature and humidity gradients within the capping inversion layer and lowers the marine boundary layer (MBL) depth by 130 m from that of the MET simulation. These differences are associated with weaker entrainment and stronger mean subsidence at the top of the MBL in AERO. Mean top-of-atmosphere outgoing shortwave fluxes, surface latent heat, and surface downwelling longwave fluxes are in better agreement with observations in AERO, compared to the MET simulation. Nevertheless, biases in some of the simulated meteorological quantities (e.g., MBL temperature and humidity) and aerosol quantities (e.g., underestimations of accumulation mode aerosol number) might affect simulated stratocumulus and energy fluxes over the Southeastern Pacific, and require further investigation. The well-simulated timing and outflow patterns of polluted and clean episodes demonstrate the model's ability to capture daily/synoptic scale variations of aerosol and cloud properties, and suggest that the model is suitable for studying atmospheric processes associated with pollution outflow over the ocean. The overall performance of the regional model in simulating mesoscale clouds and boundary layer properties is encouraging and suggests that reproducing gradients of aerosol and cloud droplet concentrations and coupling cloud-aerosol-radiation processes are important when simulating marine stratocumulus over the Southeast Pacific.

Macroscopic impacts of cloud and precipitation processes in shallow convection

This paper presents application of the EULAG model combined with a sophisticated double-moment warm-rain microphysics scheme to the model intercomparison case based on RICO (Rain in Cumulus over Ocean) field observations. As the simulations progress, the cloud field gradually deepens and a relatively sharp temperature and moisture inversions develop in the lower troposphere. Two contrasting aerosol environments are considered, referred to as pristine and polluted, together with two contrasting subgridscale mixing scenarios, the homogeneous and the extremely inhomogeneous mixing. Pristine and polluted environments feature mean cloud droplet concentrations around 40 and 150 mg−1, respectively, and large differences in the rain characteristics. Various measures are used to contrast evolution of macroscopic cloud field characteristics, such as the mean cloud fraction, the mean cloud width, or the height of the center of mass of the cloud field, among others. Macroscopic characteristics appear similar regardless of the aerosol characteristics or the homogeneity of the subgrid-scale mixing.

Indirect Impact of Atmospheric Aerosols in Idealized Simulations of Convective–Radiative Quasi Equilibrium. Part II: Double-Moment Microphysics

AbstractThis paper extends the previous cloud-resolving modeling study concerning the impact of cloud microphysics on convective–radiative quasi equilibrium (CRQE) over a surface with fixed characteristics and prescribed solar input, both mimicking the mean conditions on earth. The current study applies sophisticated double-moment warm-rain and ice microphysics schemes, which allow for a significantly more realistic representation of the impact of aerosols on precipitation processes and on the coupling between clouds and radiative transfer. Two contrasting cloud condensation nuclei (CCN) characteristics are assumed, representing pristine and polluted conditions, as well as contrasting representations of the effects of entrainment and mixing on the mean cloud droplet size. In addition, four sets of sensitivity simulations are also performed with changes that provide a reference for the main simulation set.As in the previous study, the CRQE mimics the estimates of globally and annually averaged water and energy fluxes across the earth’s atmosphere. There are some differences from the previous study, however, consistent with the slightly lower water vapor content in the troposphere and significantly reduced lower-tropospheric cloud fraction in current simulations. There is also a significant reduction of the difference between the pristine and polluted cases, from ∼20 to ∼4 W m−2 at the surface from ∼20 to ∼9 W m−2 at the top of the atmosphere (TOA). The difference between the homogeneous and extremely inhomogeneous mixing scenarios, ∼20 W m−2 in the previous study, is reduced to a mere 2 (1) W m−2 at the surface (TOA). An unexpected difference between the previous and current simulations is the lower Bowen ratio of the surface heat flux, the partitioning of the total flux into sensible and latent components. It is shown that most of the change comes from the difference in the representation of rain evaporation in the subcloud layer in the single- and double-moment microphysics schemes. The difference affects the mean air temperature and humidity near the surface, and thus the Bowen ratio. The differences between the various simulations are discussed, contrasting the process-level approach with the impact of cloud microphysics on the quasi-equilibrium state with a more appropriate system dynamics approach. The key distinction is that the latter includes the interactions among all the processes in the modeled system.

Effects of Cloud Parameterization on Radiation and Precipitation: A Comparison Between Single-Moment Microphysics and Double-Moment Microphysics

This study compares a single-moment microphysics scheme to a double-moment microphysics scheme using four observed cases of a mesoscale cloud system. Previous studies comparing a single-moment microphysics scheme to a doublemoment microphysics scheme have focused on microphysical processes or overall dynamics, precipitation and morphology of cloud systems. However, they have not focused on how the different representation of microphysical processes between a single- and double-moment microphysics scheme affects precipitation. This study shifts its focus from that of previous studies to the effect of the different representation of microphysics on precipitation. In addition, this study examines the effect of the different representation of microphysical processes on different radiation budgets between single- and double-moment microphysics schemes. The temporal evolution of precipitation simulated by a single-moment microphysics scheme is significantly different from that by a double-moment microphysics scheme in this study. This is mostly due to different physical representations of key processes (i.e., autoconversion, saturation, and nucleation). Also, a simulation by a single-moment microphysics scheme results in different radiation budgets compared to a double-moment microphysics scheme. More reflection of incident solar radiation in a simulation with a double-moment microphysics scheme than that with a single-moment microphysics scheme is simulated.

State estimation of convective storms with a two‐moment microphysics scheme and an ensemble Kalman filter: Experiments with simulated radar data

AbstractThe ability of the ensemble Kalman filter method to estimate an increased number of state variables associated with a double‐moment (DM) microphysics scheme is examined for the first time through observing system simulation experiments, assuming either a perfect or imperfect prediction model and/or observation operators. With the DM scheme, mixing ratios and total number concentrations of hydrometeor species are predicted.It is found that the increased number of state variables can be reasonably well estimated when both radial velocity (Vr) and reflectivity (ZH) observations are used and when the prediction model is assumed to be perfect. However, the errors increase significantly when ZH is used alone. In this case, the filter has difficulty in estimating independently‐varying mixing ratios and number concentrations, which are both directly involved in the calculation of ZH. The addition of Vr data helps alleviate a problem associated with the solution not being sufficiently constrained by observations.With the DM scheme, the correlations between ZH and model state variables exhibit complex spatial structures that depend on the location of the ZH observation. Collocated ZH and vertical velocity show negative correlation when the observation is taken where ice phase hydrometeors are dominant, but positive correlation when it is taken where large quantities of liquid hydrometeors exist. Further study is needed to fully understand the complex correlation structures.Imperfect model experiments were performed, with two types of model errors: (1) microphysical parametrization error due to incorrectly assumed shape parameter of the gamma particle size distribution (PSD), and (2) different ways of calculating hydrometeor scattering. The results show that the model error degrades the state estimation in general. Nevertheless, the estimated states are still reasonably good when both Vr and ZH are assimilated. Perturbing the shape parameter of gamma PSDs within the forecast ensemble improves the overall state estimation. Copyright © 2010 Royal Meteorological Society

On the effects of vertical air velocity on winter precipitation types

Abstract. The various precipitation types formed within winter storms (such as snow, wet snow and freezing rain) often lead to very hazardous weather conditions. These types of precipitation often occur during the passage of a warm front as a warm air mass ascends over a cold air mass. To address this issue further, we used a one-dimensional kinematic cloud model to simulate this gentle ascent (≤10 cm/s) of warm air. The initial temperature profile has an above 0°C inversion, a lower subfreezing layer, and precipitation falls from above the temperature inversion. The cloud model is coupled to a double-moment microphysics scheme that simulates the production of various types of winter precipitation. The results are compared with those from a previous study carried out in still air. Based on the temporal evolution of surface precipitation, snow reaches the surface significantly faster than in still air whereas other precipitation types including freezing rain and ice pellets have a shorter duration. Overall, even weak background vertical ascent has an important impact on the precipitation reaching the surface, the time of the elimination of the melting layer, and also the evolution of the lower subfreezing layer.

A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part II: Single-Column Modeling of Arctic Clouds

Abstract The new double-moment microphysics scheme described in Part I of this paper is implemented into a single-column model to simulate clouds and radiation observed during the period 1 April–15 May 1998 of the Surface Heat Budget of the Arctic (SHEBA) and First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) field projects. Mean predicted cloud boundaries and total cloud fraction compare reasonably well with observations. Cloud phase partitioning, which is crucial in determining the surface radiative fluxes, is fairly similar to ground-based retrievals. However, the fraction of time that liquid is present in the column is somewhat underpredicted, leading to small biases in the downwelling shortwave and longwave radiative fluxes at the surface. Results using the new scheme are compared to parallel simulations using other microphysics parameterizations of varying complexity. The predicted liquid water path and cloud phase is significantly improved using the new scheme relative to a single-moment parameterization predicting only the mixing ratio of the water species. Results indicate that a realistic treatment of cloud ice number concentration (prognosing rather than diagnosing) is needed to simulate arctic clouds. Sensitivity tests are also performed by varying the aerosol size, solubility, and number concentration to explore potential cloud–aerosol–radiation interactions in arctic stratus.