Mixed-Phase Arctic Cloud Experiment Research Articles

Primary and secondary ice production: interactions and their relative importance

Abstract. A discrepancy of up to 5 orders of magnitude between ice crystal and ice nucleating particle (INP) number concentrations was found in the measurements, indicating the potentially important role of secondary ice production (SIP) in the clouds. However, the interactions between primary and SIP processes and their relative importance remain unexplored. In this study, we implemented five different ice nucleation schemes as well as physical representations of SIP processes (i.e., droplet shattering during rain freezing, ice-ice collisional break-up, and rime splintering) in the Community Earth System Model version 2 (CESM2). We ran CESM2 in the single column mode for model comparisons with the DOE Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We found that the model experiments with aerosol-aware ice nucleation schemes and SIP processes yield the best simulation results for the M-PACE single-layer mixed-phase clouds. We further investigated the relative importance of ice nucleation and SIP to ice number and cloud phase as well as interactions between ice nucleation and SIP in the M-PACE single-layer mixed-phase clouds. Our results show that SIP contributes 80 % to the total ice formation and transforms ∼30 % of pure liquid-phase clouds simulated in the model experiments without considering SIP into mixed-phase clouds. The SIP is not only a result of ice crystals produced from ice nucleation, but also competes with the ice nucleation by reducing the number concentrations of cloud droplets and cloud-borne dust INPs. Conversely, strong ice nucleation also suppresses SIP by glaciating mixed-phase clouds and thereby reducing the amount of precipitation particles (rain and graupel).

Impacts of secondary ice production on Arctic mixed-phase clouds based on ARM observations and CAM6 single-column model simulations

Abstract. For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice-nucleating particle number concentrations in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent SIP processes well, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice–ice collisional breakup, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We show the important role of SIP in four types of clouds during M-PACE (i.e., multilayer, single-layer stratus, transition, and frontal clouds), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentrations, and provides a better agreement with observations. The findings of this study highlight the importance of considering SIP in GCMs.

Toward Understanding the Simulated Phase Partitioning of Arctic Single‐Layer Mixed‐Phase Clouds in E3SM

AbstractArctic mixed‐phase clouds simulated by the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) are found to be overly dominated by supercooled liquid with little ice production. Sensitivity experiments using the short‐term hindcast approach are performed to isolate the impact of several new parameterizations on the simulated mixed‐phase clouds in EAMv1. These include the Classical Nucleation Theory (CNT) ice nucleation scheme, the Cloud Layer Unified By Binormals (CLUBB) parameterization, and the updated Morrison and Gettelman microphysics scheme (MG2). Results are compared to the DOE's Atmospheric Radiation Measurement (ARM) Mixed‐Phase Arctic Cloud Experiment (M‐PACE) observations. It is found that all of these new parameterizations are responsible for the decrease of cloud ice water content in EAMv1 simulated single‐layer mixed‐phase clouds. A budget analysis of detailed cloud microphysical processes suggests that a lack of initial ice particles from ice nucleation or convective detrainment strongly diminishes the cloud ice water content through the subsequent ice mass growth processes. Reduced heterogeneous ice nucleation by CNT at temperatures warmer than −15°C along with negligible ice processes in CLUBB are primarily responsible for the problem. Because the use of MG2 does not impact initial ice formation, the MG2 cloud microphysics is not the primary reason for the underestimate of cloud ice. However, using MG2 leads to a lower total ice mass due to a higher accretion rate of liquid droplets by rain drops and a lower ice mass growth rate.

Impacts of Representing Heterogeneous Distribution of Cloud Liquid and Ice on Phase Partitioning of Arctic Mixed‐Phase Clouds with NCAR CAM5

AbstractIn this study, we conduct sensitivity experiments with the Community Atmosphere Model version 5 to understand the impact of representing heterogeneous distribution between cloud liquid and ice on the phase partitioning in mixed‐phase clouds through different perturbations on the Wegener‐Bergeron‐Findeisen (WBF) process. In two experiments, perturbation factors that are based on assumptions of pocket structure and the partial homogeneous cloud volume derived from the High‐performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole‐to‐Pole Observation (HIPPO) campaign are utilized. Alternately, a mass‐weighted assumption is used in the calculation of WBF process to mimic the appearance of unsaturated area in mixed‐phase clouds as the result of heterogeneous distribution. Model experiments are tested in both single column and weather forecast modes and evaluated against data from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program's Mixed‐Phase Arctic Cloud Experiment (M‐PACE) field campaign and long‐term ground‐based multisensor measurements. Model results indicate that perturbations on the WBF process can significantly modify simulated microphysical properties of Arctic mixed‐phase clouds. The improvement of simulated cloud water phase partitioning tends to be linearly proportional to the perturbation magnitude that is applied in the three different sensitivity experiments. Cloud macrophysical properties such as cloud fraction and frequency of occurrence of low‐level mixed‐phase clouds are less sensitive to the perturbation magnitude than cloud microphysical properties. Moreover, this study indicates that heterogeneous distribution between cloud hydrometeors should be treated consistently for all cloud microphysical processes. The model vertical resolution is also important for liquid water maintenance in mixed‐phase clouds.

A modelling study of the continuous ice formation in an autumnal Arctic mixed-phase cloud case

An autumnal Arctic mixed-phase cloud case from the Mixed-Phase Arctic Cloud Experiment is simulated with the Weather Research and Forecasting model with a prognostic ice-nucleating particle (INP) formulation to investigate the mechanisms sustaining the continuous ice formation. When the model is run with only primary ice production (PIP) processes, it needs 100 times the observed INP concentration to reproduce the observations. When a secondary ice production (SIP) process, i.e., droplet shattering when supercooled droplets freeze heterogeneously, is added, the model needs 50 times the observed INP concentration to reproduce the observations. Two factors are found to reduce the INP concentration required to reproduce the observations. First, as the cloud moves over the open ocean, the cloud top rises quickly, resulting in a cloud top entrainment rate of ~3.0 cm s−1, which is 4 times as large as that in previous large-eddy simulations of the same case. More INPs can hence be entrained into the cloud. Second, INPs are recycled when ice crystals are completely sublimated below the cloud base. Sensitivity tests show that INP recycling reduces the required INP concentration by a factor of 4. In addition, offline tests show that another two SIP processes, i.e., droplet shattering when supercooled droplets collect small ice crystals and breakup during ice-ice collision, do not substantially contribute to the ice formation in this case.

The Response of Simulated Arctic Mixed‐Phase Stratocumulus to Sea Ice Cover Variability in the Absence of Large‐Scale Advection

AbstractThis study examines the responses of Arctic mixed‐phase stratocumulus boundary layer to sea ice cover variability near the sea ice margins using large eddy simulations. The simulations are conducted for two different atmospheric conditions, based on observations from the Surface Heat Budget of the Arctic Ocean Experiment (SHEBA) (100% sea ice‐covered) and the Mixed‐Phase Arctic Cloud Experiment (M‐PACE) (open ocean). The effect of sea ice cover variability is investigated for both atmospheric conditions by conducting a series of simulations prescribed with varying amounts of sea ice cover and no large‐scale advection. As sea ice cover amount decreases, the SHEBA boundary layer deepens and becomes decoupled. The relative strength of turbulence driven by surface heating to that driven by cloud top radiative cooling increases. Cloud ice and snow grow more efficiently than cloud liquid with moisture transported from the lower boundary layer. On the other hand, as sea ice cover amount increases, the M‐PACE boundary layer becomes shallower and more coupled with the surface as turbulence mainly driven by cloud top radiative cooling. Moisture supply from the surface is reduced, while cloud droplets are generated from turbulence at cloud top with little ice formation. In both atmospheric conditions, the boundary layer turbulence structure is modified according to change in the relative strength of boundary layer turbulent sources as sea ice amount changes, resulting in the growth/decay of the cloud layer. Simulations with smaller sea ice cover amounts are associated with more cloud ice but not necessarily more cloud liquid.

A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors

Abstract. Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.

Sensitivities in Large-Eddy Simulations of Mixed-Phase Arctic Stratocumulus Clouds Using a Simple Microphysics Approach*

Abstract Arctic mixed-phase stratocumulus clouds are maintained by feedbacks between microphysical and dynamical phenomena, but the details of these interactions are incompletely understood. Although large-eddy simulations are a promising means of elucidating microphysics–turbulence relationships, the use of sophisticated microphysical schemes complicates analysis of their results. Here, the ability of a simplified one-moment scheme to capture basic features of this cloud type is investigated through simulations based on Mixed-Phase Arctic Cloud Experiment (MPACE), SHEBA/FIRE-ACE, and Indirect and Semi-Direct Aerosol Campaign (ISDAC) intercomparison studies. The results of the simple scheme show reasonable agreement with liquid and ice water path predictions reported by models using schemes of similar or greater complexity. Additional tests are performed to evaluate the sensitivity of the results to three main parameters of the scheme: the snow and ice size distribution intercept parameters and the exponent appearing in the temperature-dependent phase-partition function, which is used to diagnose cloud condensate amounts. Sensitivities of the SHEBA and ISDAC cases, both of which have low surface heat fluxes and low precipitation rates, tend to be similar, while the MPACE case, with higher surface fluxes and precipitation rates, shows somewhat different trends. Results of all three cases are found to be sensitive to the snow size distribution intercept parameter, but this quantity can be adequately estimated using a recently developed diagnostic expression based on observations of Arctic clouds.

Small Cloud Particle Shapes in Mixed-Phase Clouds

AbstractThe shapes of cloud particles with maximum dimensions Dmax between 35 and 60 μm in mixed-phase clouds were studied using high-resolution particle images collected by a cloud particle imager (CPI) during the Mixed-Phase Arctic Cloud Experiment (M-PACE) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC). The area ratio α, the projected area of a particle divided by the area of a circle with diameter Dmax, quantified particle shape. The differing optical characteristics of CPIs used in M-PACE and ISDAC had no effect on derived α provided that Dmax > 35 μm and CPI focus > 45. The fraction of particles with 35 < Dmax < 60 μm with α > 0.8 increased with the ratio of liquid water content (LWC) to total water content (TWC). The average αmean of small particles in each 10-s interval in mixed-phase clouds was correlated with LWC/TWC with a correlation coefficient of 0.60 for M-PACE and 0.43 for ISDAC. The stronger correlation seen during M-PACE was most likely associated with the presence of more liquid droplets that were larger than the CPI detection threshold contributing to αmean; the modal effective radius was larger (11 vs 6 μm), and drops with D > 35 μm had concentrations during M-PACE that were 6 times as large as those of ISDAC. This study hence suggests that area ratio can be used to identify the phase of particles with 35 < Dmax < 60 μm and questions the assumption used in previous studies that all particles in this size range are supercooled droplets.

Assessment of some parameterizations of heterogeneous ice nucleation in cloud and climate models

Abstract. Several different types of parameterization of heterogeneous ice nucleation for cloud and climate models have been developed over the past decades, ranging from empirically-derived expressions to parameterizations of ice crystal nucleation rates derived from theory, including the parameterization developed by the authors that includes simultaneous dependence on the temperature and saturation ratio, hereafter referred to as KC. Parameterizations schemes that address the deliquescence-heterogeneous-freezing (DHetF), which combines the modes of condensation freezing and immersion freezing, are assessed here in the context of thermodynamic constraints, laboratory measurements, and recent field measurements. It is shown that empirical schemes depending only on the ice saturation ratio or only on temperature can produce reasonable crystal concentrations, but ice crystal nucleation is thermodynamically prohibited in certain regions of the temperature-saturation ratio phase space. Some recent empirical parameterizations yield clouds that are almost entire liquid at temperatures as low as −35 °C in contrast to cloud climatology. Reasonable performance of the KC ice nucleation scheme is demonstrated by comparison with numerous data from several recent field campaigns, laboratory data, climatology of cloud phase-state. Several mis-applications of the KC parameterization that appeared recently in the literature are described and corrected. It is emphasized here that a correct application of the KC scheme requires integration of the individual nucleation rates over the measured size spectrum of ice nuclei that represent a fraction or several fractions of the environmental aerosol with specific ice nucleation properties. The concentration in these fractions can be substantially smaller than that of the total aerosol, but greater than the crystal concentration measured by an experimental device. Simulations with temperature-dependent active site area or with several IN fractions having different properties show that ice nucleation in the KC scheme occurs in a wide temperature range of 10–20 °C, which depends on IN properties. Simulation with a spectral bin model and correct application of KC scheme adequately describes ice nucleation via the DHetF mode and yields crystal concentrations and phase state close to those measured in the single-layer stratocumulus cloud observed in the Mixed Phase Arctic Cloud Experiment (MPACE). An assessment of some deficiencies in current parcel modeling methods and cloud chamber observations and their impact on parameterization development and evaluation is provided.

Testing cloud microphysics parameterizations in NCAR CAM5 with ISDAC and M-PACE observations

[1] Arctic clouds simulated by the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 5 (CAM5) are evaluated with observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Indirect and Semi-Direct Aerosol Campaign (ISDAC) and Mixed-Phase Arctic Cloud Experiment (M-PACE), which were conducted at its North Slope of Alaska site in April 2008 and October 2004, respectively. Model forecasts for the Arctic spring and fall seasons performed under the Cloud-Associated Parameterizations Testbed framework generally reproduce the spatial distributions of cloud fraction for single-layer boundary-layer mixed-phase stratocumulus and multilayer or deep frontal clouds. However, for low-level stratocumulus, the model significantly underestimates the observed cloud liquid water content in both seasons. As a result, CAM5 significantly underestimates the surface downward longwave radiative fluxes by 20–40 W m−2. Introducing a new ice nucleation parameterization slightly improves the model performance for low-level mixed-phase clouds by increasing cloud liquid water content through the reduction of the conversion rate from cloud liquid to ice by the Wegener-Bergeron-Findeisen process. The CAM5 single-column model testing shows that changing the instantaneous freezing temperature of rain to form snow from −5°C to −40°C causes a large increase in modeled cloud liquid water content through the slowing down of cloud liquid and rain-related processes (e.g., autoconversion of cloud liquid to rain). The underestimation of aerosol concentrations in CAM5 in the Arctic also plays an important role in the low bias of cloud liquid water in the single-layer mixed-phase clouds. In addition, numerical issues related to the coupling of model physics and time stepping in CAM5 are responsible for the model biases and will be explored in future studies.

The impact of microphysical parameters, ice nucleation mode, and habit growth on the ice/liquid partitioning in mixed-phase Arctic clouds

[1] The fundamental physical processes that maintain supercooled liquid in observed Arctic mixed-phase clouds are poorly constrained. To isolate the factors that control ice/liquid partitioning during the ascent of an air parcel, we apply an adiabatic parcel model that includes ice nucleation by deposition and immersion freezing and ice habit evolution. Simulations are performed for two different temperature regimes that resemble those observed during the Mixed-Phase Arctic Cloud Experiment (−13°C < T < −9°C) and the Surface Heat Budget of the Arctic Ocean (−22°C < T < −17°C). Effects on ice and liquid water evolution in an updraft are explored as a function of ice nucleus (IN) concentration and nucleation mode, updraft velocity, properties of cloud condensation nuclei, and assumption about ice particle shape (habit). For most conditions, ice and liquid coexist and increase simultaneously, and only at high IN concentrations or low updraft velocities do ice particles grow at the expense of droplets. The impact of the ice nucleation mode on ice/liquid distribution depends on the temperature and supersaturation regime. The assumption of spherical ice particles instead of nonspherical habits leads to an underestimate of ice growth. It is concluded that updraft velocity, IN concentrations, and particle shape can impact ice/liquid distribution to similar extents.

Parameterizing correlations between hydrometeor species in mixed-phase Arctic clouds

Mixed-phase Arctic clouds, like other clouds, contain small-scale variability in hydrometeor fields, such as cloud water or snow mixing ratio. This variability may be worth parameterizing in coarse-resolution numerical models. In particular, for modeling processes such as accretion and aggregation, it would be useful to parameterize subgrid correlations among hydrometeor species. However, one difficulty is that there exist many hydrometeor species and many microphysical processes, leading to complexity and computational expense.Existing lower and upper bounds (inequalities) on linear correlation coefficients provide useful guidance, but these bounds are too loose to serve directly as a method to predict subgrid correlations. Therefore, this paper proposes an alternative method that is based on a blend of theory and empiricism. The method begins with the spherical parameterization framework of Pinheiro and Bates (1996), which expresses the correlation matrix in terms of its Cholesky factorization. The values of the elements of the Cholesky matrix are parameterized here using a cosine row-wise formula that is inspired by the aforementioned bounds on correlations. The method has three advantages: 1) the computational expense is tolerable; 2) the correlations are, by construction, guaranteed to be consistent with each other; and 3) the methodology is fairly general and hence maymore » be applicable to other problems. The method is tested non-interactively using simulations of three Arctic mixed-phase cloud cases from two different field experiments: the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE). Benchmark simulations are performed using a large-eddy simulation (LES) model that includes a bin microphysical scheme. The correlations estimated by the new method satisfactorily approximate the correlations produced by the LES.« less

Using Doppler Spectra to Separate Hydrometeor Populations and Analyze Ice Precipitation in Multilayered Mixed-Phase Clouds

Multimodality of cloud radar Doppler spectra is used to partition cloud particle phases and separate distinct ice populations in the radar sample volume, thereby facilitating the analysis of individual ice showers in multilayered mixed-phase clouds. A 35-GHz cloud radar located at Barrow, Alaska, during the Mixed-Phase Arctic Cloud Experiment collected the Doppler spectra. Data from a pair of collocated depolarization lidars confirmed the presence of two liquid cloud layers reported in this letter. Both of these cloud layers were embedded in ice precipitation yet maintained their liquid. The spectral separation of the ice precipitation yielded two distinct ice populations: the ice initiated within the two liquid cloud layers and the ice precipitation formed in the higher cloud layers. The comparisons of ice fall velocity-versus-radar reflectivity relationships derived for distinct showers reveal that a single relationship does not properly represent the ice showers during this period.

Investigation of Microphysical Parameterizations of Snow and Ice in Arctic Clouds during M-PACE through Model–Observation Comparisons

Abstract In this study the Weather Research Forecast model is used with 1-km horizontal grid spacing to investigate the microphysical properties of Arctic mixed-phase stratocumulus. Intensive measurements taken during the Department of Energy Atmospheric Radiation Measurement Program Mixed-Phase Arctic Cloud Experiment (M-PACE) on the North Slope of Alaska, during 9–12 October 2004, are used to verify the microphysical characteristics of the model’s simulation of mixed-phase clouds (MPCs). A series of one- and two-moment bulk microphysical cloud schemes are tested to identify how the treatment of snow and ice affects the maintenance of cloud liquid water at low temperatures. The baseline two-moment simulation results in realistic liquid water paths and in size distributions of snow reasonably similar to observations. With a one-moment simulation for which the size distribution intercept parameter for snow is fixed at values taken from the two-moment simulation, reasonable snow size distributions are again obtained but the cloud liquid water is reduced because the one-moment scheme couples the number concentration to the mixing ratio. The one-moment scheme with the constant snow intercept parameter set to a value typical of midlatitude frontal clouds results in a substantial underprediction of the liquid water path. In the simulations, the number concentration of small ice crystals is found to be underestimated by an order of magnitude. A sensitivity test with the concentration of ice particles larger than 53 μm increased to the observed value results in underprediction of the liquid water path. If ice (not snow) is the primary driver for the depletion of cloud liquid water, then the results of this study suggest that the feedbacks among ice–snow–cloud liquid water may be misrepresented in the model.

Simulating mixed-phase Arctic stratus clouds: sensitivity to ice initiation mechanisms

Abstract. The importance of Arctic mixed-phase clouds on radiation and the Arctic climate is well known. However, the development of mixed-phase cloud parameterization for use in large scale models is limited by lack of both related observations and numerical studies using multidimensional models with advanced microphysics that provide the basis for understanding the relative importance of different microphysical processes that take place in mixed-phase clouds. To improve the representation of mixed-phase cloud processes in the GISS GCM we use the GISS single-column model coupled to a bin resolved microphysics (BRM) scheme that was specially designed to simulate mixed-phase clouds and aerosol-cloud interactions. Using this model with the microphysical measurements obtained from the DOE ARM Mixed-Phase Arctic Cloud Experiment (MPACE) campaign in October 2004 at the North Slope of Alaska, we investigate the effect of ice initiation processes and Bergeron-Findeisen process (BFP) on glaciation time and longevity of single-layer stratiform mixed-phase clouds. We focus on observations taken during 9–10 October, which indicated the presence of a single-layer mixed-phase clouds. We performed several sets of 12-h simulations to examine model sensitivity to different ice initiation mechanisms and evaluate model output (hydrometeors' concentrations, contents, effective radii, precipitation fluxes, and radar reflectivity) against measurements from the MPACE Intensive Observing Period. Overall, the model qualitatively simulates ice crystal concentration and hydrometeors content, but it fails to predict quantitatively the effective radii of ice particles and their vertical profiles. In particular, the ice effective radii are overestimated by at least 50%. However, using the same definition as used for observations, the effective radii simulated and that observed were more comparable. We find that for the single-layer stratiform mixed-phase clouds simulated, process of ice phase initiation due to freezing of supercooled water in both saturated and subsaturated (w.r.t. water) environments is as important as primary ice crystal origination from water vapor. We also find that the BFP is a process mainly responsible for the rates of glaciation of simulated clouds. These glaciation rates cannot be adequately represented by a water-ice saturation adjustment scheme that only depends on temperature and liquid and solid hydrometeors' contents as is widely used in bulk microphysics schemes and are better represented by processes that also account for supersaturation changes as the hydrometeors grow.

Ice formation in Arctic mixed‐phase clouds: Insights from a 3‐D cloud‐resolving model with size‐resolved aerosol and cloud microphysics

The single‐layer mixed‐phase clouds observed during the Atmospheric Radiation Measurement (ARM) program's Mixed‐Phase Arctic Cloud Experiment (MPACE) are simulated with a three‐dimensional cloud‐resolving model, the System for Atmospheric Modeling (SAM), coupled with an explicit bin microphysics scheme and a radar simulator. By implementing an aerosol‐dependent and a temperature‐ and supersaturation‐dependent ice nucleation scheme and treating IN size distribution prognostically, the link between ice crystal and aerosol properties is established to study aerosol indirect effects. Two possible ice enhancement mechanisms, activation of droplet evaporation residues by condensation followed by freezing and droplet evaporation freezing by contact freezing inside out, are scrutinized by extensive comparisons with the in situ and remote sensing measurements. Simulations with either mechanism agree well with the in situ and remote sensing measurements of ice microphysical properties but liquid water content is slightly underpredicted. These two mechanisms give similar cloud properties, although ice nucleation occurs at very different rates and locations. Ice nucleation from activation of evaporation nuclei occurs mostly near cloud top areas, while ice nucleation from the drop freezing during evaporation has no significant location preference. Both ice enhancement mechanisms contribute dramatically to ice formation with ice particle concentration of 10–15 times higher relative to the simulation without either of them. Ice nuclei (IN) recycling from ice sublimation contributes significantly to maintaining concentrations of IN and ice particles in this case, implying an important role to maintain the observed long‐term existence of mixed‐phase clouds. Cloud can be very sensitive to IN initially but become much less sensitive as cloud evolves to a steady mixed‐phase condition.

Ice nuclei characteristics from M-PACE and their relation to ice formation in clouds

This paper presents airborne measurements of ice nuclei (IN) number concentration and elemental composition from the mixed-phase Arctic cloud experiment (M-PACE) in northern Alaska during October 2004. Although the project average IN concentration was low, less than 1 L -1 STP, there was significant spatial and temporal variability, with local maximum concentrations of nearly 60 L -1 STP. Immersion and/or condensation freezing appear to be the dominant freezing mechanisms, whereas mechanisms that occur below water saturation played a smaller role. The dominant particle types identified as IN were metal oxides/dust (39%), carbonaceous particles (35%) and mixtures of metal oxides/dust with either carbonaceous components or salts/sulphates (25%), although there was significant variability in elemental composition. Trajectory analysis suggests both local and remote sources, including biomass burning and volcanic ash. Seasonal variability of IN number concentrations based on this study and data from SHEBA/FIRE-ACE indicates that fall concentrations are depleted relative to spring by about a factor of five. Average IN number concentrations from both studies compare favorably with cloud ice number concentrations of cloud particles larger than 125 μ m, for temperatures less than −10 °C. Cloud ice number concentrations also were enhanced in spring, by a factor of ∼2, but only over a limited temperature range. DOI: 10.1111/j.1600-0889.2009.00415.x

Ice nuclei characteristics from M-PACE and their relation to ice formation in clouds

This paper presents airborne measurements of ice nuclei (IN) number concentration and elemental composition from the mixed-phase Arctic cloud experiment (M-PACE) in northern Alaska during October 2004. Although the project average IN concentration was low, less than 1 L-1 STP, there was significant spatial and temporal variability, with local maximum concentrations of nearly 60 L-1 STP. Immersion and/or condensation freezing appear to be the dominant freezing mechanisms, whereas mechanisms that occur below water saturation played a smaller role. The dominant particle types identified as IN were metal oxides/dust (39%), carbonaceous particles (35%) and mixtures of metal oxides/dust with either carbonaceous components or salts/sulphates (25%), although there was significant variability in elemental composition. Trajectory analysis suggests both local and remote sources, including biomass burning and volcanic ash. Seasonal variability of IN number concentrations based on this study and data from SHEBA/FIREACE indicates that fall concentrations are depleted relative to spring by about a factor of five. Average IN number concentrations from both studies compare favorably with cloud ice number concentrations of cloud particles larger than 125 μm, for temperatures less than -10°C. Cloud ice number concentrations also were enhanced in spring, by a factor of ˜2, but only over a limited temperature range.

A Focus On Mixed-Phase Clouds

The phase composition and microphysical structure of clouds define the manner in which they modulate atmospheric radiation and contribute to the hydrologic cycle. Issues regarding cloud phase partitioning and transformation come to bear directly in mixed-phase clouds, and have been difficult to address within current modeling frameworks. Ground-based, remote-sensing observations of mixed-phase clouds can contribute a significant body of knowledge with which to better understand, and thereby more accurately model, clouds and their phase-defining processes. Utilizing example observations from the Mixed-Phase Arctic Cloud Experiment (M-PACE), which occurred at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program's Climate Research Facility in Barrow, Alaska, during autumn 2004, we review the current status of ground-based observation and retrieval methods used in characterizing the macrophysical, microphysical, radiative, and dynamical properties of stratiform mixed-phase clouds. In...