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Kara J. Sulia, Jerry Y. Harrington, and Hugh Morrison

Abstract

Arctic mixed-phase clouds are ubiquitous, and the persistence of supercooled liquid is not well understood. Prior studies of mixed-phase clouds predict a single axis length assuming spherical particles or mass–dimensional relationships derived from in situ data. These methods cannot mechanistically evolve particle shape, leading to inaccuracies in estimates of mixed-phase lifetime. Parts I and II of this study report on the development and parcel model testing of an adaptive habit parameterization that predicts two bulk crystal lengths. The method is implemented into a two-dimensional kinematic model in which the dynamic flow field is prescribed, allowing for sedimentation and separate advection of length mixing ratios.

Similar to other studies, results show that mass–dimensional relationships produce large variation of phase, despite similar choice in particle type. Results with evolving ice habit promote phase maintenance in cases where mass–dimensional methods glaciate the layers. Adaptive habit simulations with sedimentation increase cloud lifetime at higher ice concentrations but can also lead to lower liquid amounts. Radiative cooling initially increases ice growth with a subsequent enhanced sedimentation flux, altering cloud-phase partitioning dependent on ice concentration. Surface latent and sensible heat fluxes of 50 W m−2 result in an increase in overall water mass, while compensating fluxes establish sufficient energy and mass amounts for liquid and ice maintenance. These studies provide insight into the fluxes that may be necessary for mixed-phase cloud maintenance.

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Kara J. Sulia, Hugh Morrison, and Jerry Y. Harrington

Abstract

A bulk microphysics scheme predicting ice particle habit evolution has been implemented in the Weather Research and Forecasting Model. Large-eddy simulations are analyzed to study the effects of ice habit and number concentration on the bulk ice and liquid masses, dynamics, and lifetime of Arctic mixed-phase boundary layer clouds. The microphysical and dynamical evolution simulated using the adaptive habit scheme is compared with that assuming spherical particles with a density of bulk ice or a reduced density and with mass–dimensional parameterizations. It is found that the adaptive habit method returns an increased (decreased) ice (liquid) mass as compared to spheres and provides a more accurate simulation as compared to dendrite mass–size relations.

Using the adaptive habit method, simulations are then completed to understand the microphysical and dynamical interactions within a single-layer mixed-phase stratocumulus cloud observed during flight 31 of the Indirect and Semi-Direct Aerosol Campaign. With cloud-top longwave radiative cooling as a function of liquid mass acting as the primary dynamic driver of turbulent eddies within these clouds, the consumption of liquid at the expense of ice growth and subsequent sedimentation holds a strong control on the cloud lifetime. Ice concentrations ≥ 4 L−1 collapse the liquid layer without any external maintaining sources. Layer maintenance is possible at 4 L−1 when a constant cloud-top cooling rate or the water mass lost due to sedimentation is supplied. Larger concentrations require a more substantial source of latent or sensible heat for mixed-phase persistence.

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Hugh Morrison, Mikael Witte, George H. Bryan, Jerry Y. Harrington, and Zachary J. Lebo

Abstract

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.

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Edwin L. Dunnavan, Zhiyuan Jiang, Jerry Y. Harrington, Johannes Verlinde, Kyle Fitch, and Timothy J. Garrett

Abstract

Snow aggregates evolve into a variety of observed shapes and densities. Despite this diversity, models and observational studies employ fractal or Euclidean geometric measures that are assumed universal for all aggregates. This work therefore seeks to improve understanding and representation of snow aggregate geometry and its evolution by characterizing distributions of both observed and Monte Carlo–generated aggregates. Two separate datasets of best-fit ellipsoid estimates derived from Multi-Angle Snowflake Camera (MASC) observations suggest the use of a bivariate beta distribution model for capturing aggregate shapes. Product moments of this model capture shape effects to within 4% of observations. This mathematical model is used along with Monte Carlo simulated aggregates to study how combinations of monomer properties affect aggregate shape evolution. Plate aggregates of any aspect ratio produce a consistent ellipsoid shape evolution whereas thin column aggregates evolve to become more spherical. Thin column aggregates yield fractal dimensions much less than the often-assumed value of 2.0. Ellipsoid densities and fractal analogs of density (lacunarity) are much more variable depending on combinations of monomer size and shape. Simple mathematical scaling relationships can explain the persistent triaxial ellipsoid shapes that appear in both observed and modeled aggregates. Overall, both simulations and observations prove aggregates are rarely oblate. Therefore, the use of the proposed bivariate ellipsoid distribution in models will allow for similar-sized aggregates to exhibit a realistic dispersion of masses and fall speeds.

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Lianet Hernández Pardo, Hugh Morrison, Luiz A. T. Machado, Jerry Y. Harrington, and Zachary J. Lebo

Abstract

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

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Anthony J. Prenni, Jerry Y. Harrington, Michael Tjernström, Paul J. DeMott, Alexander Avramov, Charles N. Long, Sonia M. Kreidenweis, Peter Q. Olsson, and Johannes Verlinde

Mixed-phase stratus clouds are ubiquitous in the Arctic and play an important role in climate in this region. However, climate and regional models have generally proven unsuccessful at simulating Arctic cloudiness, particularly during the colder months. Specifically, models tend to underpredict the amount of liquid water in mixed-phase clouds. The Mixed-Phase Arctic Cloud Experiments (M-PACE), conducted from late September through October 2004 in the vicinity of the Department of Energy's Atmospheric Radiation Measurement (ARM) North Slope of Alaska field site, focused on characterizing low-level Arctic stratus clouds. Ice nuclei (IN) measurements were made using a continuous-flow ice thermal diffusion chamber aboard the University of North Dakota's Citation II aircraft. These measurements indicated IN concentrations that were significantly lower than those used in many models. Using the Regional Atmospheric Modeling System (RAMS), we show that these low IN concentrations, as well as inadequate parameterizations of the depletion of IN through nucleation scavenging, may be partially responsible for the poor model predictions. Moreover, we show that this can lead to errors in the modeled surface radiative energy budget of 10–100 Wm2. Finally, using the measured IN concentrations as input to RAMS and comparing to a mixed-phase cloud observed during M-PACE, we show excellent agreement between modeled and observed liquid water content and net infrared surface flux.

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J. Verlinde, J. Y. Harrington, G. M. McFarquhar, V. T. Yannuzzi, A. Avramov, S. Greenberg, N. Johnson, G. Zhang, M. R. Poellot, J. H. Mather, D. D. Turner, E. W. Eloranta, B. D. Zak, A. J. Prenni, J. S. Daniel, G. L. Kok, D. C. Tobin, R. Holz, K. Sassen, D. Spangenberg, P. Minnis, T. P. Tooman, M. D. Ivey, S. J. Richardson, C. P. Bahrmann, M. Shupe, P. J. DeMott, A. J. Heymsfield, and R. Schofield

The Mixed-Phase Arctic Cloud Experiment (M-PACE) was conducted from 27 September through 22 October 2004 over the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) on the North Slope of Alaska. The primary objectives were to collect a dataset suitable to study interactions between microphysics, dynamics, and radiative transfer in mixed-phase Arctic clouds, and to develop/evaluate cloud property retrievals from surface-and satellite-based remote sensing instruments. Observations taken during the 1977/98 Surface Heat and Energy Budget of the Arctic (SHEBA) experiment revealed that Arctic clouds frequently consist of one (or more) liquid layers precipitating ice. M-PACE sought to investigate the physical processes of these clouds by utilizing two aircraft (an in situ aircraft to characterize the microphysical properties of the clouds and a remote sensing aircraft to constraint the upwelling radiation) over the ACRF site on the North Slope of Alaska. The measurements successfully documented the microphysical structure of Arctic mixed-phase clouds, with multiple in situ profiles collected in both single- and multilayer clouds over two ground-based remote sensing sites. Liquid was found in clouds with cloud-top temperatures as cold as −30°C, with the coldest cloud-top temperature warmer than −40°C sampled by the aircraft. Remote sensing instruments suggest that ice was present in low concentrations, mostly concentrated in precipitation shafts, although there are indications of light ice precipitation present below the optically thick single-layer clouds. The prevalence of liquid down to these low temperatures potentially could be explained by the relatively low measured ice nuclei concentrations.

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