Introduction

1. Further development of a parcel model for modeling studies of aerosol-cloud interactions Trude Eidhammer*, Paul J. DeMott, Sonia M. Kreidenweis Department of Atmospheric Science, Colorado State University Susceptibility of cirrus cloud properties to heterogeneous nucleation “Warm” temperature simulation (T > -40°C; no homogeneous nucleation) Nucleation of ice from different types of IN was simulated for 2 different initial atmospheric conditions as shown below. The updraft for both conditions was 50 cm/s and the initial pressure was 800 hPa. The number concentrations were Ns = 500 cm-3 and NINS= 1.00 cm-3 (typical background distribution (see figure 1)). Only growth by vapor condensation or deposition is considered here, thus any simulations here can only represent initial cloud formation. For each of the 2 different atmospheric conditions, 6 simulations were conducted: 1 KC04: Contact angle of 60° (typical for quartz); red curves 3 DW04: Bacteria, dust (montmorillonite) and soot; green curves 2 PDA07: Dust and soot; blue curves • Introduction • Ice particles in the atmosphere can form either by freezing of pure liquid drops (homogeneous ice nucleation), or by water freezing on insoluble aerosols (heterogeneous ice nucleation). Several different types of insoluble aerosols may act as ice nuclei (IN) for heterogeneous freezing. Depending on the different types, and number concentrations of particles that are present in the atmosphere, ice is nucleated at different temperatures, supersaturations, and rates. Data from laboratory and field studies will continue to inform quantifying such information for use in modeling studies. The main objective here is to study aerosol-cloud interactions at temperatures below -0°C with a parcel model that can serve as a basis for developing larger scale model parameterizations. Several different types of parameterizations for ice nucleation have been proposed for use in models. Here we compare 3 different heterogeneous ice nucleation parameterizations in a parcel model. The parameterizations all have in common that they take into account different IN compositions. • The parcel model • An existing “warm temperature” Lagrangian parcel model, originally developed by Feingold and Heymsfield (1992), has been modified to include ice nucleation. In addition, the treatment of water activity (aw) with a single parameter (κ) (Petters and Kreidenweis, (2007)) is implemented. Water activity is now only a function of: • Aerosol dry volume (Vd) • Haze/droplet size (Vw) • Kappa (κ), which is composition dependent • The use of κallows for easy change of input aerosol types. Further, it also allows for easy implementation of internally or externally mixed aerosols, and fairly complex aerosol types can be assumed without having to determine the physiochemical constants. • Growth by ice is treated in a Lagrangian hybrid framework, where bin values grow with ice size until neighboring bins get similar in size. At this point, the bins are merged and given a new average size. • Only growth by vapor condensation or deposition is included in the parcel model. • Ice nucleation • Homogeneous nucleation rates are calculated according to Koop et al., (2000). • The 3 different heterogeneous nucleation parameterizations that are implemented: • Based on theory: Khvorostyanov and Curry (2004) [KC04]. Different types of IN can be accounted for by varying contact angle and number of active sites. • Based of laboratory studies: Diehl and Wurzler (2004) [DW04]. IN types included: Soot from kerosene; mineral particles (kaolinite, montmorillonite and illite); biological particles (pollen, leaf litter and bacteria). • Based on field work, constrained by laboratory studies: Phillips, DeMott and Andronache (2007) [PDA07]. IN types included: Dust, black carbon and organics. • Here we compare the 3 parameterization schemes for several different atmospheric conditions (temperature, updrafts and initial IN concentration). • Initial size distributions and bin structure • For all simulations, we use 2 size distributions. One with only soluble aerosols (NS) (ammonium sulfate, κ=0.61) with mean radius μS=0.02 μm and geometric width σS=2.3. The second distribution (NINS) contains internally mixed soluble and insoluble aerosols (0.5 mass fraction). Here μINS=0.4 μm and σINS=2.0. • We assume some soluble particles are always present (NS), and compete with ice crystals for vapor during the simulations. • The two size distributions are both divided into 50 bins (100 size bins total) and the spacing between bins is equal in log space. Cirrus clouds are typically formed by homogeneous nucleation of haze particles. However, when insoluble aerosols are present, heterogeneous nucleation can start at higher temperature and lower ice saturation ratio than for homogeneous nucleation. The onset of heterogeneous nucleation might curb homogeneous nucleation due to depletion of vapor when the ice crystals grow. In this case, the number concentration of ice crystals can be suppressed (negative Twomey effect) while the ice particles grow to larger sizes, changing the radiation characteristics of the cloud. Simulations were conducted here with several different updrafts and initial NINS concentrations. Both homogeneous and heterogeneous nucleation were allowed to proceed, to simulate the susceptibility ice crystal concentrations in cirrus clouds to the presence of ice nuclei. Only dust simulations are shown here. Initial conditions: T = - 40 °C, P = 340 hPa, RH = 68% and Ns = 200 cm-3. Figure 4 Ice crystal concentrations produced in an adiabatic updraft, allowing for simultaneous heterogeneous and homogeneous nucleation. Simulations are conducted for different updraft velocities and initial insoluble particle concentration (NINS). Left plot shows the total ice crystal concentration. The middle plot shows the difference (in %) between a case when only homogeneous nucleation is allowed, and a case where both homogeneous and heterogeneous nucleation is allowed. Right plot shows the regions where homogeneous or heterogeneous nucleation dominates. T = 10 °C, RH = 89 % T = -14 °C, RH = 85 % Details for selected points: Figure 2 Ice crystal nucleation simulation for 2 different initial temperatures (left: 10°C, right: -14°C). Blue curves represent the parameterization by PDA07. Solid curves are dust and dotted curves are soot. Green curves represents parameterization by DW04 where solid line is dust, dotted line is soot and dashed line is bacteria. Red curves represent parameterization by KC04 for a contact angle of 60° (quartz). Black curves in saturation ratio, drop concentration and water mixing ratio plots indicate that the 3 parameterizations start with the same values, while the colored curves show where the simulations deviate. Figure 5 Time/height evolution of ice crystal concentration (top plots), ice saturation ratio (middle plots) and average ice crystal radius (bottom plots) for 5 selected updrafts and initial insoluble aerosol concentration (NINS). Each parameterization, and the pure homogeneous case are indicated with different colors (as labeled in the legend). Each different case points to the same case in Figure 4 for KC04 simulation. However, the colored circles indicate where the respective cases are for the PDA07 and DW04 simulations. Measured IN concentration from field campaigns References DeMott, P. J., Cziczo, D. J., Prenni, A. J., Murphy, D. M., Kreidenweis, S. M., Thomson, D. S., Borys, R., and Rogers, D. C.: Measurements of the concentration and composition of nuclei for cirrus formation, P. Natl. Acad. Sci. USA, 100, 14 655–14 660, 2003. Diehl, K., and S. Wurzler, Heterogeneous drop freezing in the immersion mode: Model calculations considering soluble and insoluble particles in the drops, J. Atmos. Sci., 61 (16), 2063-2072, 2004. Feingold, G., and A.J. Heymsfield, Parameterizations of Condensational Growth of Droplets for Use in General-Circulation Models, J. Atmos. Sci., 49 (23), 2325-2342, 1992. Khvorostyanov, V.I., and J.A. Curry, The theory of ice nucleation by heterogeneous freezing of deliquescent mixed CCN. Part I: Critical radius, energy, and nucleation rate, J. Atmos. Sci. , 61 (22), 2676-2691, 2004. Koop, T., B.P. Luo, A. Tsias, and T. Peter, Water activity as the determinant for homogeneous ice nucleation in aqueous solutions, Nature, 406 (6796), 611-614, 2000. Marcolli, C., S. Gedamke, T. Peter, and B Zobrist, Efficiency of immersion mode ice nucleation on surrogates of mineral dust, Atmos. Chem. Phys. Disc., 7, 9687-9716, 2007. Petters, M.D., and S.M. Kreidenweis, A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. and Phys., 7, 1961-1971, 2007. Phillips, V.T.J., P. J. DeMott. and C. Andronache, An empirical parameterization of heterogeneous ice nucleation for multiple chemical species of aerosol, J. Atmos. Sci., In review, 2007. Prenni, A. J., Harrington, J. Y., Tjernstrm, M., DeMott, P. J., Avramov, A., Long, C. N., Kreidenweis, S. M., Olsson, P. Q., and Verlinde, J.: Can Ice-Nucleating Aerosols Affect Arctic Seasonal Climate?, B. Am. Meteorol. Soc., 88(4), 541–550, 2007. Richardson, M. S., DeMott, P. J., Kreidenweis, S. M., Cziczo, D. J., Dunlea, E., Jimenez, J. L., Thompson, D. S., Ashbaugh, L. L., Borys, R. D., Westphal, D. S., Cassucio, G. S., and Lersch, T. L.: Measurements of heterogeneous ice nuclei in the Western U.S. in springtime and their relation to aerosol characteristics, J. Geophys. Res., 112, D02209, doi:10.1029/2006JD007500, 2007. Rogers, D. C. and DeMott, P. J.: Measurements of natural ice nuclei, CCN, and CN in winter clouds, in AMS Conference on Cloud Physics, pp. 139–144, Dallas, TX, 1995. Rogers, D. C., P. J. DeMott, and S. M. Kreidenweis, 2001: Airborne measurements of tropospheric ice nucleating aerosol particles in the Arctic Spring, J. Geophys. Res., 106, 15,053-15,063. Summary The 3 ice nucleation parameterizations show large differences in the number concentrations of ice crystals formed, and in the conditions for onset of ice nucleation. For the “warm temperature” simulations, the initial insoluble/soluble aerosol loading was chosen to represent a “background” (relatively clean) case (NINS=1 cm-3). Whereas measurements show IN concentrations up to ~10 L-1 under such conditions (see figure 3), KC04 and DW04 simulations estimate ice concentrations close to 1000 L-1. The predictions from DPA07 are more in line with the measured concentrations due to constraint by both IN and aerosol concentrations. There are also large differences in the cirrus simulation. The KC04 parameterization predicts that heterogeneous nucleation dominates over homogeneous nucleation at even low NINS concentrations. The Twomey effect in this case is negative for low NINS concentration and becomes positive as the NINS increases. The DW04 simulation predicts that the Twomey effect occurs at higher NINS, but is always negative. The PDA07 simulation requires low updrafts and high NINS for heterogeneous nucleation to have any effect on ice crystal concentration. Future work Put results and findings with the parcel model into workable form for regional scale cloud models, including translating particle size classes into variables usable in the MMF. Conduct simulations of parcels in Arctic clouds along trajectories from an LES simulation. New research (Marcolli et al, 2007) shows that when it is assumed that each IN has a distribution of contact angles, nucleation rates decreases. We will include this idea into the KC04 framework to see if theory can more faithfully represent IN measurements. Test new parameterizations based on existing, and recently obtained, data from several different field campaigns, including the data shown in Figure 3. Figure 3 Ice nuclei concentration in the free atmosphere from field campaigns in the mid-latitude spring (◊) (Richardson et al., 2007), mid-latitude fall (♦) (DeMott et al., 2003), mid-latitude winter (○) Rogers and DeMott (1995), Arctic spring (□) (Rogers at al., 2001), Arctic fall (■) (Prenni et al., (2007) and two studies with measurement made in conditions of high mineral dust loading. Data were obtained primarily with a continuous flow diffusion chamber. Figure 1 Size distributions used in simulations presented here. For the soluble distribution (black curves), two different cases are used where number concentration NS = 500 and 200 cm-3. For the mixture of insoluble and soluble aerosols (colored curves), several different number concentrations are used from 1·10-4to 10 cm-3. Acknowledgements This work has been supported by the National Science Foundation Science and Technology Center for Multi-Scale Modeling of Atmospheric Processes, managed by Colorado State University under cooperative agreement No. ATM-0425247, and by the NASA MAP (Modeling and Analysis Program) No. NNG06GB60G *For further information contact: trude@atmos.colostate.edu