Proposal for DE-PS02-08ER08-23

Project Title: A Coordinated Effort to Improve Parameterization ofHigh-Latitude Cloud and Radiation Processes

Principal Investigator: Judith A. Curry

PI Organization: School of Earth and Atmospheric SciencesGeorgia Institute of TechnologyAtlanta, GA 30332-0340Phone: 404 894 3948 Fax: 404 894 5638Email: curryja@eas.gatech.edu

Co-Principal Investigators: V.I. Khvorostyanov, Central Aerological Observatory, RussiaHenian Zhang, Georgia Institute of Technology

Collaborator: H. Morrison, National Center for Atmospheric Research

1. Introduction

The goal of the proposed project is the development, evaluation and implementation of improved parameterization of cloud and radiation processes into climate models, with a focus on arctic clouds and their interactions with aerosols, towards clarifying the aerosol indirect radiative effect in the Arctic.

A comprehensive review of our understanding of Arctic clouds and radiation prior to ARM measurements in the Arctic has been prepared by Curry et al. (1996) for the special ARM issue in the Journal of Climate. Initial results from ARM, SHEBA, and ARM NSA are summarized in Curry et al. (2000), Uttal et al. (2002), Curry et al. (2002), and in the FIRE.ACE Special Section in the Journal of Geophysical Research (July 27, 2001). Measurements from the ARM site at Barrow are providing the foundation for understanding and modeling Arctic cloud and radiation processes, in particular the most recent IOPs: MPACE (Verlinde et al. 2007) and ISDAC. An issue of increasing focus of IOPs in the Arctic is the aerosol indirect radiative effect.

Curry (1995) provided an overview of arctic aerosols, their impact on climate, and their involvement in local and global climate feedbacks. Local aerosol sources in the Arctic include volcanoes (from Iceland, Kamchatka, Alaska), biomass burning (e.g., Browell et al. 1992; Polissar et al. 1998), resource extraction activities (including Prudhoe Bay), local burning of fossil fuels, sea salt, and biogenic activity (e.g., Schnell 1977; Talbot et al. 1992; Ferek et al. 1995). Layers of elevated aerosol concentration often indicate long-range transport from lower latitudes. Widespread intrusions of polluted air occur in winter and spring, with enhanced levels of carbonaceous and dust particle mass loadings (e.g., Radke et al. 1984; Schnell 1987; Barrie 1986; Brock et al. 1989; Quinn et al. 2005; Koch and Hansen 2005; Garrett and Versella 2008). This “Arctic Haze” aerosol has decreased since 1989 (Sharma et al. 2004; Garrett and Versella 2008), possibly owing to a reduction in emissions from the former USSR. Desert dust particles transported from the Takla Makan and Gobi deserts has greatly contributed to high number concentration in the arctic haze during early spring (e.g., Rahn et al. 1977 , Wetzel et al., 2003, Darmenova et al., 2005). Recent lidar and aircraft observations demonstrated that Asian dust and biomass burning aerosols transported to the Arctic exhibit a complex multilayered vertical distribution with some layers present up to altitudes of at least 8 km (Treffeisen et al. 2004; Sassen 2006; Sassen and Khvorostyanov 2008).

Aerosol from pollution sources, biomass burning, and desert dust can contribute to the concentrations of both cloud condensation nuclei (CCN) and ice nuclei (IN) and hence their impact on radiative fluxes and precipitation processes can be complex. The relevant aerosol characteristics include the number concentration, size distribution, chemical composition, and vertical distribution. Aerosol completely or partially composed of water soluble material (e.g., NaCl and,sulfates) can act as CCN when environment condition is favorable. Increasing CCN concentration in low-level arctic stratus clouds results in higher cloud droplet concentrations and smaller droplet sizes relative to clouds forming under more pristine conditions (Garret et al. 2004). The observations during June 1980 of cloud drop concentrations exceeding 200 cm -3 in summertime Arctic stratus (Curry 1986), which are much higher than the concentrations < 100 cm-3 subsequently observed in Arctic stratus clouds (e.g., Hobbs and Rangno 1998), are now believed to be associated with the eruption of Mt. St. Helens that occurred in spring and summer, 1980. Clouds with larger droplet concentration and smaller droplet sizes reflect more solar radiation, emit more longwave radiation, and are less likely to form precipitation-size drops. Insoluble aerosol (some mineral dust species, carbonaceous soot, and biogenic aerosols) may act as ice-forming nuclei (IN) (e.g., Demott et al. 2003; Twohy and Poellot 2005; Field et al. 2006).

Increasing the concentration of IN will tend to change the phase of clouds at temperatures below the freezing point from liquid to ice, resulting in enhanced precipitation and decreased shortwave reflectivity and longwave emissivity of the clouds.

Clouds observed in the Arctic may be broadly divided into three categories (e.g., Curry et al. 1996): 1) mid- and upper-level or vertically-extensive clouds associated with synoptic disturbances, 2) persistent low-level clouds occurring under weak synoptic forcing (including surface-induced clouds due to open water), and 3) summertime convective clouds over continental interior regions. In regions with significant topographic features, orographic lifting is presumably also an important cloud formation mechanism. Although the precipitation associated with weakly-forced clouds is generally light compared to the synoptically-driven and convective cloud systems, it can contribute significantly to the annual accumulated precipitation due to the long duration and frequent occurrence of these clouds. Aerosols are expected to play a role in all arctic cloud types. However, aerosols may have a particularly strong impact on weakly-forced clouds, since these clouds represent a delicate balance between the cloud microphysics, turbulence, surface fluxes, and/or radiative transfer (Curry 1986; Pinto 1998, Harrington et al. 1999; Jiang et al. 2000; Morrison and Pinto 2005a). Weakly forced low-level arctic clouds are typically mixed-phase (even at cloud temperatures < -25° C) with continuous light snow or snow showers falling to the surface (Hobbs and Rangno 1998; Pinto et al. 2001; Curry et al. 2000; Intrieri et al. 2002; Rangno and Hobbs 2001). During the brief summer season, liquid-phase stratus (often multi-layer) with frequent drizzle dominate the cloud fraction (e.g., Herman and Curry 1984; Curry and Herman 1985). The number concentration of CCN (and hence aerosols) may play an important role in initiating precipitation in both warm and cold arctic clouds (e.g., Curry et al. 2000; Rangno and Hobbs 2001).

The availability of IN is apparently a critical factor in the persistence of weakly forced arctic mixed-phase clouds and their precipitation efficiency (Pinto 1998; Harrington et al. 1999; Jiang et al. 2000; Morrison et al. 2005b; Morrison and Pinto 2005a,b). The general lack of in-situ sources of IN (Bigg 1996) and depletion of IN within cloud layers may play a key role in the longevity and evolution of these clouds (Pinto 1998; Harrington et al. 1999; Zuidema 2005; Morrison and Pinto 2005a). Morrison and Pinto (2005a) found that increased concentration of IN led to decreased precipitation rates in arctic mixed-phase stratus due to rapid depletion of liquid water, while Lohmann (2002a,b) found that increased IN led to rapid glaciation and more overall precipitation in a general circulation model. Lohmann et al. (2003) present evidence that increasing anthropogenic aerosols may actually increase precipitation in the Arctic by modifying droplet size, contrary to mid-latitude studies that show increased pollution may decrease snowfall by limiting riming (e.g., Borys et al. 2000; 2003). These studies suggest the importance of aerosols in modifying precipitation but also highlight the uncertainties in the myrad of microphysical processes in cold clouds.

With increased computing power, physical parameterizations in atmospheric models are becoming increasingly complex. Virtually all climate models currently use a prognostic bulk microphysical parameterization, whereby the mixing ratio of one or more cloud particle types are predicted (e.g. Fowler et al., 1996; Rotstayn, 1997; Lohmann et al. 2001; Del Genio et al. 2002). A recent improvement in bulk schemes has been the prediction of two moments of the hydrometeor size spectra, i.e. the number concentration in addition to the mixing ratio of the species (e.g., Levkov et al. 1992; Ferrier 1994; Harrington et al. 1995; Meyers et al. 1997; Morrison et al. 2004a), increasing the degrees of freedom associated with the particle spectra and improving calculations of the microphysical processes and radiative transfer. Such parameterizations are now included in mesoscale models (e.g. Morrison and Pinto 2006; Cotton et al. 2003) and increasingly in climate models (e.g. Gettleman et al. 2008), and a few models are

now including interactive aerosols (e.g. Stiers et al. 2005; Grell et al., 2005). In spite of this increasing complexity, deficiencies in even the most sophisticated of these microphysics schemes remain:

cloud phase diagnosed by temperature diagnostic ice nuclei or ice crystal concentration determined as a function of either the

temperature or ice supersaturation instantaneous relaxation of supersaturation with respect to ice in ice clouds assumption of homogeneous ice crystal nucleation at temperatures below –40oC, or

neglect of homogeneous freezing of haze aerosols altogether. monodisperse cloud droplet and ice size distributions lack of explicit interactions between clouds and aerosols

As a result of these simplifying assumptions, climate models using current bulk microphysical formulations may have some or all of the following deficiencies:

no ice cloud supersaturation, contrary to observed supersaturations as high as 80% ice water contents that may be an order of magnitude too high incorrect cloud phase inability to sustain mixed phase clouds inability of clouds to respond appropriately to changes in aerosol characteristics

These deficiencies result in incorrect cloud properties and hence incorrect simulations of radiative transfer and precipitation and cloud feedbacks within the climate system. These deficiencies have led several investigators to attempt incorporating an explicit bin resolving microphysics scheme into a mesoscale model (e.g. Lynn et al. 2005) and Japanese scientists have plans to incorporate a bin-resolving scheme into a global climate model (to be run on the Earth Simulator). The computational expense of a bin resolving microphysics scheme is substantial. Further, while the explicit scheme inherently improves simulation of particle size spectra and allows for explicit interaction with aerosol, it does not necessarily improve problems related to droplet and ice nucleation and supersaturation. We hypothesize here that that a combination of theoretical and numerically-based improvements to dual moment bulk microphysics schemes can be developed that produce a bulk microphysics model that is just as effective as a bin resolving microphysics scheme in simulating cloud properties and processes in cloud and climate models and can fully capture the needed interactions between the clouds and aerosols.

Towards improving the model simulation of ice and mixed phase clouds in the Arctic as well as in other regions, we propose a comprehensive program to develop, evaluate and implement new cloud microphysical parameterizations in the following overall framework: Use theory, laboratory experiments, and field measurements to develop and evaluate

parameterization elements Use process models (e.g. bin-resolving microphysics model, parcel model) to develop

parameterizations suitable for the bulk microphysics model Incorporate new parameterization elements into a single column model and a cloud resolving

model Force and evaluate the models using ARM data (focusing on IOPs such as MPACE, ISDAC) Conduct sensitivity studies to see if the complexity of the new parameterization is necessary

and sufficient Quantify the impact of parameterization improvements and variations in aerosol

characteristics on simulated arctic cloud and radiation processes. Develop numerical schemes to adapt the parameterizations to the coarser space/time

resolutions used in climate models

Incorporate the new microphysical parameterizations into the WRF mesoscale model evaluate using A-train satellite data.

Work with collaborators using the NCAR CCSM and NASA GISS models to incorporate these parameterizations into climate models

The specific parameterization elements that comprise the focus of this research are: temperature dependence of droplet activation a unified parameterization for ice nucleation that includes homogeneous nucleation and 4

different modes of heterogeneous nucleation threshold humidity for onset of ice nucleation ice crystal size spectra and integral fall speeds particle collection and aggregation

We will assess the impact of our new microphysical parameterizations on the aerosol indirect effect. The same analytical equations used to develop the microphysical parameterization elements will be used to derive new analytical relationships relating aerosol characteristics to droplet and ice crystal concentrations and to optical depth. These relationships will be evaluated using ARM data and through sensitivity simulations conducted using a single column model and the WRF model.

BER long-term measure requirement. This project will use ARM data, primarily from the NSA locale but also from recent IOPs in other regions, to improve the parameterization of cloud microphysical processes in climate model and to evaluate these parameterizations. Parameterization of cloud microphysical processes is essential to simulating cloud, precipitation, and radiation processes and is hence essential for accurate simulations of greenhouse gas induced warming.

3. Modeling tools

A hierarchy of models are used in this study, both to develop and evaluate the parameterizations. A bin-resolving explicit microphysics scheme incorporated into a parcel model is a central tool for developing microphysical parameterization elements. A bulk microphysics parameterization provides the framework for integrating the parameterization elements for incorporation into climate models. The parameterization elements are evaluated sequentially in a single column model, cloud resolving model, and mesoscale model.

a) Bin resolving explicit microphysics model Explicit microphysics models have been the primary tool for developing microphysics parameterizations. Explicit microphysics models developed in the Former Soviet Union have evolved in a different framework from those developed in the U.S. In the 1960’s, Buikov introduced a method of kinetic equations that treats growth and evaporation of the polydisperse ensemble of the drops or crystals in a continuous regime by introducing a new term in the equation that describes “advection” in the space of radii that is proportional to the vapor supersaturation. In combination with equations for supersaturation and temperature, a closed system of equations was developed that included kinetic equations for the droplet and crystal size spectra that included also coagulation terms. The explicit microphysics model developed by Khvorostyanov (see Khvorostyanov 1995 for a summary) is used in our research. Recently, this model has also been used and evaluated in the framework of NASA SUCCESS (Sassen and Khvorostyanov, 1998), FIRE-ACE (Khvorostyanov et al. 2001, 2003), CloudSat, CRYSTAL-FACE (Khvorostyanov et al. 2006). This model has also been evaluated in the context of the

Idealized Cirrus Models Comparison Project Models (ICMCP; Starr et al., 2000), and the methods developed in the previous ARM projects were validated by comparison with the data from Cirrus Parcel Model Comparison Project (CPMCP; Lin et al., 2002) as described in Khvorostyanov and Curry (2004b, 2005).

The model includes explicit microphysics for both liquid and crystalline clouds. The two key elements of the model are: 1) two kinetic equations for the droplet and crystal size spectra that account for the particles growth by diffusion and accretion, and 2) supersaturation equation. The numerical realization of the model was recently modified, so that the numbers of the horizontal and vertical grid points can be varied as well as the number of bins in spectral representation of the size spectra. In the configuration most frequently used in the current projects, the model accounts for 30 size classes from 0.1 m to 3.5 m for each of the droplet and crystal size spectra, each class with its own terminal velocity, growth/evaporation rate, rates of nucleation, freezing, and melting. The model is under continued development; recently new theories were developed for stochastic condensation in the turbulent atmosphere (Khvorostyanov and Curry, 1999a,b; 2008a,b), for aerosol size spectra and CCN activation (Khvorostyanov and Curry, 1999c,d; 2006, 2007, 2008c), for homogeneous and heterogeneous ice nucleation (Khvorostyanov and Curry 2004b, 2005), and a newer thermodynamic theory of freezing and melting (Khvorostyanov and Curry 2004a). Planned modifications to the explicit bin microphysics model include: incorporation of the kinetic corrections to the droplet and crystal growth rates caused by the

presence of surfactants at their surfaces incorporation of the new ice nucleation schemes for the main nucleation modes

(deliquescence-freezing, immersion, contact) developed in this project inclusion of additional cross-derivative terms needed to account for the effects of stochastic

condensation.

b) Bulk microphysics scheme

Based upon our previous research (ARM, SHEBA, FIRE.ACE) over the past decade on modeling arctic clouds, we have developed a sophisticated new double-moment bulk cloud microphysics module (Morrison et al. 2003, 2005a and Morrison and Pinto 2005a) that allows for explicit interactions with aerosol concentration, size distribution, and composition. This scheme has been used and evaluated extensively for simulating arctic (Morrison et al. 2003, 2005b, Morrison and Pinto 2005, 2006) and lower latitude (Khvorostyanov et al. 2005) cloud regimes. It predicts the number concentrations and mixing ratios of up to five species (cloud ice, droplets, rain, snow and graupel) and includes numerous microphysical processes. The scheme is distinguished from other bulk schemes by the inclusion of several recently-developed, physically-based parameterizations developed by Khvorostyanov and Curry (1999a, 1999b, 1999c, 1999d, 2000, 2002, 2004, 2005a, 2005b) including detailed treatments of droplet and ice nucleation from a spectrum of aerosol particles. In particular, the new parameterization of heterogeneous ice crystal nucleation (Khvorostyanov and Curry 2004, 2005a) for the first time allows both a simultaneous functional dependence on supercooling temperature and supersaturation, also allowing for ice particle nucleation at water subsaturation. Further, the treatment of droplet and ice crystal diffusional growth allows for kinetic effects that produces slower ice crystal growth and allows for the observed ice supersaturation in crystalline clouds. With conventional nucleation and diffusional growth parameterizations, ice nucleation was observed to proceed too quickly, mixed phase clouds could not be sustained, the clouds were too thick and altered the radiative fluxes too much, and precipitation formed too quickly. While aerosols directly influence clouds through nucleation, nucleation processes in turn determine droplet and ice crystal number concentrations and sizes that impact precipitation development. Cloud particle size impacts precipitation

processes through several mechanisms in the model, including warm-rain coalescence, crystal aggregation, and riming by ice particles (including a droplet size-dependent collection efficiency). Since the scheme includes a detailed treatment of both droplet and ice nucleation and warm and cold precipitation processes, it can simulate the effects of variations in the aerosol chemical and physical properties on liquid-, mixed-, and ice-phase clouds that all commonly occur in the Arctic throughout the year.

c) Cloud resolving model

The cloud resolving model with explicit microphysics used in this study has been under development for more than 30 years by V. Khvorostyanov and has been applied to a variety of cloud types (boundary layer St, multi-layered orographic cloud systems, deep convective Cu-Cb, frontal stratiform St); (see reviews in Khvorostyanov et al. 1988, 1989; Khvorostyanov, 1995; Khvorostyanov and Curry, 1999a, b). The model can be configured as 1D/2D/3D with various resolutions. A 1D version was used for simulation of arctic mid- and upper-tropospheric clouds (Khvorostyanov et al., 2001) and tropical cirrus (Khvorostyanov et al., 2006), 2D version has been applied to cirrus clouds (Khvorostyanov and Sassen, 1998, 2002), and 3D version was used for stratocumulus in the arctic (Khvorostyanov et al., 2003).

The model contains six basic units: 1) dynamics (either hydrostatic or nonhydrostatic); 2) cloud microphysics (kinetic equations for the droplet and crystal size spectra that describe condensation and aggregation) 3) thermodynamics (temperature, humidity and supersaturation); 4) aerosols, including transport and deliquescence of the aerosol; 5) longwave and shortwave radiation; and 6) heat and moisture exchange with the underlying surface.

d) Mesoscale model

While the focus of our previous research (Morrison and Pinto 2005, 2006; Morrison et al. 2008) has been the MM5 mesoscale model, we have transitioned to the Advanced Research WRF (Weather Research and Forecasting) model Version 3.0 (released on April 04 2008) for this project, which is a community model developed and supported by the NCAR/MMM to replace the MM5. To better simulate aerosol/microphysical interactions, we have incorporated into WRF 3.0 the new double-moment bulk cloud microphysics scheme developed by Morrison et al. (2003, 2005a, and 2008) that allows for explicit interactions with aerosol concentration, size distribution, and composition. WRF 3.0 also includes an option for an interactive atmospheric chemistry module (WRF-chem; Fast et al., 2002; 2006). Of particular interest to this proposal is the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol model that employs the sectional approach for the size distribution and internal mixing (Zaveri et al., 2005a,b). Aerosol-radiation-cloud feedback processes, cloud chemistry, and cloud-aerosol interactions were incorporated into WRF-chem during 2005 and 2006. Optical properties are determined from a generalized driver that computes aerosol optical depth, single scattering albedo, and asymmetry parameter for MOSAIC using either volume averaging, Maxwell-Garnett, or shell-core mixing rules. Radiative forcing capabilities include direct, semi-direct, first indirect, and second indirect effects of aerosols. Cloud aerosol interactions are currently configured through coupling with the Lin et al. microphysics parameterization [Gustafson et al., 2007; Chapman et al., 2008] that include prognostic treatments of cloud droplet number and activated (cloud-phase) aerosol species, aerosol activation and resuspension, bulk cloud chemistry, and in-cloud and below-cloud wet removal of particulates and trace gases. The Morrison dual moment microphysics scheme has recently been made interactive with WRF-chem (H. Morrison, personal communication; see supplemental letter). We will be collaborating with Morrison to evaluate the microphysics package in WRF-chem and to improve it.

4. Parameterization development and evaluation

The following general framework will be used for developing parameterization elements:1. Theoretical analysis and simulations using an explicit bin resolving microphysics model to

improve simulation of specific cloud microphysical processes, and comparison with available laboratory measurements.

2. Simulations with the explicit microphysics model incorporated into a parcel model and single column model for the full range of cloud types and thermodynamic/dynamic conditions encountered in the atmosphere

3. Development of parameterizations of microphysical processes based upon the theoretical analyses and the statistics of the parcel and single column simulations.

Our primary guiding principles for new parameterization development is a strong physical basis in theory and also computational efficiency. An additional issue is to ensure that appropriate connections are made among the parameterization elements. To account for the coarser space/time resolution in mesoscale and climate models, numerical techniques must be adopted that preserve the essence of the solution in the higher resolution single column model, yet are computationally efficient. Techniques that we will use include time splitting, table look-ups, and curve-fitting techniques to fit solutions to high-resolution model simulations, and stochastic approximations to subgrid variability.

The following parameterization elements will be addressed in this research: temperature dependence of droplet activation a unified parameterization for ice nucleation that includes homogeneous nucleation

and 4 different modes of heterogeneous nucleation threshold humidity for onset of ice nucleation size spectra and integral fall speeds particle collection and aggregation

a) Droplet activation

Kinetics of cloud drop formation and its parameterization for cloud and climate models have been developed by Khvorostyanov and Curry (2008c). To study the kinetics of drop nucleation in clouds, a differential-integral equation for integral water supersaturation in cloud is derived and analyzed. Solving the supersaturation equation with an algebraic form of the CCN activity spectrum, analytical expressions are obtained for the time of CCN activation, the maximum supersaturation, and droplet concentration limited by the total aerosol concentration at high supersaturations. Each of these quantities are expressed as functions of the vertical velocity, and characteristics of the CCN size spectra: mean geometric radius, dispersion, and parameter of solubility. A generalized power law for the drop activation is formulated that is similar to the Twomey’s power law, but both the coefficient C(s) and index k(s) are functions of supersaturation that are expressed analytically in terms of vertical velocities and CCN microphysical parameters. An extended series of numerical experiments was performed using a simple and economical numeral solution, in which the dependencies of the activation time supersaturation, drop concentration fractions activated, and C(s) and k(s) were studied as functions of vertical velocity and physico-chemical properties of the aerosol. These solutions and expressions for the parameters presented here can be used for parameterization of the drop activation process in cloud and climate models.

Towards improving parameterization of cloud droplet activation in cloud and climate models, the general integro-differential equation for water supersaturation derived by Khvorostyanov and Curry (2008c) will be solved analytically for the algebraic size spectrum of the cloud condensation nuclei (CCN). Our preliminary estimates showed that the analytical solutions can be obtained for four limiting cases that are combinations of two different values of the updraft vertical velocity (small and large), and two different values of the condensation coefficient: pure cloud drops in the diffusion regime of droplet growth and polluted cloud drops in the kinetic growth regime. The characteristics of the CCN can vary within each limit. Thus, these four limits and interpolation among them would cover the vast majority of cloudy conditions. We propose to obtain analytical expressions for the major characteristics of drop activation: time of CCN activation, maximum supersaturation, and the concentration of activated droplets. Preliminary estimates show that these quantities should be the products of the power laws by 6 variables: CCN concentration, mean radius, soluble fraction, vertical velocities, surface tension, temperature, and condensation coefficient with coefficients that are functions of the temperature and depend on pressure. This will be a generalization of the Twomey’s (1959) empirical power laws used in cloud physics over 50 years without justification and will be derived now from the first principles. An advantage of this analytical method is that it does not require running parcel models, and the drops concentrations can be obtained from lookup tables or as simple interpolation among the limiting solutions for the instantaneous model parameters.

Our research on this topic will be extended to further explore the effects of surface tension and temperature on drop activation. The specific item of interest with regards to surface tension is the effects of surfactants and organics on the drop nucleation. A decrease in surface tension of deliquescent CCN should cause an increase in concentration of nucleated drops and lead to a noticeable increase in global albedo (Facchini et al. 1999, 2000). However, any extensive quantitative assessments of this effects under various conditions have not been done so far, this hampers reliable predictions of impact of impurities in CCN on climate variations. A powerful and effective method developed in the previous ARM project and based on semi-analytical solution of the integral supersaturation equation (Khvorostyanov and Curry 2008c) allows to isolate effects of various external factors (in particular, the effects of surface tension and temperature) and study each of them separately without running hundreds times parcel models as it is often done. We propose to perform quantitative evaluation of surface tension variations on the drop concentration in various updrafts (various cloud types), various size spectra and chemical composition of CCN.

Our analysis has identified a temperature dependence for drop activation. This dependence arises from the competition of the processes of supersaturation generation by updrafts and supersaturation absorption by the newly activated drops in the course of activation. With decreasing temperature, the rate of supersaturation absorption decreases, since the droplet growth rate is proportional to the vapor density. Therefore, the maximum supersaturation is reached later, and is higher, causing increasing temperature leads to decreasing concentrations of nucleated drops. This dependence was outlined in a recent work by Saleeby and Cotton (2004). We propose to study this temperature effect of drop activation in more detail, for various properties of CCN and updrafts. The importance of a quantitative assessment of this effect is its potential positive feedback for climate warming. As the climate warms, this decrease in drop concentration would cause a decrease in planetary albedo, contributing a positive feedback. Our preliminary estimates of the magnitude of the cloud temperature-albedo feedback indicate that it can be comparable in magnitude to the 1st and 2nd aerosol indirect effects.

b) Heterogeneous ice nucleation

There are considerable uncertainties in the nucleation of ice particles (for a review, see e.g., Khvorostyanov and Curry, 2004a,b). The modes and mechanisms of ice particle nucleation are critical factors in the correct simulation of cloud processes that include the crystalline phase. Heterogeneous ice nucleation, parameterized as either a function of supercooled temperature (e.g., Fletcher, 1962) or supersaturation (e.g. Huffman, 1973; Meyers et al., 1992), has been used in microphysical models to form mixed phase and eventually crystalline clouds and also cold rain through the Bergeron-Findeisen process. Until recently, cirrus modeling studies primarily used the homogeneous freezing mode since the available parameterizations of heterogeneous nucleation [e.g., Fletcher, 1962] produced unrealistically high crystal concentration when extrapolated to cold cirrus temperatures. It is becoming increasingly apparent that heterogeneous ice nucleation can play an important role in cirrus (for a review, see Lin et al., 2002). The recent previous parameterizations of heterogeneous nucleation were based either on the laboratory data (e.g., DeMott et al. 1997, 1998; DeMott 2002), or on an analogy with homogeneous nucleation with some additional hypotheses on analytical dependences of the nucleation rates (e.g., Kärcher and Lohmann 2003; Kärcher et al. 2006). However, these hypotheses were not based on a rigorous theory of nucleation and were not directly confirmed by laboratory data.

Recent observations indicate that many if not most aerosol particles are mixed, containing both soluble and insoluble portions (e.g. Chen et al., 1998; DeMott et al., 1998; Rogers et al., 1998; 2001). These observations imply that an individual mixed aerosol particle, containing both CCN and IN, can nucleate both a water drop and an ice crystal. Immersion freezing and deliquescence freezing (e.g. Khvorostyanov and Curry 2004a) can occur on such mixed particles, while contact freezing has been hypothesized to be inhibited by the sulfate and other soluble coatings of dust aerosol (e.g. Lohmann 2002).

Building upon our theoretical and modeling studies initiated in prior ARM research, we have developed and evaluated a new theory and parameterization for heterogeneous ice nucleation by freezing of deliquescent CCN (Khvorostyanov and Curry, 2000; 2004a, b) that has been incorporated into our cloud models. This parameterization integrates both the temperature and supersaturation dependencies of ice nucleation. We plan to extend this theory to the contact and immersion-freezing modes, and thereby to develop a unified formulation for heterogeneous nucleation that includes all of the main modes of heterogeneous ice nucleation and treats them in a unified framework. This unified parameterization will be incorporated into our cloud models to study in detail crystal formation by drop freezing, and to compare the main modes of heterogeneous ice nucleation and how these are influenced by aerosol physical and chemical characteristics, including conditions for which these mechanisms will dominate over homogeneous nucleation.

In Khvorostyanov and Curry (2005b), a parcel model with explicit bin microphysics was used to examine ice nucleation processes in a broad range of meteorological conditions. This framework will be adopted here to investigate the role of different aerosol characteristics in controlling heterogeneous nucleation of ice under atmospheric conditions representative of different cloud types. Different modes of ice nucleation can be selectively “turned off” to examine specific modes of nucleation. We will extend our previous parcel model simulations using a range of aerosol size distributions and concentrations of varying composition, for a range of thermodynamical and dynamical conditions. We will explore the following issues of the impact of aerosol characteristics on ice nucleation: threshold supersaturation for heterogeneous ice nucleation; range of thermodynamical and dynamical conditions for which heterogeneous ice nucleation will dominate over homogeneous ice nucleation; the impact of various aerosol mixtures involving dust on the ice nucleation processes; and the dominating modes of nucleation (homogeneous vs the different heterogeneous modes and vs the deposition mode).

The parcel model will be used in this project to study ice nucleation in much more detail than any Eulerian cloud model can afford. The modes of nucleation will be further explored using the single-column model and 2-D cloud-resolving model, to investigate more complex dynamical and thermodynamical interactions in the clouds. The deliquescence-freezing parameterization has already been successfully incorporated into the bulk microphysics scheme. MM5 simulations using this scheme have suggested the importance of deliquescence-freezing in terms of arctic mixed-phase stratus (Morrison and Pinto 2004a, b). If aerosol properties (e.g., size, solubility) were specified so that deliquescence-freezing was allowed in the cloud layer, the formation of mixed-phase cloud was limited. This in turn had a strong impact on the surface radiative fluxes, surface temperature, and boundary layer characteristics. The importance of contact nucleation in arctic mixed-phase clouds is inferred from observations during SHEBA/FIRE-ACE, and may potentially explain observations of ice crystal concentration that far exceeded ice nuclei concentrations (which did not include contact nuclei) during M-PACE (Morrison et al. 2005c). The ice nucleation mode was found to have a strong impact on characteristics of mixed-phase stratus, particularly the sensitivity of cloud lifetime to changes in ice nuclei concentration (Morrison et al. 2005c). Significant deliquescence-freezing or deposition nucleation at water subsaturation in relatively warm conditions may lead to dehydration and limit in situ formation of arctic mixed-phase clouds (Morrison and Pinto 2005b).

c) Threshold humidity for onset of ice nucleation.

Knowledge of the threshold humidities or saturation ratios is crucial for parameterization of onset of ice nucleation in the atmosphere in both cloud and climate models, since the simulations of mixed phase and crystalline clouds typically depend on the choice of the threshold humidity. Current parameterizations of threshold humdities for homogeneous nucleation are based upon parcel model simulations and laboratory and field measurements. Threshold humidities for heterogeneous ice nucleation are much less known and are typically parameterized from empirical measurements either as functions of temperature or ice supersaturation. The relative rates of heterogeneous versus homogeneous nucleation remains uncertain, and their variations with parameters such as the size of freezing particles, their physico-chemical properties, cooling rates, temperature, and other factors are neglected (e.g., Kärcher and Lohmann, 2003). A more rigorous recent consideration of both homogeneous and heterogeneous ice nucleation was based on the general thermodynamical principles and extensions of classical nucleation theory (Khvorostyanov and Sassen 1998, 2002; Khvorostyanov and Curry, 2000, 2004a,b, 2005). We propose to invert the nucleation rates from the extended form of the classical nucleation theory to develop a parameterization for threshold humidity, applicable to both heterogeneous and homogeneous ice nucleation, that is not only a function of temperature but also nucleation rate (or cooling rate), the size of the freezing particles, contact parameter, misfit strain, and environmental pressure. This parameterization will clarify under which conditions heterogeneous vs homogeneous nucleation will dominate, and also provide for explicit variation of the threshold humidity with aerosol physico-chemical characteristics.

d) Size spectra and fall speed

Our recent research (Khvorostyanov and Curry 2008a,b) extended the kinetic equation of stochastic condensation to account for crystalline clouds and also to include the accretion/aggregation process and applied the kinetic equation to derive analytical size spectra. The size spectra are separated into small and large size fractions that correspond to cloud drops (ice) and rain (snow). Solutions for the small-size fraction have the form of generalized gamma distributions and simple analytical expressions are found for parameters of the gamma

distributions that are functions of quantities that are available in cloud and climate models: liquid or ice water content and its vertical gradient, mean particle radius or concentration, and supersaturation or vertical velocities. Equations for the gamma distribution parameters provide an explanation of the dependence of observed spectra on atmospheric dynamics, cloud temperature, and cloud liquid water or ice water content. The general solution for the size spectra of the large-size particles is represented by the product of an exponential term and a term that is an algebraic function of radius. The slope of the exponent consists of the Marshall-Palmer slope and an additional integral that is a function of radius. Both the integral and algebraic term depend on the condensation and accretion rates, vertical velocity, turbulence coefficient, terminal velocity of the particles, and the vertical gradient of the liquid (ice) water content. These solutions provide explanations of the observed dependencies of the cloud particle spectra in different phases and size regimes on temperature, height, turbulence, vertical velocities, liquid or ice water content, and other cloud properties. These analytical solutions can be used for parameterization of the spectra of precipitating particles and related quantities (e.g., optical properties, radar reflectivities) in bulk cloud microphysical parameterizations and in remote sensing techniques. We propose to extend this approach and apply it to mixed phase clouds. This requires including inclusion of additional cross-derivative terms in the kinetic equations needed to simulate mixed phase clouds. Addition of these terms considerably complicates the solution of the kinetic equations. The results of this analysis will provide important insights into the co-evolution of the liquid and ice particle spectra in mixed phase clouds.

Khvorostyanov and Curry (2002; 2005) developed a unified representation of fall velocities for both liquid and crystalline particles as a power law over the entire size range of hydrometeors observed in the atmosphere. The power law coefficients are determined as continuous analytical functions of the Best or Reynolds number or of the particle size, including turbulent corrections to the Reynolds number and to the power law coefficients that describe the continuous transition from the laminar to the turbulent flow around a falling particle. This approach provides a continuous analytical power law description of the terminal velocities of liquid and crystalline hydrometeors with sufficiently high accuracy and can be directly used in bin-resolving models or incorporated into parameterizations for cloud and large-scale models and remote sensing techniques. To parameterize integral fall speed for the bulk microphysical model, the fall speeds will be integrated over the analytical size spectra in an effort to identify a simpler bulk fall speed parameterization consistent with the analytical size spectra.

e) Particle collection and aggregation

Most bulk microphysical models use an “autoconversion” term to convert cloud water into precipitation once a threshold cloud water amount is reached. This autoconversion approach overly simplifies the precipitation process and results in erroneous cloud processes and adversely influences cloud optical properties and the simulated precipitation. The Smoluchowski integral coagulation equation (Pruppacher and Klett 1997) has applications both to the formation of aerosol particles and also for the formation of precipitation by coagulation processes. The solution of the Smoluchowski integral equation for gravitational coagulation is very time consuming and various numerical approximations may reduce the accuracy of the solution. The use of a corresponding differential equation would essentially accelerate simulations. We propose to derive a differential kinetic equation of the Fokker-Plank type for gravitational coagulation for precipitating drops or crystals in approximation of continuous collection, typically used in bulk cloud microphysical parameterizations. This equation will account for particle growth by coagulation (drift in the space of mass or radii), and diffusion in these spaces. This drift-diffusion differential equation will be much simpler for analytical and numerical solutions than the original full Smoluchowski integral equation. The importance of this new drift-diffusion differential

equation for cloud modeling is that current parameterizations of coagulation in bulk microphysical schemes are equivalent to accounting for only the drift term, and neglect the diffusion term. However, diffusion may substantially broaden the size or mass spectra, accelerate precipitation formation, and influence radar reflectivity and cloud life cycle, and this should be accounted for in a new equation.

f) Parameterization evaluation

The evaluation of the theoretical models and parameterization elements will be evaluated against laboratory and field data, where available. An integrated and comprehensive evaluation of the parameterizations in simulations of real cloud systems will be undertaken in the following manner:1. Incorporation of the parameterization elements into a bulk microphysical model framework

that is incorporated into a cloud resolving model and a single column model, and evaluation of the simulations against selected case studies used by each of the working groups in GCSS and the forthcoming A-train satellite data.

2. Conduct sensitivity studies using the single column model with bulk microphysics to assess whether the new parameterization elements are necessary and sufficient, and understand the impact of the new parameterizations relative to existing parameterizations.

3. Develop numerical schemes to adapt the parameterizations to the coarser space/time resolutions used in climate models

4. Incorporate the new microphysical parameterizations into WRF and evaluate using IOP observations and A-train satellite data.

To evaluate the parameterizations, we focus on IOPs having in situ microphysics and aerosol data available. Every effort will be made to assemble case studies that represent the full range of cloud types in the Arctic. In the Arctic, we will focus on MPACE (Verlinde et al. 2007) and ISDAC/ARCTAS (2008). In collaboration with the ARM project proposed by Russell, Lubin and Vogelmann (see attached letter), we will work to identify suitable periods from the Barrow time series for examination, focusing on cold core anticyclones in autumn, winter and spring. Outside the Arctic, we will focus on NAMMA/Niamey (2006), for which co-PI Zhang has extensive experience, and also the China IOP (2008).

Similarly to our previous studies (e.g. Khvorostyanov et al., 2003; Morrison et al. 2003; Morrison et al. 2005a; Khvorostyanov et al. 2005), model output from simulations using the bulk microphysics model will be compared with bin model results and also with available observations. The evaluation consists of direct comparison of cloud macro- and microphysical properties, and also comparison of covariances of cloud and thermodynamic variables (e.g., ratio of liquid and ice water content and cloud temperature, ice water content and relative humidity, cloud particle size or number concentration and temperature).

5. Aerosol indirect effect

Our investigation of the aerosol indirect effect includes the following two elements: Development of improved analytical expressions of the aerosol indirect effect SCM, CRM and WRF based sensitivity studies.

Quantification of aerosol indirect effect of liquid clouds requires knowledge of the basic relation between the droplet (Ndr) and aerosol (Na) concentrations, . The power index q1

plays an important role in quantification of the indirect effects, since an increase in q1 enhances

both effects. A quantitative formulation of the indirect aerosol effect (IE) was suggested in Feingold et al. (2001, 2003) as IE = q1/3. A characteristic value derived in earlier studies was q1 ~ 0.7 (Pruppacher and Klett, 1997), i.e., IE ~ 0.23. Later studies revealed a broad range of values of q1 ~ 0.06 - 0.5, and the corresponding variations in IE that were difficult to explain. While the upper limit of these values for q1 is comparable or close to the earlier data, the minimum values are much lower and the reason for these wide variations is unclear. We have begun applying an analytical aerosol model (Khvorostyanov and Curry, 2006, 2007, 2008a, 2008b) for interpretation of the index q1 and of the IE. The aerosol model uses an algebraic size spectrum that is equivalent to a lognormal size spectrum, simplifying the analytical calculations. Solutions to an integro-differential equation for supersaturation are obtained in the form of an analytical power law relation for Ndr and Na with the power index and coefficient expressed via aerosol and atmospheric parameters (modal radius, spectral dispersion, solubility, surface tension, temperature, pressure, vertical velocities in the cloud). Based on this model, the index q1 and IE are expressed via aerosol microphysical parameters. Using these expressions and ground-based remote sensing data from the SGP site used by Feingold et al. (2003) and also the IOP cases that we are investigating, we will use the analytical expression to provide a physico-chemical interpretation for variations of the index q1 and indirect effect IE. This general approach will be extended to consideration of aerosol indirect effects involving the ice phase: thermodynamic effect, glaciation indirect effect, and riming indirect effect (e.g. Lohmann and Feichter 2005).

The SCM, CRM and WRF will be used to investigate the aerosol indirect effect through a series of sensitivity studies. The purpose of these sensitivity studies are to:

(a) Assess whether the new parameterization elements are necessary and sufficient, and examine the impact of the new parameterization relative to the existing ones. The theoretical models and parameterization elements will be evaluated against laboratory and field data, where available. An integrated and comprehensive evaluation of the parameterizations in simulations of various real cloud systems will be undertaken. Sensitivity studies will be conducted by “turning off” selected parameterization elements in the model and replacing them with the old parameterizations elements, to identify the impact of the new parameterization. This will also help to pinpoint which new parameterization elements are most important in terms of model sensitivity, across a range of cloud types. We will investigate how the new parameterizations influence the cloud microphysics parameters (e.g., water contents, particle sizes, etc.), and also examine water vapor, temperature, and supersaturation budgets to determine how these parameterizations influence other cloud and thermodynamic processes and the interactions among them.

(b) Evaluate and compare the different components of the aerosol indirect effect using the new scheme and old scheme. The components of the aerosol indirect effect include cloud albedo (or Twomey) effect, cloud lifetime effect, semi-direct effect, thermodynamic effect, glaciation indirect effect, riming indirect effect, and effect on surface energy budget, following the methods summarized by Lohmann and Feichter (2005).

(c) Test the response of the models to changing aerosol properties, both to assess indirect effects of aerosol and to better understand how the individual parameterization elements respond to modification of aerosols. A major advantage of the microphysics scheme is that it can be used to test the sensitivity of liquid, mixed-phase, and ice clouds to modification of aerosols (including size, number concentration, composition and soluble fraction). In addition, the detailed treatment of ice nucleation means that the response can be tested for various modes to examine the importance of each individual mode under certain circumstances and better understand the ice-aerosol interactions.

(d) Evaluate the new microphysical parameterizations implemented on selected case studies using WRF. Simulated cloud properties from the case studies using the new and old

scheme will be compared and evaluated with using IOP observations (e.g. MPACE, ISDAC, and possibly NAMMA/Niamey and ARM China), analyses of microphysical and cloud radar/lidar observations undertaken by ARM investigators, and A-train satellite data products. Sensitivity studies that has been described above, such as “turning off” selected parameterization elements, modifying aerosol properties, and evaluating the components of the aerosol indirect effect will be performed. Moreover, using WRF, the relative importance of the microphysics scheme to large scale environmental condition, boundary layer scheme, radiation and surface properties will be identified.

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