To understand how clouds affect chemical constituents, modeling the chemistry in clouds has been an active area of research. Many processes must be taken into account to describe accurately the effect of clouds on tropospheric chemistry, including cloud dynamics, microphysics, chemistry, radiation effects and, for simulations of thunderstorms, nitrogen oxide production from lightning. To better understand the influence of clouds on chemistry, these processes should be integrated in three-dimensional numerical simulations that resolve the cloud scale yet cover synoptic-scale domains such that long-term effects of clouds on chemical species can be tracked.

Alternatively, box models and parcel models provide a useful means to focus on individual processes (e.g., chemistry) such that the process can be assessed in more detail. This paper focuses on three processes: 1) the chemistry in and around cloud drops, 2) interactions between aerosols and aqueous chemistry, and 3) cloud microphysics to exemplify how a non-chemical process changes the spatial distribution of chemical species.

Aqueous chemistry

In clear air, the chemical sources and sinks of gas-phase species, C_g, are represented as

(1)

where P_g is the gas-phase production of C_g and L_g is the first order gas-phase loss of C_g. If only non-precipitating water clouds are considered, the chemical sources and sinks of the gas-phase species are modified to

(2)

where k_t is the first order mass transfer rate, L is the liquid water content, and H_eff is the dimensionless effective Henry's Law constant. A similar equation can be written for the aqueous-phase species, C_a,

(3)

Adding equations 2 and 3 gives the total concentration of the species, C_tot = C_g + C_a,

(4)

Either equations 2 and 3 can be solved to predict C_g and C_a, or equation 4 can be solved and C_g and C_a determined by assuming Henry's Law equilibrium between phases. The production and loss terms in these equations depend on the concentration of other chemical species, thus a set of interdependent stiff equations must be solved. Several numerical techniquese.g., predictor-corrector methods, implicit methods, or hybrid explicit-implicit methodscould be utilized to solve this set of equations.

Figure 1. Time versus total CH3OOH concentration from several box model simulations. The simulated cloud is appropriate for stratus at 1.5 km altitude.

During the 5^th International Cloud Modeling Workshop (Glenwood Springs, CO, USA, August 2000), the cloud chemistry case compared numerical solver techniques for the system outlined above. Seven solvers were tested: four based on the Gear predictor-corrector method, two using an Euler Backward Iterative (EBI) method, and one using the Newton-Raphson method. Excellent agreement between model results was found for the clear-sky-only simulation. For the simulation in which a cloud with L = 0.3 x 10^–6 cm³ cm^–3 was imposed for a one-hour period, agreement between model results was quite good. Greater variability was found for species such as CH₃OOH (Figure 1), CH₂O, and HCOOH. When aqueous chemistry was introduced, increased variability between models resulted whether equations 2-3 or equation 4 (and determining C_g and C_a from equilibrium calculations) were solved, or whether a dissociated species (e.g., O₂^–) was treated as part of a family and determined by equilibrium, or whether it was predicted separately from the other members of the family. A summary of the results of this intercomparison will be published in the Cloud Modeling Workshop's report to the World Meteorological Organization, and a manuscript discussing the results in detail is in preparation.

Aerosols and aqueous chemistry

The cloud chemistry case of the Cloud Modeling Workshop also compared results of aerosol parcel models that simulated S(IV) oxidation. Not only do these parcel models need to use equations 2-4 to predict SO₂, H₂O₂ and O₃ concentrations, but they need to predict the activation of the aerosol to cloud drops and the liquid water content (L) in the air parcel.

The activation of cloud condensation nuclei (CCN) depends on the size of the CCN and its composition. Droplet growth equations can be found in textbooks on cloud physics and chemistry [e.g., Seinfeld and Pandis, 1998]. Many aerosol parcels models discretize the aerosol and cloud drop size distribution into bins. Thus, the total concentration of a species, C_pa, in the activated aerosol (i.e., cloud drops) can be determined by summing over the size range of CCN that were activated,

(5)

where C_pg(n) is the concentration of species in the aerosol in bin n, m_c is the aerosol bin number that is associated with the critical diameter for activation, and mtot is the total number of bins. The liquid water content can be found from the supersaturation, which is determined from the Clausius-Clapeyron equation [e.g., see Seinfeld and Pandis, 1998].

Results from this initial aerosol parcel model intercom-parison showed that there was much disagreement in predicting pH and final SO₂ concentration. Two types of simulations were performed: 1) where the cloud drop size is fixed at 10 mm, and 2) where the cloud drop population is distributed over size. One consistent outcome was that the results from the simulations with a size-varying cloud drop population oxidized 20% more SO₂ than the simulations that assumed a fixed cloud drop size. For a size-varying cloud droplet population, the pH is allowed to vary with drop size and reaction rates that depend on pH (e.g., S(IV) oxidation by O₃) vary with drop size. Thus, for the larger droplets that have higher pH values, O₃ oxidation by S(IV) proceeds rapidly causing greater SO₂ depletion than if a fixed cloud drop size is used. As has been noted in other studies [Hegg and Larson, 1990; Roelofs, 1993], it is important to characterize a population of cloud drops rather than fixing the cloud drop size.

Cloud physics and chemical species

The physical characteristics of clouds are quite varied and can influence the chemical distribution of species. As noted in the previous section, the effect of drop size on cloud chemistry is important to the pH-dependent chemistry. The effect of size on cloud chemistry can most notably be seen when comparing cloud drop chemistry to rain drop chemistry [Audiffren et al., 1996]. The larger-sized raindrops usually have higher pH values, which increases the extent of S(IV) oxidation, oxidation of formate and oxidation of species by the superoxide ion (O₂^–).

Ice hydrometeors also need to be considered when describing cloud chemistry because of the interaction of the ice particles with cloud drops and because of possible direct interactions with chemical species. Cloud physics texts detail the methodology used to describe microphysical processes in cloud models. When cloud drops or rain freeze to form ice, snow or hail (whether by direct freezing or by freezing during the riming process), the chemical species in the cloud water or rain may or may not be captured by the frozen hydrometeor. As an example, the transfer of a chemical constituent from cloud water to snow can be depicted to be proportional to the amount of water transferred [Hegg et al., 1984],

(6)

where C_s is the concentration of the species in snow, MP refers to the microphysical process of interest, q_c is the cloud water mass mixing ratio, dq_c/dt is the microphysical process affecting the hydrometeors (in this example, snow collecting cloud water).

In the frozen hydrometeors, chemical reactions that readily occurred in the aqueous phase are stopped. Further, because snow and hail have fall speeds quite different from each other and from rain, the chemical species that are captured by the frozen hydrometeors will be redistributed very differently than if the species were degassed during the freezing process. To exemplify the potential importance of the ice on the distribution of soluble species, results from simulations [Barth et al., 2000] of a thunderstorm that included cloud chemistry and tested the importance of a species being captured by the frozen hydrometeors show that H₂O₂ is somewhat reduced in the outflow region of the storm by aqueous chemistry but is substantially reduced when the frozen hydrometeors are allowed to capture H₂O₂ (Figure 2).

Conclusions

Figure 2. Spatially-averaged H2O2 versus height in the outflow region (shaded area) of a simulated thunder-storm.

To understand how cloud processes influence chemical species, numerical simulations can be performed. Box model studies of cloud chemistry can illuminate the significance of aqueous-phase reactions on tropospheric chemistry, but first must be evaluated to assess the model's ability to predict the chemistry accurately. Results from the cloud chemistry case of the 5^th International Cloud Modeling Workshop indicate good agreement between different box models.

Parcel models allow the researcher to understand the importance of cloud condensation nuclei on cloud drop activation and cloud chemistry. The intercomparison of parcel models showed some disagreement between different models and suggests the need for continued comparisons.

The cloud chemistry intercomparison was very successful; however, its usefulness would be even greater if the results could be compared to observations. Thus we hope to combine parcel models of cloud and aerosol chemistry with cloud chemistry observations of orographic clouds.

To understand more complex clouds, e.g., cumulonimbus, multi-dimensional cloud models are used to describe several processes. Sensitivity studies of how cloud microphysics affects the spatial distribution of soluble species indicate that the concentrations of some constituents can be substantially altered in the anvil region of a storm. Observations of these soluble species need to be made to improve our understanding of how thunderstorms influence soluble species.

Not only should cloud chemistry and microphysics be accurately described in cloud chemistry models, but transport, radiative influences, and lightning-produced NO_X must also be depicted properly. Simulations of individual clouds teach us what occurs in a short period of time for particular cases. However, long-term effects of clouds on chemical species must also be examined by resolving cloud-scale processes on large model domains.

Acknowledgments

Participants of the cloud chemistry case, box model intercomparison of photochemistry included the author, Anne Monod, Cheol-Hee Kim, Mark Jacobson, Jinyou Liang, Sandy Sillman, and Rynda Hudman. Their contribution and the contribution by Sonia Kreidenweis to this article is greatly appreciated. Charles Brock, Geoff Tyndall and Brian Ridley are thanked for their comments on this manuscript. NCAR is sponsored by the National Science Foundation.

References

1. Ackerman, A.S., O.B. Toon, D.E. Stevens, A.J. Heymsfield, V. Ramanathan, and E.J. Welton, Reduction of tropical cloudiness by soot, Science, 288 (5468), 1042-1047, 2000.
2. Albrecht, B.A., Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227-1230, 1989.
3. Brasseur, G.P., R.A. Cox, D. Hauglustaine, I. Isaksen, J. Lelieveld, D.H. Lister, R. Sausen, U. Schumann, A. Wahner, and P. Wiesen, European Scientific Assessment of the Atmospheric Effects of Aircraft Emissions, Atmos. Env., 32 (13), 2329-2418, 1998.
4. Brenguier, J.L., P.Y. Chuang, Y. Fouquart, D.W. Johnson, F. Parol, H. Pawlowska, J. Pelon, L. Schuller, F. Schroder, and J. Snider, An overview of the ACE-2 CLOUDYCOLUMN closure experiment, Tellus Series B-Chemical and Physical Meteorology, 52 (2), 815-827, 2000.
5. Coakley, J.A., Jr., R.L. Bernstein, and P.A. Durkee, Effect of ship-stack effluents on cloud reflectivity, Science, 237, 1020-1022, 1987.
6. Conover, J.H., Anomalous cloud lines, J. Atm. Sci., 23, 778-785, 1966.
7. Durkee, P.A., K.J. Noone, and R.T. Bluth, The Monterey Area Ship Track (MAST) Experiment, J. Atmos. Sci., 57 (16), 2523-2541, 2000.
8. Ferek, R.J., T. Garrett, P.V. Hobbs, S. Strader, D. Johnson, J.P. Taylor, K. Nielsen, A.S. Ackerman, Y. Kogan, Q.F. Liu, B.A. Albrecht, and D. Babb, Drizzle suppression in ship tracks, J. Atmos. Sci., 57 (16), 2707-2728, 2000.
9. Hallberg, A., J.A. Ogren, K.J. Noone, J. Heintzenberg, A. Berner, I. Solly, C. Kruisz, G. Reischl, S. Fuzzi, M.C. Facchini, H.C. Hansson, A. Wiedensohler, and I.B. Svenningsson, Phase Partitioning for Different Aerosol Species in Fog, Tellus, 44B (5), 545-554, 1992.
10. King, M.D., L.F. Radke, and P.V. Hobbs, Optical properties of marine stratocumulus clouds modified by ships, J. Geophys. Res., 98 (D2), 2729-2739, 1993.
11. Lohmann, U., and J. Feichter, Can the direct and semi-direct aerosol effect compete with the indirect effect on a global scale?, Geophys. Res. Lett., 28 (1), 159-161, 2000.
12. Noone, K.J., J.A. Ogren, A. Hallberg, J. Heintzenberg, J. Ström, H.C. Hansson, B. Svenningsson, A. Wiedensohler, S. Fuzzi, M.C. Facchini, B.G. Arends, and A. Berner, Changes in Aerosol Size and Phase Distributions Due to Physical and Chemical Processes in Fog, Tellus, 44B (5), 489-504, 1992.
13. Radke, L.F., J.A. Coakley, and M.D. King, Direct and remote sensing observations of the effects of ships on clouds, Science, 246, 1146-1149, 1989.
14. Rosenfeld, D., Suppression of rain and snow by urban and industrial air pollution, Science, 287, 1793-1796, 2000.
15. Schumann, U., H. Schlager, F. Arnold, J. Ovarlez, H. Kelder, O. Hov, G. Hayman, I.S.A. Isaksen, J. Staehelin, and P.D. Whitefield, Pollution from aircraft emissions in the North Atlantic flight corridor: Overview on the POLINAT projects, Journal of Geophysical Research-Atmospheres, 105 (D3), 3605-3631, 2000.
16. Ström, J., and S. Ohlsson, In situ measurements of enhanced crystal number densities in cirrus clouds caused by aircraft exhaust, J. Geophys. Res., 103 (D10), 11,355-11,361, 1998.
17. Toon, O.B., and R.C. Miake-Lye, Subsonic Aircraft: Contrail and Cloud Effects Special Study (SUCCESS), Geophys. Res. Lett., 25 (8), 1109-1112, 1998.
18. Twomey, S., Pollution and the planetary albedo, Atmospheric Environment, 8, 1251-1256, 1974.
19. Twomey, S., Atmospheric aerosols, Elsevier Scientific Publishing Co., Amsterdam, 302pp, 1977a.
20. Twomey, S., The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34 (7), 1149-1152, 1977b.
21. Twomey, S., H.B. Howell, and T.A. Wojciechowski, Comments on "Anomalous cloud lines", J. Atm. Sci., 25, 333-334, 1968.
22. Warner, J., A reduction in rainfall associated with smoke from sugarcane fires - An inadvertent weather modification?, J. Appl. Meteor., 7, 247-251, 1968.