Representation of Aerosols in Globalscale Chemical Transport Models and Global Climate Models

Although there are many similarities between treatment of aerosol processes in chemical transport models (CTMs) and global climate models, it is useful to distinguish the two modeling approaches. Global climate models simulate their own meteorology and couple aerosol cycles with clouds, precipitation, and radiation transfer, thereby allowing the projection of future climate under different emissions scenarios. Because climate modeling emphasizes long-term simulation of climate, treatment of aerosol processes in climate models must be greatly simplified. In contrast, with CTMs it is possible to treat aerosol processes and interactions between aerosols (and hydrometeors) and atmospheric chemistry in greater detail. CTMs are often driven by observed meteorology; in such models, the aerosol chemistry and physics do not feed back on the meteorology. CTMs and global climate models need to be driven by observed meteorology to capture detailed aerosol processes and to compare simulated aerosol fields with observations.

Since the pioneering study by Langner and Rodhe (1991), who used a coarse horizontal resolution CTM based on climatological meteorology to represent the global distribution of the mass concentration of sulfate aerosol (without explicit representation of aerosol microphysics), substantial advances have been made in the complexity of treatment of many key processes: aerosol precursor chemistry, aerosol microphysical processes, transport processes, and particle dry and wet deposition. Attempts have recently been undertaken to calculate the aerosol mass concentration as well as the particle number concentration by parameterizing aerosol formation and dynamic processes (e.g., Easter et al. 2004; Stier et al. 2005). An overview of the processes which must be understood and represented in models is given in Figure 23.3.

Most of the earlier studies concerned with the effect of aerosol particles on the climate system took only sulfate particles into account or considered sulfate to be a surrogate for all anthropogenic aerosols. Lately, most major global climate models include also carbonaceous particles, dust, and sea salt (for a synopsis of the state of model development, see Kinne et al. 2006 and the AeroCom model intercomparison project1). AeroCom has enabled a comparison of the results of aerosol simulations from more than a dozen modeling groups worldwide. Figure 23.4 provides an example of a comparison for global and annual mean aerosol optical depth and the vertical integral of aerosol extinction coefficient. Although fairly good agreement is demonstrated for most models, it is clear that there are substantial differences in the contributions of the several aerosol species.

A major source of uncertainty in present aerosol modeling is the lack of accurate time-resolved emission inventories. In particular, biogenic sources and emissions from biomass burning are highly uncertain. Both biogenic and biomass burning emissions depend on environmental conditions (e.g., weather) and exhibit high interannual variability, which has not been taken into account by climate studies. Probably the largest uncertainty is associated with organic aerosols, because current measurement techniques cannot identify the many organic species present in primary emissions (Kanakidou et al. 2004). A second issue is that the chemical pathways in the atmosphere are complex and not fully understood. Organic particles result both from primary emission and from

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Figure 23.3 Important climate-influencing aerosol processes that must be accurately represented in climate models. Aerosol particles are directly emitted as primary particles and are formed secondarily by oxidation of emitted gaseous precursors. The formation of low-volatility materials results in new particle formation and condensation onto existing particles. Aqueous-phase oxidation of gas-phase precursors within cloud droplets accretes additional mass onto existing particles but does not form new particles. Particles age by surface chemistry, coagulation and condensation. The uptake of water with increasing relative humidity increases particle size, which affects the particle optical properties as well. As clouds form, some fraction of aerosol particles are "activated" to produce cloud droplets. Within clouds, interstitial particles can become attached to cloud droplets by diffusion, and activated particles are combined when cloud droplets collide and coalesce. If cloud droplets evaporate, the particles remain in the atmosphere; if the cloud precipitates, the particles are carried below the cloud to the surface, unless the precipitating particles evaporate completely. Aerosol particles below precipitating clouds can also be removed from the atmosphere through impaction by precipitating drops or through dry deposition to the surface (from Ghan and Schwartz 2007).

gas-to-particle conversion in the atmosphere (secondary production). The total source of these organic particles is therefore a major wildcard in simulations of future scenarios. Advances in measurement techniques for particles are thus of critical importance; one such recent advance is the aerosol mass spectrometer, which permits the development of a systematic measurement database to be developed of general aerosol composition and the identification of primary and secondary organic species (Zhang et al. 2007). Simulating nitrate particles remains problematic because of their semi-volatile nature. In addition to all of the difficulties that exist in developing an understanding of the chemical and microphysical processes, the simulation of aerosol processes in large-scale models is very CPU-time consuming.

There is increasing evidence that individual aerosol particles consist predominantly of a conglomerate ofmultiple internally mixed chemical substances. In contrast, most global climate models treat aerosols as external mixtures, because internal mixtures have more degrees of freedom, are more complex,

Organic ■ Black Carbon _1 Sulfate

Figure 23.4 Simulated contributions of five aerosol components (seasalt, dust, organic, black carbon, and sulfate) to annual and global mean aerosol optical thickness (AOT), at 550 nm by 17 chemical transport models. For comparison, surface measurements taken by the AERONET network and a composite of satellite measurements are shown. Modified from Kinne et al. (2006).

Organic ■ Black Carbon _1 Sulfate

Figure 23.4 Simulated contributions of five aerosol components (seasalt, dust, organic, black carbon, and sulfate) to annual and global mean aerosol optical thickness (AOT), at 550 nm by 17 chemical transport models. For comparison, surface measurements taken by the AERONET network and a composite of satellite measurements are shown. Modified from Kinne et al. (2006).

and impose an additional computational burden. However, the mixing state of aerosol particles (externally vs. internally mixed) influences strongly their optical properties and ability to act as CCN. For example, a slight coating of a particle by only a moderately soluble organic species can drastically increase its ability to act as a CCN. Therefore, treating the degree of mixing properly is essential to represent accurately aerosol processing in global climate models, including aerosol-cloud interactions. Advanced aerosol modules in some global climate models have been expanded to include aerosol mixtures (see Lohmann and Feichter 2005 for references).

Representing the particle size distributions of aerosols and their evolution is also essential. Two kinds of aerosol dynamics models have been developed: modal schemes and bin schemes. Modal schemes represent each aerosol mode with a log-normal distribution of aerosol mass and possibly number. Bin schemes divide the aerosol spectrum into a large number of bins. Typically, modal representations of the aerosol size distribution evolve aerosol number concentration as well as mass concentration.

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