Simulating the evolution of soot mixing state with a particle‐resolved aerosol model

Abstract

[1] The mixing state of soot particles in the atmosphere is of crucial importance for assessing their climatic impact, since it governs their chemical reactivity, cloud condensation nuclei activity, and radiative properties. To improve the mixing state representation in models, we present a new approach, the stochastic particle-resolved model PartMC-MOSAIC, which explicitly resolves the composition of individual particles in a given population of different types of aerosol particles. This approach tracks the evolution of the mixing state of particles due to emission, dilution, condensation, and coagulation. To make this direct stochastic particle-based method practical, we implemented a new multiscale stochastic coagulation method. With this method we achieved high computational efficiency for situations when the coagulation kernel is highly nonuniform, as is the case for many realistic applications. PartMC-MOSAIC was applied to an idealized urban plume case representative of a large urban area to simulate the evolution of carbonaceous aerosols of different types due to coagulation and condensation. For this urban plume scenario we quantified the individual processes that contributed to the aging of the aerosol distribution, illustrating the capabilities of our modeling approach. The results showed for the first time the multidimensional structure of particle composition, which is usually lost in sectional or modal aerosol models.Citation: Riemer, N., M. West, R. A. Zaveri, and R. C. Easter (2009), Simulating the evolution of soot mixing state with a particleresolved aerosol model,

Sensitivity of remote aerosol distributions to representation of cloud–aerosol interactions in a global climate model

“…Condensation of soluble material (e.g., sulfuric acid gas and condensable organics), coagulation with soluble particles and oxidation of organic material can be involved in aging, but uncertainties remain (Pöschl et al, 2001;Riemer et al, 2004). When BC aging is represented in global models, it is either simply parameterized by prescribing a fixed aging timescale (e.g., Collins et al, 2001) or represented more explicitly by treating condensation/coagulation with some simplifications (e.g., Vignati et al, 2004;Liu et al, 2012) because it is computationally impractical to explicitly treat the most detailed representations of the aging process (Riemer et al, 2009;Zaveri et al, 2010) in global models. Koch et al (2009b) compared global BC predictions from 17 AeroCom models, including an older version of CAM, and evaluated model results against surface and aircraft measurements.…”

Metrics to quantify the importance of mixing state for CCN activity

“…We use the mixing state index (χ) proposed by Riemer and West (2013) to rigorously quantify the degree of external and internal mixing of aerosol populations. This mixing state index is a scalar quantity and varies between 0 % (for completely external mixtures) and 100 % (for completely internal mixtures) for any given aerosol population.…”

“…It is commonly assumed that models are more prone to errors in predicted cloud condensation nuclei (CCN) concentrations when the aerosol populations are externally mixed. In this work we investigate this assumption by using the mixing state index (χ) proposed by Riemer and West (2013) to quantify the degree of external and internal mixing of aerosol populations. We combine this metric with particleresolved model simulations to quantify error in CCN predictions when mixing state information is neglected, exploring a range of scenarios that cover different conditions of aerosol aging.…”

Coupling spectral‐bin cloud microphysics with the MOSAIC aerosol model in WRF‐Chem: Methodology and results for marine stratocumulus clouds

“…Regarding the representation of cloud-borne aerosol, there are several options. Cloud droplets and the aerosol material they contain can be viewed as highly dilute liquid aerosol particles, and fully representing a population of these particles requires a multidimensional bin structure or a particle-resolving approach [Riemer et al, 2009]. The most detailed representation that has been applied to atmospheric models such as WRF-Chem uses a two dimensional bin structure to explicitly treat cloud-borne aerosol material, with one dimension being the effective dry-size (or mass) of the aerosol material in a droplet, and the other being the actual size (or water mass) of a droplet [e.g., Ovchinnikov and Easter, 2010].…”