MOSAIC (MOdel for Simulating Aerosol Interactions and Chemistry)

MOSAIC focuses on addressing the long‐standing issues in solving the dynamic partitioning of semivolatile inorganic gases (HNO3, HCl, and NH3) to size‐distributed atmospheric aerosol particles.

dynamic partitioningsemivolatile inorganic gasesHNO3HClNH3atmosphericaerosol particles



Initial contribute: 2020-01-03


Pacific Northwest National Laboratory, U.S.
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Application-focused categoriesNatural-perspectiveAtmospheric regions

Detailed Description

English {{currentDetailLanguage}} English

Quoted fromZaveri, Rahul A., Richard C. Easter, Jerome D. Fast, and Leonard K. Peters. "Model for simulating aerosol interactions and chemistry (MOSAIC)." Journal of Geophysical Research: Atmospheres 113, no. D13 (2008). 

Atmospheric aerosols are ubiquitous suspended particulate matter that can range from a few nanometers up to a few microns in size. In large urban areas and megacities, anthropogenic aerosols are implicated in many human health related problems [Gauderman et al., 2000Osornio‐Vargas et al., 2003Pope et al., 2004]. On regional and global scales, these as well as other naturally occurring aerosols play an important role in the Earth's radiative balance and climate via various direct and indirect effects [Intergovernmental Panel on Climate Change, 2001], and are also involved in other environmental issues such as visibility degradation [Malm et al., 1994Park et al., 2006] and acid deposition [Norris et al., 1999Kelly et al., 2002]. Aerosols also provide reactive volumes and surfaces in the atmosphere where heterogeneous chemical reactions can proceed efficiently [Laskin et al., 2005Gibson et al., 2006Brown et al., 2006]. These interlinked scientific and policy‐related issues have motivated many field, laboratory, and theoretical studies with a common goal of developing a better understanding of the composition of atmospheric aerosols and how they form and evolve as a function of time and space. To this end, several different aerosol process modules and 3‐D models have played a central role in first synthesizing the diverse experimental findings with theoretical formulations, and subsequently in interpreting field observations and assessing the impact of aerosols on air quality and climate at different spatial and temporal scales [Peters et al., 1995Haywood and Boucher, 2000Zhang et al., 2004Textor et al., 2006].

[3] However, aerosol modeling is a scientifically challenging endeavor as well as a computationally difficult problem. Atmospheric aerosols not only span over three orders of magnitude in size, but they are also physically and chemically rather complex. Field observations show that they can be composed of a wide variety of compounds, including sulfate, nitrate, ammonium, carbonaceous materials, sea salt, and crustal species from soil dust [Seinfeld and Pandis, 1998]. Furthermore, carbonaceous aerosols include sooty particles (black carbon) formed as result of fossil fuel combustion and biomass burning as well as organic compounds of both anthropogenic and biogenic origins. Particles that are directly emitted into the atmosphere are called primary aerosols, which include sea salt, soil dust, black carbon, and organic carbon. However, a significant portion of aerosol mass is formed via the process of gas‐particle partitioning of H2SO4, HNO3, HCl, and NH3, and myriad low and semivolatile organic compounds. The resulting particulate mass, referred to as secondary aerosols, consists of various salts of sulfate, nitrate, chloride, and ammonium and many different organic species. Other important chemical and microphysical processes that affect aerosol size, number, and composition distributions include homogeneous nucleation (new particle formation), coagulation, gas‐phase chemistry, heterogeneous chemistry, cloud droplet and ice particle nucleation, dry‐deposition, and wet removal.

[4] A reliable aerosol model must therefore be able to resolve the wide particle size range and chemical complexity arising from the many different primary and secondary aerosol species; and it must also include reliable treatments for simulating the various chemical and microphysical processes mentioned above. While great advances have been made on developing model representations of these processes, several microphysical processes such as homogeneous, cloud droplet, and ice particle nucleation and gas‐particle partitioning are still somewhat problematic. The difficulties with the different types of nucleation processes and partitioning of organic species are at a fundamental level where the physics and chemistry of the phenomena under ambient atmospheric conditions still remain to be completely understood [e.g., Nadykto and Yu, 2007Curtius et al., 2006Cantrell et al., 2001Stroud et al., 2007Cziczo et al., 2004Robinson et al., 2007]. On the other hand, the theoretical formulation of the gas‐particle partitioning process for inorganic species has been well established. The difficulty primarily lies in its numerical solution.

[5] In this paper, we describe the development and evaluation of a new aerosol model, referred to as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), with a focus on resolving the outstanding issues associated with the gas‐particle partitioning process for the various inorganic gases. For other aerosol processes, we rely on the techniques and modules available in the literature. We begin in section 2 with a brief description of the overall framework of MOSAIC which serves as a host for coupling various chemical and microphysical processes. In section 3, we review the gas‐partitioning problem and the different approaches and numerical techniques developed so far. In section 4, we describe in detail the new gas‐particle mass transfer module ASTEM (Adaptive Step Time‐Split Euler Method) that resolves the outstanding issues with the gas‐particle partitioning problem. In section 5, we first verify and validate the fidelity of MOSAIC/ASTEM in a box‐model framework for several representative test cases, and then apply MOSAIC within PNNL's 3‐D Eulerian model, PEGASUS, to evaluate its performance and CPU time requirements under realistic conditions.

[6] An earlier version of MOSAIC was implemented in the chemistry version of the Weather Research and Forecasting (WRF‐chem) model [Grell et al., 2005], and successfully applied to study aerosol evolution and direct radiative forcing in the vicinity of Houston during the TexAQS 2000 field campaign [Fast et al., 2006]. The new version described here is now accessible in WRF‐chem and also available in a standalone box‐model format upon request.



MOSAIC team (2020). MOSAIC (MOdel for Simulating Aerosol Interactions and Chemistry), Model Item, OpenGMS,


Initial contribute : 2020-01-03



Pacific Northwest National Laboratory, U.S.
Is authorship not correct? Feed back

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