The objective of this project is to develop a new, highly detailed aerosol model and use it to study the aging of soot in the atmosphere. This particle resolved model (PartMC) will explicitly resolve and track the size and composition of individual particles as they undergo transformations by coagulation and condensation in the atmosphere. The model spreads the aerosol size distribution over a finite number of Monte Carlo particles and allows them to evolve using the appropriate probabilities for coagulation, along with deterministic processes such as condensation. Freshly emitted particles or particles formed via homogeneous nucleation are initially externally mixed, but can be transferred into an internal mixture by coagulation, condensation or photochemical processes, collectively known as aging. This work will improve the theoretical understanding of aerosol mixing state representations, quantify errors resulting from parameterizations of aging in regional and global models, and help to develop better models and parameterizations of aerosol aging.
An improved understanding of aerosol processes through this research will help to reduce uncertainties in climate models. Undergraduates will be integrated into the project and the participation of women and minorities in research will be actively encouraged. All computer codes will be licensed under an open source license to foster use by the atmospheric science community.
Summary The objective of the work under grant NSF-ATM 0739404 was to develop a new, higher-detail aerosol-modeling component in the model hierarchy and to use it to study the aging of black-carbon-containing particles. With this new stochastic particle-resolved model (PartMC), we explicitly resolve and track the size and composition of individual particles as they undergo transformations by coagulation and condensation in the atmosphere. Hence, this novel approach allows for an explicit treatment of mixing state and provides an invaluable tool for (1) performing detailed model simulations on a process level, (2) benchmarking more approximate aerosol model and, (3) deriving parameters used by more approximate aerosol models (coarse-graining). Intellectual Merit The development of a particle-resolved aerosol model, as implemented in the PartMC code, was only possible due to several improvements in algorithms for stochastic particle simulation. The key advances made over the course of this project were: (1) the development of a binned stochastic sampling algorithm to enable efficient simulation of multiscale populations and rates; (2) the development of parallel stochastic coagulation methods using alternating mixing and local interactions; (3) the development of spatially distributed stochastic particle transport methods to enable multi-dimensional particle-resolved simulations, (4) the evaluation and development of adaptive timestepping for stochastic methods to adapt the simulation to evolving physical conditions; (5) the development of a high-detail, particle-resolved cloud parcel model to study the impact of mixing state on cloud droplet formation; and (6) the development of a method for explicitly calculating aging time scales of black carbon aerosol. Applying this new model capability to a number of case studies, we investigated the evolution of aerosol mixing state and the associated optical and cloud-condensation-nuclei activation properties, and quantified the error that was introduced by the assumption that particles of a given size are internally mixed. Further, we carried out detailed process studies to quantify the time-scales of black carbon aging and the relative importance of coagulation and condensation contributing to this process. We used the newly developed particle-resolved cloud parcel capability to study the activation and growth of particle populations of different degrees of aging and quantified the errors in cloud microphysical properties introduced by different mixing state assumptions. Broader impacts The improved understanding of aerosol modeling processes generated by the proposed work is an important contribution towards reducing the uncertainty in the predictions of future climate change, a subject with great impact on the broader community. Over the course of this project we developed several new graduate-level and undergraduate-level courses that address the problem of modeling aerosol and cloud interactions. Undergraduate students and graduate students, in both mechanical engineering and atmospheric sciences, were actively integrated into the proposed research, and interacted regularly with the PIs. In particular we have been successful in recruiting and working with students from minorities (female students, as well as hispanic and black students). Our codes are freely available as open source code (see http://lagrange. mechse.illinois.edu/mwest/partmc/). We added extensive online documentation for all PartMC subroutines and on the format of input and output files and published journal articles on our work.
