Open AccessDescription and evaluation of a six‐moment aerosol microphysical module for use in atmospheric chemical transport models
Author(s) -
Wright D. L.,
Kasibhatla P. S.,
McGraw R.,
Schwartz S. E.
Publication year - 2001
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2001jd900098
Subject(s) - aerosol , moment (physics) , particle number , relative humidity , atmospheric sciences , troposphere , environmental science , scanning mobility particle sizer , meteorology , computational physics , physics , volume (thermodynamics) , thermodynamics , classical mechanics
We describe and evaluate a six‐moment aerosol microphysical module, 6M, designed for implementation in atmospheric chemical transport models (CTMs). The module 6M is based upon the quadrature method of moments (QMOM) [ McGraw , 1997] and the multiple isomomental distribution aerosol surrogate (MIDAS) method [ Wright , 2000]. The module 6M evolves the lowest six radial moments of H 2 SO 4 ‐H 2 O aerosols for a comprehensive set of dynamical processes including the formation of new particles via binary H 2 SO 4 ‐H 2 O nucleation, condensational growth, coagulation, evolution due to cloud processing, size‐resolved dry deposition, and water uptake and release with changing relative humidity. Performance of the moment‐based aerosol evolution is examined and evaluated by comparison with results obtained using a high‐resolution discrete model of the particle dynamics for a range of conditions representative of the boundary layer and lower troposphere. Overall, the performance of 6M is good relative to uncertainties associated with other processes represented in CTMs for the 30 test cases evaluated. Differences between 6M and the discrete model in the mass/volume moment and in the partitioning of sulfur (VI) between the gas and aerosol phases remain under 1% whenever significant aerosol is present, and differences in particle number rarely exceed 15%. Estimates of cloud droplet number from 6M are on average within 16% of those of the discrete model, with a significant part of these differences attributable to limitations of the discrete dynamics. Multimodal lognormal (MIDAS) surrogates to the underlying size distributions derived from the 6M moments are in good agreement with the benchmark size distributions.