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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-93
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model evaluation paper
01 Jun 2017
Review status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models
Tanvir R. Khan and Judith A. Perlinger Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, Michigan 49931, United States
Abstract. Despite considerable effort to develop mechanistic dry particle deposition parameterizations for atmospheric transport models, current knowledge has been inadequate to propose quantitative measures of the relative performance of available parameterizations. In this study, we evaluated the performance of five dry particle deposition parameterizations developed by Zhang et al. (2001) (Z01), Petroff and Zhang (2010) (PZ10), Kouznetsov and Sofiev (2012) (KS12), Zhang and He (2014) (ZH14), and Zhang and Sao (2014) (ZS14), respectively. The evaluation was performed in three dimensions: model ability to reproduce observed deposition velocities, Vd (accuracy), the influence of imprecision in input parameter values on the modeled Vd (uncertainty), and identification of the most influential parameter(s) (sensitivity). The accuracy of the modeled Vd was evaluated using observations obtained from five land use categories (LUCs): grass, coniferous and deciduous forests, natural water, and ice/snow. To ascertain the uncertainty in modeled Vd, and quantify the influence of imprecision in key model input parameters, a Monte Carlo uncertainty analysis was performed. The Sobol' sensitivity analysis was conducted with the objective to determine the parameter ranking, from the most to the least influential. Comparing the normalized mean bias factors (indicator of accuracy), we find that the ZH14 parameterization is the most accurate for all LUCs except for coniferous forest, for which it is second most accurate (BNMBF = −2.31). From Monte Carlo simulations, the estimated mean normalized uncertainties in the modeled Vd obtained for seven particle sizes (ranging from 0.005 to 2.5 μm) for the five LUCs are 17 %, 12 %, 13 %, 16 %, and 27 % for the Z01, PZ10, KS12, ZH14, and ZS14 parameterizations, respectively. From the Sobol' sensitivity results, we suggest that the parameter rankings vary by particle size and LUC for a given parameterization. Overall, for dp = 0.001 to 1.0 μm, friction velocity was one of the three most influential parameters in all parameterizations. For giant particles (dp = 10 μm), relative humidity was the most influential parameter. Because it is the least complex of the five parameterizations, and it has the greatest accuracy and least uncertainty, we propose that the ZH14 parameterization is currently superior for incorporation into atmospheric transport models.

Citation: Khan, T. R. and Perlinger, J. A.: Evaluation of five dry particle deposition parameterizations for incorporation into atmospheric transport models, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-93, in review, 2017.
Tanvir R. Khan and Judith A. Perlinger
Tanvir R. Khan and Judith A. Perlinger
Tanvir R. Khan and Judith A. Perlinger

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