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Volume 14, issue 17
Atmos. Chem. Phys., 14, 9171–9200, 2014
https://doi.org/10.5194/acp-14-9171-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 14, 9171–9200, 2014
https://doi.org/10.5194/acp-14-9171-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Sep 2014

Research article | 08 Sep 2014

Improvement and further development in CESM/CAM5: gas-phase chemistry and inorganic aerosol treatments

J. He and Y. Zhang J. He and Y. Zhang
  • Air Quality Forecasting Laboratory, Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, 27695, USA

Abstract. Gas-phase chemistry and subsequent gas-to-particle conversion processes such as new particle formation, condensation, and thermodynamic partitioning have large impacts on air quality, climate, and public health through influencing the amounts and distributions of gaseous precursors and secondary aerosols. Their roles in global air quality and climate are examined in this work using the Community Earth System Model version 1.0.5 (CESM1.0.5) with the Community Atmosphere Model version 5.1 (CAM5.1) (referred to as CESM1.0.5/CAM5.1). CAM5.1 includes a simple chemistry that is coupled with a 7-mode prognostic Modal Aerosol Model (MAM7). MAM7 includes classical homogenous nucleation (binary and ternary) and activation nucleation (empirical first-order power law) parameterizations, and a highly simplified inorganic aerosol thermodynamics treatment that only simulates particulate-phase sulfate and ammonium. In this work, a new gas-phase chemistry mechanism based on the 2005 Carbon Bond Mechanism for Global Extension (CB05_GE) and several advanced inorganic aerosol treatments for condensation of volatile species, ion-mediated nucleation (IMN), and explicit inorganic aerosol thermodynamics for sulfate, ammonium, nitrate, sodium, and chloride have been incorporated into CESM/CAM5.1-MAM7. Compared to the simple gas-phase chemistry, CB05_GE can predict many more gaseous species, and thus could improve model performance for PM2.5, PM10, PM components, and some PM gaseous precursors such as SO2 and NH3 in several regions as well as aerosol optical depth (AOD) and cloud properties (e.g., cloud fraction (CF), cloud droplet number concentration (CDNC), and shortwave cloud forcing, SWCF) on the global scale. The modified condensation and aqueous-phase chemistry could further improve the prediction of additional variables such as HNO3, NO2, and O3 in some regions, and new particle formation rate (J) and AOD on the global scale. IMN can improve the prediction of secondary PM2.5 components, PM2.5, and PM10 over Europe as well as AOD and CDNC on the global scale. The explicit inorganic aerosol thermodynamics using the ISORROPIA II model improves the prediction of all major PM2.5 components and their gaseous precursors in some regions as well as downwelling shortwave radiation, SWCF, and cloud condensation nuclei at a supersaturation of 0.5% on the global scale. For simulations of 2001–2005 with all the modified and new treatments, the improved model predicts that on global average, SWCF increases by 2.7 W m−2, reducing the normalized mean bias (NMB) of SWCF from −5.4 to 1.2%. Uncertainties in emissions can largely explain the inaccurate prediction of precursor gases (e.g., SO2, NH3, and NO) and primary aerosols (e.g., black carbon and primary organic matter). Additional factors leading to the discrepancies between model predictions and observations include assumptions associated with equilibrium partitioning for fine particles assumed in ISORROPIA II, irreversible gas/particle mass transfer treatment for coarse particles, uncertainties in model treatments such as dust emissions, secondary organic aerosol formation, multi-phase chemistry, cloud microphysics, aerosol–cloud interaction, dry and wet deposition, and model parameters (e.g., accommodation coefficients and prefactors of the nucleation power law) as well as uncertainties in model configuration such as the use of a coarse-grid resolution.

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