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Volume 13, issue 4
Atmos. Chem. Phys., 13, 1913–1926, 2013
https://doi.org/10.5194/acp-13-1913-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 13, 1913–1926, 2013
https://doi.org/10.5194/acp-13-1913-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Feb 2013

Research article | 19 Feb 2013

Evaluation of factors controlling global secondary organic aerosol production from cloud processes

C. He1,*, J. Liu1, A. G. Carlton2, S. Fan3, L. W. Horowitz3, H. Levy II3, and S. Tao1 C. He et al.
  • 1College of Urban and Environmental Sciences, Peking University, Beijing, China
  • 2Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, USA
  • 3Geophysical Fluid Dynamics Laboratory (GFDL), Princeton, NJ, USA
  • *now at: Department of Atmospheric and Oceanic Sciences, University of California at Los Angeles (UCLA), Los Angeles, CA, USA

Abstract. Secondary organic aerosols (SOA) exert a significant influence on ambient air quality and regional climate. Recent field, laboratorial and modeling studies have confirmed that in-cloud processes contribute to a large fraction of SOA production with large space-time heterogeneity. This study evaluates the key factors that govern the production of cloud-process SOA (SOAcld) on a global scale based on the GFDL coupled chemistry-climate model AM3 in which full cloud chemistry is employed. The association between SOAcld production rate and six factors (i.e., liquid water content (LWC), total carbon chemical loss rate (TCloss), temperature, VOC/NOx, OH, and O3) is examined. We find that LWC alone determines the spatial pattern of SOAcld production, particularly over the tropical, subtropical and temperate forest regions, and is strongly correlated with SOAcld production. TCloss ranks the second and mainly represents the seasonal variability of vegetation growth. Other individual factors are essentially uncorrelated spatiotemporally to SOAcld production. We find that the rate of SOAcld production is simultaneously determined by both LWC and TCloss, but responds linearly to LWC and nonlinearly (or concavely) to TCloss. A parameterization based on LWC and TCloss can capture well the spatial and temporal variability of the process-based SOAcld formation (R2 = 0.5) and can be easily applied to global three dimensional models to represent the SOA production from cloud processes.

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