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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 8
Atmos. Chem. Phys., 13, 4235–4251, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 13, 4235–4251, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 24 Apr 2013

Research article | 24 Apr 2013

Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint

R. H. Moore*,1, V. A. Karydis2, S. L. Capps1, T. L. Lathem2, and A. Nenes2,1 R. H. Moore et al.
  • 1School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
  • 2School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
  • *now at: NASA Postdoctoral Program, NASA Langley Research Center, Hampton, Virginia, USA

Abstract. We use the Global Modelling Initiative (GMI) chemical transport model with a cloud droplet parameterisation adjoint to quantify the sensitivity of cloud droplet number concentration to uncertainties in predicting CCN concentrations. Published CCN closure uncertainties for six different sets of simplifying compositional and mixing state assumptions are used as proxies for modelled CCN uncertainty arising from application of those scenarios. It is found that cloud droplet number concentrations (Nd) are fairly insensitive to the number concentration (Na) of aerosol which act as CCN over the continents (∂lnNd/∂lnNa ~10–30%), but the sensitivities exceed 70% in pristine regions such as the Alaskan Arctic and remote oceans. This means that CCN concentration uncertainties of 4–71% translate into only 1–23% uncertainty in cloud droplet number, on average. Since most of the anthropogenic indirect forcing is concentrated over the continents, this work shows that the application of Köhler theory and attendant simplifying assumptions in models is not a major source of uncertainty in predicting cloud droplet number or anthropogenic aerosol indirect forcing for the liquid, stratiform clouds simulated in these models. However, it does highlight the sensitivity of some remote areas to pollution brought into the region via long-range transport (e.g., biomass burning) or from seasonal biogenic sources (e.g., phytoplankton as a source of dimethylsulfide in the southern oceans). Since these transient processes are not captured well by the climatological emissions inventories employed by current large-scale models, the uncertainties in aerosol-cloud interactions during these events could be much larger than those uncovered here. This finding motivates additional measurements in these pristine regions, for which few observations exist, to quantify the impact (and associated uncertainty) of transient aerosol processes on cloud properties.

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