Abstract

Abstract. A model of carbonaceous aerosols has been implemented in the TwO-Moment Aerosol Sectional (TOMAS) microphysics module in the GEOS-Chem chemical transport model (CTM), a model driven by assimilated meteorology. Inclusion of carbonaceous emissions alongside pre-existing treatments of sulfate and sea-salt aerosols increases the number of emitted primary aerosol particles by a factor of 2.5 and raises annual-average global cloud condensation nuclei at 0.2% supersaturation (CCN(0.2%)) concentrations by a factor of two. Compared to the prior model without carbonaceous aerosols, this development improves the model prediction of condensation nuclei with dry diameter larger than 10 nm (CN10) number concentrations significantly from −45% to −7% bias when compared to long-term observations. Inclusion of carbonaceous particles also largely eliminates a tendency for the model to underpredict higher cloud condensation nuclei (CCN) concentrations. Similar to other carbonaceous models, the model underpredicts organic carbon (OC) and elemental carbon (EC) mass concentrations by a factor of 2 when compared to EMEP and IMPROVE observations. Because primary organic aerosol (POA) and secondary organic aerosol (SOA) affect aerosol number size distributions via different microphysical processes, we assess the sensitivity of CCN production, for a fixed source of organic aerosol (OA) mass, to the assumed POA–SOA split in the model. For a fixed OA budget, we found that CCN(0.2%) decreases nearly everywhere as the model changes from a world dominated by POA emissions to one dominated by SOA condensation. POA is about twice as effective per unit mass at CCN production compared to SOA. Changing from a 100% POA scenario to a 100% SOA scenario, CCN(0.2%) concentrations in the lowest model layer decrease by about 20%. In any scenario, carbonaceous aerosols contribute significantly to global CCN. The SOA–POA split has a significant effect on global CCN, and the microphysical implications of POA emissions versus SOA condensation appear to be at least as important as differences in chemical composition as expressed by the hygroscopicity of OA. These findings stress the need to better understand carbonaceous aerosols loadings, the global SOA budget, microphysical pathways of OA formation (emissions versus condensation) as well as chemical composition to improve climate modeling.

Highlights

  • Cloud condensation nuclei (CCN) are the fraction of aerosol particles that activate to become cloud droplets

  • The treatment of aging is crude, this result may be seen as broadly consistent with ambient data that show a predominance of oxygenated organic aerosol (OOA) over hydrocarbon-like organic aerosol (HOA) (Zhang et al, 2007) as well as data that show that typically 60 % of OA is water soluble (Kerminen, 1997)

  • Contributions of carbonaceous aerosol to the CN10 and CCN(0.2 %) predictions have been examined in comparison to a simulation with only sulfate and sea salt aerosols

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Summary

Introduction

Cloud condensation nuclei (CCN) are the fraction of aerosol particles that activate to become cloud droplets. Previous work has demonstrated that uncertainty in the total SOA source has a significant impact on global CCN concentrations (Pierce and Adams, 2009). Despite great uncertainty in the POA–SOA split, total organic aerosol sources, on the other hand, may be better quantified than those of POA or SOA individually. This work will explore the sensitivity of CCN concentrations to different POA–SOA split assumptions and ask whether POA or SOA, per unit mass, is better at forming CCN. We perform sensitivity simulations to test different POA–SOA split assumptions Results from these simulations are used to answer the question whether POA or SOA is more effective at CCN formation.

Overview
Improvements in particulate emissions
Carbonaceous aerosols implementation
Emissions
Secondary organic aerosol
Overview of simulations
Global aerosol distributions
Contribution of carbonaceous aerosols to CN and CCN
Aerosol number and CCN concentrations
Effects of POA–SOA split on CCN
Effect on global CCN distribution
Effect on size distributions
Effect on global CCN burdens
Conclusions
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