Abstract

Abstract. The global organic aerosol (OA) budget is highly uncertain and past studies suggest that models substantially underestimate observed concentrations. Few of these studies have examined the vertical distribution of OA. Furthermore, many model-measurement comparisons have been performed with different models for single field campaigns. We synthesize organic aerosol measurements from 17 aircraft campaigns from 2001–2009 and use these observations to consistently evaluate a GEOS-Chem model simulation. Remote, polluted and fire-influenced conditions are all represented in this extensive dataset. Mean observed OA concentrations range from 0.2–8.2 μg sm−3 and make up 15 to 70% of non-refractory aerosol. The standard GEOS-Chem simulation reproduces the observed vertical profile, although observations are underestimated in 13 of the 17 field campaigns (the median observed to simulated ratio ranges from 0.4 to 4.2), with the largest model bias in anthropogenic regions. However, the model is best able to capture the observed variability in these anthropogenically-influenced regions (R2=0.18−0.57), but has little skill in remote or fire-influenced regions. The model bias increases as a function of relative humidity for 11 of the campaigns, possibly indicative of missing aqueous phase SOA production. However, model simulations of aqueous phase SOA suggest a pronounced signature in the mid-troposphere (2–6 km) which is not supported in the observations examined here. Spracklen et al. (2011) suggest adding ~100 Tg yr−1 source of anthropogenically-controlled SOA to close the measurement-model gap, which we add as anthropogenic SOA. This eliminates the model underestimate near source, but leads to overestimates aloft in a few regions and in remote regions, suggesting either additional sinks of OA or higher volatility aerosol at colder temperatures. Sensitivity simulations indicate that fragmentation of organics upon either heterogeneous or gas-phase oxidation could be an important (missing) sink of OA in models, reducing the global SOA burden by 15% and 47% respectively. The best agreement with observations is obtained when the simulated anthropogenically-controlled SOA is increased to ~100 Tg yr−1 accompanied by either a gas-phase fragmentation process or a reduction in the temperature dependence of the organic aerosol partitioning (by decreasing the enthalpy of vaporization from 42 kJ mol−1 to 25 kJ mol−1). These results illustrate that models may require both additional sources and additional sinks to capture the observed concentrations of organic aerosol.

Highlights

  • Fine aerosols contain a substantial fraction of organic material over much of the world (Zhang et al, 2007)

  • Increasing Biogenic SOA (BSOA) in the model results in lesser improvements, and almost no model skill improvement is evident when simulated primary organic aerosol (POA) is tripled. This result is consistent with Spracklen et al (2011) who found that increasing either anthropogenic or biogenic secondary organic aerosol (SOA) sources reduced the GLOMAP model bias, but that only increasing anthropogenic sources enhanced the correlation with observations

  • The observations of elevated OA in the free troposphere from ACE-Asia (Maria et al, 2004; Huebert et al, 2004; Mader et al, 2002) remain an outlier, and contrary to Heald et al (2005), the other datasets examined here do not support a sustained source of organic aerosol in the free troposphere

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Summary

Introduction

Fine aerosols contain a substantial fraction of organic material over much of the world (Zhang et al, 2007). We use the spatial distributions of the observed organic aerosol concentrations across all these regions to investigate loading and the potential contribution of different sources and sinks as represented in the GEOS-Chem model. Hawkins et al (2010) report that ship-based AMS observations during this campaign were less than half of the FTIR measurements of OA made aboard the same platform, suggesting possible collection efficiency differences, with marine organics This may influence lower tropospheric OA concentrations for the comparable airborne instrument. This may reflect the ability of the model to better capture the strong gradients from near source to background (driven by emission inventories and the processes of dilution and removal) versus the smaller range of variability in aged air masses.

Aqueous phase SOA
Increasing Existing OA Sources
Increasing the loss of OA
Findings
Discussion and conclusions
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