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Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-171
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Feb 2018

Research article | 19 Feb 2018

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This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

Size-resolved mixing state of black carbon in the Canadian high Arctic and implications for simulated direct radiative effect

John K. Kodros1, Sarah Hanna2, Allan Bertram2, W. Richard Leaitch3, Hannes Schulz4, Andreas Herber4, Marco Zanatta4, Julia Burkart5, Megan Willis5, Jonathan P. D. Abbatt5, and Jeffrey R. Pierce1 John K. Kodros et al.
  • 1Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, 80523, USA
  • 2Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
  • 3Climate Chemistry Measurements and Research, Climate Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, Canada
  • 4Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven, Bremerhaven, Germany
  • 5University of Toronto, Department of Chemistry, Toronto, Ontario, Canada

Abstract. Transport of anthropogenic aerosol into the Arctic in the spring months has the potential to affect regional climate; however, modeling estimates of the aerosol direct radiative effect (DRE) are sensitive to uncertainties in the mixing state of black carbon (BC). A common approach in previous modeling studies is to assume an entirely external mixture (all primarily scattering species are in separate particles from BC) or internal mixture (all primarily scattering species are mixed in the same particles as BC). To provide constraints on the size-resolved mixing state of BC, we use airborne Single Particle Soot Photometer (SP2) and Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) measurements from the Alfred Wegener Institute (AWI) POLAR6 flights from the NETCARE/PAMARCMIP2015 campaign to estimate coating thickness as a function of refractory BC (rBC) core diameter as well as the fraction of particles containing rBC in the springtime Canadian high Arctic. For rBC core diameters in the range of 140 to 220nm, we find average coating thicknesses of approximately 45 to 40nm, respectively, resulting in ratios of total particle diameter to rBC core diameters ranging from 1.6 to 1.4. For total particle diameters ranging from 175 to 730nm, rBC-containing particle number fractions range from 16 to 3%, respectively. We combine the observed mixing-state constraints with simulated size-resolved aerosol mass and number distributions from GEOS-Chem-TOMAS to estimate the DRE with observed bounds on mixing state as opposed to assuming an entirely external or internal mixture. We find that the pan-Arctic average springtime DRE ranges from −1.65Wm−2 to −1.34Wm−2 when assuming entirely externally or internally mixed BC. Using the observed mixing-state constraints, we find the DRE is 0.05Wm−2 and 0.19Wm−2 less negative than the external mixing-state assumption when constraining by coating thickness of the mixed particles and by BC-containing particle number fraction, respectively. The difference between these methods is due to an underestimation of BC mass fraction in the springtime Arctic in GEOS-Chem-TOMAS compared to POLAR6 observations. Measurements of mixing state provide important constraints for model estimates of DRE.

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John K. Kodros et al.
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The mixing state of black carbon is one of the key uncertainties limiting the ability of models to estimate the direct radiative effect. In this work, we present aircraft measurements from the Canadian Arctic of coating thickness as a function of black carbon core diameter as well as black carbon containing particle number fractions. We use these measurements to inform estimates of the direct radiative effect in Arctic aerosol simulations.
The mixing state of black carbon is one of the key uncertainties limiting the ability of models...
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