Articles | Volume 11, issue 17
https://doi.org/10.5194/acp-11-8847-2011
https://doi.org/10.5194/acp-11-8847-2011
Research article
 | 
01 Sep 2011
Research article |  | 01 Sep 2011

Investigating organic aerosol loading in the remote marine environment

K. Lapina, C. L. Heald, D. V. Spracklen, S. R. Arnold, J. D. Allan, H. Coe, G. McFiggans, S. R. Zorn, F. Drewnick, T. S. Bates, L. N. Hawkins, L. M. Russell, A. Smirnov, C. D. O'Dowd, and A. J. Hind

Abstract. Aerosol loading in the marine environment is investigated using aerosol composition measurements from several research ship campaigns (ICEALOT, MAP, RHaMBLe, VOCALS and OOMPH), observations of total AOD column from satellite (MODIS) and ship-based instruments (Maritime Aerosol Network, MAN), and a global chemical transport model (GEOS-Chem). This work represents the most comprehensive evaluation of oceanic OM emission inventories to date, by employing aerosol composition measurements obtained from campaigns with wide spatial and temporal coverage. The model underestimates AOD over the remote ocean on average by 0.02 (21 %), compared to satellite observations, but provides an unbiased simulation of ground-based Maritime Aerosol Network (MAN) observations. Comparison with cruise data demonstrates that the GEOS-Chem simulation of marine sulfate, with the mean observed values ranging between 0.22 μg m−3 and 1.34 μg m−3, is generally unbiased, however surface organic matter (OM) concentrations, with the mean observed concentrations between 0.07 μg m−3 and 0.77 μg m−3, are underestimated by a factor of 2–5 for the standard model run. Addition of a sub-micron marine OM source of approximately 9 TgC yr−1 brings the model into agreement with the ship-based measurements, however this additional OM source does not explain the model underestimate of marine AOD. The model underestimate of marine AOD is therefore likely the result of a combination of satellite retrieval bias and a missing marine aerosol source (which exhibits a different spatial pattern than existing aerosol in the model).

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