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Title: The AeroCom evaluation and intercomparison of organic aerosol in global models

This paper evaluates the current status of global modeling of the organic aerosol (OA) in the troposphere and analyzes the differences between models as well as between models and observations. Thirty-one global chemistry transport models (CTMs) and general circulation models (GCMs) have participated in this intercomparison, in the framework of AeroCom phase II. The simulation of OA varies greatly between models in terms of the magnitude of primary emissions, secondary OA (SOA) formation, the number of OA species used (2 to 62), the complexity of OA parameterizations (gas-particle partitioning, chemical aging, multiphase chemistry, aerosol microphysics), and the OA physical, chemical and optical properties. The diversity of the global OA simulation results has increased since earlier AeroCom experiments, mainly due to the increasing complexity of the SOA parameterization in models, and the implementation of new, highly uncertain, OA sources. Diversity of over one order of magnitude exists in the modeled vertical distribution of OA concentrations that deserves a dedicated future study. Furthermore, although the OA / OC ratio depends on OA sources and atmospheric processing, and is important for model evaluation against OA and OC observations, it is resolved only by a few global models. The median global primary OA (POA)more » source strength is 56 Tg a–1 (range 34–144 Tg a−1) and the median SOA source strength (natural and anthropogenic) is 19 Tg a–1 (range 13–121 Tg a−1). Among the models that take into account the semi-volatile SOA nature, the median source is calculated to be 51 Tg a–1 (range 16–121 Tg a−1), much larger than the median value of the models that calculate SOA in a more simplistic way (19 Tg a–1; range 13–20 Tg a–1, with one model at 37 Tg a−1). The median atmospheric burden of OA is 1.4 Tg (24 models in the range of 0.6–2.0 Tg and 4 between 2.0 and 3.8 Tg), with a median OA lifetime of 5.4 days (range 3.8–9.6 days). In models that reported both OA and sulfate burdens, the median value of the OA/sulfate burden ratio is calculated to be 0.77; 13 models calculate a ratio lower than 1, and 9 models higher than 1. For 26 models that reported OA deposition fluxes, the median wet removal is 70 Tg a–1 (range 28–209 Tg a−1), which is on average 85% of the total OA deposition. Fine aerosol organic carbon (OC) and OA observations from continuous monitoring networks and individual field campaigns have been used for model evaluation. At urban locations, the model–observation comparison indicates missing knowledge on anthropogenic OA sources, both strength and seasonality. The combined model–measurements analysis suggests the existence of increased OA levels during summer due to biogenic SOA formation over large areas of the USA that can be of the same order of magnitude as the POA, even at urban locations, and contribute to the measured urban seasonal pattern. Global models are able to simulate the high secondary character of OA observed in the atmosphere as a result of SOA formation and POA aging, although the amount of OA present in the atmosphere remains largely underestimated, with a mean normalized bias (MNB) equal to –0.62 (–0.51) based on the comparison against OC (OA) urban data of all models at the surface, –0.15 (+0.51) when compared with remote measurements, and –0.30 for marine locations with OC data. The mean temporal correlations across all stations are low when compared with OC (OA) measurements: 0.47 (0.52) for urban stations, 0.39 (0.37) for remote stations, and 0.25 for marine stations with OC data. The combination of high (negative) MNB and higher correlation at urban stations when compared with the low MNB and lower correlation at remote sites suggests that knowledge about the processes that govern aerosol processing, transport and removal, on top of their sources, is important at the remote stations. There is no clear change in model skill with increasing model complexity with regard to OC or OA mass concentration. As a result, the complexity is needed in models in order to distinguish between anthropogenic and natural OA as needed for climate mitigation, and to calculate the impact of OA on climate accurately.« less
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [1] ;  [8] ;  [9] ;  [10] ;  [11] ;  [12] ;  [13] ;  [14] ;  [15] ;  [16] ;  [17] ;  [18] ;  [18] more »;  [19] ;  [20] ;  [21] ;  [22] ;  [4] ;  [23] ;  [24] ;  [25] ;  [26] ;  [10] ;  [27] ;  [28] ;  [29] ;  [30] ;  [31] ;  [14] ;  [3] ;  [9] ;  [32] ;  [33] ;  [2] ;  [34] ;  [35] ;  [28] ;  [36] ;  [37] ;  [7] ;  [25] ;  [7] ;  [25] ;  [38] ;  [28] ;  [33] ;  [14] ;  [32] ;  [39] ;  [40] ;  [41] ;  [20] ;  [42] ;  [43] ;  [12] ;  [44] ;  [30] ;  [45] ;  [46] ;  [18] ;  [45] ;  [47] ;  [48] ;  [46] « less
  1. Columbia Univ., New York, NY (United States); NASA Goddard Institute for Space Studies, New York, NY (United States)
  2. Univ. of Crete, Heraklion (Greece); Foundation for Research and Technology Hellas, Patras (Greece)
  3. Univ. of Crete, Heraklion (Greece)
  4. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  5. Univ. of Sao Paulo (Brazil)
  6. Univ. of California, San Diego, CA (United States)
  7. Lab. des Sciences du Climate et de l'Environment, Gif-sur-Yvette (France)
  8. Met Office Hadley Centre, Exeter (United Kingdom); Johannes Gutenberg Univ., Mainz (Germany)
  9. ECMWF, Reading (United Kingdom)
  10. Finnish Meteorological Inst. (FMI), Kuopio (Finland)
  11. Univ. of Oslo, Oslo (Norway); Center for International Climate and Environmental Research - Oslo (CICERO), Oslo (Norway)
  12. North-West Univ., Potchefstroom (South Africa)
  13. Univ. of Maryland, Baltimore, MD (United States)
  14. Univ. of Leeds, Leeds (United Kingdom)
  15. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
  16. Univ. of L'Aquila (Italy)
  17. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Universities Space Research Association, Greenbelt, MD (United States)
  18. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  19. Meteorological Service of Canada, Toronto, ON (Canada)
  20. National Center for Atmospheric Research, Boulder, CO (United States)
  21. Paul Scherrer Inst. (PSI), Villigen (Switzerland); Swiss Federal Institute for Forest Snow and Landscape Research (WFL) - Institute for Snow and Avalanche Research (SFL), Davos (Switzerland)
  22. ECMWF, Reading (United Kingdom); Univ. of Oslo, Oslo (Norway); Norwegian Meteorological Institute, Oslo (Norway)
  23. Univ. of Colorado, Boulder, CO (United States)
  24. ECMWF, Reading (United Kingdom); King's College London, London (United Kingdom); Max Planck Institute for Chemistry, Mainz (Germany)
  25. Norwegian Meteorological Institute, Oslo (Norway)
  26. Columbia Univ., New York, NY (United States); NASA Goddard Institute for Space Studies, New York, NY (United States); Office of Biological and Environmental Research, Washington, D.C. (United States)
  27. Carnegie Mellon Univ., Pittsburgh, PA (United States); Columbia Univ., New York, NY (United States); NASA Goddard Institute for Space Studies, New York, NY (United States)
  28. Univ. of Michigan, Ann Arbor, MI (United States)
  29. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Univ. of Wyoming, Laramie, WY (United States)
  30. State Univ. of New York, Albany, NY (United States)
  31. Environment Canada, Victoria (Canada); State Univ. of New York, Albany, NY (United States)
  32. Belgian Institute for Space Aeronomy, Brussels (Belgium)
  33. Center for International Climate and Environmental Research - Oslo (CICERO), Oslo (Norway)
  34. Georgia Inst. of Technology, Atlanta, GA (United States)
  35. Max Planck Institute for Meteorology, Hamburg (Germany); Finnish Meteorological Institute, Helsinki (Finland)
  36. Istanbul Technical Univ. (Turkey)
  37. Max Planck Institute for Chemistry, Mainz (Germany); Univ. of Leeds, Leeds (United Kingdom)
  38. Columbia Univ., New York, NY (United States); NASA Goddard Institute for Space Studies, New York, NY (United States); Duke Univ., Durham, NC (United States)
  39. Universities Space Research Association, Greenbelt, MD (United States)
  40. Kyushu Univ., Fukuoka (Japan)
  41. North-West Univ., Potchefstroom (South Africa); Univ. of Eastern Finland, Kuopio (Finland)
  42. Johannes Gutenberg Univ., Mainz (Germany)
  43. Royal Netherlands Meteorological Institute (KNMI), DeBilt (The Netherlands)
  44. Environment Canada, Victoria (Canada)
  45. China Meteorological Administration, Beijing (China)
  46. Chinese Academy of Meteorological Sciences, Beijing (China)
  47. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Max Planck Institute for Meteorology, Hamburg (Germany)
  48. Univ. of California, Davis, CA (United States)
Publication Date:
OSTI Identifier:
1165323
Report Number(s):
PNNL-SA--101226
Journal ID: ISSN 1680-7324; KP1703020
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 14; Journal Issue: 19; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES AeroCom; intercomparison; organic aerosol; global models