ACP Atmospheric Chemistry and Physics ACP Atmos. Chem. Phys. 1680-7324 Copernicus Publications Göttingen, Germany 10.5194/acp-14-10845-2014 The AeroCom evaluation and intercomparison of organic aerosol in global models Tsigaridis K. https://orcid.org/0000-0001-5328-819X 2 1 Daskalakis N. https://orcid.org/0000-0002-2409-0392 3 4 Kanakidou M. https://orcid.org/0000-0002-1724-9692 3 Adams P. J. 6 5 Artaxo P. https://orcid.org/0000-0001-7754-3036 7 Bahadur R. 8 Balkanski Y. https://orcid.org/0000-0001-8241-2858 9 Bauer S. E. https://orcid.org/0000-0001-7823-8690 2 1 Bellouin N. https://orcid.org/0000-0003-2109-9559 10 47 Benedetti A. https://orcid.org/0000-0002-9971-9976 11 Bergman T. https://orcid.org/0000-0002-6133-2231 12 Berntsen T. K. 13 14 Beukes J. P. 15 Bian H. 16 Carslaw K. S. https://orcid.org/0000-0002-6800-154X 17 Chin M. 18 Curci G. https://orcid.org/0000-0001-9871-5570 19 Diehl T. 18 20 Easter R. C. 21 Ghan S. J. 21 Gong S. L. 22 Hodzic A. 23 Hoyle C. R. 24 25 Iversen T. https://orcid.org/0000-0001-6875-2979 11 13 26 Jathar S. https://orcid.org/0000-0003-4106-2358 5 Jimenez J. L. https://orcid.org/0000-0001-6203-1847 27 Kaiser J. W. https://orcid.org/0000-0003-3696-9123 11 28 29 Kirkevåg A. https://orcid.org/0000-0002-3691-554X 26 Koch D. 48 2 1 Kokkola H. https://orcid.org/0000-0002-1404-6670 12 Lee Y. H 49 5 Lin G. 30 Liu X. 21 50 Luo G. 31 Ma X. https://orcid.org/0000-0003-4485-8431 32 51 Mann G. W. 33 34 Mihalopoulos N. 3 Morcrette J.-J. 11 Müller J.-F. 35 Myhre G. https://orcid.org/0000-0002-4309-476X 14 Myriokefalitakis S. https://orcid.org/0000-0002-1541-7680 3 4 Ng N. L. 36 O'Donnell D. 37 52 Penner J. E. https://orcid.org/0000-0001-5577-452X 30 Pozzoli L. https://orcid.org/0000-0003-0485-9624 38 Pringle K. J. 29 39 Russell L. M. https://orcid.org/0000-0002-6108-2375 9 Schulz M. https://orcid.org/0000-0003-4493-4158 26 Sciare J. 9 Seland Ø. 26 Shindell D. T. https://orcid.org/0000-0003-1552-4715 53 2 1 Sillman S. 30 Skeie R. B. 14 Spracklen D. 17 Stavrakou T. 35 Steenrod S. D. 20 Takemura T. https://orcid.org/0000-0002-2859-6067 40 Tiitta P. https://orcid.org/0000-0003-4694-3648 15 41 Tilmes S. https://orcid.org/0000-0002-6557-3569 23 Tost H. 42 van Noije T. https://orcid.org/0000-0002-5148-5867 43 van Zyl P. G. https://orcid.org/0000-0003-1470-3359 15 von Salzen K. https://orcid.org/0000-0002-2991-6181 32 Yu F. 31 Wang Z. 44 Wang Z. 45 Zaveri R. A. 21 Zhang H. 44 Zhang K. https://orcid.org/0000-0003-0457-6368 21 37 Zhang Q. https://orcid.org/0000-0002-5203-8778 46 Zhang X. 45 Center for Climate Systems Research, Columbia University, New York, NY, USA NASA Goddard Institute for Space Studies, New York, NY, USA Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, Greece Institute of Chemical Engineering, Foundation for Research and Technology Hellas (ICE-HT FORTH), Patras, Greece Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA University of São Paulo, Department of Applied Physics, Brazil Scripps Institution of Oceanography, University of California San Diego, CA, USA Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France Met Office Hadley Centre, Exeter, UK ECMWF, Reading, UK Finnish Meteorological Institute, Kuopio, Finland University of Oslo, Department of Geosciences, Oslo, Norway Center for International Climate and Environmental Research – Oslo (CICERO), Oslo, Norway Environmental Sciences and Management, North-West University, Potchefstroom, South Africa University of Maryland, Joint Center for Environmental Technology, Baltimore County, MD, USA School of Earth and Environment, University of Leeds, Leeds, UK NASA Goddard Space Flight Center, Greenbelt, MD, USA Department of Physics CETEMPS, University of L'Aquila, Italy Universities Space Research Association, Greenbelt, MD, USA Pacific Northwest National Laboratory; Richland, WA, USA Air Quality Research Branch, Meteorological Service of Canada, Toronto, Ontario, Canada National Center for Atmospheric Research, Boulder, CO, USA Paul Scherrer Institute, Villigen, Switzerland Swiss Federal Institute for Forest Snow and Landscape Research (WSL) – Institute for Snow and Avalanche Research (SLF), Davos, Switzerland Norwegian Meteorological Institute, Oslo, Norway University of Colorado, Department of Chemistry & Biochemistry, Boulder, CO, USA King's College London, Department of Geography, London, UK Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, MI, USA State University of New York, Albany, NY, USA Environment Canada, Victoria, Canada National Centre for Atmospheric Science, University of Leeds, Leeds, UK School of Earth and Environment, University of Leeds, Leeds, UK Belgian Institute for Space Aeronomy, Brussels, Belgium School of Chemical and Biomolecular Engineering and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA Max Planck Institute for Meteorology, Hamburg, Germany Eurasia Institute of Earth Sciences, Istanbul Technical University, Turkey Institute for Climate and Atmospheric Science, School or Earth and Environment, University of Leeds, Leeds, UK Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan Fine Particle and Aerosol Technology Laboratory, Department of Environmental Science, University of Eastern Finland, Kuopio, Finland Institute for Atmospheric Physics, Johannes Gutenberg University, Mainz, Germany Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands Laboratory for Climate Studies, Climate Center, China Meteorological Administration, Beijing, China Chinese Academy of Meteorological Sciences, Beijing, China Department of Environmental Toxicology, University of California, Davis, CA, USA now at: Department of Meteorology, University of Reading, Reading, UK now at: Department of Energy, Office of Biological and Environmental Research, Washington, DC, USA now at: Center for Climate Systems Research, Columbia University, New York, NY, USA and NASA Goddard Institute for Space Studies, New York, NY, USA now at: University of Wyoming, Department of Atmospheric Science, Laramie, WY, USA now at: State University of New York, Department of Atmospheric Science, Albany, NY, USA now at: Finnish Meteorological Institute, Helsinki, Finland now at: Nicholas School of the Environment, Duke University, Durham, NC, USA 15 10 2014 14 19 10845 10895 Copyright: © 2014 K. Tsigaridis et al. 2014 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://acp.copernicus.org/articles/14/10845/2014/acp-14-10845-2014.html The full text article is available as a PDF file from https://acp.copernicus.org/articles/14/10845/2014/acp-14-10845-2014.pdf

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. <br><br> The median global primary OA (POA) source strength is 56 Tg a<sup>−1</sup> (range 34–144 Tg a<sup>&minus;1</sup>) and the median SOA source strength (natural and anthropogenic) is 19 Tg a<sup>−1</sup> (range 13–121 Tg a<sup>&minus;1</sup>). Among the models that take into account the semi-volatile SOA nature, the median source is calculated to be 51 Tg a<sup>−1</sup> (range 16–121 Tg a<sup>&minus;1</sup>), much larger than the median value of the models that calculate SOA in a more simplistic way (19 Tg a<sup>−1</sup>; range 13–20 Tg a<sup>−1</sup>, with one model at 37 Tg a<sup>&minus;1</sup>). 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<sup>−1</sup> (range 28–209 Tg a<sup>&minus;1</sup>), which is on average 85% of the total OA deposition. <br><br> 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. <br><br> 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. However, 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.

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