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Fanourgakis GS, Kanakidou M, Nenes A, Bauer SE, Bergman T, Carslaw KS, Grini A, Hamilton DS, Johnson JS, Karydis VA, Kirkevåg A, Kodros JK, Lohmann U, Luo G, Makkonen R, Matsui H, Neubauer D, Pierce JR, Schmale J, Stier P, Tsigaridis K, van Noije T, Wang H, Watson-Parris D, Westervelt DM, Yang Y, Yoshioka M, Daskalakis N, Decesari S, Gysel-Beer M, Kalivitis N, Liu X, Mahowald NM, Myriokefalitakis S, Schrödner R, Sfakianaki M, Tsimpidi AP, Wu M, Yu F. Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation. ATMOSPHERIC CHEMISTRY AND PHYSICS 2019; 19:8591-8617. [PMID: 33273898 PMCID: PMC7709872 DOI: 10.5194/acp-19-8591-2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB -13% and -22% for updraft velocities 0.3 and 0.6 ms-1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂N d/∂N a) and to updraft velocity (∂N d/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂N d/∂N a and ∂N d/∂w; models may be predisposed to be too "aerosol sensitive" or "aerosol insensitive" in aerosol-cloud-climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.
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Affiliation(s)
- George S. Fanourgakis
- Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, 70013, Greece
| | - Maria Kanakidou
- Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, 70013, Greece
| | - Athanasios Nenes
- Laboratory of Atmospheric Processes and their Impacts, School of Architecture, Civil & Environmental Engineering, École Polytechnique Federale de Lausanne, Lausanne, 1015, Switzerland
- Institute of Chemical Engineering Sciences, Foundation for Research and Technology (FORTH/ICE-HT), Hellas, 26504, Patras, Greece
| | - Susanne E. Bauer
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Center for Climate Systems Research, Columbia University, New York, NY, USA
| | - Tommi Bergman
- Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
| | - Ken S. Carslaw
- School of Earth and Environment, University of Leeds, UK
| | | | - Douglas S. Hamilton
- Department of Earth and Atmospheric Sciences, Atkinson Center for a Sustainable Future, Cornell University, Ithaca, NY, USA
| | | | - Vlassis A. Karydis
- Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
- Forschungszentrum Jülich, Inst Energy & Climate Res IEK-8, 52425 Jülich, Germany
| | - Alf Kirkevåg
- Norwegian Meteorological Institute, Oslo, Norway
| | - John K. Kodros
- Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
| | - Ulrike Lohmann
- Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
| | - Gan Luo
- Climate Atmospheric Sciences Research Center , of the State University of New York at Albany, Albany, 12203, New York, USA
| | - Risto Makkonen
- Climate System Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
- Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
| | - Hitoshi Matsui
- Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
| | - David Neubauer
- Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
| | - Jeffrey R. Pierce
- Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
| | - Julia Schmale
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland
| | - Philip Stier
- Atmospheric, Oceanic & Planetary Physics, Department of Physics, University of Oxford, Oxford OX1 2JD, UK
| | - Kostas Tsigaridis
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Center for Climate Systems Research, Columbia University, New York, NY, USA
| | - Twan van Noije
- Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
| | - Hailong Wang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Duncan Watson-Parris
- Atmospheric, Oceanic & Planetary Physics, Department of Physics, University of Oxford, Oxford OX1 2JD, UK
| | - Daniel M. Westervelt
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
| | - Yang Yang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Nikos Daskalakis
- Laboratory for Modeling and Observation of the Earth System (LAMOS) Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
| | - Stefano Decesari
- Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Via Piero Gobetti, 101, 40129 Bologna, Italy
| | - Martin Gysel-Beer
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland
| | - Nikos Kalivitis
- Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, 70013, Greece
| | - Xiaohong Liu
- Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA
| | - Natalie M. Mahowald
- Department of Earth and Atmospheric Sciences, Atkinson Center for a Sustainable Future, Cornell University, Ithaca, NY, USA
| | - Stelios Myriokefalitakis
- Institute for Environmental Research and Sustainable Development (IERSD), National Observatory of Athens, Penteli, Greece
| | - Roland Schrödner
- Centre for Environmental and Climate Research, Lund University, Lund, Sweden
| | - Maria Sfakianaki
- Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, 70013, Greece
| | - Alexandra P. Tsimpidi
- Department of Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
| | - Mingxuan Wu
- Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA
| | - Fangqun Yu
- Climate Atmospheric Sciences Research Center , of the State University of New York at Albany, Albany, 12203, New York, USA
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