Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation

Journal Article · Mon Jul 08 00:00:00 EDT 2019 · Atmospheric Chemistry and Physics (Online)

DOI:https://doi.org/10.5194/acp-19-8591-2019· OSTI ID:1548281

Fanourgakis, George S. ^[1]; Kanakidou, Maria  ^[1]; Nenes, Athanasios  ^[2]; Bauer, Susanne E. ^[3]; Bergman, Tommi  ^[4]; Carslaw, Ken S.  ^[5]; Grini, Alf ^[6]; Hamilton, Douglas S.  ^[7]; Johnson, Jill S. ^[5]; Karydis, Vlassis A. ^[8]; Kirkevåg, Alf ^[9]; Kodros, John K.  ^[10]; Lohmann, Ulrike  ^[11]; Luo, Gan ^[12]; Makkonen, Risto ^[13]; Matsui, Hitoshi ^[14]; Neubauer, David  ^[11]; Pierce, Jeffrey R.  ^[10]; Schmale, Julia  ^[15]; Stier, Philip  ^[16] ; Tsigaridis, Kostas  ^[17]; van Noije, Twan  ^[4]; Wang, Hailong ^[18]; Watson-Parris, Duncan  ^[16]; Westervelt, Daniel M.  ^[17]; Yang, Yang  ^[18]; Yoshioka, Masaru ^[5]; Daskalakis, Nikos  ^[19]; Decesari, Stefano ^[20]; Gysel-Beer, Martin  ^[15]; Kalivitis, Nikos ^[1]; Liu, Xiaohong ^[21]; Mahowald, Natalie M. ^[7]; Myriokefalitakis, Stelios  ^[22]; Schrödner, Roland ^[23]; Sfakianaki, Maria ^[1]; Tsimpidi, Alexandra P. ^[24]; Wu, Mingxuan  ^[21]; Yu, Fangqun ^[12]

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1. Univ. of Crete, Heraklion (Greece)
2. Ecole Polytechnique Federale Lausanne (Switzlerland); Foundation for Research & Technology-Hellas, Crete (Greece)
3. NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States); Columbia Univ., New York, NY (United States)
4. Royal Netherlands Meteorological Inst. (KNMI), De Bilt (Netherlands)
5. Univ. of Leeds (United Kingdom)
6. Norwegian Meteorological Inst., Oslo (Norway); Univ. of Oslo (Norway); Centre National de Recherches Météorologiques, Toulouse (France); Univ. of Helsinki (Finland)
7. Cornell Univ., Ithaca, NY (United States)
8. Max Planck Society, Mainz (Germany); Forschungszentrum Julich (Germany)
9. Norwegian Meteorological Inst., Oslo (Norway)
10. Colorado State Univ., Fort Collins, CO (United States)
11. Univ. of Zurich (Switzerland)
12. State Univ. of New York (SUNY), Albany, NY (United States)
13. Finnish Meteorological Inst., Helsinki (Finland); Univ. of Helsinki (Finland)
14. Nagoya Univ. (Japan)
15. Paul Scherrer Inst. (PSI), Villigen (Switzerland)
16. Univ. of Oxford (United Kingdom)
17. Columbia Univ., New York, NY (United States); NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)
18. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
19. Univ. of Bremen (Germany)
20. Italian National Research Council, Bologna (Italy). Inst. of Atmospheric Sciences and Climate (ISAC)
21. Univ. of Wyoming, Laramie, WY (United States)
22. National Observatory of Athens, Penteli (Greece)
23. Lund Univ. (Sweden)
24. Max Planck Society, Mainz (Germany)

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 model shave 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 andshort-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 dataare available, of –24% and –35% for particles with dry diameters <50 and <120 nm, as well as –36% and –34% for CCNat supersaturations of 0.2% and 1.0%, respectively. Yet, 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% (CCN_0.2) 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 o naverage 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 0.6m s^–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( $\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$) and to updraft velocity ( $\partial N_{\mathrm{d}}/\partial w$). 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 $\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$ and $\partial N_{\mathrm{d}}/\partial w$; 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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); European Research Council (ERC)
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 1548281
• Report Number(s): PNNL-SA-143854
• Journal Information: Atmospheric Chemistry and Physics (Online), Vol. 19, Issue 13; ISSN 1680-7324
• Publisher: European Geosciences Union
• Country of Publication: United States
• Language: English

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