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@ARTICLE{Fanourgakis:866829,
author = {Fanourgakis, George S. and Kanakidou, Maria and Nenes,
Athanasios and Bauer, Susanne E. and Bergman, Tommi and
Carslaw, Ken S. and Grini, Alf and Hamilton, Douglas S. and
Johnson, Jill S. and Karydis, Vlassis A. and Kirkevåg, Alf
and Kodros, John K. and Lohmann, Ulrike and Luo, Gan and
Makkonen, Risto and Matsui, Hitoshi and Neubauer, David and
Pierce, Jeffrey R. and Schmale, Julia and Stier, Philip and
Tsigaridis, Kostas and van Noije, Twan and Wang, Hailong and
Watson-Parris, Duncan and Westervelt, Daniel M. and Yang,
Yang and Yoshioka, Masaru and Daskalakis, Nikos and
Decesari, Stefano and Gysel-Beer, Martin and Kalivitis,
Nikos and Liu, Xiaohong and Mahowald, Natalie M. and
Myriokefalitakis, Stelios and Schrödner, Roland and
Sfakianaki, Maria and Tsimpidi, Alexandra P. and Wu,
Mingxuan and Yu, Fangqun},
title = {{E}valuation of global simulations of aerosol particle and
cloud condensation nuclei number, with implications for
cloud droplet formation},
journal = {Atmospheric chemistry and physics},
volume = {19},
number = {13},
issn = {1680-7324},
address = {Katlenburg-Lindau},
publisher = {EGU},
reportid = {FZJ-2019-05892},
pages = {8591 - 8617},
year = {2019},
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
>120 nm, 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 m 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 (∂Nd/∂Na) and to updraft velocity
(∂Nd/∂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 ∂Nd/∂Na and
∂Nd/∂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.},
cin = {IEK-8},
ddc = {550},
cid = {I:(DE-Juel1)IEK-8-20101013},
pnm = {243 - Tropospheric trace substances and their
transformation processes (POF3-243)},
pid = {G:(DE-HGF)POF3-243},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000474457300001},
doi = {10.5194/acp-19-8591-2019},
url = {https://juser.fz-juelich.de/record/866829},
}
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