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.
Long-term observations of atmospheric aerosol, cloud condensation nuclei concentration and hygroscopicity in the Amazon rain forest – Part 1: Size-resolved characterization and new model parameterizations for CCN prediction