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.
Introduction
Aerosol particles absorb and scatter radiation, thereby modulating the
planetary radiative balance (Boucher et al., 2013; Myhre et
al., 2013). They also provide the nuclei upon which cloud droplets and ice
crystals form; variations thereof can profoundly impact cloud formation and
precipitation. Both the direct radiative effects of aerosols and their
impacts on clouds are thought to be important for climate at global and
regional scales, although they are highly uncertain and confound projections
of anthropogenic climate change (e.g., Boucher et al., 2013; Seinfeld et al.,
2016). The impacts of aerosols on clouds in particular introduce
considerable uncertainty in our estimates of equilibrium climate sensitivity
and transient climate response to the combined changes in aerosol and
greenhouse gas concentrations (e.g., Seinfeld et al., 2016; Fan et al., 2016).
Aerosols can be either directly emitted from a variety of sources (primary
aerosols) or formed by nucleation from precursor compounds (secondary
aerosols), which afterwards can grow by condensation and coagulation from a
few nanometers to a few hundred nanometers (Kerminen et al., 2012). Note
that secondary aerosol also includes the condensed material upon primary
emitted aerosol. Aerosols that have the potential to create cloud droplets
at atmospherically relevant conditions are termed cloud condensation nuclei
(CCN). The CCN number concentration depends on the particle size
distribution, chemical composition and mixing state, as well as the level of
water vapor supersaturation that develops in rising air parcels (Köhler, 1936; Seinfeld and Pandis, 2006). It is now
established that primary emissions of particulate matter and particle
formation from anthropogenic precursor gases have strongly modulated clouds
and climate at the global scale since the industrial revolution (Boucher et al., 2013). Much work
remains, however, to reduce the uncertainty associated with anthropogenic
aerosol–cloud–climate interactions.
Among the main sources of uncertainty in simulating aerosol microphysics at
regional to global scales are the amounts of particle and precursor vapor
mass emitted by anthropogenic activities or natural sources, as well as the
size distribution of the emitted particles and their representation in
models. However, Mann
et al. (2012) showed that a careful choice of the aerosol parameters
describing the aerosol distribution can reduce differences between the
sectional and the modal description of aerosol microphysics in most parts of
the atmosphere. Furthermore, carbonaceous combustion aerosol, although
assumed hydrophobic upon emission, was found to contribute up to 64 % of
global surface CCN concentrations (Spracklen et al., 2011). Although less
important than particle size for CCN formation, particle chemical
composition determines aerosol hygroscopicity (Twomey, 1977; Dusek et al., 2006; Petters and Kreidenweis, 2007; Cubison et al., 2008; Bougiatioti
et al., 2009). An adequate description of aerosol hygroscopicity is required to
accurately describe CCN and cloud droplet number variability. In this
respect, uncertainties are partially related to organic aerosol (OA),
which can be composed of thousands of compounds with different physical and
chemical properties. OA contributes to the fine aerosol mass by up to
30 %–70 % depending on location and season (Kanakidou et al., 2005; Jimenez et al., 2009), while source estimates of OA span 1 order of
magnitude (see the AEROCOM phase II intercomparison study of 31 models by Tsigaridis et al., 2014). Regionally, sea salt (SS)
and mineral dust (DU) are also significant contributors to the total aerosol
particle mass and number concentration. Atmospheric mass loads during the
first phase of AEROCOM showed a high diversity among 15 models of 54 % for SS and 40 % for
DU (Textor et al., 2006). This diversity arises from the different
parameterizations used to calculate the size-resolved fluxes and their
dependence on wind speed but also from the consideration, or not, of the
super-coarse aerosol fraction (Huneeus
et al., 2011; Tsigaridis et al., 2013). Although
nitrate (NO3-) and ammonium (NH4+) are not explicitly
studied here, differences of up to a factor of 13 in the atmospheric burden of
NO3- and 17 and 4 for NH3 and NH4+, respectively,
have been found between AEROCOM models (Bian et al., 2017).
Formation of new particles by nucleation in the atmosphere is a frequent
phenomenon in the free troposphere and in the continental boundary layer
(e.g., Kerminen
et al., 2010; Kulmala and Kerminen, 2008),
and it is an important source of aerosol particle number on a global scale (Kerminen et al., 2012; Kalivitis et al., 2015; Gordon et al., 2017). Although
it is well established that sulfuric acid, due to its low volatility, plays
a central role in new particle formation and growth, it cannot explain the
observed substantial growth of small particles in many environments where
organics and NH3 are abundant. This is due to the low concentration of
sulfuric acid and is evidenced by the observed poor correlation of its
concentration with very small particles (e.g., Pierce et al., 2011). Recently, the
involvement of organics from early stages of nucleation and growth of
particles has been established (e.g., D'Andrea et al., 2013; Spracklen et al., 2008; Makkonen et al.,
2009; Tröstl et al.,
2016). Several approaches for modeling particle growth in large-scale models
have been developed, which are very sensitive to the volatility of organic
vapor (e.g., Laaksonen et al.,
2008; Yu, 2011; D'Andrea et
al., 2013) and are being implemented in global models.
The number concentration and the size of cloud droplets depend on both the
concentrations of CCN and on the cloud updraft velocity (Pruppacher and Klett, 1997; Seinfeld and Pandis, 2006).
However, the spatial scale of updrafts governing droplet formation is
several orders of magnitude smaller than the size of the grid boxes of
global models. Therefore, parameterized aerosol–cloud interactions in
climate models require sub-grid-scale vertical velocity distributions to
calculate grid-scale relevant cloud droplet number concentration (CDNC)
(Morales and Nenes, 2010). Karydis et al. (2012) and Moore et
al. (2013) have shown that in regions with low particle number
concentrations, such as the Arctic and remote oceans, CDNC is more sensitive
to CCN uncertainty than in continental regions where particle number
concentrations exceed 104 cm-3. In contrast, Ervens et al. (2010)
pointed out that at high updraft velocities, supersaturation is controlled
by adiabatic cooling, and CDNC is not very sensitive to errors in simulated
CCN number concentration. They estimated that uncertainties in the chemical
composition of aerosol particles that could lead to a doubling of CCN
concentration would affect CDNC by only about 10 %–20 %. Therefore, there
are two distinct regimes with regard to CDNC sensitivity:
aerosol limited and updraft velocity limited (Reutter et al., 2009).
Totally different cloud radiative (indirect) effects could be computed by
climate models depending on the dominance of CDNC sensitivity to either
aerosol number or updraft velocity (Sullivan
et al., 2016). Therefore, capturing the balance between the two is critical
in understanding where and when aerosol emissions are governing the
variability of cloud properties and where the updraft velocity is the
controlling factor. The failure of state-of-the-art models to capture such
sensitivity implies that even if models exhibit a similar magnitude of aerosol
indirect effects, it may be for completely different reasons (Sullivan et al., 2016). In this case models
would show limited skills and their predictions would be associated with low
confidence.
The aims of this work are to (i) assess the accuracy of state-of-the-art
global aerosol models in simulating the chemical composition and number
concentration of aerosol particles, with a focus on CCN concentrations at
various water vapor supersaturation ratios, (ii) document the diversity of the
global models in simulating these aerosol properties, (iii) produce an ensemble
view of the global distribution of aerosol particle and CCN number
concentrations, together with the most important particle chemical
components at the Earth's surface, (iv) evaluate the agreement of inferred CDNC
from modeled and from observed CCN spectra and their sensitivity to aerosol
number concentrations and updraft velocities, (v) evaluate the potential
causes of model diversity and bias versus observations using model
uncertainty analysis, and (vi) provide recommendations for future model
improvements.
A total of 16 global models contributed to this study, and multiyear observations
of CCN, size-resolved particle number concentration distributions, and
particle chemical composition obtained from eight atmospheric monitoring
stations in Europe and one in Japan were used as an observational reference,
representing distinct atmospheric environments (Schmale et al., 2017, 2018).
MethodologyContributing models and model description
Model setup, such as spatial resolution, meteorological conditions and
emission inventories, differs significantly among models (Tables
S1 to S4 in the Supplement). The spatial resolution varies among the models from 0.94∘ by 1.3∘ to 4∘ by 5.0∘
(latitude by longitude) and from 25 to 56 vertical layers up to 10 and even
0.1 hPa. Nine of the models are general circulation models (GCMs) and six
are chemical transport models (CTMs). The CTMs use prescribed (and
different) meteorological datasets, while the GCMs (with the exception of
GISS-E2-TOMAS) are nudged to various reanalysis products. Atmospheric
transport, secondary aerosol formation and removal of aerosols are driven by
wind, temperature, radiation, precipitation and relative humidity, as well
as cloud fraction and liquid water content. In addition, most of the models
use wind-driven dust, sea salt and marine organic aerosol emissions as well
as calculated online biogenic emissions of non-methane volatile organic
compounds (NMVOCs) (Table S3). Therefore, meteorology significantly affects
number concentration, composition and other metrics of aerosol particles.
Despite the recognized importance of organic compounds in nucleation (Tröstl et al., 2016),
several global models that participated in the present study use the binary
homogeneous nucleation of sulfuric acid and water (referred to later as BHN;
e.g., Kulmala et al., 1998; Vehkamäki, 2002) and
the contribution of organics to particle growth (see Sect. S1
and Table S2 and references therein). GEOS-Chem-TOMAS assumes a ternary
nucleation mechanism when NH3 is present and a binary one when NH3 is absent. GEOS-Chem-APM and CAM5-Chem-APM employ a ternary ion-mediated
nucleation (TIMN) scheme that considers both binary and ternary as well as
ion-mediated and neutral nucleation (Yu et al., 2018). New particle
formation in TM5 is calculated as a combination of BHN and organic–sulfuric
acid nucleation (Riccobono et al., 2014).
Once in the atmosphere, aerosols undergo transformations through chemical
and physical processes, such as coagulation, condensation and evaporation,
that modify their size and physical and chemical properties. These aerosol
microphysical processes are parameterized differently in models. Eight of
the models use modal schemes in which the evolution of particle number and
mass concentrations is described by lognormal distributions, and the
remaining models use the sectional approach with various numbers of
monodisperse size bins describing aerosol particle number concentration and
chemical composition (Table S2).
Regarding the eight modal models, six of them (the three ECHAM models, EMAC,
TM4-ECPL and TM5) are based on the M7 aerosol module developed by Vignati et al. (2004) for the description of aerosol
microphysics or improved versions of M7 to account for SO2 oxidation
to sulfuric acid, the contribution of organics to growth and additional aerosol
species. Other aerosol microphysics modules used in models participating in
this study are the Modal Aerosol Modules (MAM3 and MAM4; Liu
et al., 2012, 2016), the Advanced Particle Microphysics (APM) package (Yu and
Luo, 2009; Yu, 2011; Yu et al., 2018), the TwO-Moment
Aerosol Sectional (TOMAS) microphysics package (Adams and Seinfeld, 2002), the
Multiconfiguration Aerosol Tracker of mIXing state (MATRIX) module (e.g., Bauer et al., 2008), the Aerosol Two-dimensional bin
module for formation and Aging Simulation version 2 (ATRAS2; Matsui, 2017) and a production-tagged module OsloAero5.3 used
in combination with the offline microphysics scheme AeroTab5.3 (Kirkevåg et
al., 2018). Tables S1, S2, S3 and S4 provide a summary of the
main features of the participating models and appropriate references.
Relevant to this study are also differences in the aerosol components that
are taken into consideration in the models for the CCN calculations. Nine
models (CAM5-MAM3, CAM5-MAM4, CAM5.3-Oslo, the three ECHAM models,
GEOS-Chem-TOMAS, GISS-E2-TOMAS models and TM4-ECPL) do not account for
particulate nitrate at all or in the CCN calculations (Table S2). TM4-ECPL, however, computes the NO3- and NH4+ mass
distribution in fine and coarse modes with the ISORROPIA II module (Fountoukis
and Nenes, 2007). Similarly, TM5 uses EQSAM (Metzger et al., 2002b, a)
to calculate, using a bulk aerosol approach, the partitioning of ammonium
nitrate between the gaseous and particulate phase with the particulate mass
assumed to reside in the soluble accumulation mode.
Both dry deposition and wet deposition of aerosol particles are taken into
account in the participating models as shown in Table S4.
For dry deposition, models account for gravitational settling and for
turbulence, and thus these processes depend on the aerosol particle size. The
omission of super-coarse particle sources associated with dust and sea-salt
particles results in discrepancies between models and between model results
and observations (Myriokefalitakis et al., 2016). Wet deposition
parameterizations account for both in-cloud scavenging, which is sensitive
to the solubility of aerosol particles, and below-cloud scavenging by
convective and large-scale precipitation (Seinfeld and Pandis, 2006). In
addition, while all models account for in-cloud scavenging of aerosols and
for aerosol release from the evaporation of droplets, a few models also account
for melting and sublimation of ice crystals. For the calculation of CCN
concentrations from the aerosol number and mass distributions, models need
to specify their hygroscopicity from the volume-weighted hygroscopicities of
each constituent (Table 1) following the approach of Petters and Kreidenweis (2007).
Hygroscopicity parameters used by the participating models
for water uptake calculations.
1 In CAM5.3-Oslo the hygroscopicity parameters κ for pure
ammonium sulfate or sulfuric acid are 0.507 and 0.534, respectively. For
internal mixtures, κ is a mass-weighted average of the aerosol
components, except for particles coated (>2 nm) with SO4, OA
and/or SS, where κ is a mass-weighted average of the components of
the coating (Kirkevåg et al., 2018).
2 ECHAM6-HAM2 and ECHAM6-HAM2-AP use the Abdul-Razzak and Ghan
(AR-G) activation scheme (Abdul Razzak and Ghan, 2000). The
reported values are approximated using the number of ions and osmotic
coefficients used in the AR-G scheme.
(3) EMAC model simulates the effective hygroscopicity parameter κ of each aerosol size mode in order to describe the influence of chemical
composition on the CCN activity of aerosol particles (Pringle
et al., 2010). These values are the internally mixed κ calculated
across the nucleation, Aitken, accumulation and coarse modes. The effective
aerosol hygroscopicity parameter κ is calculated according to the
simple mixing rule proposed by Petters and Kreideweis (2007)
using the volume fraction and hygroscopicity parameter of each chemical
component (23 salts from ISORROPIA-II and 4 bulk species) taken from
Petters et al. (2007) and Sullivan et al. (2009)
(4) for hydrophilic OA κ=0.1, for hydrophobic OA κ=0.01 and
(5) for hydrophilic ORG (Lee et al., 2015). For hydrophobic, κ=0.
(6) for NaCl κ=1, for Na2SO4κ=0.95. N/A: not considered in this study.
Furthermore, most of the participating models (Table S4)
follow the AEROCOM recommendation of biomass burning emission heights, which
in the boreal regions extend above 2 km and up to 6 km for the Canadian
boreal fires (Dentener et al., 2006). ECHAM6-HAM2 and ECHAM6-HAM2-AP use a
slightly different vertical distribution of biomass burning emissions, with
75 % within the planetary boundary layer (PBL), 17 % in the first and
8 % in the second level above the PBL (Tegen et al., 2019). EMAC assumes
biomass burning emissions at 140 m and GEOS-Chem-APM well mixed in the
boundary layer.
In addition to these 15 models, we include the results from perturbed
parameter ensemble (PPE) simulations using HadGEM3-UKCA (Yoshioka et al.,
2019; see details in Sect. S1). The PPE consists of 235
atmosphere-only simulations for the year 2008 with 26 parameters controlling
aerosol emissions and processes perturbed simultaneously. Simulations were
nudged to ERA-Interim wind and temperature and all aerosol feedbacks to
atmospheric dynamics are turned off. Therefore, all simulations share the
same meteorology. CCN number concentrations were calculated globally for all
member simulations and taken at geographical locations and elevations of
observation stations. These simulations were then used to create Gaussian
process emulators at each station location from which 260 000 “model
variants” were generated that densely sample the 26-dimension parameter
space. The emulator was validated against additional model simulations to
show that the emulator uncertainty is much smaller than the model parametric
uncertainty.
Observational data for model evaluation
Datasets for CCN at various supersaturations, particle number concentrations,
size distributions and particle chemical compositions measured at one
atmospheric monitoring station in Japan and eight Aerosols, Clouds, and
Trace gases Research InfraStructure (ACTRIS) atmospheric monitoring stations
in Europe (Schmale et al., 2017) were used in the
present study (Fig. 1) for the evaluation of model results. The observatories
are representative of different environments (Pacific, Atlantic and
Mediterranean marine atmospheres, high alpine and boreal forest continental
atmospheres). A brief site description of the observatories is provided in
Table S5, while more technical details are given by Schmale et al. (2017). While in general measurement
data are available from the period 2011 to 2015, each station covered
only a subperiod of those 5 years but at least one entire year (Schmale
et al., 2017). Despite using point measurements, the long period of
observations allows for the evaluation of global models without biases
associated with the model resolution (Schutgens et al., 2016).
Six out of the nine stations provided non-refractory chemical composition
data on submicron particles (based on aerosol mass spectrometry), while all
stations recorded submicron particle number size distributions and CCN
number concentrations over a variety of supersaturations. A detailed
discussion of the observational results can be found in Schmale et al. (2018).
Map showing the location of the measurement sites used in
this study.
For this study, the observations of CCN concentrations at supersaturations
spanning between 0.1 % and 1.0 %, the number concentrations of aerosols with
dry diameters larger than 50, 80 and 120 nm (denoted hereafter as N50,
N80 and N120, respectively), and PM1 (particles with
dry diameters less than 1 µm) chemical composition (mainly sulfate
(SO42-, hereafter SO4) and organic aerosol – OA) from the nine
stations are used. The CCN data for these stations cover at least 75 % of
each year (Schmale et al., 2017). Observational data have been
further filtered so that there is a minimum data requirement, which means
that daily averages are calculated from hourly data only for days with at
least six hourly measurements. Monthly averages follow a similar method, whereby
the average is calculated only for months with at least 10 daily averages.
When fewer data are available, the data are not considered representative of
this quantity and are not included in the comparisons with the model
results.
Design of the experiment
This model experiment has been designed within the BACCHUS EU project and
has been opened for participation to the entire AEROCOM global modeling
community. Global simulations have been performed for the years 2010–2015
(2010 is used as a spin-up). SO4, BC, OA, SS and DU are the aerosol
components that are considered here. Models provided hourly values for
N50, N80, N120 and CCN number concentrations for 13 supersaturations ranging from 0.05 % up to 1.0 % (these are 0.05 %,
0.075 %, 0.1 %, 0.15 % and from 0.2 % to 1.0 % in increments of
0.1 %, denoted hereafter as CCNi, where i is the supersaturation value), as
well as the chemical composition of PM1 particles at the station
locations (Table S5). The large number of different supersaturations at
which CCN are computed allows for direct comparisons with all available
observations of CCN for the nine stations as well as for the calculation of
CDNC (Sect. 2.4). Among the models that participated in the present study
GISS-E2-TOMAS and HadGEM3-UKCA did not provide any results for the stations
due to meteorology not corresponding to the measurement time period
(free running for the first one and 2008 for the second); therefore, all
multi-model medians (MMMs) for the stations presented below have been computed
excluding these models.
Beyond station data, the global annual mean surface distribution of
CCN0.2, the particle numbers N3, N50 and N120, and the
mass composition of the PM1 particles for the year 2011 are provided by
15 models (HadGEM3-UKCA did not provide such results). The MMM has been
computed as the median of the contributing models.
In addition to the data provided by the 15 global models, the results of the
PPE using HadGEM3-UKCA (Yoshioka et al., 2019) are used in this study to
quantify the model parametric uncertainty in CCN and to perform a
sensitivity analysis to quantify how each parameter contributes to the
overall uncertainty.
Data interpretation methodologyCCN persistence
To investigate the duration for which the
CCN number concentration remains similar to its earlier concentration, the
so-called persistence, the autocorrelation function (ACF) of the CCN time
series, has been calculated as in Schmale et al. (2018) (see also
Sect. S2). This ACF may provide valuable information about the
drivers of the variability of the CCN number concentration in the
atmosphere. In the present study, we chose to compute the ACF based on model
results of CCN0.2 at the nine sampling sites and compare them with the
corresponding ACF obtained from observations (Schmale et al.,
2018). For a direct comparison, we use the same time periods as for the
observations, which vary among the sampling sites. For all ACF calculations,
hourly data on CCN0.2 were used for both the observations and model
results.
CDNC calculations
While GCMs calculate CDNC using a
variety of approaches, for the present study CDNC is calculated offline
using a common parameterization for CCN spectra derived from the models or
from the observations. This approach allows for an understanding of the importance of
differences in modeled and observed CCN spectra by expressing them as
differences in CDNC that would form in a given type of cloud. We have
calculated CDNC for two different updraft velocities: one characteristic for
stratiform clouds (w=0.3 m s-1) and the
second characteristic for cumulus clouds (w=0.6 m s-1), where w is the width of the vertical velocity distribution assuming a zero mean Gaussian. Similar calculations have been
performed using the observed CCN spectra at the stations where such
information is available to enable comparison of model results with
observations. The ability of the modeled CCN spectra to reproduce the
observed sensitivity of CDNC to aerosol or to updraft velocity is also
evaluated. Note that evaluation of the differences in CDNC calculations by
the different models that are derived from both the parameterizations used
and from their input variables would require a different model
intercomparison design than here and is planned for the future.
Morales Betancourt and Nenes (2014a) provide a good example in which the source
of CDNC prediction discrepancy for two state-of-the-art parameterizations in
the CAM5 global model was unraveled using adjoint sensitivity analysis.
That study pointed to exactly which aspects of the parameterization (i.e.,
water uptake from large CCN) were not captured adequately, leading to the
highly improved droplet parameterizations (Morales-Betancourt and Nenes,
2014b) that were used in the current study.
The calculation of CDNC is based on the parameterization of Nenes and Seinfeld (2003) with the mass transfer
augmentations proposed by Fountoukis
and Nenes (2005), Barahona and Nenes (2007), and Morales Betancourt and Nenes (2014b).
Using the CCN at different supersaturations (Sect. 2.3) allows us to
consistently construct the CCN spectrum function F(s) from each simulation, which provides the CCN number as a function of supersaturation, s (Sotiropoulou et al., 2006):
Fs=N1+sba,
where N is the total number of particles, and a and b are parameters determined
using a nonlinear fitting procedure for each of the participating
models. F(s) is then computed for each station's grid point and time step of
the model outputs (with b and a being fitting parameters), and CDNC, denoted
in the figures by Nd, is computed from the parameterization for
prescribed values of the vertical velocity. This fitting approach has
also been applied to the CCN observations since they are available only for a
limited number of supersaturations and thus cannot be directly used for
accurate calculation of CDNC. A well-constrained CCN spectrum requires
concentrations for at least five different supersaturations at the same time
instance (Sotiropoulou et al., 2006). Such information was
available only at five stations (Cabauw, Finokalia, Jungfraujoch, Mace Head
and Vavihill), which is subsequently used for deriving CDNC based on
observations and compared against model-derived CDNC.
The CDNC parameterization uses as input F(s), cloud-base pressure and
temperature, and the vertical velocity characterizing the cloud updraft
(either as a single updraft or a “characteristic” value that provides a
distribution-averaged value; Morales and
Nenes, 2010). It determines the value of maximum supersaturation,
smax, that develops in the cloudy updrafts using the concept of
“population splitting” (Nenes and Seinfeld, 2003).
smax is achieved during the cloud parcel ascent and is calculated
considering the water vapor balance between its availability from cooling
and its loss from condensational growth of the CCN (Fountoukis and Nenes,
2005). CDNC is then obtained from the CCN spectrum as
Nd=F(smax). This approach works well for stratus and stratocumulus clouds (Morales and Nenes, 2010). CDNC
calculated here is from primary activation and does not consider the
influence of preexisting droplets, although modifications to the
parameterization can account for this as well (e.g., Barahona et al., 2014).
Ensemble modeling computation
The modeled hourly aerosol
particle number concentrations, mass composition, CCN and CDNC at the nine
stations have been used to calculate daily and monthly averages. Comparison
of individual model results with observations is provided in Figs. S2 and S3 because it can be used to identify
the strengths and weaknesses of each specific model and can serve as a guide for
model improvements in the future. In Sect. 3, the multi-model median (MMM)
is compared to observations. The diversity of the model results (defined as
the ratio of standard deviation to mean) and the mean of the models, which
in several cases significantly differs from the MMM, are also reported in
these comparisons.
Annual averages of the global surface distributions of N3, N50,
N120, CCN0.2 and PM1 mass concentrations of the major aerosol
components have been provided by a total of 15 models. Global fields
have first been re-gridded to a 5∘× 5∘ grid
for all models, which is close to the coarsest-resolved participating models
(4∘× 5∘). Then the MMM and diversity are calculated, as described
above, for the stations. Note that 5∘× 5∘ is a
very coarse grid size, which no doubt affects the model-to-observation
comparison, particularly when comparing to sites within small heavily
polluted areas where a large rural background is now also being added in and
vice versa. Therefore, it is worth mentioning that the surface stations used
for model comparison are representative of the larger area in which they are
located and justify our choice for a relatively large grid to re-grid all
model results. For the mountain stations, the appropriate model level has
been considered that corresponds to the station's altitude above sea level.
Annual means of the individual models are also presented in Figs. S6–S14.
Performance indexes
For the comparison of model results
with observations, a number of statistics variables have been calculated and
defined as shown in Sect. S3.2. Hereafter we discuss the following:
theindexofagreementIoA=1-∑i=1NPi-Oi2∑i=1NOi-O‾+Pi-O‾2,thenormalizedmeanbiasNMB=∑i=1N(Pi-Oi)∑i=1NOi×100%andthenormalizedmeanerrorNME=∑i=1N|Pi-Oi|∑i=1NOi×100%,
where M represents model results, O represents observations, O‾ stands for the mean of the observations, and normalized mean bias (NMB), normalized mean error (NME) and the index of agreement (IoA) are used to quantify the
performance of the models to reproduce observations. The IoA is a measure of the
agreement of model results with the observations. In this study we use all
three for the evaluation of the capability of the models to reproduce the
observations. We calculate also
thePearsonlinearregressioncoefficient,r=∑i=1NPi-P‾Oi-O‾∑i=1NPi-P‾2∑i=1NOi-O‾2,
as a measure of the ability of the model results to represent the
variability in the observations.
Evaluation against station observationsCCN number concentration comparisons with multi-model median
The models tend to underestimate the monthly CCN0.2 number
concentration in the lowest model level at all sites (Figs. 2 and
S2) for the years 2011–2015: the average NMB of all models and for the nine sites is as low as -36 %, and the
NME is 69 %, while among individual models and
stations NMB and NME vary from -88 % to 145 % and from 40 % to
159 %, respectively (see Sect. S3.2 for definitions and
Table S6 for results). The Finokalia station is an exception, where most
models overestimate CCN0.2 (average NMB around 47 %) with eight
models showing significant overestimation (NMB>10 %) and six
models smaller deviations from observations (-10 % < NMB < 10 %). Among the studied locations, Finokalia is the station with the
highest observed critical diameter (∼200 nm at a
supersaturation of 0.2 % according to Schmale et al.,
2018); therefore, potential inaccuracies in the model determination of the
critical size may be responsible for the model overestimate of CCN0.2 at this station.
Monthly ensembles for the years 2011–2015 of the CCN number
concentration for supersaturation 0.2 % (CCN0.2). The CCN0.2
obtained from observational data is shown with symbols. The continuous bold
blue and red lines show the monthly median and mean of all models,
respectively. The shaded area shows 25 % and 75 % of the model results,
while the green dashed lines show the minimum and maximum values of all models.
Such a hypothesis is supported by earlier studies that have observed a large
size dependence of sensitivity in the activation fraction at low
supersaturations and in the size ranges between 60 and 100 nm (Bougiatioti
et al., 2011). Deng et al. (2013) reported inferred critical diameters
varying by factors of 2–3 for low supersaturations from 0.06 % to 0.2 %
and suggested the use of size-resolved particle number concentrations with
inferred critical diameters or size-resolved activation ratios to predict
CCN. Errors in CCN predictions have been shown to exceed 50 % only at very
low supersaturations (Reutter et al., 2009), reaching a factor of 2.4,
while at high supersaturations CCN overestimates can be less than 5 %
(Ervens et al., 2007). The global near-surface mean CCN prediction error has
been estimated at about 9 %, and regionally the maximum error can reach
40 % (Sotiropoulou et al., 2007). The largest CCN prediction error was
found in regions with low in-cloud smax, like those affected by
long-range transport of pollution or industrial pollution plumes.
Lower CCN prediction error was found in regions where in-cloud smax is high, which
is typical for pristine areas. Sotiropoulou et al. (2007) also found that
the assumption of a size-invariant chemical composition of internally mixed
aerosol increases the error by a factor of 2.
The underestimation of the observed CCN0.2 by the models is largest at
the high alpine site of Jungfraujoch (mean NMB of all models: -73 %),
where none of the models are able to capture the maximum observed values of
CCN0.2 (∼300–600 cm-3) during summer. Deficiencies
in the models' representation of the boundary layer and mixing of air
between the boundary layer and the free troposphere in complex terrain like
the Alps, as well as the sampling of the models based on the station's
altitude, might be reasons for this systematic underestimation by the models (D'Andrea et al., 2016). Despite the quantitative differences in the estimation of the
CCN0.2 concentrations, models are able to qualitatively capture the
relative differences in CCN0.2 concentrations between stations and their seasonal variations. Comparing the CCN0.2 as calculated from
the observations and as computed from the daily MMM for the days with
available observations for the stations, we find a Pearson linear
correlation coefficient (r) that varies between 0.44 (for Melpitz) and 0.83
(for Mace Head), showing significant covariation of model results with
observations. Furthermore, ranking the stations based on the observed mean
CCN0.2 levels (Fig. S17) we find that the corresponding
MMM mean follows this station ranking with the exception of Finokalia where,
as further discussed, the models overestimate the observed CCN0.2,
although they capture (r=0.76) the observed temporal variability well. The
MMM index of agreement (IoA) varies between 0.44 and 0.82 for the different
stations, with the best for the Finokalia remote coastal station and the worst
for the Jungfraujoch alpine station. The largest difference in performance among
models is found for the Mace Head station with an IoA varying between 0.20
and 0.89 for the individual models (Table S6).
To compare the calculated MMM and the observed seasonal variability of
CCN0.2 for each station (Fig. 3), the monthly model results have been
temporally co-located with monthly mean observations. Furthermore, to
increase clarity in Fig. 3, for each station, the MMM CCN0.2 has been
multiplied by a scaling factor, f, so that the four-season mean of the
simulated MMM CCN0.2 concentrations becomes equal to the corresponding
observed value. The factor f is denoted for each station inside the frame.
Overall, the seasonal pattern is nicely captured by the models, although the
absolute values are underestimated everywhere (f>1.50) except
at Finokalia (f=0.82) as discussed earlier.
Comparison of the seasonal variations of the observed and
model median computed CCN0.2. The solid bars show the average of the
observed CCN0.2 during each season and the shaded bars the
corresponding averages of the model results. The simulated CCN0.2
concentrations have been scaled by a factor, f (denoted in each graph), so
that the four-season mean is the same as the observed one. For Puy de
Dôme the normalization is based on the mean of three seasons (winter, summer
and fall) due to data availability.
For the high-altitude continental background sites (Puy de Dôme,
Jungfraujoch) low number concentrations with high seasonal variability are
observed (winter (DJF) minimum and summer (JJA) maximum with observed summer-to-winter ratios
of 2.17 and 5.37, respectively, while the simulated MMM
ratios are 3.19 and 5.58). This strong seasonality is attributed to changes
in the height of the boundary layer that can affect these sites during
summer but not during winter when the sites are mostly in the free
troposphere (Schmale et al., 2018; Jurányi et al., 2011). At Jungfraujoch the boundary layer
virtually never reaches up to the site. Instead, increased concentrations
are caused by injections of boundary layer air into the lower free
troposphere over the mountainous terrain. The free tropospheric background
concentration of CCN is very low such that increases in the number concentration
of CCN-sized particles (90 nm in diameter) are a good indicator for boundary
layer influence (Herrmann et al., 2015).
On the other hand, high CCN0.2 number concentrations but low seasonal
variability are found for the rural background stations of Cabauw and
Melpitz, indicative of the elevated air pollution background in these
regions. At these stations the highest CCN0.2 number concentrations are
observed during spring, which are underestimated by the MMM. Furthermore,
observations show a monotonous decrease from spring to summer and fall,
while models calculated higher summertime values than in spring and fall at
Cabauw and a monotonous increase from spring to fall at Melpitz. This could
indicate that the models are not following the observed changes in the
aerosol particle number concentration and/or the critical diameter at these
stations (Schmale et al., 2018), possibly also
associated with the adopted sizes in the primary aerosol emissions at these
locations. At the other rural background station (Vavihill), both models and
observations show lower CCN0.2 concentrations and seasonal
variability than at Cabauw or Melpitz. In addition, observations indicate a
higher critical diameter at Vavihill (around 120 nm) than at the other two
stations (around 90 nm) (Schmale et al., 2018).
Different seasonal cycles are also observed among the three coastal sites
Mace Head, Finokalia and the Noto Peninsula: at the Mace Head site, due to
the clean marine conditions over the Atlantic Ocean (Ovadnevaite et al., 2014), low CCN0.2 concentrations
are observed through the year. There, the highest concentrations are
observed and simulated during spring. Both Finokalia and the Noto Peninsula are
impacted by long-range transport that occurs through the free troposphere
and affects the surface by mixing down into the boundary layer, and the
models qualitatively reproduce the observed seasonal cycles, simulating a
high variation in the number concentration over the year. At Finokalia the
observed and simulated summer seasonal maximum is also attributed to biomass
burning plumes from northeastern Europe (Bougiatioti et
al., 2016), while high CCN0.2 concentrations peaking in spring
(observations available only for May) over the Noto Peninsula are due to
pollutants originating from East Asia (Iwamoto et al., 2016; Schmale et al., 2018). However, the observed sharp
decline of CCN0.2 during the spring (May) to summer transition over the
Noto Peninsula is also reproduced by the models. At Finokalia the models
qualitatively follow the observed seasonality, although the observed
summer-to-winter ratio (4.6) is underestimated by the models (2.3; Fig. 3).
This can be due to the CCN sensitivity to loss by deposition during winter
and to OA formation and hygroscopicity during summer that combined weaken
the simulated seasonality (further discussion in Sect. 5).
Finally, at Hyytiälä, on average the models calculate relatively
small CCN0.2 number concentrations and a low seasonal variability with
a maximum in concentrations in summer, in agreement with observations,
although they slightly underestimate the observed summer-to-winter ratio
(1.5 modeled versus 1.7 observed). As discussed further in Sect. 5, at
Hyytiälä the modeled CCN0.2 is very sensitive to errors in OA
hygroscopicity and in secondary organic aerosol (SOA) formation from
biogenic organic precursors during summer. Therefore, uncertainties in OA in
the models and in particular underestimates of OA are expected to affect the
summer-to-winter ratio.
Observed CCN number concentrations at the maximum supersaturation ratios
measured at each station (which vary among stations, ranging from 0.7 % to
1.0 %) are compared to model results in Fig. 4. CCN at various
supersaturation ratios provides insights into the size distribution and the
chemical composition in the models, since at high supersaturations smaller
and less hygroscopic particles also activate. Most models underestimate
CCN at high supersaturation at all stations with available
observations (Fig. 4), indicating that an insufficient number of small
particles are predicted to activate in the model. However, observations are
captured by the maximum and minimum of the 14 models (dashed green line)
except for the alpine Jungfraujoch station. Overall, the average NMB and NME
of all models and for all stations with available observations are -34 %
and 78 %, respectively, while among individual models and stations NMB
varies from about -89 % to about 253 % (Table S6).
Same as Fig. 2 for the CCN at the maximum supersaturation
with available measurements at each station. For Puy de Dôme only
CCN0.2 data are available and are shown in Fig. 2.
Comparing model performance for CCN at low supersaturation
(CCN0.2; Fig. 2) and at high supersaturation (CCN1.0; Fig. 4),
CCN1.0 is systematically underestimated by the models across all
stations. The NME of MMM for CCN0.2 ranges from 45 % (Finokalia) to
81 % (Jungfraujoch) for the different stations with significant
correlation coefficients between 0.44 (Melpitz) and 0.86 (Mace Head),
indicating that the MMM model is able to simulate the temporal variability
in the observations. For CCN at the highest supersaturation with available
observations the NME varies from 50 % (Finokalia) to 74 % (Mace Head)
and the correlation coefficients from 0.37 (Melpitz) to 0.78 (Mace Head)
(see also Table S6). These results indicate that CCN0.2
is in general better captured than CCN at higher supersaturations, both in
absolute values and in temporal variability. Since the number concentration
of CCN depends on both the chemical composition and the number of aerosol
particles, it is worth investigating the role of these two factors
separately.
CCN number concentration comparisons with PPE
CCN0.2 concentrations in perturbed parameter ensemble (PPE) simulations
using HadGEM3-UKCA (Yoshioka et al., 2019) for 2008 at these stations are
shown in Fig. 5, together with observations. The solid blue line shows the mean of the sample of 260 000 model variants that cover the multidimensional uncertainty of the PPE (sampled using an emulator). The blue shading shows the range of 1 standard deviation around
the mean, and the dotted lines show the minimum and maximum sampled values. The
range of 1 standard deviation either side of the mean value represents
approximately 68 % of all samples, and therefore the blue shading shows
approximately the same relative range as for the multi-model comparison in
Fig. 2 (25 % and 75 % quartiles). The MMM averaged for the years 2011–2015
is also plotted in this figure for comparison purposes together with the
25 % and 75 % quartile shaded area. The means of the available observations
from the different years are shown by symbols. Since the interannual
variability of simulated MMM CCN0.2 concentrations shown in Fig. 2 is
generally small compared to inter-model variability, the difference in years
between simulations and observations is not considered to undermine the
model–data comparisons.
Monthly average CCN0.2 based on HadGEM3-UKCA perturbed
parameter ensemble simulations for the year 2008. The solid blue line shows the
mean of the sample of 260 000 model variants from the emulator for each month and station. The shaded blue
area shows the range of this mean plus and minus 1 standard deviation,
while the blue dashed lines show the minimum and maximum sampled values.
The red line shows the MMM results (mean of the years 2011–2015 shown in
Fig. 2), and the shaded red area corresponds to the 25 % and 75 %
quartiles. The CCN0.2 values obtained from observational data are shown by
symbols (mean of the available data).
Monthly ensembles for the period 2011–2015 of the number
concentration of particles with diameters larger than 50 nm (N50 – in
red) and 120 nm (N120 – in green). The continuous lines correspond to
the median of the models for each month; the shaded areas show the 25 % and 75 %
quartiles and the dashed lines the minimum and maximum of all models for the
N50 (red area) and N120 (green area). Observational data are
available for all stations except Jungfraujoch and are shown with symbols of
the corresponding color.
Except for Mace Head, the uncertainty ranges in the PPE are somewhat smaller
than the 25 % and 75 % quartiles of the models shown in Fig. 2. This
suggests that model structural differences and the emission inventories used in
different models are more important sources of diversity of estimated
CCN0.2 concentrations for the central 70 % range than the fully sampled
parametric uncertainty in a single model. However, the maximum–minimum
ranges are much larger in the PPE than in the MMM at many locations.
Therefore, the values of the sampled model variants from the PPE are more concentrated near the mean
but have longer tails on their distribution compared to values from MMM. This is to be expected
from such a relatively small sample of models in the MMM.
Model–data comparisons are qualitatively similar to the case with MMM. The
PPE simulations underestimate the observed CCN0.2 concentrations at
many stations and in many months. Exceptions are Puy de Dôme and
Hyytiälä where PPE simulations reproduce the observations well for
most of the months and Finokalia where, just like MMM, the PPE overestimates
the observations. At Melpitz and Vavihill simulations capture the observed
values in summer but underestimate them in winter and early spring. The PPE
simulations fail to capture the observed peaks in winter and early spring at
Mace Head and Cabauw as well. This is unlike the case with MMM, which does
not show a distinct wintertime underestimate (Fig. 3). The qualitative
agreement between PPE and MMM indicates that the perturbed parameters are
those with significant control on aerosol processes and emissions and
can be used for CCN uncertainty attribution in Sect. 5.
Particle number concentration and PM1 aerosol chemical composition
The observed critical diameter for particle activation into CCN at 0.2 %
supersaturation at most of the locations in this study is around 100 nm or
larger, reaching about 200 nm in spring and summer at Finokalia (Schmale et al., 2018). Therefore, in Fig. 6, the MMMs
of the simulated N50 and N120 are depicted together with the
25 % and 75 % quartiles of all models that provided station data and are
compared with observations. N120 is expected to represent a significant
portion of the activated particles at 0.2 % or higher supersaturation. The
MMM underestimates N50, and on average NMB is -51 % and NME is 55 %
for all stations. N80 is not shown in this figure but follows a
similar behavior as N50 and N120. It is not surprising that in
almost all cases both the N50 and the N120 concentrations are
underestimated (the average NMB for MMM for all stations is -50 % and the
NME is 54 %) by a factor that is only slightly lower than the
underestimation of the CCN0.2 concentration (-50 % NMB and 60 %
NME). It may therefore be concluded that the quantitative differences
of the models in the prediction of CCN originate from the underestimation of
the number concentration of aerosol particles in the relevant size ranges.
Note, however, that the aerosol number concentration cannot be used as a proxy
for CCN levels since activation of aerosols to CCN depends not only on the
size distribution but also on the chemical composition of the aerosols as
well as on the supersaturation that develops in clouds (e.g., Seinfeld and
Pandis, 2006; Kalkavouras et al., 2019).
Figure S1 is similar to Figs. 2 and 4 but shows particulate SO4, OA mass
in PM1 particles at the nine stations, and model results for DU
and SS. Strong seasonal variations of the SO4 mass of about 1 order of
magnitude are observed and simulated at the alpine site, Jungfraujoch, and
at the coastal background stations, Mace Head and Finokalia, although winter
minima are overestimated by the models at these coastal sites. Smaller variation or
no clear seasonal variation of SO4 is observed at the boreal forest
environment of Hyytiälä, the rural background station Cabauw and at
the highly polluted Melpitz station during the year. At these three
stations, the MMM underestimates the observed annual mean concentration of
SO4. Strong seasonal variations of the OA mass are observed and simulated at
Mace Head, Finokalia, Jungfraujoch and Hyytiälä, while no distinct
seasonal cycle in organic mass is seen at Cabauw and Melpitz. The MMM is
underestimating OA concentrations at all sites. The IoA between the MMM and
the observations is between 0.28 and 0.62 for all stations. A detailed
analysis of each model separately (Table S6) shows that the OA
mass concentration is underestimated (mean NMB is -37 %) by nine of the
models and overestimated by six of them (range of NMB -97 % to 216 %).
Because different models are appearing as outliers at each station, it is
difficult to conclude whether the parameterizations in one model are better
than another. This, however, is consistent with the findings of a recent OA
intercomparison study that considered 31 models (Tsigaridis et al., 2014) and several modeling studies
that suggest a missing source of OA needed to reconcile observations with
model results (Spracklen
et al., 2011; Heald
et al., 2011). It appears therefore that in addition to the aerosol number
concentration discussed earlier, a possible source of error in the
simulation of aerosol and CCN number concentrations in the present study
originates from the underestimation of the submicron OA mass at the stations
where a significant contribution of the submicron OA mass to the CCN0.2
levels has been observed (Schmale et al., 2018). The importance of the contribution
of OA to the uncertainty of CCN is also supported by the PPE simulations
further discussed in Sect. 5.
CCN persistence
The above analysis of CCN and aerosol number concentrations shows that on
average the models are able to simulate the seasonal variations in CCN
concentrations, while the model-to-observation differences in the CCN
concentrations can be attributed mainly to a systematic underestimation of
the number of aerosol particles that are large enough to act as CCN. The
ability of models to simulate short-term variations (order of days) of the
CCN number concentration is examined based on the calculated persistence of
CCN0.2 number concentrations during summer and winter (see Sect. 2.4)
for all stations and for each model. The average persistence times for all
models are compared in Fig. 7 with those derived from the observations
(Schmale et al., 2018). Depending on the season and the station, the
persistence time varies from a few hours (e.g., summer in Mace Head) to
several days (e.g., winter in Melpitz).
Comparison between the observed and the mean of the model-derived persistence (days) of CCN0.2 during winter (left bar) and
summer (right shaded bar) for each station. The observed persistence times
are shown in black for each station and the mean of the model-derived
persistence times in white. The persistence times obtained from model
simulations have been computed at the same time periods as the observed
ones.
Depending on the station, the persistence time is longer during winter (five
stations) than during summer (four stations). The average persistence of the
CCN0.2 number concentrations simulated by the individual models is
consistent with the observed change between winter and summer at six among the
nine stations. At all stations, the simulations display a much smaller change from winter to summer than indicated by the observations. Furthermore, the modeled change at Mace Head, the Noto Peninsula and Vavihill is opposite to the observed one. For the high-altitude stations, Puy de Dôme and
Jungfraujoch, the models calculate longer persistence times during summer
than during winter, in agreement with the observations. For these two
high-altitude stations, a significant increase in the number concentration
of CCN0.2 is observed during summer because the stations are subjected
to the boundary layer air mass influence during that season, while during
winter they are largely in the free troposphere. Therefore, despite the fact
that the number concentration of CCN0.2 is overall underestimated, the
models are able to reproduce the dynamical behavior of these continental
background stations, most probably because they are able to simulate the
local meteorological changes that drive CCN persistence (Fig. S4 and further discussion in Sect. S3.1).
Analyzing the factors that affect the persistence and then attributing the
differences between the observed and the model-derived values to the
underlying physical and/or chemical process parameterizations in each model
is a demanding task, which is also likely to be model and case dependent. In
addition to atmospheric transport patterns, dry and wet deposition processes
are presumably affecting the persistence time. Because the present exercise
was not focused on the deposition of aerosols, it does not have the necessary
elements to elaborate on differences in the results associated with
differences in the deposition parameterizations. However, earlier global
model comparisons provide insight into such differences. The Tsigaridis et
al. (2014) comparison of 31 global models, among which are those
participating in the present study, has shown that the representation of
aerosol microphysics in the models was important for dry deposition. In
particular, they have shown that the use of the M7 aerosol microphysics module
was associated with low dry deposition fluxes of organic aerosol, which is
mainly fine aerosol in the models, and the dry deposition rate coefficient
ranged from 0.005 to 0.13 d-1, i.e., with a max/min ratio of 26. They
also found that the effective wet deposition rate coefficient in the 31
participating models ranged from 0.09 to 0.24 d-1, i.e., with a
max/min ratio of 2.6 that is 10 times lower than for dry deposition, and
found virtually no change between AEROCOM phase I and AEROCOM phase II
models. Kim et al. (2014) compared the deposition of dust, which is mainly
coarse aerosol, calculated by a smaller subset (five) of AEROCOM models. They
pointed out that the size distribution of dust differs among these models
and found a 30 % difference in the effective dry deposition rate
coefficient and about the same in the total deposition rate varying from
0.28 to 0.37 d-1. The Kim et al. (2014) analysis also revealed
differences in the annual precipitation rate and in its seasonal
distribution in the models, as well as factor of 2 differences in the fraction of
wet to total deposition of dust among the models (ranging between 0.36
and 0.63). In addition, the PPE results (see Sect. 5) clearly show that dry
deposition is one of the major factors of uncertainty in the calculations of
CCN in 0.2 % supersaturation. Kristiansen et al. (2016) investigated the
causes of differences in aerosol lifetimes within 19 global models by making
use of an observational constraint from radionuclide measurements and found
largely underestimated accumulation-mode aerosol lifetimes due to
removal in most models that is too fast. In particular, they found that the way aerosols are
transported and scavenged in convective updrafts makes a large difference in
aerosol vertical distribution and lifetimes, as revealed in their
simulations from the same model (CAM5) but with different convective
transport and wet removal treatments (Wang et al., 2013) .
Furthermore, the size of the emitted OA and BC particles has been shown to
be an important parameter to which the persistence time and in particular
the summer-to-winter ratio of the persistence time of CCN is sensitive (see sensitivity runs
performed with one (TM4-ECPL) among the participating models in Sect. S3.1 and Fig. S5). Section 5
further attributes CCN0.2 uncertainty to various parameters.
Cloud droplet number concentration from CCN spectra
Inside a cloudy updraft, smax is reached when supersaturation
generation from expansion cooling becomes equal to its depletion by the
condensation of water vapor onto the growing droplets (Nenes and Seinfeld,
2003). Increasing updraft velocity enhances the cooling rate of the cloudy
air parcels, which in turn allows for higher supersaturation and eventually
increases smax and CDNC (Nd in the following text
and figures). Increases in CCN concentrations tend to increase Nd and
associated water vapor depletion in the early stages of cloud formation;
this in turn hinders the development of supersaturation and implies an
eventual decrease in smax. This water vapor “competition effect” is
especially strong when clouds form in the presence of large, hygroscopic
particles such as sea-salt aerosol or large amounts of accumulation aerosol (Morales Betancourt and Nenes, 2014a; Ghan et al., 1998). Competition effects in turn explain
why droplet number responses exhibit a sublinear response to modulations in
CCN; only when CCN concentrations are very low (or updraft velocities very
high) does smax become high enough so that the sensitivity of Nd to
CCN approaches unity.
Based on the behavior described above, one can understand the Nd
predicted from simulated and observed CCN spectra. This is straightforward
for the Jungfraujoch and Mace Head stations. For Cabauw and Vavihill the
observed-to-simulated ratio turns from a substantial overestimation in
CCN0.2 to an underestimation in Nd, and the opposite is found for
Finokalia. This can be explained as follows. At both Cabauw and Finokalia,
smax derived from observations is very low (approaching in the summer
0.07 % at Finokalia and 0.04 % at Cabauw; Fig. 8). The models
overestimate these low values of smax, and such values are indicative of
the presence of large particles (>250 nm) with sufficient
hygroscopicity at these stations that are not captured by the models.
Indeed, at Cabauw the available observations of CCN at 0.1 %
supersaturation show a larger underestimate by the models than for
CCN1.0 and CCN0.2 (Fig. S16), also pointing to a
model underestimate of the largest particles (>250 nm) that
induce the very low smax. The overestimate in smax leads to an
underestimate in Nd by the models for all seasons except winter at
Cabauw when the models at high updraft velocity capture the
observationally derived Nd levels. Furthermore, at Finokalia, CCN1.0
is underestimated year-round, indicating that, in addition to the largest
particles, the very small particles (smaller than 50 nm) that activate at
1.0 % supersaturation and/or their hygroscopicity are also underestimated
by the models there. On the other hand, particles larger than 120 nm that
activate at 0.2 % supersaturation are overestimated, especially in winter,
and slightly underestimated in summer. Therefore, the global models have
significant difficulties in capturing the aerosol size distribution and
hygroscopicity at Finokalia, which in turn translate into counterintuitive
discrepancies in Nd.
Comparison between the observed (symbols) and the monthly
averages of all models (continuous lines) of the cloud droplet properties:
in red for updraft velocity w=0.3 ms-1 and in green for updraft
velocity w=0.6 ms-1. For each station from top to bottom the four
graphs show (as indicated in the y axis label) the number of cloud droplets, Nd, the maximum supersaturation, smax, the sensitivity of the Nd to the total number of aerosol particles, ∂Nd/∂Na, and the sensitivity of
the Nd to the updraft velocity ∂Nd/∂w.
At Vavihill a somewhat different behavior is found; the underestimate of CCN
at supersaturations of 0.2 % and 0.7 % changes to an overestimate at
supersaturation 0.1 % mainly in summer (Fig. S16),
indicating an underestimate of fine particles and/or their hygroscopicity
and an overestimate of the largest particles and/or their hygroscopicity, in
particular during summer. This agreement of model results with observations
during winter and the overestimate of CCN at 0.1 % supersaturation during
summer can explain the similar behavior of modeled Nd.
The difference between model and observationally derived ∂Nd/∂w clearly
supports the above statements. Since observations predict a suppressed
smax compared to model distributions (Fig. 8), water vapor competition
effects in the observations are much more severe than in the model,
indicating that observations are much more (positively) sensitive to updraft
velocity. The opposite trends are seen for activation fraction
(∂Nd/∂Na), given that reductions in aerosol reduce competition
effects. The reduced water vapor competition effects at higher updraft
velocities and the trend in CCN error also generally explain why the
sensitivities are smaller for the highest updraft velocity.
As expected, both smax values and Nd for all observations and
simulations are higher for w=0.6 ms-1 than for w=0.3 ms-1. The response of smax and Nd to increasing w also depends on the
activated fraction (Fig. 8 third row). The calculated Nd increases
progressively from the low values seen for the clean marine conditions at
Mace Head and the high alpine atmospheric conditions of Jungfraujoch to the
rural background conditions at Cabauw and Vavihill, while at Finokalia the
observationally derived Nd values are the largest among the five stations
(Fig. 9a). At Jungfraujoch, Finokalia and Mace Head, the seasonal
variability of Nd is captured, despite the fact that the multi-model
median tends to underestimate the observationally derived Nd. However,
the individual models show both overpredictions and underpredictions of the
observations (Fig. S3). Owing to the water vapor competition
effect, smax decreases for increasing Nd, meaning that clouds at a
given location do not have a “characteristic smax”, but rather
depend on the given set of aerosol and dynamical conditions that develop
during the cloud formation.
Scatter plot of the average of multi-model median results
(y axis) versus observationally derived results (x axis) for (a) CDNC
(Nd) (cm-3; in red for updraft velocity w=0.3 ms-1 and in
green for updraft velocity w=0.6 ms-1); (b) CCN at supersaturation
0.2 % (gray) and CCN at maximum supersaturation (blue) with available data
(cm-3). To fit the scale all CCN number concentrations at maximum
supersaturation (blue symbols) have been divided by 2. Panel (c) is as panel (a) but for
smax (%). The letters close to the symbols indicate the station
names (C – Cabauw, F – Finokalia, H – Hyytiälä, J – Jungfraujoch, M
– Mace Head, N – Noto Peninsula, P – Puy de Dôme, V – Vavihill, Z – Melpitz).
For all stations except Finokalia, the agreement between the model and
observationally derived Nd (Fig. 8) tends to be better than for CCN
(Figs. 2, 4) and aerosol number concentrations (Fig. 6) (as expressed by the
MMM's NMB and NME for all stations provided in Table S6). Indeed, for all
stations except Finokalia, NMB and NME of the MMM for Nd vary from -7 % to -17 % and 41 % to 42 %,
respectively, with the lowest values calculated for the low updraft
velocity. For CCN0.2 NMB is -59 % and NME 63 %, averaged over the
same stations. This trend is a result of the competition effect of CCN on
smax; if observed CCN concentrations are higher than predicted, then
the “observed” smax tends to be less than the “predicted” smax, which means the discrepancy in observed and predicted Nd is
reduced compared to the CCN errors. The error reduction is substantial,
especially under lower updraft velocity conditions. As a qualitative example
we present here the ratio of the observed to the simulated average values of
CCN0.2 number concentrations: 4.0 at Jungfraujoch, 2.2 at
Cabauw, 2.1 at Mace Head, 1.5 at Vavihill and 0.8 at Finokalia (Fig. 3). In
the case of Nd the corresponding ratios for w=0.6 m s-1 are
∼1.8 at Jungfraujoch, ∼0.9 at Cabauw,
∼1.5 at Mace Head, ∼0.9 at Vavihill and
∼1.8 at Finokalia (Fig. 9). All these ratios are inversely
correlated with the observed to the simulated average values of smax
(Fig. 9), a clear indication of competition effects on Nd and
prediction error mitigation.
In agreement with our finding, Sotiropoulou et al. (2006) used a similar
approach applied to observations from the ICARTT field campaign and estimated
that a 20 %–50 % error in CCN closure results in a 10 %–25 % error in
Nd, while global simulations suggest global average CCN prediction
error between 10 % and 20 % and a smaller corresponding Nd error
between 7 % and 14 % (Sotiropoulou et al., 2007). Such a reduction in error
can be explained by self-regulation by Nd since smax decreases
with increasing aerosol number concentration, as discussed by many studies
published to date (e.g., Twomey et al., 1959; Charlson et al., 2001; Nenes
and Seinfeld, 2003; Feingold and Siebert, 2009), giving rise to regions
where Nd is relatively insensitive to changes in CCN or updraft
velocity (e.g., Rissman et al., 2004; Reutter et al., 2017). At very
high CCN levels and in the presence of sufficiently large hygroscopic CCN,
Nd may actually decrease with increases in aerosol amount (Ghan and Abdul-Razzak, 1998; Feingold, 2001; McFiggans et al., 2006; Reutter et al., 2017);
parameterizations that do not fully capture these important aspects of the
aerosol–droplet relationship may also give rise to biases in aerosol
indirect forcing assessments (e.g., Morales-Betancourt et al., 2014a).
These results clearly indicate that the number of CCN at a prescribed
supersaturation cannot be used as an indicator for the number of activated
droplets. The maximum supersaturation that develops inside the cloud (hence
droplet number) responds to changes in aerosol and vertical velocity levels
and is thus dynamically determined and can vary considerably for a given
site. This is even further complicated by the potential for model biases to
change sign at cloud-relevant supersaturations. CCN-derived
comparisons cannot even be used qualitatively, as the supersaturation levels
can be so different from a prescribed value that even the error trend in
Nd may not be reflected. For example, according to observationally
derived data, CCN0.2 at Cabauw is significantly higher than at
Finokalia, although at Finokalia Nd is larger for the observations but
not for the model results. Our analysis, however, clearly shows that the
models examined here do not exhibit the same level of Nd prediction
error as CCN error – a robust trend that is a result of the physics of
cloud droplet formation. Because of the discrepancy in the 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. Few published efforts (apart from Morales Betancourt and Nenes, 2014a, and Sullivan
et al., 2016) can demonstrate this, none over a range of models and
using a considerable aerosol dataset for evaluation as performed here.
Global distributions of surface CCN0.2 and particle number
concentrations
The global near-surface annual mean MMM distributions of the N3,
N50 and CCN0.2 number concentrations for the year 2011 (Fig. 10)
show similar patterns, i.e., larger concentrations over the continents due to
the primary anthropogenic emissions over industrialized areas in the USA,
Europe and Asia, as well as dust and biomass burning emissions in the tropics.
Global distributions of the annual multi-model median
concentrations of N3, N50 and CCN0.2 (cm-3) for the
year 2011 (a, c, e, respectively) and the corresponding diversities (b, d, f,
respectively; calculated as the ratio of standard deviation to the mean of
the models).
Multi-model median near-surface N3 number concentrations over
continental regions vary between 1000 and 10 600 cm-3, while over the
marine boundary layer (MBL) they vary between 100 and 2000 cm-3, rarely
exceeding 300 cm-3 (Fig. 10a). The MMM N3 surface distribution
is similar to the results by Spracklen
et al. (2010) and Gordon et al. (2017), who computed maximum N3 concentrations of ∼10 000 cm-3. The concentration of N3 is directly related to new
particle formation and growth as well as to primary emitted particles. Since
models use different nucleation mechanisms and emission inventories it is
expected that the diversity of the model results is higher for N3 than
for particle number concentrations with a larger (low-end) cutoff diameter.
The largest diversities in the model results (Fig. 10b) are found in the
polar regions, where concentrations are relatively low, and in the
continental boundary layer with high values (about 2) observed in the
tropics and particularly in South America and over the boreal regions in
Asia. Diversities of up to 1.5 are computed for the Mediterranean, Arabian
Peninsula, Central Africa, Indonesia and Southeast Asia, indicating
differences between models in the representation of primary and secondary
aerosol sources in these regions. Over the oceans the diversity is lower
(<1) except in the high latitudes of the Northern Hemisphere where
it exceeds 1.5. Even lower model diversity (around 0.8) is found in highly
polluted areas over North America and Europe, indicating consistency between
models in the representation of aerosols in these regions. In addition to
new particle formation, our results point mainly to biomass burning
emissions as a major source of uncertainty in the model calculations, resulting
in high model divergence in areas like southern Europe, tropical Africa and
America, southern Asia and Indonesia. Assumption of emission injection
height is also a source of discrepancy between models, leading to
differences in the calculated lifetimes (up to 30 %) and in the
tropospheric columns (up to 25 %) of pollutants (Daskalakis et al., 2015), while differences of
an order of magnitude in their concentrations are computed for the middle
troposphere (Jian and Fu, 2014). Thus, differences in the emission injection
heights in the participating models, as outlined in Sect. 2.1 and Table S4, contribute to the model result divergence. The highest maximum N3
concentrations in a 5∘× 5∘ grid box (Fig. S6) were
computed by the GISS-E2.1-MATRIX model (∼176 000 cm-3)
and the TM4-ECPL model (∼102 000 cm-3), while the lowest
were from the ECHAM6_HAM2-AP model (∼6400 cm-3). A sensitivity simulation was performed by a single model
(TM4-ECPL; discussed in Sects. 3.3 and S3.1 and Fig. S5) assuming the same primary emissions of carbonaceous aerosol in terms of
mass to be emitted at larger particle sizes. This additional simulation
shows the importance of the assumptions on size distribution of the
emissions in the models since the results of this simulation are very close
to the average of the other models. In agreement with these findings, Spracklen
et al. (2010) concluded that the sensitivity of N3 to the size of
emitted particles originating from anthropogenic activities is significantly
higher in regions close to anthropogenic sources and significantly lower at
remote boundary layer sites.
The annual global mean distribution of near-surface N50 particle number
concentrations (Fig. 10c) is similar to that of the N3 particles, but
the number concentrations are lower for these larger particle sizes that are
more relevant for CCN. The spatial distributions of N50 are similar,
but their concentrations are reduced by about a factor of 2.5 compared to
N3. The highest values of N50 are found over or close to
industrialized regions due to anthropogenic emissions and over Central
Africa and South America due to strong biomass burning emissions. Over
marine regions, N50 is higher in the Northern Hemisphere than in the
Southern Hemisphere due to the outflow from continental anthropogenic
sources. Despite the similarities of the global MMM distributions, the
models' diversity and spatial pattern of N50 (Fig. 10d) differ
significantly from that of N3. Excluding polar regions as for N3,
the highest model diversities for N50 (∼2) are observed
in regions with strong biomass burning emissions (southern America, Central
Africa and Indonesia), and high diversities are also found over the tropical
Pacific, which might be associated with marine emission representation in
the models. In all other regions the diversity of N50 simulations does
not exceed 1, even over the remaining tropical and southern oceans where
sea salt is important.
The near-surface MMM concentrations of CCN0.2 do not exceed 3500 cm-3 over polluted areas in Europe, Asia and the United States, as
shown in Fig. 10e. This value is in the range of the 3162–10 000 cm-3
CCN0.2 concentrations simulated by Spracklen et al. (2011) over China and
attributed to carbonaceous aerosols acting as CCN. In the present study,
only one model (EMAC) computes CCN0.2 levels that exceed 10 000 cm-3 over the Taklimakan Desert in Asia, while the remaining 14 models show maximum surface CCN0.2 concentrations <5000 cm-3 (see Fig. S9). The surface distribution and
magnitude of CCN0.2 are similar to N120 (Fig. S8),
with the maximum CCN0.2 concentrations only slightly lower than the
N120 values for most models, indicating that most of the N120
particles activate, implying a global mean kappa of ∼0.2 for
120 nm particles. However, analysis of the individual model results over the
polluted areas shows that the number concentration of N120 can, in most
cases, be either 50 % lower or higher than that of CCN0.2. The
modeled CCN0.2 diversity is lower than the diversity for N50 with
values <0.5 for midlatitude continental regions and around 1 over
the tropical oceans, where the CCN0.2 number concentration is usually
lower than 60 cm-3, but also over tropical southern Africa and Central
Africa where CCN0.2 number concentration is a few hundred
cubic centimeters. CCN0.2 model diversity is also lower than that of N3
simulations. The maximum reduction of the model diversity in CCN0.2 simulations compared to that in N3 simulations is found to exceed a
factor of 9 and maximizes over the high latitudes of the Northern Hemisphere
and the south Arabian Peninsula where new particle formation is high.
Overall, a global mean reduction of a factor of about 2 is found, as shown in
Fig. S18, that provides the ratio of N3 model diversity (Fig. 10b) over
CCN0.2 model diversity (Fig. 10f).
Some of the differences in global near-surface distributions of CCN
(Fig. S9) can be associated with the corresponding differences in the computed SO4
and OA surface distributions (Figs. S10 and S11, respectively). For
instance, in China and South America, models that are biased low in SO4 and
high in OA are also biased low in CCN. Significant differences are also
found for black carbon, sea salt and dust PM1 components
(Figs. S12–S14). In particular, for all models near-surface BC distributions
maximize over China, while individual models differ by a factor of 3 to 4.
Simulated SS distributions maximize over the southern oceans where the
models show the largest differences of up to 2 orders of magnitude, reflecting
large differences in the parameterized emissions of SS (see also
Table S3). Finally, DU distributions show the largest spread
among models with near-surface values that differ by up to a factor of 40. The
global surface distributions of the MMM of the chemical compound (SO4, BC,
OA, SS and DU) concentrations that contribute to PM1 are shown in Fig. S15 (left column) together with the corresponding model
diversities (right column). For all simulated PM1 components
diversities maximize south of 60∘ S and north of 60∘ N,
similarly to N3, which reflects the challenges of the models in
simulating atmospheric transport, deposition and chemistry close to the poles.
Causes of uncertainty in CCN
In this section we use the HadGEM-UKCA perturbed parameter ensemble (PPE) to
identify some potential causes of model diversity and bias compared to the
observations. We performed a variance-based sensitivity analysis at each
measurement site using the 260 000 HadGEM-UKCA model variants sampled from
the emulator following the methodology described in previous studies (Lee et al., 2013; Johnson et al., 2018).
Figure 11 shows the fraction of variance in CCN0.2 that can be
attributed to each of the perturbed parameters. Here we draw attention to
the main parameter effects and refer to Yoshioka et al. (2019) for a full
description of all parameters. The list of these parameters is provided in
the caption of Fig. 11. In the summer, the largest contributions to
uncertainty in CCN0.2 at most sites come from the biogenic volatile organic
compound (BVOC) emission flux and the assumed hygroscopicity of the organic
matter in the particles (κOA). The BVOC emissions in this model
are assumed to be α-pinene and to have an uncertainty range of
12–225 Tg SOA production per year. The κOA is assumed to have a
range of 0.1–0.6 and to be fixed during the simulation time (i.e., the
hygroscopicity does not change due to within-particle oxidation). Together,
these two mostly biogenic-related parameters account for up to 90 % of the CCN variance in summer, ranging from about 0 % at Mace Head, 20 % at
Cabauw, 40 % at Finokalia and 70 % at Melpitz to 90 % at
Hyytiälä. These results show that at Hyytiälä the organic
fraction of CCN-active aerosol is highest, while at other locations, like
Mace Head, the inorganic fraction dominates the total hygroscopicity. Except
at the Mace Head coastal site, the other important parameters in summer are
dry deposition of aerosol, anthropogenic SO2 emissions (at Finokalia,
Puy de Dôme and Jungfraujoch), the fossil fuel emission flux (at the Noto
Peninsula, Cabauw and Melpitz) and the assumed width of the accumulation
mode (at Jungfraujoch and Puy de Dôme).
Contribution to the uncertainty in monthly average
CCN0.2 based on HadGEM3-UKCA perturbed parameter ensemble simulations
for the year 2008. Each color refers to 1 of the 26 perturbed parameters as
indicated in the legend of the figure. The uncertainty is shown as the
percentage contribution of the parameter to the CCN0.2 variance. The
assumed parameter uncertainty ranges are given in Yoshioka et al. (2019). All contributions smaller than 1 % are not shown. Abbreviations are as follows. BL_Nuc: boundary layer nucleation; Aging: aging “rate” from insoluble to soluble; Acc_Width:
modal width (accumulation soluble–insoluble); Ait_Width:
modal width (Aitken soluble–insoluble); Cloud_pH: pH of
cloud drops; Carb_FF_Ems: particle mass
emission rate for BC and OC (fossil fuel); Carb_BB_Ems: particle mass emission rate for BC and OC (biomass
burning); Carb_Res_Ems: particle mass
emission rate for BC and OC (biofuel); Carb_FF_Diam: particle emitted mode diameter for BC and OC (fossil fuel);
Carb_BB_Diam: particle emitted mode diameter
for BC and OC (biomass burning); Carb_Res_Diam: particle emitted mode diameter for BC and OC (biofuel);
Prim_SO4_Frac: mass fraction of SO2
converted to new SO4-2 particles in sub-grid power plant plumes;
Prim_SO4_Diam: mode diameter of new sub-grid
SO4-2 particles; Sea_Spray: sea spray mass flux
(coarse / accumulation); Anth_SO2: SO2 emission flux
(anthropogenic); Volc_SO2: SO2 emission flux
(volcanic); BVOC_SOA: biogenic monoterpene production of
SOA; DMS: DMS emission flux; Dry_Dep_Ait:
dry deposition velocity of Aitken mode aerosol; Dry_Dep_Acc: dry deposition velocity of accumulation-mode
aerosol; Dry_Dep_SO2: dry deposition velocity
of SO2; Kappa_OC: hygroscopicity parameter kappa for
organic aerosols. Default value in UKCA is 0.06; see Petters and Kreidenweis
(2007). Sig_W: standard deviation of updraft velocity
(this affects the activation of aerosol particles to form cloud droplets). Dust: dust emission flux; Rain_Frac: the fraction of the cloudy
part of the grid box in which rain is forming and hence scavenging takes place;
Cloud_Ice_Thresh: scavenging (by both cloud
liquid and ice water) is suppressed in dynamic clouds when cloud ice
fraction is higher than this value. The parameters with no color in the legend do not contribute to the uncertainty in CCN0.2 (less than 1 %) at any station in any month.
In winter, aerosol dry deposition is an important cause of uncertainty in
CCN0.2 at all sites except Jungfraujoch and Puy de Dôme. At most
sites (except Mace Head and the Noto Peninsula) the emissions fluxes (and the
assumed particle sizes) of carbonaceous aerosol from fossil fuel and
residential combustion sources account for 10 %–20 % of the uncertainty.
Aging of aerosol through the uptake of sulfuric acid and SOA is also important
at these sites. Finally, the production of sulfate through in-cloud
oxidation by ozone (perturbed parameter marked as “Cloud pH”) accounts for
30 %–40 % of the uncertainty at Finokalia, Puy de Dôme and Jungfraujoch.
In summary, the PPE results suggest that the production of SOA from biogenic
emissions combined with the hygroscopic properties of the OA should be
investigated as a source of differences in predicted CCN between models in
summer. In winter, dry deposition, aging and in-cloud sulfate production
are the dominant sources of CCN uncertainty. Given that the importance of
CCN prediction uncertainty may not always translate to CDNC uncertainty –
especially if cloud formation occurs in a velocity-limited regime – any
future analysis should place CCN uncertainty within the context of CDNC
uncertainty.
Summary and conclusions
Within the BACCHUS–AEROCOM multi-model CCN intercomparison initiative, a
total of 16 global aerosol–climate and chemistry transport models were
compared to each other and to observations. Among them 14 provided results
for particle and CCN number concentrations and PM1 component mass
concentrations, which have been compared to surface observations at eight
sites in Europe and one in Japan to evaluate the skill of the simulations.
In this inter-model comparison, models used different meteorology and
emissions (e.g., CMIP5 and 6), as well as datasets and parameterizations. Most models (including
the multi-model median) tend to underestimate the observed aerosol number
concentrations N50, N80 and N120, as well as the CCN
concentrations, suggesting an incomplete understanding of the underlying
processes. In particular, emissions and the size distribution of emitted
particles, injection heights of biomass burning emissions, atmospheric
aging and particularly aqueous-phase chemistry, the hygroscopicity of
organic aerosol, and dry and wet deposition have been pointed out as main
sources of uncertainties in model simulations. Models are, however,
reproducing between 45 % and 86 % of the seasonal variability of
N50, N80, N120 and CCN0.2 number concentrations, as well as
SO4 and OA PM1 component mass concentrations, with the exception of
Hyytiälä where only 36 % of the SO4 variability is captured by the
MMM, as indicated by the correlation coefficient of the MMM with the
observations (Table S6). While models have improved since the 2014 AEROCOM
organic aerosol intercomparison (Tsigaridis et al.,
2014), most continue to underestimate the organic submicron aerosol mass
concentrations. Thus, the MMM underestimates observed OA PM1 mass
concentrations by 36 % (for Hyytiälä) to 77 % (for
Jungfraujoch).
The simulated N3 number concentrations, which are generally higher over
land, show high diversity among models over the Northern Hemisphere
continents, while the simulated CCN are less diverse. Overall, a global mean
reduction of a factor of about 2 is found in the model diversity in
CCN0.2 simulations compared to that in N3 simulations,
maximizing over regions where new particle formation is important. This
finding points to differences in the size distribution of primary
emissions and/or in the formation and growth of new particles as important
sources of the inter-model diversity in CCN.
CCN number concentrations are generally underestimated at all
supersaturations by the MMM by at least 34 % (Fig. 9, Table S6), with
the exception of very low supersaturations, indicating that models have most
difficulty in capturing the largest particles (>250 nm) that
activate at very low supersaturations. There is no model that performs best
at all stations. The models on average qualitatively capture the strong
seasonal variabilities of CCN observed at Finokalia, the Noto Peninsula, Puy de
Dôme and Jungfraujoch, as well as the very weak seasonality observed at the
other stations. The production of SOA from biogenic emissions combined with the
hygroscopic properties of the OA in summer and dry deposition, aging and
in-cloud sulfate production in winter have been identified by PPE
simulations as dominant sources of CCN uncertainty and should be
investigated in the future.
The short-term variability of CCN0.2 at the measurement sites has been
examined by comparing the CCN0.2 persistence time computed from the
observed data and the model results. Because persistence time is a
normalized timescale driven by the processes that “set” the CCN
concentrations, it is more sensitive to air mass changes and the
formation–removal rates of atmospheric particles than to the exact number
concentration of CCN. With the exception of two models that estimate very
large persistence times (about 16 d) during summertime at Finokalia, the
modeled persistence times of near-surface CCN0.2 are between 0.5 and 9 d depending on the model, location and season (Fig. S4), a range similar
to that derived from observations that vary between about 0.5 and 7 d. At
six out of nine stations the average relative change in modeled persistence time
between winter and summer is in agreement with observations. These
persistence times of CCN0.2 are sensitive to assumptions on the size of the
emitted particles, as shown by a sensitivity simulation with the TM4-ECPL model.
A novel aspect of this study is the comparison of ensemble global aerosol
climate model near-surface results with experimentally derived CDNC from
surface measurements of CCN at different levels of supersaturation. Note
that CDNC is not calculated by each participating model, but a common
methodology has been followed to derive the CDNC from the modeled and
observed CCN spectra. Despite the large differences between models and
observations found in the number concentration of aerosol particles and CCN,
the CDNC estimates based on the CCN spectra are in significantly better
agreement than the CCN for the stations examined here. In addition, the
inter-model spread of CDNC is smaller than that of particle and CCN number
concentrations. These trends are robust and a result of the physics
of cloud droplet response to aerosol perturbations and show
self-regulation by CDNC.
As for CCN number concentrations, in several cases models underestimate CDNC
when compared to the observationally derived CDNC (Sect. 3.5). At high
aerosol number concentrations, the maximum supersaturation is computed to be
low, limiting the fraction of particles that can activate and form CDNC. As
a result, the sensitivity of CDNC to updraft velocity prevails. In
contrast, at high updraft velocities, CDNC is controlled by the variability
in the aerosol number concentration. An anticorrelation is found between the
sensitivity of CDNC to the number of aerosols and that to the updraft
velocity, showing that the variability of these two parameters can explain
the variability in CDNC and limit CDNC formation.
Our results are in agreement with previous studies showing that CDNCs are
sensitive to the uncertainties in the CCN number concentrations, mainly in
regions where aerosol number concentrations are low and support the concept
of the existence of two distinct regimes (“aerosol limited” and
“updraft limited”). Unlike previous studies, however, we show that for a
large number of models, persistent and substantial CCN prediction biases are
considerably reduced when expressed as droplet number concentrations for
boundary-layer-type clouds. Biases in CDNC are found to be qualitatively
different from the biases in CCN0.2 and are attributed to the ability
of models to capture the levels of the largest particles that activate at
very low cloud-relevant supersaturations. These results point to the need
for observations that cover the CCN spectra down to very low supersaturation
levels and demonstrate that model–observation comparisons of CCN at a
prescribed supersaturation may be misleading in the error evaluation for
CDNC, since supersaturation is dynamically determined and can vary
considerably for a given site. The methodology proposed here, however,
overcomes this limitation and considers the dynamic nature of
supersaturation adjustment to CCN variations, thus determining appropriate
supersaturation levels for model–observation comparison. Such a methodology
can help better guide modeling efforts to focus on regions where CDNC
predictions are most biased and sensitive to CCN perturbations (e.g., in the
southern oceans).
Data availability
The data used for this study are available online at 10.5281/zenodo.3265866 (Fanourgakis et al., 2019) (contact: Maria
Kanakidou at mariak@uoc.gr). CDNC analysis tools for model and observational data can be obtained from athanasios.nenes@epfl.ch upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8591-2019-supplement.
Author contributions
GF performed TM4-ECPL model simulations, developed codes and drew all
figures for the multi-model data analysis. MK initiated and coordinated the
exercise. AN developed the codes to interpret the CCN spectra from all
models and from the observations and to calculate CDNC. MK and AN defined
the experimental protocol. GF, MK and AN analyzed data and wrote the
paper. All coauthors contributed data and to the writing of the
paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “BACCHUS – Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding (ACP/AMT/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
This work has been initiated and conducted within the European Union's Seventh Framework
Programme (FP7/2007-2013) collaborative project BACCHUS (Impact of Biogenic
versus Anthropogenic emissions on Clouds and Climate: towards a Holistic
UnderStanding). Dirk Olivie
has contributed with coding and information about the emission heights used
in CAM5.3-Oslo. Yunha Lee developed the GISS-E2-TOMAS model used in this study. The
ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, the
Max Planck Institut für Meteorologie, Forschungszentrum Jülich, the
University of Oxford, the Finnish Meteorological Institute and the Leibniz
Institute for Tropospheric Research; it is managed by the Center for Climate
Systems Modeling (C2SM) at ETH Zurich. Hailong Wang and Yang Yang acknowledge support from the U.S.
Department of Energy (DOE) Biological and Environmental Research and the
ACTIVATE project (a NASA Earth Venture Suborbital-3 investigation) funded by
NASA's Earth Science Division and managed through the Earth System Science
Pathfinder Program Office. The Pacific Northwest National Laboratory (PNNL)
is operated for the DOE by the Battelle Memorial Institute under contract
DE-AC05-76RLO1830. The CAM5_MAM3 simulation was performed
using PNNL institutional computing resources. Resources supporting this work were also provided by the NASA
High-End Computing (HEC) program through the NASA Center for Climate
Simulation (NCCS) at Goddard Space Flight Center. Martin Gysel-Beer acknowledges support
from MeteoSwiss in the framework of
the Swiss contributions (GAW-CH and GAW-CH-Plus) to the Global Atmosphere
Watch program of the World Meteorological Organization (WMO). This work
was finalized when Maria Kanakidou was visiting L. Gallardo at the Center for
Climate and Resilience Research (CR2) at the Geophysics Department of the
University of Chile where Maria Kanakidou profited from fruitful discussions.
Financial support
This research has been supported by the FP7 Environment (grant no. BACCHUS (603445)), the H2020 Environment (grant nos. CRESCENDO (641816) and ACTRIS2 (654109)), and the H2020 European Research Council (grant nos. PyroTRACH (726165) and RECAP (724602)). The contributing authors were also supported by the following: the Norwegian Research Council (grant nos. 207711/E10, 229771, nn2345k and ns2345k); the Nordic Centre of Excellence eSTICC (grant no. 57001); the Natural Environment Research Council project (grant no. NE/L01355X/1); the Japan Society for the Promotion of Science, the Ministry of Education, Culture, Sports, Science, and Technology and the Japan Society for the Promotion of Science (grant nos. JP26740014, JP17H04709, JP26241003 and JP16H01770); the MEXT Green Network of Excellence and Arctic Challenge for Sustainability projects and the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency (grant nos. 2-1403, 2-1703); the U.S. National Science Foundation Atmospheric Chemistry Program (grant nos. AGS-1559607 and 1550816); the U.S. National Oceanic and Atmospheric Administration, an Office of Science, Office of Atmospheric Chemistry, Carbon Cycle, and Climate Program (grant no. NA17OAR430001); NASA (grant nos. NNX13AK20G and NNX15AE36G); the Swiss State Secretariat for Education, Research and Innovation (SERI) (grant no. 15.0159-1); and the Swiss
National Supercomputing Centre (grant no. s652).
Review statement
This paper was edited by Joachim Curtius and reviewed by two anonymous referees.
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