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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D15, 10.1029/2001JD000887, 2002
Studies of the aerosol indirect effect from sulfate
and black carbon aerosols
Jón Egill Kristjánsson
Department of Geophysics, University of Oslo, Oslo, Norway
Received 29 May 2001; revised 25 October 2001; accepted 1 November 2001; published 6 August 2002.
[1] The indirect effect of anthropogenic aerosols is investigated using the global climate
model National Center for Atmospheric Research Community Climate Model Version 3
(NCAR CCM3). Two types of anthropogenic aerosols are considered, i.e., sulfate and
black carbon aerosols. The concentrations and horizontal distributions of these aerosols
were obtained from simulations with a life-cycle model incorporated into the global
climate model. They are then combined with size-segregated background aerosols. The
aerosol size distributions are subjected to condensation, coagulation, and humidity
swelling. By making assumptions on supersaturation, we determine cloud droplet number
concentrations in water clouds. Cloud droplet sizes and top of atmosphere (TOA) radiative
fluxes are in good agreement with satellite observations. Both components of the indirect
effect, i.e., the radius and lifetime effects, are computed as pure forcing terms. Using
aerosol data for 2000 from the Intergovernmental Panel on Climate Change (IPCC), we
find, globally averaged, a 5% decrease in cloud droplet radius and a 5% increase in cloud
water path due to anthropogenic aerosols. The largest changes are found over SE Asia,
followed by the North Atlantic, Europe, and the eastern United States. This is also the case
for the radiative forcing (‘‘indirect effect’’), which has a global average of 1.8 W m2.
When the experiment is repeated using data for 2100 from the IPCC A2 scenario, an
unchanged globally averaged radiative forcing is found, but the horizontal distribution has
been shifted toward the tropics. Sensitivity experiments show that the radius effect is 3
times as important as the lifetime effect and that black carbon only contributes marginally
INDEX TERMS: 0305 Atmospheric Composition and Structure:
to the overall indirect effect.
Aerosols and particles (0345, 4801); 1610 Global Change: Atmosphere (0315, 0325); 3359 Meteorology and
Atmospheric Dynamics: Radiative processes; 3354 Meteorology and Atmospheric Dynamics: Precipitation
(1854); KEYWORDS: aerosols, clouds, indirect effect, sulfate, radiative forcing, climate
1. Introduction
[2] In recent years it has become increasingly evident that
significant man-made climate change may be about to take
place [Lean and Rind, 1998; Jones et al., 1999]. The initial
focus was mainly on increases in greenhouse gas concentrations [e.g., Houghton et al., 1990], but the role of
anthropogenic aerosols for climate change has gradually
received wide recognition [e.g., Charlson et al., 1991;
Mitchell et al., 1995; Kiehl et al., 2000]. Whereas the
long-lived anthropogenic greenhouse gases have a warming
effect (i.e., they cause a positive radiative forcing at the
tropopause), the aerosols can have either a warming or a
cooling effect. The sign of the ‘‘direct effect’’ of an aerosol
particle is determined mainly by its ratio of absorptivity to
reflectivity of solar radiation. For instance, sulfate particles
mainly have a cooling effect, since they are effective
reflectors of solar radiation, although they absorb little of
it. However, black carbon (BC) particles absorb a significant portion of the solar radiation [Seinfeld and Pandis,
Copyright 2002 by the American Geophysical Union.
1998] and may have a warming effect, especially above
highly reflecting surfaces such as clouds, desert, or ice.
Estimates of aerosol direct effect on climate vary, but global
averages are typically between 0 and 1 W m2 for
present-day conditions [Intergovernmental Panel on Climate Change (IPCC), 2001]. The aerosols typically have a
lifetime of only a few days, so that unlike the greenhouse
gases they are not spread evenly over the globe. Consequently, aerosol radiative forcing tends to peak close to
source regions. The most prominent source regions for
anthropogenic aerosols are densely populated areas of the
Northern Hemisphere, i.e., SE Asia, Europe, and eastern
North America, as well as low-latitude regions exposed to
biomass burning, e.g., central Africa and parts of South
America [e.g., Seinfeld and Pandis, 1998]. In addition to the
two aerosol types already mentioned (sulfate and BC) these
regions are also large sources of organic carbon (OC). The
organic carbon aerosols are considered potentially important
for climate, but their concentrations are highly uncertain
[Huebert and Charlson, 2000].
[3] The aerosols also have a so-called ‘‘indirect effect’’
on climate. Since clouds only form with the aid of hygroscopic aerosols, it is clear that man-made changes in the
abundance of these aerosols can have an impact on cloud
microphysical processes. Sulfate aerosols are a good example of this. They form in the atmosphere as a result of the
release of SO2 from the burning of fossil fuels that is
subsequently transformed to sulfate (SO4) aerosols by
various processes. One of the more important of these
processes is the aqueous-phase oxidation of SO2 in cloud
droplets. Upon evaporation of the cloud droplet the ability
of the residual particle to serve as a cloud condensation
nucleus (CCN) will be enhanced [e.g., Hobbs, 1993]. The
anthropogenic sulfate aerosols lead to a general decrease in
cloud droplet size, resulting in a larger surface area of the
droplets. A larger surface area means that cloud albedo will
be enhanced; an effect that is sometimes termed the ‘‘Twomey effect’’ [Twomey, 1977] or the ‘‘first indirect effect’’
[IPCC, 2001], hereinafter referred to as the ‘‘radius effect.’’
Another result of the generally smaller cloud droplets is that
coalescence will be suppressed. This means that the clouds
will be more persistent, i.e., both their coverage and average
water content increase, again leading to an enhanced cloud
albedo. This latter effect, through cloud microphysics
[Albrecht, 1989; Liou and Ou, 1989], is sometimes termed
the ‘‘second indirect effect’’ [IPCC, 2001], hereinafter
referred to as the ‘‘lifetime effect.’’
[4] Both the radius and lifetime effects are considered to
be important, but the relative role of the two is not known.
Despite an increasing number of modeling studies dealing
with the indirect effect in recent years [e.g., Jones and
Slingo, 1996; Lohmann et al., 1999, 2000; Kiehl et al.,
2000; Rotstayn, 1999], the magnitude of the indirect effect
is still highly uncertain. Estimates for the globally averaged
indirect effect range from less than 1 W m2 to more than
2 W m2, meaning that in a globally averaged sense this
effect probably cancels at least half of the warming effect of
anthropogenic greenhouse gases [Hansen et al., 1998]. Here
it is essential to keep in mind, however, that the aerosol
indirect effect exhibits large geographical variations related
to cloud distribution and cloud height, aerosol distribution,
surface albedo, and solar zenith angle. The huge uncertainty
in aerosol indirect effect estimates [IPCC, 2001] is caused
by the complexity of the problem and a poor understanding
of the processes involved. This uncertainty can be exemplified by the studies by Ghan et al. [1998] and O’Dowd
et al. [1999] on the one hand and Ackerman et al. [2000] on
the other. The former two studies demonstrated that the
number of nucleated cloud droplets over the ocean can
either increase or decrease with increasing sea-salt concentrations depending on how large the sulfate concentrations
and the updraft wind speeds are. The study by Ackerman
et al. [2000] suggests an interaction between the absorption
of solar radiation by soot and cloud evolution, which acts in
such a way as to reduce cloud cover and hence gives a
positive radiative forcing.
[5] Here we present a global modeling study that uses a
more process-based description of the atmospheric aerosol
and its interaction with cloud. Two types of anthropogenic
aerosols are considered, i.e., sulfate and BC aerosols, and a
treatment of naturally occurring sea-salt and mineral aerosols
is also included. A particular emphasis is put on the treatment
of particle size. Both radius effect and lifetime effect are
computed in terms of pure radiative forcing at the top of
the atmosphere. Seasonal variations are also considered. Two
5-year simulations and a set of 1-year simulations are carried
out using the atmospheric general circulation model (AGCM)
National Center for Atmospheric Research Community Climate Model Version 3 (NCAR CCM3) [Kiehl et al., 1998]. In
our version of the model, cloud water is treated explicitly
[Rasch and Kristjánsson, 1998], enabling a direct coupling
between aerosols, clouds, and model dynamics.
2. Methodology
[6] Recently, Seland and Iversen [1999] developed a
process-oriented life-cycle model for sulfate and BC aerosols. Subsequently, this model has been implemented into
the NCAR CCM3 global climate model, and multiyear
simulations have been carried out. As discussed by T.
Iversen and Ø. Seland (Life cycle modelling of SO4 and
BC for on-line climate impacts, submitted to Journal of
Geophysical Research, 2001, hereinafter referred to as
Iversen and Seland, submitted manuscript, 2001), the model
yields turnover times and concentrations that compare
favorably with other models and with available observations. Monthly averaged fields from the three-dimensional
sulfate and BC fields of Iversen and Seland (submitted
manuscript, 2001) have been used by A. Kirkevåg and T.
Iversen (Global direct radiative forcing by process-parameterized aerosol optical properties, submitted to Journal of
Geophysical Research, 2001, hereinafter referred to as
Kirkevåg and Iversen, submitted manuscript, 2001) in their
model simulations of the aerosol direct effect using the same
climate model. They showed a rather small sensitivity to
running the life-cycle model on line, as opposed to a less
time consuming off-line approach using monthly averages,
except in certain regions. In the present study the same path
is followed, feeding the monthly averaged sulfate and BC
fields into the aerosol module of Kirkevåg and Iversen
(submitted manuscript, 2001) at every grid point and time
step. The main features of this module are now described.
2.1. Aerosol Module
[7] In addition to the natural and anthropogenic sulfate
and BC, the following background aerosols are treated [see
Kirkevåg et al., 1999]: Between 60N and 60S we use
prescribe sea-salt, mineral, ‘‘water-soluble,’’ and ‘‘dustlike’’ aerosols based on the study of d’Almeida et al.
[1991]. A modification here is that over land the number
of water-soluble particles has been reduced by a factor of 2
to 5000 cm3, while the number of mineral particles has
been enhanced from 0 to 944 cm3. This has been done to
avoid unrealistically high cloud droplet number concentrations over land. In arctic regions a different aerosol is
assumed on the basis of Covert et al. [1996]. The background aerosols have a prescribed size distribution
described by 44 size bins. Sources of atmospheric sulfur
are DMS in the ocean and gaseous SO2 from man-made
emissions. These species are quickly transformed to sulfate
either in the nucleation mode or in the accumulation mode
(for details, see Seland and Iversen [1999] and Iversen and
Seland (submitted manuscript, 2001)). Some of the nucleation mode particles coagulate on preexisting background
aerosols to form accumulation-mode particles that are more
relevant to the aerosol indirect effect owing to their large
size (Kirkevåg and Iversen, submitted manuscript, 2001).
Another contribution to accumulation mode sulfate comes
from oxidation of SO2 in clouds and subsequent evaporation. Sensitivity tests have shown this latter sulfate component to be somewhat more important than the former for the
indirect effect. This is probably because it contributes more
to the sulfate mass [Seland and Iversen, 1999], and thereby
increases the size of the aerosol particles, so that more of
them become activated at the assumed supersaturation. BC
aerosols are formed either through biomass burning (90% of
which is assumed to be anthropogenic) or through combustion processes, mainly in industrialized regions. The sulfate
and BC aerosols are added to the background (see Kirkevåg
et al. [1999] for details) in such a way that nucleation mode
particles are assumed externally mixed, while accumulation
mode sulfate and BC are internally mixed with the background according to condensational growth and Brownian
coagulation theory.
[8] Kirkevåg et al. [1999] calculated the dependency of
aerosol size on relative humidity, both at subsaturated and
supersaturated conditions, using the Köhler equation. In the
supersaturated case those calculations yield the number of
CCN in the air mass, i.e., the number of activated aerosol
particles for a given supersaturation. In order to save
computing time, the results from a large number of such
calculations were condensed into a lookup table with supersaturation and amount of accumulation mode sulfate as
input and the number of activated CCN as output.
[9] The amount of accumulation mode sulfate is obtained
from the calculations of Iversen and Seland (submitted
manuscript, 2001), but it is difficult to obtain a reasonable
estimate of the supersaturation (S ) in a GCM grid box.
Ghan et al. [1997] parameterized subgrid-scale vertical
velocities and thereby obtained updrafts suitable for calculating S. Similarly, Lohmann et al. [1999] devised a method
for obtaining vertical velocity as a function of turbulent
kinetic energy (TKE). This is an interesting approach, but
could not be used here since the CCM3 does not carry
information on TKE. Consequently, we have chosen a
simpler, more empirical approach: In the case of stratiform
clouds we assume a supersaturation of 0.05%. In the case of
cumuliform clouds we assume a supersaturation of 0.10%
over ocean and 0.15% in the case of a convective cloud over
land. This roughly simulates some observed features of
clouds [e.g., Rogers and Yau, 1989; Pruppacher and Klett,
1997], although our assumed supersaturations are generally
lower than those observed at cloud formation. This is
because we are not explicitly accounting for processes that
deplete droplet number, such as coalescence and evaporation due to inhomogeneous mixing. Hence we crudely
account for these effects by lowering the assumed supersaturation. Tests have shown that for larger values of S,
excessively large cloud droplet number concentrations
(CDNCs) are obtained, leading to unrealistically small
droplet radii. In reality the final number of droplets in a
cloud may be considerably different from the number
initially activated, owing to coalescence, evaporation, and
removal by precipitation, all of which would tend to reduce
CDNC from the initial value [Hudson and Yum, 1997].
Another effect that is ignored is the fact that in polluted air
with many activated CCN, S will be reduced due to
competition among the droplets. Most other studies in the
literature make similar assumptions, that is, they do not
distinguish between number of activated droplets and the
actual number of droplets in a cloud volume [e.g., Kiehl
et al., 2000]. However, Lohmann et al. [1999] solved a
continuity equation for CDNC, allowing a potentially more
consistent estimate. A problem with that approach is that
some of the terms in that continuity equation are quite
uncertain at this point in time, as evidenced by the aforementioned papers by Ghan et al. [1998] and O’Dowd et al.
[1999]. In section 4.3 we discuss the sensitivity of model
results to our assumption on S. More elaborate methods for
determining CDNC will be explored in future studies.
2.2. Distinction Between Liquid Water and Ice
[10] We assume that the indirect effect only acts through
water (not ice) clouds. This means that only clouds in the
lower troposphere will contribute to our estimate of the
aerosol indirect effect. In our model, clouds at temperatures
below 20C are considered pure ice clouds, clouds at
temperatures >0C are considered pure water clouds, while
clouds at temperatures between these two values are of a
mixed phase determined by a temperature-dependent
weighting factor [Rasch and Kristjánsson, 1998]. Very little
is currently known about the sensitivity of ice cloud microphysics to aerosol burden. We feel that more knowledge is
needed about these processes before meaningful parameterizations for them can be developed for GCMs. However,
that does not necessarily mean that the effect is small.
2.3. Lifetime Effect As a Forcing
[11] Most studies so far have presented the radius effect
of aerosols as a forcing term. In the case of the lifetime
effect the notion has been [e.g., IPCC, 2001] that it is not
possible to present this as a forcing, leading researchers to
calculate it as a difference between two separate runs. The
disadvantage of this is obvious since dynamic feedbacks can
then easily blur the original signal. Here we present a way to
get around this problem. When the radius effect is calculated as a forcing, this is done by making three parallel calls
to the radiation scheme at every time step, one for advancing the model and two diagnostic calls, one for cloud
droplets calculated from natural aerosols only, and one from
calculations using all aerosols. The difference between the
radiative forcings of the latter two then gives the radius
effect. Kiehl et al. [2000] only calculated this component of
the indirect effect. In a similar manner we make three
parallel calls to the condensation scheme of the model and
introduce three cloud water fields, one for advancing the
model and two for diagnostic purposes. The last two are
based on natural aerosols only and all aerosols, respectively.
Hence the difference in cloud water amounts between those
two cloud water fields is a result of the difference in
precipitation release due to different CCN amounts and
hence different CDNCs. The different cloud water fields
are used in the parallel calls to the radiation scheme, along
with the different cloud droplet radii, mentioned earlier in
this paragraph. Note that since cloud cover in the NCAR
CCM3 mainly depends on relative humidity and is independent of cloud water content, this approach does not
capture changes in cloud coverage. This may lead to an
underestimation of the lifetime effect.
[12] One justification for this new approach is that the
lifetime of clouds in our model is quite short. A rough
estimate of the lifetime can be obtained by dividing the
cloud water content in the model by the precipitation rate
[Berge and Kristjánsson, 1992]. This yields a typical lifetime of 30 min. Since the model time step is 20 min, this
means that instantaneous sources and sinks will tend to
dominate the continuity equation for cloud water. Furthermore, as mentioned by Rasch and Kristjánsson [1998], no
transport of cloud water is considered. As a result, the
diagnostic cloud water fields will tend to be aligned with
areas of relative humidity near 100%, i.e., areas where
clouds are being produced.
[13] Preliminary results from ‘‘response simulations,’’ in
which the aerosol forcing is allowed to drive the model (not
shown), give results similar to those of our ‘‘forcing’’ runs.
This seems to support the soundness of the approach outlined here. Similarly, Rotstayn and Penner [2001] showed
that in their model the radius effect was very similar when
estimated as a forcing compared to estimates based on
response simulations.
2.4. Retuning of Two Parameters in the Cloud
Microphysics Scheme
[ 14 ] Rasch and Kristjánsson [1998] introduced an
entirely new cloud microphysics scheme into the NCAR
CCM3. This scheme was based on more elaborate schemes
by Liou and Ou [1989] and Boucher et al. [1995]. Rate of
release of precipitation is expressed as the sum of 5 terms,
i.e., autoconversion of cloud water to rain (PWAUT);
collection of cloud water by rain (PRACW); autoconversion
of cloud ice to snow (PSAUT); collection of cloud water by
snow (PSACW); and collection of cloud ice by snow
(PSACW). The term PWAUT contains explicitly cloud
droplet number concentration (N ) and a threshold value
for cloud droplet mean volume radius (r3lc):
PWAUT ¼ ½ðCl;aut q2l ra Þ=rw ½ðql ra Þ=ðrw NÞ3
re ¼ k r3l ;
where k = 1.14 for continental clouds and k = 1.08 for
maritime clouds [Martin et al., 1994], an underestimation of
liquid water path will lead to an underestimation of cloud
droplet size. In order to resolve this, we have modified
another uncertain parameter in the cloud parameterization
scheme. This is the proportionality factor Cl,aut in equation
(1), which, as discussed by Rasch and Kristjánsson [1998],
was already reduced compared with the original theoretical
value based on work by Baker [1993]. Rasch and
Kristjánsson [1998] reduced Cl,aut by a factor of 10 when
the precipitation flux entering the grid box was <0.5 mm d1.
We have now raised this limit to 5 mm d1. The effect of
these two modifications can be perceived by looking at
Figure 1a and comparing the relative role of the terms
PWAUT and PRACW before and after the modifications. As
seen, these processes have now become less effective almost
everywhere, with the largest differences being found near
600 hPa in the tropics and below 850 hPa at high latitudes.
As a result, cloud droplet sizes have increased by typically
around 1 mm, but with even larger enhancements at high
latitudes (Figure 1b). Figure 1b also shows isolated pockets
of size reductions, e.g., near the North Pole. Here the
occurrence of liquid clouds is quite rare, so the significance
of this result is limited.
Hðr3l r3lc Þ;
where ql denotes in-cloud liquid water mixing ratio, ra is air
density, and rw is water density, while H is the Heaviside
function and r3l denotes mean volume radius, defined as:
r3l ¼ ½ð3 ql ra Þ=ð4 p rw NÞ1=3 :
value of 5 mm led to excessive rain rates and unrealistically
thin clouds. Hence we have changed the value of r3lc to
10 mm. Even after this change our cloud droplet radii were
considerably smaller than observed. This is related to the
fact, as discussed by Kiehl et al. [2000], that the vertically
integrated liquid water paths are considerably smaller than
what satellite estimates suggest. Since cloud droplet
effective radius is related to mean volume radius through
For N, values of 400 cm3 over land (decreasing with
height) and 150 cm3 over ocean were used, while for r3lc a
value of 5 mm was adopted. Both parameters were adjusted
to give reasonable top of the atmosphere radiative fluxes, as
explained by Rasch and Kristjánsson [1998]. As we are
now computing the value of N, rather than prescribing it, we
have found that the parameter values of Rasch and
Kristjánsson [1998] yield unrealistically small cloud
droplets. This suggests that the rate of release of precipitation may be too strong. Recently, Delobbe and Gallée
[1998] carried out a set of simulations of stratocumulus
cloud evolution verified against observations from the
Atlantic Stratocumulus Trade Wind Experiment (ASTEX).
Different cloud microphysics schemes were tested out. For
the Liou and Ou [1989] scheme it was found that the
parameter r3lc, which determines onset of autoconversion,
needed to be 10 mm. They found that using a threshold
3. Simulated Indirect Effect Using IPCC
Scenario A2 Data for 2000 and 2100
[15] In this section we will present results from the last 3
years of a 5-year control run, in which all parameters have
been set to those values described in section 2. The control
run has been conducted for two sets of aerosol input data
obtained from the module of Iversen and Seland (submitted
manuscript, 2001). The two sets of aerosol data were
obtained by running the aerosol module for the IPCC A2
scenario [IPCC, 2001] for 2000 with and without anthropogenic aerosols. In our simulations we investigate the
radiative forcing caused by anthropogenic aerosols interacting with clouds, while the model state evolved independently of the aerosols. Hence we get a clear picture of the first
order effect (forcing) of anthropogenic aerosols through
clouds. For comparison we also show results from 2100
of the IPCC A2 scenario in section 3.3. The simulations
presented here use prescribed climatological monthly mean
sea surface temperatures [Kiehl et al., 1998].
3.1. Changes in re, CDNC, and Liquid Water
Path (LWP)
[16] Before studying the radiative impact of aerosols
through clouds it is appropriate to look at how the clouds
themselves are changing. Hence we investigate zonally
Figure 1. Effects of retuning the condensation scheme.
(a) Percent change in the sum of contributions to total
precipitation release from autoconversion of cloud water to
rain (PWAUT) and collection of cloud water by rain
(PRACW). (b) Change of cloud droplet size in micrometers.
averaged cross sections of effective radius, CDNC, and
liquid water path (LWP = ql ra z), as well as the
changes of these quantities due to anthropogenic aerosols.
First, in Figure 2 we show CDNC at two model levels in the
lower troposphere. Note that in Figures 2, 3, 4, 5, and 6,
conditional sampling is applied such that only contributions
from grid boxes containing warm clouds (T > 273 K) are
considered. The largest concentrations of almost 1000 cloud
droplets cm3 are found near the surface in heavily populated areas of the Northern Hemisphere. This is precisely
where the aerosol number concentrations are highest. Many
of the aerosols in these regions require large supersaturations
to become activated, but due to the large number of aerosols
present, droplet concentrations of almost 1000 cm3 are
nevertheless reached. Over the remote oceans and in the
polar regions the cloud droplet number concentrations are
much smaller. Typical values over the remote oceans are
100 cm3 near the surface (Figure 2b), dropping to
50 cm3 at 1 – 1.5 km height (Figure 2a). Our values for
CDNC near the surface appear to correspond reasonably
with observations discussed by Pruppacher and Klett
[1997]. Recently, Glantz and Noone [2000] obtained values
between 28 and 238 cm3 for ‘‘clean air’’ and between 274
and 557 cm3 for ‘‘highly polluted air’’ in aircraft measurements over ocean. Figure 3 shows the corresponding cloud
droplet effective radii. To some extent these values reflect
the differences shown in Figure 2, for example, fewer
droplets over ocean than land yield larger droplets over
ocean than over land. However, as mentioned before, mean
volume radius is also determined by cloud water content,
which mostly decreases with height [Rasch and Kristjánsson, 1998]. This explains why droplet sizes are similar at
the two model levels, even though cloud droplet number
concentrations decrease rather rapidly with height. Even so,
the smallest cloud droplets of 5 mm are found in the
heavily polluted regions of SE Asia and over convective
regions of central America, where the prescribed supersaturations are large. The largest droplets, up to 17 mm in
size, are found in the polar regions where droplet concentrations are small.
[17] Having shown the horizontal distributions of CDNC
and re in Figures 2 and 3, we display their zonal averages in
Figure 4. Figure 4a shows that the cloud droplet number
concentrations decrease rather rapidly with height, as well
as toward the poles. The largest concentrations of between
200 and 500 cm3 are found near the surface between 30S
and 60N. Figure 4b shows a minimum in cloud droplet size
of <6 mm near 950 hPa at low latitudes, increasing with
height and toward the surface (see Figure 3b), as well as
with increasing latitude. The anthropogenic effect on CDNC
and re is shown in Figure 5. The largest increase in CDNC
due to anthropogenic aerosols (Figure 5a) is found in the
lowest kilometers over the Northern Hemisphere mid- to
low-latitudes. However, the largest relative increase is found
at upper levels, i.e., at 600 –800 hPa (not shown), and this is
what determines the change in effective radius (Figure 5b),
owing to the relation
dre =re 1=3 dN =N :
Hence in these regions, effective radii are found to decrease
by >1 mm, zonally averaged. This is a significant change,
considering that the average droplet sizes here are close to
11 mm (Figure 4b). We note that a large decrease in cloud
droplet size is also found between 800 and 900 hPa in the
Arctic (Figure 5b). This has to do with a large sensitivity of
the pristine arctic aerosol to changes in sulfate concentrations.
[18] In Figures 6a and 6b we compare the computed
cloud droplet radii to satellite observations given by Han
et al. [1994]. The model droplet radii are calculated at
individual model levels. In order to make them comparable
to those seen by the satellite, we search from above through
liquid cloud only for the first cloud (if any) in a vertical
column that is optically thick (black in the longwave). If
such a cloud is found, the effective radius of that cloud (rev)
is registered. If not, that column will not contribute to the
estimate in Figure 6a at the given time. The satellite data are
only shown for the region 50S – 50N owing to the use of
geostationary satellites. A generally good agreement is
Figure 2. (a) Cloud droplet number concentrations (CDNC) at h = 0.87 in the 2000 simulation.
(b) CDNC at h = 0.99 in the 2000 simulation.
found between model results and observations, in particular,
over the oceans. Here the largest radii, >13 mm, are found
over remote ocean regions of the subtropics. Elsewhere over
ocean the radii are mainly between 11 and 13 mm and are
somewhat smaller near the coastlines. Over the continents
the cloud droplet radii are typically 2 mm smaller than
over ocean, again in fair agreement with observations.
Regionally, over land there are discrepancies, but some of
them (North Africa) may be caused by problems with the
satellite retrievals.
[19] In Figure 7 we show the reduction in cloud droplet
size due to anthropogenic aerosols. The largest reductions of
>2 mm are found over SE Asia. Other regions with large
reductions are central Africa, Siberia, parts of the North and
equatorial Atlantic, and the United States. These regions
mainly coincide with areas of large sulfate concentrations
(Figure 8). Over the North Atlantic the natural aerosol
loading and CCN concentrations are low. Hence a large
relative increase is found due to anthropogenic aerosols
carried downstream from the North American continent.
This explains the large reduction in droplet size here. As
expected, the smallest changes in cloud droplet radii (<0.1
mm) are found over pristine areas south of 30S.
[20] Globally averaged droplet sizes given in Table 1
show significant differences between land (9.32 mm) and
ocean (10.60 mm), as well as between the two hemispheres
(9.81 mm in the Northern Hemisphere and 10.78 mm in the
Southern Hemisphere). The reduction due to anthropogenic
aerosols is largest over land and in the Northern Hemisphere, but this does not explain all the size difference. For
Figure 3. (a) Cloud droplet effective radius at h = 0.87 in the 2000 simulation. (b) Cloud droplet
effective radius at h = 0.99 in the 2000 simulation.
instance, there is more land in the Northern Hemisphere, and
hence the background aerosol concentrations are higher
there (6 103 cm3 near the surface as compared with
4 102 cm3 over ocean) [see Kirkevåg et al., 1999],
leading to larger CDNC and smaller rev.
[21] The reduction in cloud droplet size due to anthropogenic aerosols affects precipitation release both in theory
[Albrecht, 1989] and in our model calculations. This can be
seen by noting that the term r3l in equation (2), which enters
into equation (1) for PWAUT, is now reduced, while N is
enhanced. This reduction in precipitation release does not
affect the globally averaged precipitation considerably since
clouds are mainly a manifestation of the conversion of
converging moisture to precipitation. However, the amount
of water stored in clouds is strongly affected, and this in
turn affects the radiation. In Figure 9 we show the change in
cloud water content both in a zonally averaged cross section
(Figure 9a) and in a horizontal projection (Figure 9b). The
general patterns in Figure 9a are quite similar to those seen
for CDNC change (Figure 5a), both having the largest
changes near the surface in the Northern Hemisphere
midlatitudes and the tropics. Interesting differences between
Figure 9a and Figure 5a are found in the Arctic and at high
latitudes in the Southern Hemisphere. This has to do with a
large sensitivity to change in these areas, i.e., a large relative
change in CDNC, and hence r3l. Figure 9b shows the largest
cloud water path changes over SE Asia, followed by the
eastern United States, the North Atlantic, and Europe.
occurring over SE Asia and with large local minima
occurring over and downstream of eastern North America,
central Africa, and over Europe. There are smaller but still
significant negative forcings near industrialized centers of
the Southern Hemisphere, such as Santiago-Buenos AiresSao Paulo in South America and over parts of South
Africa. One interesting feature emerging from Figure 10a
is that in some areas, e.g., the North Atlantic between 25
and 50N, there are large indirect forcings quite far away
from aerosol source regions. This can partly be explained
by transport of pollutants downstream from the eastern
U.S. source region. The other part of the explanation is in
the distribution of clouds and the change thereof, as
suggested by Figure 9b.
[23] We note from Table 1 that the difference in radiative
forcing between land and ocean is smaller than we might
expect from the large differences in changes in droplet radius
and liquid water path. This is because over land, clouds are
typically rather dense, having a high cloud albedo, with
Figure 4. (a) Zonally averaged cross section of CDNC in
the 2000 simulation. (b) Zonally averaged cross section of
re in the 2000 simulation.
Table 1 confirms that land areas of the Northern Hemisphere
have the largest cloud water paths and also that this is where
the increase due to anthropogenic aerosols is largest, even
percent-wise (not shown). The pristine ocean regions of the
Southern Hemisphere have the smallest increase in LWP, but
the LWP itself is slightly larger here than over the Southern
Hemisphere land areas and the Northern Hemisphere
oceans. This is due to the persistent cyclonic activity
associated with the Southern Hemisphere storm tracks.
3.2. Radiative Forcing
[22] We will now investigate the radiative impact of the
simulated anthropogenic changes in cloud droplet size that
were presented in Figures 5b and 6c, as well as the
changes in the water path shown in Figure 9b. We start
by studying the annually averaged indirect radiative forcing from anthropogenic aerosols for 2000 (Figure 10a).
The annual average for 2000 is 1.8 W m2 (Table 1),
which is within the range given by Hansen et al. [1998]
and IPCC [2001]. There are large horizontal inhomogeneities in the forcing field, with the strongest negative forcing
Figure 5. (a) Zonally averaged cross section of anthropogenic change of CDNC in the 2000 simulation.
(b) Zonally averaged cross section of anthropogenic change
of re in the 2000 simulation.
Figure 6. (a) The quantity rev as observed by satellite, in the 2000 simulation. (b) The value of rev as
observed by satellite, according to Han et al. [1994].
cloud-free areas in between (e.g., the continental desert
regions). Over ocean, however, there are few cloud-free
areas, and large parts of the ocean are covered by rather thin
clouds with a comparatively low albedo. These clouds are
much more sensitive to a change in rev or LWP, and hence
cloud albedo, than the clouds over land. Such differences in
sensitivity have previously been explored by Hobbs [1993].
Finally, we note the large hemispheric differences in radiative
forcing due to the predominance of sulfate aerosols in the
Northern Hemisphere. As a result, the Northern Hemispheric
average radiative forcing is 2.6 W m2, while the corresponding value for the Southern Hemisphere is 1.1 W m2.
Since cloud radiative forcing is largest in summer, the
indirect effect is considerably larger in the Northern Hemisphere summer season (June, July, and August (JJA)) than in
Northern Hemisphere winter (December, January, and Feb-
ruary (DJF)). Global averages for the four seasons are as
follows (Table 2): 2.4 W m2 in JJA; 1.7 W m2 in
September, October, and November; 1.4 W m2 in DJF;
and 1.8 W m2 in March, April, and May. This means that
during Northern Hemisphere summer our calculations give
an aerosol indirect effect that cancels the greenhouse gas
warming of approximately +2.45 W m2 [IPCC, 2001].
Furthermore, over the Northern Hemisphere, even the annually averaged forcing (2.6 W m2) approximately cancels
the greenhouse gas warming, and during the summer the sum
of the two gives a significant cooling effect here. Considering the fairly uniform global warming observed over recent
decades [Jones et al., 1999; Levitus et al., 2000], this may
seem unrealistic, but it should be remembered that our results
only give the radiative forcing while the response to that
forcing is yet to be determined. This will involve dynamical
1 - 10
Figure 7. Change of rev due to anthropogenic aerosol emissions.
feedbacks that may alter the picture substantially. Possible
reasons for overestimations of aerosol indirect effect in our
simulations will be discussed in section 5.
[24] Even though we are here concerned with radiative
forcings rather than climate response, it is important that the
TOA radiative budget, when anthropogenic aerosols are
included, is realistic. This is investigated in Figure 11,
where we compare the shortwave cloud forcings with
estimates from the Earth Radiation Budget Experiment
(ERBE). Apart from some well-known systematic biases,
e.g., in the subtropics [Rasch and Kristjánsson, 1998], the
model realistically simulates the main features of the TOA
radiative budgets. The globally averaged cloud forcings are
within 1 – 2 W m2 of observational estimates. The shortwave cloud forcing (Figure 11) is too strong over Europe in
the model but is too weak over the Southern Hemisphere
storm track region. The latter is a preexisting model bias
that was previously pointed out by Kristjánsson et al.
Figure 8. Vertically integrated sulfate amounts in mg m2.
1 - 11
Table 1. Area and Annual Averages of Key Quantities and Their Changes Due to Aerosol-Cloud Interactions As Calculated in the 5-Year
Control Experimenta
All, global
All, NH
All, SH
Ocean, global
Ocean, NH
Ocean, SH
Land, global
Land, NH
Land, SH
Indirect Effect, W m2
rev, mm
LWP, g m2
rev, mm
LWP, g m2
Abbreviations are LWP, liquid water path; NH, Northern Hemisphere; and SH Southern Hemisphere.
[2000], although it was less prominent in the 15-year
integrations of Rasch and Kristjánsson [1998]. The longwave cloud forcing is less sensitive to the modifications of
low clouds by aerosols that we have investigated and is
therefore not shown. In general, however, the long wave
radiative budget is in excellent agreement with the ERBE
observations [Rasch and Kristjánsson, 1998; Kristjánsson
et al., 2000].
Figure 9. (a) Zonally averaged change of cloud liquid water mixing ratio due to anthropogenic aerosols.
(b) Change of vertically integrated liquid water path due to anthropogenic aerosols.
1 - 12
Figure 10. (a) Simulated indirect effect for 2000. (b) Simulated change in the indirect effect from 2000
to 2100.
3.3. Comparison of 2000 With 2100
[25] Here we compare the annually averaged indirect
radiative forcing from anthropogenic aerosols for 2000
and 2100 (Figure 10b) using the IPCC A2 scenario. The
A2 is a ‘‘middle of the road scenario’’ which expects sulfate
aerosol concentrations to decrease significantly in the
developed countries, with increases in developing countries.
Black carbon emissions due to both biomass burning and
combustion are expected to increase substantially during the
21st century in this scenario. This last assumption may be
debatable, but as discussed in section 4.2, the impact on the
indirect effect simulation is small. In 2100 the global
average is the same as in the 2000 run (see Table 3), but
this is obtained through a markedly different horizontal
distribution. Due to the different trends in sulfur emissions
according to geographical location in the A2 scenario (see
section 3), the indirect aerosol forcing is now strongly
enhanced at low latitudes (e.g., central Africa, Indian
Ocean, and Indonesia), while the Northern Hemisphere
regions that dominated in 2000 now have considerably
weaker forcings than before (Figure 10b).
4. Sensitivity Experiments
[26] In this section we describe the results of sensitivity
experiments that were carried out in order to highlight
1 - 13
Table 2. Seasonal Variation of Annual Averages of Key Quantities and Their Changes Due to Aerosol-Cloud Interactions As Calculated
in the 5-Year Control Experimenta
DJF, global
JJA, global
SON, global
MAM, global
Indirect Effect, W m2
rev, mm
LWP, g m2
rev, mm
LWP, g m2
Abbreviations are DJF, December, January, and February; JJA, June, July, and August; SON, September, October, and November; and MAM, March,
April, and May.
particular features of the results presented so far. In all cases
the model has been run for 16 months, and we use the last
12 months for our investigation.
4.1. Radius and Lifetime Effects Separately
[27] A quantitative way to estimate the relative roles of
the radius effect and the lifetime effect is to run the model
with one of the effects turned off. In Figures 12a and 12b we
show results from simulations where this was done. First, in
Figure 12a we have turned off the lifetime effect. This
‘‘radius effect only’’ run gives a globally averaged indirect
effect of 1.3 W m2, i.e., almost 3/4 of the total forcing in
the control run (Table 4). By comparison, Figure 12b gives
the indirect effect when only the lifetime effect is considered. In this case the global average is 0.46 W m2, i.e.,
1/4 of the total effect given in Table 4. In section 4.3 we
discuss the sensitivity of this result to some of the assumptions made in the parameterization of the indirect effect.
The horizontal distribution is also different; for example, the
radius effect produces a distinct local maximum over the
North Atlantic with lower values over the eastern United
States. The lifetime effect, however, has significantly larger
forcing over the eastern United States than over the North
Atlantic (Figure 12b). To understand this, we note the large
similarities in spatial patterns between Figures 7c and 12a
on the one hand and between Figures 9b and 12b on the
other hand. Evidently, a measure of the radius effect can be
obtained by looking at the change in cloud droplet radius
due to anthropogenic aerosols (Figure 6c), while the
increase in liquid water path (Figure 9b) is a measure of
the lifetime effect in our model.
4.2. Role of Black Carbon for the Aerosol
Indirect Effect
[28] One of the reasons that sulfate aerosols from anthropogenic emissions are so important for the aerosol indirect
effect is the fact that they are very efficient CCNs. This is
not the case for all anthropogenic aerosols; for example,
black carbon is hydrophilic and the BC particles can only
act as a CCN when they are in an internal mixture containing, e.g., sulfate. Remember from section 2.1 that BC in
nucleation mode is assumed externally mixed, while BC in
accumulation mode is assumed internally mixed with sulfate and background aerosols. We have carried out a
sensitivity study to see just how large the impact of
anthropogenic BC aerosols is on the indirect effect in our
model. Figure 13 shows the difference in simulated indirect
effect between the control run and a simulation in which all
BC aerosol concentrations are artificially set to 0 at every
time step. The monthly mean concentrations of the different
sulfate species are left unaltered. Figure 13 shows a slight
contribution (about 0.25 W m2) to the indirect effect
from BC in parts of central Africa, SE Asia, Europe, and
South America. Elsewhere the effect is very small, so that the
globally averaged contribution of BC is only 0.1 W m2.
The negative difference arises mainly in regions with large
concentrations of accumulation mode particles because BC
adds to their size. However, the positive values over the
remote oceans occur because here the reduced hygroscopicity due to BC is the dominating effect, due to small
concentrations of accumulation mode particles. In summary,
we find that virtually all the anthropogenic indirect effect in
our model stems from sulfate aerosols.
4.3. Sensitivity to Assumptions in the
Parameterization Scheme
[29] In order to investigate the sensitivity of our results to
some of the assumptions in the parameterization schemes,
we have carried out sensitivity tests where some of those
assumptions have been modified. In particular, we wish to
find out whether the partitioning between radius and lifetime effects (section 4.1) is sensitive to the parameterization
assumptions. Rotstayn [2000] showed that a larger value of
the autoconversion threshold, combined with changes in the
subgrid-scale cloud parameterization, led to a larger lifetime
effect and a smaller radius effect than with the original
[30] As seen in Table 4, the 1-year control run yields
results very similar to those of the 5-year control experiment
(Tables 1, 2, and 3). In the first sensitivity experiment,
termed ‘‘S*2’’ in Table 4, the supersaturation that is
assumed at cloud formation has been doubled. This leads
to an unrealistically high cloud droplet number, and hence
the cloud droplet radius is probably too low (8.5 mm).
Owing to the larger number of droplets, the decrease in
droplet size due to anthropogenic aerosols is now reduced
compared with the control experiment (Table 4). Conversely, the change in liquid water path is enhanced from
1 - 14
Figure 11. Shortwave cloud forcing (SWCF) from (a) model integration and (b) Earth Radiation Budget
Experiment (ERBE) data. Units are W m2.
Table 3. Area and Annual Averages of Key Quantities and Their Changes Due to Aerosol-Cloud Interactions As Calculated in Two
5-Year Experiments
Indirect Effect, W m2
rev, mm
LWP, g m2
rev, mm
LWP, g m2
1 - 15
Figure 12. (a) Indirect effect from the radius effect only. (b) Indirect effect from the lifetime effect only.
1.9 to 2.6 g m2. The opposite trend is found in the other
sensitivity experiment, in which the autoconversion threshold r3lc in equation (1) was set back to its original value of
5 mm instead of the value of 10 mm used in the control
experiment. With a lower threshold value, more cloud water
is released as precipitation from relatively thin clouds. As a
result, the liquid water path is reduced, and its change due to
anthropogenic aerosols is reduced by a factor of 2.
However, the reduction in cloud droplet radius due to
anthropogenic aerosols is now slightly enhanced, as fewer
droplets now experience size reduction. For this last experiment, separate simulations have been carried out by computing only the radius or lifetime effects (Table 4). As one
would expect from the changes in effective radius and liquid
water path, we then find that the partitioning is changed so
that the radius effect now contributes 1.3 W m2, while
the lifetime effect now yields 0.27 W m2. This means
that the lifetime effect now contributes 17% of the total
indirect effect, as compared with 25% in the standard
simulation (section 4.1). According to Table 4, we would
Table 4. Area and Annual Averages of the Changes in Key
Quantities Due to Aerosol-Cloud Interactions As Calculated in
1-Year Sensitivity Experiments
Indirect Effect, W m2
rev, mm
LWP, g m2
Only the radius effect was treated.
Only the lifetime effect was simulated.
1 - 16
Figure 13. Difference in indirect effect between the control run and a run where all black carbon
concentrations were set to zero.
expect an opposite effect due to a doubling of the supersaturation, i.e., an enhanced lifetime effect and a reduced
radius effect. In conclusion, we find that the partitioning
between the radius and the lifetime effect is somewhat
sensitive to the two parameters explored here, but the
indirect effect itself is less sensitive. Both quantities may
also be sensitive to other assumptions, e.g., regarding the
background aerosols. Fewer background aerosols tend to
yield a larger indirect effect since the aerosol population is
then more dominated by the presence or absence of anthropogenic aerosols. This will be explored further in future
5. Discussion
5.1. Comparison With Other Studies
[31] Comparing our results with other recent model
studies of aerosol indirect effect is not entirely straightforward since there are very large differences in approach. This
highlights the large uncertainty in estimating this forcing, as
indicated by the large range (0.0 to 2.0 W m2 for the
radius effect alone) adopted by IPCC [2001]. Many investigations, starting with Boucher and Lohmann [1995], have
used simple empirical relationships between sulfate mass
and cloud droplet number, meaning that there is no explicit
treatment of background aerosols. Here the present study
differs by treating aerosol sizes in a fairly detailed manner.
Arguably, this is an important step toward improved estimates of the indirect forcing. However, in order to exploit
fully the advantage of the size information, a more detailed
parameterization for cloud droplet number than what was
used here is needed. This will be explored in the near future.
The investigation by Kiehl et al. [2000] used the same
model tools that we have used, i.e., the NCAR CCM3 and
the Rasch-Kristjánsson condensation scheme. However,
their aerosol treatment was very different from ours; for
example, they related aerosol (sulfate) mass to droplet
number without considering particle sizes [Barth et al.,
2000]. Also, they only considered the radius effect and
not the lifetime effect. For four different assumptions on
the relationship between aerosol sizes and droplet number,
they obtained an indirect effect ranging from 0.6 to
1.8 W m2. As explained by Iversen and Seland (submitted manuscript, 2001), the vertical distribution of sulfate
of Barth et al. [2000] and Kiehl et al. [2000] is quite
different from ours since they allow a very strong vertical
transport by moist convection. This has a substantial impact
on the indirect effect because our larger sulfate concentrations at low levels will affect liquid clouds more, and these
are the only clouds for which an indirect effect is computed.
A test run (not shown) where sulfate was distributed evenly
in the vertical, keeping the column burdens unchanged,
resulted in a reduction of the indirect effect in our model by
>30% to about 1.2 W m2.
[32] In the study by Lohmann et al. [2000] a total aerosol
indirect effect of between 1.1 and 1.5 W m2 was
obtained, most of which came from organic carbon aerosols.
In their model the contribution from sulfate was considerably smaller than in both this study and that of Kiehl et al.
[2000] since sulfate aerosols were rather ineffective at
forming new cloud droplets. Rotstayn [1999] used a similar
approach to that of Boucher and Lohmann [1995] and
obtained a total indirect effect of 2.1 W m2, consisting
of approximately equal contributions from lifetime and
radius effects. A similar partitioning between lifetime and
radius effects was found by Lohmann et al. [2000]. Neither
of those studies obtained the lifetime effect as a pure forcing
term as done here, but that probably does not explain the
difference in partitioning because the response experiments
mentioned in section 2.3 gave similar changes in droplet
size and liquid water path to those obtained in our forcing
experiments. There is no doubt that in addition to the
differences in aerosol treatment, different condensation
schemes and different model climates in general contribute
to these differences between models. For example, we note
that the increase in liquid water path due to anthropogenic
aerosols found by Lohmann et al. [2000] was >10% as
compared to 5% here. Rotstayn [1999] found that the
increase was only 6%, and yet the lifetime effect was as
large as 1.0 W m2, but it should be noted that there was
an accompanying increase in the cloud cover by 1%. All
of the studies mentioned here suggest that the aerosol
indirect effect is a major climate forcing of a similar
magnitude, but with opposite sign, to the warming effect
of anthropogenic CO2.
5.2. Limitations of the Present Study
[33] All model estimates of the aerosol indirect effect
yield a uniformly negative radiative forcing at TOA. Recent
work by Ackerman et al. [2000] suggests that in some cases,
absorption of solar radiation by soot aerosols embedded in a
cloud layer may affect cloud evolution in such a way that
the total radiative forcing from anthropogenic aerosols
becomes positive. This positive forcing is not simply a
result of adding the direct effect and the indirect effect.
Rather, there is an interaction between the two, where the
direct aerosol forcing affects cloud lifetime. More research
is needed to establish how important this effect is in a global
perspective. However, it is a reminder that the direct and
indirect effects need to be studied simultaneously in order to
account for possible nonlinear interactions between them.
Hence, as a follow-up of the present study, we intend to
combine the direct effect calculations of Kirkevåg and
Iversen (submitted manuscript, 2001) with the indirect
calculations presented here. Recently, Lohmann and
Feichter [2001] presented a study of this so-called ‘‘semidirect’’ effect. They found a fairly small effect globally,
while in certain regions the indirect effect was considerably
reduced, as suggested by Ackerman et al. [2000].
[34] We have so far focused almost exclusively on the
shortwave component of the indirect effect. The longwave
effect is typically assumed to be much smaller because the
water clouds have a cloud top temperature, which does not
differ much from that of the underlying surface. Also, most
water clouds are already black bodies, so that an increase in
cloud thickness will not affect emissivity much. In our
model the longwave component is particularly small, only
about +0.01 W m2. This is an order of magnitude smaller
than that presented in many other studies [e.g., Rotstayn,
1999]. The main reason for this is that the treatment of the
longwave properties of water clouds in NCAR CCM3
[Kiehl et al., 1998] uses a simple emissivity approximation
where only liquid water path enters and not droplet size.
Hence the reduction in cloud droplet size seen in Figure 6c
will not affect the outgoing longwave radiation at TOA. Ice
clouds, which have not been considered here, have a much
lower cloud top temperature than water clouds. Hence it is
quite likely that the longwave component of the indirect
1 - 17
effect may be important for the indirect effect of ice clouds.
This is an area where more research is needed.
6. Conclusions and Future Plans
[35] In order to simulate the aerosol indirect effect, we
have combined the following three modules and applied
them in the framework of the NCAR CCM3 global climate
model: (1) a life-cycle scheme for black carbon and sulfate
aerosols described by Iversen and Seland (submitted manuscript, 2001); (2) a size-segregating scheme for background
aerosols combined with the sulfate and BC from the lifecycle scheme, accounting for aerosol condensation, incloud processes, coagulation, and hygroscopic growth
(Kirkevåg and Iversen, submitted manuscript, 2001); and
(3) the prognostic cloud water scheme of Rasch and
Kristjánsson [1998]. Cloud droplet number concentrations
were obtained from assumptions on supersaturation, enabling the use of the Köhler equation. Only the liquid phase
was considered.
[36] When the three modules were coupled together and
were coupled with the radiation scheme of the NCAR
CCM3, it was necessary to retune two of the more uncertain
parameters in the cloud microphysics scheme. This had the
effect of enhancing both cloud droplet sizes and liquid
water paths, bringing both quantities closer to observational
estimates. Following this retuning, a 5-year control experiment, a 5-year scenario experiment, and several 1-year
sensitivity simulations were carried out. The focus was on
changes in cloud droplet size (radius effect) and liquid
water path (lifetime effect), as well as the impact of these
changes in terms of radiative forcing at the top of the
atmosphere. A novelty in this investigation is that not only
the radius effect but also the lifetime effect of anthropogenic
aerosols was computed as a forcing term. The rationale for
this procedure was explained, and a set of test runs
supported its validity.
[37] The globally averaged droplet size in our simulations
is 10.31 mm, which is 0.58 mm lower than in the simulations
without anthropogenic aerosols, i.e., a 5.3% reduction. By
comparison the increase in globally averaged liquid water
path is 4.9 %. Together these two effects yield a globally
averaged radiative forcing of 1.8 W m2. When the model
is run with the radius and lifetime effects separately, the
radiative forcings are 1.3 and 0.46 W m2, respectively.
In the main simulation the radiative forcing is largest over
SE Asia, owing to a combination of high sulfate amounts
and a low solar zenith angle throughout the year. Also, this
region has rather dense liquid clouds throughout the year.
Secondary maxima are found over eastern North America,
the North Atlantic, Europe, and Siberia. In the sensitivity
experiments the spatial patterns in the experiment with
‘‘radius effect only’’ resemble those of the change in cloud
droplet size, while those in the ‘‘lifetime effect only’’
experiment resemble those associated with the change in
liquid water path. In one sensitivity experiment, black
carbon aerosol concentrations were set to 0. This turned
out to have a very small effect on the results, in particular,
the global averages.
[38] A potential weakness of our aerosol scheme is the
omission of certain aerosol types, e.g., organic carbon
aerosols. Our main reason for not including organic carbon
1 - 18
at this stage is the large uncertainties that exist concerning
concentrations and physical properties (water solubility) of
these aerosols [Huebert and Charlson, 2000]. Since
organic aerosols may potentially be quite effective at
creating new cloud droplets [e.g., Lohmann et al., 2000],
there is no doubt that this is an issue that requires further
investigation. We plan to return to this topic in future
papers. Other items that deserve more attention are the
closure assumption on supersaturations and the omission of
ice phase and longwave effects in our estimates of the
aerosol indirect effect.
[39] In the near future we plan to couple the atmospheric
component of NCAR CCM3 to a slab ocean model [Boville
and Gent, 1998] and to perform decadal simulations investigating the response of the climate system to the indirect
forcing. The focus will then be on regional climate effects in
the northern North Atlantic and Europe. Obviously, on
multidecadal and century timescales one also has to consider possible changes to the thermohaline circulation,
which would require running a fully coupled comprehensive
ocean model. Such investigations will be deferred to the
more distant future.
[40] Acknowledgments. This study has been supported by the Norwegian Research Council through the RegClim project. Furthermore, this
work has received support of the Norwegian Research Council’s Programme for Supercomputing through a grant of computer time. The author
thanks Øyvind Seland for providing the sulfate and black carbon data and
Alf Kirkevåg, who provided codes for computing CCN concentrations, as
well as the background aerosol data. The author is also grateful to them for
many illuminating discussions. The author has benefited from repeated
discussions with Philip Rasch, in particular, during the author’s sabbatical
leave at NCAR in 1999. Two anonymous reviewers provided constructive
comments that led to improvements in the manuscript.
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J. E. Kristjánsson, Department of Geophysics, University of Oslo, P.O.
Box 1022, Blindern N-0315, Oslo, Norway. ( [email protected])