JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, D24206, doi:10.1029/2005JD006299, 2005
Response of the climate system to aerosol direct and indirect forcing:
Role of cloud feedbacks
J. E. Kristjánsson, T. Iversen, A. Kirkevåg, and Ø. Seland
Department of Geosciences, University of Oslo, Oslo, Norway
J. Debernard
Norwegian Meteorological Institute, Oslo, Norway
Received 31 May 2005; revised 6 September 2005; accepted 12 October 2005; published 23 December 2005.
[1] In this study, the response of the climate system to aerosol direct and indirect radiative
forcing is investigated. Several multidecadal equilibrium simulations are carried out,
using the NCAR CCM3 framework coupled to a separately developed aerosol treatment.
The aerosol treatment includes, e.g., a life cycle scheme for particulate sulfate and black
carbon (natural and anthropogenic), calculations of aerosol size distributions and
CCN activation, as well as computations of direct and indirect forcing (1st and 2nd
indirect effect). In all the simulations the full aerosol treatment is run online, hence
responding interactively to changes in climate. By far the largest response is caused by the
indirect forcing, with a globally averaged cooling of 1.25 K due to anthropogenic
aerosols. The largest temperature reduction is found in the Northern Hemisphere because
of a larger aerosol burden there. As a result of this cooling pattern, the Intertropical
Convergence Zone is displaced southward by a few hundred kilometers. Interestingly, a
similar, though less significant displacement is also found in the experiments with the
direct effect alone, even though the globally averaged aerosol induced cooling is
much weaker in that case, i.e., 0.08 K. The direct radiative forcing is much stronger at
the surface than at the top of the atmosphere, and this leads to a slight weakening of the
hydrological cycle. Comparing simulations with direct and indirect forcing combined to
those with indirect and direct forcing separately, a residual, caused by nonlinear
model feedbacks, is manifested through a reduction in precipitation. This reduction
amounts to 0.5% in a global average and exceeds 2.5% in the Arctic, highlighting the
role of high-latitude climate feedbacks. Globally, cloud feedback is negative in the
sense that in the colder climate resulting from anthropogenic aerosol forcing, net cloud
forcing is reduced by 15% compared to the original climate state. This is caused by a
general cloud thinning, especially at high latitudes, while in the most polluted regions,
clouds are thicker through the 2nd indirect effect. The 1st indirect effect, on the
other hand, remains intact in the presence of climate feedbacks, yielding a similar
signature of cloud droplet reduction as in the pure forcing simulations.
Citation: Kristjánsson, J. E., T. Iversen, A. Kirkevåg, Ø. Seland, and J. Debernard (2005), Response of the climate system to aerosol
direct and indirect forcing: Role of cloud feedbacks, J. Geophys. Res., 110, D24206, doi:10.1029/2005JD006299.
1. Introduction
[2] Over the last 250 years, humans have considerably
altered the composition of the atmosphere. There is now
little doubt that the increased concentrations of man-made
greenhouse gases (CO2, CH4, N2O, CFC gases, etc. [e.g.,
Houghton, 2002]) will lead to a significant global warming,
unless major cooling factors come into play [Houghton et
al., 2001]. Much more uncertainty is attributed to the role
of anthropogenic aerosols. The aerosols affect climate
directly by reflecting and absorbing solar radiation, and
Copyright 2005 by the American Geophysical Union.
0148-0227/05/2005JD006299$09.00
to a lesser extent through absorption and emission of
longwave radiation. They also affect climate indirectly
by altering the amount and size of cloud condensation
nuclei (CCN) in the atmosphere. It is very difficult to
assess the impact of these two influences by measurements, although improvements have been made lately
[e.g., Brenguier et al., 2003]. Consequently, most estimates
of the direct and indirect effect have been obtained from
simulations with global climate models (GCMs). At present
model estimates vary greatly, with typical estimates of the
globally averaged direct aerosol radiative forcing ranging
from 0.5 to 1.0 W m2, and a corresponding range of 0
to 2.0 W m2 for the indirect forcing (ignoring changes
in cloud lifetime and precipitation release [Ramaswamy,
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KRISTJÁNSSON ET AL.: CLIMATE RESPONSE TO AEROSOL FORCING
2001]). If anything, the range of model estimates has
widened in recent years, as more and more detailed aerosol
treatments have been introduced, adding new degrees of
freedom to the models. Clearly, it is of crucial importance
for society at large to investigate what this range of
estimates means in terms of climate and climate change.
For instance, according to Anderson et al. [2003] and
Andreae et al. [2005] the uncertainty regarding the cooling
effect of aerosols is so large that it cannot be ruled out that
this cooling may so far have almost canceled the anthropogenic greenhouse effect, implying that a very strong
warming may be expected in the 21st century, when
greenhouse gases are expected to greatly overrun the
aerosols in terms of radiative forcing.
[3] The first studies investigating the climate response to
aerosol forcing dealt only with the indirect effect, e.g.,
Rotstayn et al. [2000], Williams et al. [2001], and Rotstayn
and Lohmann [2002]. In these studies, using three different
GCMs, qualitatively similar results were found, i.e., a
substantial cooling effect that was largest at high latitudes
of the Northern Hemisphere, and a southward shift of the
Intertropical Convergence Zone (ITCZ). The shift of the
ITCZ was caused by the stronger cooling of the Northern
Hemisphere than the Southern Hemisphere, while the large
cooling at high latitudes was explained by ice albedo
feedback mechanisms [Williams et al., 2001]. Rotstayn
and Lohmann [2002] compared their results with observations and went on to suggest that the southward shift of the
ITCZ might partly explain the drought in the Sahel region in
Africa in the 1960s and 1970s [Folland and Karl, 2001].
Previously, the drought had been attributed mainly to
natural variability, but with a possible influence from overgrazing, which tends to raise the surface albedo of this
region at the rim of the Saharan desert. In two recent papers
by Feichter et al. [2004] and Takemura et al. [2005], both
the direct and indirect forcing were treated simultaneously.
In both cases, a southward shift of the ITCZ was found, but
the shift was less pronounced in the Feichter et al. [2004]
study than in the other studies mentioned above. This may
conceivably be related to their more complex treatment of
cloud droplet nucleation, which probably tends to give less
weight to the effects of sulfate than simpler schemes do.
[4] In the present paper, the climate response of both the
direct and indirect effect is studied. Furthermore, by subtracting results from simulations where the indirect effect
and the direct effect are simulated separately from simulations in which both effects are treated simultaneously an
aerosol effect, caused by nonlinear response of the model to
imposed forcings, will be estimated. We term this effect the
‘‘residual effect.’’ We also evaluate the semidirect effect as
the change in cloud properties due to direct radiative
forcing, similarly to what was done by Lohmann and
Feichter [2001]. However, it should be noted that neither
Lohmann and Feichter [2001] nor Penner et al. [2003]
enabled the oceans to respond to the aerosol forcing in their
estimates of the semidirect effect.
[5] The purpose of this study is to investigate interactions
between aerosol forcing and the climate system, with a
special emphasis on the hydrological cycle. On the other
hand, while presenting a plausible control climate, this
study does not address the more general issue of attempting
to simulate today’s climate as well as possible. Likewise,
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the impact of a doubling of CO2 in a future climate scenario
is dealt with in a separate upcoming paper. The next
section reviews our aerosol treatment, and describes the
setup for the experiments that follow. The main results of
these experiments and their interpretation are presented in
section 3. This is followed by (section 4) an investigation
of the role of cloud feedbacks in modifying the climate
response to the aerosol forcing. Finally, section 5 presents
a summary and the main conclusions of this study.
2. Model and Experimental Setup
[6] The basic model tool for the experiments is a modified version (CCM-Oslo) of the atmospheric global climate
model NCAR Community Climate Model version 3 (CCM3
[Kiehl et al., 1998]), coupled to a slab ocean module. CCM3
is a state-of-the-art global climate model, run at T42 spectral
truncation and with 18 levels in the vertical. The modifications to the CCM3 consist of (1) the introduction of a
prognostic cloud water scheme, following Rasch and
Kristjánsson [1998], and (2) the replacement of the
simple aerosol scheme in CCM3 with a detailed aerosol
scheme, described by Iversen and Seland [2002, 2003],
Kirkevåg and Iversen [2002] and Kristjánsson [2002].
Background aerosols, consisting of sea salt, mineral and
water-soluble non-sea-salt particles are prescribed and size
distributed. These size distributions are then modified by
adding natural and anthropogenic sulfate and black carbon (BC) into an internal mixture, brought about by
condensation, coagulation in clear and cloudy air, and
wet phase chemical processes in clouds. A normally
minor fraction of sulfate and BC is externally mixed,
produced by clear-air oxidation followed by nucleation,
and by emission of primary particles. Starting from
emissions of sulfate precursor gases (SO2, DMS), sulfate
particles (SO4) and black carbon, chemical reactions,
transport and deposition are computed at every grid point.
Emission data, assumed valid for the year 2000, are the
same as used by Iversen and Seland [2002, 2003] and in
the IPCC TAR [Penner, 2001]. Biogenic emissions,
emissions from volcanoes and a minor fraction (10%)
of emissions from biomass burning are assumed natural.
The remaining emissions are assumed anthropogenic. The
largest emission sources in 2000 are fossil fuel combustion, biomass burning and industrial releases. A weakness of
the aerosol treatment is the lack of an explicit treatment of
organic carbon (OC) aerosols, which are considered to be an
important component of the global aerosol [e.g., Kanakidou
et al., 2005]. In the latest version of our aerosol scheme
[Kirkevåg et al., 2005], OC has been incorporated into the
aerosol framework described above. (3) As described in
detail by Kristjánsson [2002], cloud droplet number is
diagnosed from the aerosol size distributions, after accounting for humidity swelling, via assumptions on the supersaturation (0.05% in stratiform clouds, 0.10% in convective
clouds over ocean and 0.15% in convective clouds over
land). For a given supersaturation, the number of activated
CCN is obtained from Köhler theory, and the droplet
concentration is set to this value. The cloud droplet effective
size is then obtained from the following relationship:
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reff ¼ k½ð3 ql ra Þ=ð4 p rw NÞ1=3
ð1Þ
KRISTJÁNSSON ET AL.: CLIMATE RESPONSE TO AEROSOL FORCING
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Figure 1. Variation in the globally averaged surface
temperature (C) in the ALLTOT simulation, illustrating
the 10 year spin-up and the subsequent new equilibrium.
where k is a measure of the dispersion of the cloud droplet
number distribution, ql denotes in-cloud liquid water mixing
ratio, ra is air density, rw water density and N denotes the
cloud droplet number concentration. The indirect effect is
simulated by (1) 1st indirect effect: feeding the droplet size
from equation (1) into the radiation scheme of the model,
where it influences cloud optical properties, e.g., smaller
droplets due to anthropogenic aerosols will make the clouds
more reflective of solar radiation, and (2) 2nd indirect
effect: using the cloud droplet size from equation (1) in the
calculation of warm cloud autoconversion of cloud water to
rain, which according to Rasch and Kristjánsson [1998] is
given by:
PWAUT ¼
Cl;aut q2l ra =rw ½ðql ra Þ=ðrw NÞ1=3 Hðr3l r3lc Þ ð2Þ
In the work by Kristjánsson [2002] the parameters Cl,aut and
r3lc were retuned in order to yield more realistic cloud
droplet sizes than the original values did, e.g., the
autoconversion threshold, r3lc, was set to 10 mm. Anthropogenic aerosols will tend to make cloud droplet number
(N) large and cloud droplet size (r3l) small, and this will via
equation (2) reduce precipitation release (PWAUT), and
hence increase cloud liquid water amounts. It should be
noted that only the indirect forcing associated with clouds in
liquid phase is considered. Furthermore, since the cloud
water scheme of Rasch and Kristjánsson [1998] only treats
stratiform clouds and detraining convective clouds, the
calculation of indirect forcing is restricted to these clouds
only.
[7] In the present study, the aerosol forcing modules are
allowed to interact with the dynamics of the climate system,
and it is the response to the aerosol forcing that is the focus
of the paper. The coupling of the CCM3 to a slab ocean
model has been described in detail by Kiehl et al. [1996].
The purpose of using a slab ocean model is to obtain a
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realistic thermal inertia for the climate system on multidecadal timescales. On the other hand, potential changes in
ocean currents due to changes within the climate system are
not taken into account. The open (i.e., ice-free) ocean
component of the slab ocean model is taken from Hansen
et al. [1983]. It consists of a prognostic equation for the
ocean mixed layer temperature, subjected to fluxes to and
from the atmosphere (F) and horizontal and vertical heat
fluxes within the ocean (Q). The ocean mixed layer depth
varies according to climatological seasonally varying
observational data by Levitus [1982], but is limited to
200 meters to reduce the spin-up time of the model
[Kiehl et al., 1996]. A typical range of mixed layer
depths in the model is 20– 60 m in the tropics and the
high-latitude summer hemisphere, while the typical values
in the high-latitude winter hemisphere are near 200 m.
For the ice-covered ocean, the mixed layer Q flux below
sea ice is specified so as to yield approximately a
present-day sea ice distribution from observations. Furthermore, to avoid excessive ice growth in experimental simulations, the Q flux is constrained in a globally conserving
manner. Sea ice is divided into four layers of uniform
thickness, and in each layer a separate heat transfer equation
is solved. In the horizontal the sea ice is assumed to completely cover a CCM grid cell. Sea ice is assumed to form at
1.9C and to melt at 0C. Snowfall and consequent variations in snow depth on top of the sea ice are taken into account,
but changes in snow depth by compaction over time and by
sublimation are ignored. A minimum sea ice thickness of
0.25 m is assumed, to avoid numerical difficulties. In the
Arctic the sea ice thickness is not allowed to exceed 3 m, while
the maximum ice thickness in the Southern Ocean is 0.50 m.
[8] The simulations that follow are of 50-year duration.
There is a spin-up period covering the first 5 – 10 years,
during which the climate gradually changes, especially in
the runs with present-day aerosol conditions. After this,
the model’s climate has reached a new equilibrium
(Figure 1), and we consequently use the last 40 years
of each 50-year simulation in the analysis. In all the
simulations the life cycle scheme for sulfate and black
carbon is run interactively with the climate evolution.
This means that interactions between chemical processes
and changes in climate can take place. The importance of
these interactions has been studied in detail and is
described in a separate study by T. Iversen et al.
(manuscript in preparation, 2005). Table 1 gives a schematic overview of all the simulations, and explains the
differences in their setup.
[9] The model results have been subjected to a t-test, in
order to evaluate their statistical significance. We do not
show the results of this test explicitly, but in general,
unless explicitly stated otherwise, all the main features,
especially at low latitudes are significant at the 95%
level, assuming that all the years are statistically independent. Notable exceptions are some of the signals at
high latitudes, e.g., the temperature signal near the ice
edge off Antarctica, where the statistical significance is
often weak. Investigations of the decorrelation time [von
Storch and Zwiers, 1999] (not shown) indicate that in the
tropics, the annual averages are a suitable sample to use
for evaluating statistical significance, while in other parts
of the world, the autocorrelations are weaker, especially
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Table 1. A Schematic Overview of the Experimental Setupa
Direct Indirect Anthropogenic Natural Greenhouse Gas
Forcing Forcing
Aerosols
Aerosols Concentrations
DIRTOT
DIRNAT
INDTOT
INDNAT
ALLTOT
ALLNAT
PREIND
yes
yes
no
no
yes
yes
no
no
no
yes
yes
yes
yes
yes
yes
no
yes
no
yes
no
no
yes
yes
yes
yes
yes
yes
yes
present day
present day
present day
present day
present day
present day
preindustrial
a
All the experiments are carried out using an interactive slab ocean, as
well as online calculations of aerosol chemistry and transport. In
experiments DIRTOT and DIRNAT, the prescribed cloud droplet number
concentrations of Rasch and Kristjánsson [1998] are used, while in all the
other simulations the cloud droplet number concentrations are calculated
from activation of CCN, as in the work by Kristjánsson [2002], yielding an
interaction between the aerosols and the cloud properties.
over land, meaning that an assumption of independence
between monthly averages would be appropriate when
applying the t-test.
3. Main Results
3.1. Sulfate and Black Carbon Cycle
[10] In Figures 2a and 2b we show the horizontal distribution of the vertically integrated sulfate and black carbon
amounts at years 11 – 50 in the new simulations. The main
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features are similar to those previously reported by Iversen
and Seland [2002, 2003], but in the tropics there is a
southward shift for reasons discussed in the next subsection.
The largest column burdens of sulfate are found over SE
Asia and North America, with high values also over Europe,
Mexico, the Middle East and central Africa. Another
noticeable feature is the fact that in general the burdens
are much higher over the Northern than the Southern
Hemisphere. Compared to other studies, the fraction of total
sulfate that is anthropogenic is rather large in our simulations. This, together with little vertical transport contributed
to a large indirect forcing of 1.83 W m2 in the study
of Kristjánsson [2002]. This estimate was obtained from
5-year simulations in which SST was prescribed and there
was no response from the climate system; that is, both
the 1st (cloud droplet radius) and 2nd (cloud lifetime)
indirect effect were obtained from parallel calls to the
radiation and condensation schemes of the model. The
rationale for obtaining the 2nd indirect effect as a forcing
term was discussed in detail in Kristjánsson [2002]. The
indirect forcing was found as the instantaneous change in
cloud radiative forcing (shortwave plus longwave) at the
top of the atmosphere.
[11] Similarly, Kirkevåg and Iversen [2002] calculated the
direct aerosol forcing as the instantaneous change in net
radiative flux (positive downward) at TOA due to sulfate
and black carbon aerosols by carrying out 5-year integra-
Figure 2. Simulated column integral of sulfate (mg S m2) and black carbon (mg C m2) in the
ALLTOT simulation: (a) sulfate and (b) black carbon.
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Table 2. Global Averages of Key Quantities in the Experiments Defined in Table 1
DIRTOT
DIRNAT
INDTOT
INDNAT
ALLTOT
ALLNAT
PREIND
Temperature,
C
Precipitation,
mm/day
Cloud
Cover, %
13.8
13.9
12.6
13.9
12.6
14.0
12.4
2.89
2.90
2.80
2.90
2.77
2.89
2.81
57.2
57.1
57.1
57.1
57.2
57.2
57.2
Cloud Liquid
Water Path, g m2
40.9
41.0
41.5
41.6
38.6
tions in which the aerosol forcing did not interact with the
climate system. They obtained a globally averaged direct
forcing of 0.11 W m2, with values ranging from +1.1 W
m2 over the biomass burning regions, to 1.1 W m2 in
air masses dominated by sulfate at midlatitudes. Figure 2b
shows the spatial distribution of vertically integrated BC in
the response simulation (ALLTOT). In this case, the largest
column burdens are found over China and Europe, but the
large values associated with the biomass burning areas in
central Africa and tropical South America cause a generally
smaller difference between the two hemispheres than in the
case of sulfate.
[12] We now investigate the changes in the climate state,
some of which are caused directly by the aerosol forcing,
while others are caused by feedbacks of the climate system
to that forcing. Table 2 summarizes the main features of the
climate state in the 7 simulations.
3.2. Changes in Climate
[13] Starting with the temperature response due to the
direct effect (Figure 3a), as might be expected from the
forcing figures, the signal is weak and most of the geographical features are marginally significant. Although the
cooling effect dominates, there are also regions of warming,
e.g., over high-latitude continents and over Australia. In
particular this can be ascribed to BC aerosols above highalbedo surfaces such as ice and snow, low clouds or deserts.
In reality, the warming over Australia would tend to be
offset by the simultaneous presence of OC aerosols, which
have a cooling effect due to their reflection of solar
radiation. The globally averaged cooling effect is 0.08 K,
which would correspond to an equilibrium climate sensitivity
of 0.73 K per W m2. In the case of the indirect effect,
Figure 3b shows the simulated temperature change. The
indirect effect causes a globally averaged cooling of
1.25 K, corresponding to a climate sensitivity of about
0.68 K per W m2. By comparison Meehl et al. [2000]
found an equilibrium sensitivity value of 0.55 K per W m2
for a doubling of CO2 using the NCAR CCM3 with slab ocean
and with the same prognostic cloud water scheme [Rasch and
Kristjánsson, 1998] as in the present study. In terms of climate
efficacy, i.e., the ratio between climate sensitivity to a given
forcing over that to a doubling of CO2 [Hansen et al., 2005],
this would correspond to a value of 1.24 for indirect forcing
and 1.33 for direct forcing. The higher value for sensitivity
obtained from the direct effect probably reflects the influence
of absorbing aerosols, for which the concept of climate
sensitivity is problematic [Hansen et al., 1997]. For the
two forcings combined (direct plus indirect), we find a
somewhat larger cooling of 1.42 K (Table 3) than by
adding the previous two figures together (1.33 K).
Cloud Ice Water
Path, g m2
17.4
17.2
17.2
17.2
17.3
Cloud Droplet
Effective Radius, mm
Net SW Flux at
Surface, W m2
10.12
10.78
10.13
10.81
10.71
169.6
170.2
169.6
170.7
168.2
170.1
171.2
Figure 3c shows the vertical distribution of the temperature changes in a cross section. Even though most of the
aerosols are found in the lower troposphere, the temperature response has little vertical variation through the
troposphere.
[14] Also the horizontal distribution of the temperature
response is quite different from the distribution of the
forcing pattern. The negative direct plus indirect forcing is
most pronounced in the low to midlatitudes of the Northern
Hemisphere and is quite weak in the Arctic [Kristjánsson,
2002; Kirkevåg and Iversen, 2002], but the cooling effect is
largest in the Arctic (Figures 3a and 3b). This is a consequence of climate feedbacks, in particular associated with
the influence of ice and snow on surface albedo, but is also
partly due to the influence of ice cover on ocean-atmosphere
heat fluxes. In addition to the ice albedo feedback, there is a
cloud feedback that influences the Arctic signal. This will
be studied in more detail in the next section. The highlatitude amplification is similar, apart from the sign, to what
has been found in simulations of global warming due to
increased greenhouse gas concentrations [Houghton et al.,
2001]. We note that this result is similar to those obtained in
the Hadley Centre GCM [Williams et al., 2001] and the
Australian CSIRO model [Rotstayn and Lohmann, 2002].
[15] A set of sensitivity experiments in which the ice
albedo feedback was artificially suppressed by setting the
albedo over sea ice equal to that over ocean, gave a different
geographical distribution of the cooling effect, with enhanced cooling in regions of large negative radiative forcing
and reduced cooling over the Arctic (not shown). Nevertheless, the globally averaged cooling was almost as large as
in the simulations with the ice albedo feedback included.
Hence, with the current experimental setup the Arctic ice
albedo feedback does not influence the global climate
sensitivity much, but mainly causes a geographical redistribution of the regional response patterns.
[16] In order to check the realism in the temperature
signals in Figures 3a – 3c, a simulation was carried out in
which emissions and greenhouse gases were set to preindustrial values (PREIND), while other features of this
simulation are identical to the simulation INDNAT (see
Table 1). The globally averaged near-surface temperature
difference between the INDTOT run and PREIND is
+0.26 K, with large positive values mainly in the Southern Hemisphere and in the Arctic (not shown). At
midlatitudes in the Northern Hemisphere, there is a
cooling, and the same applies to the region underneath
the stratus cloud deck off the coast of Peru. This
indicates that the cooling signal of aerosols over land
may be exaggerated in our model. This problem is being
dealt with by introducing various major improvements to
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Figure 3. Simulated temperature changes (K) due to aerosol direct and indirect forcing in simulations:
(a) DIRTOT minus DIRNAT, (b) INDTOT minus INDNAT, and (c) ALLTOT minus ALLNAT, zonally
averaged. Note the different temperature scale in Figure 3a, compared to Figures 3b and 3c.
Table 3. Global Averages of Changes in Key Quantities Due to Different Aerosol Forcings and Associated Climate Feedbacks
DIRECT
INDIRECT
DIRECT plus INDIRECT
RESIDUAL
Temperature,
K
Precipitation,
mm/day
Cloud Cover, %
LWP,
g m2
IWP,
g m2
Net SW Flux at Surface,
W m2
0.083
1.25
1.42
0.10
0.017
0.10
0.13
0.013 (0.5%)
0.021
0.015
0.001
0.01
0.17
0.09
0.15
+0.11
0.02
+0.02
+0.06
+0.06
0.60
1.15
1.82
0.07
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Figure 4. Simulated changes in sea level pressure (hPa) due to aerosol direct and indirect forcing
(ALLTOT minus ALLNAT): (a) annual average and (b) SON season.
the aerosol schemes that will be reported elsewhere. Another
feature of the PREIND simulation worth mentioning is that
globally averaged precipitation is slightly higher than in the
corresponding run with today’s greenhouse gases (INDTOT
in Table 3). This means that the suppression of the hydrological cycle due to the indirect effect of aerosols is larger than the
enhancement of it due to greenhouse gas warming of the
oceans, as was also found by Feichter et al. [2004]. From
Table 2 we see that the net solar flux reaching the surface is
higher in the PREIND run than in any of the other simulations.
Conversely it is lower in the runs with direct plus indirect
forcing than in the simulations where only one of these effects
is treated. This illustrates the role of aerosols for suppressing
solar heating of the surface which in turn drives the hydrological cycle being responsible for precipitation.
[17] The so-called semidirect effect [e.g., Ackerman et al.,
2000] measures the modulation of clouds due to absorbing
aerosols. It appears mainly because although absorbing
aerosols warm the atmosphere, they cool the surface, hence
suppressing evaporation [e.g., Ramanathan et al., 2001]. As
mentioned above, Kirkevåg and Iversen found a globally
averaged direct radiative forcing of 0.11 W m2 in their
simulations, but at the surface the global average was
0.60 W m2, and this may cause a suppression of the
hydrological cycle. In our simulations the semidirect
effect is manifested through a reduction in cloud water
path in the simulations with direct aerosol forcing (top
row of Table 3). However, as in the study of Lohmann
and Feichter [2001], the reduction is quite small.
[18] As the climate system responds to the aerosol forcing, there are significant changes in sea level pressure,
especially due to the indirect effect. The main change is a
1– 2 hPa increase in sea level pressure over the Arctic
(Figure 4a), most pronounced in the autumn and early
winter (Figure 4b). At the surface, the cooling in this region
is largest in the winter, even though the radiative forcing is
strongest in the summer [Kristjánsson, 2002]. What happens is that the reduction in summer insolation leads to
increased sea ice extent, hence reducing the heat flux from
the ocean to the atmosphere. As a result, a shallow anticyclone tends to form, blocking the intrusion of low-pressure
systems that would normally have brought warm air masses
toward the Arctic in the fall and early winter. A similar
result, but with the opposite sign, has recently been found in
observations of sea level pressure in the Arctic [Zhang et
al., 2004], probably related to greenhouse gas warming.
[19] In Figure 5 we show the influence of aerosol forcing
on precipitation. First in Figure 5a the change in precipitation from the direct radiative forcing is shown. Considering
that the forcing and its interhemispheric differences are
significantly smaller than for the indirect effect, it is
interesting to see the southward shift of the ITCZ, even
though its statistical significance is marginal. Elsewhere,
the precipitation signal from the direct effect is weak and
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features being a southward displacement of the ITCZ by a
few degrees of latitude and some regional changes, e.g., a
drying over the Sahel region in Africa. The southward shift
is expected, since Kristjánsson [2002] found the indirect
forcing in the Northern Hemisphere to be about 2.61 W
m2, as compared to 1.06 W m2 in the Southern
Hemisphere. Furthermore, the result is consistent with other
studies referred to in the introduction. There is also a
general reduction in precipitation (Tables 2 and 3), mainly
because of less humidity being present in the atmosphere in
the colder climate, but also due the second indirect effect;
that is, the more numerous, smaller droplets are less likely
to exceed the autoconversion threshold of 10 mm radius
(equation (2)). The southward shift of the ITCZ can be
verified by viewing the changes in vertical velocity between
the simulations with and without anthropogenic aerosols,
see Figures 6a and 6b. Note how the rising branch of the
Figure 5. Simulated changes in precipitation (%), due to
aerosol forcing from (a) simulations with direct forcing
(DIRTOT minus DIRNAT), (b) simulations with indirect
forcing (INDTOT minus INDNAT), and (c) simulations
with direct plus indirect forcing (ALLTOT minus
ALLNAT).
largely insignificant. The southward shift is caused by
interhemispheric differences in radiative forcing, the average
value in the Northern Hemisphere being 0.19 W m2, as
compared to 0.04 W m2 in the Southern Hemisphere
[Kirkevåg and Iversen, 2002]. By comparison, Chung and
Seinfeld [2005] found a northward shift of the ITCZ due to
direct forcing, which can be understood by the fact that the
direct forcing in their simulations was positive, since only
BC aerosols were considered. The warming effect of BC
was largest in the Northern Hemisphere, and this led to the
northward shift of the ITCZ.
[20] The precipitation response due to indirect forcing
(Figure 5b) gives a strong and coherent signal, its main
Figure 6. Zonally averaged vertical velocity, w, in units of
hPa s1 from (a) simulation with direct plus indirect aerosol
forcing (ALLTOT) and (b) simulation without direct plus
indirect forcing (ALLNAT).
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Figure 7. Time evolution of the globally averaged
difference in cloud radiative forcing between runs with
(INDTOT) and without (INDNAT) anthropogenic aerosols.
Short wave is red, long wave is blue, and net is green. Unit
is W m2.
Hadley circulation is split in the simulation with aerosol
forcing, with a secondary rising branch occurring south of
the equator, in addition to the branch near 5N.
[21] In Table 3 the aforementioned ‘‘residual effect’’ (see
end of section 1) is evaluated by comparing the results of
the runs ALLTOT – ALLNAT on the one hand and
INDTOT – INDNAT plus DIRTOT – DIRNAT on the
other. The difference is not large (compare Figure 5c with
Figures 5a and 5b), but we note that the precipitation is
reduced by about 0.5%, which is consistent with a further
weakening of the hydrological cycle due to absorbing
aerosols. The residual effect shows no signal in cloud
cover, but this is related to an inherent feature of the host
model, largely because of the random overlap assumption.
In fact, the global cloud cover is found to be virtually
insensitive to almost any model change that we have
performed. Also, cloud water path is quite insensitive to
the residual effect (Table 3). The residual effect is probably a manifestation of the nonlinearities in the model’s
response to different forcings, causing the response to the
direct and indirect effect to be nonadditive. A thorough
investigation of such nonlinear feedbacks has been carried
out by Colman et al. [1997], who found that the main
contributions to such nonlinearities in their model were
associated with high clouds in the tropics.
4. Cloud Feedback
4.1. Main Features
[22] We here investigate the role played by changes in
cloud radiative forcing in response to the climate changes
that take place as the aerosols are allowed to cool the
climate system. In particular we ask the question of what
the sign and horizontal distribution of the cloud feedback is.
First in Figure 7 we show the time evolution of globally
averaged cloud radiative forcing estimated from the
INDTOT and INDNAT simulations. Keeping in mind that
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the imposed indirect forcing is 1.83 W m2 and that
practically all of this is coming from the shortwave part of
the spectrum [Kristjánsson, 2002], it is interesting to see
how this quantity changes with time during the simulation
(Figure 7). During the transition time of the first 10 years,
the shortwave component of the indirect forcing is gradually
reduced (in absolute value) to almost half the original value,
but this is partly compensated by a negative longwave
forcing that arises. Both these changes can be attributed to
a slight thinning of the clouds in a colder climate, but the
influence of that thinning is larger in the short wave than in
the long wave because of a larger degree of saturation for
cloud emissivity than for cloud albedo. The net effect is a
radiative perturbation of about 1.55 W m2, i.e., some
15% weaker than at the outset, representing a significant
negative cloud feedback. Another interesting feature of
Figure 7 is the large interannual variability in cloud forcing,
meaning that fairly long simulations are needed to evaluate
this quantity accurately.
[23] In order to understand the geographical distribution
of these changes in cloud radiative forcing, we now investigate changes in cloud water path, both liquid and ice. First
we note by comparing the results from INDTOT and
INDNAT in Table 3 that liquid water path (LWP), which
in simulations without climate response was increased by
5% because of the 2nd indirect effect [Kristjánsson, 2002],
is now virtually unchanged in a global average. What causes
the thinning of the clouds, and hence offsets the 2nd indirect
effect, is the reduction of atmospheric moisture in the colder
climate. Consistently with this assertion, we note that the
lowest total cloud water path (LWP plus IWP) values of all
the runs are found in the PREIND simulation (Table 2),
which has the coldest climate. Figure 8a shows the geographical distribution of the changes in LWP from the
combination of indirect forcing and climate response. As
in the work by Kristjánsson [2002], an increase is found in a
zone of large sulfate amounts stretching from North America toward Europe, as well as over China, southern Africa
and parts of S America. The main novelty here is that there
are also large areas with decreased LWP amounts, especially
over ocean regions poleward of 55 latitude in both hemispheres, but also over parts of the subtropical oceans. The
latter areas show up with an opposite sign in the signature
for ice water path (IWP, Figure 8b), since these are high
clouds that are now cold enough to consist of ice. No such
compensation is found in the Arctic where IWP is reduced
at the same time as LWP is reduced. Hence the thinning of
clouds in this region must be related to climate feedbacks. It
is of interest to find out what these feedbacks are, as well as
what sign they have, and we will deal with this in the next
subsection. Figure 8b also shows signatures of the southward displacement of the ITCZ and its associated anvil (ice)
clouds, as well as a general increase in ice water path over
the Northern Hemisphere storm tracks, partly caused by the
general cooling of the atmosphere. The lack of a signature
for the southward shift of the ITCZ in LWP (Figure 8a) is
due to the fact that only the detraining, upper part of the
convective clouds influences the prognostic cloud water
[Rasch and Kristjánsson, 1998], as mentioned in section 2.
[24] Figure 9 shows the reduction in effective radius
between the simulations INDTOT and INDNAT, which is
a measure of the 1st indirect effect. As opposed to the 2nd
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Figure 8. Simulated changes in cloud water path due to aerosol indirect forcing (INDTOT minus
INDNAT): (a) cloud liquid water path (g m2) and (b) cloud ice water path (g m2).
indirect effect (Figure 8a) this result is quite similar to that
of the pure forcing simulations of Kristjánsson [2002],
including the global averages (Tables 2 and 3). Exceptions
are slightly enhanced cloud droplet radii in some tropical
ocean areas, the Arctic and to a lesser extent near 60S. The
larger droplets in the tropical areas are mainly due to a
combination of reduced precipitation caused by the southward shift of the ITCZ (Figure 5b) and larger LWP
(Figure 8a), hence leading to an increase in cloud droplet
size through equation (1). At high latitudes, water clouds
occur less frequently in a cooler climate. This will tend to
reduce the in-cloud production of sulfate, but because of
very slow clear air oxidation rates at high latitudes
combined with reduced scavenging in the colder climate,
the fraction of sulfate produced in clouds may increase,
leading to fewer new CCN and hence larger droplets. Such
effects will be further investigated in the upcoming paper
by T. Iversen et al. (manuscript in preparation, 2005).
4.2. Clouds in the Arctic
[25] The simultaneous reduction of LWP and IWP in the
Arctic that we found in Figures 8a and 8b can be partly
understood by looking at changes in cloud cover. First, in
Figure 10a we see that effective cloud cover, given as the
product of cloud cover and cloud emissivity is reduced in
the polar regions. This reduction is related to the changes in
sea level pressure that were shown in Figure 4a, indicating a
general buildup of high pressure over the polar regions in
the colder climate, hence suppressing the supply of moisture
to these regions by extratropical cyclone activity. At the
same time effective cloud amount is increasing over the
midlatitude storm track regions. This is partly due to the 2nd
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Figure 9. Difference in cloud droplet effective radius, as seen by satellite, between the simulations
INDTOT and INDNAT (mm).
indirect effect discussed in connection with Figure 8a, but
since the signal is very strong also at upper levels where
only ice clouds are present, and hence the indirect effect is
not calculated, this cannot be the only explanation. The
geographical features of Figure 10b, showing changes in
high clouds due to aerosol indirect forcing, suggest that the
midlatitude storm tracks are shifted in position, as also
evidenced by the change in rainfall patterns in Figure 5b.
Figure 10. Simulated changes in cloud cover due to aerosol indirect forcing (INDTOT minus
INDNAT): (a) zonally averaged effective cloud cover (%) and (b) high cloud cover (%).
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Table 4. As for Table 3, Except Only for the Area North of 60N
DIRECT
INDIRECT
DIRECT plus INDIRECT
RESIDUAL
Temperature, K
Precipitation,
mm/day
Cloud Cover, %
LWP,
g m2
IWP,
g m2
Net SW Flux at Surface,
W m2
0.23
2.41
2.77
0.13
0.007
0.16
0.19
0.03 (2.5%)
+0.077
0.13
0.038
+0.014
0.63
9.18
10.04
0.25
+0.06
1.49
1.72
0.29
0.35
0.049
0.26
+0.14
The tropical signature in Figures 10a and 10b, on the other
hand, is once again a consequence of the southward
displacement of the ITCZ, discussed in the previous section.
[26] In Table 4, we display the changes due to aerosol
forcing in the same basic variables as in Table 3, but now
only for the area north of 60N. We note how the signals of
Table 3 are generally enhanced, including the residual
effect, which gives a precipitation reduction of 2.5%. Even
though the Arctic has significant BC concentrations
(Figure 2b), it seems extremely unlikely that the semidirect effect as such is acting in this environment of
relatively high static stability. Rather, this suppression of
precipitation, which comes in addition to the suppression
by the indirect forcing alone, must be related to a reduced
hydrological cycle. To demonstrate this, we have computed the vertically integrated water vapor amounts, and
found that the globally averaged suppression in the case
of the direct, indirect and direct plus indirect simulations
has values of 4.5 g m2, 91 g m2 and 104 g m2,
respectively, confirming the role of moisture suppression
for explaining the reduction in precipitation.
[27] Field experiments in the Arctic have shown this to
be a unique area in the sense that clouds have an overall
warming effect, at least at the surface [Intrieri et al.,
2002]. This is due to a combination of a low sun angle
and a high surface albedo, making the shortwave cloud
forcing small, while the longwave cloud forcing of the
surface is large because of persistent rather dense clouds
throughout much of the year. Consistently with this, in
our simulations the net cloud forcing at the surface
poleward of 60N is about +20 W m2, as compared
to about 10 W m2 at the top of the atmosphere.
However, despite this dominance of the longwave over
shortwave forcing at the surface, the change in net cloud
forcing from INDNAT to INDTOT is dominated by the
short wave and has the value +1.5 W m2; that is, there
is a negative cloud feedback in this region. This is in
contrast to a recent study by Vavrus [2004], who found
the positive cloud feedback to contribute 40% to the
Arctic warming resulting from a doubling of CO2 in simulations with the GENESIS2 model. We believe the negative
Arctic cloud feedback in our simulations may be an artifact
of the oversimplified treatment of ice cloud radiative
properties, these clouds having a too large negative net
cloud forcing, as well as to biases in Arctic cloud fraction in
CCM3, discussed by Rasch and Kristjánsson [1998].
5. Summary and Conclusions
[28] Results from multidecadal equilibrium simulations of
the response to indirect and direct aerosol forcing have been
presented. The simulations were carried out using an
atmospheric GCM (CCM-Oslo) coupled to a slab ocean.
The life cycles of sulfate and black carbon aerosols were run
interactively enabling the aerosol fields to change according
to changes in circulation patterns resulting from climate
response.
[29] The main features in the response in temperature and
precipitation to indirect forcing are similar to those previously reported by Rotstayn and Lohmann [2002] and
Williams et al. [2001], e.g., a southward displacement of
the ITCZ, and a maximum cooling in the Arctic, despite the
forcing being most negative at midlatitudes of the Northern
Hemisphere. The direct forcing causes a much weaker
Northern Hemisphere cooling than the indirect forcing does.
Nevertheless, a distinct, but marginally statistically significant southward shift of the ITCZ is found. In simulations
with direct and indirect effect together, the direct and
indirect effects do not simply add up. Rather a nonnegligible ‘‘residual effect’’ occurs, characterized by a weakening of the hydrological cycle presumably due to nonlinear
interactions between absorbing aerosols and the indirect
forcing. All the above signals are enhanced in the Arctic.
This is partly due to a chain reaction between cooling from
ice albedo feedback, via higher surface pressure, to a
general reduction in cloudiness at all levels of the troposphere. In the midlatitude storm tracks, however, clouds are
thicker when the (mainly indirect) aerosol forcing is present
because of a combination of the 2nd indirect effect and a
reduced insurgence of moisture into the polar regions. In a
global average the 2nd indirect effect is swamped by the
associated climate change, since the colder climate carries
less moisture, leading to thinner clouds. Conversely, the 1st
indirect effect appears in a similar manner as in previous
simulations that did not allow climate response to aerosol
forcing.
[30] A simulation using preindustrial aerosol precursor
emissions and preindustrial greenhouse gas concentrations
gave a globally averaged temperature which was 0.26 K
lower than in simulations with today’s emissions. Cloud
water paths were smaller than in the present-day simulations, because of less atmospheric moisture in the colder
climate, but, interestingly, precipitation amounts were enhanced. This means that in the present-day climate simulations relative to simulations of preindustrial climate the
suppression of precipitation by aerosols is stronger than the
enhancement of precipitation that inevitably occurs in a
warmer climate because of higher CO2 concentrations.
[31] Acknowledgments. This study was supported by the Norwegian
Research Council through the RegClim project. Furthermore, the work has
received support of the Norwegian Research Council’s Programme for
Supercomputing through a grant of computer time. Many thanks to the
reviewers whose insightful and constructive comments led to significant
improvements of the paper.
References
Ackerman, A. S., O. B. Toon, D. E. Stevens, A. J. Heymsfield,
V. Ramanathan, and E. J. Welton (2000), Reduction of tropical cloudiness by soot, Science, 288, 1042 – 1047.
12 of 13
D24206
KRISTJÁNSSON ET AL.: CLIMATE RESPONSE TO AEROSOL FORCING
Anderson, T. L., R. J. Charlson, S. E. Schwartz, R. Knutti, O. Boucher,
H. Rodhe, and J. Heintzenberg (2003), Climate forcing by aerosols—A
hazy picture, Science, 300, 1103 – 1104.
Andreae, M. O., C. D. Jones, and P. M. Cox (2005), Strong present-day
aerosol cooling implies a hot future, Nature, 435, 1187 – 1190.
Brenguier, J.-L., H. Pawlowska, and L. Schüller (2003), Cloud microphysical and radiative properties for parameterization and satellite monitoring
of the indirect effect of aerosols on climate, J. Geophys. Res., 108(D15),
8632, doi:10.1029/2002JD002682.
Chung, S. H., and J. H. Seinfeld (2005), Climate response of direct radiative
forcing of anthropogenic black carbon, J. Geophys. Res., 110, D11102,
doi:10.1029/2004JD005441.
Colman, R. A., S. B. Power, and B. J. McAvaney (1997), Non-linear
climate feedback analysis in an atmospheric general circulation model,
Clim. Dyn., 13, 717 – 731.
Feichter, J., E. Roeckner, U. Lohmann, and B. Liepert (2004), Nonlinear
aspects of the climate response to greenhouse gas and aerosol forcing,
J. Clim., 17, 2384 – 2398.
Folland, C. K., and T. R. Karl (Coord.) (2001), Observed climate variability
and change, in Third Assessment Report of the Intergovernmental Panel
on Climate Change, chap. 2, pp. 99 – 181, Cambridge Univ. Press, New
York.
Hansen, J., A. Lacis, D. Rind, G. Russell, P. Stone, I. Fung, R. Ruedy, and
J. Lerner (1983), Climate sensitivity: Analysis of feedback mechanisms,
in Climate Processes and Climate Sensitivity, Geophys. Monogr. Ser., vol.
29, edited by J. E. Hansen and T. Takahashi, pp. 130 – 163, AGU,
Washington, D. C.
Hansen, J., M. Sato, and R. Ruedy (1997), Radiative forcing and climate
response, J. Geophys. Res., 102, 6831 – 6864.
Hansen, J., et al. (2005), Efficacy of climate forcings, J. Geophys. Res., 110,
D18104, doi:10.1029/2005JD005776.
Houghton, J. (2002), The Physics of Atmospheres, 3rd ed., 320 pp., Cambridge Univ. Press, New York.
Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden,
X. Dai, K. Maskell. and C. A. Johnson (Eds.) (2001), Climate Change
2001: The Scientific Basis—Contribution of Working Group I to the
Third Assessment Report of the Intergovernmental Panel on Climate
Change, 881 pp., Cambridge Univ. Press, New York.
Intrieri, J. M., C. F. Fairall, M. D. Shupe, O. G. P. Persson, E. L.
Andreas, P. L. Guest, and R. M. Moritz (2002), An annual cycle of
Arctic surface cloud forcing at SHEBA, J. Geophys. Res., 107(C10),
8039, doi:10.1029/2000JC000439.
Iversen, T., and Ø. Seland (2002), A scheme for process-tagged SO4 and
BC aerosols in NCAR-CCM3: Validation and sensitivity to cloud processes, J. Geophys. Res., 107(D24), 4751, doi:10.1029/2001JD000885.
Iversen, T., and Ø. Seland (2003), Correction to ‘‘A scheme for processtagged SO4 and BC aerosols in NCAR-CCM3: Validation and sensitivity
to cloud processes,’’ J. Geophys. Res., 108(D16), 4502, doi:10.1029/
2003JD003840.
Kanakidou, M., et al. (2005), Organic aerosol and global climate modelling:
A review, Atmos. Chem. Phys., 5, 1053 – 1123.
Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L.
Williamson, and P. J. Rasch (1996), Description of the NCAR Community Climate Model (CCM3), NCAR Tech. NCAR/TN-420+STR, 152 pp.,
Natl. Cent. for Atmos. Res., Boulder, Colo.
Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, D. L. Williamson,
and P. Rasch (1998), The National Center for Atmospheric Research
Community Climate Model: CCM3, J. Clim., 11, 1131 – 1149.
Kirkevåg, A., and T. Iversen (2002), Global direct radiative forcing by
process-parameterized aerosol optical properties, J. Geophys. Res.,
107(D20), 4433, doi:10.1029/2001JD000886.
D24206
Kirkevåg, A., T. Iversen, Ø. Seland, and J. E. Kristjánsson (2005), Revised
schemes for aerosol optical parameters and cloud condensation nuclei in
CCM-Oslo, Inst. Rep. Ser. 128, 29 pp., Dep. of Geosci., Univ. of Oslo,
Oslo.
Kristjánsson, J. E. (2002), Studies of the aerosol indirect effect from
sulfate and black carbon aerosols, J. Geophys. Res., 107(D15), 4246,
doi:10.1029/2001JD000887.
Levitus, S. (1982), Climatological Atlas of the World Ocean, NOAA Prof.
Pap., 13.
Lohmann, U., and J. Feichter (2001), Can the direct and semi-direct aerosol
effect compete with the indirect effect on a global scale?, Geophys. Res.
Lett., 28, 159 – 161.
Meehl, G. A., W. D. Collins, B. A. Boville, J. T. Kiehl, T. M. L. Wigley,
and J. M. Arblaster (2000), Response of the NCAR Climate System
Model to increased CO2 and the role of physical processes, J. Clim.,
13, 1879 – 1898.
Penner, J. E. (Coord.) (2001), Aerosols, their direct and indirect effects, in Third Assessment Report of the Intergovernmental Panel on
Climate Change, chap. 5, pp. 291 – 348, Cambridge Univ. Press,
New York.
Penner, J. E., S. Y. Zhang, and C. C. Chuang (2003), Soot and smoke
aerosol may not warm climate, J. Geophys. Res., 108(D21), 4657,
doi:10.1029/2003JD003409.
Ramanathan, V., P. J. Crutzen, J. T. Kiehl, and D. Rosenfeld (2001),
Aerosols, climate and the hydrological cycle, Science, 294, 2119 –
2124.
Ramaswamy, V. (Coord.) (2001), Radiative forcing of climate change, in
Third Assessment Report of the Intergovernmental Panel on Climate
Change, chap. 6, pp. 349 – 416, Cambridge Univ. Press, New York.
Rasch, P. J., and J. E. Kristjánsson (1998), A comparison of the CCM3
model climate using diagnosed and predicted condensate parameterizations, J. Clim., 11, 1587 – 1614.
Rotstayn, L. D., and U. Lohmann (2002), Tropical rainfall trends and the
indirect aerosol effect, J. Clim., 15, 2103 – 2116.
Rotstayn, L. D., B. F. Ryan, and J. E. Penner (2000), Precipitation changes
in a GCM resulting from the indirect effects of anthropogenic aerosols,
Geophys. Res. Lett., 27, 3045 – 3048.
Takemura, T., T. Nozawa, S. Emori, T. Y. Nakajima, and T. Nakajima
(2005), Simulation of climate response to aerosol direct and indirect
effects with aerosol transport-radiation model, J. Geophys. Res., 110,
D02202, doi:10.1029/2004JD005029.
Vavrus, S. (2004), The impact of cloud feedbacks on Arctic climate under
greenhouse forcing, J. Clim., 17, 603 – 615.
von Storch, H., and F. W. Zwiers (1999), Statistical Analysis in Climate
Research, 484 pp., Cambridge Univ. Press, New York.
Williams, K. D., A. Jones, D. L. Roberts, C. A. Senior, and M. J. Woodage
(2001), The response of the climate system to the indirect effects of
anthropogenic sulfate aerosol, Clim. Dyn., 17, 845 – 856.
Zhang, X., J. E. Walsh, J. Zhang, U. S. Bhatt, and M. Ikeda (2004),
Climatology and interannual variability of arctic cyclone activity:
1948 – 2002, J. Clim., 17, 2300 – 2317.
J. Debernard, Norwegian Meteorological Institute, P.O. Box 43 Blindern,
N-0313 Oslo, Norway.
T. Iversen, A. Kirkevåg, J. E. Kristjánsson, and Ø. Seland, Department of
Geosciences, University of Oslo, P.O. Box 1022 Blindern, N-0315 Oslo,
Norway. (jegill@ulrik.uio.no)
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