Response of the climate system to aerosol direct and
indirect forcing – the role of cloud feedbacks
by
J.E. Kristjánsson1, T. Iversen1, A. Kirkevåg1, Ø. Seland1,
J.Debernard2
1
Department of Geosciences, University of Oslo, P.O.Box 1022
Blindern, 0315 Oslo, Norway
2
Norwegian Meteorological Institute, P.O.Box 43 Blindern, 0313 Oslo,
Norway
6 September 2005
1
Abstract
In this study, the response of the climate system to aerosol direct and indirect radiative
forcing is investigated. Several multi-decadal 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, i.e., the 1st and 2nd
indirect effect. In all the simulations the full aerosol treatment is run on-line, 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, due to a
larger aerosol burden there. As a result of this cooling pattern, the Intertropical
Convergence Zone is displaced southward by a few hundred km. 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
2
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.
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1. Introduction
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 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, 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 can not be ruled out
that this cooling may so far have almost cancelled the anthropogenic greenhouse effect,
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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.
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), 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 1960’s and 1970’s (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.
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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 non-linear response of the model to
imposed forcings, will be estimated. We term this effect the ‘residual effect’. We also
evaluate the semi-direct 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 semi-direct effect.
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, 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 is 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
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;
6
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: a) The introduction of a prognostic cloud water
scheme, following Rasch and Kristjánsson (1998); b) Replacing 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 in 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
7
above; c) As described in detail in 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:
reff = κ [(3 ql ρa) / (4 π ρw N)]1/3
(1)
where κ is a measure of the dispersion of the cloud droplet number distribution, ql
denotes in-cloud liquid water mixing ratio, ρa is air density, ρw water density and N
denotes the cloud droplet number concentration. The indirect effect is simulated by: (i)
1st indirect effect: feeding the droplet size from (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; (ii) 2nd
indirect effect: using the cloud droplet size from (1) in the calculation of warm cloud
autoconversion of cloud water to rain, which according to Rasch and Kristjánsson (1998)
is given by:
PWAUT = [(Cl,aut ql2 ρa) / ρw] [(ql ρa) / (ρw N)]1/3 H(r3l – r3lc)
(2)
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In 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 µm. 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.
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
realistic thermal inertia for the climate system on multi-decadal time scales. 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
9
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 –1.9ºC and to melt at 0ºC. 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 Antarctic is 0.50 m.
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
Kristjánsson et al. (2005). Table 1 gives a schematic overview of all the simulations, and
explains the differences in their setup.
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,
10
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 over land, meaning that an assumption of independence between monthly
averages would be appropriate when applying the t-test.
3. Main results
a) Sulfate and black carbon cycle
In Figures 2a-b we show the horizontal distribution of the vertically integrated
sulfate and black carbon amounts at years 11-50 in the new simulations. The main
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 m-2 in the study of
Kristjánsson (2002). This estimate was obtained from 5-year simulations in which SST
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was prescribed and there was no response from the climate system, i.e., 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 + longwave) at the top of the atmosphere.
Similarly, Kirkevåg and Iversen (2002) calculated the direct aerosol forcing as the
instantaneous change in net radiative flux (positive downwards) at TOA due to sulfate
and black carbon aerosols by carrying out 5-year integrations in which the aerosol forcing
did not interact with the climate system. They obtained a globally averaged direct forcing
of –0.11 W m-2, with values ranging from +1.1 W m-2 over the biomass burning regions,
to –1.1 W m-2 in air masses dominated by sulfate at mid-latitudes. 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.
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.
b) Changes in climate
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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 high-albedo 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 m-2. 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 m-2. By comparison Meehl et al. (2000) found an
equilibrium sensitivity value of 0.55 K per W m-2 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 + indirect), we
find a somewhat larger cooling of -1.42 K (Table 3) than by adding the previous two
figures together (-1.33 K). Figure 3c shows the vertical distribution of the temperature
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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.
Also the horizontal distribution of the temperature response is quite different from
the distribution of the forcing pattern. The negative direct+indirect forcing is most
pronounced in the low to mid-latitudes 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 (Figure 3a,b). 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 high-latitude 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).
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 icealbedo feedback included. Hence, with the current experimental setup the Arctic icealbedo feedback does not influence the global climate sensitivity much, but mainly
causes a geographical redistribution of the regional response patterns.
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In order to check the realism in the temperature signals in Figures 3a-c, 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 mid-latitudes 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 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+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.
The so-called semi-direct 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
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evaporation (e.g., Ramanathan et al., 2001). As mentioned above, Kirkevåg and Iversen
found a globally averaged direct radiative forcing of -0.11 W m-2 in their simulations, but
at the surface the global average was –0.60 W m-2, and this may cause a suppression of
the hydrological cycle. In our simulations the semi-direct 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.
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 towards 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.
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 inter-hemispheric differences are significantly
smaller than for the indirect effect, it is interesting to see the southward shift of the ITCZ,
16
even though its statistical significance is marginal. Elsewhere, the precipitation signal
from the direct effect is weak and largely insignificant. The southward shift is caused by
inter-hemispheric differences in radiative forcing, the average value in the Northern
Hemisphere being –0.19 W m-2, as compared to –0.04 W m-2 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.
The precipitation response due to indirect forcing (Figure 5b) gives a strong and
coherent signal, its main 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 m-2, as compared to –1.06 W m-2
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,
3), mainly because of less humidity being present in the atmosphere in the colder climate,
but also due the second indirect effect, i.e., the more numerous, smaller droplets are less
likely to exceed the auto-conversion threshold of 10 µm 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-b. Note
how the rising branch of the Hadley circulation is split in the simulation with aerosol
17
forcing, with a secondary rising branch occurring south of the equator, in addition to the
branch near 5°N.
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-b), 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 due to 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 non-linearities in the
model’s response to different forcings, causing the response to the direct and indirect
effect to be non-additive. A thorough investigation of such non-linear 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
a) Main features
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 Figures 7 we show the time evolution of
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globally averaged cloud radiative forcing estimated from the INDTOT and INDNAT
simulations. Keeping in mind that the imposed indirect forcing is -1.83 W m-2 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 shortwave than in the longwave due to a
larger degree of saturation for cloud emissivity than for cloud albedo. The net effect is a
radiative perturbation of about -1.55 W m-2, 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.
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%
due to 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+IWP) values of all the
runs are found in the PREIND simulation (Table 2), which has the coldest climate. Figure
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8a shows the geographical distribution of the changes in LWP from the combination of
indirect forcing and climate response. As in Kristjánsson (2002), an increase is found in a
zone of large sulfate amounts stretching from North America towards 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.
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 indirect
effect (Figure 8a) this result is quite similar to that of the pure forcing simulations of
Kristjánsson (2002), including the global averages (Tables 2-3). Exceptions are slightly
enhanced cloud droplet radii in some tropical ocean areas, the Arctic and to a lesser
20
extent near 60°S. 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 due to 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 Kristjánsson
et al. (2005).
b) Clouds in the Arctic
The simultaneous reduction of LWP and IWP in the Arctic that we found in
Figures 8a-b 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 build-up 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 mid-latitude storm track regions. This is partly due to
the 2nd 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 can not be the only explanation. The geographical features of
Figure 10b, showing changes in high clouds due to aerosol indirect forcing, suggest that
21
the mid-latitude storm tracks are shifted in position, as also evidenced by the change in
rainfall patterns in Figure 5b. The tropical signature in Figures 10a-b, on the other hand,
is once again a consequence of the southward displacement of the ITCZ, discussed in the
previous section.
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 60°N. 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 semi-direct 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+indirect simulations has values of
-4.5 g m-2, -91 g m-2 and -104 g m-2, respectively, confirming the role of moisture
suppression for explaining the reduction in precipitation.
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
short wave cloud forcing small, while the long wave cloud forcing of the surface is large
due to persistent rather dense clouds throughout much of the year. Consistently with this,
in our simulations the net cloud forcing at the surface poleward of 60°N is about +20 W
m-2, as compared to about -10 W m-2 at the top of the atmosphere. However, despite this
22
dominance of the long wave over short wave 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 m-2, i.e., 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 due to 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
Results from multi-decadal 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.
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 mid-latitudes 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
23
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 non-linear 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 mid-latitude storm
tracks, however, clouds are thicker when the (mainly indirect) aerosol forcing is present
due to 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.
A simulation using pre-industrial aerosol precursor emissions and pre-industrial
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, due to 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 pre-industrial climate the suppression of
precipitation by aerosols is stronger than the enhancement of precipitation that inevitably
occurs in a warmer climate due to higher CO2 concentrations.
Acknowledgments
24
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.
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30
Tables
Table 1: A schematic overview of the experimental setup. All the experiments are carried
out using an interactive slab ocean, as well as on-line 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 Kristjánsson (2002), yielding an interaction between the aerosols and the
cloud properties.
Table 2: Global averages of key quantities in the experiments defined in Table 1.
Table 3: Global averages of changes in key quantities due to different aerosol forcings.
Table 4: As in Table 3, except only for the area north of 60°N.
Figures
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.
Figure 2: Simulated column integral of sulfate (mg S m-2) and black carbon (mg C m-2) in
the ALLTOT simulation:
31
a) Sulfate;
b) Black carbon.
Figure 3: Simulated temperature changes (K) due to aerosol direct and indirect forcing in
simulations:
a) DIRTOT minus DIRNAT,
b) INDTOT minus INDNAT,
c) ALLTOT minus ALLNAT, zonally averaged.
Note the different temperature scale in a), compared to b) and c).
Figure 4: Simulated changes in sea-level pressure (hPa) due to aerosol direct and indirect
forcing (ALLTOT minus ALLNAT):
a) Annual average,
b) The SON season.
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),
c) Simulations with direct+indirect forcing (ALLTOT minus ALLNAT).
Figure 6: Zonally averaged vertical velocity, ω, in units of hPa s-1 from:
a) Simulation with direct+indirect aerosol forcing (ALLTOT),
b) Simulation without direct+indirect forcing (ALLNAT).
32
Figure 7: Time evolution of the globally averaged difference in cloud radiative forcing
between runs with (INDTOT) and without (INDNAT) anthropogenic aerosols.
Short wave (red), long wave (blue), net (green). Units: W m-2.
Figure 8: Simulated changes in cloud water path due to aerosol indirect forcing (INDTOT
minus INDNAT):
a) Cloud liquid water path (g m-2),
b) Cloud ice water path (g m-2).
Figure 9: Difference in cloud droplet effective radius, as seen by satellite, between the
simulations INDTOT and INDNAT (µm).
Figure 10: Simulated changes in cloud cover due to aerosol indirect forcing (INDTOT
minus INDNAT):
a) Zonally averaged effective cloud cover (%),
b) High cloud cover (%).
33
Tables
Direct
Indirect Anthropo- Natural
Forcing Forcing
genic
Greenhouse
Aerosols Gas
Aerosols
Concentrations
DIRTOT
Yes
No
Yes
Yes
Present day
DIRNAT
Yes
No
No
Yes
Present day
INDTOT
No
Yes
Yes
Yes
Present day
INDNAT
No
Yes
No
Yes
Present day
ALLTOT
Yes
Yes
Yes
Yes
Present day
ALLNAT
Yes
Yes
No
Yes
Present day
PREIND
No
Yes
No
Yes
Pre-industrial
Table 1: A schematic overview of the experimental setup. All the experiments are carried
out using an interactive slab ocean, as well as on-line 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 Kristjánsson (2002), yielding an interaction between the aerosols and the
cloud properties.
34
Temp-
Precip-
Cloud
Cloud
Cloud
Cloud
Net
erature
itation
Cover
Liquid
Ice
Droplet
SW
(°C)
(mm
(%)
Water
Water Effective flux at
Path
Path
Radius
surface
(g m-2)
(g m-
(µm)
(W m-
/day)
2
2
)
)
DIRTOT
13.8
2.89
57.2
169.6
DIRNAT
13.9
2.90
57.1
170.2
INDTOT
12.6
2.80
57.1
40.9
17.4
10.12
169.6
INDNAT
13.9
2.90
57.1
41.0
17.2
10.78
170.7
ALLTOT
12.6
2.77
57.2
41.5
17.2
10.13
168.2
ALLNAT
14.0
2.89
57.2
41.6
17.2
10.81
170.1
PREIND
12.4
2.81
57.2
38.6
17.3
10.71
171.2
Table 2: Global averages of key quantities in the experiments defined in Table 1.
35
Temper-
Precip-
Cloud
LWP
IWP
Net SW
ature
itation
Cover
(g m-2)
(g m-2)
flux at
(K)
(mm/day) (%)
surface
(W m-2)
DIRECT
-0.083
-0.017
0.021
-0.17
-0.02
-0.60
INDIRECT
-1.25
-0.10
-0.015
-0.09
+0.02
-1.15
DIRECT&INDIRECT -1.42
-0.13
-0.001
-0.15
+0.06
-1.82
-0.013
-0.01
+0.11
+0.06
-0.07
RESIDUAL
-0.10
(-0.5%)
Table 3: Global averages of changes in key quantities due to different aerosol forcings
and associated climate feedbacks.
36
Temper-
Precip-
Cloud
LWP
IWP
Net SW
ature
itation
Cover
(g m-2)
(g m-2)
flux at
(K)
(mm/day) (%)
surface
(W m-2)
DIRECT
-0.23
-0.007
+0.077
-0.63
+0.06
-0.35
INDIRECT
-2.41
-0.16
-0.13
-9.18
-1.49
-0.049
DIRECT&INDIRECT -2.77
-0.19
-0.038
-10.04
-1.72
-0.26
-0.13
-0.03
+0.014
-0.25
-0.29
+0.14
RESIDUAL
(-2.5%)
Table 4: As for Table 3, except only for the area north of 60°N.
37
Figure 1:
Figure 2:
Figure 3:
Figure 4:
Figure 5:
Figure 6:
0.5
0
W/m**2
−0.5
−1
−1.5
−2
−2.5
−3
0
5
10
15
20
25
Year
Figure 7:
30
35
40
45
50
Figure 8:
Figure 9:
Figure 10: