JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D22204, doi:10.1029/2010JD014015, 2010
Do anthropogenic aerosols enhance or suppress
the surface cloud forcing in the Arctic?
K. Alterskjær,1 J. E. Kristjánsson,1 and C. Hoose1,2
Received 7 February 2010; revised 20 July 2010; accepted 2 August 2010; published 18 November 2010.
[1] Earlier studies suggest that aerosol‐cloud interactions may have contributed to the
increase in surface air temperature recently observed in the Arctic. While those studies
focused on longwave effects of strong pollution events around Barrow, Alaska, we use a
global climate model (CAM‐Oslo) to study the annual and seasonal net radiative effect
of aerosol‐cloud interactions over the entire Arctic region. The model is validated against
and adjusted to match observations from the Surface Heat Budget of the Arctic Ocean
campaign along with measuring stations within the Arctic region. Several sensitivity
experiments were conducted which included changes in both cloud properties and aerosol
concentrations. Results show that the longwave indirect effect at the surface lies
between 0.10 and 0.85 W/m2 averaged annually north of 71°N, while the shortwave
indirect effect lies between −1.29 W/m2 and −0.52 W/m2. Due to longwave dominance
in winter, 6 of 11 simulations give a positive change in net cloud forcing between October
and May (−0.16 to 0.29 W/m2), while the change in forcing averaged over the summer
months is negative for all model simulations (from −2.63 to −0.23 W/m2). The annually
averaged change in net cloud forcing at the surface is negative in 10 of 11 simulations,
lying between −0.98 and 0.12 W/m2. In conclusion, our results point to a small decrease in
the surface radiative flux due to the aerosol indirect effect in the Arctic, but these estimates
are subject to uncertainties in the frequency of thin clouds and biases in the estimated
cloud cover.
Citation: Alterskjær, K., J. E. Kristjánsson, and C. Hoose (2010), Do anthropogenic aerosols enhance or suppress the surface
cloud forcing in the Arctic?, J. Geophys. Res., 115, D22204, doi:10.1029/2010JD014015.
1. Introduction
[2] The Arctic region is particularly sensitive to climate
change due to the positive feedback between surface temperature and surface albedo [Wang and Key, 2005] and the
increase in air temperature in the bottom layers of the
atmosphere over the past decades is almost twice as large
here as in the rest of the world [e.g., Graversen et al., 2008].
Due to the rapid changes found in this region, there has been
an increasing scientific interest in the Arctic in general. This
was made evident by the implementation of the International
Polar Year in 2007–2008.
[3] Several factors contribute to climate change in the
Arctic, among these are the increased surface radiative flux
resulting from increasing anthropogenic greenhouse gas
concentrations and reduced surface albedo due to soot
deposition on snow. Another possible cause discussed in
two empirical studies published in Nature in 2006 is the
change in Arctic clouds due to human activities ([Garrett
1
Department of Geosciences, Meteorology and Oceanography Section,
University of Oslo, Oslo, Norway.
2
Now at Karlsruhe Institute of Technology, Institute for Meteorology
and Climate Research, Karlsruhe, Germany.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2010JD014015
and Zhao, 2006] (GZ06) and [Lubin and Vogelmann,
2006] (LV06)). Of particular interest is the influence of
anthropogenic emissions of pollution on the thin, nonopaque
clouds common in the Arctic region.
[4] Clouds in the Arctic differ from clouds elsewhere in
that they have a net warming effect at the surface: There is
positive net cloud forcing [Intrieri et al., 2002a]. This
happens because the longwave (LW) radiation dominates
the radiation regime, due to large solar zenith angles
throughout the year, combined with a high surface albedo.
Consequently, the LW radiation plays a much more
important role in this region than at lower latitudes, and the
greenhouse effect of clouds in the LW leads to a net surface
warming by clouds in the Arctic. The shortwave (SW)
radiation, however, dominates during midsummer and
clouds have a net cooling effect at the surface [Intrieri et al.,
2002a].
[5] GZ06 and LV06 focused on what is known as the first
aerosol indirect effect or the Twomey effect [Twomey,
1977]. This effect is described as an increase in the cloud
optical depth through pollution aerosols leading to more
numerous, smaller sized droplets, while the water content of
the cloud is assumed to be constant. As is the case for the
cloud optical depth, the cloud LW emissivity increases with
such a change in cloud properties. Based on this, GZ06 and
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LV06 suggested that anthropogenic emissions of pollution
might increase the LW emissivity of the thin, nonopaque
clouds common in the Arctic, causing their warming effect
to increase. Results from their studies suggest that the LW
cloud forcing at the surface increases by 3.3 to 8.2 W/m2 in
the presence of large anthropogenic emissions of sulfate
precursors, due to increases in the LW emissivity of low‐
level clouds. GZ06 and LV06 also suggested a possible
contribution from the second indirect effect, through
increased liquid water content, and even changes in cloud
amount.
[6] In this study we will examine how the surface cloud
forcing in the Arctic region has changed due to interactions
between clouds and anthropogenic aerosols. We will study
this with focus on the time of year, and we will compare our
results to earlier findings whenever possible. In the following
section we describe the model tools and methods used in our
study. Section 3 presents basic features of cloud cover, sulfate
concentration, cloud water path, and cloud radiative forcing
as simulated by the model, comparing our results to observations. The influence of anthropogenic emissions on the
sulfate concentration, cloud properties, and cloud radiative
forcing is investigated in section 4. We discuss the results in
section 5 and summarize our findings with conclusions in
section 6.
stratosphere. A 20 min time step is used both for the
dynamics and the physics.
2.2. CAM‐Oslo Model Modifications
[10] Several modifications were made to better suit the
model to the focus of our study. Cloud‐aerosol interactions
lead to changes in cloud microphysics and therefore in cloud
radiative properties. Before model modification, calculations
in the LW part of the spectrum did not depend on the size of
cloud droplets. This was because this dependence is insignificant when SW radiation is dominating the radiation
regime and because water clouds at low and midlatitudes are
often optically thick in the LW. The emissivity of these
clouds is therefore not influenced by cloud droplet size. In
the Arctic, the LW radiation is much more important, and
optically thin clouds persist for large parts of the year. We can
therefore no longer neglect the LW emissivity dependence on
cloud droplet size. We derived an expression for the dependency of the LW absorption coefficient, and therefore the
LW emissivity, on cloud droplet size and implemented this
expression in the model, enhancing its capability to accurately simulate Arctic conditions.
[11] The LW cloud emissivity is given by Collins et al.
[2004] (section 4.9.5) as follows:
¼ 1 e1:66*kabs *CWP ;
2. Model Tools and Methods
[7] GZ06 and LV06 both based their findings on observational data that have been gathered under specific atmospheric conditions from the area around Barrow, on the north
slope of Alaska. In this study we wish to examine the effects
of cloud‐aerosol interactions in the Arctic region as a whole.
In order to do so, our best option as of today is to use
numerical modeling. The use of a three dimensional (3‐D)
climate model allows us to study both the spatial variations in
cloud‐aerosol interactions and the effect of these interactions
over time and for different seasons. Long‐term averages will
show the overall importance of the indirect effects. The use of
a one dimensional model allows us to study how specific
changes in cloud properties affect the surface cloud forcing in
the Arctic.
2.1. Model Description: CAM‐Oslo
[8] The atmospheric general circulation model used here
is the CAM‐Oslo, extended from NCAR‐CAM3 (National
Center for Atmospheric Research Community Atmosphere
Model version 3) [Collins et al., 2006a]. The CAM‐Oslo
includes modules for aerosol life‐cycling and interactions
with radiation described by Seland et al. [2008]. The model
also includes a prognostic calculation of cloud droplet
number concentration (CDNC) in which droplet activation
is based on chemical composition, size distribution, and
parametrized subgrid‐scale vertical velocity [Storelvmo et al.,
2006; Hoose et al., 2009]. It is run as a stand‐alone atmospheric model with prescribed climatological sea surface
temperatures.
[9] The horizontal resolution of the model is approximately
2.8° × 2.8° (T42 spectral truncation), and there are 26 layers
in the vertical. The vertical coordinate is a hybrid coordinate
that follows the terrain in the lower troposphere and gradually becomes a pressure coordinate when entering the lower
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ð1Þ
where CWP is the cloud water path, that is, the integrated
total cloud water content in a column above a certain surface
area, in units of g/m2, while 1.66 is the diffusivity factor and
kabs is the mass absorption coefficient for condensed water.
For mixed phased clouds kabs will be a weighted mean of the
absorption coefficients for liquid and solid particles,
respectively. In this study we consider the aerosol influence
on liquid cloud particles only. It is therefore the liquid water
mass absorption coefficient (kabs,liquid) which we express in
R
R
terms of effective radius (re = pr3n(r)dr/ pr2n(r)dr) in
order for the LW cloud emissivity to depend on droplet size.
The absorption coefficient for liquid particles is given by:
kabs;liquid ¼
a
;
LWC
ð2Þ
where ba is the volume absorption coefficient for liquid
droplets and LWC is the liquid water content of the
in
R cloud
r3n(r)dr,
units of mass per unit volume (LWC = 43prL
where rL is the bulk density of liquid water). The volume
absorption coefficient is given by
Z
a ¼ 1
nðrÞr2 Qa ðrÞdr;
ð3Þ
0
where n(r)dr is the cloud droplet size distribution as a
function of radius, r, while Qa is the absorption efficiency.
Based on Mie calculations following equations (3) and (6) by
Chýlek et al. [1992], the model Qa is approximated as
follows: For radii greater than a certain rmax, Qa is constant
and equal to 1.0, while for r smaller than rmax, Qa increases
linearly with r (Qa = a1r). The parameter rmax varies with
wavelength because Qa is wavelength dependent (Figure 8.4
in Paltridge and Platt [1976] and Garrett et al. [2002]).
However, in some general circulation models, including the
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Table 1. Global Annual Emissions of Dimethyl Sulfide (DMS),
SO2, Particulate SO4, Black Carbon (BC), Particulate Organic
Matter (OM), Including Secondary Organic Aerosols, Sea Salt
(SS), and Mineral Dust (DUST) (Tg yr−1)a
DMS
SO2
SO4
BC
OM
SS
DUST
PD
PI
2SOx
18.2
68.6
1.8
7.7
65.4
7925
1678
18.2
14.9
0.4
1.3
30.0
7925
1678
18.2
120.9
3.1
7.7
65.4
7925
1678
a
Emissions of DMS, SO2, and SO4 are in Tg S yr−1. Abbreviations: PD,
present‐day emissions [Dentener et al., 2006]; PI, preindustrial emissions;
2SOx, present‐day emissions except that emissions of SO2 from fossil fuel
combustion are doubled compared to present day.
CAM‐Oslo model, the cloud emissivity is constant over the
entire LW spectrum and one representative rmax must be
used. According to Paltridge and Platt [1976, p. 200] the
value of the mass absorption coefficient for 11 mm is near the
average value for the entire window region from 8 to 14 mm,
and from Wien’s displacement law [e.g., Liou, 2002] we
know that the wavelength for the intensity peak of the Earth’s
radiation field lies within this window region. Around a
wavelength of 11 mm rmax can be approximated by 10 mm and
a1 by 0.1 [Garrett et al., 2002]. These are the values used over
the whole LW spectrum in our calculations.
[12] The effective radius (re) is constant in the population
of droplets. Therefore Qa(re) does not vary with r and can be
taken outside the integral.
Z 1
a Qa
nðrÞr2 dr;
0
Qa ¼ 0:1 re ðm1 Þ f or
Qa ¼ 1:0
f or
re < 10 m
re 10 m:
ð4Þ
[13] Solving this integral, using the definition of effective
radius and LWC, leaves the following:
3 Qa
kabs;liquid ;
4 L re
Qa ¼ 0:1 re ðm1 Þ f or
Qa ¼ 1:0
f or
re < 10 m
re 10 m:
ð5Þ
[14] The mass absorption coefficient and hence the cloud
LW emissivity thus depends on the droplet effective radius.
Expression 5 is used in all model simulations of this study
unless otherwise stated. Approximating Qa in this manner is
a simplification. In reality there is a continuum between the
two regimes both because clouds have a droplet spectrum
and because thermal radiation has a wavelength spectrum
[Garrett et al., 2002]. The sensitivity of the LW emissivity to
changes in cloud droplet size may therefore be affected by our
assumptions. This will be investigated closer in section 5.3.
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sions, hereby referred to as PD, and another field based on
preindustrial emissions, hereby referred to as PI. They are
both based on the AeroCom emissions [Dentener et al.,
2006]. However, due to uncertainties about preindustrial
forest fires, we modified the AeroCom PI field such that the
emissions prior to the industrial revolution were nowhere
higher than the present‐day emissions. The total global
annual emissions of each species in each of the emission
fields are listed in Table 1.
[16] The model was run for 5 years and the results shown
are averaged over these years. Such a long integration time
diminishes variations due to specific weather events. The
summer season here includes the months of June, July,
August, and September, while the winter season is an
average over the remaining months. The model was run off‐
line, meaning that the meteorological evolution is the same
in all model runs. This allows us to study how the clouds
and the radiative balance are changed between emission
fields without feedbacks due to the aerosol forcing. The
simulated change in cloud forcing with pollution is then
only a result of aerosols interacting with the clouds and we
avoid noise from synoptic variability in our results. This
also implies that all feedbacks due to aerosol‐induced cloud
changes such as the semi‐indirect effect and changes in
cloud cover are precluded from this investigation. However,
as explained in Kristjánsson [2002], the contribution to the
indirect effect from instantaneous suppression of precipitation release is accounted for. This is not treated as a feedback in our model because a control simulation propagates
the model.
2.4. Model Description: One‐Dimensional Model
[17] A one‐dimensional column model was used to study
the radiative effects of placing specific clouds in preferred
environments. The model input includes cloud parameters
such as cloud droplet effective radius and liquid water path
(LWP), that is, the integrated liquid water content in a
column above a certain surface area (g/m2). The model uses
the radiation scheme from the NCAR CCM3 model [Kiehl et
al., 1998] to give instantaneous values of radiation fluxes.
This scheme is very similar to the one used in NCAR‐CAM3
and therefore in CAM‐Oslo [Collins et al., 2006a]. In addition to cloud parameters the input includes location given by
latitude and time of year and gas and temperature profiles
suited for the chosen environment. The output is averaged
over the chosen latitude, equal to a 24 h mean. In this study
the model ran with 26 layers in the vertical corresponding to
the vertical layers of CAM‐Oslo.
2.5. Calculation of Cloud Radiative Forcing
[18] The change in cloud forcing due to aerosol‐cloud
interactions can be taken as a measure of the aerosol indirect
effect. Cloud forcing (CF) is defined as “the radiative impact
that clouds have on the atmosphere, surface, or top‐of‐the‐
atmosphere (TOA) relative to clear skies” [Shupe and Intrieri,
2004]. In the following we will be mainly concerned with the
CF at the surface (CFS), which is given by:
2.3. CAM‐Oslo Model Setup
[15] Two different emission fields were used in order to
study the effect of increased amounts of pollution on cloud
forcing: One field based on present‐day (year 2000) emis3 of 19
LWCFS ¼ NetLWallsky NetLWclear
ð6Þ
SWCFS ¼ NetSWallsky NetSWclear ;
ð7Þ
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ALTERSKJÆR ET AL.: AEROSOL INDIRECT EFFECTS IN THE ARCTIC
where LWCFS and SWCFS denote the longwave and shortwave cloud forcings at the surface, respectively, NetLW and
NetSW denote the net downward flux at the surface (i.e.,
downward flux minus upward flux), subscript allsky refers to
the true atmospheric state including clouds, while the subscript
clear refers to a hypothesized atmosphere with all clouds
removed but otherwise identical conditions.
[19] Adding the longwave and shortwave contributions, a
net cloud forcing at the surface (Net CFS) is defined as:
Net CFS ¼ LWCFS þ SWCFS:
ð8Þ
[20] The LWCFS depends mainly on the ability of the
clouds to absorb and emit LW radiation and therefore on the
LW emissivity (equation (1)) of the cloud. The clouds
absorb a fraction of the radiation emitted by the surface and
then reemit energy toward the ground, and because the
clouds become optically thick at low LWPs (∼50 g/m2), the
LWCFS depends mainly on the lowest cloud base. The flux
density emitted toward the surface depends on the cloud
base temperature, T, and the cloud properties such as cloud
particle size and LWP (see the Stefan‐Boltzmann law and
equation (1)). The higher the emissivity, the larger the
LWCFS.
[21] The SWCFS depends mainly on the ability of the
clouds to reflect SW radiation and therefore on the cloud
albedo, A. The albedo of a cloud can be approximated by its
optical depth, t, and the asymmetry factor, g, alone [Meador
and Weaver, 1980]:
A¼
ð1 g Þ
:
1 þ ð1 g Þ
ð9Þ
The optical depth depends on cloud droplet effective radius
and LWP through [e.g., Liou, 2002]:
¼
3 LWP
:
2 L re
ð10Þ
1
ð11Þ
This gives
A¼
1 þ 23
:
L re
ð1g ÞLWP
The higher the cloud albedo, the more negative the SWCFS.
[22] When the influence of anthropogenic aerosols on
clouds and climate is considered, it is the changes in
LWCFS and SWCFS from a clean to a polluted case that are
of interest. These changes are given as follows:
DLWCFS ¼ NetLWallsky;polluted NetLWallsky;clean
NetLWclear;polluted NetLWclear;clean
ð12Þ
DSWCFS ¼ NetSWallsky;polluted NetSWallsky;clean
NetSWclear;polluted NetSWclear;clean
ð13Þ
and the sum of the two is defined as
DNet CFS ¼ DLWCFS þ DSWCFS:
ð14Þ
[23] As the focus of this investigation is on the radiative
effect of aerosol‐cloud interactions, the change in net flux
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due to aerosols in clear conditions is not contained in our
simulations. However, it is clear that the anthropogenic
aerosols may influence the Arctic also via the direct effect
(i.e., by reflection and absorption of solar radiation). In this
paper the term “change in cloud forcing” refers to the
aerosol indirect effect.
3. Basic Features of Arctic Clouds and Aerosols
[24] In this section we will check whether the model
output is consistent with observations. The main focus will
be on cloud cover, sulfate, liquid water path, and cloud
forcing, and observations made during the Surface Heat
Budget of the Arctic Ocean (SHEBA) campaign will be an
important part of this validation. The SHEBA campaign
took place in the Beaufort and Chukchi Seas (from 75.3°N,
142.7°W to 80.5°N, 166°W) from October 1997 to October
1998 [Intrieri et al., 2002b; Maslanik et al., 2001] and its
main observables include the sea ice mass balance and the
surface energy balance. The advantage of using this data set
is that it comprises one continuous year of data, something
not matched by any other campaign this far into the Arctic
region.
3.1. Cloud Cover
[25] Observational data gathered by Warren et al. [1988]
show that in general the Arctic cloud cover has a minimum
in wintertime with values just below 50% and a maximum
in late summer/early fall that peaks around 85% (see
Figure 1a, black dash‐dot line). The seasonal variation in
Arctic cloud cover is well reproduced by the CAM‐Oslo
model. Nevertheless, the model seems to underestimate the
total cloud cover from April until December. The largest
errors occur during the last half of the year when the cloud
cover is underestimated by around 10%. The data presented
by Warren et al. [1988] are, however, based on ground‐
based manual observations and are therefore likely to be
somewhat inaccurate, both due to the dark season and due to
the sparsity of measurements in the remote Arctic region.
[26] We also compare simulated average cloud cover to
observations made during the SHEBA campaign (see
Figure 1a). The SHEBA data were obtained from manual
observations, from ground‐based LIDAR/RADAR measurements as well as from satellite, and show a large spread
between the different observational methods. This highlights
the difficulty in determining cloud fraction. From Figure 1a
it seems that the CAM‐Oslo cloud fraction is lower than
most observations, sometimes by up to 30%. This may lead
to an underestimation of cloud‐aerosol interactions and
hence of the cloud radiative forcing.
[27] Contrary to this, when comparing the simulated
fraction of low‐level Arctic clouds (below 700 hPa) to satellite observations presented by Kay and Gettelman [2009,
Figure 4] we find that the fraction of these clouds is overestimated during summer (24% between 65°N and 82°N),
while it is underestimated in early fall (16%). A possible
overestimation in summer may lead to a negative bias in the
aerosol indirect effect due to SW dominance, while the net
effect of an underestimation of cloud cover in September
and October is less obvious.
[28] The vertical placement of the clouds is also of
importance, as the LW cloud forcing is temperature
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averaged and column integrated and show that the burden is
largest over northern Eurasia, gradually decreasing as we
move further into the Arctic. This is consistent with the
current understanding of the transport of air pollution into
the region [Stohl, 2006], namely that pollution in the Arctic
mainly originates in Eurasia. The annually averaged burdens
of organic and black carbon have the same spatial pattern as
sulfate.
[31] The surface concentrations of SO4 are verified
against measurements taken at several monitoring stations
(Zeppelin, Spitsbergen, Norway (78.9°N, 11.9°E); Alert,
Canada (82.5°N, 62.3°W); Janiskoski, Russia (69°N, 29°E);
and Barrow, Alaska (71.3°N, 156.6°W)) [Arctic Monitoring
and Assessment Programme, 2006, chapter 4]. We find that
the seasonal variation in SO4 concentration at all stations is
fairly well reproduced by the CAM‐Oslo model (Figure 4),
with large concentrations in winter and early spring and
minima occurring during summer. These summer minima
are caused by a shift in the east‐west pressure gradients
across Eurasia so that less pollution is transported into the
Arctic, combined with an increase in precipitation during
summer, scavenging the SO4 from the lowest layers of the
atmosphere [Barrie, 1986]. Additionally, the Arctic air mass
is less stable during the summer than during the winter. This
is associated with increased turbulent transfer [Quinn et al.,
2008] and thus removal of aerosols through dry and wet
deposition.
[32] The CAM‐Oslo model’s ability to reproduce the
mass concentration of SO4 varies over the Arctic region.
Figure 4 shows that the surface concentrations at Zeppelin
are well reproduced by the model, while simulations for
Janiskoski show an overestimation of SO4 compared to
observations, although the simulated mean SO4 concentrations are seldom larger than the maximum monthly mean
observed during the 5 year period from 1996 to 2000.
Unlike simulations for stations on the Eurasian side of the
Arctic, simulations for the North American sites generally
Figure 1. (a) Observed and simulated total cloud fraction.
The Arctic region (Black). SHEBA region (Color). (b) Monthly
variation in average cloud LWP in the SHEBA region.
Observed values reproduced from Zhang et al. [2002].
dependent. Observational data from the SHEBA campaign
show that the Arctic clouds often lie close to the surface
(cloud bases below 1 km) [Intrieri et al., 2002b, Figure 7].
This tendency is well reproduced by the CAM‐Oslo model
(see Figure 2).
3.2. Particulate Sulfate (SO4)
[29] In this work the terms sulfate and SO4 both refer to
particulate sulfate.
[30] The simulated present‐day sulfate burden over the
Arctic region is plotted in Figure 3a. The data are annually
Figure 2. Simulated zonally averaged annual cloud fraction north of 65°N. The black line indicates 1 km height
above sea level.
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Figure 3. (a) Simulated annually averaged column integrated sulfate concentrations in the Arctic region (kg S/m2),
present day. (b) Simulated changes in column integrated
SO4 concentrations from preindustrial times to the present
day (kg S/m2), annual mean. The characters plotted show
the locations of the stations in Figure 4; Z: Zeppelin; J:
Janiskoski; A: Alert; B: Barrow.
underestimate SO4 compared to observations. There can be
several reasons for this underestimation. First, these sites are
further away from the main sources of Arctic SO4 than the
Eurasian sites [Stohl, 2006]. This may point to the transport
pathways themselves being inaccurate or to important
sources of SO4 precursors being ignored. Another reason for
the small concentrations in North America may be inaccurate SO4 removal processes. In section 3.3 we will show that
the model cloud liquid water path is too high, which may
lead to an overestimated in‐cloud scavenging of SO4. For
further details on the treatment of scavenging in CAM‐Oslo
see Seland et al. [2008]. In section 5.4 the sensitivity of our
results to the SO4 concentration will be tested both by
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reducing the in‐cloud scavenging and by increasing the
emissions of SO4 precursors.
[33] We compared the accuracy of our results at Zeppelin
and Janiskoski to the accuracy of results from models participating in the Aerosol Comparisons between Observations and Models project (AeroCom; [Textor et al., 2006]).
The red lines in Figure 4 show the model median of
10 AeroCom A models simulating surface concentrations of
SO4 at Zeppelin and Janiskoski for the year 2000 (medians
for Alert and Barrow were not available) [http://nansen.ipsl.
jussieu.fr/AEROCOM/]. Note that the median seasonal
variation is opposite to what is observed. It is clear that the
CAM‐Oslo results are in general a better fit to the observations than the AeroCom model median.
[34] Comparing the CAM‐Oslo simulated vertical profiles
of SO4 to observations is challenging. First, measurements
of SO4 in the Arctic are limited both in number and in
geographical distribution. Second, there is large variability
in the observations, also when taken with short time intervals in the same regions [e.g., Dreiling and Friederich,
1997; Scheuer et al., 2003]. These measurements are generally instantaneous aircraft measurements and are limited
both in time and space. This makes it difficult to compare
observations of vertical profiles to our monthly averaged
profiles.
[35] Figure 5a shows the concentration of sulfate with
height in terms of mg S per unit volume of air. The values
which are annually and zonally averaged over the Arctic
region show that north of 70° to 75°N the largest concentrations are found at around 800 to 900 hPa. Although
we have no averaged observed vertical profiles to verify the
concentrations of this cross section, 800 hPa is the height
found by Dreiling and Friederich [1997] to have the largest
concentration of particles of all sizes. A comparison with
Scheuer et al. [2003] shows that the simulated near surface
concentrations of SO4, which are the most important for our
study, are of the same order of magnitude as measurements
taken during the Tropospheric Ozone Production about the
Spring Equinox Experiment campaign. The measurements
show mean SO4 concentrations in the bottom two kilometers
of the atmosphere of between 50 and 230 pptv during
springtime, while the simulated values along 70°W range
between 60 and 170 pptv for the same altitudes.
3.3. Liquid and Ice Water Path
[36] Measurements taken during the SHEBA campaign
were used by Zhang et al. [2002] to retrieve monthly
averaged liquid water paths for the region covered by the
campaign. A maximum of around 100 g/m2 was reached in
August, when also the cloud fraction reaches its peak value.
This is in accordance with the typical range of Arctic LWP
only seldom exceeding 150 g/m2 [Löhnert et al., 2003]. A
comparison between the monthly averaged LWP (including
its uncertainty) retrieved from measurements and the simulated LWP for the SHEBA region shows that the CAM‐Oslo
overestimates the LWP by a factor of 3 to 5, depending on
season (see Figure 1b).
[37] Model intercomparison studies by Morrison et al.
[2009] and Karlsson and Svensson [2010] have found
excessive LWPs over the Arctic region in NCAR Community Climate System Model version 3 (CCSM3) [Collins
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Figure 4. Observed (green) and simulated (blue) SO4 concentrations at selected Arctic stations (mg S/m3).
The locations of the stations are plotted in Figure 3. Observational data from Arctic Monitoring and
Assessment Programme [2006] have been averaged over 5 years and error bars show the maximum and
minimum observed monthly means. The median of 10 AeroCom A models [Textor et al., 2006] for
Spitsbergen (including Zeppelin) and Janiskoski for the year 2000 is shown in red. Simulated SO4 from both
CAM‐Oslo and the AeroCom A models are non sea salt only. Note that the axes differ.
et al., 2006b] and SCAM3, a single‐column version of the
NCAR‐CAM3. In the case studied by Morrison et al. [2009]
the SCAM3 simulated LWP averages to 298 g/m2 while the
observed LWP ranges from 55 to 121 g/m2, depending on
the retrieval method. The model intercomparison by
Karlsson and Svensson [2010] shows that the NCAR‐
CCSM3 simulated LWP over the Arctic Ocean is from 2 to
3.7 times the ensemble model mean LWP. These results
suggest that the overestimation of LWP in CAM‐Oslo may
be linked to problems in the NCAR‐CCSM3, as both the
CAM‐Oslo and the SCAM3 are developed from this model.
[38] The excessive simulated liquid water amounts may
be caused by several factors. An underestimated autoconversion rate will lead to little loss of cloud water through
precipitation. Another possibility is too little conversion
from liquid water to ice particles. The overestimated LWP
may also be caused by an overestimated transport of moisture
into the region or by stably stratified conditions allowing
model clouds to become thicker than what occurs in nature.
We return to this problem in section 3.5.
[39] Ice water path (IWP) retrievals have very high
uncertainties. Nevertheless, it should be mentioned that
Shupe et al. [2006] found observed IWPs on the order of
42 g/m2 and Morrison et al. [2003] reported IWPs on the
order of 34.6 g/m2 for the SHEBA region. Our model has an
average IWP of 24.0 g/m2. Also, Karlsson and Svensson
[2010] found that the NCAR‐CCSM3 has among the lowest ice water paths of the models in their study. Combined
with the positive bias in LWP this may point to a bias in the
conversion between solid and liquid particles or a bias in the
distinction between solid and liquid particles in our model.
Due to the limited amount of measurements in the Arctic
combined with high uncertainties we cannot conclude on the
exact reason for the too low ice water paths.
3.4. Cloud Forcing
[40] The simulated cloud forcing (CF) is compared to
observations by Intrieri et al. [2002a], ignoring the turbulent
flux that is part of their study. The simulated cloud forcing
in the SHEBA region (74°–81°N and 144°–169°W [Zhang
et al., 2002]) differs significantly from what was measured
at SHEBA, especially during the summer (Table 2 under
case names “SHEBA” and “CAM‐Oslo std. LWP”). This is
mainly caused by a large difference in the SWCFS between
the simulations and observations. However, the simulated
LWCFS is also larger than the observed forcing. We know
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Figure 5. (a) Simulated zonally averaged annual sulfate concentrations in the Arctic region (mg S/m3),
present day. (b) Simulated zonally averaged changes in sulfate concentrations from preindustrial times to
the present day (mg S/m3), annual mean. (c) Simulated annual Arctic mean of effective cloud droplet
radius (mm) in present‐day conditions. (d) Simulated zonally averaged change in annually averaged effective radius from preindustrial times to present day (mm). Note that the color bars differ.
from Figure 1a that the cloud cover is reproduced fairly well
by the model or slightly underestimated. Comparing the
model surface albedo to measurements taken over the
SHEBA region [Curry et al., 2000; Intrieri et al., 2002a] we
find that it is within reasonable range. Rough monthly estimates of aircraft measured surface albedos from Curry et al.
[2000] are 0.76, 0.67, and 0.50 for May, June, and July,
respectively. CAM‐Oslo results from the same region and
time period are 0.78, 0.66, and 0.49, in excellent agreement
with the observations. Without time‐averaged temperature
profiles for this area we cannot exclude that the LWCFS is
affected by a bias in cloud base temperature. However, based
on the simulated forcing being larger than observations in
both wavelength ranges, it is likely that the discrepancy
between modeled and observed cloud forcing is caused by the
optical depth and the emissivity of the clouds being too large.
3.5. Model Modifications to Improve the Simulated
LWP and CFS
[41] The simulated effective radius around Barrow,
Alaska, is 15 mm when averaged below 700 hPa, with larger
values in summer than in winter (16 mm versus 14 mm).
While comparing our simulated re to observations is challenging because observed values vary significantly, the
seasonal variation simulated around Barrow is consistent
with the findings of Dong and Mace [2003] in the same
region. Additionally, the simulated effective radius in the
SHEBA region is 10 mm when averaged annually over the
whole vertical column, which is consistent with the findings
of Curry et al. [2000]. Consequently, it is unlikely that the
overestimation of cloud optical depth and emissivity is
caused by an underestimation of re (see equation (10) for t
and equations (1) and (5) for ). Instead, it is most likely
associated with an excessive LWP in the model (Figure 1b).
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Table 2. Observed and Simulated Surface Cloud Forcing in the SHEBA Regiona
SHEBA
CAM‐Oslo Std. LWP CAM‐Oslo LWP/5 DCDNC CAM‐Oslo LWP/5 Dre 1st Auto‐Conv. 2nd Auto‐Conv. Auto Fice
Annual Average
LWCFS
35 to 41
SWCFS −10.5 to −9.5
Net CFS
25 to 30
39.1
−23.1
16.0
35.8
−13.6
22.1
LWCFS
SWCFS
Net CFS
25 to 30
−1 to 0
24 to 30
28.4
−4.7
23.7
27.2
−2.5
24.7
LWCFS
SWCFS
Net CFS
45 to 50
−26 to −24
19 to 21
60.7
−60.0
0.7
52.9
−36.0
16.9
38.2
−16.8
21.4
39.0
−20.5
18.6
40.9
−20.0
20.9
37.1
−18.6
18.5
28.7
−3.0
25.7
28.7
−4.1
24.6
29.4
−4.2
25.2
26.2
−3.4
22.9
57.3
−44.5
12.8
59.8
−53.2
6.5
64.0
−51.6
12.4
58.9
−49.0
9.9
Winter Average
Summer Average
Note that a turbulent heat flux of approximately −6 W/m2 has been subtracted from the SHEBA total cloud forcing described in Intrieri et al. [2002a].
a
[42] Several approaches were used in order to reduce the
liquid water amount in the model. This is of particular
importance for our study as the sensitivity of cloud emissivity and albedo to changes in LWP depends on the amount
of water that the clouds initially hold. The goal was to keep
the LWP within the range of the observed values and
simultaneously find what simulation had cloud forcing
closest to the SHEBA measurements of this quantity.
Modifying the LWP affects the global net radiation, but as
we focus on the Arctic region and on the change in cloud
forcing between PI and PD, this is not a concern.
[43] Different modifications of the autoconversion
parametrization were tried allowing more water to be lost
through precipitation. The autoconversion threshold radius,
r3lc, was successively reduced from 15 to 10 and 7.5 mm.
This radius decides the size that cloud particles must reach
before the onset of precipitation, as described in equation
(21) by Rasch and Kristjánsson [1998]. In addition, we
changed the lower limit for which autoconversion is fully
efficient, here named autlim, as described in section 2.4 by
Kristjánsson [2002], from 5.0 mm d−1 [Kristjánsson, 2002]
to 0.5 mm d−1 [Rasch and Kristjánsson, 1998] and then to
0.0 mm d−1. This parameter accounts for the decrease in
collection efficiency in a cloud droplet distribution that has
been modified by precipitation. Results from the simulations
with modified autoconversion can be seen as the light blue
and the red lines in Figure 1b. The water amounts are now
much closer to the retrieved values but are still on the high
side. From Table 2 it is clear that these simulations still
overestimate the SW and the LW components of the surface
cloud forcing (case names “1st auto‐conv.” (r3lc = 10mm
and autlim = 0.5 mmd−1) and “2nd auto‐conv.” (r3lc =
7.5mm and autlim = 0.0 mmd−1)).
[44] We also reduced the cloud liquid water path by
modifying both the autoconversion and the cloud particle ice
fraction (fice). The autoconversion threshold radius was
changed from 15 to 7.5 mm and a lower limit for fully
efficient autoconversion of 0.0 mm d−1 was used instead of
5.0 mm d−1. In the standard version of CAM‐Oslo the
fraction of cloud particles that are solid is temperature
dependent and increases linearly from 0 to 1 as the temperature decreases from 263 K to 233 K. In reality the ice
fraction is expected to be influenced by aerosol properties.
We increased fice by letting it go from 0 to 1 between 273 K
and 243 K. Results from this simulation are seen as the
purple line in Figure 1b. The LWP is now consistent with
observations, but the SW and the LW components of the
surface cloud forcing are still overestimated (Table 2 under
case name “Auto fice”).
[45] For simplicity we then conducted several idealized
experiments where we forced a reduction in the LWP and
found that reducing it by a factor of 5 through reducing the
CDNC gave the results closest to the observed values. This
can be seen from the green line in Figure 1b and from results
under case name “LWP/5 DCDNC” in Table 2. Note that
the model radiation scheme does not depend explicitly on
the CDNC but rather on LWP and re. Physically, however,
reducing the LWP while keeping the re constant is the same
as reducing the cloud droplet number concentration. A
reduction in CDNC is not in itself an improvement, as this
concentration is already low. The averaged observed CDNC
in single‐layer stratus clouds obtained during the Mixed‐
Phase Arctic Cloud Experiment (M‐PACE) during fall 2004
was 43.6 ± 30.5 cm−3 [McFarquhar et al., 2007]. The
simulated CDNC is around 17 cm−3 in the standard model
version for all clouds during this season in the same area
(around Barrow and Oliktok, Alaska). This mean gives
weight also to glaciated mixed‐phased clouds where nearly
all the water is in solid form and the CDNC consequently is
very low. It is therefore likely that the simulated CDNC of
the persistent low‐level stratus clouds is somewhat higher
than 17 cm−3.
[46] Additionally, we tried reducing the cloud droplet
effective radius, keeping the CDNC constant, in order to
obtain LWP values consistent with observations. However,
this led to surface cloud forcing much stronger than what
was observed during the SHEBA campaign (see Table 2
under case name “LWP/5 Dre”).
[47] Note that the only adjustment to the LWP that affects
the model thermodynamics is the one in which the cloud
particle ice fraction is modified. All the other methods used
to adjust the LWP are applied after the liquid water is
formed and therefore have no effect on the thermodynamic
state of the atmosphere. By similar arguments, the only
adjustments to the LWP that affect the aerosol concentration
are the modifications of the autoconversion and the cloud
particle ice fraction. We are not directly comparing these
runs to the standard model but are consistently calculating
the differences between PD and PI for two “autoconversion”
and two “fice” simulations, respectively.
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[48] The results shown in the next section are from simulations in which the LWP was reduced by a factor of 5 through
reducing the CDNC, as these simulations give the best
agreement between observed and simulated LWP and surface
cloud forcing. In section 5 we will discuss how our results are
affected by this choice.
4. Anthropogenic Influence
[49] In the following subsections we will investigate the
simulated changes in cloud properties due to anthropogenic
aerosol emissions and how these changes affect the surface
radiative balance in the Arctic region.
4.1. Changes in Sulfate (SO4) Concentrations
[50] The only change between simulations of the preindustrial and the present‐day climate is the anthropogenic
emissions of chemical compounds in to the atmosphere. The
increase in SO4 and other anthropogenic particles in this
period leads to an increase in the concentration of cloud
condensation nuclei (CCN) and is expected to influence
cloud properties. Figure 3b shows the increase in the column
burden of sulfate from preindustrial times until today. The
increase is largest over northern Eurasia, where the burden
itself is also largest (see Figure 3a). The large increase in
this area is not surprising as northern Eurasia is a significant
source region of anthropogenic sulfate precursors (see
section 3.2).
[51] Figure 5b shows the vertical distribution of the
change in sulfate concentration between PD and PI, averaged annually over the Arctic region. The largest change in
concentration occurs around 800–900 hPa. However, there
are relatively large signals of change both above and below
this level. According to Shindell et al. [2008], the upper part
of this signal may be influenced by the increase in emissions
of SO4 precursors in Asia as well as Europe. In section 3.1
we showed that both the modeled and the observed clouds in
the Arctic in general lie close to the surface (∼900–950 hPa,
see e.g., Figure 2). As seen in Figure 5b, changes in aerosol
concentrations occur at the same levels and we therefore
expect that this change will affect cloud properties.
4.2. Changes in Cloud Droplet Effective Radius (re)
[52] The simulated effective radius averaged annually
over the cloud droplet number concentration decreases from
11.7 mm in pristine conditions (PI) to 9.8 mm in the polluted
present‐day regime north of 71°N. By comparison, observations by GZ06 show an average decrease in effective radius
from 12.9 to 9.9 mm between clean and polluted conditions.
Thus, the first indirect effect is present in our simulations and
the change in re is of the same order of magnitude as observed
values.
[53] Figure 5c displays the vertical cross section of the
annually averaged effective radius over the Arctic region.
This figure shows that the layers below 500 to 600 hPa have
effective radii above 10 mm, and we note that the LW cloud
emissivity is sensitive to changes in cloud droplet size (see
equation (5)).
[54] Figure 5d shows that the changes in effective radius
due to anthropogenic emissions are largest between 500 and
800 hPa. The change in effective radius is large if the re is
large initially and there is a large relative increase in CDNC.
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This is what creates the maximum between 500 and 800 hPa.
Above this maximum the re is initially small while close to the
surface the CDNC is high preindustrially and the relative
increase in CDNC with increasing aerosol levels is therefore
small (not shown here).
[55] The largest reductions in effective radius of about
2.5 mm occur well above the height of the highest cloud
fraction (Figure 2) and have therefore only limited influence
on the surface cloud forcing. On the other hand, the decrease
in re of 0.6 to 1.0 mm close to the surface will increase both
the SW and the LW surface cloud forcing from preindustrial
times to the present day.
4.3. Changes in Liquid Water Path
[56] As displayed in Figure 6a the spatial pattern of
annually averaged change in LWP between the PD and the
PI scenario has similarities with the pattern of change in
the integrated SO4 concentration (Figure 3b). Note that the
LWP increases in the more polluted regime, as expected
from the reduced droplet size and therefore reduced loss of
water due to precipitation release. Hence, the model simulates a distinct second indirect effect. The simulated ice
water path (not shown) does not change between scenarios,
as aerosols in this study do not affect ice nucleation.
[57] The average increase in liquid water path between the
two scenarios is about 2.3 g/m2 north of 71°N in the “LWP/5
DCDNC” simulations, going from 27.8 to 30.1 g/m2. This
is within the same range as the change found by GZ06 of
2.4 g/m2, from 31.1 g/m2 in pristine conditions to 33.5 g/m2 in
an atmosphere with high aerosol concentrations. We also note
that the clouds in the preindustrial aerosol regime have liquid
water paths in the same range as the average clean clouds
observed by GZ06.
[58] The vertical distribution of changes in in‐cloud liquid
water mixing ratio (LWMR) averaged annually over the
Arctic region is shown in Figure 6b. Contrary to the changes
in effective radius, the LWMR increases the most close to
the surface. The reason for this is that the liquid water
amount is largest near the surface in preindustrial times (not
shown). This affects the change in LWMR in the following
manner: The onset of precipitation is determined by the size
of the cloud droplets (see section 3.2), but the amount of
water lost through this process increases with the in‐cloud
liquid water mixing ratio, as well as with re [equation (21);
Rasch and Kristjánsson, 1998]. This means that the change
in the precipitation amount and hence in the LWMR is large
for a given change in re if the LWMR is large. Although a
small change in CDNC leads to small changes in the
effective radius at surface levels, this change is large enough
to affect the model autoconversion and hence the amount of
water lost through precipitation.
[59] The average LWP described so far says nothing about
the thickness of each individual cloud simulated by CAM‐
Oslo. There may be episodes of very high or very low LWPs
that affect this average greatly. This is of importance
because the cloud optical properties vary nonlinearly with
LWP and the LW cloud emissivity reaches saturation for all
LWPs above about 50 g/m2. One might therefore question
whether clouds are thin enough to be affected in the LW by
a change in cloud droplet size or LWP. Figure 7 shows the
fraction of time that has vertically integrated LWPs below
50 g/m2 when clouds are present. It reaches a minimum in
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today. This increase is caused by an increase in the CCN
concentration influencing the cloud effective radius and the
liquid water path.
[62] There is a significant seasonal variation in the change
in LWCFS with anthropogenic aerosol emissions (Figures 9a
and 9b). Averaged north of 71°N the LW cloud forcing
changes by 0.99 W/m2 during summer, while it changes by
only 0.33 W/m2 during winter time. The large changes in LW
cloud forcing during summer may be highly influenced by the
fraction of low clouds being larger during the summer season
(0.75) than during winter (0.40). According to Shupe and
Intrieri [2004], clouds that are important to the LW surface
radiation balance in the Arctic typically have bases at low
altitudes (below 4 km). A high fraction of these clouds allows
changes in cloud radiative properties to occur over large areas
and therefore cause larger changes in the LW surface radiation budget during summer than during winter.
[63] In addition, results show that the cloud liquid water
path changes much more in summer than it does in winter
(4.2 g/m2 versus 1.3 g/m2). This is because the high LWPs
in the summer make the amount of water lost through
precipitation very sensitive to changes in cloud droplet size
(see section 4.3). The re at low levels changes by approximately the same amount during the summer and the winter
(−1.46 mm versus −1.51 mm below 700 hPa). In the winter re
is smaller than in summer, but the pollution events are
stronger, while in the summer re at low levels is large and
therefore sensitive to the little pollution that is present at low
levels during this season (see section 4.2). The large change
in LWP causes a large change in the LW emissivity and
therefore in the LWCFS in summer.
4.5. Changes in Shortwave Cloud Forcing
at the Surface (SWCFS)
[64] Changes in re and LWP due to an increase in the
anthropogenic aerosol concentrations will affect the SW
cloud forcing, as long as solar radiation is present. We find
that on average the simulated SWCFS changes by −0.85 W/m2
Figure 6. (a) Simulated annually averaged change in LWP
(g/m2) from preindustrial times to the present day. (b) Simulated zonally and annually averaged change in in‐cloud liquid water mixing ratio (kg/kg) from preindustrial times to
the present day.
August, simultaneously with the maximum in cloud cover
and average LWP (see Figures 1a and 1b). The fraction is
never below 59% preindustrially and never below 55% in
the present day. We conclude that a large fraction of clouds
is nonopaque in the LW and therefore sensitive to changes
in effective radius and liquid water path due to anthropogenic aerosols.
4.4. Changes in Longwave Cloud Forcing
at the Surface (LWCFS)
[60] In this subsection we will study the simulated changes in LW cloud forcing at the surface between preindustrial
times and the year 2000 (LWP/5 DCDNC simulations).
[61] The annually averaged change in surface LW cloud
forcing (LWCFS) north of 71°N is 0.55 W/m2, corresponding to a 1.6% increase from preindustrial times until
Figure 7. Fraction of time when clouds are present with
vertically integrated LWP below 50 g/m2. The presence of
clouds is defined as times when LWP > 5 g/m2. Results
are averaged north of 71°N.
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[67] Another reason for the large SW signals is the
simulated changes in re and LWMR at levels well above
the surface (sections 4.2 and 4.3). These changes will
affect the SWCFS because they affect the cloud albedo (see
equation (11)). Conversely, the effect of these changes on
LWCFS is expected to be very small as this forcing is
mainly influenced by changes that occur in the bottom cloud
layers, which are low in the Arctic (see Figure 2).
Figure 8. Solid gray lines are cloud SW albedo (thick) and
LW emissivity (thin) as a function of liquid water path (bottom axis, re = 11.6 mm). Dashed black lines are cloud SW
albedo (thick) and LW emissivity (thin) as a function of
effective radius (top axis, LWP = 43.5 g/m2). Cloud ice fraction = 0.
north of 71°N between the PI and the PD scenarios, representing a 6.5% increase in the magnitude of the SW cloud
forcing. The seasonal variation in the change in surface cloud
forcing is much stronger in the SW than in the LW, due to the
sun being absent or at high solar zenith angles through most
of the winter season. Because of the low signals during
winter, we will now focus only on the summer season.
[65] The change in surface SW cloud forcing during the
summer has an average of −2.17 W/m2 north of 71°N. From
Figure 9c it is clear that the changes are larger toward the
lower Arctic latitudes. This happens for two reasons. First,
the solar zenith angle is smaller here, causing a larger possible impact of the clouds on the radiation budget. Second,
areas around the North Pole and over Greenland are covered
by snow and ice. The cloud albedo increase will be less
important here as the clouds are above highly reflective
surfaces.
[66] The magnitudes of both the relative and the absolute
change in SWCFS during summer are larger than the corresponding magnitudes simulated for the LW case. There
are several reasons for this. Figure 8 shows that a given
change in re or LWP affects the SW cloud albedo more than
the LW cloud emissivity under averaged simulated summer
conditions (PI) (re = 11.6 mm and LWP = 43.5 g/m2).
Depending on surrounding conditions such as the surface
albedo and the vertical temperature profile, this behavior
will lead to larger changes in SWCFS than in its LW
counterpart for a given change in re or LWP. Additionally,
since cloud albedo saturates at much higher LWPs than
cloud emissivity, a larger fraction of clouds has radiative
properties sensitive to changes in re and LWP in the SW
than in the LW.
4.6. Changes in Net Cloud Forcing at the Surface
(Net CFS)
[68] The changes in cloud forcing in both the LW and the
SW have now been examined. Here, we will study the total
influence of increased aerosol levels interacting with Arctic
clouds.
[69] The annually averaged change in Arctic net CFS
between the PD and the PI scenario is −0.30 W/m2 (not
shown). This confirms that the increased magnitude of SW
cloud forcing with pollution is larger than the increased
warming by clouds due to LW effects. If the fraction of low
clouds is overestimated in summer as suggested by the
comparison to the findings of Kay and Gettelman [2009]
(section 3.1), the strong SW effects in summer will be
overestimated and it is possible that there is a negative bias
in the aerosol indirect effect.
[70] During summer the net cloud forcing in present‐day
conditions is positive over ice covered surfaces, while the
areas of open water and the southern regions of the Arctic
experience negative cloud forcing (not shown). The LW
component thus dominates where the surface albedo is high.
Despite this, the change in surface net cloud forcing with
anthropogenic aerosols is negative over most of the Arctic
region during the summer (Figure 9d). The large change in
SW cloud forcing dominates the change in net forcing
completely, even over areas covered by surface ice. The
change in net surface cloud forcing averages to −1.18 W/m2
north of 71°N during the summer.
[71] In the winter, the SW cloud forcing is of less
importance than in the summer and the change in net forcing
with pollution is dominated by LW effects. Anthropogenic
aerosols interacting with clouds lead to a net increase in the
winter surface flux on the order of 0.14 W/m2 north of 71°N.
5. Discussion
[72] We will now compare the results from the LWP/5
DCDNC simulations to earlier findings and go on to discuss
the sensitivity of our results to our assumptions, as well as to
the sulfate concentration. The LWP/5 DCDNC simulations
are highlighted in this work because the simulated surface
cloud forcing and LWP agree well with the measurements
taken during the SHEBA campaign. As this campaign was
limited both in time and space, focusing only on these
simulations may not give an adequate overall view of the
Arctic conditions. In this section we will therefore study the
sensitivity of our results to the model LWP through several
sensitivity experiments not directly linked to the SHEBA
campaign. In addition to annual, winter and summer
averages the sensitivity experiments include spring averages
(January to April) to show that results from the polluted
spring months are in agreement with what is presented in
section 4.
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Figure 9. Simulated anthropogenic change in cloud forcing at the surface (W/m2) from preindustrial
times to the present day. (a) DLWCFS, winter season (October–May). (b) DLWCFS, summer season
(June–September). (c) DSWCFS, summer season. (d) DNet CFS, summer season. Note that the color
scales are reversed in Figures 9c and 9d.
5.1. Comparison With Earlier Findings
5.1.1. Longwave
[73] The simulated annual increase in surface LW cloud
forcing of 0.55 W/m2 from preindustrial times until today is
one order of magnitude less than the change in this forcing
suggested by GZ06 and LV06. In these two articles, increases
of between 3.3 and 8.2 W/m2 were found when going from
pristine to polluted conditions under cloudy skies. We will in
the following paragraphs discuss possible reasons for this
discrepancy.
[74] First, the GZ06 and LV06 studies show the radiative
effect of increased aerosol levels in a certain area and under
certain conditions. We, however, study the overall change in
surface cloud forcing under all conditions and over the
entire Arctic region. The different goals of the studies also
result in fundamental differences in the approach used. First,
GZ06 and LV06 have looked at specific conditions for
cloud type and pollution, and it may be that these favorable
conditions are met too seldom or over too short time periods
to affect our monthly averaged results. If this is the case,
instantaneous results should include signals of change that
are significantly larger than our seasonal averages. The
fraction of time with changes in LWCFS above 3.3 W/m2 in
the Arctic region is plotted in Figure 10. The value 3.3 W/m2
is the lower limit for the increase in surface flux found by
GZ06 and LV06. The plot shows that changes of this magnitude occur throughout the year and the 3‐D model thus
simulates changes that are consistent with the range observed
by GZ06 and LV06. The fraction of time this occurs, however, is very limited, with a peak of approximately 4% in late
summer/early fall. Results around Barrow, Alaska, show
similar seasonal variation and changes of the same order of
magnitude as the Arctic average.
[75] Second, the clean and the polluted scenarios found in
GZ06 and LV06 contain the lower and the upper quartile of
present‐day aerosol concentrations respectively. This means
that they compare situations with especially large differences in aerosol conditions, while this study compares all
conditions of the present‐day regime to the clean prein-
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Figure 10. Fraction of time that the change in LW cloud
forcing at the surface from preindustrial times to the present
day is greater than 3.3 W/m2, Arctic average.
dustrial scenario. This will influence the magnitude of
change found in the surface LWCF. It is difficult to say
whether the different approaches used in the studies are
sufficient to explain why our average results are lower than
the earlier findings, as there is no information on the fraction
of time studied in the GZ06 and LV06 articles.
[76] The average results found in our study may also be
influenced by possible model artifacts. In the following
paragraphs, we investigate whether features of the LW
radiation scheme and the simulated cloud cover and cloud
properties can explain the differences in results. We used the
1‐D model to study whether the LW radiation scheme itself
was capable of reproducing the findings of GZ06, using
their observed changes in re and LWP as input. In doing this
we forced the simulated clouds to be similar to the ones
observed; the clouds are at low levels (below 1.5 km) and
are all liquid. The model was run with cloud parameter input
similar to what was observed under both pristine (re =
12.9 mm and LWP = 31.1 g/m2) and polluted conditions (re =
9.9 mm and LWP = 33.5 g/m2). GZ06 found changes in
LWCFS of between 3.3 and 5.2 W/m2, while the 1‐D model
simulates a change of 2.1 to 2.6 W/m2 depending on cloud
base height and season. The difference between observed and
modeled changes may be due to the temperature profiles of
the 1‐D model, which are averaged north of 65.5°N, not being
representative for the area studied by GZ06. It may also be
due to the fact that in these 1‐D tests we simulate July and
January only and therefore do not get an annual mean as
presented in GZ06. Despite the noted difference, the results
are of the same order of magnitude as findings by GZ06. This
suggests that the LW radiation scheme used both in the 1‐D
and the 3‐D model reacts to changes in LWP and re in
accordance with observations. The radiation scheme itself is
therefore not likely to cause the large discrepancy between the
3‐D model results and observations.
[77] The LW indirect effect at the surface will also be
affected by the simulated cloud fraction and the sensitivity
of cloud LW emissivity to changes in cloud parameters. As
noted in section 3.1 the CAM‐Oslo cloud fraction is lower
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than most observations, and underestimation of the radiative
effect of cloud‐aerosol interactions is likely (see Figure 1a).
However, a discrepancy of up to 30% in cloud cover alone
is not enough to explain the difference in results found
between this and earlier studies. The sensitivity of the LW
emissivity will also influence the LW aerosol indirect effect.
It increases with decreasing LWP and becomes especially
large for LWP below 20 g/m2 (Figure 8). Curry and Herman
[1985] observed from aircraft measurements that the LWP
of the Arctic stratus is frequently below this value. In our
simulations this occurs 36% of the time annually when
clouds are present and we cannot rule out that this time
fraction may be too low. Based on this and the underestimated cloud cover it is possible that the time fraction of
4% found to have surface indirect effects consistent with the
findings of GZ06 and LV06 is somewhat underestimated.
[78] In addition, the results will be affected by the magnitude of change in cloud parameters with increasing aerosol
load. In sections 4.2 and 4.3 we found that the changes in re
and LWP averaged in height are consistent with observations. Although the integrated LWP includes changes in
water amounts at all altitudes, the liquid water amount
changes most close to the surface (Figure 6b) and it is at
these levels that we expect the largest influence on the LW
cloud forcing. The effective radius, on the other hand,
changes much less in surface layers than it does averaged in
height. While GZ06 found a decrease in re of 3 mm between
pristine and polluted conditions, our annually averaged
results show a change of 0.6 to 1.0 mm in layers important to
the LWCFS. However, annually around Barrow, Alaska, the
model reproduces the observed reductions in low level cloud
effective radius 10% of the time.
[79] One final aspect that may lead to discrepancies in
results is differences in the weather and temperature conditions between the model and the observed cases. The
change in LW surface cloud forcing with pollution is
influenced by the temperature of the cloud base. If the
vertical temperature profiles in our simulations differ from
those common at the measuring sites used by GZ06 and
LV06, it will affect the results. However, large systematic
biases would be needed for this to greatly influence the
results. Additionally, the results shown in this section are
averaged over 5 years and no particular meteorological
event or temperature anomaly will affect the average results.
[80] In summary, it is clear that there are significant differences between this and the two earlier studies of the LW
indirect at the Arctic surface. While our study aims to show
the overall importance of the phenomenon, the GZ06 and
LV06 studies show its magnitude under specific conditions.
In addition to the differences between the studies the low
change in surface LWCF may be influenced by an underestimated cloud fraction and by a possible underestimation
of the frequency of the most sensitive clouds in our simulations. However, if the simulated indirect effect at the
surface is to reach the values found by GZ06 and LV06,
large changes are needed in these parameters.
5.1.2. Shortwave
[81] There has been less emphasis on the SW than on the
LW indirect effect in the Arctic, and observational data
similar to what were used by GZ06 and LV06 to study the
LW effect are not available for the visible through near‐
infrared wavelengths. Lubin and Vogelmann [2007] have,
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however, simulated the SW first indirect effect during
springtime for water clouds at four different Arctic latitudes.
They used a 179‐band SW discrete‐ordinates method and
used changes in re as described by GZ06. In doing this, they
found that for March and April the changes in the SW
radiative flux at the surface due to the first indirect effect are
comparable in magnitude to the increased LW flux found by
GZ06 and LV06. For May and June, the decrease in the SW
surface flux was larger than the increase in the LW, meaning
that the simulated net surface radiation under cloudy skies
decreases due to the first indirect effect. These findings are
consistent with our results (section 4).
5.2. Sensitivity to Simulated LWP and IWP
[82] We now investigate whether our results are sensitive to
the manner in which the model LWP is reduced (section 3.5)
or to the magnitude of these reductions. Table 3 shows the
change in surface cloud forcing with anthropogenic aerosols
for several simulations in which different modifications of
the LWP and the IWP were used.
[83] From Table 3 it is clear that the manner in which the
model LWP is reduced does not greatly affect the anthropogenic change in surface cloud forcing. Results from the
standard CAM‐Oslo are shown under the case name “std.
LWP” in the second column of Table 3. Columns 3 to 6 list
results from simulations where the LWP was decreased by a
factor of 5 or 10 through either reducing the cloud droplet
number concentration (LWP/5(10) DCDNC) or through
reducing the cloud droplet effective radius (LWP/5(10) Dre).
As the model re was found to be consistent with observations (section 3.5), reducing this quantity is not something
we consider to be physically accurate but rather a test of the
other “extreme” way of changing the LWP besides reducing
the CDNC.
[84] Reducing the LWP by a factor of 10 brings it well
below the values observed during the SHEBA campaign
(Figure 1b), and we consider these simulations to be tests
where the clouds are in general too thin and therefore too
sensitive to changes in CCN concentration. The changes in
LW cloud forcing simulated with these model versions are
larger than they are for simulations in which the LWP was
reduced by a factor of 5 (see Table 3 under case names
“LWP/10(5) DCDNC” and “LWP/10(5) Dre”). One exception is the wintertime change in LWCFS labeled “LWP/10
Dre,” in which reductions in cloud droplet size bring the
effective radii more frequently below 10 mm and therefore
make the LW emissivity less sensitive to changes in re
(equation (5)). Unlike in the LW, the changes in SW cloud
forcing are smaller in magnitude for LWP/10 simulations
than for simulations with smaller reductions in LWP. The
SWCFS is highly influenced by the absolute changes in re and
LWP being smaller in LWP/10 simulations than in LWP/5
simulations, while the LWCFS is influenced by a large
increase in the sensitivity of the LW cloud emissivity to
changes in re and LWP for low LWPs (see Figure 8). Note
that the case where LWP is reduced by a factor of 10 through
reducing the CDNC is the only case that simulates an increase
in surface cloud forcing with pollution on an annual basis
(0.12 W/m2).
[85] Results from simulations where the LWP is reduced
by modifying the autoconversion are shown in column 7
under the case name “1st Auto‐conv.” (r3lc = 10 mm and
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autlim = 0.5 mm d−1, see section 3.5). The LWCFS changes
less from preindustrial times to present day in this case than
in the case presented in section 4 (“LWP/5 DCDNC”),
while the opposite is true for the SWCFS. We also ran a test
where the ice fraction of the cloud (fice) was increased as in
section 3.5 (“Fice+LWP/5 DCDNC”). This case shows
smaller changes in both LW and SW cloud forcing at the
surface than results discussed in section 4. Table 3 further
contains results from modifications of both the autoconversion and the cloud particle ice fraction as described in
section 3.5 (“Auto‐conv.+Fice”). This simulation behaves
similarly to the “1st Auto‐conv.” simulation. Neither of
these cases point to an increased warming effect of the
Arctic clouds due to anthropogenic aerosols, and the magnitude of change is relatively insensitive to how the model
liquid water path is reduced.
[86] In summary Table 3 contains results both from simulations where the cloud LWP is reduced in crude manners by
simply reducing the size of the cloud particles or the CDNC
and from simulations where it is reduced in more physically
accurate manners. None of them show signals of change
significantly larger than what we found using the version in
which the LWP was reduced by a factor of 5 through
reductions in the CDNC.
5.3. Sensitivity to the Treatment of LW Absorption
Efficiency
[87] In section 2.2 we presented a new parametrization of
the LW absorption efficiency, Qa, as part of the LW
absorption coefficient, kabs. We will now investigate
whether our results are sensitive to this parametrization.
[88] As noted in section 2.2, the parametrization of Qa is a
simplification because we ignore the effects of the cloud
droplet spectrum and the thermal radiation wavelength
spectrum. One consequence of this may be that we overestimate the sensitivity of Qa to droplet size for small droplets
and therefore underestimate the sensitivity of kabs and the LW
emissivity to droplet size for re < 10 mm (kabs = 34 QL rae ¼ 34 0:1
L ).
We ran a set of test simulations in which the Qa was (unrealistically) set to one for all effective radii rendering kabs
sensitive to cloud droplet size for all re as follows:
kabs ¼
3 1
; f or all re :
4 L re
ð15Þ
The results of these simulations show a slightly increased
change in LWCFS compared to the standard “LWP/5
DCDNC” run (see Table 3 under case name “Qa = 1 LWP/5
DCDNC”), but the results do not contradict our earlier
findings. We conclude that the Qa parametrization for re <
10 mm is not what causes the small simulated changes in
LW cloud forcing at the surface from preindustrial times
until today.
5.4. Sensitivity to Aerosol Concentration
[89] In this section we examine whether the simulated
change in surface cloud forcing is underestimated because
of low aerosol concentrations (Figure 4). To do this we
increase the concentrations by either decreasing the in‐cloud
scavenging or by increasing the emissions of SO4 precursors. Results shown in this subsection are from simula-
15 of 19
16 of 19
0.16
−2.07
−1.90
0.04
−0.13
−0.09
D LWCFS
D SWCFS
D Net CFS
D LWCFS
D SWCFS
D Net CFS
0.19
−0.11
0.08
0.99
−2.17
−1.18
0.33
−0.19
0.14
0.06
−0.10
−0.04
0.53
−2.07
−1.55
0.14
−0.20
−0.06
0.27
−0.83
−0.56
LWP/5
Dre
4
0.28
−0.07
0.21
1.46
−1.69
−0.23
0.44
−0.15
0.29
0.07
−0.09
−0.02
0.65
−1.96
−1.31
0.13
−0.18
−0.05
0.30
−0.78
−0.47
LWP/10
Dre
LWP/10
DCDNC
0.78
−0.66
0.12
6
5
0.07
−0.16
−0.09
0.38
−3.01
−2.63
0.14
−0.29
−0.16
0.22
−1.20
−0.98
0.17
−0.21
−0.04
0.59
−2.50
−1.91
0.07
−0.12
−0.05
Winter Average
0.15
−0.14
0.02
Summer Average
0.57
−1.28
−0.72
Spring Average
0.09
−0.08
0.01
Auto‐conv. +
Fice
9
0.31
−0.97
−0.66
Fice + LWP/5
DCDNC
8
Annual Average
0.29
−0.52
−0.23
1st
Auto‐Conv.
7
0.24
−0.09
0.15
1.02
−2.12
−1.09
0.40
−0.19
0.21
0.61
−0.83
−0.23
Qa = 1.0 LWP/5
DCDNC
10
Sensitivity
to Model Qa
b
11
0.14
−0.07
0.06
0.87
−2.40
−1.53
0.24
−0.16
0.07
0.45
−0.91
−0.46
0.27
−0.16
0.11
1.53
−3.28
−1.74
0.51
−0.29
0.22
0.85
−1.29
−0.44
2SOx ‐ PI LWP/5
DCDNC
12
Sensitivity to Model
Aerosol Concentration
In‐Cloud Scav. +
LWP/5 DCDNC
See sections 5.2, 5.3, and 5.4 for details.
Results presented in the last column are from a simulation where the emissions of SO2 from fossil fuel combustion are doubled compared to present‐day emissions.
a
0.07
−0.22
−0.15
0.10
−0.84
−0.74
D LWCFS
D SWCFS
D Net CFS
D LWCFS
D SWCFS
D Net CFS
LWP/5
DCDNC
Std.
LWP
Case Name
0.55
−0.85
−0.30
3
2
Column
Number
Sensitivity
to Model LWP
Table 3. Simulated Changes in Surface Cloud Forcing Between Preindustrial and Present‐Day Conditions (W/m2)a,b
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ALTERSKJÆR ET AL.: AEROSOL INDIRECT EFFECTS IN THE ARCTIC
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ALTERSKJÆR ET AL.: AEROSOL INDIRECT EFFECTS IN THE ARCTIC
tions in which the LWP is reduced by a factor of 5 through
DCDNC.
[90] The high liquid water content of the CAM‐Oslo
model in the Arctic may lead to an overestimated in‐cloud
scavenging. In order to check whether this is of importance
we ran test runs in which the in‐cloud scavenging coefficient was reduced from 1 to 0.1, thus reducing the wet
deposition of SO4. This allows us to investigate whether the
low simulated change in cloud forcing is due to an underestimation of CCN available to affect cloud properties.
Results from these runs show average SO4 concentrations
that exceed the observational means by 201% at the Zeppelin
station, 228% at Janiskoski, 223% at Alert, and 176% at
Barrow.
[91] The change in cloud forcing simulated with reduced
in‐cloud scavenging shows that the increased concentration
of SO4 does not greatly affect the indirect forcing (see Table 3
under case name “In‐cloud scav.+LWP/5 DCDNC”).
Comparing these results to results under “LWP/5 DCDNC”
we see that the LWCFS changes less than for simulations
where the scavenging is left unchanged, while the SWCFS
changes more. This can be explained by the reduction in
in‐cloud scavenging leading to reduced effective radii and
increased LWP of clouds preindustrially (Dre = −0.4 mm,
DLWP = 2.3 g/m2) as well as in the present day.
[92] The sensitivity of our results to aerosol concentrations was tested further through simulations in which the
present‐day emissions of SO2 from fossil fuel combustion
were doubled (2SOx) (see Table 1). This led to an increase
in sulfate burden of approximately the same magnitude as
the increase from preindustrial times until the present day
(DSO4(2SOx ‐ PD) = 1.07 × 10−6 kg S/m2 versus DSO4
(PD ‐ PI) = 0.89 × 10−6 kg S/m2). Results from this simulation are shown under case name “2SOx‐PI LWP/5
DCDNC” in Table 3. Even though the difference in SO4
concentration between the runs nearly doubles, the forcing
only increases by about 50%. This experiment shows that
even with significantly increased concentrations of SO4, the
signals of change in LWCFS are not within the range found
by GZ06 and LV06. The SW effects dominate results from
this simulation as well as most others. Only during winter is
the net radiative effect of increased aerosol levels positive.
6. Summary and Conclusions
[93] The observed increase in surface air temperature over
the past decades is almost twice as large in the Arctic as in the
rest of the world [e.g., Graversen et al., 2008]. Garrett and
Zhao [2006] and Lubin and Vogelmann [2006] suggest that
aerosol‐cloud interactions may contribute to the observed
temperature amplification in this region. In this study we
have investigated the overall importance of the suggested
increase in surface radiative flux due to increased CCN
concentrations in clouds.
[94] Using the CAM‐Oslo global climate model we have
studied simulated changes in the radiative balance at the
Arctic surface due to aerosol‐cloud interactions. The simulated cloud cover, cloud water path, and cloud radiative
forcing were verified against observations and model modifications were made to better suit the model to the focus of
our study. The simulated SO4 concentrations were compared
both to observations and to the median of 10 models par-
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ticipating in the AeroCom project (section 3.2). We found
that results from the CAM‐Oslo model were in better
agreement with observations than the AeroCom model
median and conclude that the CAM‐Oslo global climate
model is well suited for this study. Our results show that the
indirect effects of anthropogenic aerosols are close to the
same magnitude in the LW and the SW, with a net result
of −0.30 W/m2. We have conducted several sensitivity
experiments that show that our findings are robust against
model assumptions, changes in cloud properties, and aerosol
concentrations.
[95] Below is a summary of key findings concerning
aerosol‐indirect effect at the Arctic surface. Numbers in
parentheses give the minimum and the maximum results
from the sensitivity experiments.
[96] 1. The simulated increase in LW cloud forcing at the
surface due to anthropogenic aerosols averages to 0.55
(0.10 to 0.85) W/m2 annually, to 0.99 (0.16 to 1.53) W/m2
from June to September, and to 0.33 (0.07 to 0.51) W/m2
from October to May. The seasonal variation is caused by
larger changes in cloud emissivity in summer than in winter,
combined with high fractions of low clouds in summer.
[97] 2. The simulated LW indirect effect is one order of
magnitude lower than suggested by Garrett and Zhao [2006]
and Lubin and Vogelmann [2006]. This discrepancy may be
caused by a combination of effects. GZ06 and LV06 showed
the magnitude of change in surface LWCF under specific
conditions, whereas this study includes a variety of conditions at all times of the year providing results for the average
changes. In addition, underestimation of cloud cover and a
possible underestimation of the frequency of the most sensitive clouds may influence the results. However, large
changes are needed in these parameters for the LW indirect
effect to reach the values found by GZ06 and LV06.
[98] 3. The corresponding simulated change in surface
SW cloud forcing due to anthropogenic aerosols averages to
−0.85 (−1.29 to −0.52) W/m2 annually and −2.17 (−3.28 to
−1.28) W/m2 in summer.
[99] 4. The annual change in surface net cloud forcing
averages to −0.30 (−0.98 to 0.12) W/m2. During the summer, the net surface cloud forcing decreases by 1.18 (2.63 to
0.23) W/m2, while in the winter LW effects dominate, and
changes in cloud properties due to anthropogenic aerosols
increase the surface radiative flux by 0.14 (−0.15 to 0.29)
W/m2. The net cloud forcing will be particularly sensitive to
overestimation of the summer cloud cover as the negative
SW forcing is strong during this season. A possible overestimation of the SWCFS in summer may lead to a negative
bias in the net aerosol indirect effect.
[100] 5. The sensitivity experiments show that the annually averaged changes in net CFS are positive only in 1 of 11
simulations and our results suggest that increased levels of
anthropogenic aerosols in Arctic clouds may lead to a small
decrease in the radiative flux at the surface. Our general
findings depend little on model assumptions, changes in
cloud properties and aerosol concentrations.
[101] In recent years (after the fall of the Soviet Union) the
emissions of the SO4 precursor SO2 have decreased dramatically in Europe and Russia [Karnieli et al., 2009], and
Quinn et al. [2007] found that the concentrations of non‐
sea‐salt SO4 decreased by 30%–70% from the early 1990s
to present in the Canadian, Norwegian, and Finnish Arctic.
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Additionally, Sharma et al. [2006] found a clear downward
trend in concentrations of equivalent black carbon (BC) in
the high Arctic. From the findings of this study it is likely
that a reduction in CCN amount in the Arctic will decrease
the magnitude of the negative indirect effect and therefore in
sum work to increase the net positive cloud forcing found in
this region. The less pollution that enters the Arctic region,
the larger the increase in surface radiative flux compared to
the present day.
[102] Furthermore, the expected reduction in the polar ice
cap and therefore in the surface albedo may increase the
importance of the negative SW surface cloud forcing. Larger
areas of open waters may, however, affect both the cloud
fraction and the thickness of the clouds in the region and it is
difficult to predict the net effect of sea ice reductions.
[103] Although the simulated changes in surface cloud
forcing are smaller than what is found in earlier studies, they
are of the same order of magnitude as the BC surface forcing
via snow and ice albedos in sea ice areas. Flanner et al.
[2007] estimate that the annual mean of the instantaneous
surface forcing of BC on snow in these areas are around
0.20 W/m2 during a year of average BC emissions. The
changes in Arctic surface cloud forcing due to anthropogenic aerosols may therefore be of importance and should be
studied further. To enable this, the accuracy of climate
models needs to be improved, especially in dealing with
cloud water amount and conversion between liquid water
and ice particles, and a higher frequency and larger geographical spread in Arctic measuring campaigns are needed.
The current lack of comprehensive observations limits the
possibility of verifying and improving current climate
models.
[104] Acknowledgments. This study was partly funded by the
Norwegian Research Council through the projects POLARCAT (grant
175916) and NorClim (grant 178246) and has received support from the
Norwegian Research Council’s Programme for Supercomputing through
a grant of computing time. The authors thank AeroCom for access to their
data and are also grateful to Alf Kirkevåg, Øyvind Seland, Frode Stordal,
Terje Berntsen, and Gunnar Myhre for helpful discussions. Finally, we
are thankful to three anonymous reviewers whose comments led to significant improvements of the paper.
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