JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/,
1
2
Do Anthropogenic Aerosols Enhance or Suppress the
Surface Cloud Forcing in the Arctic?
1
1
K. Alterskjær, J. E. Kristjánsson, C. Hoose
1, 2
K. Alterskjær, Department of Geosciences, Meteorology and Oceanography Section,
University of Oslo, P.O.Box 1022, 0315 Oslo, NORWAY. (karialt@geo.uio.no)
C. Hoose, Department of Geosciences, Meteorology and Oceanography Section,
University of Oslo, P.O.Box 1022, 0315 Oslo, NORWAY.
J. E. Kristjánsson, Department of Geosciences, Meteorology and Oceanography Section,
University of Oslo, P.O.Box 1022, 0315 Oslo, NORWAY.
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.
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Abstract.
Earlier studies suggest that aerosol-cloud interactions may have contributed
5
to the increase in surface air temperature recently observed in the Arctic.
6
While those studies focused on longwave effects of strong pollution events
7
around Barrow, Alaska, we use a global climate model (CAM-Oslo) to study
8
the annual and seasonal net radiative effect of aerosol-cloud interactions over
9
the entire Arctic region. The model is validated against and adjusted to match
10
observations from the SHEBA campaign along with measuring stations within
11
the Arctic region. Several sensitivity experiments were conducted which in-
12
cluded changes in both cloud properties and aerosol concentrations. Results
13
show that the longwave indirect effect at the surface lies between 0.10 W/m2
14
and 0.85 W/m2 averaged annually north of 71◦ N, while the shortwave indi-
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rect effect lies between −1.29 W/m2 and −0.52 W/m2 . Due to longwave dom-
16
inance in winter, 6 out of 11 simulations give a positive change in net cloud
17
forcing between October and May (−0.16 W/m2 to 0.29 W/m2 ), while the
18
change in forcing averaged over the summer months is negative for all model
19
simulations (from −2.63 W/m2 to −0.23 W/m2 ). The annually averaged change
20
in net cloud forcing at the surface is negative in 10 out of 11 simulations, ly-
21
ing between −0.98 W/m2 and 0.12 W/m2 . In conclusion our results point
22
to a small decrease in the surface radiative flux due to the aerosol indirect
23
effect in the Arctic, but these estimates are subject to uncertainties in the
24
frequency of thin clouds and biases in the estimated cloud cover.
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1. Introduction
25
The Arctic region is particularly sensitive to climate change due to the positive feedback
26
between surface temperature and surface albedo [Wang and Key, 2005] and the increase
27
in air temperature in the bottom layers of the atmosphere over the last decades is almost
28
twice as large here as in the rest of the world [e. g. Graversen et al., 2008]. Due to the
29
rapid changes found in this region, there has been an increasing scientific interest in the
30
Arctic in general. This was made evident by the implementation of the International
31
Polar Year in 2007-2008.
32
Several factors contribute to climate change in the Arctic, among these are the increased
33
surface radiative flux resulting from increasing anthropogenic greenhouse gas concentra-
34
tions and reduced surface albedo due to soot deposition on snow. Another possible cause
35
discussed in two empirical studies published in Nature in 2006, is the change in Arctic
36
clouds due to human activities (Garrett and Zhao [2006] (GZ06) and Lubin and Vogel-
37
mann [2006] (LV06)). Of particular interest is the influence of anthropogenic emissions
38
of pollution on the thin, non-opaque clouds common in the Arctic region.
39
Clouds in the Arctic differ from clouds elsewhere in that they have a net warming effect
40
at the surface - there is positive net cloud forcing [Intrieri et al., 2002a]. This happens
41
because the longwave (LW) radiation dominates the radiation regime, due to large solar
42
zenith angles throughout the year, combined with a high surface albedo. Consequently,
43
the LW radiation plays a much more important role in this region than at lower latitudes,
44
and the greenhouse effect of clouds in the LW leads to a net surface warming by clouds in
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the Arctic. The shortwave (SW) radiation, however, dominates during mid-summer and
46
clouds have a net cooling effect at the surface [Intrieri et al., 2002a].
47
GZ06 and LV06 focused on what is known as the first aerosol indirect effect or the
48
Twomey effect [Twomey, 1977]. This effect is described as an increase in the cloud optical
49
depth through pollution aerosols leading to more numerous, smaller sized droplets, while
50
the water content of the cloud is assumed to be constant. As is the case for the cloud
51
optical depth, the cloud LW emissivity increases with such a change in cloud properties.
52
Based on this GZ06 and LV06 suggested that anthropogenic emissions of pollution might
53
increase the LW emissivity of the thin, non-opaque clouds common in the Arctic causing
54
their warming effect to increase. Results from their studies suggest that the LW cloud
55
forcing at the surface increases by 3.3 to 8.2 W/m2 in the presence of large anthropogenic
56
emissions of sulfate precursors, due to increases in the LW emissivity of low level clouds.
57
GZ06 and LV06 also suggested a possible contribution from the second indirect effect,
58
through increased liquid water content, and even changes in cloud amount.
59
In this study we will examine how the surface cloud forcing in the Arctic region has
60
changed due to interactions between clouds and anthropogenic aerosols. We will study
61
this with focus on the time of year, and we will compare our results to earlier findings
62
whenever possible. In the following section we describe the model tools and methods used
63
in our study. Section 3 presents basic features of cloud cover, sulfate concentration, cloud
64
water path and cloud radiative forcing as simulated by the model, comparing our results
65
to observations. The influence of anthropogenic emissions on the sulfate concentration,
66
cloud properties and cloud radiative forcing is investigated in section 4. We discuss the
67
results in section 5 and summarize our findings with conclusions in section 6.
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2. Model Tools and Methods
68
GZ06 and LV06 both based their findings on observational data that have been gathered
69
under specific atmospheric conditions from the area around Barrow, on the north slope of
70
Alaska. In this study we wish to examine the effects of cloud-aerosol interactions in the
71
Arctic region as a whole. In order to do so, our best option as of today is to use numerical
72
modeling. The use of a three dimensional (3D) climate model allows us to study both
73
the spatial variations in cloud-aerosol interactions and the effect of these interactions over
74
time and for different seasons. Long term averages will show the overall importance of
75
the indirect effects. The use of a one dimensional model allows us to study how specific
76
changes in cloud properties affect the surface cloud forcing in the Arctic.
2.1. Model Description: CAM-Oslo
77
The atmospheric general circulation model used here is the CAM-Oslo, extended from
78
NCAR-CAM3 (National Center for Atmospheric Research - Community Atmosphere
79
Model version 3) [Collins et al., 2006a]. The CAM-Oslo includes modules for aerosol life-
80
cycling and interactions with radiation described by Seland et al. [2008]. The model also
81
includes a prognostic calculation of cloud droplet number concentration (CDNC) in which
82
droplet activation is based on chemical composition, size distribution and parametrized
83
subgrid-scale vertical velocity [Storelvmo et al., 2006; Hoose et al., 2009]. It is run as a
84
stand-alone atmospheric model with prescribed climatological sea surface temperatures.
85
The horizontal resolution of the model is approximately 2.8◦ x 2.8◦ (T42 spectral trunca-
86
tion), and there are 26 layers in the vertical. The vertical coordinate is a hybrid coordinate
87
that follows the terrain in the lower troposphere and gradually becomes a pressure coor-
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dinate when entering the lower stratosphere. A 20 minute time step is used both for the
89
dynamics and the physics.
2.2. CAM-Oslo Model Modifications
90
Several modifications were made to better suit the model to the focus of our study.
91
Cloud-aerosol interactions lead to changes in cloud microphysics and therefore in cloud
92
radiative properties. Before model modification, calculations in the LW part of the spec-
93
trum did not depend on the size of cloud droplets. This was because this dependence is
94
insignificant when SW radiation is dominating the radiation regime and because water
95
clouds at low- and mid-latitudes are often optically thick in the LW. The emissivity of
96
these clouds is therefore not influenced by cloud droplet size. In the Arctic, the LW ra-
97
diation is much more important, and optically thin clouds persist for large parts of the
98
year. We can therefore no longer neglect the LW emissivity dependence on cloud droplet
99
size. We derived an expression for the dependency of the LW absorption coefficient, and
100
therefore the LW emissivity, on cloud droplet size and implemented this expression in the
101
model, enhancing its capability to accurately simulate Arctic conditions.
102
The LW cloud emissivity is given by Collins et al. [2004] (section 4.9.5):
ϵ = 1 − e−1.66∗kabs ∗CW P
(1)
103
where CWP is the cloud water path i.e. the integrated total cloud water content in a
104
column above a certain surface area, in units of g/m2 , while 1.66 is the diffusivity factor
105
and kabs is the mass absorption coefficient for condensed water. For mixed phased clouds
106
kabs will be a weighted mean of the absorption coefficients for liquid and solid particles,
107
respectively. In this study we consider the aerosol influence on liquid cloud particles only.
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It is therefore the liquid water mass absorption coefficient (kabs,liquid ) which we express
109
in terms of effective radius (re =
110
emissivity to depend on droplet size. The absorption coefficient for liquid particles is given
111
by:
kabs,liquid =
∫
∫
πr3 n(r)dr/ πr2 n(r)dr), in order for the LW cloud
βa
LW C
(2)
112
where βa is the volume absorption coefficient for liquid droplets and LWC is the liquid
113
water content of the cloud in units of mass per unit volume (LW C = 43 πρL r3 n(r)dr,
114
where ρL is the bulk density of liquid water). The volume absorption coefficient is given
115
by:
∫
∫
βa = π
∞
0
n(r)r2 Qa (r)dr
(3)
116
where n(r)dr is the cloud droplet size distribution as a function of radius, r, while Qa is
117
the absorption efficiency. Based on Mie calculations following equations (3) and (6) in
118
Chýlek et al. [1992], the model Qa is approximated as follows: For radii greater than a
119
certain rmax , Qa is constant and equal to 1.0, while for r smaller than rmax , Qa increases
120
linearly with r (Qa =a1 r). The parameter rmax varies with wavelength because Qa is
121
wavelength dependent (Paltridge and Platt [1976, Figure 8.4] and Garrett et al. [2002]).
122
However, in some general circulation models, including the CAM-Oslo model, the cloud
123
emissivity is constant over the entire LW spectrum and one representative rmax must be
124
used. According to Paltridge and Platt [1976, p. 200] the value of the mass absorption
125
coefficient for 11 µm is near the average value for the entire window region from 8 µm to
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14 µm, and from Wien’s Displacement Law [e. g. Liou, 2002] we know that the wavelength
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for the intensity peak of the earth’s radiation field lies within this window region. Around
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a wavelength of 11 µm rmax can be approximated by 10 µm and a1 by 0.1 [Garrett et al.,
129
2002]. These are the values used over the whole LW spectrum in our calculations.
130
131
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.
βa ≈ πQa
{
132
∫
∞
n(r)r2 dr,
0
Qa = 0.1 re [µm−1 ] f or re < 10µm
Qa = 1.0
f or re ≥ 10µm
(4)
Solving this integral, using the definition of effective radius and LWC, leaves:
3 Qa
,
4
ρ
r
L
e
{
Qa = 0.1 re [µm−1 ] f or re < 10µm
Qa = 1.0
f or re ≥ 10µm
kabs,liquid ≈
(5)
133
The mass absorption coefficient and hence the cloud LW emissivity thus depends on the
134
droplet effective radius. Expression 5 is used in all model simulations of this study unless
135
otherwise stated. Approximating Qa in this manner is a simplification. In reality there
136
is a continuum between the two regimes both because clouds have a droplet spectrum
137
and because thermal radiation has a wavelength spectrum [Garrett et al., 2002]. The
138
sensitivity of the LW emissivity to changes in cloud droplet size may therefore be affected
139
by our assumptions. This will be investigated closer in section 5.3.
2.3. CAM-Oslo Model Setup
140
Two different emission fields were used in order to study the effect of increased amounts
141
of pollution on cloud forcing: One field based on present day (year 2000) emissions, hereby
142
referred to as PD, and another field based on pre-industrial emissions, hereby referred to
143
as PI. They are both based on the AeroCom emissions [Dentener et al., 2006]. However,
144
due to uncertainties about pre-industrial forest fires, we modified the AeroCom PI field
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such that the emissions prior to the industrial revolution were nowhere higher than the
146
present day emissions. The total global annual emissions of each species in each of the
147
emission fields are listed in Table 1.
148
The model was run for five years and the results shown are averaged over these years.
149
Such a long integration time diminishes variations due to specific weather events. The
150
summer season here includes the months of June, July, August and September, while
151
the winter season is an average over the remaining months. The model was run off-line,
152
meaning that the meteorological evolution is the same in all model runs. This allows us
153
to study how the clouds and the radiative balance are changed between emission fields
154
without feedbacks due to the aerosol forcing. The simulated change in cloud forcing
155
with pollution is then only a result of aerosols interacting with the clouds and we avoid
156
noise from synoptic variability in our results. This also implies that all feedbacks due
157
to aerosol induced cloud changes such as the semi-indirect effect and changes in cloud
158
cover are precluded from this investigation. However, as explained in Kristjánsson [2002],
159
the contribution to the indirect effect from instantaneous suppression of precipitation
160
release is accounted for. This is not treated as a feedback in our model because a control
161
simulation propagates the model.
2.4. Model Description: One Dimensional (1D) Model
162
A one dimensional column model was used to study the radiative effects of placing
163
specific clouds in preferred environments. The model input includes cloud parameters
164
such as cloud droplet effective radius and liquid water path (LWP) i.e. the integrated
165
liquid water content in a column above a certain surface area (g/m2 ). The model uses the
166
radiation scheme from the NCAR CCM3 model [Kiehl et al., 1998] to give instantaneous
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values of radiation fluxes. This scheme is very similar to the one used in NCAR-CAM3 and
168
therefore in CAM-Oslo [Collins et al., 2006a]. In addition to cloud parameters the input
169
includes location given by latitude and time of year, and gas and temperature profiles
170
suited for the chosen environment. The output is averaged over the chosen latitude, equal
171
to a 24 hour mean. In this study the model ran with 26 layers in the vertical corresponding
172
to the vertical layers of CAM-Oslo.
2.5. Calculation of Cloud Radiative Forcing
173
The change in cloud forcing due to aerosol-cloud interactions can be taken as a measure
174
of the aerosol indirect effect. Cloud forcing (CF) is defined as “the radiative impact that
175
clouds have on the atmosphere, surface, or top-of-the-atmosphere (TOA) relative to clear
176
skies” [Shupe and Intrieri , 2004]. In the following we will be mainly concerned with the
177
CF at the surface (CFS), which is given by:
LWCFS = N etLWallsky − N etLWclear
(6)
SWCFS = N etSWallsky − N etSWclear
(7)
178
where LWCFS and SWCFS denote the longwave and shortwave cloud forcings at the
179
surface, respectively, NetLW and NetSW denote the net downward flux at the surface,
180
i.e., downward flux minus upward flux, allsky refers to the true atmospheric state includ-
181
ing clouds, while clear refers to a hypothesized atmosphere with all clouds removed but
182
otherwise identical conditions.
183
184
Adding the longwave and shortwave contributions, a net cloud forcing at the surface
(Net CFS) is defined as:
Net CFS = LWCFS + SWCFS
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185
The LWCFS depends mainly on the ability of the clouds to absorb and emit LW radi-
186
ation, and therefore on the LW emissivity (Equation 1) of the cloud. The clouds absorb
187
a fraction of the radiation emitted by the surface and then re-emit energy toward the
188
ground, and because the clouds become optically thick at low LWPs (∼50 g/m2 ) the
189
LWCFS depends mainly on the lowest cloud base. The flux density emitted toward the
190
surface depends on the cloud base temperature, T, and the cloud properties such as cloud
191
particle size and LWP (see the Stefan-Boltzmann law and Equation 1). The higher the
192
emissivity, the larger is the LWCFS.
193
The SWCFS depends mainly on the ability of the clouds to reflect SW radiation, and
194
therefore on the cloud albedo, A. The albedo of a cloud can be approximated by its optical
195
depth, τ , and the asymmetry factor, g, alone [Meador and Weaver , 1980]:
(1 − g)τ
1 + (1 − g)τ
A =
(9)
196
The optical depth depends on cloud droplet effective radius and LWP through [e. g. Liou,
197
2002]:
τ =
198
(10)
This gives:
A =
199
3 LW P
2 ρL re
1
(11)
2
ρL re
1+
3 (1 − g)LW P
The higher the cloud albedo, the more negative is the SWCFS.
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:
∆LWCFS = (N etLWallsky,polluted − N etLWallsky,clean )
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−(N etLWclear,polluted − N etLWclear,clean )
(12)
∆SWCFS = (N etSWallsky,polluted − N etSWallsky,clean )
−(N etSWclear,polluted − N etSWclear,clean )
200
(13)
and the sum of the two is defined as:
∆Net CFS = ∆LWCFS + ∆SWCFS
(14)
201
As the focus of this investigation is on the radiative effect of aerosol-cloud interactions,
202
the change in net flux due to aerosols in clear conditions is not contained in our simula-
203
tions. However, it is clear that the anthropogenic aerosols may influence the Arctic also
204
via the direct effect, i.e., by reflection and absorption of solar radiation. In this paper the
205
term “change in cloud forcing” refers to the aerosol indirect effect.
3. Basic Features of Arctic Clouds and Aerosols
206
In this section we will check whether the model output is consistent with observations.
207
The main focus will be on cloud cover, sulfate, liquid water path and cloud forcing,
208
and observations made during the SHEBA (Surface Heat Budget of the Arctic Ocean)
209
campaign will be an important part of this validation. The SHEBA campaign took place
210
in the Beaufort and Chukchi Seas (from 75.3◦ N, 142.7◦ W to 80.5◦ N, 166◦ W) from October
211
1997 to October 1998 [Intrieri et al., 2002b; Maslanik et al., 2001] and its main observables
212
include the sea ice mass balance and the surface energy balance. The advantage of using
213
this data set is that it comprises one continuous year of data, something not matched by
214
any other campaign this far into the Arctic region.
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3.1. Cloud Cover
215
Observational data gathered by Warren et al. [1988] show that in general the Arc-
216
tic cloud cover has a minimum in wintertime with values just below 50 percent and a
217
maximum in late summer/early fall that peaks around 85 percent (see Figure 1a, black
218
dash-dot line). The seasonal variation in Arctic cloud cover is well reproduced by the
219
CAM-Oslo model. Nevertheless, the model seems to underestimate the total cloud cover
220
from April until December. The largest errors occur during the last half of the year when
221
the cloud cover is underestimated by around 10 percent. The data presented by Warren
222
et al. [1988] are, however, based on ground-based manual observations and are therefore
223
likely to be somewhat inaccurate, both due to the dark season and due to the sparsity of
224
measurements in the remote Arctic region.
225
We also compare simulated average cloud cover to observations made during the SHEBA
226
campaign (see Figure 1a). The SHEBA data were obtained from manual observations,
227
from ground-based LIDAR/RADAR measurements as well as from satellite, and show a
228
large spread between the different observational methods. This highlights the difficulty
229
in determining cloud fraction. From Figure 1a it seems that the CAM-Oslo cloud frac-
230
tion is lower than most observations - sometimes by up to 30%. This may lead to an
231
underestimation of cloud-aerosol interactions and hence of the cloud radiative forcing.
232
Contrary to this, when comparing the simulated fraction of low level Arctic clouds
233
(below 700 hPa) to satellite observations presented by Kay and Gettelman [2009, Figure
234
4] we find that the fraction of these clouds is overestimated during summer (24% between
235
65◦ N and 82◦ N), while it is underestimated in early fall (16%). A possible overestimation
236
in summer may lead to a negative bias in the aerosol indirect effect due to SW dominance,
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while the net effect of an underestimation of cloud cover in September and October is less
238
obvious.
239
The vertical placement of the clouds is also of importance, as the LW cloud forcing is
240
temperature dependent. Observational data from the SHEBA campaign show that the
241
Arctic clouds often lie close to the surface (cloud bases below 1 km) [Intrieri et al., 2002b,
242
Figure 7]. This tendency is well reproduced by the CAM-Oslo model (see Figure 2).
3.2. Particulate Sulfate (SO4 )
243
In this work the terms sulfate and SO4 both refer to particulate sulfate.
244
The simulated present day sulfate burden over the Arctic region is plotted in Figure
245
3a. The data are annually averaged and column integrated and show that the burden is
246
largest over northern Eurasia, gradually decreasing as we move further into the Arctic.
247
This is consistent with the current understanding of the transport of air pollution into
248
the region [Stohl , 2006], namely that pollution in the Arctic mainly originates in Eurasia.
249
The annually averaged burdens of organic and black carbon have the same spatial pattern
250
as sulfate.
251
The surface concentrations of SO4 are verified against measurements taken at sev-
252
eral monitoring stations (Zeppelin, Spitsbergen, Norway (78.9◦ N, 11.9◦ E); Alert, Canada
253
(82.5◦ N, 62.3◦ W); Janiskoski, Russia (69◦ N, 29◦ E); and Barrow, Alaska (71.3◦ N,
254
156.6◦ W)) [AMAP , 2006, chapter 4]. We find that the seasonal variation in SO4 con-
255
centration at all stations is fairly well reproduced by the CAM-Oslo model (Figure 4) -
256
with large concentrations in winter and early spring and minima occurring during sum-
257
mer. These summer minima are caused by a shift in the east-west pressure gradients
258
across Eurasia so that less pollution is transported into the Arctic, combined with an
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increase in precipitation during summer, scavenging the SO4 from the lowest layers of
260
the atmosphere [Barrie, 1986]. Additionally, the Arctic air mass is less stable during
261
the summer than during the winter. This is associated with increased turbulent transfer
262
[Quinn et al., 2008] and thus removal of aerosols through dry and wet deposition.
263
The CAM-Oslo model’s ability to reproduce the mass concentration of SO4 varies over
264
the Arctic region. Figure 4 shows that the surface concentrations at Zeppelin are well
265
reproduced by the model, while simulations for Janiskoski show an overestimation of SO4
266
compared to observations, although the simulated mean SO4 concentrations are seldom
267
larger than the maximum monthly mean observed during the five year period from 1996
268
to 2000. Unlike simulations for stations on the Eurasian side of the Arctic, simulations for
269
the North American sites generally underestimate SO4 compared to observations. There
270
can be several reasons for this underestimation. First of all these sites are further away
271
from the main sources of Arctic SO4 than the Eurasian sites [Stohl , 2006]. This may
272
point to the transport pathways themselves being inaccurate or to important sources
273
of SO4 precursors being ignored. Another reason for the small concentrations in North
274
America may be inaccurate SO4 removal processes. In section 3.3 we will show that the
275
model cloud liquid water path is too high, which may lead to an overestimated in-cloud
276
scavenging of SO4 . For further details on the treatment of scavenging in CAM-Oslo see
277
Seland et al. [2008]. In section 5.4 the sensitivity of our results to the SO4 concentration
278
will be tested both by reducing the in-cloud scavenging and by increasing the emissions
279
of SO4 precursors.
280
We compared the accuracy of our results at Zeppelin and Janiskoski to the accuracy of
281
results from models participating in the AeroCom project (Aerosol Comparisons between
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Observations and Models; Textor et al. [2006]). The red lines in Figure 4 show the model
283
median of ten AeroCom A models simulating surface concentrations of SO4 at Zeppelin
284
and Janiskoski for the year 2000 (medians for Alert and Barrow were not available) [http :
285
//nansen.ipsl.jussieu.f r/AEROCOM/]. Note that the median seasonal variation is
286
opposite to what is observed. It is clear that the CAM-Oslo results are in general a better
287
fit to the observations than the AeroCom model median.
288
Comparing the CAM-Oslo simulated vertical profiles of SO4 to observations is chal-
289
lenging. First of all, measurements of SO4 in the Arctic are limited both in number and
290
in geographical distribution. Secondly, there is large variability in the observations, also
291
when taken with short time intervals in the same regions [e. g. Dreiling and Friederich,
292
1997; Scheuer et al., 2003]. These measurements are generally instantaneous aircraft
293
measurements and are limited both in time and space. This makes it difficult to compare
294
observations of vertical profiles to our monthly averaged profiles.
295
Figure 5a shows the concentration of sulfate with height in terms of µg S per unit vol-
296
ume of air. The values which are annually and zonally averaged over the Arctic region,
297
show that north of 70 to 75◦ N the largest concentrations are found at around 800 to 900
298
hPa. Although we have no averaged observed vertical profiles to verify the concentrations
299
of this cross section, 800 hPa is the height found by Dreiling and Friederich [1997] to
300
have the largest concentration of particles of all sizes. A comparison with Scheuer et al.
301
[2003] shows that the simulated near surface concentrations of SO4 , which are the most
302
important for our study, are of the same order of magnitude as measurements taken dur-
303
ing the TOPSE (Tropospheric Ozone Production about the Spring Equinox Experiment)
304
campaign. The measurements show mean SO4 concentrations in the bottom two kilome-
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305
ters of the atmosphere of between 50 and 230 pptv during springtime, while the simulated
306
values along 70◦ W range between 60 and 170 pptv for the same altitudes.
3.3. Liquid and Ice Water Path
307
Measurements taken during the SHEBA campaign were used by Zhang et al. [2002] to
308
retrieve monthly averaged liquid water paths for the region covered by the campaign. A
309
maximum of around 100 g/m2 was reached in August, when also the cloud fraction reaches
310
its peak value. This is in accordance with the typical range of Arctic LWP only seldom
311
exceeding 150 g/m2 [Löhnert et al., 2003]. A comparison between the monthly averaged
312
LWP (including its uncertainty) retrieved from measurements and the simulated LWP for
313
the SHEBA region shows that the CAM-Oslo overestimates the LWP by a factor of 3 to
314
5, depending on season (see Figure 1b).
315
Model intercomparison studies by Morrison et al. [2009] and Karlsson and Svensson
316
(manuscript in prep.) have found excessive LWPs over the Arctic region in NCAR-CCSM3
317
(Community Climate System Model version 3) [Collins et al., 2006b] and SCAM3, a single-
318
column version of the NCAR-CAM3. In the case studied by Morrison et al. [2009] the
319
SCAM3 simulated LWP averages to 298 g/m2 while the observed LWP ranges from 55 to
320
121 g/m2 , depending on the retrieval method. The model intercomparison by Karlsson
321
and Svensson (manuscript in prep.) shows that the NCAR-CCSM3 simulated LWP over
322
the Arctic Ocean is from 2 to 3.7 times the ensemble model mean LWP. These results
323
suggest that the overestimation of LWP in CAM-Oslo may be linked to problems in the
324
NCAR-CCSM3, as both the CAM-Oslo and the SCAM3 are developed from this model.
325
The excessive simulated liquid water amounts may be caused by several factors. An
326
underestimated auto-conversion rate will lead to little loss of cloud water through precip-
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327
itation. Another possibility is too little conversion from liquid water to ice particles. The
328
overestimated LWP may also be caused by an overestimated transport of moisture into
329
the region or by stably stratified conditions allowing model clouds to become thicker than
330
what occurs in nature. We return to this problem in section 3.5.
331
Ice water path (IWP) retrievals have very high uncertainties. Nevertheless, it should
332
be mentioned that Shupe et al. [2006] found observed IWPs on the order of 42 g/m2 and
333
Morrison et al. [2003] reported IWPs on the order of 34.6 g/m2 for the SHEBA region.
334
Our model has an average IWP of 24.0 g/m2 . Also, Karlsson and Svensson (manuscript
335
in prep.) found that the NCAR-CCSM3 has among the lowest ice water paths of the
336
models in their study. Combined with the positive bias in LWP this may point to a bias
337
in the conversion between solid and liquid particles or a bias in the distinction between
338
solid and liquid particles in our model. Due to the limited amount of measurements in
339
the Arctic combined with high uncertainties we cannot conclude on the exact reason for
340
the too low ice water paths.
3.4. Cloud Forcing
341
The simulated cloud forcing (CF) is compared to observations of Intrieri et al. [2002a],
342
ignoring the turbulent flux that is part of their study. The simulated cloud forcing in the
343
SHEBA region (74◦ -81◦ N and 144◦ -169◦ W [Zhang et al., 2002]) differs significantly from
344
what was measured at SHEBA, especially during the summer (Table 2 under case names
345
’SHEBA’ and ’CAM-Oslo std. LWP’). This is mainly caused by a large difference in the
346
SWCFS between the simulations and observations. However, the simulated LWCFS is
347
also larger than the observed forcing. We know from Figure 1a that the cloud cover is
348
reproduced fairly well by the model, or slightly underestimated. Comparing the model
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349
surface albedo to measurements taken over the SHEBA region [Curry et al., 2000; Intrieri
350
et al., 2002a] we find that it is within reasonable range. Rough monthly estimates of
351
aircraft measured surface albedos from Curry et al. [2000] are 0.76, 0.67 and 0.50 for May,
352
June and July respectively. CAM-Oslo results from the same region and time period are
353
0.78, 0.66 and 0.49, in excellent agreement with the observations. Without time averaged
354
temperature profiles for this area we cannot exclude that the LWCFS is affected by a bias
355
in cloud base temperature. However, based on the simulated forcing being larger than
356
observations in both wavelength ranges, it is likely that the discrepancy between modeled
357
and observed cloud forcing is caused by the optical depth and the emissivity of the clouds
358
being too large.
3.5. Model Modifications to Improve the Simulated LWP and CFS
359
The simulated effective radius around Barrow, Alaska, is 15 µm when averaged below
360
700 hPa, with larger values in summer than in winter (16 µm vs. 14 µm). While comparing
361
our simulated re to observations is challenging because observed values vary significantly,
362
the seasonal variation simulated around Barrow is consistent with the findings of Dong
363
and Mace [2003] in the same region. Additionally, the simulated effective radius in the
364
SHEBA region is 10 µm when averaged annually over the whole vertical column, which
365
is consistent with the findings of Curry et al. [2000]. Consequently it is unlikely that the
366
overestimation of cloud optical depth and emissivity is caused by an underestimation of re
367
(see Equation 10 for τ and Equations 1 and 5 for ϵ). Instead, it is most likely associated
368
with an excessive LWP in the model (Figure 1b).
369
Several approaches were used in order to reduce the liquid water amount in the model.
370
This is of particular importance for our study as the sensitivity of cloud emissivity and
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371
albedo to changes in LWP depends on the amount of water that the clouds initially hold.
372
The goal was to keep the LWP within the range of the observed values and simultaneously
373
find what simulation had cloud forcing closest to the SHEBA measurements of this quan-
374
tity. Modifying the LWP affects the global net radiation, but as we focus on the Arctic
375
region and on the change in cloud forcing between PI and PD, this is not a concern.
376
Different modifications of the auto-conversion parametrization were tried allowing more
377
water to be lost through precipitation. The auto-conversion threshold radius, r3lc , was
378
successively reduced from 15 µm to 10 µm and 7.5 µm. This radius decides the size that
379
cloud particles must reach before the onset of precipitation, as described in Rasch and
380
Kristjánsson [1998, Equation 21]. In addition, we changed the lower limit for which auto-
381
conversion is fully efficient, here named autlim, as described in Kristjánsson [2002, section
382
2.4], from 5.0 mm day−1 [Kristjánsson, 2002] to 0.5 mm day−1 [Rasch and Kristjánsson,
383
1998] and then to 0.0 mm day−1 . This parameter accounts for the decrease in collection
384
efficiency in a cloud droplet distribution that has been modified by precipitation. Results
385
from the simulations with modified auto-conversion can be seen as the light blue and the
386
red lines in Figure 1b. The water amounts are now much closer to the retrieved values, but
387
are still on the high side. From Table 2 it is clear that these simulations still overestimate
388
the SW and the LW components of the surface cloud forcing (case names ’1st auto-conv.’
389
(r3lc = 10 µm and autlim = 0.5 mm day −1 ) and ’2nd auto-conv.’ (r3lc = 7.5 µm and
390
autlim = 0.0 mm day −1 )).
391
We also reduced the cloud liquid water path by modifying both the auto-conversion and
392
the cloud particle ice fraction (’fice’). The auto-conversion threshold radius was changed
393
from 15 to 7.5 µm and a lower limit for fully efficient auto-conversion of 0.0 mm day−1
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394
was used instead of 5.0 mm day−1 . In the standard version of CAM-Oslo the fraction of
395
cloud particles that are solid is temperature dependent and increases linearly from 0 to 1
396
as the temperature decreases form 263K to 233K. In reality the ice fraction is expected
397
to be influenced by aerosol properties. We increased ’fice’ by letting it go from 0 to 1
398
between 273K and 243K. Results from this simulation are seen as the purple line in Figure
399
1b. The LWP is now consistent with observations, but the SW and the LW components
400
of the surface cloud forcing are still overestimated (Table 2 under case name ’Auto-conv.
401
+ fice’).
402
For simplicity we then conducted several idealized experiments where we forced a re-
403
duction in the LWP, and found that reducing it by a factor of five through reducing the
404
CDNC gave the results closest to the observed values. This can be seen from the green
405
line in Figure 1b and from results under case name ’LWP/5 ∆CDNC’ in Table 2. Note
406
that the model radiation scheme does not depend explicitly on the CDNC, but rather on
407
LWP and re . Physically, however, reducing the LWP while keeping the re constant is the
408
same as reducing the cloud droplet number concentration. A reduction in CDNC is not in
409
itself an improvement, as this concentration is already low. The averaged observed CDNC
410
in single-layer stratus clouds obtained during the Mixed-Phase Arctic Cloud Experiment
411
(M-PACE) during fall 2004 was 43.6 ± 30.5 cm−3 [McFarquhar et al., 2007]. The simu-
412
lated CDNC is around 17 cm−3 in the standard model version for all clouds during this
413
season in the same area (around Barrow and Oliktok, Alaska). This mean gives weight
414
also to glaciated mixed-phased clouds where nearly all the water is in solid form and the
415
CDNC consequently is very low. It is therefore likely that the simulated CDNC of the
416
persistent low level stratus clouds is somewhat higher than 17 cm−3 .
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417
Additionally, we tried reducing the cloud droplet effective radius, keeping the CDNC
418
constant, in order to obtain LWP values consistent with observations. However, this led to
419
surface cloud forcing much stronger than what was observed during the SHEBA campaign
420
(see Table 2 under case name ’LWP/5 ∆re ’).
421
Note that the only adjustment to the LWP that affects the model thermodynamics is
422
the one in which the cloud particle ice fraction is modified. All the other methods used
423
to adjust the LWP are applied after the liquid water is formed and therefore have no
424
effect on the thermodynamic state of the atmosphere. By similar arguments, the only
425
adjustments to the LWP that affect the aerosol concentration are the modifications of the
426
auto-conversion and the cloud particle ice fraction. We are not directly comparing these
427
runs to the standard model, but are consistently calculating the differences between PD
428
and PI for two “auto-conversion” and two “fice” simulations, respectively.
429
The results shown in the next section are from simulations in which the LWP was
430
reduced by a factor of five through reducing the CDNC, as these simulations give the best
431
agreement between observed and simulated LWP and surface cloud forcing. In section 5
432
we will discuss how our results are affected by this choice.
4. Anthropogenic Influence
433
In the following subsections we will investigate the simulated changes in cloud properties
434
due to anthropogenic aerosol emissions and how these changes affect the surface radiative
435
balance in the Arctic region.
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4.1. Changes in Sulfate (SO4 ) Concentrations
436
The only change between simulations of the pre-industrial and the present day climate
437
is the anthropogenic emissions of chemical compounds in to the atmosphere. The increase
438
in SO4 and other anthropogenic particles in this period leads to an increase in the concen-
439
tration of cloud condensation nuclei (CCN) and is expected to influence cloud properties.
440
Figure 3b shows the increase in the column burden of sulfate from pre-industrial times
441
until today. The increase is largest over northern Eurasia, where the burden itself is
442
also largest (see Figure 3a). The large increase in this area is not surprising as northern
443
Eurasia is a significant source region of anthropogenic sulfate precursors (see section 3.2).
444
Figure 5b shows the vertical distribution of the change in sulfate concentration between
445
PD and PI, averaged annually over the Arctic region. The largest change in concentration
446
occurs around 800 - 900 hPa. However, there are relatively large signals of change both
447
above and below this level. According to Shindell et al. [2008], the upper part of this
448
signal may be influenced by the increase in emissions of SO4 precursors in Asia as well as
449
Europe. In section 3.1 we showed that both the modeled and the observed clouds in the
450
Arctic in general lie close to the surface (∼ 900 - 950 hPa, see e. g. Figure 2). As seen
451
in Figure 5b, changes in aerosol concentrations occur at the same levels and we therefore
452
expect that this change will affect cloud properties.
4.2. Changes in Cloud Droplet Effective Radius (re )
453
The simulated effective radius averaged annually over the cloud droplet number con-
454
centration decreases from 11.7 µm in pristine conditions (PI) to 9.8 µm in the polluted
455
present day regime north of 71◦ N. By comparison, observations by GZ06 show an average
456
decrease in effective radius from 12.9 µm to 9.9 µm between clean and polluted conditions.
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457
Thus, the first indirect effect is present in our simulations and the change in re is of the
458
same order of magnitude as observed values.
459
Figure 5c displays the vertical cross section of the annually averaged effective radius over
460
the Arctic region. This figure shows that the layers below 500 to 600 hPa have effective
461
radii above 10 µm, and we note that the LW cloud emissivity is sensitive to changes in
462
cloud droplet size (see Equation 5).
463
Figure 5d shows that the changes in effective radius due to anthropogenic emissions
464
are largest between 500 and 800 hPa. The change in effective radius is large if the re is
465
large initially and there is a large relative increase in CDNC. This is what creates the
466
maximum between 500 and 800 hPa. Above this maximum the re is initially small while
467
close to the surface the CDNC is high pre-industrially and the relative increase in CDNC
468
with increasing aerosol levels is therefore small (not shown here).
469
The largest reductions in effective radius of about 2.5 µm occur well above the height
470
of the highest cloud fraction (Figure 2), and have therefore only limited influence on the
471
surface cloud forcing. On the other hand, the decrease in re of 0.6 to 1.0 µm close to the
472
surface will increase both the SW and the LW surface cloud forcing from pre-industrial
473
times to present day.
4.3. Changes in Liquid Water Path (LWP)
474
As displayed in Figure 6a the spatial pattern of annually averaged change in LWP
475
between the PD and the PI scenario has similarities with the pattern of change in the
476
integrated SO4 concentration (Figure 3b). Note that the LWP increases in the more
477
polluted regime, as expected from the reduced droplet size and therefore reduced loss of
478
water due to precipitation release. Hence, the model simulates a distinct second indirect
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479
effect. The simulated ice water path (not shown) does not change between scenarios, as
480
aerosols in this study do not affect ice nucleation.
481
The average increase in liquid water path between the two scenarios is about 2.3 g/m2
482
north of 71◦ N in the ’LWP/5 ∆CDNC’ simulations, going from 27.8 to 30.1 g/m2 . This
483
is within the same range as the change found by GZ06 of 2.4 g/m2 , from 31.1 g/m2 in
484
pristine conditions to 33.5 g/m2 in an atmosphere with high aerosol concentrations. We
485
also note that the clouds in the pre-industrial aerosol regime have liquid water paths in
486
the same range as the average clean clouds observed by GZ06.
487
The vertical distribution of changes in in-cloud liquid water mixing ratio (LWMR)
488
averaged annually over the Arctic region is shown in Figure 6b. Contrary to the changes
489
in effective radius, the LWMR increases the most close to the surface. The reason for
490
this is that the liquid water amount is largest near the surface in pre-industrial times
491
(not shown). This affects the change in LWMR in the following manner: The onset of
492
precipitation is determined by the size of the cloud droplets (see section 3.2), but the
493
amount of water lost through this process increases with the in-cloud liquid water mixing
494
ratio, as well as with re [Rasch and Kristjánsson, 1998, eq. 21]. This means that the
495
change in the precipitation amount and hence in the LWMR is large for a given change
496
in re if the LWMR is large. Although a small change in CDNC leads to small changes
497
in the effective radius at surface levels, this change is large enough to affect the model
498
auto-conversion and hence the amount of water lost through precipitation.
499
The average LWP described so far says nothing about the thickness of each individual
500
cloud simulated by CAM-Oslo. There may be episodes of very high or very low LWPs
501
that affect this average greatly. This is of importance because the cloud optical properties
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502
vary non-linearly with LWP and the LW cloud emissivity reaches saturation for all LWPs
503
above about 50 g/m2 . One might therefore question whether clouds are thin enough to be
504
affected in the LW by a change in cloud droplet size or LWP. Figure 7 shows the fraction
505
of time that has vertically integrated LWPs below 50 g/m2 when clouds are present. It
506
reaches a minimum in August, simultaneously with the maximum in cloud cover and
507
average LWP (see Figures 1a and 1b). The fraction is never below 59 % pre-industrially
508
and never below 55 % in present day. We conclude that a large fraction of clouds is non-
509
opaque in the LW and therefore sensitive to changes in effective radius and liquid water
510
path due to anthropogenic aerosols.
4.4. Changes in Longwave Cloud Forcing at the Surface (LWCFS)
511
512
In this subsection we will study the simulated changes in LW cloud forcing at the surface
between pre-industrial times and the year 2000 (’LWP/5 ∆CDNC’ simulations).
513
The annually averaged change in surface LW cloud forcing (LWCFS) north of 71◦ N is
514
0.55 W/m2 , corresponding to a 1.6 % increase from pre-industrial times until today.
515
This increase is caused by an increase in the CCN concentration influencing the cloud
516
effective radius and the liquid water path.
517
There is a significant seasonal variation in the change in LWCFS with anthropogenic
518
aerosol emissions (Figures 9a and 9b). Averaged north of 71◦ N the LW cloud forcing
519
changes by 0.99 W/m2 during summer, while it changes by only 0.33 W/m2 during
520
winter time. The large changes in LW cloud forcing during summer may be highly influ-
521
enced by the fraction of low clouds being larger during the summer season (0.75) than
522
during winter (0.40). According to Shupe and Intrieri [2004], clouds that are important
523
to the LW surface radiation balance in the Arctic typically have bases at low altitudes
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524
(below 4 km). A high fraction of these clouds allows changes in cloud radiative properties
525
to occur over large areas and therefore cause larger changes in the LW surface radiation
526
budget during summer than during winter.
527
In addition, results show that the cloud liquid water path changes much more in summer
528
than it does in winter (4.2 g/m2 vs. 1.3 g/m2 ). This is because the high LWPs in the
529
summer make the amount of water lost through precipitation very sensitive to changes
530
in cloud droplet size (see section 4.3). The re at low levels changes by approximately the
531
same amount during the summer and the winter (-1.46 µm vs. -1.51 µm below 700 hPa).
532
In the winter re is smaller than in summer, but the pollution events are stronger, while in
533
the summer re at low levels is large and therefore sensitive to the little pollution that is
534
present at low levels during this season (see section 4.2). The large change in LWP causes
535
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)
536
Changes in re and LWP due to an increase in the anthropogenic aerosol concentrations
537
will affect the SW cloud forcing, as long as solar radiation is present. We find that on
538
average the simulated SWCFS changes by −0.85 W/m2 north of 71◦ N between the PI
539
and the PD scenarios, representing a 6.5% increase in the magnitude of the SW cloud
540
forcing. The seasonal variation in the change in surface cloud forcing is much stronger
541
in the SW than in the LW, due to the sun being absent or at high solar zenith angles
542
through most of the winter season. Because of the low signals during winter, we will now
543
focus only on the summer season.
544
545
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 towards
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546
the lower Arctic latitudes. This happens for two reasons. First, the solar zenith angle
547
is smaller here, causing a larger possible impact of the clouds on the radiation budget.
548
Secondly, areas around the North Pole and over Greenland are covered by snow and ice.
549
The cloud albedo increase will be less important here as the clouds are above highly
550
reflective surfaces.
551
The magnitudes of both the relative and the absolute change in SWCFS during summer
552
are larger than the corresponding magnitudes simulated for the LW case. There are several
553
reasons for this. Figure 8 shows that a given change in re or LWP affects the SW cloud
554
albedo more than the LW cloud emissivity under averaged simulated summer conditions
555
(PI)(re = 11.6 µm and LWP = 43.5 g/m2 ). Depending on surrounding conditions such
556
as the surface albedo and the vertical temperature profile, this behaviour will lead to
557
larger changes in SWCFS than in its LW counterpart for a given change in re or LWP.
558
Additionally, since cloud albedo saturates at much higher LWPs than cloud emissivity, a
559
larger fraction of clouds has radiative properties sensitive to changes in re and LWP in
560
the SW than in the LW.
561
Another reason for the large SW signals is the simulated changes in re and LWMR at
562
levels well above the surface (section 4.2 and 4.3). These changes will affect the SWCFS
563
because they affect the cloud albedo (see Equation 11). Conversely, the effect of these
564
changes on LWCFS is expected to be very small as this forcing is mainly influenced by
565
changes that occur in the bottom cloud layers, which are low in the Arctic (see Figure 2).
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4.6. Changes in Net Cloud Forcing at the Surface (Net CFS)
566
The changes in cloud forcing in both the LW and the SW have now been examined.
567
Here, we will study the total influence of increased aerosol levels interacting with Arctic
568
clouds.
569
The annually averaged change in Arctic net CFS between the PD and the PI scenario
570
is −0.30 W/m2 (not shown). This confirms that the increased magnitude of SW cloud
571
forcing with pollution is larger than the increased warming by clouds due to LW effects. If
572
the fraction of low clouds is overestimated in summer as suggested by the comparison to
573
the findings of Kay and Gettelman [2009] (section 3.1), the strong SW effects in summer
574
will be overestimated and it is possible that there is a negative bias in the aerosol indirect
575
effect.
576
During summer the net cloud forcing in present day conditions is positive over ice
577
covered surfaces, while the areas of open water and the southern regions of the Arctic
578
experience negative cloud forcing (not shown). The LW component thus dominates where
579
the surface albedo is high. Despite this, the change in surface net cloud forcing with
580
anthropogenic aerosols is negative over most of the Arctic region during the summer
581
(Figure 9d). The large change in SW cloud forcing dominates the change in net forcing
582
completely, even over areas covered by surface ice. The change in net surface cloud forcing
583
averages to −1.18 W/m2 north of 71◦ N during the summer.
584
In the winter, the SW cloud forcing is of less importance than in the summer and the
585
change in net forcing with pollution is dominated by LW effects. Anthropogenic aerosols
586
interacting with clouds lead to a net increase in the winter surface flux on the order of
587
0.14 W/m2 north of 71◦ N.
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5. Discussion
588
We will now compare the results from the ’LWP/5 ∆CDNC’ simulations to earlier
589
findings and go on to discuss the sensitivity of our results to our assumptions, as well as to
590
the sulfate concentration. The ’LWP/5 ∆CDNC’ simulations are highlighted in this work
591
because the simulated surface cloud forcing and LWP agree well with the measurements
592
taken during the SHEBA campaign. As this campaign was limited both in time and
593
space, focusing only on these simulations may not give an adequate overall view of the
594
Arctic conditions. In this section we will therefore study the sensitivity of our results to
595
the model LWP through several sensitivity experiments not directly linked to the SHEBA
596
campaign. In addition to annual, winter and summer averages the sensitivity experiments
597
include spring averages (January to April) to show that results from the polluted spring
598
months are in agreement with what is presented in section 4.
5.1. Comparison With Earlier Findings
599
5.1.1. Longwave
600
The simulated annual increase in surface LW cloud forcing of 0.55 W/m2 from pre-
601
industrial times until today is one order of magnitude less than the change in this forcing
602
suggested by GZ06 and LV06. In these two articles, increases of between 3.3 and 8.2
603
W/m2 were found when going from pristine to polluted conditions under cloudy skies.
604
We will in the following paragraphs discuss possible reasons for this discrepancy.
605
First of all, the GZ06 and LV06 studies show the radiative effect of increased aerosol
606
levels in a certain area and under certain conditions. We, on the other hand, study the
607
overall change in surface cloud forcing under all conditions and over the entire Arctic
608
region. The different goals of the studies also result in fundamental differences in the
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609
approach used. First, GZ06 and LV06 have looked at specific conditions for cloud type
610
and pollution, and it may be that these favorable conditions are met too seldom or over too
611
short time periods to affect our monthly averaged results. If this is the case, instantaneous
612
results should include signals of change that are significantly larger than our seasonal
613
averages. The fraction of time with changes in LWCFS above 3.3 W/m2 in the Arctic
614
region is plotted in Figure 10. The value 3.3 W/m2 is the lower limit for the increase in
615
surface flux found by GZ06 and LV06. The plot shows that changes of this magnitude
616
occur throughout the year and the 3D model thus simulates changes that are consistent
617
with the range observed by GZ06 and LV06. The fraction of time this occurs, however,
618
is very limited, with a peak of approximately 4% in late summer/early fall. Results
619
around Barrow, Alaska, show similar seasonal variation and changes of the same order of
620
magnitude as the Arctic average.
621
Secondly, the clean and the polluted scenarios found in GZ06 and LV06 contain the lower
622
and the upper quartile of present day aerosol concentrations respectively. This means
623
that they compare situations with especially large differences in aerosol conditions, while
624
this study compares all conditions of the present day regime to the clean pre-industrial
625
scenario. This will influence the magnitude of change found in the surface LWCF. It is
626
difficult to say whether the different approaches used in the studies are sufficient to explain
627
why our average results are lower than the earlier findings, as there is no information on
628
the fraction of time studied in the GZ06 and LV06 articles.
629
The average results found in our study may also be influenced by possible model arti-
630
facts. In the following paragraphs, we investigate whether features of the LW radiation
631
scheme and the simulated cloud cover and cloud properties can explain the differences in
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632
results. We used the 1D model to study whether the LW radiation scheme itself was ca-
633
pable of reproducing the findings of GZ06, using their observed changes in re and LWP as
634
input. In doing this we forced the simulated clouds to be similar to the ones observed; the
635
clouds are at low levels (below 1.5 km) and are all liquid. The model was run with cloud
636
parameter input similar to what was observed under both pristine (re = 12.9 µm and
637
LWP = 31.1 g/m2 ) and polluted conditions (re = 9.9 µm and LWP = 33.5 g/m2 ). GZ06
638
found changes in LWCFS of between 3.3 and 5.2 W/m2 , while the 1D model simulates
639
a change of 2.1 to 2.6 W/m2 depending on cloud base height and season. The difference
640
between observed and modeled changes may be due to the temperature profiles of the 1D
641
model, which are averaged north of 65.5◦ N, not being representative for the area studied
642
by GZ06. It may also be due to the fact that in these 1D tests we simulate July and
643
January only and therefore do not get an annual mean as presented in GZ06. Despite
644
the noted difference, the results are of the same order of magnitude as findings by GZ06.
645
This suggests that the LW radiation scheme used both in the 1D and the 3D model reacts
646
to changes in LWP and re in accordance with observations. The radiation scheme itself
647
is therefore not likely to cause the large discrepancy between the 3D model results and
648
observations.
649
The LW indirect effect at the surface will also be affected by the simulated cloud frac-
650
tion and the sensitivity of cloud LW emissivity to changes in cloud parameters. As noted
651
in section 3.1 the CAM-Oslo cloud fraction is lower than most observations, and under-
652
estimation of the radiative effect of cloud-aerosol interactions is likely (see Figure 1a).
653
However, a discrepancy of up to 30 % in cloud cover alone is not enough to explain the
654
difference in results found between this and earlier studies. The sensitivity of the LW
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655
emissivity will also influence the LW aerosol indirect effect. It increases with decreasing
656
LWP and becomes especially large for LWP below 20 g/m2 (Figure 8). Curry and Her-
657
man [1985] observed from aircraft measurements that the LWP of the Arctic stratus is
658
frequently below this value. In our simulations this occurs 36 % of the time annually
659
when clouds are present and we cannot rule out that this time fraction may be too low.
660
Based on this and the underestimated cloud cover it is possible that the time fraction of
661
4 % found to have surface indirect effects consistent with the findings of GZ06 and LV06
662
is somewhat underestimated.
663
In addition, the results will be affected by the magnitude of change in cloud parameters
664
with increasing aerosol load. In sections 4.2 and 4.3 we found that the changes in re and
665
LWP averaged in height are consistent with observations. Although the integrated LWP
666
includes changes in water amounts at all altitudes, the liquid water amount changes most
667
close to the surface (Figure 6b) and it is at these levels that we expect the largest influence
668
on the LW cloud forcing. The effective radius, on the other hand, changes much less in
669
surface layers than it does averaged in height. While GZ06 found a decrease in re of 3 µm
670
between pristine and polluted conditions, our annually averaged results show a change
671
of 0.6 to 1.0 µm in layers important to the LWCFS. However, annually around Barrow,
672
Alaska, the model reproduces the observed reductions in low level cloud effective radius
673
10 % of the time.
674
One final aspect that may lead to discrepancies in results is differences in the weather
675
and temperature conditions between the model and the observed cases. The change in
676
LW surface cloud forcing with pollution is influenced by the temperature of the cloud
677
base. If the vertical temperature profiles in our simulations differ from those common at
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678
the measuring sites used by GZ06 and LV06, it will affect the results. However, large sys-
679
tematic biases would be needed for this to greatly influence the results. Additionally, the
680
results shown in this section are averaged over five years and no particular meteorological
681
event or temperature anomaly will affect the average results.
682
In summary, it is clear that there are significant differences between this and the two
683
earlier studies of the LW indirect at the Arctic surface. While our study aims to show the
684
overall importance of the phenomenon, the GZ06 and LV06 studies show its magnitude
685
under specific conditions. In addition to the differences between the studies the low change
686
in surface LWCF may be influenced by an underestimated cloud fraction and by a possible
687
underestimation of the frequency of the most sensitive clouds in our simulations. However,
688
if the simulated indirect effect at the surface is to reach the values found by GZ06 and
689
LV06, large changes are needed in these parameters.
690
5.1.2. Shortwave
691
There has been less emphasis on the SW than on the LW indirect effect in the Arctic,
692
and observational data similar to what were used by GZ06 and LV06 to study the LW effect
693
are not available for the visible through near-infrared wavelengths. Lubin and Vogelmann
694
[2007] have, however, simulated the SW first indirect effect during springtime for water
695
clouds at four different Arctic latitudes. They used a 179-band SW discrete-ordinates
696
method and used changes in re as described by GZ06. In doing this, they found that
697
for March and April the changes in the SW radiative flux at the surface due to the first
698
indirect effect are comparable in magnitude to the increased LW flux found by GZ06 and
699
LV06. For May and June, the decrease in the SW surface flux was larger than the increase
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700
in the LW, meaning that the simulated net surface radiation under cloudy skies decreases
701
due to the first indirect effect. These findings are consistent with our results (section 4).
5.2. Sensitivity to Simulated LWP and IWP
702
We now investigate whether our results are sensitive to the manner in which the model
703
LWP is reduced (section 3.5) or to the magnitude of these reductions. Table 3 shows
704
the change in surface cloud forcing with anthropogenic aerosols for several simulations in
705
which different modifications of the LWP and the IWP were used.
706
From Table 3 it is clear that the manner in which the model LWP is reduced does not
707
greatly affect the anthropogenic change in surface cloud forcing. Results from the standard
708
CAM-Oslo are shown under the case name ’std. LWP’ in the second column of the table.
709
Columns 3 to 6 list results from simulations where the LWP was decreased by a factor of
710
five or ten through either reducing the cloud droplet number concentration (’LWP/5(10)
711
∆CDNC’) or through reducing the cloud droplet effective radius (’LWP/5(10) ∆re ’). As
712
the model re was found to be consistent with observations (section 3.5), reducing this
713
quantity is not something we consider to be physically accurate, but rather a test of the
714
other “extreme” way of changing the LWP besides reducing the CDNC.
715
Reducing the LWP by a factor of ten brings it well below the values observed during the
716
SHEBA campaign (Figure 1b), and we consider these simulations to be tests where the
717
clouds are in general too thin and therefore too sensitive to changes in CCN concentration.
718
The changes in LW cloud forcing simulated with these model versions are larger than they
719
are for simulations in which the LWP was reduced by a factor of five (see Table 3 under
720
case names ’LWP/10(5) ∆CDNC’ and ’LWP/10(5) ∆re ’). One exception is the wintertime
721
change in LWCFS labeled ’LWP/10 ∆re ’, in which reductions in cloud droplet size bring
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722
the effective radii more frequently below 10 µm and therefore make the LW emissivity
723
less sensitive to changes in re (Equation 5). Unlike in the LW, the changes in SW cloud
724
forcing are smaller in magnitude for LWP/10 simulations than for simulations with smaller
725
reductions in LWP. The SWCFS is highly influenced by the absolute changes in re and
726
LWP being smaller in LWP/10 simulations than in LWP/5 simulations, while the LWCFS
727
is influenced by a large increase in the sensitivity of the LW cloud emissivity to changes
728
in re and LWP for low LWPs (see Figure 8). Notice that the case where LWP is reduced
729
by a factor of ten through reducing the CDNC is the only case that simulates an increase
730
in surface cloud forcing with pollution on an annual basis (0.12 W/m2 ).
731
Results from simulations where the LWP is reduced by modifying the auto-conversion
732
are shown in column 7 under the case name ’1st Auto-conv’ (r3lc = 10 µm and autlim =
733
0.5 mm day −1 , see section 3.5). The LWCFS changes less from pre-industrial times to
734
present day in this case than in the case presented in section 4 (’LWP/5 ∆CDNC’), while
735
the opposite is true for the SWCFS. We also ran a test where the ice fraction of the cloud
736
(fice) was increased as in section 3.5 (’Fice + LWP/5 ∆CDNC’). This case shows smaller
737
changes in both LW and SW cloud forcing at the surface than results discussed in section
738
4. The table further contains results from modifications of both the auto-conversion and
739
the cloud particle ice fraction as described in section 3.5 (’Auto-conv. + Fice’). This
740
simulation behaves similarly to the ’1st Auto-conv’ simulation. Neither of these cases
741
point to an increased warming effect of the Arctic clouds due to anthropogenic aerosols,
742
and the magnitude of change is relatively insensitive to how the model liquid water path
743
is reduced.
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744
In summary Table 3 contains results both from simulations where the cloud LWP is
745
reduced in crude manners by simply reducing the size of the cloud particles or the CDNC
746
and from simulations where it is reduced in more physically accurate manners. None of
747
them show signals of change significantly larger than what we found using the version in
748
which the LWP was reduced by a factor of five through reductions in the CDNC.
5.3. Sensitivity to the Treatment of LW Absorption Efficiency
749
In section 2.2 we presented a new parametrization of the LW absorption efficiency, Qa ,
750
as part of the LW absorption coefficient, kabs . We will now investigate whether our results
751
are sensitive to this parametrization.
752
As noted in section 2.2, the parametrization of Qa is a simplification because we ignore
753
the effects of the cloud droplet spectrum and the thermal radiation wavelength spectrum.
754
One consequence of this may be that we overestimate the sensitivity of Qa to droplet
755
size for small droplets and therefore underestimate the sensitivity of kabs and the LW
756
emissivity to droplet size for re <10 µm (kabs =
757
simulations in which the Qa was (unrealistically) set to one for all effective radii rendering
758
kabs sensitive to cloud droplet size for all re :
kabs =
3 Qa
4 ρ L re
=
3 0.1
).
4 ρL
3 1
, f or all re
4 ρL r e
We ran a set of test
(15)
759
The results of these simulations show a slightly increased change in LWCFS compared
760
to the standard ’LWP/5 ∆CDNC’ run (see Table 3 under case name ’Qa =1 LWP/5
761
∆CDNC’), but the results do not contradict our earlier findings. We conclude that the
762
Qa parametrization for re < 10 µm is not what causes the small simulated changes in LW
763
cloud forcing at the surface from pre-industrial times until today.
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5.4. Sensitivity to Aerosol Concentration
764
In this section we examine whether the simulated change in surface cloud forcing is un-
765
derestimated because of low aerosol concentrations (Figure 4). To do this we increase the
766
concentrations by either decreasing the in-cloud scavenging or by increasing the emissions
767
of SO4 precursors. Results shown in this subsection are from simulations in which the
768
LWP is reduced by a factor of five through ∆CDNC.
769
The high liquid water content of the CAM-Oslo model in the Arctic may lead to an
770
overestimated in-cloud scavenging. In order to check whether this is of importance we
771
ran test runs in which the in-cloud scavenging coefficient was reduced from 1 to 0.1,
772
thus reducing the wet deposition on SO4 . This allows us to investigate whether the low
773
simulated change in cloud forcing is due to an underestimation of CCN available to affect
774
cloud properties. Results from these runs show average SO4 concentrations that exceed
775
the observational means by 201% at the Zeppelin station, 228% at Janiskoski, 223% at
776
Alert and 176% at Barrow.
777
The change in cloud forcing simulated with reduced in-cloud scavenging shows that the
778
increased concentration of SO4 does not greatly affect the indirect forcing (see Table 3
779
under case name ’In-cloud scav. + LWP/5 ∆CDNC’). Comparing these results to results
780
under ’LWP/5 ∆CDNC’ we see that the LWCFS changes less than for simulations where
781
the scavenging is left unchanged, while the SWCFS changes more. This can be explained
782
by the reduction in in-cloud scavenging leading to reduced effective radii and increased
783
LWP of clouds pre-industrially (∆re = -0.4 µm, ∆LWP= 2.3 g/m2 ) as well as in present
784
day.
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785
The sensitivity of our results to aerosol concentrations was tested further through simu-
786
lations in which the present day emissions of SO2 from fossil fuel combustion were doubled
787
(2SOx) (see Table 1). This led to an increase in sulfate burden of approximately the same
788
magnitude as the increase from pre-industrial times until present day (∆SO4 (2SOx-PD)=
789
1.07 * 10−6 kg S/m2 vs. ∆SO4 (PD-PI)= 0.89 * 10−6 kg S/m2 ). Results from this simu-
790
lation are shown under case name ’2SOx - PI LWP/5 ∆CDNC’ in Table 3. Even though
791
the difference in SO4 concentration between the runs nearly doubles, the forcing only
792
increases by about 50 %. This experiment shows that even with significantly increased
793
concentrations of SO4 , the signals of change in LWCFS are not within the range found by
794
GZ06 and LV06. The SW effects dominate results from this simulation as well as most
795
others. Only during winter is the net radiative effect of increased aerosol levels positive.
6. Summary and Conclusions
796
The observed increase in surface air temperature over the last decades is almost twice
797
as large in the Arctic as in the rest of the world [e. g. Graversen et al., 2008]. Garrett
798
and Zhao [2006] and Lubin and Vogelmann [2006] suggest that aerosol-cloud interactions
799
may contribute to the observed temperature amplification in this region. In this study
800
we have investigated the overall importance of the suggested increase in surface radiative
801
flux due to increased CCN concentrations in clouds.
802
Using the CAM-Oslo global climate model we have studied simulated changes in the
803
radiative balance at the Arctic surface due to aerosol-cloud interactions. The simulated
804
cloud cover, cloud water path and cloud radiative forcing were verified against observations
805
and model modifications were made to better suit the model to the focus of our study.
806
The simulated SO4 concentrations were compared both to observations and to the median
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807
of ten models participating in the AeroCom project (section 3.2). We found that results
808
from the CAM-Oslo model were in better agreement with observations than the AeroCom
809
model median, and conclude that the CAM-Oslo global climate model is well suited for
810
this study. Our results show that the indirect effects of anthropogenic aerosols are close
811
to the same magnitude in the LW and the SW, with a net result of −0.30 W/m2 . We have
812
conducted several sensitivity experiments that show that our findings are robust against
813
model assumptions, changes in cloud properties and aerosol concentrations.
814
Below is a summary of key findings concerning aerosol-indirect effect at the Arctic
815
surface. Numbers in parentheses give the minimum and the maximum results from the
816
sensitivity experiments.
817
• The simulated increase in LW cloud forcing at the surface due to anthropogenic
818
aerosols averages to 0.55 (0.10 to 0.85) W/m2 annually, to 0.99 (0.16 to 1.53) W/m2
819
from June to September and to 0.33 (0.07 to 0.51) W/m2 from October to May. The
820
seasonal variation is caused by larger changes in cloud emissivity in summer than in
821
winter, combined with high fractions of low clouds in summer.
822
• The simulated LW indirect effect is one order of magnitude lower than suggested
823
by Garrett and Zhao [2006] and Lubin and Vogelmann [2006]. This discrepancy may be
824
caused by a combination of effects. GZ06 and LV06 showed the magnitude of change
825
in surface LWCF under specific conditions, whereas this study includes a variety of con-
826
ditions at all times of the year providing results for the average changes. In addition,
827
underestimation of cloud cover and a possible underestimation of the frequency of the
828
most sensitive clouds may influence the results. However, large changes are needed in
829
these parameters for the LW indirect effect to reach the values found by GZ06 and LV06.
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830
• The corresponding simulated change in surface SW cloud forcing due to anthro-
831
pogenic aerosols averages to −0.85 (−1.29 to −0.52) W/m2 annually and −2.17 (−3.28
832
to −1.28) W/m2 in summer.
833
• The annual change in surface net cloud forcing averages to −0.30 (−0.98 to 0.12)
834
W/m2 . During the summer, the net surface cloud forcing decreases by 1.18 (2.63 to 0.23)
835
W/m2 , while in the winter LW effects dominate, and changes in cloud properties due to
836
anthropogenic aerosols increase the surface radiative flux by 0.14 (−0.15 to 0.29) W/m2 .
837
The net cloud forcing will be particularly sensitive to overestimation of the summer cloud
838
cover as the negative SW forcing is strong during this season. A possible overestimation
839
of the SWCFS in summer may lead to a negative bias in the net aerosol indirect effect.
840
• The sensitivity experiments show that the annually averaged changes in net CFS are
841
positive only in one of eleven simulations and our results suggest that increased levels of
842
anthropogenic aerosols in Arctic clouds may lead to a small decrease in the radiative flux
843
at the surface. Our general findings depend little on model assumptions, changes in cloud
844
properties and aerosol concentrations.
845
In recent years (after the fall of the Soviet Union) the emissions of the SO4 precursor
846
SO2 have decreased dramatically in Europe and Russia [Karnieli et al., 2009], and Quinn
847
et al. [2007] found that the concentrations of non-seasalt SO4 decreased by 30-70 % from
848
the early 1990s to present in the Canadian, Norwegian and Finnish Arctic. Additionally,
849
Sharma et al. [2006] found a clear downward trend in concentrations of equivalent black
850
carbon (BC) in the high Arctic. From the findings of this study it is likely that a reduction
851
in CCN amount in the Arctic will decrease the magnitude of the negative indirect effect
852
and therefore in sum work to increase the net positive cloud forcing found in this region.
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853
The less pollution that enters the Arctic region, the larger the increase in surface radiative
854
flux compared to present day.
855
Furthermore, the expected reduction in the polar ice cap and therefore in the surface
856
albedo may increase the importance of the negative SW surface cloud forcing. Larger areas
857
of open waters may, on the other hand, affect both the cloud fraction and the thickness
858
of the clouds in the region and it is difficult to predict the net effect of sea ice reductions.
859
Although the simulated changes in surface cloud forcing are smaller than what is found
860
in earlier studies, they are of the same order of magnitude as the BC surface forcing via
861
snow and ice albedos in sea ice areas. Flanner et al. [2007] estimate that the annual
862
mean of the instantaneous surface forcing of BC on snow in these areas are around 0.20
863
W/m2 during a year of average BC emissions. The changes in Arctic surface cloud forcing
864
due to anthropogenic aerosols may therefore be of importance and should be studied
865
further. To enable this, the accuracy of climate models needs to be improved, especially
866
in dealing with cloud water amount and conversion between liquid water and ice particles,
867
and a higher frequency and larger geographical spread in Arctic measuring campaigns are
868
needed. The current lack of comprehensive observations limits the possibility of verifying
869
and improving current climate models.
870
Acknowledgments. This study was partly funded by the Norwegian Research Council
871
through the projects POLARCAT (grant No. 175916) and NorClim (grant No. 178246),
872
and has received support from the Norwegian Research Council’s Programme for Super-
873
computing through a grant of computing time. The authors would like to thank Aero-
874
Com for access to their data, and are also grateful to Alf Kirkevåg, Øyvind Seland, Frode
875
Stordal, Terje Berntsen and Gunnar Myhre for helpful discussions. Finally, we are thank-
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X - 43
876
ful to three anonymous reviewers whose comments led to significant improvements of the
877
paper.
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878
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879
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X - 48
Figure 1.
ALTERSKJÆR ET AL.: AEROSOL INDIRECT EFFECTS IN THE ARCTIC
a) Observed and simulated total cloud fraction. Black: The Arctic region. Color:
SHEBA region. b) Monthly variation in average cloud LWP in the SHEBA region. Observed
values reproduced from Zhang et al. [2002].
Figure 2.
Simulated zonally averaged annual cloud fraction north of 65◦ N. The black line
indicates 1 km height above sea level.
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 pre-industrial times to 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.
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July 20, 2010, 6:20am
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ALTERSKJÆR ET AL.: AEROSOL INDIRECT EFFECTS IN THE ARCTIC
Figure 4.
X - 49
Observed (green) and simulated (blue) SO4 concentrations at selected Arctic
stations [µg S/m3 ]. The locations of the stations are plotted in Figure 3. Observational data
from AMAP [2006] have been averaged over five 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.
Figure 5.
a) Simulated zonally averaged annual sulfate concentrations in the Arctic region
[µg S/m3 ], present day. b) Simulated zonally averaged changes in sulfate concentrations from
pre-industrial times to present day [µg S/m3 ], annual mean. c) Simulated annual Arctic mean
of effective cloud droplet radius [µm] in present day conditions. d) Simulated zonally averaged
change in annually averaged effective radius from pre-industrial times to present day [µm]. Note
that the color bars differ.
Figure 6. a) Simulated annually averaged change in LWP [g/m2 ] from pre-industrial times to
present day. b) Simulated zonally and annually averaged change in in-cloud liquid water mixing
ratio [kg/kg] from pre-industrial times to present day.
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.
Figure 8.
Solid grey lines: Cloud SW albedo (thick) and LW emissivity (thin) as a function
of liquid water path (bottom axis, re = 11.6 µm). Dashed black lines: 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.
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July 20, 2010, 6:20am
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X - 50
Figure 9.
ALTERSKJÆR ET AL.: AEROSOL INDIRECT EFFECTS IN THE ARCTIC
Simulated anthropogenic change in cloud forcing at the surface [W/m2 ] from pre-
industrial times to present day. a) ∆LWCFS, winter season (October - May). b) ∆LWCFS,
summer season (June - September). c) ∆SWCFS, summer season. d) ∆Net CFS, summer
season. Note that the color scales are reversed in c) and d).
Figure 10.
Fraction of time that the change in LW cloud forcing at the surface from pre-
industrial times to present day is greater than 3.3 W/m2 , Arctic average.
D R A F T
July 20, 2010, 6:20am
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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/,
Tables
Copyright 2010 by the American Geophysical Union.
0148-0227/10/$9.00
1
X-2
: TABLES
Global annual emissions of
Table 1.
DiMethyl Sulphide (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 ]. Emissions of DMS, SO2
and SO4 are in Tg S yr−1 .
PD: Present
day emissions [Dentener et al., 2006], PI:
Pre-industrial emissions, 2SOx: Present day
emissions except that emissions of SO2 from
fossil fuel combustion are doubled compared
to present day.
PD
PI
2SOx
DMS
18.2
18.2
18.2
SO2
68.6
14.9
120.9
SO4
1.8
0.4
3.1
BC
7.7
1.3
7.7
OM
65.4
30.0
65.4
SS
7925
7925
7925
DUST
1678
1678
1678
X-3
: TABLES
Table 2.
Observed and simulated surface cloud forcing in the SHEBA region. 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].
SHEBA
CAM-Oslo
CAM-Oslo
CAM-Oslo
1st
2nd
Auto
std. LWP
LWP/5 ∆CDNC
LWP/5 ∆re
auto-conv.
auto-conv.
fice
Annual average
LWCFS
35 to 41
39.1
35.8
38.2
39.0
40.9
37.1
SWCFS
-10.5 to -9.5
-23.1
-13.6
-16.8
-20.5
-20.0
-18.6
Net CFS
25 to 30
16.0
22.1
21.4
18.6
20.9
18.5
LWCFS
25 to 30
28.4
27.2
28.7
28.7
29.4
26.2
SWCFS
-1 to 0
-4.7
-2.5
-3.0
-4.1
-4.2
-3.4
Net CFS
24 to 30
23.7
24.7
25.7
24.6
25.2
22.9
LWCFS
45 to 50
60.7
52.9
57.3
59.8
64.0
58.9
SWCFS
-26 to -24
-60.0
-36.0
-44.5
-53.2
-51.6
-49.0
Net CFS
19 to 21
0.7
16.9
12.8
6.5
12.4
9.9
Winter average
Summer average
0.10
-0.84
-0.74
0.07
-0.22
-0.15
0.16
-2.07
-1.90
0.04
-0.13
-0.09
Winter average
∆ LWCFS
∆ SWCFS
∆ Net CFS
Summer average
∆ LWCFS
∆ SWCFS
∆ Net CFS
Spring average
∆ LWCFS
∆ SWCFS
∆ Net CFS
0.19
-0.11
0.08
0.99
-2.17
-1.18
0.33
-0.19
0.14
0.55
-0.85
-0.30
0.06
-0.10
-0.04
0.53
-2.07
-1.55
0.14
-0.20
-0.06
0.27
-0.83
-0.56
0.28
-0.07
0.21
1.46
-1.69
-0.23
0.44
-0.15
0.29
0.78
-0.66
0.12
0.07
-0.09
-0.02
0.65
-1.96
-1.31
0.13
-0.18
-0.05
0.30
-0.78
-0.47
0.07
-0.16
-0.09
0.38
-3.01
-2.63
0.14
-0.29
-0.16
0.22
-1.20
-0.98
0.09
-0.08
0.01
0.57
-1.28
-0.72
0.15
-0.14
0.02
0.29
-0.52
-0.23
0.07
-0.12
-0.05
0.59
-2.50
-1.91
0.17
-0.21
-0.04
0.31
-0.97
-0.66
0.24
-0.09
0.15
1.02
-2.12
-1.09
0.40
-0.19
0.21
0.61
-0.83
-0.23
Sensitivity to
model Qa
10
Qa = 1.0
LWP/5 ∆CDNC
fossil fuel combustion are doubled compared to present day emissions.
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
Sensitivity to model
aerosol concentration
11
12
In-cloud scav. +
2SOx - PI
LWP/5 ∆CDNC LWP/5 ∆CDNC
presented in the last column are from a simulation where the emissions of SO2 from
Sensitivity to
model LWP
2
3
4
5
6
7
8
9
std. LWP/5 LWP/5 LWP/10 LWP/10
1st
Fice + LWP/5 Auto-conv.
LWP ∆CDNC
∆re
∆CDNC
∆re
Auto-conv.
∆CDNC
+ Fice
Annual average
∆ LWCFS
∆ SWCFS
∆ Net CFS
Column number
Case name
Simulated changes in surface cloud forcing between pre-industrial and
present day conditions* [W/m2 ]. See sections 5.2, 5.3 and 5.4 for details. *) Results
Table 3.
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: TABLES