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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, D00D04, doi:10.1029/2008JD011066, 2009
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How do aerosol histories affect solar ‘‘dimming’’ and ‘‘brightening’’
over Europe?: IPCC-AR4 models versus observations
Christian Ruckstuhl1 and Joel R. Norris1
Received 29 August 2008; revised 22 December 2008; accepted 8 January 2009; published 21 March 2009.
[1] A multidecadal decrease in downward surface solar radiation (solar ‘‘dimming’’)
followed by a multidecadal increase in surface radiation (solar ‘‘brightening’’) have been
reported over Europe. The trends mainly occur under cloud-free skies, and they are
primarily caused by the direct aerosol radiative effect. The present study compares
observed cloud-free solar ‘‘dimming’’ and ‘‘brightening’’ trends with corresponding
output from IPCC-AR4 20th century simulations and furthermore examines how sulfate
and black carbon aerosol histories, used as model input, affect simulated surface radiation
trends. Outputs from 14 models are compared to observed cloud-free surface radiation
fluxes derived from a combination of (1) satellite cloud observations, synoptic cloud
reports, and surface solar irradiance measurements and (2) sunshine duration
measurements and variability of the atmospheric transmittance derived from solar
irradiance measurements. Most models display a transition from decreasing to increasing
solar irradiance, but the timing of the reversal varies by about 25 years. Consequently,
large discrepancies in sign and magnitude occur between modeled and observed
‘‘dimming’’ and ‘‘brightening’’ trends (up to 4.5 Wm 2 per decade for Europe).
Considering all models with identical aerosol histories, differences in cloud-free radiation
trends are in all but one case less than 0.7 Wm 2 per decade. Thirteen of the fourteen
models produce a transition from ‘‘dimming’’ to ‘‘brightening’’ that is consistent with the
timing of the reversal from increasing to decreasing aerosol emissions in the input aerosol
history. Consequently, the poor agreement between modeled and observed ‘‘dimming’’
and ‘‘brightening’’ is due to incorrect aerosol emission histories rather than other factors.
Citation: Ruckstuhl, C., and J. R. Norris (2009), How do aerosol histories affect solar ‘‘dimming’’ and ‘‘brightening’’ over Europe?:
IPCC-AR4 models versus observations, J. Geophys. Res., 114, D00D04, doi:10.1029/2008JD011066.
1. Introduction
[2] Solar radiation at the Earth’s surface is a driving
factor for the Earth’s temperature and hydrological cycle,
thus influencing the entire climate and ecosystem [e.g.,
Ramanathan et al., 2001; Wild et al., 2004]. Studies based
on surface radiation measurements have shown that solar
irradiance declined from about 1950 to the mid 1980s [e.g.,
Gilgen et al., 1998; Stanhill and Cohen, 2001], popularly
called solar ‘‘dimming.’’ Recent publications report that
solar radiation began to recover over large parts of the
world [Wild et al., 2005; Ohmura, 2006], known as solar
‘‘brightening.’’ The increase in solar irradiance in Europe
largely occurs under cloud-free skies [Ruckstuhl et al.,
2008] or under conditions where the radiative effects of
cloud cover anomalies have been removed [Norris and
Wild, 2007]. This suggests that the recent ‘‘brightening’’
in Europe is related to decreasing aerosol emissions reported
by Streets et al. [2006] and others.
1
Scripps Institution of Oceanography, University of California, San
Diego, La Jolla, California, USA.
Copyright 2009 by the American Geophysical Union.
0148-0227/09/2008JD011066$09.00
[3] Trends in aerosol emissions and associated ‘‘dimming’’ and ‘‘brightening’’ have likely influenced changes
in the Earth’s temperature [Wild et al., 2007]. Model
simulations [e.g., Meehl et al., 2004] also show that future
global warming depends on radiative forcings induced by
natural and anthropogenic aerosols in addition to atmospheric greenhouse gas concentrations and internal feedback mechanisms. Large uncertainty in anthropogenic
aerosol radiative forcings, however, is reported in the
Intergovernmental Panel of Climate Change Fourth Assessment Report IPCC-AR4 [Solomon et al., 2007]. On the
basis of an older set of models, Kiehl [2007] found that the
wide range in anthropogenic forcings is caused by poor
knowledge of past changes in aerosol emissions and by
different treatment of aerosol processes in the models. In
particular, there was a negative correlation between total
anthropogenic forcing and climate sensitivity and a positive
correlation between the total anthropogenic forcing and the
aerosol forcing. Knutti [2008] confirmed the correlation
between total anthropogenic forcing and climate sensitivity
with IPCC-AR4 models, officially called the World Climate
Research Programme’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel data set.
These correlations imply that models with high climate
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sensitivity simulate the observed 20th century warming by
partially compensating positive greenhouse gas radiative
forcing with large negative aerosol radiative forcing and
models with low climate sensitivity simulate the observed
warming by compensating with small negative aerosol
forcing. Consequently, we can reduce uncertainty in climate
sensitivity and future global warming by narrowing uncertainty in past anthropogenic aerosol radiative forcing.
[4] Since current temperature projections for the 21st
century are mostly based on coupled atmosphere-ocean
general circulation models (CGCMs), it is of particular
importance to determine how well these models simulate
solar irradiance at the Earth’s surface and its decadal
variability due to changes in aerosol content by comparing
them with surface observations. It has been shown recently
that the ability of general circulation models (GCMs) to
realistically simulate surface solar irradiance has generally
improved [Wild, 2005; Wild et al., 2006], although relatively
large discrepancies between models still exist in certain
regions [e.g., Sorteberg et al., 2007]. In the work of
Romanou et al. [2007], all-sky surface solar radiation trends
from the Baseline Surface Radiation Budget Network
(BSRN) are compared with nine IPCC-AR4 models. The
average trend of the nine models is considerably lower than
the observed BSRN trend at each of the eight sites. Since
such all-sky short-term trends are largely influenced by
weather related cloud cover variations and CGCMs do
not simulate the same weather as occurred historically,
the discrepancy between models and observations is not
unexpected.
[5] Aerosol emission histories and scenarios used as
model input are another large source of model uncertainty.
Substantial efforts have been made with the Special Report
on Emission Scenarios (SRES) [Nakićenović et al., 2000] to
have consistent emission scenarios for the IPCC-AR4
model runs, but many different emission histories were
nonetheless used in the IPCC-AR4 20th century experiments (20C3M) [e.g., Smith et al., 2001; Boucher and
Pham, 2002]. Moreover, not all IPCC-AR4 models consider
the same aerosol types.
[6] The present study compares two independent data sets
of observed solar irradiance under cloud-free skies with
cloud-free solar irradiance from IPCC-AR4 20C3M simulations over the region of Europe. One data set is based
on cloud-free irradiance measurements [Ruckstuhl and
Philipona, 2008] (RP08), and the other is estimated from
all-sky measurements from which the radiative effects of
monthly cloud cover anomalies have been removed [Norris
and Wild, 2007] (NW07). We focus on cloud-free solar
irradiance trends because (1) the direct aerosol radiative
effect (scattering and absorption of solar radiation by
aerosols) is included in all IPCC-AR4 models, (2) indirect
aerosol radiative effects (modification of clouds by aerosols) are included in fewer than one third of the IPCC-AR4
models, and (3) regional all-sky solar irradiance on short
timescales depends mainly on cloud cover variations produced by weather that differ among the models and observations. Although all-sky fluxes ultimately affect Earth’s
climate and hydrological cycle, our approach provides
insight into how well IPCC-AR4 models simulate the direct
aerosol radiative effect and how well they represent the
solar ‘‘dimming’’ and ‘‘brightening’’ observed under cloud-
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free conditions over Europe since the 1970s. We will
furthermore show how the simulated cloud-free irradiance
trends are closely related to the aerosol emission histories
used by the models and demonstrate how better emission
histories may help reduce model discrepancies and enable
more accurate projections of future climate change.
2. Data and Models
2.1. Solar Irradiance Observational Data
[7] Cloud-free shortwave downward radiation (SDRcf)
from IPCC-AR4 models is compared with measured SDRcf
(RP08) and with estimated SDRcf independently derived
from monthly all-sky measurements from which the effects
of cloud cover anomalies have been removed (NW07). The
latter data set is not truly SDRcf since it may include
radiative effects of cloud albedo variations that are uncorrelated with cloud cover variations, but we nonetheless use
the term SDRcf to describe it because it closely resembles
measured SDRcf (shown later on).
[8] The method to detect cloud-free skies from RP08 is
based on the observed sunshine duration (SSD) and the
variability of the atmospheric transmission derived from
shortwave downward radiation (SDR) measurements. Both
measurements, SDR and SSD, require a time resolution of
1 hour. An hour is defined as cloud-free, if SSD is 60 min and
the variability of the atmospheric transmission is smaller
than an empirically pre-defined value. This method has been
applied to data from the German Weather Service (DWD) in
northern Germany (8 sites) and from the MeteoSwiss
automatic meteorological network (ANETZ) at 25 lowland
sites in Switzerland [Ruckstuhl et al., 2008]. In the work of
Ruckstuhl et al. [2008], cloud-free solar irradiance is scaled
by observed SSD to obtain results weighted by the frequency
of cloud-free conditions, whereas in this study results are
provided for only 100% cloud-free skies. Hence, the
reported anomalies and trends in this study are larger than
those reported by Ruckstuhl et al. [2008]. Between 1981
and 2005, there was a SDRcf increase of +3.5 [+2.2 to +4.8]
Wm 2 per decade (dec) in northern Germany and a smaller
+1.8 [+0.9 to +2.8] Wm 2 dec 1 increase in Switzerland. In
the following we refer to RP08 SDRcf data as ‘‘surface
observations.’’
[9] NW07 investigated trends in surface solar radiation
and cloud cover using data from the Global Energy Balance
Archive (GEBA) [Gilgen and Ohmura, 1999], synoptic
cloud reports [Hahn and Warren, 2003], and the International Satellite Cloud Climatology Project (ISCCP) [Rossow
and Schiffer, 1999]. The effect of cloud cover anomalies on
solar irradiance (called cloud cover radiative effect (CCRE)
anomalies by NW07) was calculated by multiplying monthly
cloud cover anomalies by the ratio of climatological cloud
radiative effect divided by climatological cloud cover.
NW07 then removed CCRE anomalies from GEBA solar
irradiance anomalies via linear regression to produce residual anomalies, hereafter referred to as ‘‘residual flux.’’
Residual fluxes include contributions from radiation anomalies under cloud-free conditions and the effects of anomalies
in cloud albedo that are linearly uncorrelated to cloud cover
anomalies. Multidecadal variations in residual flux are
likely primarily controlled by changes in the direct aerosol
radiative effect and, if it is substantial, the first indirect
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Figure 1. Time series of SDRcf anomalies in (a) northern Germany and (b) Switzerland. The solid line
stands for observational fluxes according to RP08. The dashed line is derived from the NW07 method,
referred to as residual flux. (c) Scatterplot of the SDRcf anomalies with linear regression lines. Dotted line
is the 1:1 line. Root mean square (RMS) differences and correlation coefficients (R) are also given.
aerosol radiative effect (enhancement of cloud albedo
due to more cloud droplets by an increasing number of
aerosols acting as cloud condensation nuclei [Twomey,
1974]). Pan-European residual flux decreased between
2.7 and 3.5 Wm 2 dec 1 from 1971 to 1986 and started
to recover between 1987 and 2002 with a rate of +2.0 to
+2.3 Wm 2 dec 1, depending on the trend calculation
method.
[10] We tested whether NW07 residual flux is an appropriate measure to evaluate modeled SDRcf trends by comparing it to measured SDRcf, available for northern Germany
and Switzerland. For Germany, the residual flux time series
from five ISCCP grid boxes were averaged together with
weighting according to the number of surface radiation sites
located therein and used by Ruckstuhl et al. [2008]. Because
no Swiss measurements entered the GEBA archive and thus
were unavailable to NW07, the NW07 procedure was applied
to average of the 25 Swiss ANETZ sites, the average of
15 MeteoSwiss stations providing synoptic cloud reports,
and ISCCP data for the grid box centered on Switzerland.
[11] Figures 1a and 1b display the time series of residual
flux anomalies and SDRcf anomalies from surface observations in northern Germany (Figure 1a) and in Switzerland
(Figure 1b). In Figure 1c, the correlation between residual
flux and surface observation SDRcf anomalies is shown.
Root mean square (RMS) difference and correlation coefficient (R) are 1.86 Wm 2 and 0.89 in northern Germany and
1.63 Wm 2 and 0.73 in Switzerland. The independent
methods reveal good agreement of SDRcf anomalies and
trends even if only a few (northern Germany) or a single
ISCCP grid box is considered (Switzerland). Since residual
fluxes include direct and first indirect aerosol radiative
effects, and SDRcf from surface observations includes only
the direct effect, an assessment of the magnitude of the first
indirect aerosol effect is possible. Although the aerosol optical depth (AOD) strongly declined in this region [Ruckstuhl
et al., 2008] no significant difference between the SDRcf
trends from these two methods is apparent. This implies
that the first indirect aerosol effect is much smaller than the
direct aerosol effect and hence the NW07 residual flux data
can justifiably be used to evaluate SDRcf from the IPCCAR4 models.
2.2. IPCC-AR4 Models
[12] The 14 models used in this study are listed in Table 1
with the corresponding abbreviation, center of origin, reference, number of runs performed for the IPCC-AR4
20C3M experiments which provide SDRcf, and references
for the sulfate (SO4) or sulfur dioxide (SO2) (precursor of
sulfate aerosol) and black carbon (BC) input data sets.
Furthermore, Table 1 indicates which models include natural forcings such as solar variability and volcanic forcing.
Output data from the model runs are available from the
WCRP CMIP3 multimodel data set, which is maintained by
the Program for Climate Model Diagnosis and Intercomparison (PCMDI).
[13] To compare SDRcf from observations with model
outputs, the model data were linearly interpolated to the
exact location of each surface radiation site. The averages of
the 8 locations in northern Germany and the 25 locations in
Switzerland were then calculated. For comparison to residual
flux, the model data were linearly interpolated to the centers
of the 44 ISCCP grid boxes displayed in Figure 1 of NW07
and then averaged to what we call a pan-European mean.
2.3. Aerosol Emission/Burden Data
[14] Sulfate and BC are the most important anthropogenic
aerosols affecting the climate system [e.g., Ramanathan et
al., 2001]. Although sulfate mainly scatters solar radiation
and BC mainly absorbs solar radiation, the effect of these
two aerosol types on the surface radiation budget is thought
to be on the same order of magnitude on a global scale.
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UKMO_HadGEM1
UKMO_HadCM3
MRI_CGCM2.3.2
NCAR_CCSM3.0
MPI_ECHAM5
MIROC3.2hires/
medres
MIUB_ECHO-G
IPSL_CM4
INM_CM3.0
IAP_FGOALS-g1.0
GFDL_CM2.0/2.1
CNRM_CM3
Abbreviation
Hadley Centre for Climate Prediction and
Research/Met Office, United Kingdom
Hadley Centre for Climate Prediction and
Research/Met Office, United Kingdom
Météo-France/Centre National de
Recherches Meteorologiques,
France
Geophysical Fluid Dynamics
Laboratory, New Jersey,
United States
Institute of Atmospheric Physics,
Beijing, China
Institute for Numerical Mathematics,
Moscow, Russia
Institut Pierre Simon Laplace,
France
Center for Climate System Research,
Japan
Meteorological Institute of the
University of Bonn, Meteorological
Research Institute of KMA, and
Model and Data group, Germany/Korea
Max-Planck-Institute for Meteorology,
Germany
Meteorological Research Institute, Japan
National Center for Atmospheric Research,
Colorado, United States
Model Center
Reference
Martin et al. [2006]
Johns et al. [2003]
Yukimoto et al. [2006]
Collins et al. [2006]
2
2
1
8
3
5
Legutke and Voss [1999]
Roeckner et al. [2003]
1/3
1
1
3
3/3
1
Number
of Runs
K-1 Developers [2004]
Marti et al. [2005]
Galin et al. [2003]
Yu et al. [2004]
Delworth et al. [2006]
Salas-Mélia et al. [2005]
Table 1. List of the 14 IPCC-AR4 Models Used in This Study
Smith et al. [2001, 2004]
Smith et al. [2001, 2004]
Mitchell and Johns [1997]
Smith et al. [2001, 2004]
Boucher and Pham [2002]
Roeckner et al. [1999]
Nozawa et al. [2007]
Boucher and Pham [2002]
Smith et al. [2001, 2004]
Boucher and Pham [2002]
Horowitz [2006]
Boucher and Pham [2002]
Sulfate/Sulfur Dioxide
Nozawa et al. [2007]
no
Collins et al. [2002]
scaled by global
population
no
no
no
Nozawa et al. [2007]
no
no
Tanré et al. [1984]
scaled by Novakov
et al. [2003]
Horowitz [2006], based
on the work of
Cooke et al. [1999]
no
Black Carbon
Solanki and Krivova
[2003]
Lean et al. [1995]
Lean et al. [1995]
Lean et al. [1995]
no
Crowley [2000] based
on the work of Lean
et al. [1995]
Lean et al. [1995]
no
Lean et al. [1995]
Lean et al. [1995]
Lean et al. [1995]
no
Solar Forcing
Sato et al. [1993]
Sato et al. [1993]
Sato et al. [1993]
Ammann et al. [2003]
no
Crowley [2000]
Sato et al. [1993]
no
Ammann et al. [2003]
Sato et al. [1993]
and Ramachandran
et al. [2000]
no
no
Volcanic Forcing
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Figure 2. (a) SO2 emission (dashed lines) and SO4 burden (solid lines) from different data sources used
in IPCC-AR4 simulations. Data represent pan-European averages (44 equal-area ISCCP grid boxes used
in NW07) and are normalized to the 1960 – 1990 long-term mean for better comparability. (b) same as
Figure 2a, but for BC.
According to the aerosol transport-radiation model study of
Takemura et al. [2005], clear-sky total (shortwave plus
longwave) surface forcing (pre-industrial to present-day) is
about 0.94 Wm 2 and 0.41 Wm 2 for BC and sulfate,
respectively. Because of the positive longwave forcing of
BC, shortwave BC forcing will be slightly larger (more
negative) than the total forcing. Consequently, the fact that
only 7 out of the 14 analyzed IPCC-AR4 models use BC
emissions is likely to have a significant impact on the
magnitude of the modeled SDRcf changes during the 20th
century. Moreover, the crude assumptions made by some of
the modeling groups to generate BC fields in the absence of
accurate data become important. Brief descriptions of the
spatial and temporal distributions of sulfate and BC aerosols
over Europe used in the IPCC-AR4 models follow, with
pan-European time series displayed in Figure 2.
2.3.1. Sulfur Dioxide/Sulfate
[15] In the GFDL_CM2.0/2.1 runs, SO2 emission data
from the EDGAR V2.0 [Olivier et al., 1996] and EDGARHYDE V1.3 [Van Aardenne et al., 2001] are used according
to the assimilation described by Horowitz [2006]. Sulfur
dioxide emissions are most pronounced in the United
Kingdom and eastern Germany. The U.K. emissions show
decreasing trends since the 1970s, whereas pan-European
emissions peak in 1990.
[16] The SO2 emissions provided by Nozawa et al. [2007]
show decreasing tendencies in the United Kingdom since
1960, whereas pan-European emissions are increasing until
1980. Since then, pan-European SO2 emissions also decline,
most distinctly in northwestern Germany.
[17] The Climate and Global Dynamics Division of the
National Center for Atmospheric Research (CGD/NCAR)
provides a frequently used data set of SO2 emission data
constructed from country-level emissions inventories and
regional fossil fuel sulfur content information by Smith, et
al. [2001] and Smith et al. [2004] (available at http://
www.earthsystemgrid.org/). These data show an emission
increase from 1950 to 1970/1980, depending on the region.
From 1980 to 2000 the emission decrease is most pronounced in the United Kingdom, northwestern and eastern
Germany, and Poland.
[18] Four of the fourteen models make use of the sulfate
burden data set provided by Boucher and Pham [2002],
which generally shows an increase from the 1950s to 1980
in Europe. The magnitude of the trend gradually increases
from northern Europe (United Kingdom and Scandinavian
countries) to southeastern Europe (Bulgaria). A decrease of
sulfate burden occurs from 1980 to 2000.
[19] The MRI_CGCM2.3.2 uses sulfate burden data from
Mitchell and Johns [1997], which exhibits a larger increase
over eastern Europe than western Europe and a maximum in
eastern Poland and Romania. According to this data set,
sulfate burden reaches its pan-European maximum in 1990.
[20] The sulfate burden data set used in MIUB_ECHO-G
simulations, described by Roeckner et al. [1999], rises in
Europe until the 1970s and lessens afterward. Sulfate
burden is largest in eastern Europe (Czech Republic,
Poland).
[21] Sulfur dioxide emission (dashed lines) and sulfate
burden (solid lines) data used in the IPCC-AR4 20C3M
experiments are summarized in Figure 2a. Time series are
pan-European averages normalized by the 1960 – 1990 longterm mean of the corresponding data set to overcome the
problem of differing input parameters used in the models.
Although the lack of direct comparability between SO2
emission and SO4 aerosol burden due to chemistry and
transport processes will introduce some uncertainty into the
regional pattern and timing of the reversal from increasing
to decreasing aerosols, we believe the short lifetime of the
aerosols (around 1 week) will ensure that this uncertainty is
small. It is apparent from Figure 2 that the differences
between data sets providing either emissions or burdens are
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Figure 3. Pan-European SDRcf time series from observations (red), fourteen IPCC-AR4 20C3M
simulations, and as average over the fourteen models (black).
as large as the differences between burden and emission
data.
2.3.2. Black Carbon
[22] Horowitz [2006] describes the BC aerosol assimilation for the GFDL_CM2.0/2.1 model runs. BC emissions
decrease in the United Kingdom since the 1960s, whereas
the pan-European decline does not start until after 1990. The
United Kingdom and eastern Germany/Poland are the two
main emission centers in the Horowitz [2006] assimilation.
[23] Nozawa et al. [2007] BC emissions are based on
energy consumption. Pan-European emissions peak around
1970 and show a continuous decrease since then. Emissions
are most pronounced in eastern Germany and Poland.
[24] The BC spatial distribution in the NCAR_CCSM3.0
model runs is described by Collins et al. [2002], and the
temporal evolution of BC burden follows global average
population and consequently does not show the transition
from increasing to decreasing BC content as do the other
data sets.
[25] In the CNRM_CM3 simulation [Salas-Mélia et al.,
2005], BC concentration from Tanré et al. [1984] is scaled
by the total fossil fuel BC emissions estimated by Novakov
et al. [2003]. These data show highest concentrations at the
end of the 1980s.
[26] Pan-European time series for BC emission (dashed
line) and BC burden (solid line) are displayed in Figure 2b.
The time series are normalized to the average from 1960 to
1990. Large differences occur between the data sets, which
reveals the urgent need for more accurate BC emission
histories for further studies.
3. Results
3.1. Comparison Between Models and Observations in
the 20th Century
[27] Figure 3 depicts pan-European SDRcf time series
from individual models and the average over all models
(black line). If a model center provides several runs (see
Table 1), the displayed time series is the average of these
runs. Estimated SDRcf anomalies from NW07 (‘‘residual
flux’’) are also shown (red line). The multimodel average
exhibits a decline of about 1.5 Wm 2 dec 1 from the
1950s to the mid 1970s, then a leveling off, and finally a
slight recovery after about 1980 (+0.8 Wm 2 dec 1). The
leveling off, however, is actually an artifact of averaging
rather than the behavior of individual models. The timing of
the transition from ‘‘dimming’’ to ‘‘brightening’’ shows a
wide range between the models, with the earliest reversal
occurring in the early 1960s (MIROC3.2 models) and the
latest around 1990 (MRI_CGCM2.3.2). According to the
multimodel average, SDRcf did not reach the level it had in
the middle of the 20th century until the end of the century.
[28] We now compare modeled trends with observational
trends during the periods when solar ‘‘dimming’’ (1971–
1983) and ‘‘brightening’’ (1984 – 1999) occurred according
to the observations. Although solar ‘‘dimming’’ began
before 1971, the lack of complete observational data prevents comparison prior to that time. Output is also not
available from all models after 1999. Figure 4 presents
trends for the observations (orange bars) as well as the individual models (red bars) during the ‘‘dimming’’ (Figures 4a, 4c,
and 4e) and ‘‘brightening’’ (Figures 4b, 4d, and 4f) periods.
The blue error bars indicate the 95% confidence interval of
the linear trend for the average of all available runs from
each model, and the green error bars indicate ± one standard
deviation of the trends of the different runs performed by each
model. Pan-European trends show a wide spread between
models, ranging from SDRcf decrease of 2.3 Wm 2 dec 1
to an increase of +1.2 Wm 2 dec 1 for the period from 1971
to 1983, when solar ‘‘dimming’’ was observed (Figure 4a).
The multimodel average underestimates the observed
SDRcf decline.
[29] The models show an even larger spread on subEuropean scales. Trends for northern Germany range from
2.2 to +3.1 Wm 2 dec 1 (Figure 4c), and for the multimodel average, nearly no trend is found. It is not clear,
however, which models are more realistic since the residual
flux trend is also highly uncertain for this region and time
period. Over Switzerland (Figure 4e), the models present
a similarly inconsistent pattern, ranging from 1.6 to
+2.1 Wm 2 dec 1. Lack of observational data does not
allow us to compare the models with observations for this
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Figure 4. Bar plot of SDRcf trends for the (a, c, and e) solar ‘‘dimming’’ period (1971 –1983) and (b, d,
and f) ‘‘brightening’’ period (1984 – 1998) for observations (orange bars) and IPCC-AR4 20C3M
simulations (red bars). The blue error bars indicate the 95% confidence interval of the linear trend of the
average from each model; the green error bars indicate ± one standard deviation of the trend from the
different model runs. The models with statistically significant differences from the observed residual flux
are marked with an asterisk.
period and location. During the ‘‘brightening’’ period
(Figures 4b, 4d, and 4f) observations and models exhibit
better agreement. On average, the models reproduce the
‘‘brightening,’’ although the magnitude is smaller than is
shown by the observations. Trends for the surface observations and residual flux are both more positive for northern
Germany than for Switzerland, and only the two MIROC3.2
models capture this regional difference in trend magnitude.
Although the observed trends in Switzerland are not significant for 1984– 1999, they are significant and show similar
magnitude if measurements until 2005 are considered.
3.2. Relation Between SDRcf Trends and Emission
Histories
[30] To investigate how input emission histories affect the
modeled solar ‘‘dimming’’ and ‘‘brightening,’’ the radiation
trends from 1984 to 1999 are plotted versus the trends from
1971 to 1983 for each model for pan-Europe (Figure 5a)
and from 1984 to 1999 versus 1960 to 1983 for northern
Germany and Switzerland (Figures 5b and 5c). The different
time spans have been chosen as a compromise between
availability of observational data and more robust model
trends due to longer time periods. Pan-European residual
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Figure 5. Scatterplots of SDRcf trends (a) in pan-Europe from 1971 to 1983 versus 1984 – 1999, (b) in
northern Germany, and (c) in Switzerland from 1960 to 1983 versus 1984 – 1999. Gray box indicates 95%
confidence interval of residual flux tends in Figure 5a, whereas in Figures 5b and 5c, observations are
only available for the solar ‘‘brightening’’ period. The marker colors indicate the SO2 or SO4 data set
used in the simulations, and the marker form stands for the BC data set. Error bars denote the 95%
confidence interval of the linear trends for the respective period.
fluxes are available for both periods; the box shows the 95%
confidence area of the trends. In northern Germany and
Switzerland, observations are only available for the ‘‘brightening’’ period; their range is represented with the gray bars.
Marker colors and marker types stand for the sulfate and BC
data sets used as model input, respectively. The circle
marker indicates models without BC aerosol. Error bars
show the 95% confidence interval of the linear trends.
[31] Aside from the GFDL_CM2.0/2.1 models, panEuropean model trends are very significantly different from
the residual flux trends, and solar ‘‘dimming’’ is especially
poorly represented. Even the GFDL models have imperfect
agreement, since the SO2 and BC input data used in
GFDLCM2.0/2.1 models [Horowitz, 2006] show steadily
increasing pan-European emission until 1990 (see Figure 2),
which causes the largest SDRcf ‘‘dimming’’ trends but also
the underestimation of the ‘‘brightening’’ trend. Contrastingly, the observations show that the reversal clearly occurs
before 1990. Even when model output is processed according to the method of NW07 rather than using actual SDRcf,
no better agreement with the observed residual flux trends
exists (not shown).
[32] Large spatial variability in aerosol over Europe and
the occurrence of nation-scale emission changes (e.g.,
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Table 2. Number Out of the Total Models Showing Solar ‘‘Dimming’’ and ‘‘Brightening’’ Consistent With the Timing of Rising and
Declining Sulfur Dioxide or Sulfate Aerosols and BC Aerosolsa
Sulfur Dioxide/Sulfate
Solar dimming
Solar bightening
BC
Pan-Europe
Northern
Germany
Switzerland
Pan-Europe
Northern
Germany
Switzerland
14 (13)/14
13 (12)/14
14 (13)/14
12 (10)/14
14 (13)/14
12 (10)/14
7 (7)/7
6 (6)/6
7 (7)/7
6 (4)/6
7 (7)/7
6 (5)/6
a
With numbers of models having statistical significant (95% confidence interval) solar radiation trends in brackets.
United Kingdom and eastern Germany) may blur direct
aerosol effects in the pan-European average. Focusing on
smaller areas allows better comparison between modeled
SDRcf trends and aerosol input data. In northern Germany
(Figure 5b), only three models (MIROC3.2hires/medres and
UKMO_HadGEM1) show trustworthy agreement with the
observations for the ‘‘brightening’’ period. These three
models use SO2 emission data from either Nozawa et al.
[2007] or Smith et al. [2001, 2004] and are the only ones
using BC emission data from Nozawa et al. [2007]. This
emission data set has the most pronounced BC decrease for
pan-Europe (see Figure 2b) and northern Germany (not
shown). On the other hand, these three models incorrectly
exhibit SDRcf increases for the expected ‘‘dimming’’ period,
which is likely driven by the Nozawa et al. [2007] transition
from increasing to decreasing BC emission in the early
1960s for northern Germany. All other models show negative trends for this period. In Switzerland (Figure 5c),
where the observed trends are smaller, the pattern of model
trends is less clear, but the MIROC3.2hires/medres and
UKMO_HadGEM1 models again have large positive or
the smallest negative trends for the ‘‘dimming’’ period.
[33] Would better emission data sets help the models
reproduce solar ‘‘dimming’’ and ‘‘brightening’’ as seen in
the observations? Do the models represent solar radiation
trends according to their input aerosol emission histories?
To answer these questions, we calculated model SDRcf
trends for the time periods when their respective aerosol
emissions / burdens were increasing and when they were
decreasing. This takes into account the timing of the
transition from ‘‘dimming’’ to ‘‘brightening’’ that differs
from model to model, according on the emission / burden
data set used. The 2 years following the eruptions of El
Chichón in 1981 and Mount Pinatubo in 1991 have been
omitted from linear trend analysis for the models using
volcanic forcings (see Table 1), since these years can bias
the trends in different ways depending on the onset of the
‘‘brightening’’. Table 2 gives the number of models (out of
all models) showing decreasing (Solar Dimming) and increasing solar irradiance (Solar Brightening) for the time
periods during which the aerosol histories show rising and
declining trends. Numbers in brackets indicate the models
with statistical significant trends (95% confidence interval).
Two of the fourteen models do not display an increase in
SDRcf in northern Germany and in Switzerland for the
period during which SO2 emission or SO4 burden shows a
decline. These are CNRM_CM3 and IAP_FGOALS-g1.0 for
northern Germany and GFDL_CM2.1 and IAP_FGOALSg1.0 for Switzerland. CNRM_CM3 and GFDL_CM2.1
consider also BC aerosols, but with a later transition timing
from increasing to decreasing emissions than sulfate. This
caused the disagreement between the sign of the SDRcf
trend and the reversal of sulfate burden. Hence, only the
IAP_FGOALS-g1.0 model does not represent SDR cf
according to the input sulfate burden data set. Considering
the reversal from increasing to decreasing BC aerosols, all
models using BC as forcing agent show signs of SDRcf
trends that are consistent with their input BC data set.
[34] The negative correlation between aerosol emission/
burden and SDRcf (Figure 6) strengthens the hypothesis that
better aerosol data sets would improve the models’ ability to
represent solar irradiance trends. Correlation coefficients
(R) are given for all data points and for ‘‘dimming’’ (data
points in the upper half of the diagram) and ‘‘brightening’’
(data points in the lower half of the diagram) periods
separately. For ‘‘dimming’’ and ‘‘brightening’’ periods the
negative correlation is weaker than the overall correlation,
but Figure 6 still indicates that higher (lower) percentage
changes in aerosol are associated with higher (lower) trends
in SDRcf. This suggests that, in addition to the model
response to aerosol, the magnitude of the change in the
input aerosol history is a major contributor to the change in
SDRcf. Models with the similar radiation codes but different
aerosol histories produce different solar radiation trends (not
shown), and most models with similar aerosol histories
produce similar solar radiation trends.
[35] An investigation of seasonal trends in aerosol emission/burden and simulated SDRcf produced no additional
information. This is because input aerosol histories have no
or only negligible differences in the seasonal timing from
increasing to decreasing emissions/burden. Modeled SDRcf
trends consequently exhibit no notable seasonal differences
apart from greater trend magnitude in seasons when insolation is larger.
4. Summary and Conclusion
[36] The large spread in the timing of the transition from
solar ‘‘dimming’’ to ‘‘brightening’’ under cloud-free skies
between the IPCC-AR4 models in 20C3M simulations
reflects the variability of aerosol histories used in these
simulations. For instance, sulfate burden or sulfur dioxide
emission peaks anytime between the mid 1960s and 1990,
and BC starts to decline in the early 1960s or shows a
gradual increase through the second half of the 20th century.
These facts reveal that the uncertainty of the aerosol forcing
reported by Solomon et al. [2007] is not only related to the
aerosol treatment in the models, but also to the aerosol
histories used. Differences in SDRcf trends among models
using the same aerosol histories are generally small (less
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Figure 6. (a) Negative correlation between SO2 emission/SO4 burden trends and SDRcf trends and
(b) between BC emission/burden trends and SDRcf trends. SDRcf trends are calculated for the time periods
when aerosol emissions/burden show increasing and decreasing tendencies; therefore they have different
periods. Correlation coefficient (R) is given for all data points, for data points belonging the ‘‘dimming’’
(data points in the upper half of the graphs), and ‘‘brightening’’ period (data points in the lower half of the
graphs).
than 0.7 Wm 2 dec 1). The maximum SDRcf trend
difference among models using the same aerosol history is
2.5 Wm 2 dec 1 for the IAP_GGOALS-g1.0 model compared to the MPI_ECHAM5 and IPS_CM4 models. This is
a further indication that the IAP_FGOALS-g1.0 model has
difficulty representing the direct aerosol effect correctly.
[37] If the model trends are calculated for time periods
when the respective aerosol history shows an increasing or
decreasing trend, virtually all models reproduce the transition form solar ‘‘dimming’’ to ‘‘brightening’’ correctly. This
leads to the conclusion that the models would much better
reproduce observed solar ‘‘dimming’’ and ‘‘brightening’’ if
the input data (i.e., aerosol history) were more realistic. At
present, we cannot determine which emission or burden
data set is most reliable for the European region, as some
better represent the ‘‘dimming’’ period and some the
‘‘brightening’’ period. Nevertheless, aerosol histories should
have their maximum emissions or burdens for Europe as a
whole before the 1990s, in contrast to some of the data sets
that have a maximum around 1990. Additionally, our
analysis emphasizes the necessity to consider sulfate and
BC aerosols to obtain the magnitude of the observed solar
irradiance trends. This is consistent with the modeling
results of Takemura et al. [2005], who show that BC has
a slightly larger impact on the surface forcing than sulfate
has under clear-sky conditions over land as well as on a
global scale. The incorporation of newly available BC
emission data sets [Bond et al., 2007; Junker and Liousse,
2008] will be a important step toward producing more
realistic historical climate change simulations.
[38] Although we investigated trends only over Europe,
we expect that input aerosol histories also exert a dominant
influence on simulated trends in SDRcf on a global scale.
Hence, considering the large differences between BC aero-
sol histories in the data sets used in the 20C3M simulations
and the facts that fewer than half of the IPCC-AR4 models
use BC as a forcing agent and fewer than one third of the
models include indirect aerosol effects, a final question
remains: Why do the models reconstruct the 20th century
global surface temperature so well and so consistently [e.g.,
Hegerl et al., 2007]? This is especially the case if BC is
thought to play such a crucial role on the surface temperature [Ramanathan and Carmichael, 2008]. One possible
reason for the apparent good agreement may be compensation between high climate sensitivity and large negative
aerosol radiative forcing and vice versa, as discussed by
Kiehl [2007] and Knutti [2008]. Another possible reason
may be compensation between errors in the magnitude of
sulfate and BC aerosol forcing. A third possible reason is
compensation between errors in the magnitude of aerosol
direct and indirect radiative forcing. We did not examine
trends in aerosol indirect forcing, but [e.g., Quaas et al.,
2008] suggest that indirect aerosol effects might be generally overestimated, and fewer than a third of the IPCC-AR4
models include indirect aerosol effects. More research is
needed, and in other regions of the world. We believe
observed multidecadal variations in downward solar irradiance at the surface can provide a useful constraint on the
time history of past aerosol forcing and thereby model
climate sensitivity and future projects of climate change.
[39] Acknowledgments. The work of Christian Ruckstuhl is supported by the Swiss National Science Foundation (SNSF), Grant no.
119674. An NSF CAREER award, ATM02 – 38527, supported the work
by Joel R. Norris. GEBA database is maintained at ETH Zurich. ISCCP
cloud and radiation flux data were obtained from the NASA Goddard
Institute for Space Studies. The authors thank MeteoSwiss for providing
synoptic cloud observations and solar radiation data. We also acknowledge
the modeling groups, the PCMDI and the WCRP’s Working Group on
Coupled Modeling (WGCM) for their roles in making available the WCRP
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RUCKSTUHL AND NORRIS: MODELED AND OBSERVED SOLAR RADIATION
CMIP3 multimodel data set. Support of this data set is provided by the
Office of Science, U.S. Department of Energy. The authors are grateful for
emission data and comments from Olivier Boucher, William Collins, Larry
W. Horowitz, Seung-Ki Min, Tihomir Novakov, Jean-Francois Royer,
David Salas, Gary Strand, Evgeny Volodin, Martin Wild, and Seiji
Yukimoto.
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