Robust response of Asian summer monsoon to
anthropogenic aerosols in CMIP5 models
1
1
1
M. Salzmann , H. Weser , and R. Cherian
An edited version of this paper was published by AGU.
Copyright (2014) American Geophysical Union.
Salzmann, M, H. Weser, and R. Cherian (2014), Robust response of Asian summer monsoon to anthropogenic aerosols in
CMIP5 models, J. Geophys. Res. Atmos., 119, 11321-11337,
doi:10.1002/2014JD021783.
To view the published open abstract, go to http://dx.doi.org and
enter the DOI.
Corresponding author: M. Salzmann, Institut für Meteorologie, Universität Leipzig, vor dem
Hospitaltore 1, D-04146 Leipzig, Germany. ([email protected])
1
Institute for Meteorology, Universität
Leipzig, Germany
Abstract.
The representation of aerosol processes and the skill in sim-
ulating the Asian summer monsoon vary widely across climate models. Yet,
for the second half of the twentieth century, the models from the Coupled
Model Intercomparison Project Phase 5 (CMIP5) show a robust decrease of
average precipitation in the South- and Southeast Asian continental region
(SSEA) due to the increase of anthropogenic aerosols. When taking into account anthropogenic aerosols as well as greenhouse gases (GHGs), the 15 CMIP5
models considered in this study yield an average June-September precipitation least-squares linear trend of −0.20 ± 0.20 mm day−1 (50 years)−1 , or
-2.9%, for all land points in the SSEA region (taken from 75 to 120◦E and
5 to 30◦ N) in the years from 1950 to 1999 (multi-model average ± one standard deviation) in spite of an increase in the water vapor path of +0.99 ±
0.65 kg m−2 (50 years)−1 (+2.5%). This negative precipitation trend differs
markedly from the positive precipitation trend of +0.29±0.14 mm day−1 (50
years)−1 , or +4.1%, which is computed for GHG forcing only. Taking into
account aerosols both decreases the water vapor path and slows down the
monsoon circulation as suggested by several previous studies. At smaller scales,
however, internal variability makes attributing observed precipitation changes
to anthropogenic aerosols more difficult. Over Northern Central India (NCI),
the spread between precipitation trends from individual model realizations
is generally comparable in magnitude to simulated changes due to aerosols,
and the model results suggest that the observed drying in NCI might in part
be explained by internal variability.
1. Introduction
The Asian summer monsoon not only affects one of the most densely populated areas
of the world, but is also one of the most studied topics in climate change research with
rapidly increasing scientific consensus on several important issues. Of particularly large
environmental, societal, and economic concern is the observed trend toward more heavy
rain events but less frequent light precipitation in India [Goswami et al., 2006; Dash et al.,
2009, 2011] and eastern China [Qian et al., 2007] since it leads to an increase in the risk
of both, drought, as well as flood damage [e.g. Auffhammer et al., 2012]. This change
in precipitation characteristics is broadly consistent with the general expectations from
GHG warming [Trenberth, 2011] and is expected to continue into the future.
In addition, a large majority of studies agrees that in the absence of other forcings,
GHG warming acts on the average to increase Asian monsoon rainfall due to the increased
availability of water vapor [e.g. Meehl and Washington, 1993; Mitchell and Johns, 1997;
Ueda et al., 2006; May, 2011; Lee and Wang, 2014] and that, conversely, the cooling due
to sulfate aerosols leads to a decrease in summer monsoon precipitation [Mitchell and
Johns, 1997; Bollasina et al., 2011; Sajani et al., 2012; Cherian et al., 2013].
Cooling by anthropogenic sulfate aerosols not only lowers water vapor availability, but
also reduces the north-south and the land-sea surface temperature contrast and has therefore been associated with a weakening of the monsoon circulation [e.g. Mitchell and Johns,
1997; Bollasina et al., 2011].
Furthermore, a number of studies have suggested that the Asian monsoon is strongly
influenced by absorbing aerosols, especially during the onset period [e.g. Menon et al.,
2002; Ramanathan et al., 2005; Ramanathan and Carmichael , 2008; Wang et al., 2009;
Lau and Kim, 2010].
Aerosols have a tropospheric residence time of days to weeks while carbon dioxide
emissions partially accumulate in the atmosphere. In addition, several future emission
scenarios suggest a pronounced decrease in aerosol emissions toward the end of the 21st
century [Lamarque et al., 2011; Smith and Bond , 2014]. The resulting decrease in the
relative importance of aerosols versus GHG concentrations is expected to contribute to
the projected moistening in East and South Asia [H. Levy II et al., 2013].
Although several early model studies suggested that, in principle, the monsoon circulation might intensify under the influence of GHG warming due to the an enhanced land-sea
temperature contrast [Meehl and Washington, 1993; Mitchell and Johns, 1997], a majority
of the more recent model sensitivity studies suggests a weakening of the Asian summer
monsoon circulation due to the influence of GHGs [Kitoh et al., 1997; May, 2004; Cherchi et al., 2011; May, 2011]. This weakening is accompanied by an eastward shift in the
Walker circulation [May, 2004; Ueda et al., 2006; Bollasina et al., 2011; May, 2011]. It is
also consistent with the expectation of a general slowdown of the overturning circulation
in a warmer climate [Allen and Ingram, 2002; Held and Soden, 2006; Trenberth, 2011].
In spite of the recent scientific progresses, the observed overall trend of precipitation in
the Asian summer monsoon region shows a rather complex regional pattern [e.g. Yao et al.,
2008] that remains difficult to explain, especially since projections from individual climate
model simulations generally show little agreement regarding the regional distribution of
rainfall trends.
Recently, Bollasina et al. [2011] attributed the observed drying in central-north India
mainly to anthropogenic aerosol emissions. They found that in their model, the aerosolinduced cooling of the source regions was realized mainly through the indirect aerosol
effect. On the other hand, Golaz et al. [2011, 2013] have indicated that the indirect
aerosol effect in this particular model is very sensitive to model tuning and that the
model simulates a very strong indirect aerosol cooling effect that is in part compensated
by a continual release of heat from the ocean into the atmosphere during the twentieth
century [Golaz et al., 2013]. Furthermore, aerosol emissions have also been increasing
in Southeast Asia while Southeast Asia did not experience a comparable decrease in the
overall amount precipitation, but an increase. Instead of this observed increase, a decrease
has been simulated by Bollasina et al. [2011]. It is therefore useful and necessary to extend
the study by Bollasina et al. [2011] to other models and a larger domain.
The Coupled Model Intercomparison Project Phase 5 [CMIP5, Taylor et al., 2012]
provides typically several realizations per climate model from a set of model experiments
that allows us not only to compare the combined effects of anthropogenic aerosols (due
to aerosol-radiation and aerosol-cloud interactions) and other forcings to the individual
effects of anthropogenic GHGs and of natural forcings. It also enables us to better quantify
the uncertainties associated with applying different model formulations and to investigate
the effects of simulated internal variability as evidenced by the spread between trends
from individual ensemble members. More importantly, averaging over a large ensemble
of model runs effectively filters out influences of internal variability and thus allows us to
concentrate on the influence of the physical processes that determine the average trends.
These “filtered” results do not necessarily correspond to observations. Instead, they can
be deemed to be “consistent with observations” as long as one or more individual ensemble
members represent the observations. Due to the large spread in geographical precipitation
distributions, the individual ensemble members can significantly deviate from the model
average on the regional scale.
The CMIP5 runs and the observational datasets used in this study are described in the
next section. Section 3.1 provides an overview of the geographical distributions of the
precipitation and aerosol trends. The dynamic and thermodynamic responses to anthropogenic aerosols and greenhouse gases are discussed in Section 3.2. In Section 3.3 the
results from the individual models are further analyzed in order to separately asses the
roles of internal variability and model uncertainty. The radiative effects of anthropogenic
aerosols and their effects on the thermal structure of the troposphere are discussed in Sections 3.4 and 3.5, respectively, and the additivity of the effects of GHGs and anthropogenic
aerosols as well as the effects of land-use changes are investigated in Section 3.6.
2. Methods
We analyze a total of 239 runs by 15 CMIP5 models (see table 1). For each of these models, the CMIP5 participants have performed runs for a set of two sensitivity experiments in
addition to the “standard” historical experiment. The historicalGHG experiment includes
only the effect of well mixed GHGs and the historicalNat experiment takes into account
only the effect of natural forcings, in particular volcanic aerosols and solar variability. Preindustrial (year 1850) aerosols are kept at constant levels that vary from model to model.
The “standard” historical simulations, on the other hand, take into account effects of anthropogenic aerosols, landuse changes, and changes of tropospheric ozone as well [Taylor
et al., 2012]. The global mean radiative forcing due to land use changes (-0.15 W m−2 ) and
tropospheric ozone (+0.4 W m−2 ) are estimated to be smaller than the combined effective
radiative forcing (ERF) of anthropogenic aerosols (-0.9 W m−2 ) due to aerosol-radiation
and aerosol-cloud interactions [Myhre et al., 2013]. Furthermore, the forcing due to tropospheric ozone is of the opposite sign of the aerosol ERF. As the study region is considered
a “hotspot” of anthropogenic aerosol emissions, we assume that differences between the
historicalGHG and the historical experiment can be attributed mainly to anthropogenic
aerosols, although the effects of the other forcings might not be negligible.
Results for the historicalGHG and the historicalNat experiment have been provided
by a subset of the CMIP5 participants including many of the well established modelling
groups. The majority of these groups has provided an ensemble of several realizations for
each of the two sensitivity experiments. In total, we analyze results from 71 historical,
45 historicalNat and 46 historicalGHG runs. We also analyze 29 simulations from the
historicalMisc collection of experiments that include the effects of increasing anthropogenic
aerosol (“AA”) concentrations, but not those of increasing anthropogenic GHGs. We will
refer to these runs as historicalAA runs, noting that the models usually allow changes in
sea salt and dust emissions as well. Finally, 18 historicalMisc runs that only take into
account landuse changes are analyzed. These will be referred to as historicalLU runs.
Roughly half of the 15 models have performed historicalAA runs, while results from the
historicalLU runs are available for only one third of the models (see table 1 for details).
Our analysis focuses on the South- and Southeast Asian continental region (SSEA)
taken from 75 to 120◦ E and 5 to 30◦ N which is characterized by comparatively high
summer monsoon precipitation, and on Northern Central India (NCI) taken from 76◦
to 87E◦ , and from 20◦ to 28◦ N following e.g. Bollasina et al. [2011]. Model averages
for the SSEA region are computed for land points while maps show both sea and land
areas. Like Bollasina et al. [2011], we focus on the bulk anthropogenic aerosol effect, i.e.
the combined effect of anthropogenic aerosols due to aerosol-radiation and aerosol-cloud
interactions. Since we are mainly interested in the effect of aerosols acting on top of the
increase in GHGs and since the number of models that have performed historicalAA runs
is rather small, we primarily investigate the differences between the historicalGHG and
the historical experiment.
Precipitation and temperature over land from the historical experiment are compared
to the Climate Research Unit (CRU) TS 3.10.01 [based on Mitchell and Jones, 2005] and
the University of Delaware [UDel, e.g. Legates and Willmott, 1990] observation-derived
datasets.
Statistical significances of least-squares linear trends are computed using a two-tailed
student t−test. For maps showing ensemble averages, statistical significance is computed
for the trend of the ensemble average over the available runs. This choice was made
since the pointwise calculated ensemble average trends in this study are generally smaller
than the standard deviation of the individual trends. For maps combining data from
several models, the model output has been regridded to a 2◦ x2◦ grid. Divergence has
been calculated using centered finite differences.
The term “ensemble average” refers to an average in which each model run is weighted
equally in this study, while in a “multi-model average” the contribution from each model is
weighted equally. All 15 models treat the direct effects of sulfate, black carbon, and organic
carbon aerosol, and 12 of them take into account aerosol-cloud interactions (table 1).
3. Results
3.1. Geographical distribution of precipitation and aerosol trends
The CMIP5 ensemble average reproduces several of the main features of the observed
precipitation pattern in the SSEA region (Figure 1). In particular, the transition from
wet to dryer conditions to the North of the region and the precipitation maximum that
extends from the Ganges delta southward along the coast of Myanmar are reproduced. In
the Northeast, however, dry conditions extend too far east into NCI as some models fail
to capture the transition between dry and moist conditions east of India, while others do
(Figure S1).
The observed drying in NCI (Figure 2a and b), on the other hand, is at least qualitatively reproduced by the CMIP5 ensemble average (Figure 2c), although individual model
realizations (e.g. Figure 2d) show a moistening instead of the observed drying. The observed moistening in China, on the other hand, is reproduced in individual realizations
from several models including HadGEM2, CanESM2, and CSIRO-Mk-3-6-0 (Figure 2h
and Figure S3) but not by the CMIP5 ensemble average. Instead, the drying pattern
roughly follows that of the aerosol optical thickness (AOT) in Figure 3 (which has been
computed for the nine models for which AOT output was available). Nevertheless, for
most models for which more than one realization has been submitted at least one ensemble
member comes fairly close to the observed precipitation trend (Figure S3).
When only well-mixed GHGs are taken into account (Figure 2i), moistening dominates in much of SSEA. Only south central China does not show a clear moistening trend and thus appears to be more susceptible to drying in the historical experiment. The historicalNat experiment yields a drying tendency for Indochina, but on
the whole, the drying is small compared to the historical experiment (Figure 4a). The
multi-model average precipitation trend is −0.20 ± 0.20 mm day−2 (50 years)−1 for the
historical and +0.29 ± 0.14 mm day−1 (50 years)−1 for the historicalGHG experiment. The
SSEA precipitation trend from CRU is −0.28 mm day−1 (50 years)−1 and from UDel it is
−0.55 mm day−1 (50 years)−1 .
3.2. Dynamic and thermodynamic responses to anthropogenic forcing
In SSEA, precipitation decreases in the historical experiment while at the same time
surface temperature and water vapor path increase (Figures 4c and d). The reason
for this is the slowdown of the monsoon circulation (Figure 4e and f).
This slow-
down is clearly stronger than that associated with GHG forcing alone. A similar, albeit
slightly less clear picture, also arises for the NCI region (Figure 5). Taking into account
anthropogenic aerosols in the historical experiment also leads to significantly reduced
temperature and water vapor availability compared to the historicalGHG experiment.
The multi-model average water vapor path trend is +0.99 ± 0.65 for the historical and
+3.10 ± 0.86 kg m−2 (50 years)−1 for the historicalGHG experiment.
Neglecting water storage, the net advection of water vapor into SSEA must be balanced
by the precipitation excess P − E, where P is surface precipitation and E surface evaporation. P − E is then balanced by river discharge. For the historicalGHG experiment, the
average P − E trend in Figure 4b and the precipitation trend in Figure 4a are of similar
magnitude. This indicates that the simulated precipitation increase is mainly fueled by
net vapor advection from outside the domain. A smaller contribution is associated with
the increase in local evaporation.
The simulated precipitation decrease in the historical experiment, on the other hand,
can not be explained by a decrease in net horizontal vapor advection alone. Instead, it is
also associated with a pronounced decrease in local evaporation and a general slowdown
of the local hydrological cycle. This slowdown of the local hydrological cycle can be
understood as constituting a part of the general slowdown of the monsoon circulation.
It is in line with Ganguly et al. [2012], who found that summer monsoon precipitation
changes are strongly influenced by local aerosol emissions. In NCI, local evaporation and
precipitation both increase in the historicalGHG experiment and both decrease in the
historical experiment (Figure 5).
Anthropogenic aerosol emissions increase the north-south near-surface temperature gradient over East Asia and the Pacific (compare Figure 6a to Figure 6c noting that the
average trends have been subtracted). They also partially counteract the increase in
land-sea temperature contrast that is associated with anthropogenic greenhouse gases.
This increase in the sea-land surface temperature contrast due to GHGs is strongest in
arid regions. It is damped in the moist monsoon region. The dynamic response to aerosols
and the corresponding changes in the hydrological cycle, on the other hand, are found to
be particularly strong in the moist SSEA monsoon region (compare Figure 7b to Figure 7d
and see also Figure 8). The dynamic response to well-mixed GHGs is particularly strong
within 10◦ latitude of the equator.
The Intertropical Convergence Zone (ITCZ) shifts equatorward over the Pacific due to
GHG warming [compare Huang et al., 2013] and also from the Indian to the Pacific ocean.
The region of reduced upward velocity northeast of Australia (Figure 7) corresponds to a
region of weaker warming (relative cooling) in Figure 6. A similar pattern is also found
south of the Gulf of Mexico. In both cases, the surrounding land masses have warmed
relative to the ocean. This similarity suggests that the decreased ascent over Indonesia
and the decreased ascent over the Caribbean sea could have the same reason, namely the
warming of the adjacent land-masses relative to the ocean. This would imply that the
change in the land-sea surface temperature contrast is helping to drive the eastward shift
of the Walker-circulation.
Ultimately, the increasing land-sea temperature contrast could lead to a reduction of
the monsoon strength via its influence on the Walker circulation. On the whole, however,
the CMIP5 ensemble average suggests only a slight weakening of the ascent over SSEA.
Many of the historicalGHG runs show a strengthening instead (Figure 4e and f and next
section).
In addition to an enhanced land-sea contrast, Figure 6a and c also reveals an enhanced
temperature contrast between moist continental SSEA and the arid regions of West Asia
including Saudi-Arabia and Iran. Furthermore, Figure 7a together with Figure 8c and
8d suggests that the decreased ascent in the SSEA is partially compensated by decreased
descent over continental West Asia. This anomalous circulation between continental SSEA
and West Asia is consistent with the enhanced temperature contrast, i.e. with cooling in
the SSEA ascent region and warming in the West Asia descent region. Considering the
direction of the 850 hPa divergent wind over India (Figure 8a) suggests that the enhanced
thermal contrast between SSEA and West Asia might play a role for the weakening of the
Monsoon circulation.
3.3. Internal variability
The simulated drying trends for NCI vary significantly not only between the different
models but also between the individual realizations (Figure 9). This indicates a potentially large influence of internal variability on the precipitation trend. At the same time,
the observed drying trend in NCI is qualitatively reproduced only in a small fraction of
the individual realizations (Figure 9). For the historical experiment, one third of the 15
models have produced realizations with a drying trend that is within 30% of the CRU
and UDel observed trends. The precipitation trends from these five models as well as that
from CSIRO-Mk-3-6-0 can be considered consistent with the observations, even though
the corresponding model average drying trends are all smaller than the ones in the observations.
In the light of the large uncertainties associated with the representation of aerosols
in global climate models and the uncertainties regarding inventories of historical aerosol
emissions, it can not be ruled out that even the models which simulate the strongest drying
still underestimate the local aerosol effect. On the other hand, several of the models are
thought to simulate an overly strong rather than an overly weak global aerosol effect, and
we are at present not aware of any indications of an underestimate of the emission trend
in this particular region. The difference between the average simulated and the observed
precipitation trend can, however, readily be explained by the effect of internal variability.
Since the observations can be thought of as a single realization of all the possible climate
states for a given forcing, even for a perfect model and perfect observations, one would not
expect an exact match between the average of the realizations from that model and the
observation. Furthermore, although there is some uncertainty regarding the observations,
the observed drying in NCI is backed by several datasets [see discussion in Bollasina et al.,
2011].
For the model with the strongest drying (CanESM2), the model average drying is
−0.72 mm day−1 (50 years)−1 compared to −0.86 mm day−1 (50 years)−1 based on the average of the CRU and UDel data, suggesting that 17% of the observed drying in NCI might
be explained by internal variability. For the model that simulates the second strongest
drying (-0.50 mm day−1 (50 years)−1 ), 42% of the observed drying is explained by internal
variability. It is therefore likely that internal variability has contributed significantly to
the observed trend, even though the present results do not allow an exact estimate of this
contribution. In particular, a few of the models appear to place the drying to far to the
east (Figure S2).
In order to better quantify the potential role of internal variability, it is nevertheless
useful to further examine the results for the models for which more than one realization
of the historical experiment is available. In these models, the standard deviation of the
precipitation trends for the historical runs, σhist , varies from 0.08 to 0.76 mm day−1 (50
−1
(50 years)−1 . The standard
years)−1 with a multi-model average σ m
hist = 0.39 mm day
deviation of the model average precipitation trends (“multi-model standard deviation”)
for the historical experiment, σav , is 0.33 mm day−1 (50 years)−1 . The ratio
a=
σm
hist
σav
(1)
equals 1.17 for NCI and 0.91 for SSEA (Figure 10), i.e. the internal variability and the
spread of the model results are of comparable magnitude, even though one should keep in
mind that not all of the models produce results that are consistent with the observations.
For a single model, the effect of anthropogenic aerosols on the precipitation trend
can be estimated from the difference between the average precipitation trend from the
historicalGHG realizations (thistGHG ) and from the historical realizations (thist ). The
multi-model average of the ratio
b=
m
σhist
|thistGHG − thist |
(2)
m
is b = 0.91 for NCI and b = 0.43 for SSEA, i.e. for NCI, the effects of aerosols and of
internal variability are of comparable magnitude, while for the SSEA average the effect
of aerosols dominates over the effect of internal variability.
In spite of large differences between the models, the simulated response to anthropogenic
aerosols in SSEA is robust.
3.4. Robustness of aerosol radiative effects
In most of the 15 CMIP5 models, anthropogenic aerosols have reduced the average
JJAS absorption of solar radiation (SWABS = F↓sw,T OA − F↑sw,T OA, where F↓sw,T OA is
the incoming and F↑sw,T OA the outgoing short-wave (SW) radiation flux at the top of
the atmosphere (TOA)) in the SSEA region during the second half of the last century
(Figures 11a and S4). This is in part due to a robust decrease in clear-sky absorption
(SWABScs, Figure 11b) which helps to explain the fairly robust response to aerosols in the
CMIP5 models. At the same time as SWABS has decreased, the short-wave cloud radiative
effect (SWCRE=SWABS−SWABScs) tends to become less negative (Figure 11c) and the
cloud cover decreases (Figure 11d). The cloud cover changes, however, differ strongly
between the models (Figure S4).
While a cloud cover decrease is consistent with a less negative SWCRE, it should be
noted that the SWCRE decreases when scattering aerosols are added even in the absence
of any cloud changes. This is because scattering aerosols partially mask the clouds. In
the hypothetical case of adding a completely reflecting aerosol layer, the SWCRE would
become zero. Similarly, clouds also mask the radiative effect of sulfate aerosols, so that
the trend in SWABScs is expected to be larger than the trend in SWABS, even in the
absence of cloud changes. While these effects are certainly important, they are difficult to
quantify in the absence of a radiative kernel for (scattering) aerosols. At present, radiative
kernels are available for water vapor, temperature, and also carbon dioxide [Soden et al.,
2008; Block and Mauritsen, 2013], but not for aerosols. In the case of absorbing aerosols,
the change induced in SWCRE assuming fixed cloud properties depends on the location of
the aerosol: an aerosol layer above a cloud layer acts to decrease SWCRE (in the absence
of cloud changes) while a layer of purely absorbing aerosol below or between clouds would
tend to increase the SWCRE over a relatively brighter land surface (again assuming the
absence of actual cloud changes).
In order to better isolate cloud feedbacks from cloud adjustments, one would have
to consider simulations with fixed sea-surface temperature. In particular, the CMIP5
sstClimAeros and sstClim experiments [Taylor et al., 2012] could be useful, even though
they are not available for all the models and only cover the last 20 years of the 50 year
episode. Unfortunately, however, these model runs still allow for a change in the land-sea
temperature contrast, so that they might be less suited for monsoon-related studies.
The net effect of an increase in scattering aerosols is a surface cooling via the direct
and indirect radiative effects. The net effect of an increase in absorbing aerosol on the
surface temperature, on the other hand, depends on the chemical composition and on the
magnitude of the semi-direct effect. Ramanathan and Carmichael [2008] have suggested
that the large scale effect of absorbing aerosols on the monsoon circulation is a slowdown
caused by net surface cooling and reduced evaporation. More recently, Bond et al. [2013]
noticed a lack of conclusive model data on this issue. Furthermore, atmospheric heating
by absorbing aerosols can impact deep convection depending on the vertical distribution
of the aerosol heating (a semi-direct effect). Unfortunately, the available CMIP5 data
does not allow us to clearly distinguish between the effects of scattering and absorbing
aerosols.
3.5. Effects on stability and thermal structure
On average, the CMIP5 models suggest that anthropogenic aerosols and GHGs induce
roughly opposite trends of the equivalent potential temperature θe and the temperature
(Figure 13), in spite of the rather different vertical profiles of GHGs and aerosols. This
is because the vertical profile of the tropospheric temperature trend partially reflects the
response to surface warming or cooling. Since the tropical troposphere does not support
large horizontal temperature gradients, deep convection acts to relax the temperature
profile toward a moist adiabat, which explains both the enhanced upper tropospheric
warming due to anthropogenic GHGs as well as the enhanced upper tropospheric cooling
due to anthropogenic aerosols. Deep convection thus partially inhibits the buildup of large
changes in stability as evidenced by Figure 13a. For NCI, the overall picture is similar
(Figure S5).
3.6. Non-linearity and landuse changes
Thus far, we have mainly focused on analyzing aerosol effects that occur in combination
with GHG warming, and we have not yet excluded the possibility that landuse changes
had a strong impact on the simulations. In particular, Bollasina et al. [2011] found that
the combined effect of aerosols and GHG gases does not equal the sum of the two effects.
For the SSEA region, however, taking the difference between corresponding historical
minus historicalGHG runs leads to trends that are roughly comparable to those from the
historicalAA runs (Figure 12). Generally speaking, this is true for the individual model
realizations as well (Figure S6), although the GFDL model which was analyzed for NCI
by Bollasina et al. [2011] yields a somewhat larger non-linearity compared to most other
models even for the larger SSEA region. For NCI, non-linearity is important in some
models, but not in others (Figure S7). For smaller regions, non-linearities are generally
not negligible (Figure S8).
4. Summary and Discussion
The CMIP5 results suggest that anthropogenic aerosols had a larger influence on the
summer monsoon circulation over continental SSEA than GHG warming during the
second half of the twentieth century. While the dynamic response to GHG warming
is concentrated within 10◦ latitude of the equator, taking into account anthropogenic
aerosol acts to reduce the average ascent over continental SSEA in good agreement with
Bollasina et al. [2011]. In addition, the net cooling due to anthropogenic aerosols leads
to a pronounced decrease in the water vapor availability, also in agreement with previous
studies [e.g. Mitchell and Johns, 1997; Bollasina et al., 2011]. The result is a decrease of
average precipitation in continental SSEA, even though the positive water vapor trend
due to GHG warming is not completely offset by the aerosol cooling.
In addition to the circulation changes already highlighted by Bollasina et al. [2011], we
find that anthropogenic aerosols introduce an anomalous circulation between the summer
monsoon region and West Asia including Saudi-Arabia and Iran. Furthermore, we suggest
a mechanism by which the increase in land-sea surface temperature contrast due to GHG
warming could help to drive the eastward shift of the Walker-circulation.
For the SSEA average precipitation trend, the effect of aerosols dominates over internal
variability, while smaller scale regional trends are more strongly influenced by internal
variability. For NCI internal variability and aerosols play similarly important roles in
determining the 1950–1999 precipitation trend.
Internal variability makes attributing the observed trend in NCI to aerosol forcing more
difficult, as only a few realizations of the historical experiment qualitatively agree with
the observed trend, while at the same time a few individual historicalGHG simulations
also yield a considerable drying trend. Based on the very small number of historicalGHG
simulations that yield a drying (Figure 9), it is, however, unlikely that the observed drying
would have occurred without the influence of aerosols. On the other hand, even the
models which simulate the strongest drying yield a smaller ensemble average drying trend
compared to the observations. As some models are thought to overestimate rather than
underestimate the aerosol effect, this suggests that internal variability has contributed to
the observed drying trend.
The large spread between individual realizations also suggests that internal variability
can restrict the prediction of regional scale multi-decadal precipitation trends even under
a fairly strong forcing. The spread between model realizations due to internal variability
is of similar magnitude as the spread between the model averages for both SSEA and
NCI.
In the north-eastern part of SSEA (mainly China), the simulated precipitation increase
due to GHGs is weakest, while the simulated decrease due to aerosols is particularly
strong. Together, this leads to a large discrepancy between the observed and the ensemble average simulated precipitation trend. Possible explanations for this discrepancy
include uncertainties in the historical estimates of anthropogenic aerosol sources and uncertainties related to the long term stability of precipitation observations. On the other
hand, as suggested by the HadGEM2 results presented in Sect. 3.1 and by Figure S3, the
discrepancy could also be explained by the effect of internal variability on precipitation
patterns.
The trend in the net TOA clear-sky SW aerosol radiative effect in SSEA is dominated
by an increase of scattering aerosols which more than compensates the increase in aerosol
absorption in all the models and which helps to explain the robustness of the simulated
drying trend. The picture for the indirect and semi-direct aerosol radiative effects is,
however, much less clear as expected based on the significant differences between the
model formulations. An analysis of the effect of anthropogenic aerosols on the domain
average thermal structure of the troposphere over SSEA suggests that deep convection
acts to produce similar but opposite responses to anhropogenic GHGs and aerosols.
Non-linearities between GHG and aerosol forcing [e.g Bollasina et al., 2011] play an
important role in some models, but not in others for the NCI region. For the SSEA
region, on the other hand, the non-linearity is strongly reduced.
Unfortunately, we have not been able to distinguish between the effects of scattering
and absorbing aerosols based on the CMIP5 data, although this would certainly have
been very interesting. For example, Gu et al. [2006] finds that the simulated precipitation
trend in China depends on the fraction of absorbing aerosols. However, since the effect
of absorbing aerosols on surface temperature tends to differ between models [Bond et al.,
2013], it is unclear how much absorbing aerosols would contribute to a robust response.
More research on the roles of absorbing aerosols is clearly necessary.
Furthermore, it would be extremely desirable to better distinguish between direct and
indirect aerosol effects, although we realize that designing the appropriate sensitivity studies, especially in the framework of a future model intercomparison project, will not be an
easy task. Separating between the direct and indirect radiative effects is particularly important since Bollasina et al. [2011] have pointed out that the aerosol cooling was mainly
realized via aerosol-cloud interactions in their model. In addition, Wang et al. [2014] have
recently demonstrated that aerosol-cloud interactions play an important role for the effect
of Asian pollution on mid-latitude storm tracks. Although the bulk aerosol effect is of
great interest in itself, being unable to distinguish between the effects of scattering and
absorbing aerosols and also between the roles of direct and indirect radiative effects constitutes a serious shortcoming of this multi-model study. While promising methods have recently been suggested to better seperate the effects of aerosol-radiation- and aerosol-cloud
interactions [Ghan, 2013; Zelinka et al., 2014], their application remains a challenging
task.
Decreasing future aerosol emissions imply a moistening tendency due to increased water
vapor availability and a strengthening of the monsoon circulation that partially counter-
acts the weakening associated with GHGs, i.e. future decreases in aerosol emissions are
expected to add to the moistening trend associated with rising GHG concentrations in
agreement with e.g. H. Levy II et al. [2013]. As aerosol emissions decrease, the dynamical response to the total anthropogenic forcings is expected to become qualitatively more
similar to the response in the historicalGHG runs. A planned set of idealized model experiments will help us to further investigate the role of the enhanced of land-sea temperature
contrast under GHG warming for the eastward shift of the Walker circulation.
Acknowledgments. We would like thank the groups that have contributed to CMIP5
and we are very thankful to three anonymous reviewers for their constructive comments on this manuscript as well as to two anonymous reviewers for their constructive comments on a shorter previous version.
We also acknowledge a useful com-
ment by the editor (S. Ghan) and input from discussions with D. Rosenfeld, C.
Venkataraman, and several colleagues on particular aspects of the mansucript.
R.
Cherian’s work was funded by the European Union FP7/2007–2013, grant no. 282688.
The CRU Time Series (TS) high resolution gridded data set used here is available
from http://badc.nerc.ac.uk/view/badc.nerc.ac.uk ATOM dataent 1256223773328276.
UDel AirT Precip data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado,
USA, from their web site at http://www.esrl.noaa.gov/psd/. The NCAR Command Language (Version 6.0.0) Software (2011), Boulder, Colorado: UCAR/NCAR/CISL/VETS.
http://dx.doi.org/10.5065/D6WD3XH5 has been used for data analysis and plotting.
Climate data operators (cdo) developed at the MPI for Meteorology in Hamburg
have also been used for data processing.
http://pcmdi9.llnl.gov/esgf-web-fe.
CMIP5 data is available for example via
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(a)
(b)
(c)
Figure 1.
Average June-September (JJAS) rainfall in mm day−1 for the years 1950 to 1999
derived from observations by (a) CRU, (b) UDel, and (c) simulated by 15 CMIP5 models. The
small black box indicates the NCI region and the large black box the SSEA region.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
Figure 2.
Rainfall trend for 1950 to 1999 (JJAS) in mm day−1 (50 years)−1 from (a) CRU
and (b) UDel, and the ensemble average for the (c) historical, (f) historicalNat, and the (i)
historicalGHG experiment. The remaining figures (d), (e),(g), and (h) are from four realizations
of the historical experiment with the HadGEM2 model. Black dots indicate points where the
trend is significant at the 95% confidence level.
Figure 3.
Trend of aerosol optical thickness at 550 nm for 1950 to 1999 from CMIP5 models.
Figure 4.
Trends for the SSEA region from the historical (blue), the historicalNat (green),
and the historicalGHG (red) experiment as medians of individual realizations (left) and medians
of model averages (right) for (a) precipitation, (b) precipitation minus evaporation (P-E), (c)
surface temperature Tsurf , (d) water vapor path, (e) 850 hPa, and (f) 500 hPa pressure vertical
velocity ω.
Figure 5.
Same as Figure 4 for the NCI region.
(a)
(b)
(c)
Figure 6. Deviation from the average trend for near-surface air temperature in K (50 years)−1
for the (a) historical, (b) historicalNat, and (c) historicalGHG experiment.
(a)
(b)
(c)
(d)
Figure 7.
Pressure vertical velocity ω in Pa s−1 at 500 hPa from the (a) historical experiment
and trends in Pa s−1 (50 years)−1 from the (b) historical, (c) historicalNat, and (d) historicalGHG
experiment. Black dots indicate areas where the trend is significant at the 95% confidence level.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 8. Divergence in s−1 (contours) and divergent wind in m s−1 (vectors) from the historical
experiment in (a) 850 hPa and (b) 200 hPa and trends in s−1 (50 years)−1 and m s−1 (50 years)−1
at 850 hPa (left column) and 200 hPa (right column) or the historical (c,d), historicalNat (e,f),
and historicalGHG (g,h) experiment.
Figure 9.
Trends of June-September rainfall for the NCI region in mm day−1 (50 years)−1
(upper panel), temperature in K (50 years)−1 (middle panel), and 500 hPa pressure vertical
velocity ω in hPa s−1 (50 years)−1 . Observations are from CRU (grey) and the UDel (orange).
Dots represent individual realizations from the historical (blue), the historicalNat (green), and
the historicalGHG (red) model experiment. Large dots represent statistically significant trends
at the 95% level. Vertical bars indicate averages plus minus one standard deviation. Small
numbers at the top of the plot indicate the number of ensemble members per experiment. A
filled square at the top of the plot indicates that the corresponding model takes into account the
first aerosol indirect effect. A filled triangle indicates that the second aerosol indirect effect is
taken into account.
Figure 10.
Figure 11.
Same as Figure 9 for the SSEA region.
Same as Figure 4 for the trends of the absorption of solar radiation (SWABS),
clear-sky absorption of solar radiation (SWABScs), short-wave cloud radiative effect (SWCRE),
and total cloud cover.
Figure 12.
Same as Figure 4 for the difference of the historical minus the historicalGHG
(violet), the historicalAA (dark blue) and the historicalLU (orange) experiment.
(a)
(b)
Figure 13. Multi-model 1950 to 1999 average trend of (a) equivalent potential temperature θe
and (b) temperature for the SSEA region ± one standard deviation for the historical (solid blue
lines), the historicalGHG (dotted red lines), and the historicalAA (dashed dark blue lines) runs.
Indirect aerosol effects in the 15 CMIP5 modelsa
No. of runsb albedo life
Reference
time
bcc-csm1-1
3/1/1/0/0
Wu et al. [2010]
CanESM2
5/5/5/5/5
x
Arora et al. [2011], Ma et al. [2010]
CCSM4c
6/4/3/3/3
Gent et al. [2011]
GFDL-CM3
5/3/3/3/0
x
x
Donner et al. [2011]
GFDL-ESM2
1/1/1/0/1
Anderson et al. [2004], Dunne et al. [2012]
CNRM-CM5
10/6/6/0/0
x
Voldoire et al. [2012]
CSIRO-Mk3-6-0 10/5/5/5/0
x
x
Rotstayn et al. [2012]
GISS-E2-Hc
5/5/5/5/5
x
Schmidt et al. [2014]
GISS-E2-Rc
5/5/5/5/5
x
Schmidt et al. [2014]
HadGEM
4/4/4/0/0
x
x
Chalmers et al. [2013]
IPSL-CM5c
6/3/3/1/0
x
Dufresne et al. [2013]
MIROC-ESM
3/1/1/0/0
x
x Watanabe et al. [2011], Takemura et al. [2009]
MIROC-ESM-C 1/1/1/0/0
x
x Watanabe et al. [2011], Takemura et al. [2009]
c
MRI-CGCM3
1/1/1/0/0
x
x
Yukimoto et al. [2012]
NorESM
3/1/1/1/0
x
x
Kirkevåg et al. [2013]
a
The table is in part based on Jiang et al. [2012].
Table 1.
model
b
Number of historical/historicalNat/historicalGHG/historicalAA/historicalLU runs.
c
physics version 1