The South Asian summer monsoon supplies over 80 % of India's
precipitation. Industrialization over the past few decades has resulted in
severe aerosol pollution in India. Understanding monsoonal sensitivity to
aerosol emissions in general circulation models (GCMs) could improve
predictability of observed future precipitation changes. The aims here are
(1) to assess the role of aerosols in India's monsoon precipitation and (2)
to determine the roles of local and regional emissions. For (1), we study
the Precipitation Driver Response Model Intercomparison Project experiments.
We find that the precipitation response to changes in black carbon is highly
uncertain with a large intermodel spread due in part to model differences in
simulating changes in cloud vertical profiles. Effects from sulfate are
clearer; increased sulfate reduces Indian precipitation, a consistency
through all of the models studied here. For (2), we study bespoke
simulations, with reduced Chinese and/or Indian emissions in three GCMs. A
significant increase in precipitation (up to ∼20 %) is
found only when both countries' sulfur emissions are regulated, which has
been driven in large part by dynamic shifts in the location of convective
regions in India. These changes have the potential to restore a portion of
the precipitation losses induced by sulfate forcing over the last few
decades.
Significance statement
The aims here are to assess the role of aerosols in India's monsoon
precipitation and to determine the relative contributions from Chinese and
Indian emissions using CMIP6 models. We find that increased sulfur emissions
reduce precipitation, which is primarily dynamically driven due to spatial
shifts in convection over the region. A significant increase in
precipitation (up to ∼20 %) is found only when both Indian
and Chinese sulfate emissions are regulated.
Introduction
The South Asian summer monsoon is the dominant weather pattern over India,
lasting typically from June to September. Over this period, southwesterly
winds transport warm, moist air from the Arabian Sea onto the Indian
subcontinent, supplying roughly 80 % of the region's annual rainfall
(Turner and Annamalai, 2012). Since the monsoon provides such a significant
source for India's water supply, changes in its strength and position would
have important socioeconomic implications including though not simply
confined to agricultural production (Kumar et al., 2004; Douglas et al.,
2009) and drought frequency (Subbiah, 2004). Given the rugged orography of
the surrounding region and difficulties in modeling intense precipitation,
resolving the future roles of natural variability and the externally forced
signal for the monsoon is a fundamentally difficult – but important –
problem.
Interannual changes in the monsoon have been linked to internal (natural)
variability inherent to the climate system. For instance, numerous studies
have found a potential connection between variability in the El
Niño–Southern Oscillation and the monsoon (Sikka, 1980; Shukla and
Paolino, 1983; Annamalai and Liu, 2005). Such links could be used to improve
predictability of Indian rainfall. While internal variability likely plays a
non-negligible role in modulating the South Asian summer monsoon – and is
expected to continue to do so in the future, even in high-emissions
scenarios (Annamalai et al., 2007) – changes in the monsoon's mean state
associated with external forcings are also of fundamental importance.
Specifically, determining the anthropogenic impacts on monsoonal changes
associated with emissions of greenhouse gases (GHGs) and aerosols can
provide critical insights that can help better inform policymaking decisions
regarding emission regulations.
The steady rise in GHGs over the 20th century has increased the
atmosphere's average temperature and water vapor content through the
Clausius–Clapeyron relation, and it might be expected as a result to contribute
to increased rainfall events over India (Goswami et al., 2006; Turner and
Slingo, 2009; Salzmann et al., 2014). CMIP6 models run with just an increase
in CO2 forcing generally exhibit such an increase uniformly across
India (Fig. S1 in the Supplement). However, in reality the picture is more complex as the
literature has indicated no such observed trend for India over the last half
century (Ramesh and Goswami, 2014; Saha and Ghosh, 2019). Observed monsoon
precipitation aggregated over all of continental India (Fig. 1) actually
indicates a slight drying trend over the last few decades. While these
trends are not statistically significant at a 95 % confidence level, the
purpose of Fig. 1 is to illustrate that the increase in monsoon
precipitation expected from the growing greenhouse forcing has certainly not
been detected. There may be several mechanisms invoked to explain why Indian
monsoon precipitation has not increased. Land use changes over the
Indo-Gangetic Plain have been implicated as one of the causes, where
decreased evapotranspiration may have limited the amount of available
precipitable water in the region (Paul et al., 2016). It has been shown also
that aerosol effects have counterbalanced the precipitation changes
attributable to the greenhouse warming (Bollasina et al., 2011; Turner and
Annamalai, 2012; Westervelt et al., 2020). Ramanathan et al. (2005) found
that aerosols over India reduce surface shortwave radiation, which limits
the amount of evaporation and thereby reduces monsoon precipitation.
Additionally, it has been shown that the atmospheric brown cloud (originally
so termed in Ramanathan and Crutzen (2003), referring to the pervasive
light-absorbing aerosol layer akin to the stratocumulus cloud decks observed over
the oceans) over the northern Indian Ocean is associated with a stable
atmosphere that limits convection. Atmospheric brown clouds consist
primarily of black and organic carbon, dust, and other anthropogenic
aerosols. Sources of aerosols and their precursors in South and East Asia
(indicated in Fig. S2) are tied particularly to energy production and
biomass combustion, which have grown steadily in response to
industrialization in the region, though recent trends in these two regions
differ. Meehl et al. (2008) similarly found that an increased aerosol load
reduced precipitation over India during the monsoon season but that it also
increased rainfall in the pre-monsoon season. Wang et al. (2009) found that
absorbing aerosols were particularly important in influencing the summer
monsoon system. This has been validated further by a number of studies
(highlighted in Li et al., 2016) that found aerosols can influence the
atmospheric dynamics and the formation of clouds, with consequent impacts on
daily (Singh et al., 2019), seasonal (Lau et al., 2017) and intraseasonal
(Hazra et al., 2013) precipitation. The issue with many of these studies is
that they focus on individual models. There is a large spread in the
precipitation response across models reflecting differing representations of
cloud and aerosol processes (e.g., Wilcox et al., 2015), factors that may
bias results given the already complex nature of modeling precipitation
over India (Ramanathan et al., 2005; Bollasina et al., 2011; Turner and
Annamalai, 2012; Ramesh and Goswami, 2014; Paul et al., 2016; Saha and
Ghosh, 2019). Multimodel ensembles can improve our understanding and help
constrain uncertainty about the impacts of different aerosol constituents on
the monsoon.
Average cumulative summer (JJAS) precipitation [cm] over land in
all of India from 1900 to 2016 for two observational datasets: (red)
University of Delaware (UDel; Willmott and Matsuura, 2001) and (blue) the Global
Precipitation Climatology Center (GPCC; Schneider et al., 2018). Data are
smoothed using a moving mean with a window size of 5 years. Linear trend
lines are indicated for the last 40 years for each dataset as dashed lines,
and the slopes [cm yr-1] are denoted by the arrows.
Here, we analyze results from two climate model intercomparisons to better
understand the summer monsoonal impacts from sulfur and black carbon (BC)
aerosols, two of the dominant constituents of India's aerosol pollution.
First, we study the Precipitation Driver Response Model Intercomparison
Project (PDRMIP; Samset et al., 2016) experiments to assess the summer
monsoon response to extreme aerosol conditions. The purpose of the PDRMIP
experiments here is to determine if a precipitation signal in the South
Asian summer monsoon can be detected in scenarios with large emissions
perturbations of sulfur and black carbon. Previous analysis of a set of
PDRMIP experiments which increase global BC levels 10-fold found a slight
enhancement in precipitation minus evaporation during the South Asian summer monsoon, driven by a
strengthened land–sea temperature gradient (Xie at al., 2020). We focus the
first section of our analysis on Asian perturbation experiments as
significant emissions changes are expected over this region in the coming
decades (e.g., Samset et al., 2019). We note that these experiments use
artificially large emission perturbations to enable isolation of signal
detection from climatic variability. Second, we study a set of regional
aerosol emissions intercomparison experiments (labeled RAEI experiments for
the rest of the paper for convenience) to assess the relative contributions
of Indian and Chinese anthropogenic aerosol emissions to the monsoon.
Because emissions outside of India may play an important role in its summer
monsoon (Bollasina et al., 2014; Shawki et al., 2018), in addition to Indian
emissions we choose to study emissions from China because this country is
presently the world's leading emitter of BC and SO2, it is in close
proximity to India, and its emissions of both pollutants are expected to
decline rapidly over the coming decade. Emissions in more remote regions are
less likely to change in a major way. A robust analysis of these
intercomparisons should refine our understanding of the anthropogenic
influence on the South Asian summer monsoon and reduce uncertainty about future
changes given that India's anthropogenic emissions are expected to increase
at least in the near term, while China's will likely decrease (Rao et al., 2013). We decompose precipitation changes into dynamic (i.e., circulation
changes) and thermodynamic (i.e., specific humidity changes) components to
assess how aerosols interact with the monsoon. The rest of the paper is
structured as follows: Sect. 2 discusses the simulations used in the
analysis, Sect. 3 presents and analyzes potential monsoonal impacts
associated with sulfur and black carbon emissions, and Sect. 4 summarizes
the study and highlights needs for future work.
Data and methodsPDRMIP intercomparison
We first study the Precipitation Driver Response Model Intercomparison
Project experiments. PDRMIP is an idealized set of modeling
experiments used to better understand drivers of regional precipitation
change. We focus specifically on two experiments that involve perturbations
to Asian concentrations or emissions (see Table 1), where Asia is defined by
the regional box of 10–50∘ N and 60–140∘ E. The first is
BC10xASIA, representing a 10-fold increase in present-day BC concentrations
or emissions in Asia at all vertical levels, and the second is SULF10xASIA,
which explores a similar 10-fold increase in present-day sulfate
concentrations or emissions. The BC10xASIA and SULF10xASIA scenarios are
compared with control simulations (henceforth called CTRLPDRMIP) where
aerosol concentrations or emissions are maintained at near-current values
(either year 2000 or 2005 for each model). We study the six models involved
in the PDRMIP experiments that conduct the Asian perturbation experiments
(Table 1). These experiments will be used to better constrain uncertainty about
the direction of precipitation and circulation changes under anthropogenic
aerosol emissions changes. Since these are extreme perturbations to aerosol
concentrations, we use these scenarios not as representative of a future
emissions trajectory but rather as a way to check if different models with
different process representations indicate a consistent response. Due to
intermodel differences in spatial resolution, all data are rescaled to the
lowest model resolution (3.75∘×2.0∘) when
comparing model output. Variations in aerosol schemes and direct and
indirect aerosol effects across the six models will affect the spread in
predicted precipitation changes associated with the increased aerosol
concentrations (Table 1). The different schemes and their effects on
precipitation will be discussed further in the Sect. 3.
Details of the models analyzed in this work. For the models
participating in the PDRMIP Asian aerosol perturbation simulations, each
simulation lasts 100 years. Cloud scheme refers to the microphysical cloud
scheme that describes cloud formation, where a one-moment scheme considers
only changes in mass and a two-moment scheme considers changes in mass and
number concentration. The first indirect effect refers to the aerosol effect
on cloud albedo, and the second indirect effect refers to the aerosol effect on cloud
lifetime.
ModelSpatial resolutionCloud schemeIndirect effectsModel referenceAerosol micro- physicsMIPCESM1-CAM5b2.5∘×1.875∘Two momentFirst, secondNeale et al. (2012)Full aerosolPDRMIP, RAEIGISS-E2-R2.5∘×2.0∘One momentNoneaSchmidt et al. (2014)No aerosolPDRMIP, RAEIHadGEM31.875∘×1.25∘One momentFirst, secondHewitt et al. (2011)No BC; aerosol- cloud interaction includedPDRMIPIPSL-CM3.75∘×1.875∘Two momentFirstDufresne et al. (2013)Aerosol mi- crophysics for Twomey effectPDRMIPMIROC-SPRINTARSb1.41∘×1.41∘One momentFirst, secondWatanabe et al. (2011)Full aerosolPDRMIPNorESM2.5∘×1.875∘Two momentFirst, secondBentsen et al. (2013)Full aerosolPDRMIPUKESM1-0-LL1.875∘×1.25∘Two momentFirst, secondSellar et al. (2019)Full aerosolRAEI
a Indirect effects in the PDRMIP simulations were turned off since these simulations had prescribed aerosol fields and so changes in the hydrologic cycle could not change the aerosols. The first effect was included in the GISS RAEI simulations, however, as those are emissions-driven and hence physically consistent.b Indicate models that change emissions in the PDRMIP experiments. Rows that do not include this mark indicate models that change concentrations in the PDRMIP experiments.
RAEI experiments
The purpose of the RAEI experiments is to assess the relative contributions
of aerosol emissions from China and India on monsoon precipitation over
India. Three general circulation models (GCMs) with coupled chemistry–climate components are used to
study the effects of regional perturbations in aerosol emissions on the
Indian monsoon: GISS-E2-R (Schmidt et al., 2014), CESM1-CAM5 (Neale et al.,
2012) and UKESM1-0-LL (Sellar et al., 2019). Past research has used some of
these models to explore the effects of regional aerosol reductions on global
precipitation, including emissions changes in the US, Europe, China and
India. Some of the experiments from RAEI have been used to study the global
effects of US SO2 emissions on global precipitation (Westervelt et al.,
2017) as well as local and remote precipitation responses to regional
reductions in aerosols (Westervelt et al., 2018). Here, we study the South
Asian summer monsoon response to reductions in anthropogenic aerosol
emissions in China and India, focusing specifically on a set of three
experiments: (1) no SO2 emissions in India (IND NO SO2), (2)
80 % SO2 emissions reduction in China (CHN 20 % SO2), and (3)
no SO2 emissions in India and China (IND + CHN NO SO2). We have
run additional BC experiments that are included only in the Supplement because we
find that changes in BC do not have a clear impact on precipitation in the
summer monsoon. The three SO2 experiments will be compared to control
simulations (CTRL) with emissions set near present-day values (year 2000 or
2005 depending on the model) to determine the relative importance on summer
monsoon precipitation of regional aerosol emissions from India and China.
The UKESM experiments were run over a shorter period (40 years) relative to
the other models (∼200 years). We found from resampling that
40 years is sufficient to observe the general seasonally aggregated
precipitation statistics over India. For climatological variables studied in
our PDRMIP and RAEI analysis, we take mean values over the full simulation
period, excluding the first 10 years to allow for spin-up.
Precipitation decomposition
In addition to calculating overall precipitation changes due to sulfur and
BC emissions, we seek also to determine the dynamic and thermodynamic
components of the changes attributable to these forcing agents. The dynamic
component is representative of precipitation changes caused by a change in
atmospheric circulation, and the thermodynamic component is representative
of variations in precipitation due to changes in moisture under constant
circulation. To perform this decomposition, we follow the methodology of
Chadwick et al. (2016). The total precipitation change ΔP can be
expressed as
ΔP=ΔqM*+qΔM*+ΔqΔM*,
where q is the near-surface specific humidity and M* is a proxy for
convective mass flux (M*=P/q). The first term on the right-hand side is
representative of thermodynamic changes (ΔPtherm), the second
dynamic changes (ΔPdyn) and the third the nonlinear interaction
of these two components (ΔPcross). ΔPdyn can be
further decomposed into shifts in the circulation patterns (ΔPshift) and changes in the mean strength of the tropical circulation
(ΔPstrength) as
ΔPshift=qΔMshift*,ΔPshift=qΔMstrength*,
where ΔMstrength*=-αMstrength* (where α= tropical mean ΔM*/ tropical mean M*). ΔMshift* is computed as
the residual of ΔM* and ΔMstrength*.
This decomposition follows the methodology in Chadwick et al. (2016) and
Monerie et al. (2019).
ResultsPDRMIP analysis: summertime Indian precipitation response to large BC and sulfur perturbations
We start with an evaluation using the PDRMIP experiments (Table 1) of
summertime Indian precipitation changes caused by large BC and sulfate
concentration increases over all of Asia. The difference in summer
precipitation between the BC10xASIA and CTRLPDRMIP experiments provides
an estimate for the role of BC in monsoonal changes and is shown in Fig. 2a–g. From the individual models (Fig. 2a–f), there is a noticeably large
ensemble spread in the precipitation response over India due to the increase
in BC. In north India, for example, HadGEM3 shows a precipitation decrease
of up to 70 %, while SPRINTARS exhibits effectively a null response and
GISS is identified with a strong precipitation increase of ∼50 %. PDRMIP simulations that globally increase BC 10-fold also do not
show a consistent multimodel response over India (Samset et al., 2016). The
first regional analysis of the PDRMIP experiments by Liu et al. (2018) found
also a weak precipitation response to BC changes, attributed to
insignificant circulation changes relative to those induced by the sulfur
experiments. While HadGEM3 and GISS generally underestimate precipitation
over India (Fig. S3), it does not appear that these biases are manifest in
consistent precipitation changes in the BC10xASIA experiments. The weak
precipitation over India in HadGEM3 in the CTRL simulation (Fig. S3) also
likely explains the large percent changes indicated in the BC and sulfate
experiments. Additionally, while two of the six models studied increase BC
emissions rather than BC concentrations, this does not appear to alter the
BC vertical profile except in the stratosphere (see Fig. S4). It is likely
that different aerosol schemes across models (Table 1) may be implicated as
one of the dominant sources of the large ensemble spread by altering
simulated clouds' radiative properties and lifetimes, as has been shown in
previous studies testing different aerosol schemes in the same coupled
climate model (Nazarenko et al., 2017). Additionally, both the boundary
layer scheme and modeling impacts of absorbing aerosols on cloud formation
could play important roles. Specifically, Koch and Del Genio (2010) note
that cloud formation is affected significantly by the BC vertical profile;
BC within the cloud layer can burn off moisture and reduce cloud cover, BC
below the cloud layer can enhance convection and increase cloud cover, and BC
above the cloud layer can either increase or decrease cloud cover according
to the cloud type. Because of the complexities of the semi-direct effects of
absorbing aerosols that are currently not heavily constrained by
observations, the role of BC generally has a diverse response across climate
models (Koch et al., 2009; Stjern et al., 2017). Large variance in the cloud
fraction vertical profile is also apparent in the PDRMIP BC10xASIA
simulations (Fig. 3). This large uncertainty does not consistently favor
an increase or decrease in cloud fraction across vertical layers except in
NorESM and CESM, where a slight increase (on the order of a couple of
percent) can be detected across all layers. Variations in the BC vertical
profile as well as its lifetime can result in significant changes in cloud
cover and precipitation even within an individual model by changing
atmospheric stability and humidity (Samset and Myhre, 2015). These effects
are manifest in the diverse shortwave responses (Fig. S5), which indicate
a large spread between models in magnitude and sign over parts of India.
Additionally, changes in the top-of-the-atmosphere net radiative forcing between BC10xASIA and
PDRMIPCTRL are generally consistent in magnitude and direction across
models over India (Fig. S6a–f). By contrast, the change in cloud
radiative effect (Fig. S6g–l) is not consistent in sign across
models, suggesting that the models agree on the direct aerosol effects but
differ on the aerosol–cloud interactions. While there are more causative
factors in precipitation than cloud fraction, the important point is that,
because of the large cloud uncertainty that varies in both magnitude and
sign, it is difficult to attribute future changes in Indian precipitation to
changes in BC concentration. This is reflected in the precipitation change,
which fails to demonstrate a clear spatial coherence in the multimodel mean
(Fig. 2g).
Percent change in summertime (JJAS) precipitation between (a–f)
the BC10xASIA and the CTRLPDRMIP runs; (g) the multimodel mean of the
change. Similarly, panels (h–m) represent the precipitation change in JJAS
precipitation between the SULF10xASIA scenarios and the CTRLPDRMIP
runs, and panel (n) represents the multimodel mean of the change. Stippled grid
cells in panels (g) and (n) denote regions where at least five of the six models
agree on the sign of the change. Grey contours indicate mean JJAS
precipitation from the control experiment for each model at 5 mm d-1
intervals.
JJAS difference in cloud fraction between (blue) the BC10xASIA and
the CTRLPDRMIP runs and (red) the SULF10xASIA scenarios and the
CTRLPDRMIP runs. The bold lines represent the mean difference, and the
shadings represent 25th and 75th percentiles.
The role of sulfate in Indian precipitation is much clearer. The percent
change in precipitation between the SULF10xASIA and CTRL PDRMIP experiments
is shown in Fig. 2h–n. The sign of the precipitation change is generally
consistent across models, with a large decrease in precipitation
(∼50 %) over all of India in response to a 10-fold increase
in sulfate. There is also large uncertainty in the cloud fraction profile
response to sulfate emissions (Fig. 3), similar to the BC PDRMIP
experiments. However, five of the six models on average favor a decrease in
cloud fraction with increased SO2 emissions, consistent with the
precipitation response. So, while there is a comparable measure of
intermodel spread for the BC10xASIA and SULF10xASIA cloud responses, the
mean change is more consistent in the SULF10xASIA experiments. The results
from the PDRMIP experiments, with their higher sulfate concentrations,
constrain uncertainty about the sign of precipitation changes and can be used
as a frame of reference for the country-specific aerosol experiments
described in Sect. 3.2 and beyond.
RAEI analysis: Indian aerosol burden response to Chinese and Indian aerosol emissions changes
We now consider the RAEI emissions scenarios for China and India. Percent
changes in sulfate burden between the sulfate reduction scenarios and
control runs are shown in Fig. S7a–i. Indian sulfate emissions play an
important role in local sulfate concentrations, contributing up to 60 % of
the country's aerosol burden, while China's emissions can contribute up to
60 % over the Himalayas. The change in Indian aerosol burden for sulfate
is notably consistent in terms of both the magnitude of the change and
the spatial pattern across the three models studied. Since the
temperature gradient between the Arabian Sea and Bay of Bengal and the
Himalayas has been invoked as a modulator of the South Asian monsoon (e.g.,
Priya et al., 2017), both Indian and Chinese emissions could influence
monsoon precipitation over India by modifying the optical properties of the
atmosphere not only over the country but also over surrounding regions.
JJAS precipitation percentage difference between the SO2
regional emissions scenarios and the CTRL runs. The columns represent the
different models, and rows represent the different emissions scenarios.
Stippled regions denote areas where the difference is significant at a
90 % confidence level for a two-sample t test. Grey contours indicate mean
JJAS precipitation from the control experiment for each model at 5 mm d-1 intervals.
The precipitation response associated with SO2 emissions is significant
over parts of India (Fig. 4a–i), in agreement with the PDRMIP results.
All scenarios across the multimodel ensemble (with the exception of CESM's
CHN 20 % SO2 scenario) show an increase in summer precipitation in
India when SO2 emissions in China and/or India are reduced. The
strongest response requires reductions from both China and India, with an
increase of nearly 20 % in precipitation in some regions of India when
SO2 emissions are reduced across the three models studied here. From
these results, changes in India's precipitation depend not only on local
SO2 emissions but also on regional sources. These emissions can have a
measurable impact on India's water availability by altering the underlying
statistics in favor of greater precipitation events (e.g., Sillmann et al.,
2019). That being said, the spatial patterns associated with these
precipitation changes vary to a large degree between models. For instance,
precipitation changes in GISS exhibit greater consistency across scenarios
than they do with the CESM or UKESM. Additionally, UKESM tends to estimate
larger precipitation changes than the other RAEI models, consistent with the
HadGEM3 results indicated in Fig. 2, which uses the same physical model.
There is, however, general consistency in the increase in precipitation when
SO2 emissions are reduced in both China and India. The precipitation
responses to lower BC regional emissions are indicated in Fig. S8. BC
emissions play a much lesser role in GISS and CESM relative to SO2
emissions and cause an inconsistent response in UKESM across the three
regional emissions experiments. For all reduced-BC scenarios (with the
exception of two UKESM scenarios), the changes in India's precipitation are
generally small (∼5 % locally) and not statistically
significant at a 90 % confidence level. The strongest precipitation
response occurs when both Chinese and Indian BC emissions are eliminated,
but there is a spread in the direction of change across models. This spread
in precipitation change is consistent with that of the PDRMIP results in
that the intermodel spread in precipitation change due to BC emissions
changes tends to be larger than the magnitude of the precipitation response
from any individual model. This may highlight large process uncertainty
generally. Bond et al. (2013), for example, note that the impact of BC on
the cloud radiative forcing in models is highly sensitive to the nucleation
regime in the background atmosphere.
Boxplots indicating the decomposition of area-averaged JJAS
precipitation anomalies [mm d-1] into (a)ΔPtherm, (b)ΔPdyn, (c)ΔPcross, (d)ΔPstrength and
(e)ΔPshift components over India. Different colors represent
the three RAEI scenarios relative to the respective CTRL run, with green
representing the IND NO SO2 experiment, purple the CHN 20 % SO2
experiment and orange the IND + CHN NO SO2 experiment. The range for
each boxplot corresponds to intermodel variability from the three different
models studied in the RAEI experiments.
RAEI analysis: physical understanding of the SO2 precipitation response
Physical explanations for the precipitation changes induced by SO2
emissions changes are explored here. Circulation changes are typically
connected to sulfate increases in India; a weakened land–sea temperature
gradient associated with SO2 emissions would inhibit monsoonal
advection of moisture from the Arabian Sea onto the Indian subcontinent.
Warming over the Himalayas can be seen in most of the simulations (Fig. S9), as well as changes in 850 hPa winds, where there is a clear
strengthening of the coastal winds when SO2 emissions are reduced
(Fig. S10). The fact that the land–sea temperature gradient and 850 hPa
winds change suggests that precipitation changes due to SO2 emissions
may be dynamically rather than thermodynamically driven, which motivates the
precipitation decomposition analysis discussed later. A similar analysis by
Shawki et al. (2018) also found that reduced Chinese SO2 emissions
strengthened the land–sea temperature contrast and consequently
precipitation over India. As shown in Fig. 4, strengthening of the
monsoonal winds is largely consistent across models and scenarios, though
there are slight differences in the location of the strongest zonal wind
increases; in GISS and UKESM, the greatest increase is over India itself for
most scenarios, while it is further south in CESM. This suggests that a high
sulfate burden reduces the strength of the monsoon winds, consistent with
prior studies that connect these changes to the dimming of the downward
solar flux (Kim et al., 2007). The relative contributions of thermodynamic
(i.e., specific humidity) changes to dynamic (i.e., circulation) changes are
indicated in Fig. 5. The thermodynamic precipitation response to sulfur
emissions reductions is positive for the three emissions experiments,
consistent with the Clausius–Clapeyron relation as less SO2 increases
surface temperatures and consequently specific humidity. The interaction of
dynamic and thermodynamic components (panel c, ΔPcross) plays a
minimal role. The magnitude of the thermodynamic response is on the order of
50 % of that of the dynamic component – i.e., the dynamic component
dominates. Figure 5d and e indicate that this effect is
driven primarily by shifts in the convective regions, with changes in the
tropical mean circulation having a minimal or slightly negative effect. It
is of note that the magnitude of each component is consistent across the
three models studied here, suggesting consistency in the mechanistic reasons
for increased monsoon precipitation over India when sulfur emissions are
reduced. Changing circulation patterns are suggested as a consequence of
changes in CO2 as well, and potential nonlinear effects of sulfur and
greenhouse emissions on monsoon precipitation highlight an important
challenge in predicting future changes to the South Asian summer monsoon.
Conclusions
The main purpose of this study was to better understand, through the use of
several GCM experiments, the sensitivity of the South Asian summer monsoon
to regional anthropogenic aerosol emission changes. Given that this is a
modeling study, there are a number of caveats that must be acknowledged.
There are often questions of how well GCMs can simulate the Indian monsoon
since their spatial resolution may be too coarse to resolve the complex
orography of India and the surrounding regions (Prell and Kutzbach, 1992).
Additionally, representation of cloud microphysical processes is a known
limitation of GCMs (e.g., Wilcox et al., 2015). We find a large intermodel
spread in cloud profile and precipitation changes in the various BC
emissions scenarios studied here. This suggests that discrepancies in
representation of cloud processes within GCMs constrain uncertainty in the
precipitation response from BC perturbations, which cannot be accounted for
simply by differences in the BC vertical profiles (Fig. S4). In contrast,
the precipitation responses to SO2 emission changes and the
dynamic mechanism for these responses are largely consistent across models,
suggesting that there is relative certainty in the models' ability to
simulate precipitation changes due to SO2 emissions. So, while it may
be difficult to extrapolate on the basis of these simulations from modeled
to real-world monsoon precipitation changes induced by anthropogenic
aerosols, consistency in the SO2 response across models lends
confidence in a potential observed response for future emissions changes.
On investigating the response of the monsoon to a 10-fold increase of Asian
BC and sulfate concentrations, we found that the role of BC in Indian
precipitation is uncertain but that increased sulfate concentrations over
India reduce precipitation across five of the six models studied. Large
uncertainty in the precipitation response to changing Asian BC is notably
consistent with previous PDRMIP analysis studying monsoon changes to a
10-fold increase in global BC levels (Xie et al., 2020). Consistency between
the global and regional PDRMIP simulations in this context suggests further
that a BC signal is difficult to detect for the South Asian summer monsoon
(a result found also in Liu et al., 2018).
When assessing the relative contributions of Chinese and Indian
anthropogenic SO2 emissions to aerosol loading over South Asia (the
RAEI emissions experiments), and the consequent precipitation responses, we
find that there is only a statistically significant difference in monsoon
precipitation when there is reduction of both China and India's SO2
emissions, which leads to a precipitation increase on the order of a 20 %
locally. Consistency in the precipitation responses between the increased-sulfate scenario (PDRMIP SULF10xASIA) and the decreased-sulfate scenario
(RAEI) suggests that the aerosol–precipitation link may be a reversible
process and is attributable in large part to dynamical changes, specifically
shifts in convective patterns over the region. Additionally, these results
are significant because Chinese emissions of SO2 have declined over the
past decade, while Indian emissions have grown steadily. There is also
anticipated growth in CO2 emissions and concentrations over the coming
decades, and this is expected to result in an increase in the atmospheric
water vapor content. These concurrent events will have important
implications for policy going forward, as water deficits present a major
issue for India that may be exacerbated given the country's exponential
population growth. Regions that exhibit large variability in summertime
precipitation such as Chennai and Delhi (as indicated in Fig. S11) may be
particularly sensitive to future monsoon changes because interannual shifts
between wet and dry years at present impose important strains on the
available water resource. Moreover, the benefits of policies to control
SO2 emissions will have significant impacts not only on mitigating
water deficits but also in terms of alleviation of air pollution, estimated
to be responsible for hundreds of thousands of premature deaths per year in
India (Health Effects Institute, 2019). It is, however, important to bear in
mind that SO2 emissions reductions could also increase flooding and
extreme precipitation generally (Sillmann et al., 2019).
While China's pollution is expected to decline in most socioeconomic
projections, India's is expected to grow except under strong emissions
controls (Samset et al., 2019). Regardless of the realism of these
scenarios, the results should be seen as further impetus for regional
policies to reduce SO2 emissions given that we have found that combined
emissions reductions from China and India can increase monsoon precipitation
over India by 5 % on average and by up to 20 % locally. This
effect, in combination with consequent impacts of continued growth in GHGs
(Fig. S1), could result in an overabundance. This calls therefore for
careful consideration of implications for both precipitation and health over
multiple timescales.
Code and data availability
All code and model data to make the figures used in this paper will be made
publicly available through Zenodo following acceptance of the paper. The
ESRL database makes gridded precipitation data publicly available for both
the University of Delaware data
(https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html, Willmott and Matsuura, 2001) and the GPCC data
(https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html, Schneider et al., 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-3593-2021-supplement.
Author contributions
ATA, NLA, JFL, DS and GF ran the RAEI experiments for their respective GCMs. PS
prepared the manuscript with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was supported by the Harvard Global Institute. Alex T. Archibald and Nathan Luke Abraham thank
NERC through NCAS for funding for the ACSIS project and NE/P016383/1. The
UKESM work used Monsoon2, a collaborative high-performance computing
facility funded by the Met Office and the Natural Environment Research
Council. This work used JASMIN, the UK collaborative data analysis facility.
The NCAR-CESM work is supported by the National Science Foundation and the
Office of Science (BER) of the US Department of Energy. NCAR is sponsored
by the National Science Foundation. Climate modeling at GISS is supported by
the NASA Modeling, Analysis and Prediction program. GISS simulations used
resources provided by the NASA High-End Computing (HEC) Program through the
NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center.
Financial support
This research has been supported by the Harvard Global Institute and the National Centre for Atmospheric Science (grant no. NE/P016383/1).
Review statement
This paper was edited by Pedro Jimenez-Guerrero and reviewed by Matthew Kasoar and one anonymous referee.
ReferencesAnnamalai, H. and Liu, P.: Response of the Asian Summer Monsoon to changes
in El Niño properties, Q. J. Roy. Meteor. Soc., 131, 805–831,
10.1256/qj.04.08, 2005.Annamalai, H., Hamilton, K., and Sperber, K. R.: South Asian summer monsoon
and its relationship with ENSO in the IPCC AR4 simulations, J. Climate, 20, 1071–1083,
10.1175/JCLI4035.1, 2007.Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, 10.5194/gmd-6-687-2013, 2013.Bollasina, M. A., Ming, Y., and Ramaswamy, V.: Anthropogenic aerosols and the
weakening of the South Asian Summer Monsoon, Science, 6055, 502–505,
10.1126/science.1204994, 2011.Bollasina, M. A., Ming, Y., Ramaswamy, V., Schwarzkopf, M. D., and Naik, V.:
Contribution of local and remote anthropogenic aerosols to the twentieth
century weakening of the South Asian Monsoon, Geophys. Res. Lett., 41, 680–687,
10.1002/2013GL058183, 2014.Bond, T. C, Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T.,
DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne,
S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M.,
Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K.,
Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U.,
Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.:
Bounding the role of black carbon in the climate system: A scientific
assessment, J. Geophys. Res.-Atmos., 118, 5380–5552,
10.1002/jgrd.50171, 2013.Chadwick, R., Good, P., and Willett, K. M.: A simple moisture advection model
of specific humidity change over land in response to SST warming, J. Climate, 29,
7613–7632, 10.1175/JCLI-D-16-0241.1, 2016.Douglas, E. M., Beltrán-Przekurat, A., Niyogi, D., Pielke, R. A., and
Vörösmarty, C. J.: The impact of agricultural intensification and
irrigation on land-atmosphere interactions and Indian monsoon precipitation
– a mesoscale modeling perspective, Glob. Planet. Change, 67, 117–128,
10.1016/j.gloplacha.2008.12.007, 2009.Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 10, 2123–2165,
10.1007/s00382-012-1636-1, 2013.Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S., and Xavier,
P. K.: Increasing trend of extreme rain events over India in a warming
environment, Science, 314, 1442–1445,
10.1126/science.1132027, 2006.Hazra, A., Goswami, B. N., and Chen, J.-P.: Role of Interactions between
Aerosol Radiative Effect, Dynamics, and Cloud Microphysics on Transitions of
Monsoon Intraseasonal Oscillations, J. Atmos. Sci., 70, 2073–2087,
10.1175/JAS-D-12-0179.1, 2013.
Health Effects Institute: State of Global Air 2019, Special Report, Boston, MA, Health Effects Institute, 2019.Hewitt, H. T., Copsey, D., Culverwell, I. D., Harris, C. M., Hill, R. S. R., Keen, A. B., McLaren, A. J., and Hunke, E. C.: Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system, Geosci. Model Dev., 4, 223–253, 10.5194/gmd-4-223-2011, 2011.Kim, M.-K., Lau, W. K. M., Kim, K.-M., and Lee, W.-S.: A GCM study of effects
of radiative forcing of sulfate aerosol on large scale circulation and
rainfall in East Asia during boreal spring, Geophys. Res. Lett., 34, L24701,
10.1029/2007GL031683, 2007.Koch, D. and Del Genio, A. D.: Black carbon semi-direct effects on cloud cover: review and synthesis, Atmos. Chem. Phys., 10, 7685–7696, 10.5194/acp-10-7685-2010, 2010.Koch, D., Schulz, M., Kinne, S., McNaughton, C., Spackman, J. R., Balkanski, Y., Bauer, S., Berntsen, T., Bond, T. C., Boucher, O., Chin, M., Clarke, A., De Luca, N., Dentener, F., Diehl, T., Dubovik, O., Easter, R., Fahey, D. W., Feichter, J., Fillmore, D., Freitag, S., Ghan, S., Ginoux, P., Gong, S., Horowitz, L., Iversen, T., Kirkevåg, A., Klimont, Z., Kondo, Y., Krol, M., Liu, X., Miller, R., Montanaro, V., Moteki, N., Myhre, G., Penner, J. E., Perlwitz, J., Pitari, G., Reddy, S., Sahu, L., Sakamoto, H., Schuster, G., Schwarz, J. P., Seland, Ø., Stier, P., Takegawa, N., Takemura, T., Textor, C., van Aardenne, J. A., and Zhao, Y.: Evaluation of black carbon estimations in global aerosol models, Atmos. Chem. Phys., 9, 9001–9026, 10.5194/acp-9-9001-2009, 2009.Kumar, K. K., Kumar, R. K., Ashrit, R. G., Deshpande, N. R., and Hansen, J. W.:
Climate impacts on Indian agriculture, Int. J. Clim., 24, 1375–1393,
10.1002/joc.1081, 2004.Lau, W. K. M., Kim, K.-M., Shi, J.-J., Matsui, T., Chin, M., Tan, Q.,
Peters-Lidard, C., and Tao, W. K.: Impacts of aerosol-monsoon interaction on
rainfall and circulation over Northern India and the Himalaya Foothills,
Clim. Dynam., 49, 1945–1960, 10.1007/s00382-016-3430-y,
2017.Li, Z., Lau, W. K.-M., Ramanathan, V., et al: Aerosol and monsoon climate interactions over Asia, Rev. Geophys. 54, 866–929, 10.1002/2015RG000500, 2016.Liu, L., Shawki, D., Voulgarakis, A., Kasoar, M., Samset, B. H., Myhre, G.,
Forster, P. M., Hodnebrog, Ø., Sillman, J., Aalbergsjø, S. G., Boucher,
O., Faluvegi, G., Iversen, T., Kirkevåg, A., Lamarque, J.-F.,
Olivié, D., Richadson, T., Shindell, D., and Takemura, T.: A PDRMIP
multimodel study on the impacts of regional aerosol forcings on global and
regional precipitation, J. of Climate, 31, 4429–4447,
10.1175/JCLI-D-17-0439.1, 2018.Meehl, G. A., Arblaster, J. M., and Collins, W. D.: Effects of black carbon
aerosols on the Indian Monsoon, J. Climate, 21, 2869–2882,
10.1175/2007JCLI1777.1, 2008.Monerie, P.-A., Robson, J., Dong, B., Hodson, D. L. R., and Klingaman, N. P.:
Effect of the Atlantic multidecadal variability on the global monsoon,
Geophys. Res. Lett., 46, 1765–1775, 10.1029/2018GL080903, 2019.Nazarenko, L., Rind, D., Tsigaridis, K., Genio, A. D., Kelley, M., and
Tausnev, N.: Interactive nature of climate change and aerosol forcing, J. Geophys. Res.-Atmos.,
122, 3457–3480, 10.1002/2016JD025809, 2017.
Neale, R. B., Gettelman, A., Park, S., Conley, A. J., Kinnison, D., Marsh, D.,
Smith, A. K., Vitt, F., Morrison, H., Cameron-Smith, P., Collins, W. D.,
Iacono, M. J., Easter, R. C., Liu, X., Taylor, M. A., Chen, C.-C., Lauritzen,
P. H., Williamson, D. L., Garcia, R., Lamarque, J.-F., Mills, M., Tilmes, S.,
Ghan, S. J., and Rasch, P. J.: Description of the NCAR Community Atmosphere
Model (CAM 5.0), NCAR Technical Note TN-486, Boulder, CO, USA, 274 pp., 2012.Paul, S., Ghosh, S., Oglesby, R., Pathak, A., Chandrasekharan, A., and
Ramasankaran, R.: Weakening of Indian summer monsoon rainfall due to changes
in land use land cover, Sci. Rep., 6, 32177,
10.1038/srep32177, 2016.Prell, W. L. and Kutzbach, J. E.: Sensitivity of the Indian monsoon to
forcing parameters and implications for its evolution, Nature, 360, 647–652,
10.1038/360647a0, 1992.Priya, P., Krishnan, R., Mujumdar, M., and Houze Jr., R. A.: Changing monsoon
and midlatitude circulation interactions over the Western Himalayas and
possible links to occurrences of extreme precipitation, Clim. Dynam., 49, 2351–2364,
10.1007/s00382-016-3458-z, 2017.Ramanathan, V. and Crutzen, P.: New directions: Atmospheric brown
“clouds”, Atmos. Env., 37, 4033–4035,
10.1016/S1352-2310(03)00536-3, 2003.Ramanathan, V., Chung, C., Kim, D., Bettge, T., Buja, L., Kiehl, J. T.,
Washington, W. M., Fu, Q., Sikka, D. R., and Wild, M.: Atmospheric brown
clouds: Impacts on South Asian climate and hydrological cycle, P. Natl. Acad. Sci. USA, 102,
5326–5333, 10.1073/pnas.0500656102, 2005.Ramesh, K. V. and Goswami, P.: Assessing reliability of climate projections:
the case of Indian monsoon, Sci. Rep., 4, 161–174,
10.1038/srep04071, 2014.Rao, S., Pachauri, S., Dentener, F., Kinney, P., Klimont, Z., Riahi, K., and
Schöpp, W.: Better air for better health: Forging synergies in policies
for energy access, climate change and air pollution, Global Environ. Chang., 23, 1122–1130,
10.1016/j.gloenvcha.2013.05.003, 2013.Saha, A. and Ghosh, S.: Can the weakening of Indian Monsoon be attributed to
anthropogenic aerosols?, Environ. Res. Commun., 1, 061006,
10.1088/2515-7620/ab2c65, 2019.Salzmann, M., Weser, H., and Cherian, R.: Robust response of Asian summer
monsoon to anthropogenic aerosols in CMIP5 models, J. Geophys. Res., 119, 11321–11337,
10.1002/2014JD021783, 2014.Samset, B. H. and Myhre, G.: Climate response to externally mixed black
carbon as a function of altitude, J. Geophys. Res., 120, 2913–2927,
10.1002/2014JD022849, 2015.Samset, B. H., Myhre, G., Forster, P. M., Hodnebrog, Ø., Andrews, T.,
Faluvegi, G., Fläschner, D., Kasoar, M., Kharin, V., Kirkevåg, A.,
Lamarque, J.-F., Olivié, D., Richardson, T., Shindell, D., Shine, K. P.,
Takemura, T., and Voulgarakis, A.: Fast and slow precipitation responses to
individual climate forcers: A PDRMIP multimodel study, Geophys. Res. Lett., 43, 2782–2691,
10.1002/2016GL068064, 2016.Samset, B. H., Lund, M. T., Bollasina, M., Myhre, G., and Wilcox, L.: Emerging
Asian aerosol patterns, Nat. Geosci., 12, 582–584,
10.1038/s41561-019-0424-5, 2019.Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L.,
Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., Canuto, V.,
Chen, Y., Cheng, Y., Clune, T. L., Del Genio, A., de Fainchtein, R.,
Faluvegi, G., Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A.
A., LeGrande, A. N., Lerner, J., Lo, K. K., Matthews, E. E., Menon, S.,
Miller, R. L., Oinas, V., Oloso, A. O., Perlwitz, J. P., Puma, M. J.,
Putman, W. M., Rind, D., Romanou, A., Sato, M., Shindell, D. T., Sun, S.,
Syed, R. A., Tausnev, N., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao,
M.-S., and Zhang, J.: Configuration and assessment of the GISS ModelE2
contributions to the CMIP5 archive, J. Adv. Model. Earth Syst., 1, 141–184,
10.1002/2013MS000265, 2014.Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., and Ziese, M.:
GPCC Full Data Monthly Product Version 2018 at 0.5∘: Monthly
Land-Surface Precipitation from Rain-Gauges built on GTS-based and
Historical Data, 10.5676/DWD_GPCC/FD_M_V2018_050, 2018.Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A.,
O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., Mora,
L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E.,
Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T.,
Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J.,
Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A.,
Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat,
S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A.,
Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat,
M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Syst., 11, 4513–4558, 10.1029/2019MS001739, 2019.Shawki, D., Voulgarakis, A., Chakraborty, A., Kasoar, M., and Srinivasan,
J.: The South Asian Monsoon response to remote aerosols: global and regional
mechanisms, J. Geophys. Res., 123, 11585–11601,
10.1029/2018JD028623, 2018.Shukla, J. and Paolino, D. A.: The Southern Oscillation and long-range
forecasting of the summer monsoon rainfall over India, Mon. Weather Rev., 111, 1830–1837,
10.1175/1520-0493(1983)111<1830:TSOALR>2.0.CO;2, 1983.Sikka, D. R.: Some aspects of the large-scale fluctuations of summer monsoon
rainfall over India in relation to fluctuations in the planetary and
regional scale circulation parameters, Proc. Indian Natl. Acad. Sci., 89, 179–195,
10.1007/BF02913749, 1980.Sillmann, J., Stjern, C. W., Myhre, G., Samset, B. H., Hodnebrog, Ø.,
Andrews, T., Boucher, O., Faluvegi, G., Forster, P., Kasoar, M. R., Kharin,
V. V., Kirkevåg, A., Lamarque, J.-F., Olivié, D. J. L., Richardson,
T. B., Shinndell, D., Takemura, T., Voulgarakis, A., and Zwiers, F. W.: Extreme
wet and dry conditions affected differently by greenhouse gases and
aerosols, NPJ Clim. Atmos. Sci., 2, 24, 10.1038/s41612-019-0079-3,
2019.Singh, D., Bollasina, M., Ting, M., and Diffenbaugh, N. S.: Disentangling the
influence of local and remote anthropogenic aerosols on South Asian monsoon
daily rainfall characteristics, Clim. Dynam., 52, 6301–6320,
10.1007/s00382-018-4512-9, 2019.Stjern, C. W., Samset, B. H., Myhre, G., Forster, P. M., Hodnebrog, Ø.,
Andrews, T., Boucher, O., Faluvegi, G., Iversen, T., Kasoar, M., Kharin, V.,
Kirkevåg, A., Lamarque, J.-F., Olivié, D., Richardson, T., Shawki,
D., Shindell, D., Smith, C., Takemura, T., and Voulgarakis, A.: Rapid
adjustments cause weak surface temperature response to increased black
carbon concentrations, J. Geophys. Res., 122, 11462–11481,
10.1002/2017JD027326, 2017.
Subbiah, A.: Initial report on the Indian monsoon drought of 2002, Asian Disaster Preparedness Center, Bangkok, Thailand, 29 pp., 2004.Turner, A. G. and Annamalai, H.: Climate change and the South Asian Summer
Monsoon, Nat. Clim. Change, 2, 1–9, 10.1038/nclimate1495, 2012.Turner, A. G. and Slingo, J. M.: Subseasonal extremes of precipitation and
active-break cycles of the Indian summer monsoon in a climate change
scenario, Q. J. Roy. Meteor. Soc., 135, 549–567, 10.1002/qj.401,
2009.
Wang, C., Kim, D., Ekman, A. M. L., Barth, M. C., and Rasch, P. J.: Impact of
anthropogenic aerosols on Indian summer monsoon, Geophys. Res. Lett., 36, L21704,
10.1029/2009GL040114, 2009.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, 10.5194/gmd-4-845-2011, 2011.Westervelt, D. M., Conley, A. J., Fiore, A. M., Lamarque, J.-F., Shindell,
D., Previdi, M., Faluvegi, G., Correa, G., and Horowitz, L. W.: Multimodel
precipitation responses to removal of U.S. sulfur dioxide emissions, J. Geophys. Res., 122,
5024–5038, 10.1002/2017JD026756, 2017.Westervelt, D. M., Conley, A. J., Fiore, A. M., Lamarque, J.-F., Shindell, D. T., Previdi, M., Mascioli, N. R., Faluvegi, G., Correa, G., and Horowitz, L. W.: Connecting regional aerosol emissions reductions to local and remote precipitation responses, Atmos. Chem. Phys., 18, 12461–12475, 10.5194/acp-18-12461-2018, 2018.Westervelt, D. M., You, Y., Li, X., Ting, M., Lee, D. E., and Ming, Y.:
Relative importance of greenhouse gases, sulfate, organic carbon, and black
carbon aerosol for South Asian monsoon rainfall changes, Geophys. Res. Lett., 47, 13, 10.1029/2020GL088363, 2020.Wilcox, L. J., Highwood, E. J., Booth, B. B. B., and Carslaw, K. S.: Quantifying
sources of inter-model diversity in the cloud albedo effect, Geophys. Res. Lett., 42,
1568–1575, 10.1002/2015GL063301, 2015.Willmott, C. J. and Matsuura, K.: Terrestrial Air Temperature and
Precipitation: Monthly and Annual Time Series (1950–1999), available at:
http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (last access: 8 March 2021), 2001.Xie, X., Myhre, G., Liu, X., Li, X., Shi, Z., Wang, H., Kirkevåg, A., Lamarque, J.-F., Shindell, D., Takemura, T., and Liu, Y.: Distinct responses of Asian summer monsoon to black carbon aerosols and greenhouse gases, Atmos. Chem. Phys., 20, 11823–11839, 10.5194/acp-20-11823-2020, 2020.