Using outputs from 10 CMIP5 models with fixed sea surface temperature, we investigate the fast response of the East Asian summer monsoon (EASM) and summer precipitation in East China to anthropogenic aerosols. To address this topic, we employ two commonly used EASM indices that can represent zonal and meridional land-sea thermal contrast, respectively. The results reveal that the notion of a weakened EASM in response to increased anthropogenic aerosols is a robust one, as well as decreased precipitation in East China. The ensemble mean of decreased precipitation in the aerosol run was about 6.6% in comparison to the CTL run and could be enlarged to 8.3% by excluding the experiments with the aerosol direct effect only. Convective precipitation was found to be the primary contributor (>80%) to the reduction of total precipitation. The combination of direct and indirect effects of aerosols can decrease solar radiation reaching the Earth’s surface and eventually modulate large-scale EASM circulation and suppress summer precipitation in East China. The uncertainties and discrepancies among the models highlight the complexity of interaction in aerosol-precipitation processes when investigating present and future changes of the EASM.
The East Asian summer monsoon (EASM) has tremendous impacts on socioeconomic development throughout East Asia [
It is generally recognized that aerosols affect climate change through a reduction of solar radiation (direct effect) and interactions with cloud microphysical processes (indirect effect) [
Numerical models with improved skill provide advances in detecting the climatological features induced by anthropogenic aerosols. The climate response to aerosol forcing is more localized over land [
In contrast to previous studies, we focus on the fast response of the EASM and summer precipitation over East China to anthropogenic aerosols. The main purpose of this study is to verify whether the weakening effect of anthropogenic aerosols on the EASM is a robust feature. The remainder of the paper is organized as follows. The model simulations and methodologies used in this study are introduced in Section
We use the simulation results from 10 models that participated in CMIP5. There are two sets of fixed SST experiments for each model: one is designed with natural aerosols only at the 1850AD level (control run; hereafter CTL run) and the other with both anthropogenic and natural aerosols at the 2000AD level (hereafter aerosol run). The climate variations response to the effect of anthropogenic aerosols is illuminated by the difference between the aerosol run and CTL run (the aerosol run minus the CTL run). In order to extract the effect of anthropogenic aerosols, other boundary conditions and forcing agents (greenhouse gas concentrations, solar radiative forcing, and SST) are fixed at 1850AD levels in both experiments. Among these simulations, eight models run with the direct and indirect effects of aerosols together, while the other two models (BCC-CSM1.1 and FGOALS-s2) only take the aerosol direct effect into account. Six models simulate aerosol chemistry and physics online, while the other four models utilize prescribed aerosol fields. The model simulations use horizontal resolutions of 64 × 128 to 160 × 320, with vertical layers varying from 17 to 23 levels in the atmosphere. Each of these models is run for 30–60 years, and 30 years of simulations are used in our analysis. Although these models are developed as atmosphere-ocean coupled models, as the SST is fixed in the simulations, they can be considered as atmospheric models here. As we focus our assessment on the fast response of the EASM to anthropogenic aerosols, these simulations, excluding the impacts of aerosol-SST interactions, provide us with the means to explore this issue. Detailed information on the atmospheric components, resolutions, aerosol schemes, and the modeling centers responsible for the development of the models is provided in Table
List of the CMIP5 models used in this study.
Model name | Atmospheric component | Resolution |
Aerosol |
Country/center |
---|---|---|---|---|
BCC-CSM1.1 | BCC-AGCM2.1 | 64 |
Prescribed |
China/BCC |
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||||
CanESM2 | CanAM4 | 64 |
Prescribed |
Canada/CCCMA |
|
||||
CSIRO-Mk3.6.0 | Mk3.6 atmosphere component | 96 |
Online |
Australia/CSIRO |
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||||
FGOALS-s2 | SAMIL2 | 108 |
Prescribed |
China/LASG-IAP |
|
||||
GFDL-CM3 | AM3p9 | 90 |
Prescribed |
USA/NOAA |
|
||||
HadGEM2-A | HadGAM2 | 144 |
Online |
UK/MOHC |
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||||
IPSL-CM5A-LR | LMDZ4 v5 | 96 |
Online |
France/IPSL |
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||||
MIROC5 | CCSR/NIES/FRCGC AGCM | 128 |
Online |
Japan/MIROC |
|
||||
MRI-CGCM3 | GSMUV | 160 |
Online |
Japan/MRI |
|
||||
NorESM1-M | CAM4-Oslo | 96 |
Online |
Norwegian/NCC |
DE, direct effect; IDE, indirect effect.
All the model simulations are interpolated onto a 2.5° × 2.5° grid via bilinear interpolation. The ensemble result is achieved by averaging the 10 simulations with equivalent weight. To assess the overall performance of the climate models in simulating summer climatology, we examine the mean sea level pressure (SLP), 200 hPa zonal wind, and 850 hPa winds between the CTL run from the multimodel ensemble and ECMWF 40-year reanalysis data (ERA40) [
Due to rapid industrialization in recent decades, East Asia has become one of the dominant emission sources of anthropogenic aerosols in the world. Figure
Spatial distribution of the 550 nm aerosol optical depth difference between two experiments for summer (JJA) derived from the CSIRO-Mk3.6.0 model. The purple box represents the study region (East China; 25°–45°N, 105°–120°E).
Since the EASM encompasses the tropics, subtropics, and midlatitudes, it is difficult to measure its strength due to its complex spatiotemporal structures [
We calculate the EASMI with simulations from 10 models using the Guo and Han indices. The monsoon indices derived from all the models shown in Table
The EASMI calculated by the Guo and Han indices in the CTL and aerosol runs.
Model | Guo index |
Guo index |
Han index |
Han index |
---|---|---|---|---|
BCC-CSM1.1 | −31.41 | −30.93 | −4.82 | −5.03 |
CanESM2 | −48.66 | −46.9 |
−7.99 | −8.7 |
CSIRO-Mk3.6.0 | −55.31 | −53.4 |
−9.13 | −9.31 |
FGOALS-s2 | −39.86 | −39.29 | −4.44 | −5.10 |
GFDL-CM3 | Null | −28.84 | −9.40 | −10.8 |
HadGEM2-A | −47.21 | −46.6 |
−3.47 | −4.7 |
IPSL-CM5A-LR | −31.07 | −30.80 | −13.50 | −13.40 |
MIROC5 | −54.13 | −49.0 |
−5.40 | −7.3 |
MRI-CGCM3 | −21.10 | −20.68 | −13.98 | −14.4 |
NorESM1-M | −44.02 | −42.74 | −5.62 | −6.9 |
Ensemble results | −41.00 | −39.4 |
−7.10 | −7.7 |
Precipitation is one of the main regimes of the monsoon climate. The summer precipitation in East China is largely contributed to the intensity of the EASM [
(a) Summer precipitation of the aerosol run (red bars) and CTL run (black bars) for each model over East China. (b) Same as (a), except for the precipitation difference between the aerosol run and CTL run (aerosol minus CTL). (c) Ratio of precipitation differences in (b) to the CTL run in each model. The far right bar and purple line in each panel are the ensemble mean and standard deviation from all simulations, respectively.
The precipitation can be divided into convective precipitation and large-scale precipitation by the producing mechanisms in the model simulation. The range of convective precipitation reduction is from 0.016 to 0.73 mm/day in the aerosol run in comparison to the CTL run, and the decrease in large-scale precipitation is relatively less (Table
Convective precipitation (PRCC) and large-scale precipitation (PRCL) difference between the aerosol run and CTL run and the ratio of the difference to the total precipitation decrease.
Model name | Total decrease |
PRCC | PRCL | ||
---|---|---|---|---|---|
Decrease |
Percentage |
Decrease |
Percentage | ||
BCC-CSM1.1 | −0.01 | −0.016 | 153.3 | 0.006 | −53.3 |
CanESM2 | −0.4 | −0.24 | 58.8 | −0.16 | 41.2 |
CSIRO-Mk3.6.0 | −0.5 | −0.47 | 94.0 | −0.03 | 6.0 |
FGOALS-s2 | −0.01 | −0.03 | 300.0 | 0.02 | −200.0 |
GFDL-CM3 | −0.88 | −0.72 | 81.8 | −0.16 | 18.2 |
HadGEM2-A | −0.2 | −0.12 | 57.9 | −0.08 | 42.1 |
IPSL-CM5A-LR | −0.17 | −0.27 | 160.1 | 0.10 | −60.1 |
MIROC5 | −0.89 | −0.73 | 82.5 | −0.16 | 17.5 |
MRI-CGCM3 | −0.15 | −0.08 | 55.1 | −0.07 | 44.9 |
NorESM1-M | −0.17 | −0.15 | 88.2 | −0.02 | 11.8 |
Ensemble | −0.34 | −0.28 | 82.4 | −0.06 | 17.6 |
Based on the above analysis, we know that the EASM can be weakened by the effect of anthropogenic aerosols and induced precipitation decrease in East China. The EASM is a subtropical monsoon; the low-level winds reverse primarily from southerlies [
Multimodel climatological mean (30 years) of the difference (aerosol run minus CTL run) of summer surface temperature (shading; units: K), SLP (contours; units: hPa), and 850 hPa winds (vectors; units: m/s).
During summer, East China is dominated by southerly winds at 850 hPa, bringing moisture from the ocean. To examine how the decreased precipitation is affected by regional circulation over East China, we calculate regionally averaged values of specific humidity and moisture flux variables at 850 hPa and compare the results between the aerosol run and CTL run (Table
Comparisons between the aerosol run and the CTL run in East China in terms of specific humidity, moisture flux, and convergence/divergence at 850 hPa.
Model name | Specific humidity (g/kg) | Water vapor flux |
Convergence/divergence | ||||||
---|---|---|---|---|---|---|---|---|---|
CTL | Aerosol | Aerosol−CTL | CTL | Aerosol | Aerosol−CTL | CTL | Aerosol | Aerosol−CTL | |
BCC-CSM1.1 | 9.33 | 9.37 | 0.04 | 32.63 | 31.58 | −1.05 | −0.75 | −0.69 | 0.06 |
CanESM2 | 11.79 | 11.54 | −0.24 | 31.69 | 29.13 | −2.56 | −0.66 | −0.56 | 0.10 |
CSIRO-Mk3.6.0 | 12.30 | 12.16 | −0.14 | 26.50 | 23.74 | −2.76 | −0.21 | −0.07 | 0.14 |
FGOALS-s2 | 8.60 | 8.70 | 0.10 | 40.70 | 39.09 | −1.61 | −0.77 | −0.81 | −0.04 |
GFDL-CM3 | 10.05 | 9.66 | −0.38 | 21.78 | 19.10 | −2.68 | −0.03 | 0.18 | 0.21 |
HadGEM2-A | 10.90 | 10.76 | −0.14 | 23.38 | 20.12 | −3.26 | −0.91 | −0.91 | 0.00 |
IPSL-CM5A-LR | 9.10 | 9.07 | −0.03 | 15.85 | 15.03 | −0.82 | −0.35 | −0.31 | 0.04 |
MIROC5 | 11.50 | 11.10 | −0.40 | 37.65 | 33.77 | −3.88 | −1.53 | −1.08 | 0.45 |
MRI-CGCM3 | 7.25 | 7.33 | 0.08 | 24.19 | 23.92 | −0.27 | 0.28 | 0.33 | 0.05 |
NorESM1-M | 11.08 | 10.89 | −0.19 | 43.00 | 36.05 | −6.94 | −0.44 | −0.32 | 0.12 |
Ensemble | 10.19 | 10.06 | −0.13 | 29.74 | 27.15 | −2.59 | −0.54 | −0.42 | 0.12 |
Aerosols can perturb the energy balance of the earth by scattering and absorbing sunlight and by modifying clouds, hence inducing climate change [
In general, the surface temperature decreased in response to the aerosol effect in East China, except in the MRI-CGCM3 model. The decrease ranges from 0.1 to 0.3 K, with the average cooling in the 10 models being about 0.15 K (Figure
Difference between the aerosol run and CTL run in East China of (a) summer surface (a) temperature; (b) total radiation (black), shortwave radiation (red), and longwave radiation (blue) of aerosol forcing; and (c) direct (red) and indirect (blue) radiation of aerosol forcing. The far right bar and purple line in each panel are the ensemble mean and standard deviation from all simulations, respectively.
The models produce a surface shortwave radiative forcing (RF) estimate from aerosol of −4.2 (−7.8 to −3.0) W/m2, while the longwave RF estimate from aerosol of +0.5 (+0.1 to +1.2) W/m2 partially offsets the changes in shortwave RF (Figure
In summary, direct and indirect RF impact the shortwave and longwave radiative effect together, leading to a cooling of East China, inducing a decrease of the land-sea thermal contrast, and thus weakening the EASM and reducing precipitation in East China.
By analyzing simulations from 10 CMIP5 models, we found that the weakening effect of the EASM and the reduction in precipitation over East China with increased anthropogenic aerosols are robust. The combination of direct and indirect effects of anthropogenic aerosols can reduce surface total radiative forcing and cool continental East Asia. Subsequently, the decreased land-sea thermal contrast and SLP gradients can modulate the large-scale meridional circulation, decreasing moisture transport into East China and eventually suppressing precipitation in this region. There exists the weakening trend of the EASM and the decreasing of precipitation in East China since late 1970s [
The weakening of the EASM’s intensity and the concomitant reduction in precipitation are more significant in experiments that consider both the aerosol direct and indirect effects, rather than with the aerosol direct effect only. The ensemble mean of decreased precipitation in the aerosol run was circa 0.34 mm/day (6.6%) in comparison to CTL run, and the reduction could be enlarged to 8.3% except in the experiments with the direct effect only. This may indicate an important role of the aerosol indirect effect in the EASM’s circulation and local precipitation. Additionally, more than 80% of the decreased precipitation could be attributed to convective precipitation, which is consistent with the findings of Guo et al. [
However, we also found large discrepancies among the spatial distributions of aerosol-induced summer precipitation in the 10 models, despite the common feature that precipitation was reduced by anthropogenic aerosols over East China (Figure S2). It is possible that the uncertainties and discrepancies of the simulated precipitation are due to the complexity of aerosol-precipitation processes and model limitations in representing the features of precipitation in East China. There is great potential for improvement in the parameterizations of aerosol-cloud-precipitation interaction, as well as model skill in simulating the precipitation distribution over East China.
Finally, it is important to note that we focused on the fast component of anthropogenic aerosols in this study and did not pay attention to the slow response of SST change. Ganguly et al. [
The authors declare that they have no conflict of interests regarding the publication of this paper.
This work was jointly supported by the National Basic Research Program of China (2011CB403406), the Chinese Academy of Sciences Strategic Priority Research Program (XDA05110101), and the National Natural Science Foundation of China (41105071 and 41290255).