Simulation of the responses of rainstorm in the Yangtze River Middle Reaches to changes in anthropogenic aerosol emissions

Journal Pre-proof Simulation of the responses of rainstorm in the Yangtze River Middle Reaches to changes in anthropogenic aerosol emissions Yongqing Bai, Haixia Qi, Tianliang Zhao, Yue Zhou, Lin Liu, Jie Xiong, Zhimin Zhou, Chunguang Cui PII:

S1352-2310(19)30720-4

DOI:

https://doi.org/10.1016/j.atmosenv.2019.117081

Reference:

AEA 117081

To appear in:

Atmospheric Environment

Received Date: 7 March 2019 Revised Date:

14 October 2019

Accepted Date: 26 October 2019

Please cite this article as: Bai, Y., Qi, H., Zhao, T., Zhou, Y., Liu, L., Xiong, J., Zhou, Z., Cui, C., Simulation of the responses of rainstorm in the Yangtze River Middle Reaches to changes in anthropogenic aerosol emissions, Atmospheric Environment (2019), doi: https://doi.org/10.1016/ j.atmosenv.2019.117081. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Simulation of the Responses of Rainstorm in the Yangtze River

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Middle Reaches to Changes in Anthropogenic Aerosol Emissions

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Yongqing Bai 1, Haixia Qi 1, Tianliang Zhao2*, Yue Zhou1*, Lin Liu1, Jie Xiong1, Zhimin

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Zhou1, Chunguang Cui1

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1 Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute

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of Heavy Rain, China Meteorological Administration, Wuhan 430205, China

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2 Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological

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Administration, Nanjing University of Information Science and Technology, Nanjing

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210044, China

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Corresponding authors:

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Tianliang Zhao ([email protected])

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Address: Nanjing University of Information Science & Technology, 210044 Ningliu

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Road, Nanjing 210044 , China.

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Yue Zhou ([email protected])

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Address:

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430205, China

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.

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Institute of Heavy Rain, China Meteorological Administration, Wuhan

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Abstract: The model WRF-Chem sensitivity simulation experiments with changing

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intensity of anthropogenic emissions sources were applied to simulate a rainstorm

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process in the Yangtze River Middle Reaches (YRMR) during June 18–19, 2018 to

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study the responses of clouds and precipitation in the rainstorm to changes in aerosol

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concentrations in this region.

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aerosol-cloud interaction during low and high emission phases tended to inhibit and to

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enhance the precipitation process with the precipitation peak lagging 1–2 h. In the

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later period of rainstorm, high concentrations of aerosols improved precipitation

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efficiency significantly, resulting in more centralized clusters of intense precipitation.

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The cloud droplet number concentrations and cloud water contents demonstrated an

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increasing logarithmic relationship with increasing PM2.5 concentrations. The PM2.5

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concentration of about 25 µg m-3 was estimated as the response threshold of cloud

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droplet number concentrations from sharp to smooth changes. Before and after the

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peak precipitation, the relationship between the average precipitation rates and PM2.5

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concentrations presented an inverse power function. Aerosol-induced precipitation

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changes were sensitive to ambient relative humidity (RH). When 80% ≤ RH < 85%,

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the response of precipitation to aerosol emissions was in equilibrium. When RH < 80%

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or RH > 85% increasing anthropogenic aerosol emissions tended to inhibit or enhance

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precipitation, especially in the case of low (high) aerosol emissions.

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Keywords: Yangtze River Middle Reaches; Anthropogenic aerosol emissions;

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Rainstorm; Aerosol-Cloud Interaction; WRF-Chem

The simulation experiments revealed that the

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1. Introduction

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Increased aerosol particles in the atmosphere emitted from human activities have

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a significant effect on weather and climate (Li, 1998; Bollasina et al., 2011; Menon et

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al., 2002; Ramanathan et al., 2005). Atmospheric aerosols directly affect the radiation

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balance of the earth-atmosphere system by absorbing and scattering solar radiation

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(Seinfeld, 2008; Zhang et al., 2009; Bollasina et al., 2013; Bond et al., 2013), and then

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further influence the climate. Atmospheric aerosols act as cloud condensation nuclei

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(CCN) or ice cores (IN) and affect the energy budget between the atmosphere and

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earth by changing the characteristics of clouds via microphysical processes

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(Ramanathan et al., 2001, 2005; Rosenfeld, 2006; Fan et al., 2008, 2015; Lee et al.,

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2014) that indirectly influence the climate. By absorbing solar radiation, black carbon

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aerosols heat the air, altering regional atmospheric stability and vertical motions, and

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affects large-scale circulation and the hydrologic cycle with significant regional

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climate effects in China and India (Menon et al., 2002). Precipitation in the northern

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Arabian Sea and northwest India increases by 16% from June to July. The

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corresponding increase in precipitation due to the presence of aerosol-like heating

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over South Asia (local impact) and East Asia (remote impact) was 28 and 13%,

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respectively (Chakraborty et al., 2014). Aerosol concentrations over India vary

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substantially with precipitation intensity, and aerosols modify cloud characteristics by

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aerosol-induced modulation of the active–break cycle of Indian summer monsoons

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(Bhattacharya et al., 2017).

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The mechanisms that cause aerosols to impact clouds and precipitation (the

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indirect effect) are a complicated and controversial issue in weather and climate

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research (Muhlbauer et al., 2010; Duong et al., 2011; Fan et al., 2016). Atmospheric

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aerosols and their interactions with cloud and precipitation involve complex physical

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and chemical processes and present a large degree of uncertainty (Jones et al., 2010;

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Wang et al., 2012; Bhattacharya et al., 2014; Takuro et al., 2016). Similar to aircraft

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and satellite observations, there is a positive correlation between aerosol column

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content and cloud droplet column concentrations from marine clouds in stratiform

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clouds over China, based on the aerosol number and cloud droplet number

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concentrations (Rosenfeld and Givati, 2005; Huang et al., 2005). Some studies show

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that the smoke from forest fires in the Amazon basin causes the cloud droplet number

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concentration to increase and the particle radius to decrease (Reid et al., 1999; Mircea

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et al., 2005). In contrast, observations of clouds over the Atlantic showed smaller

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cloud droplets and thinner clouds caused by atmospheric aerosol pollution (Brenguier

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et al., 2000; Schwartz et al., 2002). The increases in aerosol particles could lead to

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increases in concentrations and decreases in the radii of cloud particles, strengthening

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cloud albedo and thus inhibiting precipitation (Rosenfeld, 1999, 2000, 2006). The

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inhabitation of atmospheric aerosols on the precipitation of warm clouds could also

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produce strong ice cloud precipitation. Increases in aerosol particles leads to a

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reduction in the radius of cloud particles and an increase in their total water content,

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composed of numerous small droplets, resulting in more intense deep convection rain

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processes (Williams et al., 2002; Koren, 2005; Lin et al., 2006; Bell et al., 2008). In

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addition, the increase in aerosol particles causes an increase in cloud droplet

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concentration and the release of latent heat from the cloud droplet and cloud water

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formation process, which could alter the stability of the atmosphere (Khai et al., 2005;

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van den Heever et al., 2006; Tao et al., 2007; Fan et al., 2012; Fan et al., 2013;

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Morrison and Grabowski, 2012). The atmospheric stability is also greatly modulated

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by the radiative effects of aerosols (Chakraborty et al., 2004, 2014).

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Frequent extreme weather events are closely related to anthropogenic-induced

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climate change. Observations in recent decades show higher frequency and intensity

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of extreme precipitation in China (Zhang and Cui, 2012; Ye et al., 2013；Fu and Dan,

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2014; Sheikh et al., 2014). These changes have made China an important area for

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studying aerosols and climate, and regional water cycles (Li et al., 2007; Guo et al.,

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2014; Zhang and Wu, 2014). The Yangtze River Middle Reaches (YRMR) is a typical

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inland wetland with typical East Asian monsoons and it is susceptible to extreme

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weather and climate events (Takuro et al., 2016). The YRMR region is located in the

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developed Yangtze River Economic Zone with intense anthropogenic aerosol

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emissions. It is vulnerable to intensive emissions of complex aerosols from sources

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including ship traffic, and industrial and agricultural activities. Atmospheric aerosol

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pollution has become increasingly severe (Zhang et al., 2012). The effect of aerosols

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on precipitation varies from region to region, mainly based on the degree of cloud

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pollution and cloud type (Rosenfeld and Givati, 2005; Lynn et al., 2006; Mahen et al.,

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2012). Regional aerosols with different pollution sources and weather conditions exert

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different impacts on regional precipitation. The YRMR is the major area in the East

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Asia monsoon region with frequent extreme precipitation and heavy haze pollution

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and is, therefore, a key area for studying the effects of aerosols on clouds and

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precipitation. In this paper, the weather research and forecast model coupled with

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chemistry WRF/Chem model that considers anthropogenic aerosol emissions was

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applied to simulate a rainstorm in the YRMR region during June 18–19, 2018.

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Multiple sets of modeling sensitivity experiments were designed with changing

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anthropogenic emission intensities in order to determine a response relationship

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between cloud-precipitation and anthropogenic aerosol emissions. The results could

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provide information about, the interaction processes between atmospheric aerosols,

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clouds, and precipitation.

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2. Numerical Simulation Scheme and Analysis

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2.1 Model Settings

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The weather research and forecast model coupled with chemistry (WRF-Chem;

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Grell et al., 2005) version 3.4 was used in this study. This model can couple complex

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physicochemical processes such as transport, sedimentation, emission, chemical

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conversion, aerosol effect, photolysis, and radiation of chemical substances

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(Gustafson et al., 2007). The WRF/Chem model used an aerosol activation module to

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describe the process of cloud droplet growth from aerosols (Fast et al., 2006), the

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Lin’s single and two parameter cloud microphysical schemes (Lin et al., 1983).

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However, the WRF/Chem model has not yet implemented the ice nuclei activation

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process and thus does not reflect the effects of aerosols as ice nuclei.

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To characterize the response relationship of each physical element, the vertical

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dimension was processed as follows: the vertical integrations for water compound

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mixing ratios (cloud water, rain water, ice crystal, snow, and sleet), cloud droplet

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number concentrations, and the aerosol number concentrations in clouds were

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calculated. The maximum value of the vertical velocity in the whole layer was used.

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The surface values were taken for variables such as water vapor mixing ratio (Vapor),

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relative humidity (RH), wind speed, convective available potential energy (CAPE),

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and PM2.5 mass concentration. Num_cw02 represented the number concentration of

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second-order aerosol particles sized 0.1–1.0 µm in clouds. It was quite consistent with

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the spatial and temporal distribution of cloud droplet number concentrations,

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indicating that the range of this particle size was reasonable for activating the aerosols

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in clouds into CCN.

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2.2 Experimental Design

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The simulation employed a 3-domain nesting design (Fig. 1), with grid

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resolutions of 27 km, 9 km, and 3 km, respectively, for each of the domains. The

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innermost layer was the Yangtze River Middle Basin, with 28 layers in the vertical

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direction and a pressure of 50 hPa in the top model layer. The Lin’s two-parameter

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cloud microphysical scheme was selected for calculating cloud droplet number

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concentrations (Chapman et al., 2009). A MOSAIC (4 bins) aerosol module (Zaveri et

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al., 2008) is a scheme that includes 8 aerosol components of elemental carbon or

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black carbon, organic carbon, sulfate, nitrate, dust, NaCl, NH4 and inorganic aerosols,

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where each component is divided into 4 particle size ranges: 0.039–0.1 µm, 0.1–1.0

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µm, 1.0–2.5 µm, and 2.5–10 µm. We selected the Goddard short-wave radiation and

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RRTM long-wave radiation as the aerosol-radiation interaction process, the CBMZ

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gas phase chemical reaction mechanism, the YSU boundary layer scheme, and the

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Noah LSM land surface module for the simulation experiments. The outer two

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domains adopt a G3 cumulus convection scheme, where the convection

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parameterization process in the innermost domain is shut down and the explicit

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scheme is employed to simulate the aerosol–cloud–precipitation process entirely.

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The initial and lateral boundary conditions were taken from National Center for

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Environmental Prediction (NCEP) Final (FNL) Operational Global analysis

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(http://rda.ucar.edu/datasets/ds083.2/#!access) The model simulation time is from

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00:00 UTC on June 18, 2018 to 00:00 UTC on June 19, 2018 (same as below), with a

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time integration of 24 hours and output interval of 1 hour. 06:00-24:00 hours is the

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time period from the simulation that is analyzed, using the first 6 hours as spin-up

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time.

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2.3 Emission Scenarios

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Anthropogenic emission data is from the a mosaic Asian anthropogenic emission

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inventory MIX for 2016(Li et al, 2017). The MIX inventory provides anthropogenic

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pollution sources and greenhouse gas emission data for 30 countries and regions in

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Asia, including ten major atmospheric chemical components such as SO2, NOx, CO,

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NH3, NMVOC, PM10, PM2.5, BC, OC, and CO2. The inventory provides monthly

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grid-based 0.25° × 0.25° emissions data with a spatial resolution for 5

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emission-related sources (electricity, industry, civil, transportation, and agriculture),

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that can meet the simulation requirements for the atmospheric chemical transmission

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models at multiple scales. The inventory is widely recognized and applied

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internationally (Wang et al., 2014; Zheng et al., 2015, 2018).

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To compare the response relationship of clouds and precipitation in the YRMR to

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regional emission intensity changes, the innermost anthropogenic aerosol emission

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intensity is multiplied by coefficients of 0, 0.1, 0.5, 1, 2, 4, 6, 8, 10, 15, 20, 30, 40,

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and 100 respectively in 14 simulation experiments labeled with E0, E0.1, E0.5,

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E2, E4, E6, E8, E10, E15, E20, E30, E40 and E100 in Table 1 for the control

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experiment E1 and the rest 13 sensitivity experiments.

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2.4 Simulation Evaluation

E1,

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The YRMR area saw the first rainstorm in the summer monsoonal rain period on

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June 18, 2018 with a heavy rainfall center of the cumulative precipitation peaking at

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155.7 mm in the site Dangyang (30.82° N, 111.78° E) . Figure 2 shows the actual and

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simulated cumulative precipitation during the rainstorm and the average PM2.5

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concentration distribution on that day. The distribution of the major rainband in Figure

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2a is zonally oriented. The intensity reached rainstorm level or above and was

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categorized as a local heavy rainstorm. The red frame indicates the range within 30.5–

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31.8° N, 110.2–113° E and is the region of study of this rainstorm.

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Figure 2c shows the model coupled PM2.5 emission rates in the d03 region of the

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MIX inventory in June 2016. As shown in Figure 2c, the cities with larger populations

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and industrial densities such as Xiangyang, Yichang, and Shiyan have stronger

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emission intensities. The simulation analysis of the emissions intensity changes from

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anthropogenic sources in multiple groups shows that the simulation is largely

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dependent on the emission source inventory. The PM2.5 concentration simulation

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matches well with the distribution of regional emission sources. The high-value areas

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are also at the three centers , Xiangyang, Yichang, and Shiyan. However, the regional

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average concentration value was much lower than the actual situation. The emission

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source intensity was obviously underestimated. The emission source inventory has a

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series of problems, such as a high uncertainty in emission calculation results,

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inadequate spatial and temporal resolution, and late data updates, that directly affect

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the numerical simulation of atmospheric pollutants (Wang et al., 2014; Ma et al., 2018;

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Zhang et al., 2018). Some simulations and measurement studies have also shown that

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while emissions estimates in China may be lower than 50% (Tan et al., 2004; Cao et

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al., 2011), the error in aerosol emission factors are generally ±5% to 500% (Streets et

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al., 2003), and the uncertainty of PM2.5 emissions in East Asia is also ±130% (Zhang

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et al., 2009). The emission intensity of anthropogenic sources is multiplied by 6 (E6)

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in this simulation. PM2.5 concentrations in the heavily polluted center of Yichang and

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its surrounding areas are similar to the actual situation (Figure 2d). Due to the wet

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deposition in precipitation, there is low correlation between PM2.5 concentrations and

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the region of major rainband. Meanwhile, the major rainband pattern and the

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precipitation intensity of cumulative precipitation in simulation E6 are quite close to

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the observations (Figure 2b).

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Figure 2 shows the hourly observed and simulated precipitation under different

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emission scenarios at site Dangyang (Figure 2e), and the regional average (Figure 2f),

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using the different emission scenarios that simulate the center of heavy precipitation.

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As illustrated in Figure 2e, the peak precipitation is 43.5 mm from 00:00–01:00 (LTC)

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at the site Dangyang, and the intensity and trends of all simulated precipitation are

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consistent with the observations. However, for the low-middle emission scenarios, the

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simulated peak precipitation is slightly ahead of the observed peak and is also lower.

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However, in the high-emission scenario, the simulated precipitation peak is close to

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the observations. The intensity and evolution trends of the simulation and

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observations correspond well, but the simulated precipitation peak in all emission

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scenarios is ahead of the observed data by 2-4 hours (Figure 2f). In comparison, the

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simulated precipitation peak in the middle-high emission scenarios lags behind the

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observations.

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In summary, although there are some deviations in the simulation results, the

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spatial and temporal distribution of regional precipitation and the distribution of

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simulated PM2.5 concentrations caused by changes in anthropogenic emission

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intensity are, in general, in good agreement with the observations. By designing the

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anthropogenic source emission intensity response tests, the uncertainty of a single

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source can be reduced and the effects of anthropogenic aerosol emissions changes on

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rainstorm processes can be analyzed.

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3. Results

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3.1 Effects of Changes in Anthropogenic Aerosol Emissions on Precipitation

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The effect of a change in anthropogenic aerosol emission intensity on

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precipitation is nonlinear. As shown in Figure 3, comparisons were made among the

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spatial distribution of precipitation under different emission intensities. For the low

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emission pollution simulation, E0.1, an increase in anthropogenic emissions causes

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the effect of aerosols on precipitation to become enhanced after first being inhibiting.

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The precipitation at the rainstorm center is obviously weakened in simulation E4. The

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major rainband is longer and narrower, and the center of the rain cluster is more

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centralized in simulations E10 and E30. Comparing the distributions of precipitation

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frequency in the study area (Figure 4a), for the precipitation in the clean atmosphere

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of simulation E0, when there is an increase in anthropogenic emissions, the aerosol

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effect causes a decrease in precipitation frequency in areas below 40 mm. For areas

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above 60 mm, it first decreases and then increases. High emission pollution aerosols

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shrink the area of general precipitation, while the areas of intense precipitation are

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widened and more centralized, which makes it easier to trigger a rainstorm under the

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same environmental conditions.

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Long-term observations made by Guo et al. (2016) in the Pearl River Delta

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region of China showed that atmospheric aerosol pollution could significantly delay

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the triggering of precipitation. Bhattacharya et al. (2014, 2017) also came to the same

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conclusion using both satellite observations and WRF-Chem simulations. Figure 4b

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shows the average hourly precipitation under different emission intensities in the

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rainstorm region. Comparing with the simulation experiment E0, the precipitation

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peaking time of simulated rainstorm lagged by 1–2 h (Figure 4). After the peak

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precipitation, high levels of aerosol emissions significantly enhance precipitation in

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the E10 and E30 scenarios, resulting in an increase in total precipitation. Thus, with

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an increase in anthropogenic aerosol emissions, aerosols can inhibit (enhance)

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precipitation before (after) the peak precipitation.

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3.2 Effects of Changes in Anthropogenic Aerosol Emissions on Cloud

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Microphysics

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Figure 5 shows the statistical analysis of each 18-h regional average physical

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quantity in the simulation with the 14 groups of emission intensity sensitivity tests. As

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shown in Figure 5a, the responses of aerosol concentrations in the cloud (Num_cw02)

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to the anthropogenic emissions are saturated. With an increase in anthropogenic

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emissions, surface PM2.5 concentrations increase linearly while Num_cw02 increases

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non-linearly, where it increases rapidly in the low emission phase and increases

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relatively slowly in the high emission phase. Num_cw02 tends to saturate in

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extremely high emission scenarios.

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When aerosols enter the cloud, they can be activated into cloud condensation

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nuclei under certain conditions that affect cloud droplet formation. Based on the

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simulation, the cloud droplet number concentration has an approximately linear power

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function relationship with increases in cloud aerosols (Figure 5b). The higher the

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Num_cw02 value, the greater the cloud droplet number concentration, but when

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Num_cw02 decreases and the cloud droplet number concentration tends to saturate in

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the extremely high emission scenarios. The increase in anthropogenic aerosols leads

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to an increase in the cloud condensation nuclei concentration number, then the cloud

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droplet number concentration increases, which has been verified in other studies with

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many observations.

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In the past, aircraft observations in different regions (Taylor and McHaffie, 1994;

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Gultepe et al., 1996; Snider and Brenguier, 2000; Chuang, 2003) have found that there

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is a nonlinear relationship in the growth rate between cloud droplet number

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concentrations and aerosol concentrations. This simulation also revealed that there is a

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good logarithmic relationship between the cloud droplet number concentration and

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surface PM2.5 concentration in the YRMR (Figure 5c). When the PM2.5 concentration

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is relatively low (a daily average less than approximately 25 µg/m3), the cloud droplet

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number concentration increases rapidly with the increase in PM2.5 concentration. As

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PM2.5 concentrations continue to increase, the cloud droplet number concentration

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growth trend slows down.

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Atmospheric aerosols can change cloud droplet number concentrations, cloud

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water mass, and water vapor content. The simulation reveals that the water vapor and

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cloud water mixture ratio also have a good logarithmic relationship with surface PM2.5

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concentrations (Figure 5d). When PM2.5 concentrations are relatively low, the cloud

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water (water vapor) mixing ratio increases (decreases) rapidly with the increase of

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PM2.5 concentration. As PM2.5 concentrations continue to increase, the cloud water

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(water vapor) increase (decrease) slows down.

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When the PM2.5 concentration is relatively low (less than 25 µg/m3) in the

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response-sensitive phase of cloud water content (less than approximately 0.3 g/kg),

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the precipitation rate decreases as the cloud water mixing ratio increases. The CAPE

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value is inhibited simultaneously (Figure 5e) and the atmosphere tends to stabilize.

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Then, as the cloud water content continues to increase, precipitation begins to

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strengthen. ACI enhances precipitation when the environmental CAPE is relatively

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constant. This is similar to the long-term observations of the aerosol–cloud–

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precipitation conducted by Li et al. (2001) in the Great Plains in the southern United

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States. In the cloud layer with a high liquid water content, the increase in aerosols

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cause an increase in precipitation, and only the increase in aerosols in the cloud layer

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with low liquid water content reduces precipitation. As the aerosol and cloud water

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content increases, the mass-mixing ratio of ice phase particles (sleet, snow, and ice

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crystals) also decreases first and then increases (Figure 5f).

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The mechanism of the aerosol–cloud–precipitation effect has been verified by a

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large number of experiments with observations. The increases in aerosol

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concentrations leads to an increase in the concentration of CCN and cloud droplet

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number concentration, then the radii of the cloud droplets become smaller. The falling

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velocity and collision efficiency of the cloud droplets therefore decrease, the

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automatic conversion process of clouds and rain is inhibited, and eventually

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precipitation is affected (Warner and Twomey, 1967; Rosenfeld, 1999 ， 2000;

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Rosenfeld and Ulbrich, 2003; Lin et al., 2006; Bell et al., 2007). With higher aerosol

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concentrations, more cloud droplets reach the high altitude and follow the ascending

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airflow in the cloud, enhances the cloud droplet freezing process and releases the

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condensation of latent heat; this further enhances convection and the cold rain process

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(Rosenfeld and Woodley, 2000; Orille et al., 2001; Williams et al., 2002; Tao et al.,

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2007). Figure 6 shows the time-height profiles of the average differences of the water

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compounds, the number of cloud droplets, and the vertical velocity between E6 and

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E0 and E30 and E6 in the study area. When there are aerosol transitions from a low to

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a high concentration, the precipitation inhibition process corresponds to cloud

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droplets and the number of warm clouds (those below 0 °C) increase significantly.

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Then the ice phase particle-mixing ratio and vertical velocity decrease significantly.

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The precipitation enhancement process corresponds to the development of

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supercooled cloud droplets and cloud water above 0 °C; the ice phase particle mixing

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ratio and vertical velocity increase significantly.

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The entire microphysical process can, therefore, be described as follows. When

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aerosol concentrations increase, it will continue to activate into cloud droplets, then

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the cloud water mass grows. In the cloud water content sensitive response phase (less

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than about 0.3/kg), relatively low aerosol concentrations inhibit warm cloud

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precipitation and the formation of ice phase particles is inhibited simultaneously, so

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precipitation is then reduced. Later, as the cloud droplet concentration and cloud

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water mass continue to grow, the development of supercooled cloud droplets and

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cloud water above 0 °C will significantly enhance the formation of ice phase particles,

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increasing precipitation.

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3.3 Effects of Anthropogenic Aerosol Emissions Changes on the Precipitation

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Process before (after) the Precipitation Peak

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As shown in Figure 4b, ACI has different effects on the pre- and post-peak

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periods of precipitation. As the aerosol concentrations increase, the precipitation is

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inhibited (enhanced) before (after) the peak. The precipitation simulation time can be

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divided into a pre-peak period from 06:00–12:00 (denoted Bef-Peak) and a post-peak

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period from 13:00–24:00 (denoted Aft-Peak). The effects of changes in anthropogenic

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aerosol emissions on the process before and after the precipitation peak can then

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analyzed based on this time division.

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As shown in Figure 7a, the average hourly precipitation rate before and after the

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precipitation peak has an inverse power relationship to surface PM2.5 concentrations.

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The precipitation is inhibited (enhanced) before (after) the peak value and has a

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saturation effect. When PM2.5 concentrations are relatively low, the average

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precipitation rate is rapidly weakened (strengthened) before (after) the peak as PM2.5

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concentrations increase. As PM2.5 concentrations continue to increase, the inhibition

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(enhancement) trend slows down.

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The cloud droplet number concentration is significantly positively correlated with

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the ground PM2.5 concentrations both before and after the precipitation peak (Figure

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7b). The cloud droplet number concentration before the peak increases more rapidly

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with PM2.5 and the impact on cloud microphysics becomes more saturated. Thus, a

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PM2.5 concentration of about 25 µg/m3 is cloud droplet number concentration change

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response sensitivity threshold. The cloud droplet number concentration increases

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rapidly with the increase in PM2.5 concentrations when PM2.5 is lower than 25 µg/m3,

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but as the PM2.5 concentration continues to increase, the growth trend in the cloud

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droplet number concentration slows down.

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From the view of microphysical process of cloud precipitation, the inhibition due

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to changes in cloud droplet number concentration on the average rainfall content

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before the peak has a decreasing nonlinear trend, where the decrease is saturated at

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high concentrations. In comparison, the enhancement of the average rainfall content

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after the peak has an increasing linear trend (Figure 7c). As the cloud droplet number

378

concentration increases, the cloud water content is relatively low in the pre-peak

379

process and the growth is slow (Figure 7d). This changes the cloud droplet number

380

concentration and cloud water content, which reduces the size of the cloud droplets

381

and inhibits the automatic conversion rate of cloud rain. As the cloud droplet number

382

concentration increases, the freezing process is simultaneously weakened, and the

383

content of sleet, snow, and ice crystals are nonlinearly reduced (Figure 7e and f), then

384

the precipitation amount is reduced. The cloud water content in the post-peak process

385

also increases rapidly with the increase in cloud droplets (Figure 7d). However, this

386

change has little effect on the automatic conversion of cloud rain. Meanwhile, the ice

387

phase particles, especially the sleet and snow content, also increases linearly and

388

nonlinearly (Figure 7e and f), when the precipitation increases.

389

The effect of ACI also produces a feedback effect on atmospheric dynamic

390

conditions by affecting microphysical processes. As shown in Figure 8, before and

391

after the precipitation peak, the atmospheric aerosol concentration changes, and

392

surface wind speed and vertical velocity also have a good inverse power function

393

relationship (Figure 8a and b). As PM2.5 concentrations increase, the process before

394

(after) the peak inhibits (enhances) the horizontal and vertical wind speeds, and the

395

influence also has a saturation effect. In addition, the average precipitation rate of the

396

two processes present a good increasing linear relationship with wind speed and

397

vertical velocity (Figure 8c and d). This indicates that the changes in dynamic

398

conditions could cause the inhibition (enhancement) process on precipitation before

399

(after) the peak to become more significant.

400

In summary, the effects of the microphysical mechanisms of ACI on precipitation

401

are as follows: when the aerosol concentrations increase and the cloud droplets are

402

continuously activated, the cloud water content increases. The cloud water content is

403

relatively low and growth is slow before the precipitation peak. The changes in the

404

number of cloud droplets and cloud water content reduce the size of the cloud droplets

405

and inhibit the automatic conversion rate of cloud rain. As the of cloud droplet

406

number concentration increases, the freezing process simultaneously weakens. The

407

sleet, snow, and ice crystal content then decreases nonlinearly, resulting in a decrease

408

in precipitation amount. The cloud water content after the precipitation peak also

409

increases rapidly with the increase in the number of cloud droplets. This change has

410

little effect on the automatic conversion of cloud rain. Meanwhile, ice phase particles,

411

especially the sleet and snow content, also increases rapidly and nonlinearly, and the

412

precipitation amount then increases. The effect of ACI, therefore, also has a feedback

413

effect on atmospheric dynamic conditions by affecting microphysical processes.

414

3.4 Effects of Aerosol on Precipitation and Relative Humidity of Ambient

415

Atmosphere

416

The inhibition or enhancement of precipitation by atmospheric pollution often

417

depends on factors such as aerosol and cloud type, meteorological condition, and

418

underlying surface characteristics (Khain et al., 2005；Tao et al., 2012). The effect of

419

aerosols on cloud properties is also strongly dependent on the relative humidity of the

420

environment (Fan et al., 2007). In humid regions or seasons, an increase in aerosol

421

concentration significantly increases the frequency and intensity of precipitation.

422

Conversely, in dry areas or seasons, an increase in aerosol concentration inhibits

423

precipitation (Li et al., 2011). It is found that, the effect of anthropogenic aerosol

424

emissions changes on precipitation also depends on the relative humidity of the

425

atmospheric environment in the YRMR region.

426

Figure 9 shows the relationship between changes in anthropogenic aerosol

427

emission intensity and the hourly precipitation rate, and the corresponding change in

428

the cloud droplet number concentration and the precipitation rate under different

429

relative humidity conditions. The effect of changes in anthropogenic aerosol

430

emissions on precipitation is very sensitive to the relative humidity of the

431

environment. As the intensity of the anthropogenic emission increases, the effect of

432

ACI on this rainstorm in the YRMR region has the tendency to enhance (high

433

emission phase) after inhibition (low emission phase) (Figure 9a and b). When 80% ≤

434

RH < 85% the change in anthropogenic aerosol emissions does not affect the change

435

in precipitation, as RH is in equilibrium. When RH is lower than (higher than) 80%

436

(85%), the increase in anthropogenic aerosol emissions has a tendency to decrease

437

(increase) the precipitation intensity, and the tendency to inhibit (enhance) the

438

precipitation with a RH decrease (increase) is more pronounced, and the change of

439

inhibition (enhancement) in the low (high) emission phase is more intense. Under

440

relatively dry (wet) conditions, pollution aerosols inhibit (enhance) precipitation, and

441

the effect of inhibition (enhancement) of low (high) pollution emissions is more

442

sensitive to changes in ambient humidity.

443

4. Conclusions

444

Multiple groups of emission source sensitivity tests were used to simulate a

445

summer rainstorm event in the YRMR from June 18 to 19 2018. The response of

446

clouds and precipitation in the region to changes in anthropogenic aerosol emissions

447

and its microphysical mechanism were studied. The major conclusions are as follows:

448

(1) With an increase in anthropogenic emissions, ACI has the tendency to inhibit

449

(low emission phase) and enhance (high emission phase) the rainstorm. ACI causes

450

the peak precipitation time to lag by 1 to 2 hours, and the aerosols inhibit (enhance)

451

precipitation before (after) the peak precipitation. High concentration pollution

452

aerosols significantly change the spatial and temporal distributions of precipitation,

453

especially precipitation efficiency in the latter period. An increase in total

454

precipitation shrinks the area of general precipitation and widens the area of intense

455

precipitation, leading to a more centralized intense precipitation center. This makes it

456

easier for a rainstorm to form under the same environmental conditions, and the

457

control of regional anthropogenic emissions is, therefore, relatively important for

458

reducing local rainstorm disasters.

459

(2) There is a saturation effect (low level sensitivity but high-level saturation) in

460

the cloud and precipitation response to changes in anthropogenic aerosol emissions.

461

The aerosol number concentration in clouds and cloud droplets tend to saturate in

462

extremely high emission scenarios. Cloud droplet number concentration, cloud water

463

content, and surface PM2.5 concentrations have good logarithmic relationships. The

464

average precipitation rate has an inverse power relationship to PM2.5 concentrations

465

before and after the precipitation peak. PM2.5 concentrations of about 25 µg m-3 are

466

the response threshold of cloud particle from sharp to smooth changes. When PM2.5

467

concentrations are less than 25 µg m-3, the cloud droplet number concentration and

468

cloud water content increase rapidly with an increase in PM2.5, and the pre- (post-)

469

precipitation peak rate decreases (increases) rapidly as PM2.5 increases. As PM2.5

470

concentrations continue to increase, the cloud growth trend slows down and

471

precipitation inhibition (enhancement) also slows down.

472

(3) Before the precipitation peak, the average cloud water content increases

473

slowly with an increase in cloud droplets. This reduces the sizes of the cloud droplets,

474

which inhibits the automatic conversion of cloud rain and the formation of ice phase

475

particles, so the precipitation amount reduces. After the precipitation peak, the

476

average cloud water content increases rapidly with the increase in cloud droplets. This

477

has little effect on the automatic conversion of cloud rain, and the content of sleet and

478

snow increase non-linearly, and then the precipitation amount increases. ACI,

479

therefore, has a feedback effect on atmospheric dynamic conditions by affecting

480

microphysical processes.

481

(4) The simulations reveal that the tendency of ACI to inhibit or enhance

482

precipitation is related to the relative humidity of the ambient atmosphere. When 80%

483

≤ RH < 85%, RH is considered to be in an equilibrium phase, and the aerosol

484

concentration changes do not change precipitation. When RH is lower than 80%

485

(higher than 85%), an increase in anthropogenic aerosol emissions have a tendency to

486

inhibit (enhance) precipitation. Low (high) pollution emission scenarios are very

487

sensitive in inhibiting (enhancing) precipitation based on ambient relative humidity.

488

Based on the simulation of the effects of anthropogenic aerosols on a rainstorm

489

process during summer 2018 over the YRMR region, this study revealed the

490

responses of a rainstorm to changes in anthropogenic aerosol emissions, which would

491

be further investigated with climate analyses of long-term observation and more

492

comprehensive modeling of precipitation and aerosols.

493 494

Acknowledgements: This study acknowledges the supports of the National Key

495

R&D Program of China (2016YFC0203304), and the National Natural Science

496

Foundation of China (41830965; 41705034). Our special thanks go to Dr. Yi Deng

497

and Dr. Ling Zhang for their supports in improving the manuscript.

498 499

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665

666 667

Figure 1. Three nesting domains of the WRF-Chem simulations with domain d03

668

covering the YRMR region.

669 670

Table 1 The simulation experiments of multiple emission scenarios Emission zero-low low-middle middle-high high-extremely Scenarios experiment E0, E0.1 E1, E2, E4, E6 E10, E15, E20 E40 and E100 labels and E0.5 and E8 and E30

671

672 (c)

(d)

(b)

(a)

673 7 OBS E0 E0.1 E6 E40

40 30

Precipitation rate (mm)

Precipitation (mm)

50

(e)

20 10

6

OBS E0 E0.1 E6 E40

(f)

5 4 3 2 1 0

0 06

674

08

10

12

14 16 18 Time (UTC)

20

22

24

06

08

10

12

14 16 18 Time (UTC)

20

22

24

675

Figure 2. Distributions of 18-hr cumulative precipitation (mm) during 06:00-24:00

676

UTC on June 18, 2018 from (a) observation, (b) simulation experiment E6 and (c)

677

PM2.5 emission rates (µg m-2 s-1) over the region d03 as well as, (d) daily averaged

678

PM2.5 concentrations (µg m-3) from observation (dots) and simulation E6 (colour

679

contours), the hourly changes of precipitation from observation and simulation

680

experiments with different emission scenarios (e) at site Dangyang and (f) from

681

regional averages over the domain 3.

682 (a)

(b)

(c)

(d)

683

684

685

Figure 3. Distributions of 18-hr cumulative precipitation (mm) during 06:00-24:00

686

UTC on June 18, 2018 from the simulation experiments (a) E0.1, (b) E4, (c) E10

687

and (d) E30. 7

80 60

Precipitation (mm)

E0 E1 E2 E10 E30

40 20

6

4 3

1 0

150

130

110

95

85

75

65

55

45

35

25

15

0.1

24h Precipitation (mm)

688

E0 E1 E2 E10 E30

2

(a)

0

(b)

5

5

Probability distribution (%)

100

06

08

10

12

14 16 18 Time (UTC)

20

22

24

689

Figure 4. (a) Frequency distribution of regional averages of 18-hr accumulated

690

precipitation during 06:00-24:00 UTC on June 18, 2018 and (b) hourly changes of

691

regional averaged precipitation from the simulation experiments with different

692

emission scenarios. 100

300

PM2.5 (μg m-3)

50

240

40

180

PM2.5 Num_cw02

120

y = 3.6985x + 2.4324 R² = 0.9995

60

(a)

0

30 20 10

Num_cw02 (10-8 kg-1)

60

40 20

(c)

70 60 y = 17.298ln(x) - 9.3815 R² = 0.9653

50 40 30 20 10 0 0

25

50

75 100 125 150 175 PM2.5 (μg m-3)

Cloud water mixing ratio (g

kg-1)

80

(b)

0 0

90 Droplet number mixing ratio (10-8 kg-1)

60

10 20 30 40 50 60 70 80 90 100 Multiples of emissions

693

y = 3.8896x0.7116 R² = 0.9989

80

0 0

694

Droplet number mixing ratio (10-8 kg-1)

70

R² = 0.999

360

10

20 30 40 50 60 Num_cw02 (10-8 kg-1)

70

0.6

80

16.8

(d)

0.5

y = 0.0671ln(x) + 0.1748 R² = 0.987

16.7

0.4

16.6

0.3

16.5

0.2

16.4

y = -0.092ln(x) + 16.703 R² = 0.9549

0.1

16.3

Cloud Vapor

0 0

25

16.2 50

75 100 125 PM2.5 (μg m-3)

150

175

Water vapor mixing ratio (k kg-1)

420

200

2.3

190 2.1

180

1.9

PRE CAPE

170 R² = 0.9307

0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 Cloud water mixing ratio (g kg-1)

160

0.2

(f) 0.8

R² = 0.8085 0.15

R² = 0.7195

0.6

0.1 0.4 Graup Snow Ice

0.2 0

R² = 0.9226

0.05

Hydrometeors (g kg-1)

210 R² = 0.8366

Graupel mixing ratio (g kg-1)

(e) 2.5

1.7

695

1

220

CAPE (J kg-1)

Precipitation rate (mm h-1)

2.7

0

0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 Cloud water mixing ratio (g kg-1)

696

Figure 5. Based on the WRF-Chem simulations of 06:00-24:00 UTC on June 18 2018,

697

the fitting relationships (a) of the regional averages of multiple emission intensity

698

respectively with PM2.5 concentrations and Num_cw02, (b) between Num_cw02 and

699

cloud droplet number concentration, (c) between PM2.5 and cloud droplet number

700

concentration, the fitting relationships of (d) PM2.5 concentrations with cloud water

701

and water vapor mixing ratio, (e) as well as cloud water mixing ratio with hourly

702

precipitation rate and CAPE and (f) cloud water mixing ratio with mixing ratios of

703

sleet and snow and ice crystals.

704 (a)

(b)

(c)

(d)

705

706 707

Figure 6. Time-height profiles of regional averaged differences between E6 and E0

708

with (a) rainfall (colour contours), cloud water (black contour lines), sleet (blue

709

contour lines), snow (red contour lines), ice crystal mixing ratio (green contour lines,

710

unit: g kg-1), and 0°C air temperature (a yellow straight line), as well as with (b)

711

vertical velocity (colour contours, unit: m s-1), mixing ratio of cloud droplets number

712

(black contour lines, unit: 10-8 kg-1), 0 °C air temperature (a yellow straight line); (c)

713

Same as Fig. 6a but for the regional averaged differences between E30 and E6 as well

714

as (d) same as Fig. 6b but for the regional averaged differences between E30 and E6. 4.5 4

2.5

3.5

2

3

1.5 Bef-Peak Aft-Peak

1

y = 3.1851x-0.112 R² = 0.9635

2.5 2

(a)

0.5

1.5 0

715

Droplet number mixing ratio (10-8 kg-1)

3

120

5

y = 2.0113x0.1377 R² = 0.9148

Precipitation rate (mm h-1)

y = 16.08ln(x) - 15.859 R² = 0.9668

40 Bef-Peak

20

0

(d) (b)

50

100

150 200 250 PM2.5 (μg m-3)

300

350

0.7 Cloud water mixing ratio (g kg-1)

1 0.8 0.6 0.4

R² = 0.9643 Bef-Peak Aft-Peak

0.2

(c)

0

716 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

y = 0.1242x0.3503 R² = 0.9755

0.6 0.5 0.4

y = 0.1717x0.1952 R² = 0.9886

0.3 0.2 Bef-Peak

0.1 0

20 40 60 80 100 Droplet number mixing ratio (10-8 kg-1)

0

20 40 60 80 100 Droplet number mixing ratio (10-8 kg-1) 0.1

0.24 y = 0.0052x + 0.4086 R² = 0.8702

R² = 0.9453 Bef-Peak Aft-Peak 0

(e)

20 40 60 80 100 Droplet number mixing ratio (10-8 kg-1)

Snow water mixing ratio (g kg-1)

Rain water mixing ratio (g kg-1)

60

50 100 150 200 250 300 350 400 450 500 PM2.5 (μg m-3) y = 0.0075x + 0.5695 R² = 0.9122

0

Graupel mixing ratio (g kg-1)

80

0

1.2

717

y = 22.172ln(x) - 8.8103 R² = 0.98

100

R² = 0.8609 0.2 R² = 0.7659

0.08

0.16 R² = 0.9739 0.12

Bef-Peak(Snow) Aft-Peak(Snow) R² = 0.9725 Bef-Peak(Ice) (f) Aft-Peak(Ice)

0.08 0.04 0

0.06

0.04

Ice water mixing ratio (g kg-1)

Precipitation rate (mm h-1)

3.5

0.02

20 40 60 80 100 Droplet number mixing ratio (10-8 kg-1)

718

Figure 7. Before and after the precipitation peak, the fitting relationships of the

719

regional averages (a) between PM2.5 concentrations and the hourly precipitation rates,

720

(b) between PM2.5 and cloud droplet number concentration, (c) between cloud droplet

721

number concentration and rainfall mixing ratio, (d) between cloud droplet number

722

concentration and cloud water mixing ratio, (e) between cloud droplet number

723

concentration and sleet mixing ratio and (f) the fitting relationships of cloud droplet

724

number concentration with snow and ice crystal mixing ratios.

0.8 y = 5.7219x-0.018 R² = 0.9202

5.5

0.6 W (m s-1)

5 4.5 y = 3.792x0.0222 R² = 0.9385

4

0

100

400

4.5 4

2.5

3.5

2

3

1.5

2.5 Bef-Peak Aft-Peak

y = 2.5867x - 11.695 R² = 0.9831

3.8

726

2

(c)

0.5 3.5

(b)

4.1 4.4 4.7 5 5.3 5.6 Surface wind speed (m s-1)

100

200 300 PM2.5 (μg m-3)

400

500

3.5 Precipitation rate (mm/h)

Precipitation rate (mm h-1)

0

500

y = 4.3379x - 14.472 R² = 0.9087

1

Bef-Peak Aft-Peak

0

3.5 3

y = 0.679x-0.06 R² = 0.9578

0.3 0.1

(a) 200 300 PM2.5 (μg m-3)

725

0.4 0.2

Bef-Peak Aft-Peak

3

0.5

1.5

4.5 Bef-Peak Aft-Peak

3

4

y = 9.5907x - 1.4735 R² = 0.9741

2.5

3.5 3

2 y = 7.6066x - 2.0107 R² = 0.9856

1.5

2.5 2

1

(d)

0.5 0.35

5.9

0.4

0.45

0.5 0.55 W (m s-1)

0.6

0.65

Precipitation rate (mm/h)

3.5

y = 0.3661x0.0871 R² = 0.8698

0.7

Precipitation rate (mm h-1)

Surface wind speed (m s-1)

6

1.5 0.7

727

Figure 8. The fitting relationships of the regional averages

728

precipitation peak (a) between PM2.5 concentrations and surface wind speed, (b) PM2.5

729

and vertical velocity, (c) surface wind speed and hourly precipitation rate and (d)

730

vertical velocity and hourly precipitation rate. 2.6

Precipitation rate (mm h-1)

Precipitation rate (mm h-1)

2.8 2.4 2.2 R² = 0.8569

2 1.8 1.6 1.4

(a)

1.2

E40

20

40 60 80 100 120 140 160 Droplet number mixing ratio (10-8 kg-1)

0.4

0.3

Precipitation rate (mm h-1)

55-65%RH 65-75%RH

0.25 0.2

R² = 0.8175

0.15 0.1 R² = 0.8867

0.05

(c)

0

55-65%RH 65-75%RH

0.35 0.3 0.25

R² = 0.7546

0.2 0.15 0.1

R² = 0.9261

0.05

(d)

E100

E40

E30

Multiples of emissions

E20

E15

E10

E8

E6

E4

E2

E1

E0.5

E0

0 E0.1

Precipitation rate (mm h-1)

(b) 0

0.35

732

R² = 0.7462

E100

731

E30

Multiples of emissions

E20

E15

E8

E10

E6

E4

E2

E1

E0.5

E0

E0.1

1

2.8 2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1

before and after the

0

20 40 60 80 100 120 Droplet number mixing ratio (10-8 kg-1)

140

1.4

1.2

Precipitation rate (mm h-1)

y = 0.0038x + 0.9305 R² = 0.0205

1 0.8 0.6 0.4 y = -0.008x + 0.5829 R² = 0.212

(e)

1 0.8 0.6 0.4

E40

0

5 4 3 2

E40

E30

E100

Multiples of emissions

E20

E15

E10

E8

E6

y = 0.0338x + 2.0138 R² = 0.6034 (g) E4

E2

E1

E0.1

E0

0

734

Precipitation rate (mm h-1)

6

85-95%RH 95-100%RH

y = -0.0007x + 0.5679 R² = 0.1862 (f)

20 40 60 80 100 Droplet number mixing ratio (10-8 kg-1)

120

7

R² = 0.7205

7

E0.5

Precipitation rate (mm h-1)

8

1

75-80%RH 80-85%RH

0.2

E100

E30

E20

E15

E8

Multiples of emissions

733

y = 0.0005x + 0.9321 R² = 0.0321

1.2

0 E6

E4

E0.5

E0

E0.1

0

E2

75-80%RH 80-85%RH

E10

0.2

E1

Precipitation rate (mm h-1)

1.4

6 R² = 0.6273

5 4 3 2 1

85-95%RH 95-100%RH

0 0

y = 0.0051x + 2.0712 R² = 0.6756

(h)

20 40 60 80 Droplet number mixing ratio (10-8 kg-1)

735

Figure 9. Based on the WRF-Chem simulations of multiple emission scenarios of

736

00:06-24:00 UTC on June 18, 2018, the fitting relationships (a, c, e and g) between

737

multiple emission scenarios and hourly precipitation rates, and (b, d, f and h) cloud

738

droplet number concentrations and hourly precipitation rates under changing relative

739

humidity conditions of RH < 100% (a and b), 55% ≤ RH < 65% and 65% ≤ RH < 75%

740

(c and d), 75% ≤ RH < 80% and 80% ≤ RH < 85% (e and f), and 85% ≤ RH < 95%

741

and RH ≥ 95% (g and h) .

742 743

  

Low (high) aerosol emissions tended to inhibit (enhance) rainstorm precipitation. PM2.5 concentration of 25 μg m-3 was a threshold f cloud droplet number changes. Aerosol- induced precipitation changes were sensitive to ambient relative humidity.

Author contributions statement Zhao Tianliang and Zhou Yue conceived the experiment, Bai Yongqing and Qi Haixia conducted the simulation, Bai Yongqing, Liu Lin, Xiong Jie, Zhou Zhimin, and Cui Chunguang analyzed results. All authors reviewed the manuscript.

Additional information The authors have declared that no competing interests exist.