The Impact of the Aerosol Direct Radiative Forcing ... - AGU Publications

Download PDF
0 downloads 0 Views 1MB Size Report
Comment
radiative forcing (DRF) on deep convection and air quality in the Pearl River ... found that cloud responses induced by aerosol direct effects could dominate the ...
Geophysical Research Letters RESEARCH LETTER 10.1029/2018GL077517 Key Points: • The aerosol direct radiative forcing suppresses deep convection that causes a reduction in cloud formation in the middle-upper troposphere • The cloud response forcing is positive and offsets 20% of the aerosol direct radiative forcing • The aerosol direct radiative forcing reduces planetary boundary layer height that leads to an increase in surface aerosol concentration

Supporting Information: • Supporting Information Correspondence to: S. H. L. Yim, [email protected]

Citation: Liu, Z., Yim, S. H. L., Wang, C., & Lau, N. C. (2018). The impact of the aerosol direct radiative forcing on deep convection and air quality in the Pearl River Delta region. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL077517 Received 8 FEB 2018 Accepted 10 APR 2018 Accepted article online 23 APR 2018

The Impact of the Aerosol Direct Radiative Forcing on Deep Convection and Air Quality in the Pearl River Delta Region Z. Liu1,2, Steve H. L. Yim1,2,3

, C. Wang4

, and N. C. Lau1,2,3

1

Institute of Space and Earth Information Science, Chinese University of Hong Kong, Sha Tin, Hong Kong, 2Institute of Environment, Energy and Sustainability, Chinese University of Hong Kong, Sha Tin, Hong Kong, 3Department of Geography and Resource Management, Chinese University of Hong Kong, Sha Tin, Hong Kong, 4Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Abstract

Literature has reported the remarkable aerosol impact on low-level cloud by direct radiative forcing (DRF). Impacts on middle-upper troposphere cloud are not yet fully understood, even though this knowledge is important for regions with a large spatial heterogeneity of emissions and aerosol concentration. We assess the aerosol DRF and its cloud response in June (with strong convection) in Pearl River Delta region for 2008–2012 at cloud-resolving scale using an air quality-climate coupled model. Aerosols suppress deep convection by increasing atmospheric stability leading to less evaporation from the ground. The relative humidity is reduced in middle-upper troposphere due to induced reduction in both evaporation from the ground and upward motion. The cloud reduction offsets 20% of the aerosol DRF. The weaker vertical mixing further increases surface aerosol concentration by up to 2.90 μg/m3. These findings indicate the aerosol DRF impact on deep convection and in turn regional air quality.

Plain Language Summary Aerosol may affect regional climate and in turn air quality. While previous research has focused on the aerosol impact on low-level cloud, the impact on middle-upper troposphere needs to be further understood. This study aims to assess the impact of the aerosol direct radiative forcing (DRF) on deep convection and air quality in the Pearl River Delta region where it has a large spatial heterogeneity of emissions and aerosol concentration. We use an air quality-climate coupled model to simulate the aerosol DRF and its cloud response in June (with strong convection) in Pearl River Delta region for 2008–2012. Aerosols suppress deep convection by increasing atmospheric stability leading to less evaporation from the ground. The relative humidity is reduced in middle-upper troposphere due to induced reduction in evaporation from both the ground and upward motion. The cloud reduction offsets 20% of the aerosol DRF. The weaker vertical mixing further increases surface aerosol concentration by up to 2.90 μg/m3. Our findings are anticipated to provide important information for regional climate and air-quality forecast. 1. Introduction Climate change and air pollution not only pose severe threats to human beings but also strongly interact with each other. Aerosols affect the radiation budget by absorbing and scattering solar radiation (IPCC, 2013), referred to as the direct radiative forcing (DRF) of aerosols. The redistribution of energy between the atmosphere and the surface may subsequently affect cloud formation, further changing the radiation budget (Forkel et al., 2012), termed as the semidirect effect of aerosols (Hansen et al., 1997). Due to the heterogeneity of the spatial and temporal distributions of aerosols, the radiative impact of aerosols may vary at different locations and times, and sometimes can be greater than that of greenhouse gases at regional scale (Hatzianastassiou et al., 2004). The regional climate impact of aerosols has thus drawn scientific attention in the last decade (Feng et al., 2016; Lee & Kim, 2010; Zhao et al., 2011, 2013). The Pearl River Delta (PRD) is one of the most polluted regions in China (Lee et al., 2006). Its rapid urbanization and economic development have led to severe air pollution. Wu et al. (2005) reported that the aerosol optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer is typically higher than 0.6 in Guangzhou, one of the major cities in the PRD region. The relatively high aerosol concentration has been reported to cause a serious negative impact on public health (Gu & Yim, 2016).

©2018. American Geophysical Union. All Rights Reserved.

LIU ET AL.

Besides reducing visibility and inducing respiratory diseases, aerosols also have impacts on regional climate. Significant decreasing trend in cloud cover in China is observed over the last half of the 20th century

1

Geophysical Research Letters

10.1029/2018GL077517

(Qian et al., 2006). Meanwhile, the incoming solar radiation at surface has also decreased from 1954 to 2001. These findings are expected to correlate with aerosols which alter the radiation balance and cloud process (Qian et al., 2006; Xia, 2012). Gao et al. (2014) estimated that aerosols exert a DRF at the top of atmosphere (TOA) about 6.5 W/m2 over China, which is much larger than the global average value of 0.35 ( 0.85 to +0.15) W/m2 assessed by IPCC (2013), highlighting the significant impact in this region. Forkel et al. (2012) found that cloud responses induced by aerosol direct effects could dominate the changes in surface solar radiation than the direct effect itself. These findings highlight the importance of the aerosol DRF and its induced cloud responses in regional climate. Previous studies reported that the cloud response to absorbing aerosols varies under different conditions. Absorbing aerosols within clouds may stimulate the dissipation of these clouds (Hansen et al., 1997). On the other hand, absorbing aerosols below clouds may increase cloud cover by destabilizing the planetary boundary layer or reduce cloud cover by reducing surface fluxes (Feingold et al., 2005), while absorbing aerosols above clouds tend to enhance stratocumulus but reduce cumulus clouds (Fan et al., 2008; Johnson et al., 2004). In addition, both observation and numerical studies revealed that deep convection could be remarkably affected by aerosols serving as cloud condensation nuclei (Fan et al., 2007, 2012; Khain et al., 2005; Wang, 2005). This impact of aerosols on cloud condensation nuclei is usually termed as the indirect effect of aerosols (Twomey, 1977). Feng et al. (2016) conducted a 1-month simulation and reported the importance of rapid thermodynamic responses for understanding aerosol radiative impacts in premonsoon seasons. With a 2-D cloud-resolving Goddard Cloud ensemble model with spectral-bin cloud microphysics, Fan et al. (2008) found that the aerosol absorption effect could lead to a more stable atmosphere and thus decrease the convection in the Houston region in several-hour simulations. While the direct effect of aerosols may have potential impact on deep convection, our understanding is still limited. Especially, Qian et al. (2006) and Xia (2012) analyzed station observations and found the trend of less cloud cover in most part of China in the last half of the 20th century and speculated that the cloud reduction was related to aerosols. Whether the middle-upper troposphere cloud is also reduced due to aerosols needs to be further investigated. This knowledge is particularly important for regions with large heterogeneity of emissions and aerosol concentrations (Giorgi et al., 2002; Kim et al., 2007). An in-depth investigation on the DRF and its effect on deep convection should be investigated. It is, however, difficult to quantify aerosol-radiation-climate interactions due to spatial and temporal variations in both aerosol compositions and concentration (Giorgi et al., 2002; Kim et al., 2007). Moreover, the aerosol-meteorology interactions can in turn change surface aerosol concentrations. Recent studies reveal that both aerosol-radiation and aerosol-cloud interactions increase surface aerosol concentration (Wang, Wang, et al., 2014; Zhang et al., 2018, 2017). Nevertheless, the underlying mechanism is rarely discussed in the PRD region and needs to be further investigated. This study aims to understand the impact of the aerosol DRF on deep convection and air quality in the PRD region. We apply the WRF-Chem model (Grell et al., 2005) at relatively high spatial (3 km) and temporal (hourly) resolutions with aqueous chemistry and modification of the brown carbon absorption effect in the model as reported in recent literature (Feng et al., 2013; Wang, Heald, et al., 2014). A typical summer month, June, in which the rainfall normally peaks with abundant deep convections over the PRD region, is selected to facilitate an assessment of the impacts of aerosol DRF on deep convection. Two model simulations are conducted. The first is a baseline simulation (BASE) that includes both aerosol direct and indirect effects (including semidirect effect), while the other (ARIoff) is designed to exclude aerosol direct and semidirect effects. The simulations of June in 5 years from 2008 to 2012 are conducted for each simulation to better isolate robust signals out of the model’s natural variations. The main model configurations are described in section 2. The model evaluation results compared with satellite data and in situ observations are presented in Text S2 in the supporting information. In section 3.1, The DRF and its effect on deep convection are discussed, respectively. The surface energy budget and atmospheric heating rate response are analyzed in section 3.2. The results of atmospheric dynamic and thermodynamic responses and impact of aerosol-radiation interactions (ARI) on air quality are discussed in sections 3.3 and 3.4, respectively. The main findings are summarized in section 4.

2. Model Configuration WRF-Chem 3.8 is used with 2005 carbon bond (Wang, Zhang, et al., 2015; Yarwood, 2005) gas-phase mechanism coupled with the Modal Aerosol Dynamics Model for Europe. The main aerosol species simulated in the

LIU ET AL.

2

Geophysical Research Letters

10.1029/2018GL077517

model include sulfates, nitrates, ammonium, black carbon (BC), organic carbon, other inorganics, dust, and sea salt. The absorption effect of organic carbon is considered in the model based on parameters reported in the literature (Updyke et al., 2012; Wang, Wang, et al., 2014; Zhang et al., 2013). Three nested domains with a horizontal grid resolution of 27, 9, and 3 km are used as shown in Figure S1a in the supporting information. The innermost domain is cloud-resolving covering the PRD region, and no cumulus scheme is used. The RRTMG radiation (Iacono et al., 2008) and Morrison 2-moment microphysics (Morrison et al., 2009) schemes are implemented to capture the aerosol direct and indirect effects, respectively. The detailed configuration of WRF-Chem alongside the observations used in the study is described in Text S1.

3. Results 3.1. Radiative Forcing Due to Aerosol and Its Induced Cloud Response The aerosol DRF is defined as the all-sky radiation differences between with and without aerosol in the BASE simulation (Ghan et al., 2012), while the all-sky radiation differences between BASE and ARIoff simulations are attributed to the total aerosol radiative forcing from both the aerosol DRF and its induced cloud responses (Gregory & Webb, 2008). The difference between total aerosol radiative forcing and DRF is attributed to the radiative forcing due to cloud response. Figures 1a–1f show the spatial distribution of radiative forcing due to aerosol DRF and cloud response at the TOA, within the atmosphere (ATM), and at the surface (SFC) in the PRD region. A positive (negative) forcing effect at the TOA refers to import (export) energy to the Earth system that leads to a warming (cooling) effect (Yu et al., 2006). The aerosol DRF at the SFC is estimated to be negative over the whole domain (Figure 1c) with an average of 10.77 (the interannual variation range [IVR]: 8.10 to 13.68) W/m2, which is comparable with Gao et al. (2014). This indicates that aerosol shortwave (SW) absorption and scattering has a cooling effect at the surface. The largest differences are found to occur in the center of PRD region, which are related to the higher aerosol concentrations in that area as shown in the Figure S6. Within the atmosphere, Figure 1b shows that aerosols absorb SW radiation by 5.39 (IVR: 4.47 to 6.57) W/m2 averaged over the whole domain and warm the atmosphere. We note that the radiation change follows a spatial pattern similar to the spatial distribution of BC (Figure S6). This means that BC is the most important absorbing aerosol in the PRD region. The aerosol warming effect compensates 50% of the aerosol cooling effect in PRD region, which is comparable with the study in China (Gao et al., 2014). In total, the aerosol DRF at the TOA is 5.38 (IVR: 3.62 to 7.12) W/m2 (Figure 1a), thus contributing an overall cooling effect in the PRD region. As shown in Figures 1d–1f, the radiative forcing due to cloud response changes the sign at all layers. The domain-averaged values at the TOA, ATM, and SFC are 1.06 (IVR: 0.41 to 2.26) W/m2, 1.17 (IVR: 0.79 to 1.43) W/m2, and 2.23 (IVR: 1.20 to 3.67) W/m2, respectively. The smaller magnitudes compared with that of the aerosol DRF at all layers indicate that the cloud response offsets about 20% of the aerosol direct effect in the PRD region. The opposite radiative forcing due to the aerosol direct effect and semidirect effect has been discussed in previous regional and global numerical studies (Archer-Nicholls et al., 2016; Forkel et al., 2012; Ghan et al., 2012; Wilcox, 2012). The aerosol DRF could be countered by the aerosol semidirect effect (Ghan et al., 2012). However, the magnitude of radiative forcing due to semidirect effect is highly sensitive to the model resolution. It is lower at a higher resolution, especially without cumulus parameterization used because the cloud is more cellular (Archer-Nicholls et al., 2016). Wilcox (2012) found the semidirect cloud thickening effect offsets more than 60% of the direct radiative effect. This may be because only smoke is considered in the simulation. These findings indicate that the aerosol semidirect effect plays an important role in radiation balance but with a large uncertainty. Even so, our result shows the damping effect of cloud response to the aerosol direct effect at all layers. It is worth noting that the pattern of cloud forcing is similar with the aerosol DRF, implying the localized cloud response to aerosols which is consistent with Figures S7a and S7b. The redistribution of radiation energy in the atmosphere and at the ground surface changes the stability of atmosphere and thereby affects cloud formation. The corresponding cloud cover response at various vertical levels is shown in Figures 1g–1i. The vertical levels of cloud cover are based on sigma values defined in the European Centre for Medium-Range Weather Forecasts products (Dee et al., 2011). Low cloud cover altitude is for 0.8 < σ < 1.0 (from surface to 806 hPa), medium cloud cover is for 0.45 < σ ≤ 0.8 (from 806 to 466 hPa), and high cloud cover is for σ ≤ 0.45 (from 466 to 50 hPa). The pressure levels are calculated as the averaged LIU ET AL.

3

Geophysical Research Letters

10.1029/2018GL077517

Figure 1. The spatial distribution of the (a–c) aerosol direct radiative forcing and (d–f) its induced cloud forcing. TOA represents the top of the atmosphere and positive refers to downward direction (warming); ATM represents within the atmosphere and positive refers to warming; SFC represents ground surface, and positive refers to downward direction (warming). Values in the top-right corner of each panel are the domain averaged values. Cloud cover fraction changes (unit: %) averaged in June due to aerosol radiation interactions. (g–i) The vertical level of cloud cover is based on sigma values defined in the European Centre for Medium-Range Weather Forecasts products. Low cloud cover is for 0.8 < σ < 1.0 (from surface to 806 hPa), medium cloud cover is for 0.45 < σ ≤ 0.8 (from 806 to 466 hPa), and high cloud cover is for σ ≤ 0.45 (from 466 to 50 hPa). The pressure levels are calculated as the averaged pressure at the corresponding sigma level in the BASE simulation.

pressure at corresponding sigma level in the BASE simulation. The total cloud fraction changes from high to low are 0.58%, 0.54%, and 0.03%, respectively. The negative value represents the cloud reduction. Most of the cloud cover reduction happens in the middle and upper atmosphere, especially at the estuary, which is the most polluted area in the PRD region. The thinner cumulus cloud reflects less solar radiation back to space but blocks less longwave (LW) radiation within the atmosphere, contributing to a warming effect at the SFC but cooling effect in the ATM. The relative smaller fraction changes in low cloud suggest that the low cloud response to DRF could be negligible, because the SW effect is closely related with cloud fraction changes. Meanwhile, the low cloud does not greatly affect the infrared radiation emitted to space due to the fact that its temperature is very close to that of the Earth’s surface. In total, the cloud forcing at the TOA is 1.06 (IVR: 0.41 to 2.26) W/m2, which leads to a warming in the system. We also separate the cloud forcing at the TOA into SW forcing 1.39 (IVR: 0.49 to 2.91) W/m2 and LW forcing 0.33 (IVR: 0.08 to 0.65) W/m2, respectively. The opposite sign represents a compensate effect from SW and LW due to cloud response. The medium and high cloud reductions lower the SW reflection (warming effect) but at the same time decrease the otherwise a warming effect of these clouds through their outgoing LW radiation at temperatures much lower than that of the Earth’s surface (cooling effect). Besides, the spatial distribution of the cloud forcing changes in Figures 1d–1f shows a strong association with the medium and high cloud cover changes, especially at the estuary. These results imply the negligible forcing contribution from low

LIU ET AL.

4

Geophysical Research Letters

10.1029/2018GL077517

Figure 2. (a) Diurnal cycle of surface energy budget differences averaged over the Pearl River Delta (PRD) region due to aerosol radiation interactions (ARI). LW, SW, LH, and SH respectively refer to longwave radiation, shortwave radiation, latent heat, and sensible heat. Total refers to the sum of all energy fluxes. Positive (negative) means more (less) energy fluxes into (out of) the surface. (b) Monthly mean potential temperature tendency profile changes due to ARI averaged over the PRD center (21.7–24.4°N, 113–114°E). The physical processes contributing to potential temperature tendency include SW radiation, LW radiation, planetary boundary layer mixing, and cloud microphysics (micro) latent heat. Total refers to the sum of all heating rate due to different processes.

cloud response. Figure S7b shows the spatial distribution of the cloud top temperature (CTT). The increase in CTT indicates that the cloud top becomes lower. The lower cloud top radiates more LW back to space as shown in Figure S7a. Figure S7c shows the probability distribution function of monthly averaged radar reflectivity from both the BASE and ARIoff simulations. Radar is commonly used for predicting deep convection. The higher radar reflectivity value refers to the stronger intensity of the precipitation. In the weather forecast, 50 dBZ represents heavy rainfall using the Marshall-Palmer formula (Marshall & Palmer, 1948). Here we choose 50 dBZ as the criteria for radar reflectivity to denote deep convection. With ARI, the percentage of radar reflectivity decreases at nearly the whole spectrum. The averaged reduction in radar reflectivity (0.95%) demonstrates that aerosols suppress deep convection in the PRD region in June. Figure S7d shows the profile differences of cloud water path and ice water path between two simulations. Ice water path dominates the cloud reduction in the middle-upper troposphere. There are also some reductions in cloud water path in the lower troposphere, which is consistent with the cloud cover change shown in Figure 1i. 3.2. Surface Energy Budget and Atmospheric Heating Rate Response To investigate the mechanism of reduction in deep convection induced by the aerosol DRF, the surface energy budget at the ground surface and atmospheric heating rate response in the atmosphere are analyzed. Figure 2a shows the ARI impact on the diurnal cycle of surface energy budget at local time averaged in the PRD region during the study period. The results show that the SW fluxes reaching the surface are reduced during daytime (7 a.m. to 5 p.m. local standard time). This is attributed to the absorption and scattering effects of aerosols. As a consequence, the surface that receives less SW becomes cooler, hence reducing sensible and latent heat fluxes. The strongest cooling effect occurs at noon time when the incoming solar radiation is the strongest. During nighttime (7 p.m. to 6 a.m. local standard time), the impact of aerosols on sensible and latent heat fluxes is negligible due to lack of solar radiation. Our results also show that aerosols increase LW fluxes into the surface throughout the day, but the magnitude of LW changes is marginal compared to that of SW changes. This is because the cooling of the land surface makes the land surface radiate less LW upward. Similar mechanism is also found in Zhao et al. (2011). Nevertheless, aerosols overall reduce the net energy flux into the surface leading to a cooling effect during the daytime, while the aerosol effect is weak during nighttime. In addition to ground surface energy budget, ARI also affects the heating rate in the atmosphere. The atmospheric heating rate (dT/dt) in WRF-Chem is calculated as a function of four processes, including SW radiation, LW radiation, planetary boundary layer (PBL) mixing, and cloud microphysics latent heat release. We note that our results are based on simulations that directly resolve cumulus clouds. Figure 2b shows the ARI impact on

LIU ET AL.

5

Geophysical Research Letters

10.1029/2018GL077517

Figure 3. (a) Meridional-vertical cross section of the wind (arrow) and cloud fraction (color; left) in baseline simulation (BASE); (b) the meridional-vertical cross section of temperature (contour) and relative humidity (color) changes due to aerosol-radiation interactions (averaged at 113–114°E); (c) vertical velocity changes averaged at the Pearl River Delta center (21.7–24.4°N, 113–114°E) due to aerosol-radiation interactions.

the individual physical heating rates in the atmosphere. As shown in the total potential temperature tendency profile changes, the absorbing aerosols warm the atmosphere especially the lower troposphere (below 800 hPa) with a rate up to 0.3 K/d. The positive heating rate in the middle-upper troposphere indicates the vertical transport of absorbing aerosols by the deep convection. The surface dimming and atmospheric heating inhibit the heat flux transport within the PBL by stabilizing the atmosphere, and thus cooling the lower troposphere (near 975 hPa) by up to 0.55 K/d. At the same time, the surface dimming also causes less LW radiation remitted back to the atmosphere, which leads to a cooling effect to the atmosphere. In contrast, the lower troposphere is heated by up to 0.2 K/d through cloud microphysical processes by less evaporation, offsetting part of the cooling effect of other processes. Overall, aerosols exert a cooling effect near the surface by up to 0.35 K/d even though absorbing aerosols heat the lower troposphere. This finding is consistent with previous studies for other regions (Feng et al., 2016; C. Zhao et al., 2011) and also highlights the important role of the fast response induced by the aerosol DRF. In the middle and upper troposphere, the change in potential temperature tendency is dominated by latent heat change from cloud condensation and evaporation (micro in Figure 2b). The lower latent heat release in the middle and upper troposphere indicates the suppression of deep convection due to ARI in accordance with cloud cover reduction illustrated in Figure 1. The reduction in medium and upper cloud cover amounts results in more solar radiation reaching the ground exerting a warming effect at the SFC and blocks less LW within the atmosphere exerting a cooling effect in the ATM due to increased CTT. It is worth noting that there are two peaks of microphysics heating rate change located at the lower (~850 hPa) and upper (~250 hPa) troposphere, respectively, which are related to clouds with different phases (water cloud and ice cloud as Figure S7d). This result is quite different from Feng et al. (2016) in which there is no significant latent heating rate change in the upper troposphere during the premonsoon period over South Asia when the cloud occurrence is low. This indicates the different radiative impact of aerosols under the deep convection conditions. Though less cloud evaporation warms the lower atmosphere, less cloud condensation in the middle troposphere cools the atmosphere. In total, the atmosphere is cooled as indicated by relative larger cloud fraction changes in the middle-upper troposphere in Figures 1g and 1h and also the negative cloud forcing in the ATM in Figure 1e. This result highlights the importance of high cloud response to the aerosol DRF. 3.3. Atmospheric Dynamic and Thermodynamic Response The previous sections have demonstrated that a reduction in medium and high cloud formation contributes to positive cloud radiative forcing, which offsets part of the aerosol DRF. This section examines the associated dynamic and thermodynamic processes. Figure 3a shows the meridional vertical cross section of wind (arrow) and cloud fraction (color). The meridional vertical atmospheric circulation depicts a strong monsoon in June, strong downward motion over the ocean, and upward motion over the land. The largest cloud fraction in the middle and upper troposphere indicates a strong deep convection in the inland areas. Figure 3b shows the meridional vertical cross section of the temperature (contour) and relative humidity (color) changes due to

LIU ET AL.

6

Geophysical Research Letters

10.1029/2018GL077517

Figure 4. Spatial distribution of the differences in monthly (a) temperature (K) at 2 m above ground, (b) planetary boundary layer height (m), and (c) surface aerosol 3 concentration (μg/m ) in June between BASE and ARIoff runs.

ARI. The temperature of lower troposphere decreases because less LW radiation is reemitted from the surface and thus the PBL mixing becomes weaker as discussed in section 3.2. Although water vapor evaporation is reduced from the ground as indicated by the latent heat flux reduction shown in Figure 2a, the relative humidity in the lower atmosphere increases. This means that the reduction in temperature plays a more important role than the reduction of water vapor in influencing the relative humidity in the lower troposphere. On the other hand, the averaged upward motion decreases, which further suppresses the vertical transport of water vapor from the lower troposphere as shown in Figure 3c. The peak of vertical velocity reduction is also corresponding with that of the cloud reduction. The reductions in both relative humidity and vertical air motion thus inhibit the cloud formation in the middle and upper troposphere. This finding is also consistent with Fan et al. (2008). 3.4. Impact of ARI on Air Quality As discussed in the previous sections, the redistribution of energy at the surface and in the PBL by aerosols certainly leads to changes in dynamic and thermodynamic processes. These changes in meteorology could further affect air quality, that is, surface aerosol concentrations. Figure 4 shows the spatial distribution of differences in monthly temperature at 2 m above ground, PBL height, and surface aerosol concentration due to ARI in June. Aerosols lower the near surface temperature over the land by up to 0.27 (IVR: 0.24 to 0.29) K and PBL height by 45 (IVR: 41 to 63) m in that month. These results are consistent with our previous findings indicating less energy reaching the ground and weakening of PBL mixing (Figures 2a and 2b). The weaker turbulent mixing in the PBL thus reduces vertical dilution of aerosols, increasing the surface aerosol concentration as seen in Figure 4c. The magnitude can be up to 2.90 (IVR: 2.55 to 6.67) μg/m3. The similar mechanism has also been proven in other regions (Petäjä et al., 2016; Wang, Shi et al., 2015).

4. Conclusions This study applies the WRF-Chem to assess the aerosol DRF and its induced cloud response in the PRD region during a month (June) with strong deep convection from 2008 to 2012. The simulation results show that the aerosol DRF is 5.38 (IVR: 3.62 to 7.12) W/m2 at the TOA. Moreover, both less water vapor evaporates from the ground and reduction in the upward motion contributes to a reduction in cloud formation in the middleupper troposphere. Correspondingly, the increase in both outgoing LW radiation and CTT indicates that the aerosol-radiation interactions lower the cloud top and suppress the deep convection, leading to less cloud formation, especially in the middle-upper troposphere. The cloud forcing is estimated to be 1.06 (IVR: 0.41 to 2.26) W/m2, which offsets 20% of the aerosol DRF. This result pinpoints an overall cooling effect of aerosol in the PRD region. Our results also show that the aerosol radiative forcing causes a reduction in near surface temperature by up to 0.27 (IVR: 0.24 to 0.29) K and PBL height by 45 (IVR: 41 to 63) m, leading to a further increase in air pollution concentration, that is, the surface aerosol concentration increases by 2.90 (IVR: 2.55 to 6.67) μg/m3. While previous studies have focused on the aerosol semidirect effect on low cloud (Jacobson, 2002; Johnson et al., 2004; Sakaeda et al., 2011), our results highlight the necessity to combine both modeling and measurement effort to comprehensively investigate the aerosol-induced cloud response in the upper troposphere in the PRD region at least in summer.

LIU ET AL.

7

Geophysical Research Letters Acknowledgments This work is jointly supported by the Early Career Scheme of Research Grants Council of Hong Kong (grant CUHK24301415), the National Key Basic Research Program of China (grant 2015CB954103), the Improvement on Competitiveness in Hiring New Faculties Fund (2013/2014) of The Chinese University of Hong Kong (grant 4930059), and the Vice-Chancellor’s Discretionary Fund of The Chinese University of Hong Kong (grant 4930744). We thank the two anonymous reviewers for their constructive comments and suggestions. The authors thank the MIT Greater China Fund for Innovation 2015 for facilitating the collaboration between the CUHK and MIT research teams. We would also like to thank Prof. Kai Wang from NCSU for helping with model configuration of the 2005 carbon bond mechanism and the Information Technology Services Centre (ITSC) at CUHK for technical support. The ERA-Interim data are available from European Centre for Medium-Range Weather Forecasts (http://apps.ecmwf.int/datasets/data/ interim-full-moda/levtype=sfc/). The downward SW radiation is available from the World Radiation Data Center (http://wrdc.mgo.rssi.ru/). The Level-3 Moderate Resolution Imaging Spectroradiometer MYD08 monthly cloud product (collection 6) is provided by the Land Processes Distributed Active Archive Center (LP DAAC) managed by NASA (ftp://ladsweb.nascom. nasa.gov/allData/6/MYD08_M3). The L2B radar reflectivity and cloud mask products (2B-GEOPROF) are provided by CloudSat Data Processing Center (ftp:// ftp.cloudsat.cira.colostate.edu). The in situ meteorology and air-quality observations are provided by Hong Kong Observatory (http://www.hko.gov.hk/ cis/climat_e.htm) and Hong Kong Environmental Protection Department (https://cd.epic.epd.gov.hk/EPICDI/air/ station/?lang=en).

LIU ET AL.

10.1029/2018GL077517

References Archer-Nicholls, S., Lowe, D., Schultz, D. M., & McFiggans, G. (2016). Aerosol-radiation-cloud interactions in a regional coupled model: The effects of convective parameterisation and resolution. Atmospheric Chemistry and Physics, 16(9), 5573–5594. https://doi.org/10.5194/ acp-16-5573-2016. https://doi.org/10.5194/acp-16-5573-2016 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/ 10.1002/qj.828 Fan, J., Rosenfeld, D., Ding, Y., Leung, L. R., & Li, Z. (2012). Potential aerosol indirect effects on atmospheric circulation and radiative forcing through deep convection. Geophysical Research Letters, 39, L09806. https://doi.org/10.1029/2012GL051851 Fan, J., Zhang, R., Li, G., & Tao, W. K. (2007). Effects of aerosols and relative humidity on cumulus clouds. Journal of Geophysical Research, 112, D14204. https://doi.org/10.1029/2006JD008136 Fan, J., Zhang, R., Tao, W.-K., & Mohr, K. I. (2008). Effects of aerosol optical properties on deep convective clouds and radiative forcing. Journal of Geophysical Research, 113, D08209. https://doi.org/10.1029/2007JD009257 Feingold, G., Jiang, H., & Harrington, J. Y. (2005). On smoke suppression of clouds in Amazonia. Geophysical Research Letters, 32, L02804. https://doi.org/10.1029/2004GL021369 Feng, Y., Kotamarthi, V. R., Coulter, R., Zhao, C., & Cadeddu, M. (2016). Radiative and thermodynamic responses to aerosol extinction profiles during the pre-monsoon month over South Asia. Atmospheric Chemistry and Physics, 16(1), 247–264. https://doi.org/10.5194/ acp-16-247-2016 Feng, Y., Ramanathan, V., & Kotamarthi, V. R. (2013). Brown carbon: A significant atmospheric absorber of solar radiation. Atmospheric Chemistry and Physics, 13(17), 8607–8621. https://doi.org/10.5194/acp-13-8607-2013 Forkel, R., Werhahn, J., Hansen, A. B., McKeen, S., Peckham, S., Grell, G., & Suppan, P. (2012). Effect of aerosol-radiation feedback on regional air quality—A case study with WRF/Chem. Atmospheric Environment, 53, 202–211. https://doi.org/10.1016/j.atmosenv.2011.10.009 Gao, Y., Zhao, C., Liu, X., Zhang, M., & Leung, L. R. (2014). WRF-Chem simulations of aerosols and anthropogenic aerosol radiative forcing in East Asia. Atmospheric Environment, 92, 250–266. https://doi.org/10.1016/j.atmosenv.2014.04.038 Ghan, S. J., Liu, X., Easter, R. C., Zaveri, R., Rasch, P. J., Yoon, J. H., & Eaton, B. (2012). Toward a minimal representation of aerosols in climate models: Comparative decomposition of aerosol direct, semidirect, and indirect radiative forcing. Journal of Climate, 25(19), 6461–6476. https://doi.org/10.1175/JCLI-D-11-00650.1 Giorgi, F., Bi, X., & Qian, Y. (2002). Direct radiative forcing and regional climatic effects of anthropogenic aerosols over East Asia: A regional coupled climate-chemistry/aerosol model study. Journal of Geophysical Research, 107(D20), 4439. https://doi.org/10.1029/2001JDOO1066 Gregory, J. M., & Webb, M. (2008). Tropospheric adjustment induces a cloud component in CO2 forcing. Journal of Climate, 21(1), 58–71. https://doi.org/10.1175/2007JCLI1834.1 Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., & Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37), 6957–6975. https://doi.org/10.1016/j.atmosenv.2005.04.027 Gu, Y., & Yim, S. H. L. (2016). The air quality and health impacts of domestic trans-boundary pollution in various regions of China. Environment International, 97, 117–124. https://doi.org/10.1016/j.envint.2016.08.004 Hansen, J., Sato, M., & Ruedy, R. (1997). Radiative forcing and climate response. Journal of Geophysical Research, 102(D6), 6831–6864. https:// doi.org/10.1029/96JD03436 Hatzianastassiou, N., Katsoulis, B., & Vardavas, I. (2004). Sensitivity analysis of aerosol direct radiative forcing in ultraviolet-visible wavelengths and consequences for the heat budget. Tellus Series B: Chemical and Physical Meteorology, 56(4), 368–381. https://doi.org/10.1111/ j.1600-0889.2004.00110.x Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103. https://doi.org/10.1029/ 2008JD009944 IPCC (2013). IPCC fifth assessment report, climate change 2013: The physical science basis. IPCC (Vol. AR5). Jacobson, M. Z. (2002). Control of fossil-fuel particulate black carbon and organic matter, possibly the most effective method of slowing global warming. Journal of Geophysical Research, 107(D19), 4410. https://doi.org/10.1029/2001JD001376 Johnson, B. T., Shine, K. P., & Forster, P. M. (2004). The semi-direct aerosol effect: Impact of absorbing aerosols on marine stratocumulus. Quarterly Journal of the Royal Meteorological Society, 130(599), 1407–1422. https://doi.org/10.1256/qj.03.61 Khain, A., Rosenfeld, D., & Pokrovsky, A. (2005). Aerosol impact on the dynamics and microphysics of deep convective clouds. Quarterly Journal of the Royal Meteorological Society, 131(611), 2639–2663. https://doi.org/10.1256/qj.04.62 Kim, J., Lee, J., Lee, H. C., Higurashi, A., Takemura, T., & Song, C. H. (2007). Consistency of the aerosol type classification from satellite remote sensing during the Atmospheric Brown Cloud-East Asia Regional Experiment campaign. Journal of Geophysical Research, 112, D22S33. https://doi.org/10.1029/2006JD008201 Lee, W. S., & Kim, M. K. (2010). Effects of radiative forcing by black carbon aerosol on spring rainfall decrease over Southeast Asia. Atmospheric Environment, 44(31), 3739–3744. https://doi.org/10.1016/j.atmosenv.2010.06.058 Lee, Y. C., Savtchenko, A., Lee, Y. C., & Savtchenko, A. (2006). Relationship between air pollution in Hong Kong and in the Pearl River Delta Region of South China in 2003 and 2004: An analysis. Journal of Applied Meteorology and Climatology, 45(2), 269–282. https://doi.org/ 10.1175/JAM2332.1 Marshall, J. S., & Palmer, W. M. (1948). The distribution of raindrops with size. Journal of Meteorology, 5(4), 165–166. https://doi.org/10.1175/ 1520-0469(1948)005%3C0165:TDORWS%3E2.0.CO;2 Morrison, H., Thompson, G., & Tatarskii, V. (2009). Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Monthly Weather Review, 137(3), 991–1007. https://doi.org/10.1175/ 2008MWR2556.1 Petäjä, T., Järvi, L., Kerminen, V.-M., Ding, A. J., Sun, J. N., Nie, W., et al. (2016). Enhanced air pollution via aerosol-boundary layer feedback in China. Scientific Reports, 6(1), 18998. https://doi.org/10.1038/srep18998 Qian, Y., Kaiser, D. P., Leung, L. R., & Xu, M. (2006). More frequent cloud-free sky and less surface solar radiation in China from 1955 to 2000. Geophysical Research Letters, 33, L01812. https://doi.org/10.1029/2005GL024586 Sakaeda, N., Wood, R., & Rasch, P. J. (2011). Direct and semidirect aerosol effects of southern African biomass burning aerosol. Journal of Geophysical Research, 116, D12205. https://doi.org/10.1029/2010JD015540 Twomey, S. (1977). The influence of pollution on the shortwave albedo of clouds. Journal of the Atmospheric Sciences, 34(7), 1149–1152. https://doi.org/10.1175/1520-0469(1977)034%3C1149:TIOPOT%3E2.0.CO;2

8

Geophysical Research Letters

10.1029/2018GL077517

Updyke, K. M., Nguyen, T. B., & Nizkorodov, S. A. (2012). Formation of brown carbon via reactions of ammonia with secondary organic aerosols from biogenic and anthropogenic precursors. Atmospheric Environment, 63, 22–31. https://doi.org/10.1016/ j.atmosenv.2012.09.012 Wang, C. (2005). A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1. Dynamics and microphysics. Journal of Geophysical Research, 110, D21211. https://doi.org/10.1029/2004JD005720 Wang, H., Shi, G. Y., Zhang, X. Y., Gong, S. L., Tan, S. C., Chen, B., et al. (2015). Mesoscale modelling study of the interactions between aerosols and PBL meteorology during a haze episode in China Jing-Jin-Ji and its near surrounding region—Part 2: Aerosols’ radiative feedback effects. Atmospheric Chemistry and Physics, 15(6), 3277–3287. https://doi.org/10.5194/acp-15-3277-2015 Wang, J., Wang, S., Jiang, J., Ding, A., Zheng, M., Zhao, B., et al. (2014). Impact of aerosol-meteorology interactions on fine particle pollution during China’s severe haze episode in January 2013. Environmental Research Letters, 9, 094002. https://doi.org/10.1088/1748-9326/9/9/ 094002 Wang, K., Zhang, Y., Yahya, K., Wu, S. Y., & Grell, G. (2015). Implementation and initial application of new chemistry-aerosol options in WRF/Chem for simulating secondary organic aerosols and aerosol indirect effects for regional air quality. Atmospheric Environment, 115, 716–732. https://doi.org/10.1016/j.atmosenv.2014.12.007 Wang, X., Heald, C. L., Ridley, D. A., Schwarz, J. P., Spackman, J. R., Perring, A. E., et al. (2014). Exploiting simultaneous observational constraints on mass and absorption to estimate the global direct radiative forcing of black carbon and brown carbon. Atmospheric Chemistry and Physics, 14(20), 10,989–11,010. https://doi.org/10.5194/acp-14-10989-2014 Wilcox, E. M. (2012). Direct and semi-direct radiative forcing of smoke aerosols over clouds. Atmospheric Chemistry and Physics, 12(1), 139–149. https://doi.org/10.5194/acp-12-139-2012 Wu, D., Tie, X., Li, C., Ying, Z., Lau, A. K. H., Huang, J., et al. (2005). An extremely low visibility event over the Guangzhou region: A case study. Atmospheric Environment, 39(35), 6568–6577. https://doi.org/10.1016/j.atmosenv.2005.07.061 Xia, X. (2012). Significant decreasing cloud cover during 1954–2005 due to more clear-sky days and less overcast days in China and its relation to aerosol. Annales Geophysicae, 30(3), 573–582. https://doi.org/10.5194/angeo-30-573-2012 Yarwood, G. (2005). Updates to the carbon bond chemical mechanism: CB05. Technical Report, 115(14), 6687–6718. https://doi.org/10.1021/ cr500486u Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., et al. (2006). A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmospheric Chemistry and Physics, 6(3), 613–666. https://doi.org/10.5194/acp-6-613-2006 Zhang, X., Lin, Y. H., Surratt, J. D., & Weber, R. J. (2013). Sources, composition and absorption Ångström exponent of light-absorbing organic components in aerosol extracts from the Los Angeles basin. Environmental Science and Technology, 47(8), 3685–3693. https://doi.org/ 10.1021/es305047b Zhang, X., Zhang, Q., Hong, C., Zheng, Y., Geng, G., Tong, D., et al. (2018). Enhancement of PM2.5 concentrations by aerosol-meteorology interactions over China. Journal of Geophysical Research: Atmospheres, 123(2), 1179–1194. https://doi.org/10.1002/2017JD027524 Zhao, B., Liou, K. N., Gu, Y., Li, Q., Jiang, J. H., Su, H., et al. (2017). Enhanced PM2.5 pollution in China due to aerosol-cloud interactions. Scientific Reports, 7(1), 4453. https://doi.org/10.1038/s41598-017-04096-8 Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J. F., Zaveri, R. A., & Huang, J. (2013). Uncertainty in modeling dust mass balance and radiative forcing from size parameterization. Atmospheric Chemistry and Physics, 13(21), 10,733–10,753. https://doi.org/10.5194/acp-13-10733-2013 Zhao, C., Liu, X., Leung, L. R., & Hagos, S. (2011). Physics radiative impact of mineral dust on monsoon precipitation variability over West Africa. Atmospheric Chemistry and Physics, 11, 1879–1893. https://doi.org/10.5194/acp-11-1879-2011

LIU ET AL.

9