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Baolin Jiang, Bo Huang, Wenshi Lin, and Suishan Xu

1. Introduction The annual discharge of anthropogenic aerosols into the atmosphere is considerable, but the effects of those aerosols on weather and climate remain very uncertain ( IPCC 2007 ). Aerosols can absorb and reflect solar radiation, thereby reducing the surface temperature and planetary boundary layer height, but they also act as cloud condensation nuclei (CCN) or ice nuclei, affecting cloud microphysics and subsequent precipitation rates, and increasing cloud coverage, albedo, and

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Andrew R. Jongeward, Zhanqing Li, Hao He, and Xiaoxiong Xiong

attribution more reliably than any single source can suggest where the strengths of one data type can augment the deficiencies of others. This work expands on previous studies by considering the anthropogenic effects downwind of any significant anthropogenic sources. This paper is presented as follows. Section 2 describes the data products employed for this work. Section 3 contains results of AOD trend analysis from satellite and surface observational datasets. Section 4 discusses the attribution of

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Stacey Kawecki, Geoffrey M. Henebry, and Allison L. Steiner

. 2006 ). We include the direct and indirect radiative effects of aerosols with anthropogenic emissions and online chemistry to predict CN. In this version of the model, the chemistry module calculates the number and mass of aerosols that will activate as CCN based on hygroscopicity and supersaturation (Köhler curves), and provides this prognostic CCN to the microphysics module. Gas-phase chemistry is simulated with the Regional Acid Deposition Model, version 2, (RADM2) chemical mechanism ( Stockwell

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Jiwen Fan, Yuan Wang, Daniel Rosenfeld, and Xiaohong Liu

0.27 ± 0.10 W m −2 from two GCMs (CAM5 and ECHAM5). X. Liu et al. (2009) estimated the changes in cloud forcing from anthropogenic aerosol effects on cirrus clouds and found that anthropogenic soot, which is assumed to be an efficient IN for heterogeneous ice nucleation, changes the shortwave and longwave cloud radiative forcing by −1.14 ± 0.39 (cooling) and 1.67 ± 0.11 W m −2 (warming), respectively, as a result of an increase in cloud ice number from preindustry (PI) to present days (PD

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Yan Yang, Jiwen Fan, L. Ruby Leung, Chun Zhao, Zhanqing Li, and Daniel Rosenfeld

carbon from anthropogenic emissions. Biomass burning and dust are not significant emission sources. The simulation using emissions described above is named P_ALL, the base case simulation ( Table 1 ). To investigate the effects of anthropogenic emissions, a sensitivity test is carried out based on P_ALL, but with anthropogenic emissions scaled by a factor of 0.3 (referred to as “C_ALL”). The factor was obtained based on the approximate ratio of the SO 2 emissions in the early 1980s in China before

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Yun Lin, Yuan Wang, Bowen Pan, Jiaxi Hu, Yangang Liu, and Renyi Zhang

effects . J. Geophys. Res. Atmos. , 118 , 5361 – 5379 , doi: 10.1002/jgrd.50432 . Wang , Y. , A. Khalizov , M. Levy , and R. Y. Zhang , 2013b : New directions: Light absorbing aerosols and their atmospheric impacts . Atmos. Environ. , 81 , 713 – 715 , doi: 10.1016/j.atmosenv.2013.09.034 . Wang , Y. , K.-H. Lee , Y. Lin , M. Levy , and R. Zhang , 2014a : Distinct effects of anthropogenic aerosols on tropical cyclones . Nat. Climate Change , 4 , 368 – 373 , doi: 10

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Jie Peng, Zhanqing Li, Hua Zhang, Jianjun Liu, and Maureen Cribb

, 2012 ; Van den Heever et al. 2011 ) and explained by a conceptual theory proposed by Rosenfeld et al. (2008a) and a revised theory ( Fan et al. 2013 ). Whether aerosols invigorate or suppress cloud and thunderstorms seems to depend on the joint effects of aerosol radiative and microphysical effects: suppression for absorbing aerosols and enhancement for hygroscopic aerosols ( Yang et al. 2013a , b ; Yang and Li 2014 ). Both observational and modeling studies have shown that the AIV can lead to

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Eyal Ilotoviz, Alexander P. Khain, Nir Benmoshe, Vaughan T. J. Phillips, and Alexander V. Ryzhkov

( Ramanathan et al. 2001 ; Andreae et al. 2004 ; Rosenfeld et al. 2008 ; Freud et al. 2008 ; Khain 2009 ), it would be expected that CCN would have a strong effect on the mass and the size of hail particles. To describe aerosol effects on hail mass content (HMC) and hail size, models with advanced microphysics are required ( Levin and Cotton 2009 ; Khain 2009 ; Tao et al. 2007 , 2012 ). In numerous bulk-parameterization schemes, number size distributions of precipitating particles, including those

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Daniel Rothenberg and Chien Wang

the average size of the droplets ( Twomey 1974 ). Such a change could enhance a cloud’s albedo, an effect that could be further amplified through microphysical feedbacks since smaller droplets impede the production of drizzle and thus lengthen cloud lifetime ( Albrecht 1989 ). Mechanisms whereby aerosol influence the properties of clouds (and ultimately climate) are generally known as “aerosol indirect effects” ( Haywood and Boucher 2000 ; Lohmann and Feichter 2005 ) and provide a path for

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Tianmeng Chen, Jianping Guo, Zhanqing Li, Chuanfeng Zhao, Huan Liu, Maureen Cribb, Fu Wang, and Jing He

1. Introduction Aerosols can play an important role in Earth’s climate by altering the energy and water cycles ( Ramanathan et al. 2001 ; Rosenfeld et al. 2014 ). Various effects have been proposed that are now broadly referred to as the aerosol–radiation interactions (ARI) and aerosol–cloud–interactions (ACI) ( IPCC 2013 ). The mechanisms for ARI have been much better understood than ACI, even though very large uncertainties still exist in the former chiefly owing to a poor knowledge of the

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