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Peter J. Marinescu, Susan C. van den Heever, Max Heikenfeld, Andrew I. Barrett, Christian Barthlott, Corinna Hoose, Jiwen Fan, Ann M. Fridlind, Toshi Matsui, Annette K. Miltenberger, Philip Stier, Benoit Vie, Bethan A. White, and Yuwei Zhang

: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models . J. Geophys. Res. , 113 , D13103 , https://doi.org/10.1029/2008JD009944 . 10.1029/2008JD009944 Iguchi , T. , S. A. Rutledge , W.-K. Tao , T. Matsui , B. Dolan , S. E. Lang , and J. Barnum , 2020 : Impacts of aerosol and environmental conditions on maritime and continental deep convective systems using a bin microphysical model . J. Geophys. Res. Atmos. , 125 , e2019JD030952

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Nicholas R. Nalli, William L. Smith, and Quanhua Liu

water-droplet clouds are typically opaque in the IR spectrum, EDR retrievals must assume some degree of cloud-free radiative transfer within the sensor field of view (FOV) or field of regard (FOR). In the case of sounding systems, radiances from microwave (MW) sounders—for example, the Advanced Technology Microwave Sounder (ATMS) on board SNPP ( Weng et al. 2012 )—are utilized to “cloud clear” the IR spectra within partly cloudy FORs (e.g., Susskind et al. 2003 ). In the case of narrowband imager

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

profile are provided based on estimates of fluxes and heating rates using the radiative transfer model described by Stephens et al. (2001) . For any grid box with available AOD or AI retrievals, the CRF used is the mean value of the 2B-FLXHR-estimated CRF at the TOA for all single-layer cloudy profiles contained within the grid box. Figure 4 shows SW CRF (SW-CRF), LW CRF (LW-CRF), and net cloud radiative forcing (NET-CRF) at the TOA as a function of AI over oceans and AOD over land for different

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

adds to the microphysical invigoration and leads to more significant increase of CTH and cloud fraction. Aerosol impacts on mesoscale convective systems (MCSs) have not been established. However, those results strongly suggest that the stratiform regions of MCSs (which account for a lot of the precipitation and effect on radiative transfer) may be affected by the ingestion of aerosols. Fig . 2. Mechanism describing the impact of aerosols on the whole life cycle of deep convective clouds ( Fan et al

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

(NH 4 + ), BC, organic matter (OM), sea salt, and mineral dust are simulated in the model. Aerosol optical properties such as extinction, single-scattering albedo, and asymmetry factor for scattering are computed as a function of wavelength at each model grid box. Aerosol–radiation interaction is coupled with the Rapid Radiative Transfer Model (RRTM) with GCM applications (RRTMG) ( Iacono et al. 2008 ) for both shortwave (SW) and longwave radiation as implemented by Zhao et al. (2013a

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Wojciech W. Grabowski and Hugh Morrison

on convective dynamics. Because the analysis shows a strong microphysical effect on upper-tropospheric ice clouds, we also apply a radiative transfer model offline to further quantify the impacts. Section 4 provides a brief discussion of sensitivity simulations prompted by results discussed in section 3 . A discussion in section 5 concludes the paper. 2. The models a. Cloud model and modeling setup The cloud model used in this study, the same as in Grabowski (2014) , G15 , and Grabowski

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

processes are explicitly parameterized in this microphysics scheme. The deposition nucleation parameterization is based on Pruppacher and Klett (1996) , and the immersion-freezing parameterization follows Bigg (1953) . The homogeneous freezing is parameterized following Milbrandt and Yau (2005) . The Rapid Radiative Transfer Model (RRTM) scheme ( Mlawer et al. 1997 ) and the Goddard scheme ( Chou and Suarez 1999 ) are employed as the longwave and shortwave radiation parameterizations, respectively

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

Level-3 monthly mean product (MxD08_M3; Hubanks et al. 2015 ) with a 1° × 1° global spatial resolution from both Collection 051 and 006 are employed. Specifically, analysis using the AOD at 550 nm is explored using data from NASA’s MODIS sensors on board Aqua . The AOD is obtained from the Level-2 aerosol product (MxD04_L2) and is derived from precomputed radiative transfer lookup tables (LUTs) at seven wavelengths between 0.47 and 2.12 µ m. Over ocean, the “best” AOD is taken as the weighted

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

scheme ( Grell and Dévényi 2002 ) was used only in domains D01 and D02; no cumulus parameterization scheme was introduced in domain D03. Other major physics schemes involved included the Rapid Radiative Transfer Model for Global climate models ( Mlawer et al. 1997 ; Iacono et al. 2000 ) shortwave–longwave radiation scheme, the Yonsei University planetary boundary layer scheme ( Hong et al. 2006 ), and the National Centers for Environmental Prediction, Oregon State University, Air Force, and

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Christina S. McCluskey, Thomas C. J. Hill, Francesca Malfatti, Camille M. Sultana, Christopher Lee, Mitchell V. Santander, Charlotte M. Beall, Kathryn A. Moore, Gavin C. Cornwell, Douglas B. Collins, Kimberly A. Prather, Thilina Jayarathne, Elizabeth A. Stone, Farooq Azam, Sonia M. Kreidenweis, and Paul J. DeMott

× 10 5 aerosol particles ( Rogers et al. 1998 )], INPs can quickly transform a liquid-dominated cloud into an ice-dominated cloud via the Wegener–Bergeron–Findeisen process ( Pruppacher and Klett 1997 ; Korolev 2007 ), thereby modifying the cloud’s precipitation rates, lifetime, and radiative properties. Numerical representation of ice processes in clouds remains poorly constrained in global climate models, as demonstrated by McCoy et al. (2015) , who revealed that the glaciation temperature (i

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