Articles | Volume 10, issue 4
https://doi.org/10.5194/gmd-10-1817-2017
https://doi.org/10.5194/gmd-10-1817-2017
Development and technical paper
 | 
27 Apr 2017
Development and technical paper |  | 27 Apr 2017

An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model

Daniel Rothenberg and Chien Wang

Related authors

Impacts on cloud radiative effects induced by coexisting aerosols converted from international shipping and maritime DMS emissions
Qinjian Jin, Benjamin S. Grandey, Daniel Rothenberg, Alexander Avramov, and Chien Wang
Atmos. Chem. Phys., 18, 16793–16808, https://doi.org/10.5194/acp-18-16793-2018,https://doi.org/10.5194/acp-18-16793-2018, 2018
Short summary
The Fifth International Workshop on Ice Nucleation phase 2 (FIN-02): laboratory intercomparison of ice nucleation measurements
Paul J. DeMott, Ottmar Möhler, Daniel J. Cziczo, Naruki Hiranuma, Markus D. Petters, Sarah S. Petters, Franco Belosi, Heinz G. Bingemer, Sarah D. Brooks, Carsten Budke, Monika Burkert-Kohn, Kristen N. Collier, Anja Danielczok, Oliver Eppers, Laura Felgitsch, Sarvesh Garimella, Hinrich Grothe, Paul Herenz, Thomas C. J. Hill, Kristina Höhler, Zamin A. Kanji, Alexei Kiselev, Thomas Koop, Thomas B. Kristensen, Konstantin Krüger, Gourihar Kulkarni, Ezra J. T. Levin, Benjamin J. Murray, Alessia Nicosia, Daniel O'Sullivan, Andreas Peckhaus, Michael J. Polen, Hannah C. Price, Naama Reicher, Daniel A. Rothenberg, Yinon Rudich, Gianni Santachiara, Thea Schiebel, Jann Schrod, Teresa M. Seifried, Frank Stratmann, Ryan C. Sullivan, Kaitlyn J. Suski, Miklós Szakáll, Hans P. Taylor, Romy Ullrich, Jesus Vergara-Temprado, Robert Wagner, Thomas F. Whale, Daniel Weber, André Welti, Theodore W. Wilson, Martin J. Wolf, and Jake Zenker
Atmos. Meas. Tech., 11, 6231–6257, https://doi.org/10.5194/amt-11-6231-2018,https://doi.org/10.5194/amt-11-6231-2018, 2018
Short summary
Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG
Benjamin S. Grandey, Daniel Rothenberg, Alexander Avramov, Qinjian Jin, Hsiang-He Lee, Xiaohong Liu, Zheng Lu, Samuel Albani, and Chien Wang
Atmos. Chem. Phys., 18, 15783–15810, https://doi.org/10.5194/acp-18-15783-2018,https://doi.org/10.5194/acp-18-15783-2018, 2018
Short summary
On the representation of aerosol activation and its influence on model-derived estimates of the aerosol indirect effect
Daniel Rothenberg, Alexander Avramov, and Chien Wang
Atmos. Chem. Phys., 18, 7961–7983, https://doi.org/10.5194/acp-18-7961-2018,https://doi.org/10.5194/acp-18-7961-2018, 2018
Short summary
Uncertainty in counting ice nucleating particles with continuous flow diffusion chambers
Sarvesh Garimella, Daniel A. Rothenberg, Martin J. Wolf, Robert O. David, Zamin A. Kanji, Chien Wang, Michael Rösch, and Daniel J. Cziczo
Atmos. Chem. Phys., 17, 10855–10864, https://doi.org/10.5194/acp-17-10855-2017,https://doi.org/10.5194/acp-17-10855-2017, 2017
Short summary

Related subject area

Atmospheric sciences
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024,https://doi.org/10.5194/gmd-17-399-2024, 2024
Short summary
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024,https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024,https://doi.org/10.5194/gmd-17-381-2024, 2024
Short summary
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024,https://doi.org/10.5194/gmd-17-321-2024, 2024
Short summary
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024,https://doi.org/10.5194/gmd-17-117-2024, 2024
Short summary

Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 2. Multiple aerosol types, J. Geophys. Res., 105, 6837, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H. and Ghan, S. J.: Parameterization of the influence of organic surfactants on aerosol activation, J. Geophys. Res.-Atmos., 109, D3, https://doi.org/10.1029/2003JD004043, 2004.
Adams, B. M., Ebeida, M. S., Eldred, M. S., Jakeman, J. D., Swiler, L. P., Stephens, J. A., Vigil, D. M., Wildey, T. M., Bohnhoff, W. J., Dalbey, K. R., Eddy, J. P., Hu, K. T., Bauman, L. E., and Hough, P. D.: DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.0 User's Manual, Tech. rep., Sandia National Laboratories, Albuquerque, New Mexico, 2014.
Albani, S., Mahowald, N. M., Perry, A. T., Scanza, R. A., Zender, C. S., Heavens, N. G., Maggi, V., Kok, J. F., and Otto-Bliesner, B. L.: Improved dust representation in the Community Atmosphere Model, J. Adv. Model. Earth Sys., 6, 541–570, https://doi.org/10.1002/2013MS000279, 2014.
Barahona, D. and Nenes, A.: Parameterization of cloud droplet formation in large-scale models: Including effects of entrainment, J. Geophys. Res., 112, D16206, https://doi.org/10.1029/2007JD008473, 2007.
Download
Short summary
Climate models include descriptions of how cloud droplets form from particles in the atmosphere. We have developed an efficient parameterization of this process by building an emulator of a detailed model, which can accurately predict cloud droplet number concentrations and potentially include additional physics and chemistry. We further show that using different parameterizations could influence droplet number estimates in global models and their aerosol indirect effect on climate.