Articles | Volume 6, issue 1
https://doi.org/10.5194/gmd-6-57-2013
https://doi.org/10.5194/gmd-6-57-2013
Development and technical paper
 | 
17 Jan 2013
Development and technical paper |  | 17 Jan 2013

Performance of McRAS-AC in the GEOS-5 AGCM: aerosol-cloud-microphysics, precipitation, cloud radiative effects, and circulation

Y. C. Sud, D. Lee, L. Oreopoulos, D. Barahona, A. Nenes, and M. J. Suarez

Related authors

Diurnal aging of biomass burning emissions: Impacts on secondary organic aerosol formation and oxidative potential
Maria P. Georgopoulou, Kalliopi Florou, Angeliki Matrali, Georgia Starida, Christos Kaltsonoudis, Athanasios Nenes, and Spyros N. Pandis
EGUsphere, https://doi.org/10.5194/egusphere-2025-2728,https://doi.org/10.5194/egusphere-2025-2728, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3
Yusuf Bhatti, Duncan Watson-Parris, Leighton Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Irfan Muhammed, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-2848,https://doi.org/10.5194/egusphere-2025-2848, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Aircraft In-situ Measurements from SOCRATES Constrain the Anthropogenic Perturbations of Cloud Droplet Number
Ci Song, Daniel T. McCoy, Isabel L. McCoy, Hunter Brown, Andrew Gettelman, Trude Eidhammer, and Donifan Barahona
EGUsphere, https://doi.org/10.5194/egusphere-2025-2009,https://doi.org/10.5194/egusphere-2025-2009, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Deep learning representation of the aerosol size distribution
Donifan Barahona, Katherine Breen, Karoline Block, and Anton Darmenov
EGUsphere, https://doi.org/10.5194/egusphere-2025-482,https://doi.org/10.5194/egusphere-2025-482, 2025
Short summary
Impact of mineral dust on the global nitrate aerosol direct and indirect radiative effect
Alexandros Milousis, Klaus Klingmüller, Alexandra P. Tsimpidi, Jasper F. Kok, Maria Kanakidou, Athanasios Nenes, and Vlassis A. Karydis
Atmos. Chem. Phys., 25, 1333–1351, https://doi.org/10.5194/acp-25-1333-2025,https://doi.org/10.5194/acp-25-1333-2025, 2025
Short summary

Related subject area

Atmospheric sciences
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025,https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025,https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025,https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025,https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary

Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A Parameterization of Aerosol Activation. Part 2: Multiple Aerosol Types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak H. and Ghan, S. J.: A Parameterization of Aerosol Activation. Part 3: Sectional Representation, J. Geophys. Res., 107, 4026, https://doi.org/10.1029/2001JD000483, 2002.
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-Present), J. Hydrometeor., 4, 1147–1167, 2003.
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, 1989.
Andreae, M. O. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Revi., 89, 13–41, 2008.
Download
Share