Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1635-2020
https://doi.org/10.5194/gmd-13-1635-2020
Model description paper
 | 
30 Mar 2020
Model description paper |  | 30 Mar 2020

Coupling aerosols to (cirrus) clouds in the global EMAC-MADE3 aerosol–climate model

Mattia Righi, Johannes Hendricks, Ulrike Lohmann, Christof Gerhard Beer, Valerian Hahn, Bernd Heinold, Romy Heller, Martina Krämer, Michael Ponater, Christian Rolf, Ina Tegen, and Christiane Voigt

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Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res.-Atmos., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. a, b, c, d
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004. a
Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a, b, c
Afchine, A., Rolf, C., Costa, A., Spelten, N., Riese, M., Buchholz, B., Ebert, V., Heller, R., Kaufmann, S., Minikin, A., Voigt, C., Zöger, M., Smith, J., Lawson, P., Lykov, A., Khaykin, S., and Krämer, M.: Ice particle sampling from aircraft – influence of the probing position on the ice water content, Atmos. Meas. Tech., 11, 4015–4031, https://doi.org/10.5194/amt-11-4015-2018, 2018. a
Altaratz, O., Koren, I., Reisin, T., Kostinski, A., Feingold, G., Levin, Z., and Yin, Y.: Aerosols' influence on the interplay between condensation, evaporation and rain in warm cumulus cloud, Atmos. Chem. Phys., 8, 15–24, https://doi.org/10.5194/acp-8-15-2008, 2008. a
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Short summary
A new cloud microphysical scheme is implemented in the global EMAC-MADE3 aerosol model and evaluated. The new scheme features a detailed parameterization for aerosol-driven ice formation in cirrus clouds, accounting for the competition between homogeneous and heterogeneous ice formation processes. The comparison against satellite data and in situ measurements shows that the model performance is in line with similar global coupled models featuring ice cloud parameterizations.