Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1643-2019
https://doi.org/10.5194/gmd-12-1643-2019
Model evaluation paper
 | 
24 Apr 2019
Model evaluation paper |  | 24 Apr 2019

The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 1: Aerosol evaluation

Ina Tegen, David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Isabelle Bey, Nick Schutgens, Philip Stier, Duncan Watson-Parris, Tanja Stanelle, Hauke Schmidt, Sebastian Rast, Harri Kokkola, Martin Schultz, Sabine Schroeder, Nikos Daskalakis, Stefan Barthel, Bernd Heinold, and Ulrike Lohmann

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Cited articles

Abdul-Razzak, H. and Ghan, S.: 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
Andreas, E. L., Jones, K. F., and Fairall, C. W.: Production velocity of sea spray droplets, J. Geophys. Res.-Oceans, 115, C12065, https://doi.org/10.1029/2010JC006458, 2010. a
Arimoto, R., Duce, R. A., Ray, B. J., Ellis, W. G., Cullen, J. D., and Merrill, J. T.: Trace elements in the atmosphere over the North Atlantic, J. Geophys. Res.-Atmos., 100, 1199–1213, https://doi.org/10.1029/94JD02618, 1995. a
Asmi, A., Wiedensohler, A., Laj, P., Fjaeraa, A.-M., Sellegri, K., Birmili, W., Weingartner, E., Baltensperger, U., Zdimal, V., Zikova, N., Putaud, J.-P., Marinoni, A., Tunved, P., Hansson, H.-C., Fiebig, M., Kivekäs, N., Lihavainen, H., Asmi, E., Ulevicius, V., Aalto, P. P., Swietlicki, E., Kristensson, A., Mihalopoulos, N., Kalivitis, N., Kalapov, I., Kiss, G., de Leeuw, G., Henzing, B., Harrison, R. M., Beddows, D., O'Dowd, C., Jennings, S. G., Flentje, H., Weinhold, K., Meinhardt, F., Ries, L., and Kulmala, M.: Number size distributions and seasonality of submicron particles in Europe 2008–2009, Atmos. Chem. Phys., 11, 5505–5538, https://doi.org/10.5194/acp-11-5505-2011, 2011a. a, b, c, d
Asmi, A., Wiedensohler, A., Laj, P., Fjaeraa, A.-M., Sellegri, K., Birmili, W., Weingartner, E., Baltensperger, U., Zdimal, V., Zikova, N., Putaud, J.-P., Marinoni, A., Tunved, P., Hansson, H.-C., Fiebig, M., Kivekäs, N., Lihavainen, H., Asmi, E., Ulevicius, V., Aalto, P., Swietlicki, E., Kristensson, A., Mihalopoulos, N., Kalivitis, N., Kalapov, I., Kiss, G., de Leeuw, G., Henzing, B., Harrison, R. M., Beddows, D., O'Dowd, C., Jennings, G. S., Flentje, H., Weinhold, K., Meinhardt, F., Ries, L., and Kulmala, M.: EUSAAR size distribution analysis database. PANGAEA, https://doi.org/10.1594/PANGAEA.861856, 2011b. a
Short summary
We describe a new version of the aerosol–climate model ECHAM–HAM and show tests of the model performance by comparing different aspects of the aerosol distribution with different datasets. The updated version of HAM contains improved descriptions of aerosol processes, including updated emission fields and cloud processes. While there are regional deviations between the model and observations, the model performs well overall.
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