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Aerosol-Cloud-Precipitation Interactions in the Context of Convective Self-Aggregation

Beydoun, H.ORCIDiD
Hoose, C.ORCIDiD
DOI: https://doi.org/10.1029/2018MS001523
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9155
Beydoun, H.; Hoose, C., 2019: Aerosol-Cloud-Precipitation Interactions in the Context of Convective Self-Aggregation. In: Journal of Advances in Modeling Earth Systems, Band 11, 4: 1066 - 1087, DOI: 10.1029/2018MS001523.
 
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  • Abstract
We investigate the sensitivity of self-aggregated radiative-convective-equilibrium cloud-resolving model simulations to the cloud condensation nuclei (CCN) concentration. Experiments were conducted on a long (2,000-km × 120-km) channel domain, allowing the emergence of multiple convective clusters and dry regions of subsidence. Increasing the CCN concentration leads to increased moisture in the dry regions, increased midlevel and upper level clouds, decreased radiative cooling, and decreased precipitation. We find that these trends follow from a decrease in the strength of the self-aggregation as measured by the moist static energy (MSE) variance. In our simulations, precipitation is correlated, both locally and in total, with the distribution of MSE anomalies. We thus quantify changes in the adiabatic/diabatic contributions to MSE anomalies (Wing & Emanuel, 2014, https://doi.org/10.1002/2013MS000269) and relate those changes to changes in precipitation. Through a simple two-column conceptual model, we argue that the reduction in precipitation can be explained thermodynamically by the reduction in mean net radiative cooling and mechanistically by the weakening of the area-weighted radiatively driven subsidence velocity—defined as the ratio of the total radiative cooling over the dry regions and the static stability. We interpret the system's response to increasing CCN as a thermodynamically constrained realization of an aerosol indirect effect on clouds and precipitation.
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  • Geophysik, Extraterrestische Forschung [936]
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cloud microphysics
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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