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Abstract:
Aerosol-cloud-climate interactions (ACI) involve the activation of aerosol particles, acting as cloud condensation nuclei (CCN), into cloud droplets and their influence on planetary albedo and climate, which have been incorporated in a general circulation model. CCN activation is calculated using aerosol physical and chemical characteristics in cloud droplet nucleation (CDN) schemes. We applied three different CDN schemes to study the variability of climate predictions from aerosol-cloud interactions using the ECHAM5/MESSy Atmospheric Chemistry-climate model (EMAC). Tuning has been performed for each simulation to achieve net radiative balance at the top of the atmosphere. EMAC generally performs well in simulating climate in comparison to observations. The choice of CDN scheme has a large impact on estimated cloud radiative effects especially in regions with high anthropogenic fine particle pollution. Most pronounced regional variability is found in Europe, India, and China. This is mainly due to differences in the calculated CCN activation of small (Aitken) size particles. Köhler methods, which consider both aerosol physical and chemical properties in the CDN calculations, are recommended for investigating anthropogenic aerosol effects on climate. The tuning process towards radiative balance confines these impacts and can be regarded as important as the choice of CDN scheme. Regional variability is also found in Central Africa and South America, related to the high sensitivity of cloud optical parameter tuning as well as differences among CDNC. The tunable parameters affect the water budget and cloud optical properties, and the sensitivity of CDN. The overall variability of simulated climate, induced by different CDN schemes, is found to be less than 27%, which can be restricted by parameter tuning. In general, tuning tends to improve the general performance of the climate model, but not necessarily for all variables. We find that it is important to consider the regional sensitivity of various CCN activation and tuning parameters to optimally simulate climate.
