Sensitivity of aerosol-cloud interactions to autoconversion schemes in mixed-phase orographic clouds

Abstract Autoconversion is an important role in describing initial formation of raindrops through droplets collision-coalescence, especially in discussing aerosol-cloud interactions. Seven autoconversion schemes have been adopted to investigate aerosol particles severing as cloud condensation nuclei (CCN) effect on mixed-phase orographic cloud. As CCN represented by initial droplet concentration is increased, more cloud droplets caused by a suppression of autoconversion rate benefit for snow growth by accreting droplets, leading to a delay in precipitation. Moreover, the loss of rain growth induced by a decrease in the accretion of cloud droplets by rain with increasing CCN can, to some extent, be offset by an enhancement of snow growth. As the freezing level is decreased, ice-phase particles become more effective, and then the decrease in precipitation becomes less obvious. Compared analysis finds that sensitive degrees of surface precipitation, hydrometeors and their related microphysical processes vary from different autoconversion schemes. Among them, the decrease in precipitation induced by increasing CCN is found to range from 3.2% to 36.3% under different schemes, while the increase in spillover is changed from 2.9% to 86.4%. The decrease in the accretion of cloud droplets by rain varies from 4.47% to 62.5%, and then the increase in snow melting can be changed from 7.32% to 31.8%. Hence, it should be pay attention to autoconversion scheme in estimating aerosol-cloud-precipitation interactions. Finally, based on the SCE (the stochastic collection equation) scheme, Berry scheme and Seifert and Beheng scheme are more appropriate for clean background condition, while Khairoutdinov and Kogan scheme and Liu and Daum scheme are applicable to polluted condition.