The numerical study on the sensitivity of different auto-conversion parameterization to CCN concentration

Abstract

The auto-conversion from cloud droplet to raindrop is a process whereby rain drops formed by collision-coalescence of cloud droplets. As an essential link connecting aerosol-cloud interaction, it significantly influences the changes in cloud morphology and precipitation. In order to explore the sensitivity of auto-conversion schemes to cloud condensation nuclei (CCN) concentration, using the auto-conversion scheme in the Thompson scheme (TH-AU) and Milbrandt-Yau scheme (MY-AU), we set four groups of CCN concentrations to simulate a strong convection process in Ningxia region of China. The results show that: The sensitivity of different auto-conversion schemes to changes in CCN concentrations varies significantly, and the aerosol-induced changes in precipitation and convection strongly depend on the auto-conversion scheme. With the increase of CCN concentration, the mixing ratio of cloud droplets increases, and the particle size decreases, resulting in a decrease in the auto-conversion intensity for the two schemes, which makes more supercooled water participate in the ice phase process. Compared with the TH-AU, the MY-AU has lower auto-conversion intensity at the same CCN concentration, the proportion of supercooled cloud droplets participating in the ice phase process is higher than that in the TH-AU, which leads to the raindrop mixing ratio of 4000–6000 m in MY-AU is lower than that in TH-AU at the same CCN concentration, and the mixing ratio of ice phase particles in MY-AU scheme is higher in the convective mature stage, especially snow and graupel particles, and the graupel particle generation height of MY-AU is lower than that of TH-AU. In terms of dynamic structure, with the increase of CCN concentration, more cloud droplets are activated and frozen which makes the enhancement of updraft mainly occur in the upper layer in both schemes, but the stronger gravitational drag caused by graupel particles in MY-AU may enhance the downdraft in the middle and lower layers, which makes the convection of MY-AU decay early at higher CCN concentration. In addition, changes in microphysical processes also lead to differences in cumulative precipitation and accumulated ground graupel-fall of the two schemes. The cumulative precipitation and the accumulated ground graupel-fall of the MY-AU decrease strongly with the increase of CCN concentration because the warm rain process of MY-AU is strongly inhibited. Compared with MY-AU, the warm rain process of TH-AU is not significantly inhibited, which leads to the cumulative precipitation and the accumulated ground graupel-fall of the TH-AU scheme increases when the CCN concentration is 50–200 cm−3 and slightly decreases when the CCN concentration is 200–10000 cm−3.

The Role of In‐Cloud Wet Removal in Simulating Aerosol Vertical Profiles and Cloud Radiative Forcing

“…Also, future aerosol schemes should take into account the advection of cloud‐borne aerosols. Besides, more effort is needed to reduce uncertainties in cloud microphysics simualtion that indirectly impact aerosol wet removal (Lin et al., 2021; Lou et al., 2023; Shan et al., 2020, 2022).…”