Mineral dust is the most important natural source of atmospheric ice nuclei (IN) which may significantly mediate the properties of ice cloud through heterogeneous nucleation and lead to crucial impacts on hydrological and energy cycle. The potential dust IN effect on cloud top temperature (CTT) in a well-developed mesoscale convective system (MCS) was studied using both satellite observations and cloud resolving model (CRM) simulations. We combined satellite observations from passive spectrometer, active cloud radar, lidar, and wind field simulations from CRM to identify the place where ice cloud mixed with dust particles. For given ice water path, the CTT of dust-mixed cloud is warmer than that in relatively pristine cloud. The probability distribution function (PDF) of CTT for dust-mixed clouds shifted to the warmer end and showed two peaks at about −45 °C and −25 °C. The PDF for relatively pristine cloud only show one peak at −55 °C. Cloud simulations with different microphysical schemes agreed well with each other and showed better agreement with satellite observations in pristine clouds, but they showed large discrepancies in dust-mixed clouds. Some microphysical schemes failed to predict the warm peak of CTT related to heterogeneous ice formation.
Abstract. Diurnal variation of surface PM2.5 concentration (diurnal PM2.5)
could dramatically affect aerosol radiative and health impacts and can also
well reflect the physical and chemical mechanisms of air pollution formation
and evolution. So far, diurnal PM2.5 and its modeling capability over
East China have not been investigated and therefore are examined in this
study. Based on the observations, the normalized diurnal amplitude of
surface PM2.5 concentrations averaged over East China is weakest (∼1.2) in winter and reaches ∼1.5 in other seasons. The diurnal PM2.5 shows the peak concentration during the night in spring and fall and during the daytime in summer. The simulated diurnal PM2.5 with WRF-Chem and its contributions from multiple physical and chemical processes are examined in the four seasons. The simulated diurnal PM2.5 with WRF-Chem is primarily controlled by planetary boundary layer (PBL) mixing and emission variations and is significantly overestimated against the observation during the night. This modeling bias is likely primarily due to the inefficient PBL mixing of primary PM2.5 during the night. The simulated diurnal PM2.5 is sensitive to the PBL schemes and vertical-layer configurations with WRF-Chem. Besides the PBL height, the PBL mixing coefficient is also found to be the critical factor determining the PBL mixing of pollutants in WRF-Chem. With reasonable PBL height, the increase in the lower limit of the PBL mixing coefficient during the night can significantly reduce the modeling biases in diurnal PM2.5 and also the mean concentrations, particularly in the major cities of East China. It can also reduce the modeling sensitivity to the PBL vertical-layer configurations. The diurnal variation and injection height of anthropogenic emissions also play roles in simulating diurnal PM2.5, but the impact is relatively smaller than that from the PBL mixing. This study underscores that more efforts are needed to improve the boundary mixing process of pollutants in models with observations of PBL structure and mixing fluxes in addition to PBL height, in order to simulate reasonably the diurnal PM2.5 over East China. The diurnal variation and injection height of anthropogenic emissions must also be
included to simulate the diurnal PM2.5 over East China.
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