Changes and predictions of vertical distributions of global light-absorbing aerosols based on CALIPSO Observation

Abstract

Knowledge of the vertical distribution of absorbing aerosols is crucial for radiative forcing assessment, and its quasi real-time prediction is one of the keys for the atmospheric correction of satellite remote sensing. In this study, we investigated the seasonal and interannual changes of the vertical distribution of global absorbing aerosols based on satellite measurement from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and proposed a neural network (NN) model to predict the vertical distribution of global absorbing aerosols. Gaussian fitting was proposed to derive the maximum fitted particle number concentration (MFNC), altitude corresponding to MFNC (MFA), and standard deviation (MFASD) for vertical distribution of dust and smoke aerosols. Results showed that higher MFA values of dust and smoke aerosols mainly occurred over deserts and tropical savannas, respectively. For dust aerosol, the MFA is mainly observed at 0.5 to 6 km above deserts, and low MFNC values occur in boreal spring and winter while high values in summer and autumn. The MFA of smoke is systematically lower than that of dust, ranging from 0.5 to 3.5 km over tropical rainforest and grassland. Moreover, we found that the MFA of global dust and smoke had decreased by 2.7 m yr-1 (statistical significance p = 0.02) and 1.7 m yr-1 (p = 0.02) over 2007-2016, respectively. The MFNC of global dust has increased by 0.63 cm-3 yr-1 (p = 0.05), whereas that of smoke has decreased by 0.12 cm-3 yr-1 (p = 0.05). In addition, the determination coefficient (R2) of the established prediction models for vertical distributions of absorbing aerosols were larger than 0.76 with root mean square error (RMSE) less than 1.42 cm-3, which should be helpful for the radiative forcing evaluation and atmospheric correction of satellite remote sensing.