Long-term variation of cloud droplet number concentrations from space-based Lidar

This study presents a new 10 year of liquid water cloud droplet number concentration (N-d) climatology, and analyzes its long-term variation on both regional and global scales based on accurate depolarization ratio measurement from CALIPSO and 3.7 mu m cloud effective radius retrieval from MODIS. Compared with the widely used passive retrieval method (e.g., MODIS retrieval), which considers N-d as function of cloud optical depth, geometry thickness and effective radius, retrieval method of the new N-d dataset has a weak dependence upon the cloud adiabatic assumption and eliminates the possible bias caused by multilayer clouds. Statistical results show that the annual cycle and long-term variability of N-d retrieved by CALIPSO agree reasonably well with those obtained from MODIS retrieval method, especially over the stratocumulus regions (correlation coefficient > 0.9). Multiple regression models and contribution calculation verify that the variability of sulfate mass concentration dominates the long-term variation of N-d over most regions, even though the contribution factors and rates vary with different regions, temperatures and methods. In addition, our study also indicates that the impact of BC and OC on N-d should not be ignored, especially for supercooled water clouds over those important biomass burning regions. These results demonstrate the temperature-dependent N-d climatology derived from CALIOP has potential to be beneficial to climate research and reduce the uncertainties in estimates of the aerosol indirect effect in the model simulations.