ABSTRACT
Present study investigates the role of aerosol types in modulating warm cloud microphysics over the north Indian Ocean using long-term (2006–2016) satellite and re-analysis data. Analysis of well-mixed aerosol and marine warm cloud layers observed from daily co-located Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite over northern Indian Ocean revealed the occurrence of reverse Twomey effect in warm clouds having liquid water path (LWP) less than 75 g m−2. Such clouds are found to occur under a rather stable (Estimated Inversion Strength (EIS) > 2) and dry atmospheric conditions (Free Tropospheric Humidity (FTH) < 40%). Analysis of estimated cloud-top entrainment index (ECTEI) and cloud-top entrainment instability (CTEI) quantitatively suggested that entrainment of dry air can have a significant role in reversing the first indirect effect. Further, investigation on the role of various aerosol types in well mixed clouds revealed that dust aerosols have predominant role in reversing the Twomey effect over the north Indian Ocean.
Acknowledgement
Author acknowledges the principal investigators and science team of CALIPSO satellite, MODIS satellite and ERA-5 for providing the data used in the study. Author acknowledges Gkikas et al. (2021) for providing ModIs Dust AeroSol (MIDAS) data. Author acknowledges the anonymous reviewers for their constructive comments.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability
MODIS data used in the study is available at https://doi.org/10.1080/2150704X.2022.2115861; CALIPSO dataset are available at https://doi.org/10.1080/2150704X.2022.2115861 and ERS-5 reanalysis data used in the study are available at https://doi.org/10.1080/2150704X.2022.2115861. ModIs Dust AeroSol data set used in this study are available at https://doi.org/10.1080/2150704X.2022.2115861. Figures in this study made with Matplotlib version 3.5 available under the Matplotlib licence at https://doi.org/10.1080/2150704X.2022.2115861 and QGIS 3.6 software available under https://doi.org/10.1080/2150704X.2022.2115861.