Disentangle Aerosol-Cloud-Interactions (ACIs) using a new Aerosol-Cloud Data Base from Passive Remote Sensors and Reanalysis

Aerosol-Cloud Interactions (ACIs) remain a major uncertainty in climate predictions. While satellites provide valuable climatology to constrain ACIs and estimate their radiative forcing (e.g., Twomey effect), discrepancies among studies and measurement limitations persist. Passive sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) cannot simultaneously retrieve aerosol and cloud properties, and biases arise when absorbing aerosols above clouds (AACs) affect cloud retrievals. Such biases are particularly evident during extreme events like wildfires, dust storms, or volcanic eruptions, where AACs distort measurements of Cloud Effective Radius (CER) and Cloud Optical Thickness (COT) (e.g., Haywood et al., 2004; Alfaros and Contreras, 2013; Constantino and Bréon, 2010-2013).

 

Hitherto, existing AAC studies focus on biomass burning aerosols (BBAs) over the Southeast Atlantic, limiting their scope.

Then, the objective of this work is to globalize the AAC study to several types of absorbing aerosols all over the world.  To do so, we created a new database combining aerosols and clouds properties as well as new aerosols products based on specific aerosol-cloud scenarios. We used L3 and L2 data from TROPOMI (on board the Sentinel-5P satellite) and MODIS respectively, as well as reanalysis from CAMS: ERA5 and EAC4. We covered the period from 2019 to 2023, containing noticeable events such as the Australian and Californian Wildfires (2019/2020) or regular Saharan dust storms.

In a second time, we conducted statistical analyses on COT, CER and Cloud Droplets Number Concentration (N_d), over specific regions where N_d retrievals are reliable (McCoy, 2017). The primarily results show strong responses to AAC on cloud properties during strong events. The indirect aerosol effect is not as visible as expected, but still, this work is encouraging. The next objective is to combine satellite observations with Radiative Transfer simulations (on RTTOV) to reproduce the AAC scenes and confirm our observations and better quantify the AAC biases on cloud properties as well as ACIs more generally.