Advancing aerosol retrievals in the vicinity of clouds through remote sensing, 3-D radiative transfer, and state-of-the-art cloud modeling
Aerosols continue to be one of the largest sources of uncertainty in quantifying the responses of the climate system to human impacts. Observational constraints on aerosol properties and their radiative effect depend critically on satellite-based measurements.
Satellite retrievals currently discard data near clouds due to 3D radiation effects, exacerbated by enhanced humidity, and aerosol hygroscopic growth. This causes a significant underestimation of aerosol optical depth and the direct aerosol effect, and uncertainty in the assessment of the role of aerosols for climate.
We will characterize aerosol hygroscopic growth and advance aerosol optical depth and other aerosol property retrievals applicable to complex, highly 3-D, cloud-aerosol environments. The retrieval is unique, based on a machine-learning approach that accounts for both 3-D radiative effects and aerosol hygroscopic growth. To provide observational constraints for aerosol retrievals, we will characterize the aerosol field near clouds using data acquired during the ATOMIC and EUREC4A field programs.