ADVANCING RETRIEVALS OF NEAR-CLOUD AEROSOLS: MACHINE LEARNING DEVELOPMENT AND ASSESSMENT OF RADIATIVE EFFECTS

Abstract

Atmospheric aerosols play a central role in Earth’s radiation budget and hydrological cycle, yet they remain a major source of uncertainty in estimates of anthropogenic climate forcing. Passive satellite observations have become a primary means of monitoring and characterizing aerosols. However, aerosol retrievals remain particularly challenging in the vicinity of clouds. Most operational retrieval algorithms exclude near-cloud pixels because the measured top-of-atmosphere radiance is strongly affected by three-dimensional (3D) radiative interactions between clouds and the surrounding atmosphere. As a result, observed reflectances near cloud boundaries can be more than 50% higher than predicted by the one-dimensional (1D) radiative transfer assumptions that underlie current retrieval methods. Consequently, a substantial fraction of the atmosphere surrounding clouds is omitted from standard aerosol products.This limitation would be less consequential for radiative forcing estimates if aerosol properties near clouds were similar to those in far-from-cloud regions, where retrievals are generally more reliable. However, extensive observational evidence indicates that near-cloud aerosol populations are physically and optically distinct. High humidity near cloud edges promotes hygroscopic growth, increasing particle size and scattering efficiency over several kilometers. In addition, interactions with clouds alter aerosol size distributions through activation, collection, and evaporation, producing larger and more internally mixed particles. As a result, excluding near-cloud pixels not only biases radiative forcing estimates toward clear-sky conditions, but also represents a missed opportunity to characterize aerosol properties that encode detailed aerosol processes and aerosol–cloud interactions. To address this limitation, we develop a machine learning method that jointly retrieves aerosol optical depth (AOD) and particle size from multi-channel reflectance observations in near-cloud environments, enabling us to begin disentangling aerosol number concentration and size and then to investigate their underlying processes. The retrieval model is trained using diverse cloud fields from large-eddy-simulation and 3D radiative transfer calculations, explicitly accounting for cloud-induced radiative effects and hygroscopic growth. Based on test data, the retrieval uncertainties are approximately 0.002 plus 3.9% of AOD for aerosol optical depth and 0.021 µm plus 1.1% of effective radius for aerosol size. These uncertainties meet the accuracy targets defined for NASA missions. In particular, the requirement for aerosol effective radius is demanding, and the method developed here achieves performance comparable to polarization-based retrievals. Consistent with previous studies, near-cloud aerosols modify estimates of the aerosol direct radiative effects, enhancing both shortwave cooling and longwave warming. Approaching cloud edge, the shortwave radiative effect increases from approximately −15.1 W m–2 in background regions more than 6 km away from clouds to about −17 W m–2 within 0.5 km of the cloud edge. Meanwhile, the longwave effect increases from around 0.56 to 0.7 W m–2 over the same distance. The near-cloud enhancement in the longwave corresponds to an additional 0.14 W m–2 of regional radiative flux, which is comparable in magnitude to the local radiative perturbation associated with an increase of ~11 ppm in atmospheric CO2 (assuming a baseline concentration of 425 ppm). Overall, both shortwave and longwave effects are amplified by roughly 20% in the transition zones near cloud edges. Since cloud transition zones have been estimated to occupy more than 20% of the global domain, their enhanced radiative effects may represent a potentially important contribution to aerosol radiative forcing estimates. The newly developed retrieval model will be applied to GOES-16 observations for a case study during the Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC4A) campaign, leveraging aircraft measurements for independent evaluation of the retrieval. Although the impact of parallax shifts associated with off-nadir viewing angles has been minimized, preliminary results indicate that the primary limitation for improving GOES aerosol retrievals is the relatively coarse spatial resolution (~1 km). At this resolution, individual pixels are likely to contain unresolved clouds, making it difficult to separate their radiative influence from that of aerosols. Future work will focus on alleviating this limitation by incorporating high-resolution simulations and additional collocated observations.