EC-02. Surface Solar Irradiance in Complex Cloud-Aerosol Environments

Abstract
Ubiquitous shallow cumulus clouds exhibit detailed three-dimensional (3D) spatial structure leading to complex variability in surface solar irradiance (SSI). Furthermore, hygroscopic aerosol embedded in the cloud field introduces additional scattering and absorption, adding to the variability in SSI. Recent studies have shown that the SSI variability in this environment is captured concisely by the shape of the SSI probability density function (PDF), which is typically bi-modal representing separately the cloud shadows and the gaps between. However, detailed understanding of the relationship between cloud and aerosol properties, and the SSI PDF shape, has remained elusive. In this work, we compare observations of SSI at the Southern Great Plains Atmospheric Observatory in Oklahoma, USA, with simulated SSI derived from large eddy simulation and Monte-Carlo 3D radiative transfer for a variety of shallow cumulus cases. First, this presentation will reveal the stark differences between simulated SSI when 3-D radiative effects are included or neglected. The observed SSI PDF shape is only reproduced when accounting for horizontal photon transport, providing direct observational evidence for 3D radiative effects. Next, a machine learning framework will be introduced for predicting the SSI PDF using just a handful of key cloud and aerosol properties. It will be demonstrated how well the SSI PDF can be predicted relative to traditional 1D radiative transfer, while bypassing the computational expense of 3D radiative transfer. Finally, inference techniques will be discussed that quantify the relative importance of each predictor and therefore aid in the understanding of the physical controls on SSI variability, highlighting the key role of aerosol embedded in the cloud field. The new findings have important implications for solar renewable energy assessment, highlight the significance of the absence of shortwave 3D radiative effects in weather and climate modeling, and provide a route forward for efficient parameterization of shortwave 3D radiative effects at the surface.