Influence of Aerosol Embedded in Shallow Cumulus Cloud Fields on the Surface Solar Irradiance

Abstract

Ubiquitous shallow cumulus clouds are associated with complex variability in surface solar irradiance (SSI). Aerosol embedded in the cloud field typically has a much smaller overall radiative effect, but can significantly perturb the shape of the SSI probability density function (PDF). These perturbations have important implications for several applications that utilize SSI, but are poorly quantified and are the subject of this study. Multiple cases of shallow cumulus cloud fields with embedded aerosol are simulated at the Southern Great Plains Atmospheric Observatory using large eddy simulation (LES). The LES‐derived cloud and aerosol fields are then ingested into Monte Carlo three‐dimensional (3D) radiative transfer to simulate SSI. We find a variety of perturbations to the SSI PDF that depend on aerosol presence and optical properties. The processes leading to these perturbations include extinction of the direct beam that often increases from the clear‐sky region toward cloud edge due to aerosol hygroscopic growth, and scattering of radiation by aerosol into cloud shadows. The ability to predict the SSI PDF in the presence of aerosol is assessed by adding three representative aerosol optical properties into an existing machine learning framework. We show that machine learning accurately predicts the SSI PDF across a wide range of conditions with negligible computational expense. Importance metrics reveal the relatively high influence of aerosol optical properties in making the predictions. These new findings highlight the important role that aerosol plays in SSI variability for highly 3D cloud‐aerosol environments and provides a computationally efficient route forward for its simulation.

Observed patterns of surface solar irradiance under cloudy and clear‐sky conditions

“…Hygroscopic growth causes aerosols to scatter more despite constant aerosol amount, and more so for shorter wavelengths. This was found by Gristey et al (2022), in cases of shallow cumuli, to be an important contribution to enhanced diffuse irradiance in cloud shadows from extra scattered light around the cloud. Given its wavelength dependence, it may in our analysis contribute to more "blue" cloud shadows.…”

“…Resolving 3D radiation more accurately is possible in academic setups, where specific mechanisms can be studied in a controlled manner. For example, Veerman et al (2022) coupled a 3D Monte Carlo ray tracer to a cloud‐resolving model to study cumulus, Gristey et al (2022) used a similar technique but in an uncoupled setup to study cumulus with aerosol effects, Villefranque et al (2023) functionalised a ray tracer to study the effect of surface albedo on irradiance in cumulus fields, and Pincus and Evans (2009) demonstrated an alternative to ray tracing altogether. However, even in the best‐studied case of boundary‐layer shallow cumulus, one can question the realism with which large‐eddy simulations (LESs) can resolve such clouds (Romps et al, 2021).…”

A Case Study of Cumulus Convection Over Land in Cloud‐Resolving Simulations With a Coupled Ray Tracer

“…However, differences in the PDFs may also be attributed to uncertainties in the radiative transfer computations or the observations. The simulations do not include aerosols and therefore likely overestimate GHI and underestimate DIF (Gristey et al., 2022). Furthermore, the modeled direct radiation is defined as all non‐scattered radiation, whereas in observations it is the radiation from a small cone with an opening half‐angle of 2.5° centered around the sun (Blanc et al., 2014; Kipp & Zonen, 2001) Radiation scattered in almost forward direction would therefore be counted as direct radiation in observations, but as diffuse in the ray tracer.…”

An Efficient Parameterization for Surface Shortwave 3D Radiative Effects in Large‐Eddy Simulations of Shallow Cumulus Clouds

“…(We compared the aerosol optical depths from the McClear model (Gschwind et al, 2019), not shown.) For broken cloud conditions, Schmidt et al (2009) and Gristey et al (2022) showed that aerosols reduce the irradiance in the gaps between the clouds, by scattering radiation to the cloudy regions. In 1D simulations, the radiation scattered by aerosols cannot propagate horizontally to the cloudy regions, thus it will reach the surface in the gaps between the clouds.…”