August 15, 2018
Feature

The Shady Role of Shallow Clouds

Cloud shadows are found to influence shallow cloud size and water content

shallow clouds

Shallow clouds over land are usually only several kilometers wide and rarely produce rain, but their shadows influence the exchange of water and energy between the land and the atmosphere.

The Science

Shallow clouds over land are usually only several kilometers wide and rarely produce rain, but they are numerous and quite effective at blocking the sun and modulating evaporation of water from the land surface. It is important that Earth system models accurately take into account the effect of these shallow clouds on the exchange of water and energy between the land surface and the atmosphere.

A study by researchers at the U.S. Department of Energy's (DOE) Pacific Northwest National Laboratory showed that shadows from shallow clouds create surface variability that affects the growth and evolution of the shallow cloud population itself and the exchange of water and energy between the surface and the clouds. Furthermore, the researchers found that the angle of the sun largely influences the way in which cloud shadows affect the exchange of water and energy between the surface and the clouds.

The Impact

This study shows the need to accurately consider the effect of shallow clouds and their shadows in atmospheric models used to simulate weather and climate. It could help guide improvements to the representation of shallow convection in Earth system models.

Summary

To improve predictions of Earth systems in response to environmental changes, researchers must take into account massive amounts of data across space and time. A key component of this puzzle is the role of the small yet energetic eddies that are not represented explicitly in state-of-the-art Earth system models.

Researchers performed large-eddy simulations—high-resolution numerical simulations that explicitly resolve these energetic eddies in the atmosphere—of continental shallow clouds observed at the DOE Atmospheric Radiation Measurement (ARM) user facility's Southern Great Plains (SGP) atmospheric observatory. The large-eddy simulation domain was embedded inside a coarser-resolution domain and coupled to an interactive land surface model to accurately simulate the conditions over a range of different spatial scales.

The large-eddy simulations used very high spatial resolution (100 meters), and their domain size was comparable to that of a typical Earth system model grid box (30-100 kilometers). With this setup, researchers could explicitly model shallow clouds, their shadows at the surface, and other small-scale surface features (e.g., crop patches). By comparing simulations with and without small surface features, researchers could estimate their effect on the mean cloud properties. They found that the cooler surfaces in the cloud shadows influenced the circulations around the shallow clouds overhead and affected the size and water content of these clouds. Furthermore, the angle of the sun in the sky played a large role in this process due to its control over the shadow relative to the cloud.

 

Reference: H. Xiao, L. K. Berg, M. Huang, "The Impact of Surface Heterogeneities and Land-Atmosphere Interactions on Shallow Clouds Over ARM SGP Site." Journal of Advances in Modeling Earth Systems 10, 6 (2018). [https://doi.org/10.1029/2018MS001286]

Key Capabilities

Facilities

###

About PNNL

Pacific Northwest National Laboratory draws on its distinguishing strengths in chemistry, Earth sciences, biology and data science to advance scientific knowledge and address challenges in sustainable energy and national security. Founded in 1965, PNNL is operated by Battelle for the Department of Energy’s Office of Science, which is the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science. For more information on PNNL, visit PNNL's News Center. Follow us on Twitter, Facebook, LinkedIn and Instagram.

Published: August 15, 2018

PNNL Research Team

Heng Xiao, Larry K. Berg, Maoyi Huang