Uncovering Global Effects of Clouds on Climate: Multi-scale model provides global view of Asian pollution impacts on Pacific storm track
Scientists from the University of Texas and Pacific Northwest National Laboratory provided a first-time global perspective of the impacts of Asian pollution on the Pacific storm track and subsequent weather. They found that a unique modeling technique allowed them to understand the global scale effect of tiny pollution particles to strengthen storm clouds and rain. Developed at PNNL, the technique is essential to revealing the small-scale effects of clouds in a large-scale model. The study, published in the Proceedings of the National Academy of Sciences (PNAS), was reported in the online Early Edition April 14.
"The higher resolution improved the cloud simulations," said Dr. Minghuai Wang, PNNL climate scientist. "Conventional global scale models usually show a decrease in precipitation in response to man-made aerosols, but we see the increase that we know happens in real life."
In this study, the researchers used a multi-scale global aerosol-climate model that embeds a two-dimensional cloud-resolving model, explicitly simulating aerosol effects on deep clouds to examine aerosol effects on Pacific storm track. They compared results from the multi-scale aerosol-climate model to a conventional aerosol-climate model and showed that increased activity in the Pacific storm track is due to man-made aerosol particles. Unlike conventional climate model results without the cloud-resolving detail, their findings are consistent with observations.
For more, see PNNL news release, “Researchers reconstruct Pacific storm track in climate model”.
Man-made pollution particles, also called aerosols, can affect the climate by altering cloud formations. Until now, their effects on storm cloud systems were either missing or crudely represented in global models used to understand climate trends or possible future climate scenarios. This research allows a way to include the aerosol effects on storm cloud systems—the small-scale details—in global models that are at large-scale resolutions.
One of the largest uncertainties in simulating climate change remains the magnitude of cloud adjustment by aerosols, which is poorly quantified in climate models. Atmospheric aerosols affect weather and global general circulation by modifying cloud and precipitation processes and are essential to understanding climate change. A research team, including DOE scientists from Pacific Northwest National Laboratory, assessed the effects of anthropogenic aerosols on the Pacific storm track using a multiscale global aerosol–climate model. Their simulations of two aerosol scenarios—present day and preindustrial conditions—revealed long-range transport of anthropogenic aerosols across the north Pacific. They showed how this aerosol transport results in large changes in the aerosol optical depth, cloud droplet number concentration, cloud and ice water paths, shortwave and longwave cloud radiative forcing at the top of atmosphere, and increased precipitation and poleward heat transport. These results indicate that anthropogenic aerosols intensify the Pacific storm track. The team’s work shows for the first time the global impact of Asian pollution aerosols using a multi-scale modeling framework in GCM simulations. Their results suggest that the multiscale modeling framework is essential to show the aerosol invigoration effect of deep convective clouds on a global scale.
The research was supported by a NASA Graduate Student Fellowship in Earth System Science; the Ministry of Science and Technology of China; the NASA Research Opportunities in Space and Earth Sciences, Enhancing the Capabilities of Computational Earth System Models and NASA Data for Operation and Assessment program at the Jet Propulsion Laboratory, California Institute of Technology; and the U.S. Department of Energy (DOE) Office of Science Regional and Global Climate Modeling Program, for the Decadal and Regional Climate Prediction using Earth System Models (EaSM) project. This research used computing resources at the Oak Ridge Leadership Computing Facility, supported by DOE’s Office of Science.