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Atmospheric Sciences & Global Change
Research Highlights

November 2015

Storm Clouds Take Rain on Rollercoaster Ride

PNNL's new modeling technique describes water's up and down journey inside turbulent storm clouds

anvil and cumulus convective clouds
Tall cumulus clouds portend a coming storm. Strong updrafts within the cloud propel their tops high into the atmosphere—up to 59,000 feet—where they flatten out to resemble the shape of an anvil head. Understanding the forces inside these clouds is a challenge, especially at the microscopic scale where tiny particles are carried through the cloud and tossed about, lengthening their lifetime, accumulating water vapor or ice, and growing in size. Scientists are working to depict these cloud forces and describe their impact within global models to more accurately portray Earth’s climate. Image courtesy of Fir0002/Flagstaffotos at Wikipedia Commons.zoom Enlarge Image.

Results: Most of us think that when rain forms in a cloud, it will instantly fall down. That's what climate models typically assume, too. But in reality, rising plumes that form turbulent storm clouds can often carry raindrops, snowflakes, and even hailstones upward before they fall out. This lengthened journey prolongs their growth stage and boosts the eventual intensity and amount of precipitation. To replicate this roundabout route in climate models, a team of Pacific Northwest National Laboratory researchers found a way to compute the complex fluxes using statistical distributions of the vertical velocity and the kinds of precipitating particles within the convective clouds.

Why It Matters: To understand convective clouds in climate models, the tall and turbulent storm clouds promising rain, scientists need to describe the strong forces within them. These strong upward and downward drafts can carry precipitation through the cloud before it falls out. It is challenging to include these processes in global models, where the atmosphere is divided into grids much larger than a single cloud occupies. Researchers must turn to formulas to represent these strong drafts and their possible consequences, changing how precipitation is viewed in the model.

"In climate models, we can't possibly track how snow, rain or hail moves within individual storm clouds. But we know that in nature, stronger vertical motions carry heavier precipitation loads," said lead researcher Dr. Mikhail Ovchinnikov, atmospheric scientist at PNNL. "The newly developed formulas allow us to account for this important correlation in models."

By changing the treatment of precipitation, researchers can also improve the model's ability to predict high anvil clouds emanating from the storms. These anvil cloud tops, which form from moisture not harvested by precipitation in tall, convective tower clouds, operate as Earth's thermal blanket and therefore are an important component of the climate system.

Methods: The researchers demonstrated that an adequate representation of strong precipitating storms in climate models is not possible without an accurate description of rain and snow transport by infrequent and vigorous upward and downdrafts. However, neither convective drafts nor spatial cloud structure are explicitly resolved by coarse-resolution global circulation models, so the convective vertical transport of hydrometeors (all forms of precipitation) must be parameterized.

The researchers showed that by conditionally sampling joint (two-dimensional) distributions of vertical velocity and hydrometeor mass mixing ratios into quadrants, they could obtain the needed hydrometeor fluxes. Then, they scaled the mean quadrant fluxes to account for within-quadrant correlations between vertical velocity and the microphysics.

To develop and evaluate this new approach required detailed knowledge of three-dimensional distributions of vertical motions and cloud properties. Because obtaining this information from observations is not currently possible, the research team conducted a series of high-resolution numerical simulations of summertime convection over the continental United States using supercomputers at the National Energy Research Scientific Computing Center (NERSC) in Oakland, Calif. Then, they harnessed the statistical properties of hundreds of simulated clouds to derive relationships between vertical precipitation fluxes and probability density functions for vertical air velocity and condensate loading.

They finished with a diagnostic evaluation showing that the newly developed representation was successful in reproducing the vertical hydrometeor fluxes.

What's Next? The analysis technique is being tested for various environmental conditions in several regions of the globe, including the tropical Pacific Ocean. The researchers will then code the approach so it can be used in coarse-resolution global climate models.

Acknowledgments

Sponsors: This research was supported by the U.S. Department of Energy (DOE) Office of Science, Office of Biological and Environmental Research under the Atmospheric System Research Program.

Facilities: The National Energy Research Scientific Computing Center (NERSC) provided computing resources for the simulations. The team acquired forcing data from the ARM Climate Research Facility program archive, sponsored by DOE's Office of Science.

Research Team: May Wong (current affiliation is the National Center for Atmospheric Research) and Mikhail Ovchinnikov, PNNL; Minghuai Wang, Nanjing University, China

Research Area: Climate & Earth Systems Science 

Reference: Wong MWS, M Ovchinnikov, and M Wang. 2015. "Evaluation of Subgrid-scale Hydrometeor Transport Schemes using a High-Resolution Cloud-Resolving Model." Journal of Atmospheric Sciences 72: 3715-3731. DOI: 10.1175/JAS-D-15-0060.1.

November 16, 2015


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In one sentence: Pacific Northwest National Laboratory researchers explained the complex fluxes in turbulent storm clouds using statistical distributions of the vertical velocity and various kinds of precipitating particles within the clouds.

In 100 characters: PNNL designs new formula to capture convective clouds in global models, changing precipitation views

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