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Researchers improve predictions of cloud formation for better global climate modeling


Researchers are developing improved methods for representing cloud formation in global climate models because of increased aerosol pollution, which gives clouds more cooling power and affects precipitation. Here, cumulonimbus clouds develop over the mountains of western North Carolina. Photo by Grant W. Goodge, Courtesy of NOAA

Atmospheric scientists have developed simple, physics-based equations that address some of the limitations of current methods for representing cloud formation in global climate models – important because of increased aerosol pollution that gives clouds more cooling power and affects precipitation.

These researchers – led by the Georgia Institute of Technology -- have also developed a new instrument for measuring the conditions and time needed for a particle to become a cloud droplet. This will help scientists determine how various types of emissions affect cloud formation. The research is funded by the National Science Foundation.

Clouds play a critical role in climate, Nenes explained. Low, thick ones cool the earth by reflecting solar radiation whereas high, thin clouds have warming properties by trapping infrared radiation emitted by the earth. Scientists have learned that human activities influence cloud formation. Airborne particles released by smokestacks, charcoal grills and car exhaust restrict the growth of cloud droplets, causing condensing water to spread out among a larger number of smaller droplets. Known as the "indirect aerosol effect," this gives clouds more surface area and reflectivity, which translates into greater cooling power. The clouds may also have less chance of forming rain, which allows cloud to remain longer for cooling.

"Of all the components of climate change, the aerosol indirect effect has the greatest potential cooling effect, yet quantitative estimates are highly uncertain," said Nenes, who holds dual appointments in the Georgia Tech School of Earth and Atmospheric Sciences and the School of Chemical and Biomolecular Engineering. "We need to get more rigorous and accurate representation of how particles modify cloud properties. Until the aerosol indirect effect is well understood, society is incapable of assessing its impact on future climate."

Current computer climate models can’t accurately predict cloud formation, which, in turn, hinders their ability to forecast climate change from human activities. "Because of their coarse resolution, computer models produce values on large spatial scales (hundreds of kilometers) and can only represent large cloud systems," Nenes said.

Aerosol particles, however, are extremely small and are measured in micrometers. This means predictive models must address processes taking place on a very broad range of scale. "Equations that describe cloud formation simply cannot be implemented in climate models," Nenes said. "We don’t have enough computing power -- and probably won’t for another 50 years. Yet somehow we still need to describe cloud formation accurately if we want to understand how humans are affecting climate."

Scientists have tried to predict cloud formation through empirical "parameterization" – techniques that rely on empirical information or correlations, such as comparing the number of particles in the atmosphere with the number of cloud droplets. "Yet there’s no real physical link, no causality between those two numbers," Nenes said.

To address both the lack of computer power and shortcomings of existing parameterization, Nenes and his research team have developed simple, physics-based equations that link aerosol particles and cloud droplets. Then these offline equations can be scaled up to a global level, providing accurate predictions literally thousands of times faster than more detailed models.

For example, by determining an algebraic equation for maximum supersaturation (the point in a cloud where all droplets that could form, have formed), it is then possible to calculate how many cloud droplets can form. That droplet number reveals the optical (reflective) properties of a cloud, as well as its potential for forming rain.

This modeling method has proven successful in two field tests. In situ aircraft data was collected from cumulus clouds off the coast of Key West, Fla., in 2002, and from stratocumulus clouds near Monterrey, Calif., in 2003. Compared with this real-world data, predictions from Nenes’ model were accurate within 10 to 20 percent.

That was a pleasant surprise for the research team, which included Georgia Tech postdoctoral scholar Nicholas Meskhidze and graduate student Christos Fountoukis. "We never expected to capture the physics to that degree," Nenes explained. "We were hoping for a 50 percent accuracy rate."

Another key challenge in predicting climate change is to understand how aerosols’ chemistry affects cloud formation. Each particle has a different potential for forming a cloud drop, which depends on its composition, location and how long it has been in the atmosphere. Up to now, people have measured and averaged properties over long periods of time. "Yet particles are mixing and changing quickly," Nenes said. "If you don’t factor in the chemical aging of the aerosol, you can easily have a large error when predicting cloud droplet number."

Working with Gregory Roberts at the Scripps Institution of Oceanography, Nenes developed a new type of cloud condensation nuclei (CCN) counter. This instrument exposes different aerosol particles to a supersaturation, which enables researchers to determine: 1) how many droplets form and 2) how long they take to form.

Providing fast, reliable measurements, this CCN counter can be used either on the ground or in an aircraft. "It gives us a much needed link for determining how different types of emissions will affect clouds formation," Nenes explained.

Nenes and Roberts have patented the CCN instrument, and a paper describing the technology will be published in an upcoming issue of Aerosol Science and Technology.

The CCN counter is being commercialized by Droplet Measurement Technologies in Boulder, Colo., and a number of research universities and government agencies have already placed orders. "There is also a great deal of interest from Asia," Nenes said, "Because of its economic boom, Asia has been generating considerable emissions, which are thought to have a major impact on local climate."

Both the new modeling method and CCN instrument have far-reaching applications for predicting climate change and precipitation patterns.

The indirect aerosol effect is counteracting greenhouse warming right now, but this will stop at some point, Nenes explained: "One of our goals as scientists is to figure out how long we’ll have this cooling effect so that we can respond to changes. Being able to predict climate change can help countries with sustainability – from agricultural planning to global emission policies."

Jane Sanders | EurekAlert!
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