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The Laboratory
in the News

Carbon Capture Technique Goes Green
Major efforts are under way to reduce carbon dioxide (CO₂) emissions from burning fossil fuel. CO₂ is a heat-trapping gas (also known as greenhouse gas) that must first be separated from its source, a step known as “capture,” before it can be sequestered in an underground reservoir. Laboratory scientist Amitesh Maiti has developed a screening method that would use ionic liquids—a special type of molten salt that becomes liquid under the boiling point of water (100°C)—to separate CO₂ from its source. Chemists recently became interested in ionic liquids as solvents because they have almost no vapor pressure and do not evaporate, even under high-temperature conditions.

Over the last few years, several ionic liquids have been experimentally tested and found to be efficient solvents for CO₂, providing data that could be useful in optimizing the choice of ionic liquids for CO₂ capture. “However,” says Maiti, “each new experiment costs time and money and is often hindered because a specific ionic liquid may not be readily available.” Maiti developed a quantum-chemistry-based thermodynamic approach to compute the chemical potential of a solute (CO₂ in this case) in any solvent at an arbitrary dilution. The computations yielded accurate solubility values in a large number of solvents, including ionic liquids. Maiti confirmed these computational results by comparing computed solubilities with experimental values that have been accumulated.

Maiti used this method to predict new solvent classes that would possess CO₂ solubility nearly two times as high as the most efficient solvents experimentally demonstrated. The accuracy of the computational method will allow scientists to see useful trends, which could potentially lead to the discovery of practical solvents with significantly higher CO₂ capture efficiency than what is used today. The research appeared as the cover article in the July 2009 issue of ChemSusChem, a new journal focused on chemistry and sustainability.
Contact: Amitesh Maiti (925) 422-6657 (maiti2@llnl.gov).

Climate Models Agree on Human “Fingerprints”
Laboratory scientists and a group of international researchers have found that climate model quality does not affect the ability to identify human effects on atmospheric water vapor. The team first tested each of 22 models individually, calculating 70 different measures of model performance. These metrics provided insight on how well the models simulated today’s average climate, its seasonal changes, and geographical patterns of climate variability. The information enabled the researchers to grade and rank the models in a multitude of ways and to identify many groups of “top 10” and “bottom 10” models out of the full set of 22 models. From each of these groups, the scientists obtained estimates of the response of water vapor to human factors (the “fingerprint”) and the “noise” of natural climate variability. They then repeated the search for a human effect on water vapor with more than
100 combinations of climate model fingerprint and noise data sets. In every case, a water vapor fingerprint arising from human influences could be clearly identified in the satellite data.

“Climate model quality didn’t make much of a difference,” says Ben Santer of Livermore’s Program for Climate Modeling and Intercomparison. “Even with the computer models that performed relatively poorly, we could still identify a human effect on climate. The physics that drive changes in water vapor are very simple and are reasonably well-portrayed in all climate models.” The atmosphere’s water vapor content has increased by about 0.4 kilograms per cubic meter per decade since 1988, and natural variability alone cannot explain this moisture change, according to Santer. “The most plausible explanation is that it’s due to human-caused increases in greenhouse gases,” he says. More water vapor—which is itself a greenhouse gas—amplifies the warming effect of increased atmospheric levels of CO₂.

This new study links Livermore’s “fingerprint” research with its longstanding work in assessing climate model quality. It tackles the general question of how to best make use of the information from a large collection of models, which often perform very differently in reproducing key aspects of present-day climate. The team’s findings appeared in the August 10, 2009, online issue of the Proceedings of the U.S. National Academy of Sciences.
Contact: Ben Santer (925) 422-3840 (santer1@llnl.gov).