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An Integrated View on Coupled Aerosol-Cloud Interactions

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Improving cloud formation description in atmospheric models

Understanding the dynamic and complex relationship between aerosol particles and atmospheric water vapour can lead to more accurate climate and air quality models.

The most abundant condensable vapour in the atmosphere is water, which turns into droplets when it comes into contact with a cold surface. That’s why your glasses steam up when you move from a cold environment to a warmer one. In addition to water vapour, the atmosphere also contains aerosol particles. These are microscopic specks suspended in the air that come from natural sources such as desert dust and sea spray, as well as man-made pollutants such as industrial emissions. “Small aerosol particles in the atmosphere act like the surface of your glasses,” says INTEGRATE(opens in new window) project coordinator Ilona Riipinen from Stockholm University(opens in new window). “Every cloud droplet in the present atmosphere has been seeded by a particle.”

From molecules to climate science

Aerosol particles are therefore critical to cloud formation. This interaction between aerosol particles and vapour plays a key role in determining cloud size and levels of precipitation, as well as the Earth’s energy budget, meaning the balance between incoming solar radiation and outgoing terrestrial radiation, through the ability of clouds to reflect solar radiation. The INTEGRATE project, which was supported by the European Research Council(opens in new window), sought to advance fundamental knowledge of these interactions, with a view to better understanding how clouds and precipitation affect climate and govern air quality. “These interactions involve complex chains of events,” adds Riipinen. “To accurately estimate, for example, the evolution of temperature or precipitation patterns in an Earth systems model, you need to be able to numerically describe non-linear processes like changes in aerosol properties and cloud microphysics. This is very hard to do.”

Looking at particle interactions

To achieve its aims, INTEGRATE brought together scientists working at the fundamental molecular level, for example on what determines if a molecule condenses, with those looking at completely different scales, for example forest-atmosphere interactions. This helped to place molecular-level phase transitions within the context of climate science. “In terms of fieldwork, we gathered air samples from remote Arctic environments where there are few man-made particulates, as well as more polluted regions like the Po valley in northern Italy,” notes Riipinen. “We then used different techniques to measure particles from the molecular level up to the size of cloud droplets, which are orders of magnitude larger.” The team also focused on nitrates, a group of compounds where there has been a lack of knowledge. As sulphate emissions decrease, nitrates are expected to grow in importance as a source of man-made particulates. The project team developed new knowledge about how different nitrates interact with water.

Recommendations for climate scientists

A key success of the project has been to encourage a rethink about how scientists consider particulates in cloud formation. Instead of the conventional thinking of particles having to be a certain size to become ‘cloud seeds’, Riipinen believes this process should be considered more along the lines of a continuum. Results from the project have also been turned into recommendations for climate scientists, to help them implement these findings into Earth systems and air quality models. “If we want to make predictions about precipitation pattern changes, or air quality, we need better descriptions of aerosol vapour interactions, and the physics that are involved,” she says. “Descriptions in models need to be based on fundamental understandings.” Similarly, Riipinen believes that the project has shown the need for theoretical scientists to work with applied science communities, to ensure that their work is understandable and usable in predictive models.

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