Summary
Climate projections are essential for guiding society’s response to climate change but feature significant uncertainty. According to the latest assessment of the Intergovernmental Panel on Climate Change (IPCC), clouds remain the largest source of this uncertainty. The main culprit are mesoscale cloud fields, which organize into striking patterns, and cover hundreds of kilometers over the subtropical and tropical oceans. While conspicuous in satellite imagery, we lack the concepts and tools to adequately model their evolution.
To overcome this mesoscale cloud-climate uncertainty, the project will develop a framework to conceptually understand and quantitatively predict mesoscale cloudiness from time series of satellite imagery. This requires a fundamental change of perspective: Instead of investigating cloud processes from the bottom up, the new approach will directly focus on the emergent behavior at the mesoscale. The new framework will capture mesoscale cloudiness as a data-driven complex system. This characterization will enable an assessment of the role of clouds in climate projections that is novel in two aspects: First, it will include observational information that has not been used before to reduce cloud-climate uncertainty. Second, the reliability of state-of-the-art lines of evidence will be objectively judged based on how well they capture different scales of cloud processes. The new methodology will be equipped to tap into the next generation of data and unlock additional lines of evidence.
As a comprehensive tool for mastering mesoscale cloudiness, the new framework will have broad and lasting impact: It will steer future cloud research, it will notably reduce uncertainty in the next IPCC assessment, and it will be an essential guide for the upcoming data-driven revolution of atmospheric and climate modeling.
To overcome this mesoscale cloud-climate uncertainty, the project will develop a framework to conceptually understand and quantitatively predict mesoscale cloudiness from time series of satellite imagery. This requires a fundamental change of perspective: Instead of investigating cloud processes from the bottom up, the new approach will directly focus on the emergent behavior at the mesoscale. The new framework will capture mesoscale cloudiness as a data-driven complex system. This characterization will enable an assessment of the role of clouds in climate projections that is novel in two aspects: First, it will include observational information that has not been used before to reduce cloud-climate uncertainty. Second, the reliability of state-of-the-art lines of evidence will be objectively judged based on how well they capture different scales of cloud processes. The new methodology will be equipped to tap into the next generation of data and unlock additional lines of evidence.
As a comprehensive tool for mastering mesoscale cloudiness, the new framework will have broad and lasting impact: It will steer future cloud research, it will notably reduce uncertainty in the next IPCC assessment, and it will be an essential guide for the upcoming data-driven revolution of atmospheric and climate modeling.
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| Web resources: | https://cordis.europa.eu/project/id/101117462 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 1 499 070,00 Euro - 1 499 070,00 Euro |
Cordis data
Original description
Climate projections are essential for guiding society’s response to climate change but feature significant uncertainty. According to the latest assessment of the Intergovernmental Panel on Climate Change (IPCC), clouds remain the largest source of this uncertainty. The main culprit are mesoscale cloud fields, which organize into striking patterns, and cover hundreds of kilometers over the subtropical and tropical oceans. While conspicuous in satellite imagery, we lack the concepts and tools to adequately model their evolution.To overcome this mesoscale cloud-climate uncertainty, the project will develop a framework to conceptually understand and quantitatively predict mesoscale cloudiness from time series of satellite imagery. This requires a fundamental change of perspective: Instead of investigating cloud processes from the bottom up, the new approach will directly focus on the emergent behavior at the mesoscale. The new framework will capture mesoscale cloudiness as a data-driven complex system. This characterization will enable an assessment of the role of clouds in climate projections that is novel in two aspects: First, it will include observational information that has not been used before to reduce cloud-climate uncertainty. Second, the reliability of state-of-the-art lines of evidence will be objectively judged based on how well they capture different scales of cloud processes. The new methodology will be equipped to tap into the next generation of data and unlock additional lines of evidence.
As a comprehensive tool for mastering mesoscale cloudiness, the new framework will have broad and lasting impact: It will steer future cloud research, it will notably reduce uncertainty in the next IPCC assessment, and it will be an essential guide for the upcoming data-driven revolution of atmospheric and climate modeling.
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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