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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Rochester Institute of Tech |
| Country | United States |
| Start Date | Jan 01, 2025 |
| End Date | Dec 31, 2026 |
| Duration | 729 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2434716 |
Our climate system is a complex network of interacting phenomena that impose a significant influence on society. This project aims to provide new insights into these interacting phenomena through the usage of network science, an interdisciplinary field comprising computational and mathematical tools designed to study complex networks. First, the investigator will synthesize large-scale climate records to investigate the stability of emerging network patterns associated with tipping elements, large-scale parts of the climate system that can change abruptly and irreversibly.
The investigative focus will be on the Atlantic basin and its global influence, given the well-documented weakening of the Atlantic Meridional Overturning Circulation as a potential tipping element. Second, the investigator will develop hybrid algorithms that integrate machine learning techniques with network science methods to improve the seasonal forecast of climate indices like the Southern Oscillation Index and the North Atlantic Oscillation. The project will support an early-career scientist and train students.
One of the most alarming potential consequences of climate change is the risk of future irreversible collapse of tipping elements, such as the Atlantic Meridional Overturning Circulation. However, it is unclear how the effects of collapsing tipping elements will spread within the climate system. This project can help us understand the cascading effects of this tipping behavior, possibly revealing previously unknown vulnerabilities in the global climate system.
This project will build on existing successes of network science in the study of climate by introducing novel, low-complexity algorithms for predicting climate indices. These advancements will provide new efficient tools for climate scientists and allow for the exploration of other complex systems in nature and society. The support from the project and subsequent scientific findings will allow the investigator to promote climate science among students and faculty at the investigator’s institution which does not have an academic department focused on Earth sciences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Rochester Institute of Tech
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