This project explores the application of Koopman evolution operators to the study of climate variability. Koopman operators were derived in the early 1930s and used to prove an important theorem on the behavior of idealized dynamical systems. But they remained largely unknown beyond dynamical systems theory until the early 2000s, when their usefulness for complicated, data intensive problems was recognized. Since that time they have been applied in such data-driven applications as fluid flow, stability analysis in power networks, the development of financial trading algorithms, and brain activity research. Like these applications, climate research is often data-driven and involves analysis of a complex system, thus Koopman operators could be a valuable addition to the climate research toolkit.Research conducted here applies the Koopman operator framework to three problems in climate dynamics: 1) Understanding the variability and structure of El Nino in paleoclimate and current climate; 2) decadal-scale variability in the climate system and its implications for predictability; and 3) dynamics of the South Pacific Convergence Zone (SPCZ) across multiple timescales. Research in these areas typically relies on methods such as principle component analysis to characterize variability in terms of spatial patterns and their time evolution. But these methods yield statistical characterizations that are not dynamically based and may not be optimal for relating the variability patterns to underlying dynamical processes. Koopman operators directly express the dynamics of the system, thus they may prove more insightful for understanding how the dynamics of the climate system give rise to the forms of variability that we observe.The work has broader impacts through its potential to enhance understanding of the climate system and thus improve the guidance available to stakeholders and decision makers concerned with climate impacts. In addition, the Principal Investigator (PI) works with the Rutgers Research in Science and Engineering (RiSE) program, through which he hosts undergraduate students from underrepresented groups. The project also provides support and training for a postdoctoral research associate, thereby providing for the future workforce in this research area.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.
|Effective start/end date||9/1/18 → 8/31/20|
- National Science Foundation (National Science Foundation (NSF))